From 2224f14937cbb351f3283d82d50a9c88a4eaf412 Mon Sep 17 00:00:00 2001 From: James Overmeyer Date: Wed, 16 Oct 2024 01:34:47 -0400 Subject: [PATCH 1/9] Added Test Cases for Masking, Noise Injection, and Zoom --- testing/Masking-CNN.ipynb | 227 +++++++++++++++++++++++++ testing/Noise-Injection-CNN.ipynb | 206 ++++++++++++++++++++++ testing/Zoom-CNN.ipynb | 219 ++++++++++++++++++++++++ testing/__pycache__/Aug.cpython-39.pyc | Bin 0 -> 2765 bytes 4 files changed, 652 insertions(+) create mode 100644 testing/Masking-CNN.ipynb create mode 100644 testing/Noise-Injection-CNN.ipynb create mode 100644 testing/Zoom-CNN.ipynb create mode 100644 testing/__pycache__/Aug.cpython-39.pyc diff --git a/testing/Masking-CNN.ipynb b/testing/Masking-CNN.ipynb new file mode 100644 index 0000000..f59a5bf --- /dev/null +++ b/testing/Masking-CNN.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def cutout(image, mask_size=16):\n", + " \"\"\"Applies Cutout augmentation to an image.\"\"\"\n", + " h, w, _ = image.shape\n", + " x = np.random.randint(0, w)\n", + " y = np.random.randint(0, h)\n", + "\n", + " # Ensure the cutout does not go out of bounds\n", + " x1 = np.clip(x - mask_size // 2, 0, w)\n", + " x2 = np.clip(x + mask_size // 2, 0, w)\n", + " y1 = np.clip(y - mask_size // 2, 0, h)\n", + " y2 = np.clip(y + mask_size // 2, 0, h)\n", + "\n", + " # Create a copy of the image to avoid modifying the original\n", + " image_copy = image.copy()\n", + " image_copy[y1:y2, x1:x2, :] = 0 # Set the cutout region to black\n", + "\n", + " return image_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1688/1688 [==============================] - 16s 6ms/step - loss: 0.4519 - accuracy: 0.8605 - val_loss: 0.0979 - val_accuracy: 0.9708\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.2385 - accuracy: 0.9232 - val_loss: 0.0929 - val_accuracy: 0.9715\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1929 - accuracy: 0.9388 - val_loss: 0.0865 - val_accuracy: 0.9735\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1733 - accuracy: 0.9447 - val_loss: 0.0838 - val_accuracy: 0.9742\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1555 - accuracy: 0.9495 - val_loss: 0.0480 - val_accuracy: 0.9850\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1418 - accuracy: 0.9539 - val_loss: 0.0420 - val_accuracy: 0.9867\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1336 - accuracy: 0.9560 - val_loss: 0.0547 - val_accuracy: 0.9843\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1258 - accuracy: 0.9596 - val_loss: 0.0440 - val_accuracy: 0.9853\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1209 - accuracy: 0.9608 - val_loss: 0.0292 - val_accuracy: 0.9908\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1115 - accuracy: 0.9640 - val_loss: 0.0400 - val_accuracy: 0.9878\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1100 - accuracy: 0.9642 - val_loss: 0.0342 - val_accuracy: 0.9898\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1038 - accuracy: 0.9666 - val_loss: 0.0334 - val_accuracy: 0.9893\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1012 - accuracy: 0.9669 - val_loss: 0.0352 - val_accuracy: 0.9895\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0951 - accuracy: 0.9694 - val_loss: 0.0349 - val_accuracy: 0.9888\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1904 - accuracy: 0.9495 - val_loss: 0.0818 - val_accuracy: 0.9750\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0673 - accuracy: 0.9805 - val_loss: 0.1008 - val_accuracy: 0.9662\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0472 - accuracy: 0.9854 - val_loss: 0.0501 - val_accuracy: 0.9858\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0377 - accuracy: 0.9892 - val_loss: 0.0407 - val_accuracy: 0.9878\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 31s 18ms/step - loss: 0.0290 - accuracy: 0.9908 - val_loss: 0.0456 - val_accuracy: 0.9850\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0234 - accuracy: 0.9926 - val_loss: 0.0535 - val_accuracy: 0.9837\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0186 - accuracy: 0.9940 - val_loss: 0.0358 - val_accuracy: 0.9885\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0166 - accuracy: 0.9950 - val_loss: 0.0433 - val_accuracy: 0.9882\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0141 - accuracy: 0.9954 - val_loss: 0.0515 - val_accuracy: 0.9845\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0136 - accuracy: 0.9956 - val_loss: 0.0319 - val_accuracy: 0.9908\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0121 - accuracy: 0.9963 - val_loss: 0.0414 - val_accuracy: 0.9887\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 0.0388 - val_accuracy: 0.9897\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0087 - accuracy: 0.9974 - val_loss: 0.0487 - val_accuracy: 0.9863\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 0.0440 - val_accuracy: 0.9867\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0463 - val_accuracy: 0.9872\n", + "Test accuracy with Mask Injection: 0.9682\n", + "Test loss with Mask Injection: 0.1215\n", + "Test accuracy without Mask Injection: 0.9705\n", + "Test loss without Mask Injection: 0.1298\n" + ] + } + ], + "source": [ + "best_model_mask = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_mask = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_mask = ImageDataGenerator(\n", + " preprocessing_function = lambda x: cutout(x, mask_size=16) # Zoom in on the image\n", + ")\n", + "datagen_no_mask = ImageDataGenerator()\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_mask = datagen_mask.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_no_mask = datagen_no_mask.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "history_mask = best_model_mask.fit(train_generator_mask,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_no_mask = best_model_no_mask.fit(train_generator_no_mask,\n", + " epochs=20,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "\n", + "# Evaluate on the test set\n", + "test_loss_mask, test_accuracy_mask = best_model_mask.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_no_mask, test_accuracy_no_mask = best_model_no_mask.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Mask Injection: {test_accuracy_mask:.4f}\")\n", + "print(f\"Test loss with Mask Injection: {test_loss_mask:.4f}\")\n", + "print(f\"Test accuracy without Mask Injection: {test_accuracy_no_mask:.4f}\")\n", + "print(f\"Test loss without Mask Injection: {test_loss_no_mask:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/Noise-Injection-CNN.ipynb b/testing/Noise-Injection-CNN.ipynb new file mode 100644 index 0000000..a3d464e --- /dev/null +++ b/testing/Noise-Injection-CNN.ipynb @@ -0,0 +1,206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1688/1688 [==============================] - 16s 7ms/step - loss: 0.1925 - accuracy: 0.9488 - val_loss: 0.0775 - val_accuracy: 0.9753\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0710 - accuracy: 0.9797 - val_loss: 0.0457 - val_accuracy: 0.9870\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0489 - accuracy: 0.9851 - val_loss: 0.1010 - val_accuracy: 0.9700\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0387 - accuracy: 0.9882 - val_loss: 0.0380 - val_accuracy: 0.9887\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0330 - accuracy: 0.9901 - val_loss: 0.0405 - val_accuracy: 0.9875\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0260 - accuracy: 0.9919 - val_loss: 0.0506 - val_accuracy: 0.9857\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0220 - accuracy: 0.9931 - val_loss: 0.0569 - val_accuracy: 0.9842\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0199 - accuracy: 0.9937 - val_loss: 0.0338 - val_accuracy: 0.9902\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0168 - accuracy: 0.9948 - val_loss: 0.0494 - val_accuracy: 0.9855\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0151 - accuracy: 0.9951 - val_loss: 0.0434 - val_accuracy: 0.9890\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0139 - accuracy: 0.9957 - val_loss: 0.0519 - val_accuracy: 0.9845\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0124 - accuracy: 0.9962 - val_loss: 0.0424 - val_accuracy: 0.9873\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 23s 14ms/step - loss: 0.0101 - accuracy: 0.9970 - val_loss: 0.0341 - val_accuracy: 0.9898\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1930 - accuracy: 0.9477 - val_loss: 0.0863 - val_accuracy: 0.9748\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0655 - accuracy: 0.9813 - val_loss: 0.0846 - val_accuracy: 0.9742\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0461 - accuracy: 0.9861 - val_loss: 0.0448 - val_accuracy: 0.9872\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0368 - accuracy: 0.9886 - val_loss: 0.0566 - val_accuracy: 0.9845\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0279 - accuracy: 0.9912 - val_loss: 0.0459 - val_accuracy: 0.9867\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0223 - accuracy: 0.9929 - val_loss: 0.0416 - val_accuracy: 0.9898\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0186 - accuracy: 0.9943 - val_loss: 0.0354 - val_accuracy: 0.9902\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0485 - val_accuracy: 0.9872\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0143 - accuracy: 0.9953 - val_loss: 0.0380 - val_accuracy: 0.9892\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0106 - accuracy: 0.9966 - val_loss: 0.0339 - val_accuracy: 0.9903\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0119 - accuracy: 0.9959 - val_loss: 0.0409 - val_accuracy: 0.9898\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.0406 - val_accuracy: 0.9897\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0303 - val_accuracy: 0.9920\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0381 - val_accuracy: 0.9887\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0087 - accuracy: 0.9970 - val_loss: 0.0396 - val_accuracy: 0.9905\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.0414 - val_accuracy: 0.9892\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0069 - accuracy: 0.9976 - val_loss: 0.0390 - val_accuracy: 0.9908\n", + "Epoch 18/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.0349 - val_accuracy: 0.9897\n", + "Test accuracy with Noise Injection: 0.9624\n", + "Test loss with Noise Injection: 0.1567\n", + "Test accuracy without Noise Injection: 0.9701\n", + "Test loss without Noise Injection: 0.1244\n" + ] + } + ], + "source": [ + "best_model_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_noise = ImageDataGenerator(\n", + " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1) # Noise injection\n", + ")\n", + "datagen_no_noise = ImageDataGenerator()\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_noise = datagen_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_no_noise = datagen_no_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "history_noise = best_model_noise.fit(train_generator_noise,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_no_noise = best_model_no_noise.fit(train_generator_no_noise,\n", + " epochs=20,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "\n", + "# Evaluate on the test set\n", + "test_loss_noise, test_accuracy_noise = best_model_noise.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_no_noise, test_accuracy_no_noise = best_model_no_noise.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Noise Injection: {test_accuracy_noise:.4f}\")\n", + "print(f\"Test loss with Noise Injection: {test_loss_noise:.4f}\")\n", + "print(f\"Test accuracy without Noise Injection: {test_accuracy_no_noise:.4f}\")\n", + "print(f\"Test loss without Noise Injection: {test_loss_no_noise:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/Zoom-CNN.ipynb b/testing/Zoom-CNN.ipynb new file mode 100644 index 0000000..16a3707 --- /dev/null +++ b/testing/Zoom-CNN.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the input shape and number of classes\n", + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1688/1688 [==============================] - 15s 7ms/step - loss: 0.2087 - accuracy: 0.9423 - val_loss: 0.1137 - val_accuracy: 0.9658\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0753 - accuracy: 0.9775 - val_loss: 0.0649 - val_accuracy: 0.9793\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0531 - accuracy: 0.9838 - val_loss: 0.0761 - val_accuracy: 0.9757\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0439 - accuracy: 0.9867 - val_loss: 0.0511 - val_accuracy: 0.9842\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0341 - accuracy: 0.9894 - val_loss: 0.0619 - val_accuracy: 0.9822\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0282 - accuracy: 0.9911 - val_loss: 0.0494 - val_accuracy: 0.9847\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0259 - accuracy: 0.9918 - val_loss: 0.0556 - val_accuracy: 0.9835\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 0.0394 - val_accuracy: 0.9895\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0475 - val_accuracy: 0.9858\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0169 - accuracy: 0.9944 - val_loss: 0.0350 - val_accuracy: 0.9890\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 16s 9ms/step - loss: 0.0151 - accuracy: 0.9951 - val_loss: 0.0323 - val_accuracy: 0.9903\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0134 - accuracy: 0.9956 - val_loss: 0.0275 - val_accuracy: 0.9915\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.0131 - accuracy: 0.9958 - val_loss: 0.0308 - val_accuracy: 0.9915\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 0.0357 - val_accuracy: 0.9895\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.0309 - val_accuracy: 0.9915\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0098 - accuracy: 0.9967 - val_loss: 0.0457 - val_accuracy: 0.9868\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0090 - accuracy: 0.9970 - val_loss: 0.0366 - val_accuracy: 0.9902\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1967 - accuracy: 0.9468 - val_loss: 0.0794 - val_accuracy: 0.9768\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0673 - accuracy: 0.9807 - val_loss: 0.0819 - val_accuracy: 0.9728\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0470 - accuracy: 0.9857 - val_loss: 0.0720 - val_accuracy: 0.9777\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0380 - accuracy: 0.9886 - val_loss: 0.0441 - val_accuracy: 0.9853\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0279 - accuracy: 0.9911 - val_loss: 0.0610 - val_accuracy: 0.9798\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0219 - accuracy: 0.9928 - val_loss: 0.0459 - val_accuracy: 0.9875\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0202 - accuracy: 0.9936 - val_loss: 0.0399 - val_accuracy: 0.9897\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0174 - accuracy: 0.9943 - val_loss: 0.0421 - val_accuracy: 0.9875\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0144 - accuracy: 0.9952 - val_loss: 0.0371 - val_accuracy: 0.9883\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0127 - accuracy: 0.9960 - val_loss: 0.0433 - val_accuracy: 0.9872\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0112 - accuracy: 0.9965 - val_loss: 0.0369 - val_accuracy: 0.9912\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.0377 - val_accuracy: 0.9885\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.0525 - val_accuracy: 0.9862\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 0.0463 - val_accuracy: 0.9880\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0063 - accuracy: 0.9979 - val_loss: 0.0348 - val_accuracy: 0.9898\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0060 - accuracy: 0.9978 - val_loss: 0.0337 - val_accuracy: 0.9902\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.0476 - val_accuracy: 0.9893\n", + "Epoch 18/20\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0070 - accuracy: 0.9979 - val_loss: 0.0497 - val_accuracy: 0.9883\n", + "Epoch 19/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0061 - accuracy: 0.9980 - val_loss: 0.0479 - val_accuracy: 0.9887\n", + "Epoch 20/20\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0068 - accuracy: 0.9978 - val_loss: 0.0489 - val_accuracy: 0.9888\n", + "Test accuracy with Zoom Injection: 0.9731\n", + "Test loss with Zoom Injection: 0.1234\n", + "Test accuracy without Zoom Injection: 0.9625\n", + "Test loss without Zoom Injection: 0.2174\n" + ] + } + ], + "source": [ + "best_model_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_zoom = ImageDataGenerator(\n", + " zoom_range=0.1 # Zoom in on the image\n", + ")\n", + "datagen_no_zoom = ImageDataGenerator()\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_zoom = datagen_zoom.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_no_zoom = datagen_no_zoom.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "history_zoom = best_model_zoom.fit(train_generator_zoom,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_no_zoom = best_model_no_zoom.fit(train_generator_no_zoom,\n", + " epochs=20,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "\n", + "# Evaluate on the test set\n", + "test_loss_zoom, test_accuracy_zoom = best_model_zoom.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_no_zoom, test_accuracy_no_zoom = best_model_no_zoom.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Zoom Injection: {test_accuracy_zoom:.4f}\")\n", + "print(f\"Test loss with Zoom Injection: {test_loss_zoom:.4f}\")\n", + "print(f\"Test accuracy without Zoom Injection: {test_accuracy_no_zoom:.4f}\")\n", + "print(f\"Test loss without Zoom Injection: {test_loss_no_zoom:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/__pycache__/Aug.cpython-39.pyc b/testing/__pycache__/Aug.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b2cc4eeb5adbc146a5073fadbbd849417ee813be GIT binary patch literal 2765 zcmai0&u<$=6rP#=>2>0yY1F3tC=~~g5k!<$P}0gXX#Q;` zYjWlulQXH)3wmL%vg@9IMlul=GnMntz0d%voCZ7E74@d)!x1sR|^kO z_jRPJ^K75*^6}K8m8Z*4MthF z4S0>y%?eOpQxztlZA|N@RF!P_M#IVDK8dx4qvDGO;x)t94Bs+5Hhi@qR#PJs`kk~P zKE!wzeH&aEB>B1}Ln&%>U#;X-oT?-T;7BKypNC+)#$7SRLuU@!9G??4UK0ygo8`|V 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2/9] Added some augmentation approaches, made some changes, tried, but failed to implement VAE --- testing/Cropping-CNN.ipynb | 253 ++++++++++++++++++ testing/Flipping-testing.ipynb | 225 ++++++++++++++++ testing/Noise-Injection-CNN.ipynb | 198 +++++++++++--- testing/Trying_VAE.ipynb | 421 ++++++++++++++++++++++++++++++ testing/generated_images.npy | Bin 0 -> 62848 bytes testing/generated_labels.npy | Bin 0 -> 208 bytes 6 files changed, 1054 insertions(+), 43 deletions(-) create mode 100644 testing/Cropping-CNN.ipynb create mode 100644 testing/Flipping-testing.ipynb create mode 100644 testing/Trying_VAE.ipynb create mode 100644 testing/generated_images.npy create mode 100644 testing/generated_labels.npy diff --git a/testing/Cropping-CNN.ipynb b/testing/Cropping-CNN.ipynb new file mode 100644 index 0000000..621a064 --- /dev/null +++ b/testing/Cropping-CNN.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the input shape and number of classes\n", + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1688/1688 [==============================] - 85s 49ms/step - loss: 0.1924 - accuracy: 0.9488 - val_loss: 0.0915 - val_accuracy: 0.9717\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 79s 47ms/step - loss: 0.0664 - accuracy: 0.9803 - val_loss: 0.0615 - val_accuracy: 0.9822\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 75s 45ms/step - loss: 0.0476 - accuracy: 0.9854 - val_loss: 0.0462 - val_accuracy: 0.9853\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 81s 48ms/step - loss: 0.0346 - accuracy: 0.9896 - val_loss: 0.0928 - val_accuracy: 0.9743\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 75s 44ms/step - loss: 0.0272 - accuracy: 0.9917 - val_loss: 0.0510 - val_accuracy: 0.9855\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 76s 45ms/step - loss: 0.0238 - accuracy: 0.9926 - val_loss: 0.0366 - val_accuracy: 0.9890\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 80s 47ms/step - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0403 - val_accuracy: 0.9882\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 78s 46ms/step - loss: 0.0175 - accuracy: 0.9943 - val_loss: 0.0329 - val_accuracy: 0.9905\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 79s 47ms/step - loss: 0.0148 - accuracy: 0.9950 - val_loss: 0.0386 - val_accuracy: 0.9882\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 75s 45ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0411 - val_accuracy: 0.9883\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 77s 45ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0480 - val_accuracy: 0.9880\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 77s 45ms/step - loss: 0.0099 - accuracy: 0.9967 - val_loss: 0.0343 - val_accuracy: 0.9915\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 78s 46ms/step - loss: 0.0100 - accuracy: 0.9966 - val_loss: 0.0487 - val_accuracy: 0.9878\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2021 - accuracy: 0.9456 - val_loss: 0.1029 - val_accuracy: 0.9678\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0684 - accuracy: 0.9795 - val_loss: 0.0736 - val_accuracy: 0.9748\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0477 - accuracy: 0.9864 - val_loss: 0.0605 - val_accuracy: 0.9815\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0350 - accuracy: 0.9895 - val_loss: 0.0518 - val_accuracy: 0.9852\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0283 - accuracy: 0.9911 - val_loss: 0.0448 - val_accuracy: 0.9858\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0238 - accuracy: 0.9925 - val_loss: 0.0451 - val_accuracy: 0.9853\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0193 - accuracy: 0.9938 - val_loss: 0.0453 - val_accuracy: 0.9885\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0153 - accuracy: 0.9948 - val_loss: 0.0416 - val_accuracy: 0.9885\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0147 - accuracy: 0.9950 - val_loss: 0.0389 - val_accuracy: 0.9878\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0131 - accuracy: 0.9957 - val_loss: 0.0861 - val_accuracy: 0.9742\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.0315 - val_accuracy: 0.9908\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0312 - val_accuracy: 0.9920\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 0.0379 - val_accuracy: 0.9898\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 0.0348 - val_accuracy: 0.9898\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 0.0328 - val_accuracy: 0.9912\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0083 - accuracy: 0.9974 - val_loss: 0.0409 - val_accuracy: 0.9897\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0058 - accuracy: 0.9980 - val_loss: 0.0445 - val_accuracy: 0.9895\n", + "Test accuracy with Cropping: 0.9692\n", + "Test loss with Cropping: 0.1259\n", + "Test accuracy without Cropping: 0.9707\n", + "Test loss without Cropping: 0.1367\n" + ] + } + ], + "source": [ + "best_model_crop = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_crop = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_crop = ImageDataGenerator(\n", + " preprocessing_function=lambda x: tf.image.random_crop(\n", + " x, size=[28, 28, 1]\n", + " ) # Random crop to original size\n", + ")\n", + "datagen_no_crop = ImageDataGenerator()\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_crop = datagen_crop.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_no_crop = datagen_no_crop.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "history_crop = best_model_crop.fit(train_generator_crop,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_no_crop = best_model_no_crop.fit(train_generator_no_crop,\n", + " epochs=20,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "\n", + "# Evaluate on the test set\n", + "test_loss_crop, test_accuracy_crop = best_model_crop.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_no_crop, test_accuracy_no_crop = best_model_no_crop.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Cropping: {test_accuracy_crop:.4f}\")\n", + "print(f\"Test loss with Cropping: {test_loss_crop:.4f}\")\n", + "print(f\"Test accuracy without Cropping: {test_accuracy_no_crop:.4f}\")\n", + "print(f\"Test loss without Cropping: {test_loss_no_crop:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to apply random cropping\n", + "def apply_random_crop(image):\n", + " return tf.image.random_crop(image, size=[28, 28, 1]).numpy()\n", + "\n", + "# Select a few random samples\n", + "num_samples = 5\n", + "sample_indices = np.random.choice(x_test.shape[0], num_samples, replace=False)\n", + "\n", + "# Create a figure to display the samples\n", + "fig, axes = plt.subplots(num_samples, 2, figsize=(10, 5*num_samples))\n", + "fig.suptitle(\"Comparison of Original and Randomly Cropped Images\", fontsize=16)\n", + "\n", + "for i, idx in enumerate(sample_indices):\n", + " original_image = x_test[idx]\n", + " cropped_image = apply_random_crop(original_image)\n", + " \n", + " axes[i, 0].imshow(original_image.squeeze(), cmap='gray')\n", + " axes[i, 0].set_title(f\"Original (Class: {np.argmax(y_test[idx])})\")\n", + " axes[i, 0].axis('off')\n", + " \n", + " axes[i, 1].imshow(cropped_image.squeeze(), cmap='gray')\n", + " axes[i, 1].set_title(\"Randomly Cropped\")\n", + " axes[i, 1].axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/Flipping-testing.ipynb b/testing/Flipping-testing.ipynb new file mode 100644 index 0000000..ff4b13c --- /dev/null +++ b/testing/Flipping-testing.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1688/1688 [==============================] - 12s 6ms/step - loss: 0.4703 - accuracy: 0.8598 - val_loss: 0.3402 - val_accuracy: 0.8907\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2213 - accuracy: 0.9314 - val_loss: 0.2294 - val_accuracy: 0.9295\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1677 - accuracy: 0.9469 - val_loss: 0.1468 - val_accuracy: 0.9542\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1428 - accuracy: 0.9556 - val_loss: 0.2086 - val_accuracy: 0.9358\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1246 - accuracy: 0.9606 - val_loss: 0.1991 - val_accuracy: 0.9362\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1118 - accuracy: 0.9649 - val_loss: 0.1508 - val_accuracy: 0.9548\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1012 - accuracy: 0.9684 - val_loss: 0.1087 - val_accuracy: 0.9667\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0929 - accuracy: 0.9716 - val_loss: 0.1118 - val_accuracy: 0.9667\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0877 - accuracy: 0.9722 - val_loss: 0.0867 - val_accuracy: 0.9732\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0794 - accuracy: 0.9748 - val_loss: 0.0898 - val_accuracy: 0.9725\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0771 - accuracy: 0.9760 - val_loss: 0.0909 - val_accuracy: 0.9705\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0733 - accuracy: 0.9765 - val_loss: 0.0855 - val_accuracy: 0.9738\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0677 - accuracy: 0.9790 - val_loss: 0.1183 - val_accuracy: 0.9648\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0639 - accuracy: 0.9797 - val_loss: 0.0778 - val_accuracy: 0.9760\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0600 - accuracy: 0.9814 - val_loss: 0.0751 - val_accuracy: 0.9763\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0586 - accuracy: 0.9811 - val_loss: 0.0886 - val_accuracy: 0.9720\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0561 - accuracy: 0.9825 - val_loss: 0.0845 - val_accuracy: 0.9715\n", + "Epoch 18/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0527 - accuracy: 0.9833 - val_loss: 0.0660 - val_accuracy: 0.9803\n", + "Epoch 19/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0539 - accuracy: 0.9823 - val_loss: 0.0670 - val_accuracy: 0.9783\n", + "Epoch 20/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0683 - val_accuracy: 0.9790\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1866 - accuracy: 0.9496 - val_loss: 0.1014 - val_accuracy: 0.9708\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0662 - accuracy: 0.9808 - val_loss: 0.0612 - val_accuracy: 0.9798\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0462 - accuracy: 0.9861 - val_loss: 0.0730 - val_accuracy: 0.9780\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0344 - accuracy: 0.9895 - val_loss: 0.0408 - val_accuracy: 0.9870\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0279 - accuracy: 0.9916 - val_loss: 0.0518 - val_accuracy: 0.9842\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0218 - accuracy: 0.9931 - val_loss: 0.0379 - val_accuracy: 0.9897\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0186 - accuracy: 0.9944 - val_loss: 0.0393 - val_accuracy: 0.9885\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0167 - accuracy: 0.9947 - val_loss: 0.0338 - val_accuracy: 0.9885\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0133 - accuracy: 0.9958 - val_loss: 0.0720 - val_accuracy: 0.9780\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0126 - accuracy: 0.9960 - val_loss: 0.0399 - val_accuracy: 0.9885\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0111 - accuracy: 0.9965 - val_loss: 0.0319 - val_accuracy: 0.9903\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.0347 - val_accuracy: 0.9912\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0087 - accuracy: 0.9973 - val_loss: 0.0343 - val_accuracy: 0.9905\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0090 - accuracy: 0.9972 - val_loss: 0.0325 - val_accuracy: 0.9922\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0074 - accuracy: 0.9978 - val_loss: 0.0394 - val_accuracy: 0.9890\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.0315 - val_accuracy: 0.9920\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.0385 - val_accuracy: 0.9902\n", + "Epoch 18/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0393 - val_accuracy: 0.9903\n", + "Epoch 19/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.0321 - val_accuracy: 0.9913\n", + "Epoch 20/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0054 - accuracy: 0.9981 - val_loss: 0.0319 - val_accuracy: 0.9927\n", + "Test accuracy with Flipping: 0.9475\n", + "Test loss with Flipping: 0.1920\n", + "Test accuracy without Flipping: 0.9669\n", + "Test loss without Flipping: 0.1587\n" + ] + } + ], + "source": [ + "best_model_flip = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_flip = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_flip = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True\n", + ")\n", + "datagen_no_flip = ImageDataGenerator()\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_flip = datagen_flip.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_no_flip = datagen_no_flip.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "history_flip = best_model_flip.fit(train_generator_flip,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_no_flip = best_model_no_flip.fit(train_generator_no_flip,\n", + " epochs=20,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "\n", + "# Evaluate on the test set\n", + "test_loss_flip, test_accuracy_flip = best_model_flip.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_no_flip, test_accuracy_no_flip = best_model_no_flip.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Flipping: {test_accuracy_flip:.4f}\")\n", + "print(f\"Test loss with Flipping: {test_loss_flip:.4f}\")\n", + "print(f\"Test accuracy without Flipping: {test_accuracy_no_flip:.4f}\")\n", + "print(f\"Test loss without Flipping: {test_loss_no_flip:.4f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/Noise-Injection-CNN.ipynb b/testing/Noise-Injection-CNN.ipynb index a3d464e..29f8326 100644 --- a/testing/Noise-Injection-CNN.ipynb +++ b/testing/Noise-Injection-CNN.ipynb @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -79,89 +79,143 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - "1688/1688 [==============================] - 16s 7ms/step - loss: 0.1925 - accuracy: 0.9488 - val_loss: 0.0775 - val_accuracy: 0.9753\n", + "1688/1688 [==============================] - 14s 6ms/step - loss: 0.2187 - accuracy: 0.9408 - val_loss: 0.1093 - val_accuracy: 0.9652\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0710 - accuracy: 0.9797 - val_loss: 0.0457 - val_accuracy: 0.9870\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0732 - accuracy: 0.9789 - val_loss: 0.0625 - val_accuracy: 0.9797\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0489 - accuracy: 0.9851 - val_loss: 0.1010 - val_accuracy: 0.9700\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0502 - accuracy: 0.9850 - val_loss: 0.0482 - val_accuracy: 0.9850\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0387 - accuracy: 0.9882 - val_loss: 0.0380 - val_accuracy: 0.9887\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0392 - accuracy: 0.9881 - val_loss: 0.0445 - val_accuracy: 0.9865\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0330 - accuracy: 0.9901 - val_loss: 0.0405 - val_accuracy: 0.9875\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0326 - accuracy: 0.9898 - val_loss: 0.1013 - val_accuracy: 0.9715\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0260 - accuracy: 0.9919 - val_loss: 0.0506 - val_accuracy: 0.9857\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0290 - accuracy: 0.9909 - val_loss: 0.0487 - val_accuracy: 0.9847\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0220 - accuracy: 0.9931 - val_loss: 0.0569 - val_accuracy: 0.9842\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0225 - accuracy: 0.9931 - val_loss: 0.0533 - val_accuracy: 0.9843\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0199 - accuracy: 0.9937 - val_loss: 0.0338 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0180 - accuracy: 0.9938 - val_loss: 0.0848 - val_accuracy: 0.9750\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0168 - accuracy: 0.9948 - val_loss: 0.0494 - val_accuracy: 0.9855\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0170 - accuracy: 0.9946 - val_loss: 0.0318 - val_accuracy: 0.9903\n", "Epoch 10/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0151 - accuracy: 0.9951 - val_loss: 0.0434 - val_accuracy: 0.9890\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0137 - accuracy: 0.9956 - val_loss: 0.0382 - val_accuracy: 0.9887\n", "Epoch 11/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0139 - accuracy: 0.9957 - val_loss: 0.0519 - val_accuracy: 0.9845\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0143 - accuracy: 0.9951 - val_loss: 0.0361 - val_accuracy: 0.9900\n", "Epoch 12/20\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0124 - accuracy: 0.9962 - val_loss: 0.0424 - val_accuracy: 0.9873\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 0.0407 - val_accuracy: 0.9885\n", "Epoch 13/20\n", - "1688/1688 [==============================] - 23s 14ms/step - loss: 0.0101 - accuracy: 0.9970 - val_loss: 0.0341 - val_accuracy: 0.9898\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0108 - accuracy: 0.9963 - val_loss: 0.0397 - val_accuracy: 0.9903\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0098 - accuracy: 0.9967 - val_loss: 0.0374 - val_accuracy: 0.9913\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1906 - accuracy: 0.9488 - val_loss: 0.0982 - val_accuracy: 0.9687\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0688 - accuracy: 0.9800 - val_loss: 0.0857 - val_accuracy: 0.9718\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0462 - accuracy: 0.9862 - val_loss: 0.0464 - val_accuracy: 0.9870\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0353 - accuracy: 0.9893 - val_loss: 0.0430 - val_accuracy: 0.9865\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0280 - accuracy: 0.9910 - val_loss: 0.0515 - val_accuracy: 0.9858\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0233 - accuracy: 0.9929 - val_loss: 0.0401 - val_accuracy: 0.9882\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0187 - accuracy: 0.9942 - val_loss: 0.0459 - val_accuracy: 0.9862\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0179 - accuracy: 0.9944 - val_loss: 0.0462 - val_accuracy: 0.9865\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0156 - accuracy: 0.9950 - val_loss: 0.0370 - val_accuracy: 0.9893\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0131 - accuracy: 0.9959 - val_loss: 0.0358 - val_accuracy: 0.9905\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0110 - accuracy: 0.9967 - val_loss: 0.0556 - val_accuracy: 0.9852\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0108 - accuracy: 0.9962 - val_loss: 0.0414 - val_accuracy: 0.9882\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0095 - accuracy: 0.9969 - val_loss: 0.0333 - val_accuracy: 0.9913\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0096 - accuracy: 0.9969 - val_loss: 0.0558 - val_accuracy: 0.9845\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0085 - accuracy: 0.9972 - val_loss: 0.0428 - val_accuracy: 0.9890\n", + "Epoch 16/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0076 - accuracy: 0.9975 - val_loss: 0.0438 - val_accuracy: 0.9892\n", + "Epoch 17/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0073 - accuracy: 0.9977 - val_loss: 0.0345 - val_accuracy: 0.9912\n", + "Epoch 18/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0059 - accuracy: 0.9982 - val_loss: 0.0576 - val_accuracy: 0.9860\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1930 - accuracy: 0.9477 - val_loss: 0.0863 - val_accuracy: 0.9748\n", + "1688/1688 [==============================] - 10s 5ms/step - loss: 0.1970 - accuracy: 0.9463 - val_loss: 0.1205 - val_accuracy: 0.9612\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0655 - accuracy: 0.9813 - val_loss: 0.0846 - val_accuracy: 0.9742\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0658 - accuracy: 0.9809 - val_loss: 0.0586 - val_accuracy: 0.9825\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0461 - accuracy: 0.9861 - val_loss: 0.0448 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0491 - accuracy: 0.9849 - val_loss: 0.0884 - val_accuracy: 0.9728\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0368 - accuracy: 0.9886 - val_loss: 0.0566 - val_accuracy: 0.9845\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0371 - accuracy: 0.9886 - val_loss: 0.0570 - val_accuracy: 0.9812\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0279 - accuracy: 0.9912 - val_loss: 0.0459 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0287 - accuracy: 0.9912 - val_loss: 0.0494 - val_accuracy: 0.9858\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0223 - accuracy: 0.9929 - val_loss: 0.0416 - val_accuracy: 0.9898\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0235 - accuracy: 0.9931 - val_loss: 0.0561 - val_accuracy: 0.9833\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0186 - accuracy: 0.9943 - val_loss: 0.0354 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0198 - accuracy: 0.9936 - val_loss: 0.0434 - val_accuracy: 0.9880\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0485 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0179 - accuracy: 0.9942 - val_loss: 0.0447 - val_accuracy: 0.9862\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0143 - accuracy: 0.9953 - val_loss: 0.0380 - val_accuracy: 0.9892\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0145 - accuracy: 0.9952 - val_loss: 0.0630 - val_accuracy: 0.9838\n", "Epoch 10/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0106 - accuracy: 0.9966 - val_loss: 0.0339 - val_accuracy: 0.9903\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0131 - accuracy: 0.9960 - val_loss: 0.0463 - val_accuracy: 0.9880\n", "Epoch 11/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0119 - accuracy: 0.9959 - val_loss: 0.0409 - val_accuracy: 0.9898\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0110 - accuracy: 0.9961 - val_loss: 0.0412 - val_accuracy: 0.9882\n", "Epoch 12/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.0406 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0476 - val_accuracy: 0.9877\n", "Epoch 13/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0303 - val_accuracy: 0.9920\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.0450 - val_accuracy: 0.9885\n", "Epoch 14/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0381 - val_accuracy: 0.9887\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.0668 - val_accuracy: 0.9843\n", "Epoch 15/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0087 - accuracy: 0.9970 - val_loss: 0.0396 - val_accuracy: 0.9905\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0080 - accuracy: 0.9973 - val_loss: 0.0444 - val_accuracy: 0.9885\n", "Epoch 16/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.0414 - val_accuracy: 0.9892\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0363 - val_accuracy: 0.9882\n", "Epoch 17/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0069 - accuracy: 0.9976 - val_loss: 0.0390 - val_accuracy: 0.9908\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0072 - accuracy: 0.9976 - val_loss: 0.0369 - val_accuracy: 0.9907\n", "Epoch 18/20\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 0.0349 - val_accuracy: 0.9897\n", - "Test accuracy with Noise Injection: 0.9624\n", - "Test loss with Noise Injection: 0.1567\n", - "Test accuracy without Noise Injection: 0.9701\n", - "Test loss without Noise Injection: 0.1244\n" + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.0361 - val_accuracy: 0.9905\n", + "Epoch 19/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0060 - accuracy: 0.9978 - val_loss: 0.0427 - val_accuracy: 0.9898\n", + "Epoch 20/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0060 - accuracy: 0.9979 - val_loss: 0.0329 - val_accuracy: 0.9920\n", + "Test accuracy with Random Noise Injection: 0.9705\n", + "Test accuracy with Radial Noise Injection: 0.9738\n", + "Test accuracy without Noise Injection: 0.9672\n", + "Test loss with Random Noise Injection: 0.1289\n", + "Test loss with Radial Noise Injection: 0.1271\n", + "Test loss without Noise Injection: 0.1600\n" ] } ], "source": [ - "best_model_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_rand_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "best_model_no_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", - "datagen_noise = ImageDataGenerator(\n", + "best_model_radial_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "datagen_rand_noise = ImageDataGenerator(\n", " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1) # Noise injection\n", ")\n", + "datagen_radial_noise = ImageDataGenerator(\n", + " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape) * np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2)).reshape(1, x.shape[1], x.shape[2], 1) / np.max(np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2))), 0, 1) # Radial noise injection\n", + " )\n", "datagen_no_noise = ImageDataGenerator()\n", "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", "datagen_val = ImageDataGenerator()\n", "# Wrap the datagen with our custom generator\n", - "train_generator_noise = datagen_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_rand_noise = datagen_rand_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", + "train_generator_radial_noise = datagen_radial_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", "train_generator_no_noise = datagen_no_noise.flow(x_train_aug, y_train_aug, batch_size=32)\n", "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", "# Train the model with custom augmentation\n", - "history_noise = best_model_noise.fit(train_generator_noise,\n", + "history_rand_noise = best_model_rand_noise.fit(train_generator_rand_noise,\n", + " epochs=20,\n", + " validation_data=val_generator, \n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + "history_radial_noise = best_model_radial_noise.fit(train_generator_radial_noise,\n", " epochs=20,\n", " validation_data=val_generator, \n", " verbose=1,\n", @@ -173,13 +227,71 @@ " callbacks=[early_stopping])\n", "\n", "# Evaluate on the test set\n", - "test_loss_noise, test_accuracy_noise = best_model_noise.evaluate(x_test, y_test, verbose=0)\n", + "test_loss_rand_noise, test_accuracy_rand_noise = best_model_rand_noise.evaluate(x_test, y_test, verbose=0)\n", "test_loss_no_noise, test_accuracy_no_noise = best_model_no_noise.evaluate(x_test, y_test, verbose=0)\n", - "print(f\"Test accuracy with Noise Injection: {test_accuracy_noise:.4f}\")\n", - "print(f\"Test loss with Noise Injection: {test_loss_noise:.4f}\")\n", + "test_loss_radial_noise, test_accuracy_radial_noise = best_model_radial_noise.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Test accuracy with Random Noise Injection: {test_accuracy_rand_noise:.4f}\")\n", + "print(f\"Test accuracy with Radial Noise Injection: {test_accuracy_radial_noise:.4f}\")\n", "print(f\"Test accuracy without Noise Injection: {test_accuracy_no_noise:.4f}\")\n", + "print(f\"Test loss with Random Noise Injection: {test_loss_rand_noise:.4f}\")\n", + "print(f\"Test loss with Radial Noise Injection: {test_loss_radial_noise:.4f}\")\n", "print(f\"Test loss without Noise Injection: {test_loss_no_noise:.4f}\")" ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Function to apply random noise\n", + "def apply_random_noise(image):\n", + " return np.clip(image + np.random.normal(0, 0.1, image.shape), 0, 1)\n", + "\n", + "# Function to apply radial noise\n", + "def apply_radial_noise(image):\n", + " radial_factor = np.sqrt(np.sum(np.square(np.indices(image.shape[1:3]).T - np.array(image.shape[1:3])/2), axis=2))\n", + " radial_factor = radial_factor.reshape(1, image.shape[1], image.shape[2], 1) / np.max(radial_factor)\n", + " return np.clip(image + np.random.normal(0, 0.1, image.shape) * radial_factor, 0, 1)\n", + "\n", + "# Select a few random samples\n", + "num_samples = 5\n", + "sample_indices = np.random.choice(x_test.shape[0], num_samples, replace=False)\n", + "\n", + "# Create a figure to display the samples\n", + "fig, axes = plt.subplots(num_samples, 3, figsize=(15, 5*num_samples))\n", + "fig.suptitle(\"Comparison of Original, Random Noise, and Radial Noise\", fontsize=16)\n", + "\n", + "\n", + "for i, idx in enumerate(sample_indices):\n", + " original_image = x_test[idx]\n", + " random_noise_image = apply_random_noise(original_image)\n", + " radial_noise_image = apply_radial_noise(original_image)\n", + " \n", + " axes[i, 0].imshow(original_image.squeeze(), cmap='gray')\n", + " axes[i, 0].set_title(f\"Original (Class: {np.argmax(y_test[idx])})\")\n", + " axes[i, 0].axis('off')\n", + " \n", + " axes[i, 1].imshow(random_noise_image.squeeze(), cmap='gray')\n", + " axes[i, 1].set_title(\"Random Noise\")\n", + " axes[i, 1].axis('off')\n", + " \n", + " axes[i, 2].imshow(radial_noise_image.squeeze(), cmap='gray')\n", + " axes[i, 2].set_title(\"Radial Noise\")\n", + " axes[i, 2].axis('off')\n", + "plt.show()\n" + ] } ], "metadata": { diff --git a/testing/Trying_VAE.ipynb b/testing/Trying_VAE.ipynb new file mode 100644 index 0000000..59c52c6 --- /dev/null +++ b/testing/Trying_VAE.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import os\n", + "import sys\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "from tensorflow.keras.layers import Input, Dense, Lambda, Reshape, Conv2D, Conv2DTranspose, Flatten\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras import backend as K\n", + "from scipy.stats import norm\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", + "from models.CNN import create_cnn_model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def load(f):\n", + " # Move up one directory when loading the data\n", + " file_path = os.path.join('../../../', f)\n", + " return np.load(file_path)['arr_0']\n", + "\n", + "# Load the data\n", + "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "y_train = load('kmnist-train-labels.npz')\n", + "y_test = load('kmnist-test-labels.npz')\n", + "# Reshape the data for CNN input\n", + "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "x_test = x_test.reshape(-1, 28, 28, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the input shape and number of classes\n", + "input_shape = x_train.shape[1:] # 784 for KMNIST\n", + "num_classes = y_train.max() + 1\n", + "x_train_aug, x_val, y_train_aug, y_val = train_test_split(x_train, y_train, test_size=0.1, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot some random images from x_train_aug\n", + "# Select 5 random indices\n", + "random_indices = np.random.choice(x_train_aug.shape[0], 5, replace=False)\n", + "\n", + "# Create a figure with subplots\n", + "fig, axes = plt.subplots(1, 5, figsize=(15, 3))\n", + "\n", + "# Plot each random image\n", + "for i, idx in enumerate(random_indices):\n", + " img = x_train_aug[idx].squeeze() # Remove single-dimensional entries\n", + " axes[i].imshow(img, cmap='gray')\n", + " axes[i].axis('off')\n", + " axes[i].set_title(f\"{y_train_aug[idx]}\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameters = {\n", + " 'num_layers': 3,\n", + " 'filters': [32, 64, 64],\n", + " 'kernel_sizes': [(3, 3), (3, 3), (3, 3)],\n", + " 'activations': ['relu', 'relu', 'relu'],\n", + " 'dense_units': 64,\n", + " 'dense_activation': 'relu',\n", + " 'pooling': 'avg',\n", + " 'batch_normalization': True\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Define VAE parameters\n", + "latent_dim = 2\n", + "input_shape = (28, 28, 1)\n", + "\n", + "# Encoder\n", + "# Input layer\n", + "inputs = Input(shape=input_shape, name='encoder_input')\n", + "# First convolutional layer \n", + "x = Conv2D(32, 3, activation='relu', strides=2, padding='same')(inputs)\n", + "# Second convolutional layer\n", + "x = Conv2D(64, 3, activation='relu', strides=2, padding='same')(x) \n", + "# Flatten layer\n", + "x = Flatten()(x)\n", + "# Dense layer\n", + "x = Dense(16, activation='relu')(x)\n", + "# Dense layer for z_mean\n", + "z_mean = Dense(latent_dim, name='z_mean')(x)\n", + "# Dense layer for z_log_var\n", + "z_log_var = Dense(latent_dim, name='z_log_var')(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Reparameterization trick\n", + "def sampling(args):\n", + " # Extract z_mean and z_log_var from args\n", + " z_mean, z_log_var = args \n", + " # Get the batch size and dimensions\n", + " batch = K.shape(z_mean)[0]\n", + " dim = K.int_shape(z_mean)[1]\n", + " # Generate random normal samples\n", + " epsilon = K.random_normal(shape=(batch, dim))\n", + " # Return the sampled z\n", + " return z_mean + K.exp(0.5 * z_log_var) * epsilon" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply the sampling function to z_mean and z_log_var\n", + "z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Classification output\n", + "label_output = Dense(10, activation='softmax', name='label_output')(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "422/422 [==============================] - 5s 9ms/step - loss: 308.3031 - val_loss: 285.5771\n", + "Epoch 2/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 280.2912 - val_loss: 278.7205\n", + "Epoch 3/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 276.0619 - val_loss: 276.5463\n", + "Epoch 4/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 274.3221 - val_loss: 275.6158\n", + "Epoch 5/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 273.1137 - val_loss: 274.1030\n", + "Epoch 6/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 272.4483 - val_loss: 274.2292\n", + "Epoch 7/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 271.6632 - val_loss: 273.3286\n", + "Epoch 8/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 271.1301 - val_loss: 272.9931\n", + "Epoch 9/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 270.7764 - val_loss: 273.7676\n", + "Epoch 10/10\n", + "422/422 [==============================] - 4s 9ms/step - loss: 270.3046 - val_loss: 272.5928\n", + "here\n", + "1/1 [==============================] - 0s 314ms/step\n", + "1/1 [==============================] - 0s 61ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 15ms/step\n", + "here\n", + "1/1 [==============================] - 0s 14ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "here\n", + "1/1 [==============================] - 0s 15ms/step\n", + "1/1 [==============================] - 0s 14ms/step\n", + "Generated and saved 20 images and labels.\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Decoder \n", + "# Input layer for latent space\n", + "latent_inputs = Input(shape=(latent_dim,), name='z_sampling')\n", + "# Dense layer\n", + "x = Dense(7 * 7 * 64, activation='relu')(latent_inputs) \n", + "# Reshape layer\n", + "x = Reshape((7, 7, 64))(x)\n", + "# First convolutional transpose layer\n", + "x = Conv2DTranspose(64, 3, activation='relu', strides=2, padding='same')(x)\n", + "# Second convolutional transpose layer\n", + "x = Conv2DTranspose(32, 3, activation='relu', strides=2, padding='same')(x)\n", + "# Output layer\n", + "outputs = Conv2DTranspose(1, 3, activation='sigmoid', padding='same')(x)\n", + "\n", + "# Define VAE model\n", + "# Create encoder model\n", + "encoder = Model(inputs, [z_mean, z_log_var, z, label_output], name='encoder')\n", + "encoder.summary()\n", + "# Create decoder model\n", + "decoder = Model(latent_inputs, outputs, name='decoder')\n", + "decoder.summary()\n", + "# Create VAE model\n", + "outputs = decoder(encoder(inputs)[2])\n", + "label_output = encoder(inputs)[3]\n", + "# Add the decoder output to the encoder output\n", + "vae = Model(inputs, [outputs, label_output], name='vae')\n", + "\n", + "# VAE loss\n", + "reconstruction_loss = K.sum(K.binary_crossentropy(inputs, outputs), axis=[1, 2, 3])\n", + "kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\n", + "vae_loss = K.mean(reconstruction_loss + kl_loss)\n", + "vae.add_loss(vae_loss)\n", + "\n", + "# Compile and train VAE\n", + "vae.compile(optimizer='adam')\n", + "vae.fit(x_train_aug, epochs=10, batch_size=128, validation_data=(x_val, None))\n", + "\n", + "# Generate a large amount of VAE images and labels\n", + "num_samples = 20 # Number of samples to generate\n", + "generated_images = []\n", + "generated_labels = []\n", + "\n", + "for _ in range(num_samples):\n", + " print('here')\n", + " # Generate a random point in the latent space\n", + " z_sample = np.random.normal(size=(1, latent_dim))\n", + " \n", + " # Decode the point to generate an image\n", + " x_decoded = decoder.predict(z_sample)\n", + " \n", + " # Reshape the image\n", + " generated_image = x_decoded[0].reshape(digit_size, digit_size, 1)\n", + " \n", + " # Use the encoder to get the label (classification) for the generated image\n", + " _, _, z_encoded = encoder.predict(np.expand_dims(generated_image, axis=0))\n", + " \n", + " # Use the encoder output to determine the label\n", + " # Here we're using the first dimension of the latent space as a simple heuristic\n", + " # You might want to use a more sophisticated method depending on your specific VAE\n", + " label = int(z_encoded[0][0] * 10) # Scale to 0-9 range and convert to int\n", + " # Ensure the label is within the valid range [0, 9]\n", + " label = max(0, min(9, label))\n", + " \n", + " # Print for debugging\n", + " if label < 0 or label > 9:\n", + " print(f\"Warning: Invalid label {label} generated. Clamped to {max(0, min(9, label))}.\")\n", + " generated_images.append(generated_image)\n", + " generated_labels.append(label)\n", + "\n", + "# Convert lists to numpy arrays\n", + "generated_images = np.array(generated_images)\n", + "generated_labels = np.array(generated_labels)\n", + "\n", + "# Save the generated dataset\n", + "np.save('generated_images.npy', generated_images)\n", + "np.save('generated_labels.npy', generated_labels)\n", + "\n", + "print(f\"Generated and saved {num_samples} images and labels.\")\n", + "\n", + "# Visualize a few examples\n", + "n_examples = 5\n", + "fig, axes = plt.subplots(1, n_examples, figsize=(15, 3))\n", + "for i in range(n_examples):\n", + " axes[i].imshow(generated_images[i].squeeze(), cmap='gray')\n", + " axes[i].set_title(f\"Label: {generated_labels[i]}\")\n", + " axes[i].axis('off')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'train_images.npy'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[24], line 7\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Load the training data\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# Assuming you have saved your training data similarly to the generated data\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m train_images \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain_images.npy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 8\u001b[0m train_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_labels.npy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# Select random indices\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\jdove\\anaconda3\\envs\\GPU_TF\\lib\\site-packages\\numpy\\lib\\npyio.py:405\u001b[0m, in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[0;32m 403\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 404\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 405\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 406\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 408\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'train_images.npy'" + ] + } + ], + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": 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zh@1q=*+D4fav?E&2lY5%UX-N7N&-R!aCrdLXv; z|1)M;gPqV_7>~;MdvFzYXC4B-wyv=Vg-c^$et8#Gh0PJzt8zG1JDrZr@ggZtE%=O1 zp6kwS6MMcY5y`n1i?($piJPw95Z@edU#wZ^Ddgd#xV0|NxTcQtyyjg`a{iDs^|p_q zZykDQ-V+gSmb+ojvP5{#%Yz%PqAC9r{-k^0rK%y0T^)eWQ_s+YrNe1v;So~v-&TIx U%KcpTposH literal 0 HcmV?d00001 From 2806979107bd7dc261ee2ca96dec729756e7e1b8 Mon Sep 17 00:00:00 2001 From: James Overmeyer Date: Thu, 24 Oct 2024 22:51:02 -0400 Subject: [PATCH 3/9] Created a bunch of figures and compiled data into dfs --- testing/CNN-testing.ipynb | 888 +++++++++++++----- testing/Noise-Injection-CNN.ipynb | 2 +- testing/augmentation_accuracy.csv | 16 + testing/augmentation_accuracy.png | Bin 0 -> 2396 bytes testing/augmentation_loss.csv | 16 + testing/augmentation_loss.png | Bin 0 -> 2396 bytes .../confusion_matrix_avg_False_2024-10-24.png | Bin 0 -> 46797 bytes .../confusion_matrix_avg_True_2024-10-24.png | Bin 0 -> 42089 bytes ...fusion_matrix_best_avg_True_2024-10-24.png | Bin 0 -> 42686 bytes ...fusion_matrix_best_max_True_2024-10-24.png | Bin 0 -> 43757 bytes testing/confusion_matrix_flip_2024-10-24.png | Bin 0 -> 48737 bytes testing/confusion_matrix_mask_2024-10-24.png | Bin 0 -> 46256 bytes .../confusion_matrix_max_False_2024-10-24.png | Bin 0 -> 47836 bytes .../confusion_matrix_max_True_2024-10-24.png | Bin 0 -> 43208 bytes .../confusion_matrix_no_noise_2024-10-24.png | Bin 0 -> 46617 bytes ...nfusion_matrix_radial_noise_2024-10-24.png | Bin 0 -> 46865 bytes ..._matrix_random_noise_+_zoom_2024-10-24.png | Bin 0 -> 48157 bytes ...nfusion_matrix_random_noise_2024-10-24.png | Bin 0 -> 46590 bytes testing/confusion_matrix_zoom_2024-10-24.png | Bin 0 -> 45816 bytes testing/figures.ipynb | 148 +++ testing/hyperparameters.json | 1 + .../learning_curve_avg_False_2024-10-24.png | Bin 0 -> 37970 bytes .../learning_curve_avg_True_2024-10-24.png | Bin 0 -> 34317 bytes ...earning_curve_best_avg_True_2024-10-24.png | Bin 0 -> 42022 bytes ...earning_curve_best_max_True_2024-10-24.png | Bin 0 -> 41371 bytes .../learning_curve_max_False_2024-10-24.png | Bin 0 -> 36973 bytes .../learning_curve_max_True_2024-10-24.png | Bin 0 -> 36257 bytes testing/test_accuracy.csv | 21 + testing/test_augmentation_accuracy.png | Bin 0 -> 2396 bytes testing/test_augmentation_loss.png | Bin 0 -> 2396 bytes testing/test_loss.csv | 21 + 31 files changed, 874 insertions(+), 239 deletions(-) create mode 100644 testing/augmentation_accuracy.csv create mode 100644 testing/augmentation_accuracy.png create mode 100644 testing/augmentation_loss.csv create mode 100644 testing/augmentation_loss.png create mode 100644 testing/confusion_matrix_avg_False_2024-10-24.png create mode 100644 testing/confusion_matrix_avg_True_2024-10-24.png create mode 100644 testing/confusion_matrix_best_avg_True_2024-10-24.png create mode 100644 testing/confusion_matrix_best_max_True_2024-10-24.png create mode 100644 testing/confusion_matrix_flip_2024-10-24.png create mode 100644 testing/confusion_matrix_mask_2024-10-24.png create mode 100644 testing/confusion_matrix_max_False_2024-10-24.png create mode 100644 testing/confusion_matrix_max_True_2024-10-24.png create mode 100644 testing/confusion_matrix_no_noise_2024-10-24.png create mode 100644 testing/confusion_matrix_radial_noise_2024-10-24.png create mode 100644 testing/confusion_matrix_random_noise_+_zoom_2024-10-24.png create mode 100644 testing/confusion_matrix_random_noise_2024-10-24.png create mode 100644 testing/confusion_matrix_zoom_2024-10-24.png create mode 100644 testing/figures.ipynb create mode 100644 testing/hyperparameters.json create mode 100644 testing/learning_curve_avg_False_2024-10-24.png create mode 100644 testing/learning_curve_avg_True_2024-10-24.png create mode 100644 testing/learning_curve_best_avg_True_2024-10-24.png create mode 100644 testing/learning_curve_best_max_True_2024-10-24.png create mode 100644 testing/learning_curve_max_False_2024-10-24.png create mode 100644 testing/learning_curve_max_True_2024-10-24.png create mode 100644 testing/test_accuracy.csv create mode 100644 testing/test_augmentation_accuracy.png create mode 100644 testing/test_augmentation_loss.png create mode 100644 testing/test_loss.csv diff --git a/testing/CNN-testing.ipynb b/testing/CNN-testing.ipynb index 72bdec5..a9541e1 100644 --- a/testing/CNN-testing.ipynb +++ b/testing/CNN-testing.ipynb @@ -2,25 +2,32 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ + "\n", "import numpy as np\n", + "import tensorflow as tf\n", "import os\n", "import sys\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))\n", - "from models.CNN import create_cnn_model" + "from models.CNN import create_cnn_model\n", + "import datetime\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "import pandas as pd\n", + "import json\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -30,263 +37,169 @@ " return np.load(file_path)['arr_0']\n", "\n", "# Load the data\n", - "x_train = load('kmnist-train-imgs.npz')/ 255.0\n", + "X_train = load('kmnist-train-imgs.npz')/ 255.0\n", "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", - "y_train = load('kmnist-train-labels.npz')\n", + "Y_train = load('kmnist-train-labels.npz')\n", "y_test = load('kmnist-test-labels.npz')\n", "# Reshape the data for CNN input\n", - "x_train = x_train.reshape(-1, 28, 28, 1)\n", + "X_train = x_train.reshape(-1, 28, 28, 1)\n", "x_test = x_test.reshape(-1, 28, 28, 1)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Define the input shape and number of classes\n", - "input_shape = x_train.shape[1:] # 784 for KMNIST\n", - "num_classes = y_train.max() + 1" + "input_shape = X_train.shape[1:] # 784 for KMNIST\n", + "num_classes = Y_train.max() + 1" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Define data augmentation parameters\n", - "datagen = ImageDataGenerator(\n", - " rotation_range=10, # Randomly rotate images in the range (degrees, 0 to 180)\n", - " width_shift_range=0.1, # Randomly shift images horizontally (fraction of total width)\n", - " height_shift_range=0.1, # Randomly shift images vertically (fraction of total height)\n", - " zoom_range=0.1, # Randomly zoom image\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Testing configuration: {'batch_normalization': False, 'pooling': 'max', 'datagen': True}\n", - "Epoch 1/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.7079 - accuracy: 0.7683 - val_loss: 0.6037 - val_accuracy: 0.8184\n", - "Epoch 2/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.3379 - accuracy: 0.8929 - val_loss: 0.4694 - val_accuracy: 0.8582\n", - "Epoch 3/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2566 - accuracy: 0.9187 - val_loss: 0.4327 - val_accuracy: 0.8744\n", - "Epoch 4/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2136 - accuracy: 0.9312 - val_loss: 0.3877 - val_accuracy: 0.8922\n", - "Epoch 5/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1914 - accuracy: 0.9396 - val_loss: 0.3491 - val_accuracy: 0.9040\n", - "Epoch 6/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1725 - accuracy: 0.9465 - val_loss: 0.2866 - val_accuracy: 0.9201\n", - "Epoch 7/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.1577 - accuracy: 0.9505 - val_loss: 0.2824 - val_accuracy: 0.9220\n", - "Epoch 8/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1442 - accuracy: 0.9538 - val_loss: 0.2620 - val_accuracy: 0.9267\n", - "Epoch 9/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1370 - accuracy: 0.9563 - val_loss: 0.2761 - val_accuracy: 0.9277\n", - "Epoch 10/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1278 - accuracy: 0.9592 - val_loss: 0.2451 - val_accuracy: 0.9375\n", - "Testing configuration: {'batch_normalization': True, 'pooling': 'max', 'datagen': True}\n", + "Testing configuration: {'batch_normalization': False, 'pooling': 'max'}\n", "Epoch 1/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.3189 - accuracy: 0.9088 - val_loss: 0.2237 - val_accuracy: 0.9310\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.4779 - accuracy: 0.8454 - val_loss: 0.2424 - val_accuracy: 0.9187\n", "Epoch 2/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1320 - accuracy: 0.9601 - val_loss: 0.2399 - val_accuracy: 0.9290\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1746 - accuracy: 0.9455 - val_loss: 0.1632 - val_accuracy: 0.9475\n", "Epoch 3/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0978 - accuracy: 0.9700 - val_loss: 0.1817 - val_accuracy: 0.9492\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1203 - accuracy: 0.9623 - val_loss: 0.1420 - val_accuracy: 0.9543\n", "Epoch 4/10\n", - "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0816 - accuracy: 0.9741 - val_loss: 0.1485 - val_accuracy: 0.9578\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0896 - accuracy: 0.9717 - val_loss: 0.1250 - val_accuracy: 0.9615\n", "Epoch 5/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0724 - accuracy: 0.9778 - val_loss: 0.1494 - val_accuracy: 0.9602\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0714 - accuracy: 0.9772 - val_loss: 0.1177 - val_accuracy: 0.9675\n", "Epoch 6/10\n", - "1875/1875 [==============================] - 16s 9ms/step - loss: 0.0633 - accuracy: 0.9802 - val_loss: 0.1626 - val_accuracy: 0.9589\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0564 - accuracy: 0.9818 - val_loss: 0.1095 - val_accuracy: 0.9668\n", "Epoch 7/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0583 - accuracy: 0.9818 - val_loss: 0.1262 - val_accuracy: 0.9663\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0484 - accuracy: 0.9846 - val_loss: 0.1419 - val_accuracy: 0.9608\n", "Epoch 8/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0546 - accuracy: 0.9823 - val_loss: 0.1730 - val_accuracy: 0.9573\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0374 - accuracy: 0.9876 - val_loss: 0.1219 - val_accuracy: 0.9670\n", "Epoch 9/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0529 - accuracy: 0.9836 - val_loss: 0.1212 - val_accuracy: 0.9710\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0343 - accuracy: 0.9882 - val_loss: 0.1108 - val_accuracy: 0.9715\n", "Epoch 10/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0482 - accuracy: 0.9841 - val_loss: 0.1162 - val_accuracy: 0.9699\n", - "Testing configuration: {'batch_normalization': False, 'pooling': 'avg', 'datagen': True}\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0294 - accuracy: 0.9903 - val_loss: 0.1394 - val_accuracy: 0.9638\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Testing configuration: {'batch_normalization': True, 'pooling': 'max'}\n", "Epoch 1/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.8871 - accuracy: 0.7038 - val_loss: 0.7104 - val_accuracy: 0.7739\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2068 - accuracy: 0.9444 - val_loss: 0.0855 - val_accuracy: 0.9750\n", "Epoch 2/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4324 - accuracy: 0.8619 - val_loss: 0.5055 - val_accuracy: 0.8411\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0674 - accuracy: 0.9803 - val_loss: 0.0682 - val_accuracy: 0.9787\n", "Epoch 3/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.3269 - accuracy: 0.8960 - val_loss: 0.4188 - val_accuracy: 0.8696\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0451 - accuracy: 0.9866 - val_loss: 0.0569 - val_accuracy: 0.9832\n", "Epoch 4/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2688 - accuracy: 0.9129 - val_loss: 0.3988 - val_accuracy: 0.8768\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0333 - accuracy: 0.9903 - val_loss: 0.0452 - val_accuracy: 0.9857\n", "Epoch 5/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2329 - accuracy: 0.9259 - val_loss: 0.3193 - val_accuracy: 0.9055\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0255 - accuracy: 0.9919 - val_loss: 0.0856 - val_accuracy: 0.9747\n", "Epoch 6/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.2105 - accuracy: 0.9335 - val_loss: 0.2928 - val_accuracy: 0.9105\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0211 - accuracy: 0.9936 - val_loss: 0.0746 - val_accuracy: 0.9778\n", "Epoch 7/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1874 - accuracy: 0.9400 - val_loss: 0.2830 - val_accuracy: 0.9132\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0180 - accuracy: 0.9942 - val_loss: 0.0435 - val_accuracy: 0.9867\n", "Epoch 8/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1710 - accuracy: 0.9461 - val_loss: 0.3038 - val_accuracy: 0.9107\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0150 - accuracy: 0.9949 - val_loss: 0.0456 - val_accuracy: 0.9878\n", "Epoch 9/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1615 - accuracy: 0.9491 - val_loss: 0.2778 - val_accuracy: 0.9200\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0115 - accuracy: 0.9965 - val_loss: 0.0559 - val_accuracy: 0.9848\n", "Epoch 10/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1506 - accuracy: 0.9520 - val_loss: 0.2431 - val_accuracy: 0.9248\n", - "Testing configuration: {'batch_normalization': True, 'pooling': 'avg', 'datagen': True}\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0580 - val_accuracy: 0.9837\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Testing configuration: {'batch_normalization': False, 'pooling': 'avg'}\n", "Epoch 1/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.3119 - accuracy: 0.9084 - val_loss: 0.3626 - val_accuracy: 0.8903\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6600 - accuracy: 0.7839 - val_loss: 0.3370 - val_accuracy: 0.8948\n", "Epoch 2/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1225 - accuracy: 0.9626 - val_loss: 0.2259 - val_accuracy: 0.9345\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.2646 - accuracy: 0.9176 - val_loss: 0.2276 - val_accuracy: 0.9302\n", "Epoch 3/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0922 - accuracy: 0.9710 - val_loss: 0.1676 - val_accuracy: 0.9508\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1853 - accuracy: 0.9413 - val_loss: 0.1736 - val_accuracy: 0.9457\n", "Epoch 4/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0768 - accuracy: 0.9753 - val_loss: 0.1860 - val_accuracy: 0.9517\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1452 - accuracy: 0.9551 - val_loss: 0.1435 - val_accuracy: 0.9523\n", "Epoch 5/10\n", - "1875/1875 [==============================] - 16s 8ms/step - loss: 0.0683 - accuracy: 0.9790 - val_loss: 0.2578 - val_accuracy: 0.9327\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1190 - accuracy: 0.9628 - val_loss: 0.1163 - val_accuracy: 0.9642\n", "Epoch 6/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0617 - accuracy: 0.9801 - val_loss: 0.1404 - val_accuracy: 0.9619\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1007 - accuracy: 0.9691 - val_loss: 0.1121 - val_accuracy: 0.9650\n", "Epoch 7/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0539 - accuracy: 0.9834 - val_loss: 0.1144 - val_accuracy: 0.9710\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0852 - accuracy: 0.9731 - val_loss: 0.1128 - val_accuracy: 0.9622\n", "Epoch 8/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0516 - accuracy: 0.9840 - val_loss: 0.1122 - val_accuracy: 0.9705\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0734 - accuracy: 0.9764 - val_loss: 0.1235 - val_accuracy: 0.9608\n", "Epoch 9/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0485 - accuracy: 0.9847 - val_loss: 0.1191 - val_accuracy: 0.9701\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0629 - accuracy: 0.9800 - val_loss: 0.0907 - val_accuracy: 0.9728\n", "Epoch 10/10\n", - "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0430 - accuracy: 0.9866 - val_loss: 0.1331 - val_accuracy: 0.9679\n", - "Testing configuration: {'batch_normalization': False, 'pooling': 'max', 'datagen': False}\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0567 - accuracy: 0.9813 - val_loss: 0.0939 - val_accuracy: 0.9713\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Testing configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.4810 - accuracy: 0.8462 - val_loss: 0.2719 - val_accuracy: 0.9128\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1940 - accuracy: 0.9486 - val_loss: 0.0727 - val_accuracy: 0.9782\n", "Epoch 2/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1831 - accuracy: 0.9430 - val_loss: 0.1757 - val_accuracy: 0.9442\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0668 - accuracy: 0.9805 - val_loss: 0.0744 - val_accuracy: 0.9775\n", "Epoch 3/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1235 - accuracy: 0.9611 - val_loss: 0.1461 - val_accuracy: 0.9558\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0457 - accuracy: 0.9863 - val_loss: 0.0427 - val_accuracy: 0.9872\n", "Epoch 4/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0935 - accuracy: 0.9707 - val_loss: 0.1290 - val_accuracy: 0.9608\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0346 - accuracy: 0.9895 - val_loss: 0.0598 - val_accuracy: 0.9837\n", "Epoch 5/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0737 - accuracy: 0.9770 - val_loss: 0.1362 - val_accuracy: 0.9595\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0263 - accuracy: 0.9916 - val_loss: 0.0419 - val_accuracy: 0.9882\n", "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0573 - accuracy: 0.9812 - val_loss: 0.1207 - val_accuracy: 0.9652\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0209 - accuracy: 0.9935 - val_loss: 0.0416 - val_accuracy: 0.9860\n", "Epoch 7/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0449 - accuracy: 0.9857 - val_loss: 0.1474 - val_accuracy: 0.9630\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0171 - accuracy: 0.9947 - val_loss: 0.0428 - val_accuracy: 0.9877\n", "Epoch 8/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0377 - accuracy: 0.9877 - val_loss: 0.1225 - val_accuracy: 0.9702\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0168 - accuracy: 0.9944 - val_loss: 0.0531 - val_accuracy: 0.9843\n", "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0324 - accuracy: 0.9891 - val_loss: 0.1222 - val_accuracy: 0.9712\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0144 - accuracy: 0.9956 - val_loss: 0.0420 - val_accuracy: 0.9877\n", "Epoch 10/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0264 - accuracy: 0.9910 - val_loss: 0.1189 - val_accuracy: 0.9738\n", - "Testing configuration: {'batch_normalization': True, 'pooling': 'max', 'datagen': False}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 10s 5ms/step - loss: 0.2036 - accuracy: 0.9450 - val_loss: 0.1045 - val_accuracy: 0.9692\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0671 - accuracy: 0.9805 - val_loss: 0.0595 - val_accuracy: 0.9820\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0442 - accuracy: 0.9867 - val_loss: 0.0565 - val_accuracy: 0.9842\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0331 - accuracy: 0.9899 - val_loss: 0.0792 - val_accuracy: 0.9790\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0254 - accuracy: 0.9925 - val_loss: 0.0674 - val_accuracy: 0.9812\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0210 - accuracy: 0.9935 - val_loss: 0.0785 - val_accuracy: 0.9777\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0174 - accuracy: 0.9943 - val_loss: 0.0826 - val_accuracy: 0.9788\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0154 - accuracy: 0.9950 - val_loss: 0.0627 - val_accuracy: 0.9847\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0125 - accuracy: 0.9956 - val_loss: 0.0531 - val_accuracy: 0.9880\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0108 - accuracy: 0.9965 - val_loss: 0.0709 - val_accuracy: 0.9805\n", - "Testing configuration: {'batch_normalization': False, 'pooling': 'avg', 'datagen': False}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.6621 - accuracy: 0.7830 - val_loss: 0.3313 - val_accuracy: 0.8950\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.2744 - accuracy: 0.9132 - val_loss: 0.2161 - val_accuracy: 0.9338\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1913 - accuracy: 0.9405 - val_loss: 0.1646 - val_accuracy: 0.9477\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1470 - accuracy: 0.9544 - val_loss: 0.1557 - val_accuracy: 0.9510\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1233 - accuracy: 0.9611 - val_loss: 0.1384 - val_accuracy: 0.9563\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1012 - accuracy: 0.9678 - val_loss: 0.1304 - val_accuracy: 0.9598\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0861 - accuracy: 0.9721 - val_loss: 0.1206 - val_accuracy: 0.9633\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0737 - accuracy: 0.9762 - val_loss: 0.1004 - val_accuracy: 0.9723\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0636 - accuracy: 0.9796 - val_loss: 0.1009 - val_accuracy: 0.9715\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0536 - accuracy: 0.9826 - val_loss: 0.1036 - val_accuracy: 0.9718\n", - "Testing configuration: {'batch_normalization': True, 'pooling': 'avg', 'datagen': False}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1921 - accuracy: 0.9490 - val_loss: 0.1315 - val_accuracy: 0.9608\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0648 - accuracy: 0.9811 - val_loss: 0.0713 - val_accuracy: 0.9798\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0448 - accuracy: 0.9872 - val_loss: 0.0704 - val_accuracy: 0.9803\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0351 - accuracy: 0.9893 - val_loss: 0.0515 - val_accuracy: 0.9850\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0282 - accuracy: 0.9914 - val_loss: 0.0455 - val_accuracy: 0.9873\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0221 - accuracy: 0.9931 - val_loss: 0.0411 - val_accuracy: 0.9888\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0181 - accuracy: 0.9947 - val_loss: 0.0415 - val_accuracy: 0.9895\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0174 - accuracy: 0.9944 - val_loss: 0.0481 - val_accuracy: 0.9885\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0486 - val_accuracy: 0.9883\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0131 - accuracy: 0.9957 - val_loss: 0.0459 - val_accuracy: 0.9892\n", - "Best configuration: {'batch_normalization': True, 'pooling': 'avg', 'datagen': False}\n", - "Best accuracy: 0.9891666769981384\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9963 - val_loss: 0.0449 - val_accuracy: 0.9888\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Best configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", + "Best accuracy: 0.9888333082199097\n", "Training best model on full training set...\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1949 - accuracy: 0.9474 - val_loss: 0.0890 - val_accuracy: 0.9718\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1919 - accuracy: 0.9486 - val_loss: 0.1733 - val_accuracy: 0.9468\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0692 - accuracy: 0.9798 - val_loss: 0.0710 - val_accuracy: 0.9795\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0663 - accuracy: 0.9811 - val_loss: 0.0569 - val_accuracy: 0.9845\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0486 - accuracy: 0.9856 - val_loss: 0.0613 - val_accuracy: 0.9815\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0466 - accuracy: 0.9863 - val_loss: 0.0710 - val_accuracy: 0.9765\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0367 - accuracy: 0.9889 - val_loss: 0.0781 - val_accuracy: 0.9795\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0345 - accuracy: 0.9895 - val_loss: 0.0448 - val_accuracy: 0.9862\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0278 - accuracy: 0.9915 - val_loss: 0.0618 - val_accuracy: 0.9820\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0276 - accuracy: 0.9915 - val_loss: 0.0489 - val_accuracy: 0.9873\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0238 - accuracy: 0.9925 - val_loss: 0.0432 - val_accuracy: 0.9888\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0232 - accuracy: 0.9925 - val_loss: 0.0357 - val_accuracy: 0.9907\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.0382 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0182 - accuracy: 0.9943 - val_loss: 0.0413 - val_accuracy: 0.9873\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0371 - val_accuracy: 0.9892\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.0398 - val_accuracy: 0.9885\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.0484 - val_accuracy: 0.9848\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.0454 - val_accuracy: 0.9862\n", "Epoch 10/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 0.0378 - val_accuracy: 0.9895\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0134 - accuracy: 0.9955 - val_loss: 0.0317 - val_accuracy: 0.9910\n", "Epoch 11/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0116 - accuracy: 0.9962 - val_loss: 0.0492 - val_accuracy: 0.9868\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0099 - accuracy: 0.9970 - val_loss: 0.0791 - val_accuracy: 0.9773\n", "Epoch 12/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0099 - accuracy: 0.9971 - val_loss: 0.0401 - val_accuracy: 0.9905\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0104 - accuracy: 0.9966 - val_loss: 0.0433 - val_accuracy: 0.9892\n", "Epoch 13/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0092 - accuracy: 0.9966 - val_loss: 0.0403 - val_accuracy: 0.9898\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0095 - accuracy: 0.9969 - val_loss: 0.0440 - val_accuracy: 0.9878\n", "Epoch 14/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0081 - accuracy: 0.9974 - val_loss: 0.0514 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0079 - accuracy: 0.9975 - val_loss: 0.0308 - val_accuracy: 0.9910\n", "Epoch 15/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0449 - val_accuracy: 0.9888\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0087 - accuracy: 0.9972 - val_loss: 0.0360 - val_accuracy: 0.9892\n", "Epoch 16/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0068 - accuracy: 0.9977 - val_loss: 0.0513 - val_accuracy: 0.9862\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.0318 - val_accuracy: 0.9912\n", "Epoch 17/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0073 - accuracy: 0.9975 - val_loss: 0.0405 - val_accuracy: 0.9907\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.0340 - val_accuracy: 0.9918\n", "Epoch 18/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0072 - accuracy: 0.9977 - val_loss: 0.0490 - val_accuracy: 0.9888\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0353 - val_accuracy: 0.9918\n", "Epoch 19/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0411 - val_accuracy: 0.9900\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0395 - val_accuracy: 0.9913\n", "Epoch 20/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 0.0434 - val_accuracy: 0.9892\n", - "Test accuracy: 0.9650\n", - "Test loss: 0.1843\n" + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0060 - accuracy: 0.9980 - val_loss: 0.0248 - val_accuracy: 0.9930\n", + "Test accuracy: 0.9720\n", + "Test loss: 0.1486\n", + "313/313 [==============================] - 0s 1ms/step\n" ] } ], @@ -300,36 +213,67 @@ " 'dense_activation': 'relu'\n", "}\n", "configs = [\n", - " {'batch_normalization': False, 'pooling': 'max', 'datagen': True},\n", - " {'batch_normalization': True, 'pooling': 'max', 'datagen': True},\n", - " {'batch_normalization': False, 'pooling': 'avg', 'datagen': True},\n", - " {'batch_normalization': True, 'pooling': 'avg', 'datagen': True},\n", - " {'batch_normalization': False, 'pooling': 'max', 'datagen': False},\n", - " {'batch_normalization': True, 'pooling': 'max', 'datagen': False},\n", - " {'batch_normalization': False, 'pooling': 'avg', 'datagen': False},\n", - " {'batch_normalization': True, 'pooling': 'avg', 'datagen': False}\n", + " {'batch_normalization': False, 'pooling': 'max'},\n", + " {'batch_normalization': True, 'pooling': 'max'},\n", + " {'batch_normalization': False, 'pooling': 'avg'},\n", + " {'batch_normalization': True, 'pooling': 'avg'}\n", "]\n", - "\n", + "x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=42)\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", "best_config = None\n", "best_accuracy = 0\n", "histories = []\n", + "\n", + "test_loss_df = pd.DataFrame()\n", + "test_accuracy_df = pd.DataFrame()\n", + "\n", + "\n", + "\n", "for config in configs:\n", " print(f\"Testing configuration: {config}\")\n", " hyperparameters.update(config)\n", " model = create_cnn_model(input_shape, num_classes, hyperparameters)\n", - " if config['datagen']:\n", - " history = model.fit(datagen.flow(x_train, y_train, batch_size=32),\n", - " epochs=10,\n", - " validation_data=(x_test, y_test),\n", - " verbose=1)\n", - " else:\n", - " history = model.fit(x_train, y_train,\n", - " epochs=10,\n", - " batch_size=32,\n", - " validation_split=0.1,\n", - " verbose=1)\n", + " history = model.fit(x_train, y_train,\n", + " epochs=10,\n", + " batch_size=32,\n", + " validation_data=(x_val, y_val),\n", + " verbose=1,\n", + " callbacks=[early_stopping])\n", + " # Learning curve\n", + " plt.figure(figsize=(10, 5))\n", + " plt.plot(history.history['accuracy'], label='Training Accuracy')\n", + " plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n", + " plt.title(f'Learning Curve for Config: {config[\"pooling\"]} {config[\"batch_normalization\"]}')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Accuracy')\n", + " plt.legend()\n", + " now = datetime.datetime.now()\n", + " plt.savefig(f'learning_curve_{config[\"pooling\"]}_{config[\"batch_normalization\"]}_{now.strftime(\"%Y-%m-%d\")}.png')\n", + " plt.close()\n", + " # Append the last value of val_loss and val_accuracy an additional ten times\n", + " last_val_loss = history.history['val_loss'][-1]\n", + " last_val_accuracy = history.history['val_accuracy'][-1]\n", + " for _ in range(10):\n", + " history.history['val_loss'].append(last_val_loss)\n", + " history.history['val_accuracy'].append(last_val_accuracy)\n", + " test_loss_df[f\"{config['pooling']} {config['batch_normalization']}\"] = history.history['val_loss']\n", + " test_accuracy_df[f\"{config['pooling']} {config['batch_normalization']}\"] = history.history['val_accuracy']\n", + "\n", + "\n", + " # Predictions on the validation set\n", + " y_pred = model.predict(x_test)\n", + " y_pred_class = np.argmax(y_pred, axis=1)\n", + " # Confusion matrix\n", + " confusion = confusion_matrix(y_test, y_pred_class)\n", + " disp = ConfusionMatrixDisplay(confusion_matrix=confusion)\n", + " disp.plot()\n", + "\n", + " plt.title(f'Confusion Matrix for Config: {config[\"pooling\"]} {config[\"batch_normalization\"]}')\n", + " plt.savefig(f'confusion_matrix_{config[\"pooling\"]}_{config[\"batch_normalization\"]}_{now.strftime(\"%Y-%m-%d\")}.png')\n", + " plt.close()\n", " histories.append(history)\n", " final_acc = history.history['val_accuracy'][-1]\n", + "\n", " if final_acc > best_accuracy:\n", " best_accuracy = final_acc\n", " best_config = config\n", @@ -344,70 +288,538 @@ "best_model = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "\n", "# Train on the full training set\n", - "if best_config['datagen']:\n", - " history = best_model.fit(datagen.flow(x_train, y_train, batch_size=32),\n", - " epochs=20,\n", - " validation_split=0.1,\n", - " verbose=1)\n", - "else:\n", - " history = best_model.fit(x_train, y_train, \n", - " epochs=20, \n", - " batch_size=32, \n", - " validation_split=0.1, \n", - " verbose=1)\n", + "history = best_model.fit(x_train, y_train, \n", + " epochs=20, \n", + " batch_size=32, \n", + " validation_data=(x_val, y_val),\n", + " verbose=1)\n", "\n", "# Evaluate on the test set\n", "test_loss, test_accuracy = best_model.evaluate(x_test, y_test, verbose=0)\n", "print(f\"Test accuracy: {test_accuracy:.4f}\")\n", "print(f\"Test loss: {test_loss:.4f}\")\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(history.history['accuracy'], label='Training Accuracy')\n", + "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n", + "plt.title(f'Learning Curve for Best Model: {best_config[\"pooling\"]} {best_config[\"batch_normalization\"]}')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "now = datetime.datetime.now()\n", + "plt.savefig(f'learning_curve_best_{best_config[\"pooling\"]}_{best_config[\"batch_normalization\"]}_{now.strftime(\"%Y-%m-%d\")}.png')\n", + "plt.close()\n", + "# Predictions on the test set\n", + "y_pred = best_model.predict(x_test)\n", + "y_pred_class = np.argmax(y_pred, axis=1)\n", + "\n", + "# Confusion matrix\n", + "confusion = confusion_matrix(y_test, y_pred_class)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=confusion)\n", + "disp.plot()\n", + "plt.title(f'Confusion Matrix for Best Model: {best_config[\"pooling\"]} {best_config[\"batch_normalization\"]}')\n", + "plt.savefig(f'confusion_matrix_best_{best_config[\"pooling\"]}_{best_config[\"batch_normalization\"]}_{now.strftime(\"%Y-%m-%d\")}.png')\n", + "plt.close()\n", + "# Add the \"best + config\" column\n", + "test_loss_df[f\"best {best_config['pooling']} {best_config['batch_normalization']}\"] = history.history['val_loss']\n", + "test_accuracy_df[f\"best {best_config['pooling']} {best_config['batch_normalization']}\"] = history.history['val_accuracy']\n", + "\n", + "# Save the dataframes to CSV files\n", + "test_loss_df.to_csv('test_loss.csv', index=False)\n", + "test_accuracy_df.to_csv('test_accuracy.csv', index=False)\n", + "\n", + "\n", "\n" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "with open('hyperparameters.json', 'w') as f:\n", + " json.dump(hyperparameters, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "def cutout(image, mask_size=16):\n", + " \"\"\"Applies Cutout augmentation to an image.\"\"\"\n", + " h, w, _ = image.shape\n", + " x = np.random.randint(0, w)\n", + " y = np.random.randint(0, h)\n", + "\n", + " # Ensure the cutout does not go out of bounds\n", + " x1 = np.clip(x - mask_size // 2, 0, w)\n", + " x2 = np.clip(x + mask_size // 2, 0, w)\n", + " y1 = np.clip(y - mask_size // 2, 0, h)\n", + " y2 = np.clip(y + mask_size // 2, 0, h)\n", + "\n", + " # Create a copy of the image to avoid modifying the original\n", + " image_copy = image.copy()\n", + " image_copy[y1:y2, x1:x2, :] = 0 # Set the cutout region to black\n", + "\n", + " return image_copy" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "{'num_layers': 3, 'filters': [32, 64, 64], 'kernel_sizes': [[3, 3], [3, 3], [3, 3]], 'activations': ['relu', 'relu', 'relu'], 'dense_units': 64, 'dense_activation': 'relu', 'batch_normalization': True, 'pooling': 'avg'}\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1963 - accuracy: 0.9473 - val_loss: 0.0956 - val_accuracy: 0.9685\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0700 - accuracy: 0.9792 - val_loss: 0.0603 - val_accuracy: 0.9827\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0469 - accuracy: 0.9859 - val_loss: 0.0517 - val_accuracy: 0.9817\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0405 - accuracy: 0.9873 - val_loss: 0.0437 - val_accuracy: 0.9868\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0312 - accuracy: 0.9905 - val_loss: 0.0539 - val_accuracy: 0.9830\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0270 - accuracy: 0.9918 - val_loss: 0.0376 - val_accuracy: 0.9897\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0234 - accuracy: 0.9923 - val_loss: 0.0404 - val_accuracy: 0.9880\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0185 - accuracy: 0.9938 - val_loss: 0.0423 - val_accuracy: 0.9880\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0173 - accuracy: 0.9944 - val_loss: 0.0383 - val_accuracy: 0.9900\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0396 - val_accuracy: 0.9880\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0138 - accuracy: 0.9956 - val_loss: 0.0468 - val_accuracy: 0.9885\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0139 - accuracy: 0.9954 - val_loss: 0.0356 - val_accuracy: 0.9903\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0116 - accuracy: 0.9959 - val_loss: 0.0327 - val_accuracy: 0.9913\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0095 - accuracy: 0.9967 - val_loss: 0.0396 - val_accuracy: 0.9888\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0098 - accuracy: 0.9971 - val_loss: 0.0378 - val_accuracy: 0.9898\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1965 - accuracy: 0.9486 - val_loss: 0.2134 - val_accuracy: 0.9262\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0676 - accuracy: 0.9799 - val_loss: 0.0632 - val_accuracy: 0.9808\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0485 - accuracy: 0.9849 - val_loss: 0.0750 - val_accuracy: 0.9775\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0355 - accuracy: 0.9891 - val_loss: 0.0419 - val_accuracy: 0.9880\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0289 - accuracy: 0.9912 - val_loss: 0.0646 - val_accuracy: 0.9788\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0233 - accuracy: 0.9925 - val_loss: 0.0428 - val_accuracy: 0.9883\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 0.0458 - val_accuracy: 0.9872\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0173 - accuracy: 0.9944 - val_loss: 0.0520 - val_accuracy: 0.9857\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0162 - accuracy: 0.9943 - val_loss: 0.0553 - val_accuracy: 0.9858\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0118 - accuracy: 0.9961 - val_loss: 0.0447 - val_accuracy: 0.9882\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0108 - accuracy: 0.9965 - val_loss: 0.0408 - val_accuracy: 0.9892\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0116 - accuracy: 0.9961 - val_loss: 0.0435 - val_accuracy: 0.9878\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0089 - accuracy: 0.9972 - val_loss: 0.0371 - val_accuracy: 0.9902\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0095 - accuracy: 0.9968 - val_loss: 0.0395 - val_accuracy: 0.9905\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.0381 - val_accuracy: 0.9895\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.4931 - accuracy: 0.8508 - val_loss: 0.4760 - val_accuracy: 0.8453\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2212 - accuracy: 0.9314 - val_loss: 0.2596 - val_accuracy: 0.9150\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1703 - accuracy: 0.9466 - val_loss: 0.2252 - val_accuracy: 0.9265\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1432 - accuracy: 0.9554 - val_loss: 0.1832 - val_accuracy: 0.9428\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1219 - accuracy: 0.9608 - val_loss: 0.1059 - val_accuracy: 0.9680\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1122 - accuracy: 0.9658 - val_loss: 0.1185 - val_accuracy: 0.9650\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1042 - accuracy: 0.9673 - val_loss: 0.1422 - val_accuracy: 0.9568\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0946 - accuracy: 0.9709 - val_loss: 0.1196 - val_accuracy: 0.9647\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0861 - accuracy: 0.9734 - val_loss: 0.1603 - val_accuracy: 0.9517\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0822 - accuracy: 0.9736 - val_loss: 0.1076 - val_accuracy: 0.9672\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0785 - accuracy: 0.9749 - val_loss: 0.0845 - val_accuracy: 0.9753\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0723 - accuracy: 0.9776 - val_loss: 0.0972 - val_accuracy: 0.9693\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0707 - accuracy: 0.9771 - val_loss: 0.0920 - val_accuracy: 0.9723\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0695 - accuracy: 0.9779 - val_loss: 0.0799 - val_accuracy: 0.9777\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0626 - accuracy: 0.9805 - val_loss: 0.1149 - val_accuracy: 0.9657\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2027 - accuracy: 0.9449 - val_loss: 0.1071 - val_accuracy: 0.9672\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0743 - accuracy: 0.9780 - val_loss: 0.0726 - val_accuracy: 0.9788\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0526 - accuracy: 0.9843 - val_loss: 0.0628 - val_accuracy: 0.9818\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0440 - accuracy: 0.9863 - val_loss: 0.0479 - val_accuracy: 0.9867\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0346 - accuracy: 0.9895 - val_loss: 0.0499 - val_accuracy: 0.9852\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0283 - accuracy: 0.9911 - val_loss: 0.0347 - val_accuracy: 0.9902\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0258 - accuracy: 0.9915 - val_loss: 0.0344 - val_accuracy: 0.9890\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0223 - accuracy: 0.9923 - val_loss: 0.0418 - val_accuracy: 0.9875\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0181 - accuracy: 0.9945 - val_loss: 0.0638 - val_accuracy: 0.9815\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0169 - accuracy: 0.9946 - val_loss: 0.0443 - val_accuracy: 0.9882\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0158 - accuracy: 0.9950 - val_loss: 0.0335 - val_accuracy: 0.9903\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 0.0627 - val_accuracy: 0.9838\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0129 - accuracy: 0.9956 - val_loss: 0.0330 - val_accuracy: 0.9908\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0439 - val_accuracy: 0.9888\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0104 - accuracy: 0.9969 - val_loss: 0.0462 - val_accuracy: 0.9885\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.4530 - accuracy: 0.8600 - val_loss: 0.0945 - val_accuracy: 0.9707\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2316 - accuracy: 0.9260 - val_loss: 0.1051 - val_accuracy: 0.9693\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1934 - accuracy: 0.9377 - val_loss: 0.0597 - val_accuracy: 0.9792\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.1695 - accuracy: 0.9454 - val_loss: 0.0512 - val_accuracy: 0.9845\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1527 - accuracy: 0.9502 - val_loss: 0.0508 - val_accuracy: 0.9825\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1415 - accuracy: 0.9538 - val_loss: 0.0563 - val_accuracy: 0.9820\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1264 - accuracy: 0.9593 - val_loss: 0.0526 - val_accuracy: 0.9825\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1224 - accuracy: 0.9594 - val_loss: 0.0372 - val_accuracy: 0.9892\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1176 - accuracy: 0.9610 - val_loss: 0.0306 - val_accuracy: 0.9902\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1109 - accuracy: 0.9629 - val_loss: 0.0303 - val_accuracy: 0.9910\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1043 - accuracy: 0.9667 - val_loss: 0.0444 - val_accuracy: 0.9872\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1007 - accuracy: 0.9666 - val_loss: 0.0335 - val_accuracy: 0.9907\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0989 - accuracy: 0.9677 - val_loss: 0.0333 - val_accuracy: 0.9908\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0943 - accuracy: 0.9689 - val_loss: 0.0317 - val_accuracy: 0.9907\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0937 - accuracy: 0.9702 - val_loss: 0.0324 - val_accuracy: 0.9913\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.2142 - accuracy: 0.9411 - val_loss: 0.1341 - val_accuracy: 0.9570\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0808 - accuracy: 0.9766 - val_loss: 0.0613 - val_accuracy: 0.9835\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0583 - accuracy: 0.9826 - val_loss: 0.0592 - val_accuracy: 0.9842\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0480 - accuracy: 0.9850 - val_loss: 0.0418 - val_accuracy: 0.9873\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0403 - accuracy: 0.9876 - val_loss: 0.0553 - val_accuracy: 0.9833\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0322 - accuracy: 0.9900 - val_loss: 0.0325 - val_accuracy: 0.9913\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 0.0324 - val_accuracy: 0.9918\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0249 - accuracy: 0.9919 - val_loss: 0.0458 - val_accuracy: 0.9863\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0221 - accuracy: 0.9931 - val_loss: 0.0445 - val_accuracy: 0.9867\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0216 - accuracy: 0.9932 - val_loss: 0.0300 - val_accuracy: 0.9908\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0157 - accuracy: 0.9949 - val_loss: 0.0336 - val_accuracy: 0.9910\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0181 - accuracy: 0.9942 - val_loss: 0.0311 - val_accuracy: 0.9920\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0142 - accuracy: 0.9953 - val_loss: 0.0300 - val_accuracy: 0.9913\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.0351 - val_accuracy: 0.9910\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0331 - val_accuracy: 0.9902\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 10s 5ms/step - loss: 0.1946 - accuracy: 0.9469 - val_loss: 0.0766 - val_accuracy: 0.9775\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0674 - accuracy: 0.9802 - val_loss: 0.1005 - val_accuracy: 0.9683\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0461 - accuracy: 0.9862 - val_loss: 0.0664 - val_accuracy: 0.9775\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0354 - accuracy: 0.9890 - val_loss: 0.0619 - val_accuracy: 0.9815\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0291 - accuracy: 0.9911 - val_loss: 0.0470 - val_accuracy: 0.9865\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0225 - accuracy: 0.9929 - val_loss: 0.0532 - val_accuracy: 0.9832\n", + "Epoch 7/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0179 - accuracy: 0.9939 - val_loss: 0.0411 - val_accuracy: 0.9882\n", + "Epoch 8/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0154 - accuracy: 0.9953 - val_loss: 0.0361 - val_accuracy: 0.9892\n", + "Epoch 9/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0142 - accuracy: 0.9951 - val_loss: 0.0435 - val_accuracy: 0.9862\n", + "Epoch 10/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0119 - accuracy: 0.9960 - val_loss: 0.0534 - val_accuracy: 0.9848\n", + "Epoch 11/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0416 - val_accuracy: 0.9877\n", + "Epoch 12/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0882 - val_accuracy: 0.9777\n", + "Epoch 13/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0097 - accuracy: 0.9968 - val_loss: 0.0335 - val_accuracy: 0.9907\n", + "Epoch 14/15\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0083 - accuracy: 0.9969 - val_loss: 0.0339 - val_accuracy: 0.9912\n", + "Epoch 15/15\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0074 - accuracy: 0.9974 - val_loss: 0.0362 - val_accuracy: 0.9903\n" + ] } ], "source": [ - "# Plot training history\n", - "plt.figure(figsize=(12, 4))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(history.history['accuracy'], label='Training Accuracy')\n", - "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n", - "plt.title('Model Accuracy')\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Accuracy')\n", - "plt.legend()\n", + "# Load the hyperparameters\n", + "hyperparameters = json.load(open('hyperparameters.json'))\n", + "print(hyperparameters)\n", + "# Create the models with different noise injection\n", + "best_model_rand_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_radial_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_flip = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_mask = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_rand_noise_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_no_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(history.history['loss'], label='Training Loss')\n", - "plt.plot(history.history['val_loss'], label='Validation Loss')\n", - "plt.title('Model Loss')\n", - "plt.xlabel('Epoch')\n", - "plt.ylabel('Loss')\n", - "plt.legend()\n", + "# Random noise\n", + "datagen_rand_noise = ImageDataGenerator(\n", + " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1) # Noise injection\n", + ")\n", + "\n", + "# Radial noise\n", + "datagen_radial_noise = ImageDataGenerator(\n", + " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape) * np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2)).reshape(1, x.shape[1], x.shape[2], 1) / np.max(np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2))), 0, 1) # Radial noise injection\n", + ")\n", + "\n", + "# Flip\n", + "datagen_flip = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True\n", + ")\n", + "\n", + "# Zoom\n", + "datagen_zoom = ImageDataGenerator(\n", + " zoom_range=0.1 # Zoom in on the image\n", + ")\n", + "\n", + "# Mask\n", + "datagen_mask = ImageDataGenerator(\n", + " preprocessing_function = lambda x: cutout(x, mask_size=16) # Zoom in on the image\n", + ")\n", + "datagen_rand_noise_zoom = ImageDataGenerator(\n", + " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1), # Noise injection\n", + " zoom_range=0.1 # Zoom in on the image\n", + ")\n", + "# No noise\n", + "datagen_no_noise = ImageDataGenerator()\n", + "\n", + "datagen_val = ImageDataGenerator()\n", + "# Wrap the datagen with our custom generator\n", + "train_generator_rand_noise = datagen_rand_noise.flow(x_train, y_train, batch_size=32)\n", + "train_generator_radial_noise = datagen_radial_noise.flow(x_train, y_train, batch_size=32)\n", + "train_generator_flip = datagen_flip.flow(x_train, y_train, batch_size=32)\n", + "train_generator_zoom = datagen_zoom.flow(x_train, y_train, batch_size=32)\n", + "train_generator_mask = datagen_mask.flow(x_train, y_train, batch_size=32)\n", + "train_generator_rand_noise_zoom = datagen_rand_noise_zoom.flow(x_train, y_train, batch_size=32)\n", + "train_generator_no_noise = datagen_no_noise.flow(x_train, y_train, batch_size=32)\n", + "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", + "# Train the model with custom augmentation\n", + "augmentation_loss_df = pd.DataFrame()\n", + "augmentation_accuracy_df = pd.DataFrame()\n", + "\n", + "# Random noise\n", + "history_rand_noise = best_model_rand_noise.fit(train_generator_rand_noise,\n", + " epochs=15,\n", + " validation_data=val_generator, \n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"rand_noise\"] = history_rand_noise.history['val_loss']\n", + "augmentation_accuracy_df[f\"rand_noise\"] = history_rand_noise.history['val_accuracy']\n", + "\n", + "# Radial noise\n", + "history_radial_noise = best_model_radial_noise.fit(train_generator_radial_noise,\n", + " epochs=15,\n", + " validation_data=val_generator, \n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"radial_noise\"] = history_radial_noise.history['val_loss']\n", + "augmentation_accuracy_df[f\"radial_noise\"] = history_radial_noise.history['val_accuracy']\n", + "\n", + "\n", + "# Flip\n", + "history_flip = best_model_flip.fit(train_generator_flip,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"flip\"] = history_flip.history['val_loss']\n", + "augmentation_accuracy_df[f\"flip\"] = history_flip.history['val_accuracy']\n", + "\n", + "# Zoom\n", + "history_zoom = best_model_zoom.fit(train_generator_zoom,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"zoom\"] = history_zoom.history['val_loss']\n", + "augmentation_accuracy_df[f\"zoom\"] = history_zoom.history['val_accuracy']\n", "\n", - "plt.tight_layout()\n", - "plt.show()" + "# Mask\n", + "history_mask = best_model_mask.fit(train_generator_mask,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"mask\"] = history_mask.history['val_loss']\n", + "augmentation_accuracy_df[f\"mask\"] = history_mask.history['val_accuracy']\n", + "\n", + "# Rand noise and zoom\n", + "history_rand_noise_zoom = best_model_rand_noise_zoom.fit(train_generator_rand_noise_zoom,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"rand_noise_zoom\"] = history_rand_noise_zoom.history['val_loss']\n", + "augmentation_accuracy_df[f\"rand_noise_zoom\"] = history_rand_noise_zoom.history['val_accuracy']\n", + "\n", + "# No noise\n", + "history_no_noise = best_model_no_noise.fit(train_generator_no_noise,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1\n", + " )\n", + "augmentation_loss_df[f\"no_noise\"] = history_no_noise.history['val_loss']\n", + "augmentation_accuracy_df[f\"no_noise\"] = history_no_noise.history['val_accuracy']\n", + "\n", + "augmentation_loss_df.to_csv('augmentation_loss.csv', index=False)\n", + "augmentation_accuracy_df.to_csv('augmentation_accuracy.csv', index=False)\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rand noise test accuracy: 0.9705\n", + "Rand noise test loss: 0.1497\n", + "Radial noise test accuracy: 0.9721\n", + "Radial noise test loss: 0.1365\n", + "Flip test accuracy: 0.9229\n", + "Flip test loss: 0.2834\n", + "Zoom test accuracy: 0.9672\n", + "Zoom test loss: 0.1553\n", + "Mask test accuracy: 0.9626\n", + "Mask test loss: 0.1412\n", + "Rand noise and zoom test accuracy: 0.9667\n", + "Rand noise and zoom test loss: 0.1692\n", + "No noise test accuracy: 0.9665\n", + "No noise test loss: 0.1730\n" + ] + } + ], + "source": [ + "rand_noise_loss, rand_noise_accuracy = best_model_rand_noise.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Rand noise test accuracy: {rand_noise_accuracy:.4f}\")\n", + "print(f\"Rand noise test loss: {rand_noise_loss:.4f}\")\n", + "radial_noise_loss, radial_noise_accuracy = best_model_radial_noise.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Radial noise test accuracy: {radial_noise_accuracy:.4f}\")\n", + "print(f\"Radial noise test loss: {radial_noise_loss:.4f}\")\n", + "flip_loss, flip_accuracy = best_model_flip.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Flip test accuracy: {flip_accuracy:.4f}\")\n", + "print(f\"Flip test loss: {flip_loss:.4f}\")\n", + "zoom_loss, zoom_accuracy = best_model_zoom.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Zoom test accuracy: {zoom_accuracy:.4f}\")\n", + "print(f\"Zoom test loss: {zoom_loss:.4f}\")\n", + "mask_loss, mask_accuracy = best_model_mask.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Mask test accuracy: {mask_accuracy:.4f}\")\n", + "print(f\"Mask test loss: {mask_loss:.4f}\")\n", + "rand_noise_zoom_loss, rand_noise_zoom_accuracy = best_model_rand_noise_zoom.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Rand noise and zoom test accuracy: {rand_noise_zoom_accuracy:.4f}\")\n", + "print(f\"Rand noise and zoom test loss: {rand_noise_zoom_loss:.4f}\")\n", + "no_noise_loss, no_noise_accuracy = best_model_no_noise.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"No noise test accuracy: {no_noise_accuracy:.4f}\")\n", + "print(f\"No noise test loss: {no_noise_loss:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 1ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 1ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n" + ] + } + ], + "source": [ + "# List of models and their names\n", + "models = [\n", + " (best_model_rand_noise, \"Random Noise\"),\n", + " (best_model_radial_noise, \"Radial Noise\"),\n", + " (best_model_flip, \"Flip\"),\n", + " (best_model_zoom, \"Zoom\"),\n", + " (best_model_mask, \"Mask\"),\n", + " (best_model_rand_noise_zoom, \"Random Noise + Zoom\"),\n", + " (best_model_no_noise, \"No Noise\")\n", + "]\n", + "\n", + "# Create and save confusion matrices for each model\n", + "for model, name in models:\n", + " # Predictions on the test set\n", + " y_pred = model.predict(x_test)\n", + " y_pred_class = np.argmax(y_pred, axis=1)\n", + "\n", + " # Confusion matrix\n", + " confusion = confusion_matrix(y_test, y_pred_class)\n", + " disp = ConfusionMatrixDisplay(confusion_matrix=confusion)\n", + " \n", + " # Plot\n", + " fig, ax = plt.subplots(figsize=(10, 8))\n", + " disp.plot(ax=ax)\n", + " plt.title(f'Confusion Matrix for {name}')\n", + " \n", + " # Save the figure\n", + " now = datetime.datetime.now()\n", + " plt.savefig(f'confusion_matrix_{name.replace(\" \", \"_\").lower()}_{now.strftime(\"%Y-%m-%d\")}.png')\n", + " plt.close()\n", + "\n", + "\n" + ] } ], "metadata": { diff --git a/testing/Noise-Injection-CNN.ipynb b/testing/Noise-Injection-CNN.ipynb index 29f8326..0eb1f30 100644 --- a/testing/Noise-Injection-CNN.ipynb +++ b/testing/Noise-Injection-CNN.ipynb @@ -200,7 +200,7 @@ ")\n", "datagen_radial_noise = ImageDataGenerator(\n", " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape) * np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2)).reshape(1, x.shape[1], x.shape[2], 1) / np.max(np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2))), 0, 1) # Radial noise injection\n", - " )\n", + ")\n", "datagen_no_noise = ImageDataGenerator()\n", "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", "datagen_val = ImageDataGenerator()\n", diff --git a/testing/augmentation_accuracy.csv b/testing/augmentation_accuracy.csv new file mode 100644 index 0000000..6aae28b --- /dev/null +++ b/testing/augmentation_accuracy.csv @@ -0,0 +1,16 @@ +rand_noise,radial_noise,flip,zoom,mask,rand_noise_zoom,no_noise 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "# Read the CSV files\n", + "accuracy_df = pd.read_csv('augmentation_accuracy.csv')\n", + "loss_df = pd.read_csv('augmentation_loss.csv')\n", + "x_values = np.arange(1, 16)\n", + "# Accuracy vs Epoch plot\n", + "plt.figure(figsize=(10, 6))\n", + "for technique in accuracy_df.columns[1:]: # Skip the 'epoch' column\n", + " plt.plot(x_values, accuracy_df[technique], label=technique)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Accuracy vs Epoch for Different Augmentation Techniques')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "# Save the accuracy plot\n", + "plt.savefig('augmentation_accuracy.png')\n", + "plt.close() # Close the current figure to start fresh for the next plot\n", + "\n", + "# Loss vs Epoch plot\n", + "plt.figure(figsize=(10, 6))\n", + "for technique in loss_df.columns[1:]: # Skip the 'epoch' column\n", + " plt.plot(x_values, loss_df[technique], label=technique)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.title('Loss vs Epoch for Different Augmentation Techniques')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "# Save the accuracy plot\n", + "plt.savefig('augmentation_loss.png')\n", + "plt.close() # Close the current figure to start fresh for the next plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xl2fa77ffflMA5a233jJte/zxxxVAefvtt0scs3379krHjh1LPdbN2NnZlWjHiRMnKg0aNFCSk5NL7Dd27FjFycnJdN7lec+oyPO1Iud0/XNn+PDhio2NjXLp0iXTtlOnTikajabE87Ei70/lbYdhw4aV+p8gREXIsEAhKmjlypU4OTlx7733kpycbLp07NgRe3t7/vzzT+DqnJTffvsNnU5XaY//yy+/oFarGTVqlGnbww8/zO+//15iyNLq1atxd3fn2WefLXWM4p6T1atXo1KpSvXiXLvP7XjmmWdKbbt2Dkp+fj7Jycl069YNgMOHDwPGb6rXrVvH0KFDy+w1K47pwQcfxMbGhmXLlplu27p1K8nJybecZzBy5EgsLCz49ddfTdtOnDjBqVOneOihh0zbnJ2dOXnyJGfPni3PKZfy1ltvsX379lIXV1fXEvvddddddOzY0fR7QEAAw4YNY+vWrej1evR6Pdu2bWP48OEl5lY0aNCARx55hD179pCZmQkY/55t27ZlxIgRpeK5/u85YcKEEvOPevbsCUBkZGSFzzU+Pp6jR4/yxBNPlDi/Nm3acO+997J58+ZS9ynrOXK77O3tycrKqrTjlfc1Xqxhw4YMHDiw1DF69uyJi4tLiWP0798fvV5faujnQw89hIuLi+n3O/l7FCsqKuLXX3/loYceMv39i4e9XfvaqYjiYXZTp04tsb2s/zPFri18otFo6NSpE4qiMHHiRNN2Z2dnmjZtWuJ8V65cSfPmzWnWrFmJNiwuXnL936F///4lelfatGmDo6Njudvw2v9RGRkZJCcn07t3byIjI8nIyCjXMa518OBBEhMTmTp1aok5RUOGDKFZs2Zs2rSp1H2uf1307Nnzjp4DiqKwevVqhg4diqIoJdpx4MCBZGRkmP7/luc9o1hFnq8VPSe9Xs/WrVsZPnw4AQEBpu3Nmzcv9Torr4q0g7OzMzExMaWGOQpRXpJcCVFBZ8+eJSMjA09PTzw8PEpcsrOzTRP+e/fuzahRo5g9ezbu7u4MGzaM77//vtRcgYpaunQpXbp0ISUlhXPnznHu3Dnat29PYWEhK1euNO13/vx5mjZtetMKTOfPn8fHx6fUB/471bBhw1LbUlNTee655/Dy8kKr1eLh4WHar/iDS1JSEpmZmabhcDfi7OzM0KFDS4yBX7ZsGb6+viWqxpXF3d2dfv36sWLFCtO2X3/9FQsLC9PQTIC3336b9PR0mjRpQuvWrXnppZc4duzYrU/+itatW9O/f/9Sl+sLKjRu3LjUfZs0aUJubi5JSUkkJSWRm5tL06ZNS+3XvHlzDAYD0dHRgPHveau2K3bthxbA9EHp+jlF5XHp0iWAG8aYnJxMTk5Oie1lPUduV3Z2Ng4ODpV2vPK+xouVdS5nz55ly5Ytpe5fXHjm+mNU5t+j2LZt20hKSqJLly6m/xUXLlygb9++/Pzzz7e1hMOlS5dQq9WlzrlRo0Y3vM/15+bk5ISNjU2JYarF268937Nnz3Ly5MlSbdikSRPg1m0IxnYsbxv+888/9O/f3zRn0MPDwzR88naSq5u9Lpo1a2a6vVjxnNfbjb8sSUlJpKens3jx4lLtOGHCBOBqO5bnPaNYeZ+vt3NOSUlJ5OXllfm/say2LI+KtMMrr7yCvb09Xbp0oXHjxkybNo1//vnnth5X1E8y50qICjIYDDf95rf4jaR4odZ9+/axceNGtm7dypNPPsn8+fPZt2/fbZXGLS6pDGV/KF+2bFmJ8ruV4UY9WDebZF1WpbQHH3yQ0NBQXnrpJdq1a4e9vT0Gg4FBgwbd1oe88ePHs3LlSkJDQ2ndujUbNmxg6tSpqNW3/s5o7NixTJgwgaNHj9KuXTtWrFhBv379SnzY69WrF+fPn2f9+vVs27aNb775hk8++YSFCxdWawnyqnJ9Nb1iyh3Mf6qIyqqmp9PpiIiIKHdSWR7lfY0XK+tcDAYD9957Ly+//HKZxyhOEIpVxd+jOP4HH3ywzNt37dpF3759gdt7nZdXWedWnvM1GAy0bt2ajz/+uMx9/f39K3zMGzl//jz9+vWjWbNmfPzxx/j7+2NlZcXmzZv55JNPqmUtwRvFfyeK4x43bhyPP/54mfuUZ57v9crb1lVxTtcq7/O2Iu3QvHlzzpw5w2+//caWLVtYvXo1X3/9NW+99VaZxZCEuJ4kV0JUUEhICDt27KBHjx7l+oDYrVs3unXrxpw5c1i+fDmPPvoov/zyC0899VSFh94tW7YMS0tLfvrpp1JvWnv27OHzzz8nKiqKgIAAQkJC2L9/PzqdrsQE4+vPZevWraSmpt6w96r4G8nrF928/lvXm0lLS2Pnzp3Mnj2bt956y7T9+iF3Hh4eODo6cuLEiVsec9CgQXh4eLBs2TK6du1Kbm4ujz32WLniGT58OJMnTzYNDYyIiOC1114rtZ+rqysTJkxgwoQJZGdn06tXL2bNmlWpyVVZww4jIiKwtbU1fYi3tbXlzJkzpfY7ffo0arXa9CEzJCSkXG1X2YoXEb5RjO7u7lVWjn/VqlXk5eXd9nChslT0NX6jY2RnZ1fqEgkV+X9RvFzDQw89VOaSADNmzDAVxgHj67yshXWvf50HBgZiMBi4cOFCiS94blVp8naEhIQQFhZGv3797miY8rVudJyNGzdSUFDAhg0bSvTKXD/08GbHuN61r4vre9TPnDlTpYtvF/Pw8MDBwQG9Xn/L52J53jOqg4eHB1qttsz/jdf/jynv+1NF2gHAzs6Ohx56iIceeojCwkJGjhzJnDlzeO2116RsvLglGRYoRAU9+OCD6PV63nnnnVK3FRUVmf7Jp6WllfoWr127dgCmoYG2trZA6TeGG1m2bBk9e/Y0fWC69lJcYri4DPmoUaNITk4uUaK6WHFco0aNQlGUMr+NK97H0dERd3f3UnNEvv7663LFDFe/vby+Pa6vmKZWqxk+fDgbN240lYIvKyYACwsLHn74YVasWMEPP/xA69aty/0NrLOzMwMHDmTFihX88ssvWFlZMXz48BL7XF8i3t7enkaNGt3xsM7r7d271zTWHyA6Opr169czYMAA03pNAwYMYP369aaS0WCsfLV8+XLuvvtuHB0dAePfMywsjLVr15Z6nKrskWrQoAHt2rXjxx9/LPFcPnHiBNu2bWPw4MFV8rhhYWE8//zzuLi4mKq5VYbyvsZvdYy9e/eydevWUrelp6dTVFRU4biKE9TyPP7atWvJyclh2rRppf5XFJdlX716ten5HBISQkZGRomhr/Hx8aWeS8VJ7PWv/y+++KLC53MrDz74ILGxsSxZsqTUbXl5eaWGmpbHjdqwrP9RGRkZfP/992Ueozx/g06dOuHp6cnChQtL/N/4/fffCQ8Pv2mFxcqi0WgYNWoUq1evLvOLl6SkJNP18rxnVAeNRsPAgQNZt24dUVFRpu3h4eGlXk/lfX+qSDtc/7/fysqKFi1aoChKpc6fFnWX9FwJUUG9e/dm8uTJvP/++xw9epQBAwZgaWnJ2bNnWblyJZ999hmjR4/mxx9/5Ouvv2bEiBGEhISQlZXFkiVLcHR0NH3Y1Gq1tGjRgl9//ZUmTZrg6upKq1atyhziVFxSefr06WXG5evrS4cOHVi2bBmvvPIK48eP5//+7/944YUX+Pfff+nZsyc5OTns2LGDqVOnMmzYMPr27ctjjz3G559/ztmzZ01D9P7++2/69u1reqynnnqKDz74gKeeeopOnTqxe/duIiIiyt1mjo6O9OrVi3nz5qHT6fD19WXbtm1cuHCh1L7vvfce27Zto3fv3qbyy/Hx8axcuZI9e/aUKF08fvx4Pv/8c/7880/mzp1b7njAOCF73LhxfP311wwcOLDUQrItWrSgT58+dOzYEVdXVw4ePMiqVatu2P7X+/vvv8tce6xNmzYlksBWrVoxcODAEqXYgRIJ77vvvsv27du5++67mTp1KhYWFixatIiCggLmzZtn2u+ll15i1apVjBkzhieffJKOHTuSmprKhg0bWLhwIW3btq1IE1XIhx9+yH333cddd93FxIkTTaXYnZycyrV2260Ut6deryclJYV//vmHDRs24OTkxNq1a/H29r7zk7iivK/xm3nppZfYsGED999/P0888QQdO3YkJyeH48ePs2rVKi5evFhqztGtFBc+mTFjBgMHDkSj0TB27Ngy9122bBlubm507969zNsfeOABlixZwqZNmxg5ciRjx47llVdeYcSIEcyYMYPc3FwWLFhAkyZNSiT/HTt2ZNSoUXz66aekpKSYSrEX/z+orB4mgMcee4wVK1bwzDPP8Oeff9KjRw/0ej2nT59mxYoVprXFKqK4Dd944w3Gjh2LpaUlQ4cOZcCAAVhZWTF06FAmT55MdnY2S5YswdPTk/j4+FLHWLBgAe+++y6NGjXC09OzzLmelpaWzJ07lwkTJtC7d28efvhhUyn2oKCgUktJVJUPPviAP//8k65duzJp0iRatGhBamoqhw8fZseOHaSmpgKU6z2jusyePZstW7bQs2dPpk6dSlFRkWntqevnvpb3/am87TBgwAC8vb3p0aMHXl5ehIeH8+WXXzJkyJBKndsp6rDqLU4oRO1zo/LHixcvVjp27KhotVrFwcFBad26tfLyyy8rcXFxiqIoyuHDh5WHH35YCQgIUKytrRVPT0/l/vvvL1F2W1EUJTQ0VOnYsaNiZWV107Lszz77rAKUKMd9vVmzZimAEhYWpiiKsbTwG2+8oTRs2FCxtLRUvL29ldGjR5c4RlFRkfLhhx8qzZo1U6ysrBQPDw/lvvvuUw4dOmTaJzc3V5k4caLi5OSkODg4KA8++KCSmJh4w9LLSUlJpWKLiYlRRowYoTg7OytOTk7KmDFjlLi4uDLP+dKlS8r48eMVDw8PxdraWgkODlamTZtWonR4sZYtWypqtVqJiYm5YbuUJTMzU9FqtQqgLF26tNTt7777rtKlSxfF2dlZ0Wq1SrNmzZQ5c+YohYWFNz3urUqxX3uugDJt2jRl6dKlSuPGjRVra2ulffv2plLO1zp8+LAycOBAxd7eXrG1tVX69u2rhIaGltovJSVFmT59uuLr66tYWVkpfn5+yuOPP24qP3yj0uY3K2lc1vmVVRp9x44dSo8ePRStVqs4OjoqQ4cOVU6dOlVin5s9R272eMUXS0tLxcPDQ+nVq5cyZ84cJTExsdR97rQUe7FbvcYVxViK/UYl+7OyspTXXntNadSokWJlZaW4u7sr3bt3Vz766CPT86i43T/88MNS97/++VJUVKQ8++yzioeHh6JSqW5Ylr14uYbHHnuszNsVxfiatrW1LVHyf9u2bUqrVq0UKysrpWnTpsrSpUtLlWJXFEXJyclRpk2bpri6uir29vbK8OHDlTNnzihAifLqN/pbV+RvUVhYqMydO1dp2bKlYm1trbi4uCgdO3ZUZs+erWRkZJRoq2nTppU6ZmBgYKnlGd555x3F19dXUavVJZ4nGzZsUNq0aaPY2NgoQUFByty5c03lya99Ll2+fFkZMmSI4uDgoACmsuzXl2Iv9uuvvyrt27dXrK2tFVdXV+XRRx8t9f/qRm1SVvvfyvWl2BXF+JyYNm2a4u/vb3ov6Nevn7J48eIS+93qPaMiz9eKnFNZ7wO7du0yvTcGBwcrCxcuLPO+5X1/Km87LFq0SOnVq5fi5uamWFtbKyEhIcpLL71U4vkmxM2oFKUa+3qFEKKStW/fHldXV3bu3GnuUCpMpVIxbdq0MofhCFGbHD16lPbt27N06VIeffRRc4cj6qhZs2Yxe/bsah2mKERFyZwrIUStdfDgQY4ePcr48ePNHYoQ9UZeXl6pbZ9++ilqtZpevXqZISIhhKg5ZM6VEKLWOXHiBIcOHWL+/Pk0aNCgxOK/QoiqNW/ePA4dOkTfvn2xsLDg999/5/fff+fpp58uVR5dCCHqG+m5EkLUOqtWrWLChAnodDp+/vlnKY0rRDXq3r07qampvPPOO/z3v/8lIiKCWbNm8dVXX5k7NCGEMDuZcyWEEEIIIYQQlUB6roQQQgghhBCiEkhyJYQQQgghhBCVQApalMFgMBAXF4eDg0OlLogohBBCCCGEqF0URSErKwsfHx/U6pv3TUlyVYa4uDipeCSEEEIIIYQwiY6Oxs/P76b7SHJVBgcHB8DYgI6OjmaOpm7T6XRs27aNAQMGYGlpae5w6gVp8+ol7V39pM2rn7R59ZM2r17S3tWvJrV5ZmYm/v7+phzhZiS5KkPxUEBHR0dJrqqYTqfD1tYWR0dHs79w6gtp8+ol7V39pM2rn7R59ZM2r17S3tWvJrZ5eaYLSUELIYQQQgghhKgEklwJIYQQQgghRCWQ5EoIIYQQQgghKoEkV0IIIYQQQghRCSS5EkIIIYQQQohKIMmVEEIIIYQQQlQCSa6EEEIIIYQQohJIciWEEEIIIYQQlUCSKyGEEEIIIYSoBJJcCSGEEEIIIUQlkORKCCGEEEIIISqBJFdCCCGEEEIIUQkkuRJCCCGEEEKISiDJlRBCCCGEEEJUAkmuhBBCCCGEEKISSHIlhBBCCCGEEJVAkishhBBCCCFEjaFPTyd3/34cjoaZO5QKszB3AEIIIYQQQoj6R1EUihITyT91yngJD6fgVDi6uDgAPG1sUF571cxRVowkV0IIIYQQQogqpRgM6KKiyA8Pv5JMhZMfHo4+NbXM/S38/MhydkbJyQErq2qO9vZJciWEEEIIIYSoNIpOR8H586YEKv/UKQpOn8aQk1N6Z40G6+BgbFo0x6ZFC6ybN8emeXMMNjac2ryZ9vb21X8Cd0CSKyGEEEIIIcRtMeTmkn/mjHFIX3g4+SdPUXD2LIpOV2pflbU11k2bYnMlgbJp2QLrxo1R29iUPm4Z968NJLkSQgghhBB1gqLToc/IQJ+ejj4tjaL0dNAb7vi4en0R9seOk21hgUZz5x+f1Vob1Pb2qO0d0DjYo3ZwQG1nh0pds2vN6dPTr/REXe2RKrx4EQyl21jt4HA1iWrRHOvmzbEODkZlUbfTj7p9dkIIIYQQolYqlSilpaFPSzf9rk9LQ5+eTlH61e2GzMwqi8cHuLxsWZUdH0BtZ4fa4UrCZWd/9bq9A2oHezT211x3cLiyz5XrDg5o7OxQVcL8JEVRKEpIuJJElS40cT2Nhzs2LVpcSaZaYNOiOZZ+fqhUqjuOpbaR5EoIIYQQQlSpak2UVCo0Tk5onJ3RODmhsrS84/gNikJqaiqurq6o7zBhUFBQ8vLRZ2dhyMrGkJVlGkJnyMnBkJND0eXbP77K2tqYaNnbG3vHHOzR2BuTL7W9nem6KWmzt0djb4cuLq5Er9SNCk1Y+vtf6Y0yJlE2zZtj4eFx+wHXMZJcCSGEEEKI22bIzSXv2HHyT56gKCm56hIlFxfjxdkZjYszFqbr1253QePoiEqjqdRz1Ol0HNu8mTaDB2NZCcna9QyFhRiysjBkZaHPysaQnYU+O9uYfGVnoc+6kojlZBtvz8oyJmfZOVeuZ6Pk5gKgFBSgLyhAn5x8Z0HdoNCExsGhEs647pLkSgghRJ1RlJZG8uLFeJ4+g3LvvVAFH4JE/VEYHU3Ghg3khYVhFRSEtk1btG1aY+nvXy+HOxXTJSSSd+QwuYcPk3f4CPmnT0NR0a3vWEMSpZpIbWWF2s0N3Nxu+xhKURGG7Gz02TkYssuRqGVnX9mehSE7G427e7kKTYibk+RKCGFWSlEReWFhWHh4YBUQYO5wRC1lKCggbelSkhcuwpCVhTOQ9t33eE2fZu7QRC2jz8wkc8sWMtZvIO/QIdP2nN1/k8ZPAGicnLBp0wZt69bYtGmNtk0bLFxdzRVylVIMBgrOnruaTB06jC42ttR+Ft7eaNu1w9LXp94nSuaisrAwtrWzs7lDqdckuRJCmEVB5AUy1q4hfd069EnGoQu2nTrhNHoUjgMGoLa1NXOEojZQFIXMzZtJ+vgT0wc+C19fimJjSV20COfBg7EObmjmKEVNp+h0ZP+9h4wNG8j+4w+UwkLjDSoVdnd1w75PHwqjY8g/dsw4FyUjg5y//ybn779Nx7D09UXbtg02rdugbdMamxYtUGu1Zjqj21c8xM+YTB0h7+hRDFlZJXdSq7Fu2hTb9u3RduiAbYf2WPr4mCdgIWoYSa6EENXGkJND5patpK9eTd7hw6btaicnDFlZ5B48SO7BgyS88y6OQ4bgPGokNm3a1OvhN+LGcg8fJmHuXPLDjgFg4eWFx3+ex3bQII49+BB2ERFcnjmTgB9/qPHljUX1UxSF/BMnyVi/nszNm0tM3rdu3AinYcNwvP9+LL29S96vsJD8iLPkHQsj/9hx8o4fpzAyEl1sLLrYWDI3/27cUaPBunHja3q32mLdKKTG9dyUGuIXHg56fYl9VLa2aNu2wbZ9B7QdOqBt1xZNLVvYVYjqIsmVEKJKKYpC3pGjpK9ZTdbm3zFcmXCLRoN9r144jxqJfe/eFKWkkLFuHemr16CLjiZ9xQrSV6wwfsgZOQqnYQ/U2WE3omIKo6JI/Gg+Wdu2AcYPfu6TnsL1iSdQa7XodDoSRgwn5PPPyT1wgIw1a3AePdrMUYuaQhcXR8bG38jYsIHC8+dN2zVubjjdfz9Owx7AunnzG36po7KyQtuqJdpWLeER4zZ9Vhb5J08aizocP0Ze2DGKEhMpOH2agtOnYeVK431tbdG2aGEcUtimNdrWrbHw8am2L5BMQ/wOHzL2Sh2+8RA/2w7t0bbvgLZDe2yaNq3zaxMJUVnklSKEqBJFyclkrF9P+uo1FEZGmrZbBQbiNHoUTg8Mw9LL07Td0tsb92eewe3pp8k9cJCMNavJ3LqNgrPnSJw7l8T583Ho2xenUSOxv/tueaOvh/Tp6SQvWEjq8uWg04FajfPo0Xg8O71UGeAiV1dcp00j5aP5JMz7EPs+fbBwdzdT5MLc9Nk5ZG3bRsb69eT++y8oCmAsWe3Qrx9Ow4dh1737bf9f0Tg4YNetG3bdupm26RISyDt2zNS7lX/8OIacHFMPvem+bm5oW7e+OqSwdSs0Tk53dsJXyBA/IaqffDoRQlQapaiI7N27SV+9huy//jINLVFptTgOGoTz6FFoO3S46be0KrUau65dsOvaBa///Y/MTZtJX7OG/GPHyNq+nazt27Hw9MRp+HCcR43EKjCwms5OmItSWEjq8uUkL1iIISMDALuePfF86UVsmjS54f2cH32UnM2/k3/qFAnvvYfvxx9XV8iiBlCKisjZu5eM9RvI2rEDJT/fdJttly44DXsAhwEDqqystKWXF5b33ovjvfca4zEYKLxwgbywY+QdNyZd+WfOoE9JIfuvv4z/M6+wCgw0FczQtmmNdfPmqK2tb/mYMsRPCPOT5EoIcccKIi+QsWY16evXm4pTAGjbtcNp1Egc7xuMxt6uwsfVODjgMvYhXMY+RP6ZCDLWrCFjwwaKEhNJWbyYlMWLpQhGHaYoClnbtpM4fz66qCgArJs0wfPll7G/u8ct76+ysMD7nbe5OOZBMjf/juMDD+DQp08VRy3MLf/0aTLWrSdj028l/h9ZNWyI07BhOA29H0tf32qPS6VWYx0SgnVICM4jRwDGKpcF4eHG3qXjx8k7FobuUhSFly5ReOkSmRs3Gu9saYlN06bGQhlXCmaofHywio8n49dfKQg7JkP8hKgh5NUlhLgtxuIUW0hfvaZEcQqNmxtOw4bhPHIE1o0aVdrj2TRtgs1rr+L53xfI+vMv0levImfPPyWLYAwejPPoUVIEow7ICwsjYe4803NL4+GO53PP4TRiRIUKAmhbtsT18cdJ/f57Lr/9NnadO6O2q3iiXxclffUVGWvWYunri1VwQ6wbNsSqYUOsgoOxbNCgxhVeuBldQiKZv/1Gxvr1FEREmLZrnJ1xHDIEp+HDsGnVqsb9X1BbW6Nt1w5tu3ambfr0dPKOnzD1buUdO4Y+NZX8EyfIP3EC+PnKndUEGQwklTigDPETwtwkuRJClJupOMXqVWT+vsW0Gvz1xSlUVbhwq8rKCseBA3AcOADd5csli2CsXEn6ypVXi2A8MBSLO1iQUVS/wphYkj7+mMzNmwFQ2djg9uSTuE188qZJUaG+kJjsGGKyYriUfomD+QeJOR6DWqNG3ceGzr85oo2LZ8ebT3H+8V7VdTo1ltuhSFp/8RsAuthY4zykaxgsNeQ2cCa3gQt5Pi7k+rqQ28CFXB9n9Lalh6cZ9AYi8iNMbV4d1Pk63A+cx3v3aVyOR6O6Mo/KYKEmpWNDLvdqTmq7QBQLDRj2wLE91RJXpXAEeqigRxtQWmOTlIXD+cs4nkvA4VwCDhcS0RQUobPSkNW4AZnNfMho2oDMxt7X/H2iICmKktmXuF3meI7Xdwa9gXP55xjMYHOHUiEqRbny30iYZGZm4uTkREZGBo6OjuYOp07T6XRs3ryZwYMHY1mFH8jFVbfT5kVJSWRs2FC6OEVQEE6jRpYqTlHdFIOhRBEM09wKCwuzF8GozOe4UlgIGk2t6lEoL31mJsmLFpH2fz+h6HSgUuE0YgQez83A0ssLRVFIL0gnJiuG6KxoYrKv/Lzye2JuIgo3fjtre97AGysMGFTwxngN531qVg9GdXLNVJj3nR7HPNjZVkW4vwqfVAWfFPBJUWiQBpb6G98/1R7iXFXEuV39GeumItkRFHXVtqvKoNAySqHXCYWuZxS0hVdvO+0Hu1up2dtMRY62bv991QYFt0xIcQRDFbe5EOZkgQX/PvKv2T8jViQ3kJ4rIUSZjItq/n1HxSmqS10pgqEoCvr0dHTR0RRGRaOLufIzOprC6GiKEhJQ29lh27UrdnfdhV337lg1DKoRf4Pbpeh0pP26guQvv0Sfng6ArkNzLj7Wm3MeemLC5xL9rzGJytZl3/RYtha2+Dn44WvnS3ZiNoEBgaiL17dqAlFR/xCw7xKv/OHAzrcGoVjUw2+fDQZ6z/sDx7xE0gJdyJg2AG9LDQYg5soFgwG75Bwc4jNxuJx15Wcm9pcz0Wbk45oNrtkKraKAaxLaIo2K7AaOZHs7kuXtSFaDqz+LtHf2wcghNoPA0AsE7L2IbVquaXu2hz2XujckqnsQOZ4OuAJD7uiRag+DwUBUVBQBAQFXn+eiykh7Vz+DwUBMVIy5w6gwSa6EqEci0yNZdWYV8fnx+Cb50tarLZaakh96CiIjyVizptKLU1SXEkUwIiLIWF2zimAoOh26+HgKo68mTbroGNPvhuybJxCG7Gyyd+4ke+dOACwaNMCuuzHRsrvrrhq/FlhWYZaxtykzipy/duHz4w4cLxtLQ8e4qfjpHhVHQiIg8Swklr6/p60nfvZ++Dv44+dw9aefvR+uNq6oVKqrvYVdSvYWFjVJIXLwEJyj05lyxhe3p56qrtOuMZK++orkM4mobW3ptGg53YOCKnR/fVYWhRcuUHjhAgWRF65cj6Tg4iUsdDqcYzJwjskodT8LD48r87nKP7erKCWFzE2byFi3nvxTp0zb1Y6OON53H07DhqFt347OtfjLhTuh0+nYnFz6eS6qhrR39Stu89pGkish6oHorGgWHF3ApgubMCgGAHZs34HWQks7j3Z0dW5L55OFOGw7QP6RI6b7mYpTjBqJdUiIucK/bTZNriuCsWY1OX/vqfIiGPqsLFPvU/6li3j+E0rs2nUUxcSgi48vVRr5ehaenlgG+GPl52/86e+P0sCXJHtX7DNTsTxykJzQUPIOH6YoPt6YQK5eA4B1i+bYd++OXffuaDt2LFf55sqkN+hJzE0sMWzv2qF86QXpNIxXeOwPA62ijL0eGbawoqeane1UWFraEGLvVypx8nfwx8feBxsLm9uOzcLNDc9XXiH+9ddJ+vIrHAYMwCogoLJOvcbLPXCA5K++BsB71kysKphYgfHLC22bNmjbtCmxvTA/nx3Ll9M9MAh9dBSFFy5SGBlJwcUL6JOSKUpKoigpqdTcLpWVFVZBQcZkq2EQ1sHBoFKT+dtvZO/Zc/W1YmGBfa9eOA0bhn2f3tX+vBZCiPKS5EqIOuxyzmUWH1vM2rNrKVKKAOjt25vkxGRiVTF4RabTdv0eOoT/jaUO8gGDGtLaB+M4cjghQx7Fyqb2lzcvXQRjPelr1qCLijIVwbBqFILzqNGoBvYmQpXA2bSzeNl60ce/D1YaqxLHU/R6ihISKIyOQRcddeVntKn3qXh4WzFnIO+6eCz9jUmT6aefH9nu3sTZuBCVU0RUSh5RqblEpeYQdTyXhH/igXjUKugW3Iah0wYxKMQJy1PHyAndS05oKAVnzlBwKpyCU+GkfPMtKmtrbDt2xK6HMdmybtoUVSUOZ8nV5bIjagfHk46biknEZseiM+jK3N8tU2HaLgO9TxiTqiJLNTGD21H06AM86tWIVx38cde6o1ZV3ZAbpxHDydiwgdx9+7g8azb+335Tq4dVlldRWhqxL70MBoOxHPkDD1Tq8VUaDTo3N+x69Sz1rX7ZvV0XKLx4EaWwkIKIiBIV/q5l06YNTsMewHHwYCxcXCo1ZiGEqAqSXAlRB6XkpfDtiW/59fSvFBqMM757+PTg2fbPElzowoFNH+ETnoHuYorpPpfdNOxsrbC7lYo0hyjI/hzt6iW082hHZ+/OdPbuTEu3lqWGEdY2lt7euD8zGddJTxH7z3aSVvyC1e5DFJ47T+LcuRR9OJfTjVX80UZFspOK9dm29FY3pXWBJ3ZJWeiiotHFxhqLLtyExtUVK39/NL6+XCrIp3mfvlgEBJLs4Ea0ypbo9HwupeReSaByiTqXS25h5E2PaWelIadQT+j5FELPp/CmWkWvJh48MOBR7n3+Bawz08jZt4+cf0LJCQ2lKDGRnFDj9eKY7Lp1MyVblg0aVLj9FEUhLCmMtefWsuXCFnKLckvtY6G2wNfeF78rPVCBFp403XQSh9V/QaGxJ8LxgaF4Pv88rau5TLRKpaLB7FlEPjCMnNBQMjdswGnYsGqNobopikL8G/+j6PJlrAID8X7rzWp9/Bv1dil6Pbq4uCtJV6Sxt+vCBfSZmdj36Y3TA8OwDm5YrbEKIcSdkuRK1Ak6vY698Xtp6dYSN239Lb2dWZjJD8e+Y/O/S3FOzqNHOrQt9KKLPgD7pBR00RO5mJGBB6CjZHGKJu3b4ZcRSYvLBziYcJADlw+QXpDO3vi97I3fC2AaRljbki2DYiAqM4rTqacJTw03/kwJJ60gDTqDtrVCj3A1fcMMNI6HrmeMlciMsoCDAORce1ALCyx9fbDyD8DS3884hM/fnwJPb+K0rkTlq4hKzeVicjZHz0aTc86Wy4cTMShlTCS6QqWCBo42BLjZEuBqS6CbHf6utgS6Gn93trUkJi2Pjcfi2BgWT3h8Jn+cTuSP04nYWKrp18yLoW0702fQYBpYqCk8f96YXP0TSs6BA+hTU8ncvNlU5tyqYUPjXK0e3bHt0gWNvf0NY0vOS+a387+x9txaIjOuJoEBDgH0C+hHoGOgaSifl60XGrUGpaiI9FWrSfriC/QpxkTetlMnPF95BW3rVrfzp6wUVoGBuE+dStInn5Dw/gfY9epVp3tF0pYtJ/uPP1BZWuL7ycc1Zp0vlUaD1ZWeW/teUh5fCFE3SHIlar0iQxEv7HqBv6L/wkJtQf+A/jzY9EE6eXWqs8N9DDk5FMbEmoak5V6MJOrMAQqjo+iTbuDeElN6jMPJ8q/ZkhcQQMCTE3C5f2iJ4hSNXRrT2KUxjzR/BINi4Hz6eQ7UsmRLZ9ARmR7JqZRTnE49bbqU1cOiVqkJdgqmuWtzmt3djAZuzfFMsaBow1YyNm0yDlnydiHavoDjNklcdlK47Axprtb4Bd5NiP29qPJDiE4zDuG7FJpLVn4UEHX9I8GVv4DWUkOAq60pgbr2up+LFmuLm5dZ93e1ZWqfRkzt04izCVlsDItjQ1gcF1Ny2XQ8nk3H43GwtmBAS28eaOdD90fH4Tp+PEphIXlhYWSHhpIbupe848dNw7PSli0DjQZt27bGZKt7d7RtWqNXw57YPaw9u5bdMbtNQ0u1FlruDbyXEY1G0NGrY6nXmaIoZO/eTcK8eRSeOw8YExrPl17Evl+/GvG6dHtyApmbNlEQEUHiB3PxmfuBuUOqEvnh4STOnQuA50svYdOihZkjEkKIuk3WuSqDrHNVfe50DSBFUXh739usiliFClWJdW6CHIMY02QMwxoNw8naqTLDrnKKolCUlHR1Hk9UNIUxxT9j0Ccn3/z+FhqsfP2M3woH+GPpd+Wnvz8qLy+27NpV4Ta/UbJ1repOtnJ1uUSkRZh6pMJTwjmXfq7MOT/WGmsaOzemmVszmrs2p7lrcxq7NL5hgYT4jDzCotNNQ/ciUxOIzNtNttVe1NaXTfsZCl3RZXREl94RpcgZAE8Ha1Pi5OdsQ2p0BEP73kVDTwc87K0rPblQFIUTsZlXerTiiM+4mkq72lkxuLU3D7T1pVOgC+ora+LoMzPJ2b/fNGxQd6lkQqjTWnIqUM3BAB3HglTEu0Ibj7aMaDyCQUGDsLcqu5cr//RpEufNIyfUmIBrnJ1xnzYNl7EPVeni0iViL+f/lbywMC6OfRgUBf9vv8G+R49qia+6GHJyuDB6DIUXLmDfty9+X39VZYmtrFlY/aTNq5e0d/WrSW0u61yJemPhsYWmxOrjPh/ja+/LyoiVbIrcxMXMi3x48EM+O/wZA4IG8GDTB2nn0a5GfGsOYCgoQBcbS2FUFLromKtrGsVEUxgdc3Uh3BsostcS61RErGMRCc6g83bj7s6j6NF5FNY+Pjcsb6y7xVyhG1Gr1Gbt2cooyDAO6Uu5kkilhnMp85Kp+uG1HCwdaOralGauzWjh1oJmrs1o6NQQC3X5/uVtP5XA1GWH0Omv/+6pK9AFa9s4HD0Po7M5DFapWHtsx8ZjB23duzCm6UgGNexlKoJhfHM4Q4cA5yp7c1CpVLT2c6K1nxOvDmrGwUtpbAyLY/PxeFJyClm6L4ql+6Jo4GTD/W0aMLStD619nXC8914c770XgMyL5zj023ek/b0LvzOpOOTpaHsa2p6+8iDeHjj1CMIOLTbuhXBdxXddQiJJn39Gxpq1oCioLC1xeewx3J+ZjKaGfkmlbdsWl0cfJW3pUi7Pmk3whvWotVpzh1VpLr87h8ILF7Dw8qLBe3NqzP8+IYSoyyS5ErXWqohVfH3UWFb49a6v0z+wPwBv3fUW/+30XzZf2MzKMysJTw3nt8jf+C3yNxo5N2J0k9EMDRmKo1XVf+DTZ2Yah15dW1UuKorCmBiKEhLgZh3HajWWDRqUKMlt4evLv+pLLExey1l9PADedr480+YZHmj0AJbq6vtmp6qSLUVRSMxNNCVQp1OMw/ricuLKjMNd604zV2NvVDPXZjR3a46fvd9tf5D883SiKbFq4mVPM2/HUsP4vB1tUKtV5BXlsePSDtaeW8uBywc4mryfo8n7mXfQifuD72dEoxEEOwTfVhy3S61W0aWhK10aujJzaAtCz6ewISyOrScuE5+Rz5K/L7Dk7wsEudlyf5sGNAtM5WDqNn6/8Du5DrkwGDT3WTLC0JaBSQ3wPnmZ/CNHUS4nlVny3fauu8g7cpSUb79FyTPWRHQcfB8eL7yAlZ9ftZ777fB4/nmyduxAFx1N8tdf4/nf/5o7pEqRsXEjGWvXglqNz4fz6vScMiGEqEkkuRK10p9Rf/LOvncAmNR6EmObjS1xu52lHWOajGF049GcTDnJyoiV/H7hd86ln+ODfz/g00OfMqjhIMY0GUNr99ZV8o1u+rp1XH7zrZtWlVPb2mIZEHBNSW4/LP0DjD99fEzDqBRFYUfUDr468hXnM4xzWFxtXHm6zdOMbjIaa43513y5k2Sro1dHcotyTfOjUvNTy3wMP3s/mrsZk6jihMrD1qPSzmFXRBKTlxoTqyFtGvDZQ+2w0Ny4LLjWQsvQkKEMDRlKVGYU686tY/359STmJrIsfBnLwpfR3LU5jQoa0bOwJ66W1bvAr4VGTa8mHvRq4sG7w1uxKyKJDWFx7Iw4R6yym+8uHURz+WqBDR87P8Y0HcUDIQ/gaetp2m7IyyP34MEblnw3tUe7dni9+gradu2q8zTviMbeDu+33iJm6lRSvvsex8GDsWne3Nxh3ZHCS5e4PHMWAO5TpmDXpYt5AxJCiHpEkitR6xxNPMrLu1/GoBgY3mg4z7Z/9ob7qlQqWrm3opV7K17s9CK/Rf7GijMrOJd+jnXn1rHu3DqauTZjTJMxDAkegp1l5VTRyt69m/g3/gd6PRYeHlgFBhqTp+vmP2lcXG6a2CmKwp7YPXxx5AvCU8MBcLRyZEKrCTzS7BFsLWvuGlS3k2wV06g0NHRqWKI3qqlr0yrtbfznXDJP/99BCosMDGrpzae3SKyuF+AYwIwOM5jWbhqhcaGsPbeWP6P/NPbAEc62tdvoH9ifEY1G0Nm7c5Wu5VQWC42CjeMZLLzXYFO0G/2V4hSKwZKizNbo0jtxJq8hmxJdUGfkcH+bfDwdjfPR1Fot9j17Yt+zJwBFSUlXS77v34/G3g73adNwGDiwVg49c7inLw4DB5K1dSvxb75F0K+/3HBYbU2nFBYS+8J/MeTmou3UEfcpz5g7JCGEqFckuRK1SmRGJNP/mE6+Pp+evj156663yv1hzsHKgYebPczYpmMJSwpjZcRKtlzYwunU07yz7x3mH5zP4ODBjGkyhhZut19RK3bfQdKfnYFar2dvcGdUr81kQs+QCh/nwOUDfHHkC44kHgHA1sKWx1o8xviW46tlSGNlu1mydTTxKPZW9qbeqJsVmqgKe8+nMPHHAxQUGejf3JPPH26PZQUSq2tp1Bp6+vWkp19PUvNT2XB2A0uPLiVBn8CmyE1sityEr70vwxsNZ3ij4XjbeVfy2ZR0MeMia8+tZcP5DSTnXS2E0sa9DSMaj+Aur3v4+0w2G8Li2BuZwpGodI5EpfPOplN0a+jGA+18uK+VN862VxdStvDwwGnoUJyGDq3S2KuT1xuvkxMaSv6JE6QtXYrr44+bO6TbkvjxJ+SfPInGyQnfDz9EZSFv80IIUZ3kv66oNRJzE3lm+zNkFGTQ2r01H/X+6LbmGKlUKtp5tqOdZzte7vwyG85vYMWZFVzMvMiqiFWsilhFK7dWjGk6hkFBg27ZO6Q3KByNTuevM4mc2H+CySvfx6mwgEOeTXiv1Sj0m08T7OVI7yblG752POk4nx/5nH3x+wBjlbuxTcfyZOsncbWp3mFlVen6ZMtcDlxMZeKPB8jXGejb1IOvHu2AlUXl9Cq52rjyaLNHcT7vTFC3IDZe2MjmC5uJzY7lq6Nf8fXRr+nu053hjYdzj/89piIYdypXl8u2S9tYe3YthxMPl4ineC5YI5dGpu1ju7gytksAiZn5bDoez8awOA5HpbM3MoW9kSm8ue6EcbHitj70b+GFvXXde+uw9PTE88UXuTxzJomffY5D//5Y+vqaO6wKyd61i9QffgCgwXtzbmuRaCGEEHem7r1DijopqzCLKTumEJ8TT6BjIF/2+7JShsQ5WTvxWIvHGNd8HAcTDrLyzEq2R23nRMoJToSe4MMDH3J/8P2MaTqGJi5NTPdLzSlkd0QSf55JZFdEEum5OlzyM5m/+0ucCnOI8Qgk5aXZDMlRWH80jhk/H+G3Z+/G3/XGMZ9JPcOXR7/kr+i/ALBQWzCq8SiebvN0ifkvovIcupTGE9/9S26hnp6N3VkwruMt15m6HSqVipZuLWnn3Y4XO79YogjGP3H/8E/cPzhZXy2C0dS1aYUfQ1EUwpLCWHdunbE4xZV1vdQqNXf73s2IRiPo7df7ptUaPR1tmNCjIRN6NCQ6NZffjsWzISzuBosV+9CnqQc2lrVz+FxZnMeMJmPDBvIOHeLy2+/gt3BBrRnmqEtIJO7V1wBwGTcOh379zByREELUT5JciRqvUF/Ic38+R0RaBG42bizsv7DSe3BUKpWpgl1qfirrz61nZcRKorOi+eXML/xy5hcaO7XCR92XqOjGHI/JLVHoz1NTxIdh/4dnbipqXz/6/rqUe93dKSjSczEll7DodCb/dIjVU7qjtSr5YfRixkW+Pvo1Wy5uQUFBrVIzNHgoz7R9Bj+Hml9trbY6Gp3OE9/9S06hnu4hbiwZ36laEoVri2BEZ0az9tzaUkUwWri1YGSjkdwXfN8th4Am5yXz2/nfWHtuLZEZkabtAQ4BjGg8olRxivLyd7VlSp8QpvQJ4VxiFhvCjD1aF5JzSi1W3LeZB0FudgS42eJoU3vXf1Gp1TR4ezYXho8ge9cusn7/HcfBg80d1i0pej1xr7yCPi0N62bN8HzpRXOHJIQQ9ZYkV6JGMygGXt/zOgcuH8DO0o4F/RdUecLhauPKhFYTGN7wEX44sp1NF9aSoD/I2YwTnOUEilaLlUcH/C37MaBxa/qGuOD5/uvkxl9E4+pK0HffYOHuDoC1hYYFj3Zg6Bd7OBWfyRtrjzP/wbaoVCrisuNYGLaQDec3oFf0AAwMGsjUdlMJdqre8t31zfGYDMZ/u5+sgiK6NnTl28c7m6UHxt/Rv8wiGKdSTnEq5RQfHvywzCIYRYYi/on9hzVn17A7ZjdFV4pTaC203Bt4LyMajaCjV8dK63Vp5OnAC/c68J/+jUstVrz6cAyrD8eY9nWxtbxSut6OAFftlfL1xsTL29EGjbpm9wRZh4TgNnkyyV9+yeU572HXowcap5q9CHnKkiXk7tuHytYW348/Rm1t/uqhQghRX0lyJSos648/SHjvfdynT8N5+PAqexxFUfjwwIdsvbgVC7UFn/T5hOZuVVciWVEUwuOz+PNMIn+dSeRwVDp6gwoYiUpzL3buh7Fx/ZcCTQpWbv+QwD+cMnSgz8dF5IYeRmVri/+iRVgFBpY4ro+zli8eac+4b/az5kgsjXwMpFttYWXESooMxg/Fvf16M739dJq5Nquy8xNGJ+MyGPftfjLzi+gc5MJ3T3Qu1ZtY3a4vgrEpchNrzq7hXPq5UkUw8ovy2XB+A0l5Sab7FxenGBQ0CHsr+yqL8/rFig9FGRcrPh6bQXRqLsnZhaTl6kjLzSAsJqPU/a00avxctCXWDAtwtSXQzQ5/Vy22VjXjLcnt6Ulk/v47hefPk/jRRzR45x1zh3RDuYcPk/TFlwB4/+9/WAc3NHNEQghRv9WMdzJRq6T9+iu6mBjiX30NfVo6bhOeqJLH+eHkDywNXwrAuz3e5S6fuyr9MbLydfxzLpm/zhjnTyVkFpS4vbGnPX2bedKnqQedAh9Eo1YIjQtlZcRKdsXsotmvB3DYr6BXw6FpPdEGOhBYxuN0D3Hn+QG+fHVkCV+f24tKbVz7qqt3V6a3n047z3aVfm6itNOXMxn3zX4y8nS0D3Dm+wldsKthxRlcbVxN8wBPppxk7dm1JYpgXLtfWcUpqotaraJzkCudg64O0c0uKCI6NZdLKbnGn6k5RKXmEZWSQ0xaHoV6A5HJOUQm55R5THd7awJLJV7Gnx4O1tU2/0ltZUWDt2dz6dFxpK9chePQoTVyrSh9ejqxL74Iej2OQ4fiNGK4uUMSQoh6r2Z9qhA1nmIwkHfkqOn3xLlz0ael4fGf5yv1g8/G8xv5+NDHALzY6UWGBA+plOMqisK5xGz+PJPIn6eTOHAxlSLD1clTWksNPRq50aepMaHycyldgKK4h+Hiki/J22/8sLtgsJrdFjv5aO1OujboypgmY7jH/x4sNZZkF2bzf6f+j+Xx/4eVm/FDpaogkA/7vcLAkJ6Vcl7i1s4mZPHokv2k5epo6+fEj092qdFV70qs0XalCMbmC5uxVFvyQMgDtyxOYQ721hY0b+BI8wal54npDQrxGXlEpeQSlZrLpVTjz+LfM/J0JGcXkJxdwKFLaaXub2OpvjrE8Jqky9/VFn9XbaUXIrHt2BHnhx4i/ddfufzWTBquX1ejhtspikL8m29SFBePZUAA3jNn1priG0IIUZfV3E8WokYqPH8eQ2YmKhsb3J+ZTNKnn5GyeDH6tDS8Z82slIU3Q2NDeeuftwAY32I8j7e8s/VmcguLCD2XcmW4XxKx6Xklbg92t6N3Uw/6NvWkS0PXcs29ydi0ibz5xsTK7YXnGT2oCYaIFfwT+w/74/ezP34/bjZu9Pbvzc6onWQUGIdINXZuQnJ0X6Jiglii19B3kqHSyn6LGzuflM3DS/aTklNIK19H/u/JrrWq8MK1RTBqK41ahZ+LLX4utnQv4/aMXN2VpCunRNIVlZpLXHoe+ToDEQnZRCRkl7qvSgXejjam3i4/ZxviE1TkHIpBcwf/k9R9H6Tp1u1w8SK7Z84jYVTNWfvKdcdG/LbvwKCx4NTElzgUngKkmC0evV7PsUpoc1F+0ubVS9q7+un1ek4kqqj5ZYVKkuRKVEjuYeOCtto2bXB/5hk0rq5cnjWb9JUr0Wdm4vPhPNRWt79Wz8mUkzz/1/MUKUXc1/A+/tvpv7d1nAvJOfx5OpE/zySyPzKVQr3BdJuVhZq7gt3o29SDPk09CXK3q9Cxc/btK1Hy2GPS0/RVqegb0JfY7FhWR6xm7bm1JOcls+bsGgCCHIOY1n4aAwIHcDE5l2Ff/sPBS2m8tzmcWQ+0vK1zFOVzITmHhxfvIzm7gOYNHFk6sStOtrUnsaovnGwtaW1rnM91vcIiA3Hpedf0dhkTsEtXErDcQj3xGfnEZ+Sz/0LqlXtp+CXy1B3H1aPx/fzvwP/huv5X3sr0Icqxahd9Lo+gjDg+27UQgG+a38faIwVw5LiZo4LKanNREdLm1Uvau7pZqNTMNncQFSTJlaiQvMPGBUm1HdoD4PLgg2icnIl78UWytm4lJisTvy++QG1XsYQFIDozmqk7ppJXlEfXBl2Z02OOqTraregNCnvOJZsSqkspuSVu93PRcs+VuVN3BbvfdgGD/PBwYqZNB50Oh0GD8Hrt1RJDcXztfZnRYQZT2k3hr+i/CI0LpZ1HO4YED8FCbXy5BXvY8/FD7Zj0fwf5IfQibf2dGNFeSq5XhaiUXB5Zso/ErAKaejmw7KmuONtWzkK9ovpYWagJcrcr84sQRVFIySks0dt1ITmbsxdj8PT0Qn2n1Qmb9eNc+nEanT3CzDPr+Pmx/0E5/y9VBYvCAsb98DFWhiIiQ9qSdf8o+psxnmIGg0JiYkLltLkoF2nz6iXtXf0MBoXkpARzh1FhklyJCsk9auy5su3QwbTNceAANA4LiZ7+LDmhe7k04Un8Fy3EwsWl3MdNzU9l8o7JpOan0sy1GZ/2+bRc80kMBoUtJy/z8fYIziVeHS5kqVHRpaErfZt60qepJyEednc8H6EwJpaop5/GkJODbefO+Mz94IbDIC3VltwbeC/3Bt5b5u33tvBixj2N+PyPc7y25jhNvBxo6VOzyz3XNtGpuTy8ZB/xGfk08rRn2aSuuNpJYlXXqFQq3O2tcbe3pkOA8X+OTqdj8+YoBg9uj6XlnfdS6gbOJ3LI/fjEnuND60hcHn74jo95u+L+9z8yUuKw8PBg4E9fM8S1ctf8u13GNt9caW0ubk3avHpJe1e/4javbcz/dZeoNYqSk9FdigKVCm27diVus+vencAfvkfj7Ez+sWNcGvcYusuXy3XcAqWAGX/NIDorGl97Xxb0X3DLctKKovDn6USGfrmHqcsOcy4xGyetJQ93CWDRYx058tYAlj3Vjad6BtPI0/6OE6uitDSin3oKfVIy1k2a4PfVl3c8uf25/k3o3cSDfJ2BZ5YeIj238I6OJ66KS8/jkW/2EZueR7C7Hcuf6oq7fc0pRiBqF8sGDfB44QUAEj+ajy7BPN+kZmzaRMaq1aBS4fPhPCxqSGIlhBDiKkmuRLnlHjH2Wlk3aoTGsXQ1MG2bNgQuW4qFtzeF589z8ZFHKLhw4abH1Bl0/JLzC6dST+Fi7cLC/gtx17rf9D57z6cwZuFeJvxwgJNxmdhZaZhxTyN2v9yX90e2ZmBL70qtAmfIzSX6mWcovHgRC58G+C9ZXOb5V5RGreKzse3wd9USnZrHc78cRX9N5UJxey5n5PPwkn1Ep+YR6GbL8knd8HS0MXdYopZzeXgsNm3bYMjJIeHdd6v98Qujo7n81kwA3CY/jV23btUegxBCiFuT5EqUW15xMYtrhgRezzokhKDly7AKCqIoLp5Lj44j7+TJMvdVFIV39r/D2aKz2Ghs+LLflwQ5Bd3w2Eej0xn3zX4eXrKPg5fSsLZQM6lnQ3a/3JcXBjTFSVv53fRKURGxL/yX/LBjqJ2cCFiyBEsvr0o7vrOtFYvGdcLGUs2uiCQ+2xFRaceujxIz83lkyT4upeTi76rl50nd8HaSxErcOZVGQ4O33wELC7K27yBz+/Zqe2ylsJDY/76IIScHbYcOeEyfXm2PLYQQomIkuRLlVlzMwvZKMYsbsfTxIXD5MmxatkSfmkrU+MfJ2f9vqf0+O/wZv134DTVq5t49lzYebco8Xnh8Jk/9eJDhX/3DnnPJWGpUjOsWwO6X+/LGkBa4VdFwL0VRiJ81i+y//kJlbY3/ggVYh4RU+uO08HHk/ZGtAfj8j3NsP1X7Jm/WBElZBTy8ZB+RyTn4OhsTKx9nrbnDEnWITdMmuE2cCEDCO++iz8qqlsdN/Owz8o8dQ+3oiO+H81BZyHRpIYSoqSS5EuViyM8n75Sx/OjNeq6KWbi6EvDjD9h27YohJ4foSZPI2rnTdPuy8GV8e+JbAIZph9HTt/RiupFJ2Tz78xEGf/43O8ITUKtgdEc//vhvH94d3hqvKh7qlfzFF8b5DWo1vh/Pv2VSeSdGtPfjie5BALzw61Eik0qv5SNuLCW7gEe/2cf5pBwaONnw86RuZS4ALcSdcp/yDJaBARQlJpL0ySdV/njZf+8h9dvvAGjw7jtY+vpW+WMKIYS4fZJciXLJP3ECdDo0Hu5Y+pWvbLjG3h7/xYuw798PpbCQmGdnkL5mLVsvbmXuv3MBmNpmKh2tO5a4X0xaLi+vCuPeT3azMSwORYEhbRqw7T+9+WhMW/xdq/5Dc9ovv5D89QIAvGfOxKFfvyp/zNcHN6dzkAtZBUU8s/QQOQVFVf6YdUFaTiGPfrOfiIRsvByt+XlSNwLcJLESVUNtY0OD2cZVV9J+/sW09l9VKEpKIu7VVwFwfngsjgMGVNljCSGEqBySXIlyKf4AYduufYUq76mtrfH79FOcRo4Eg4H4119n90cvoqDwUNOHmNhyomnfxMx8Zq4/Qd+P/mLFwRj0BoV+zTzZNONuvnqkA408b15BsLJk7djB5bffAcB92jRcHnqwWh7XykLNV490wNPBmoiEbF5efQxFkQIXN5ORq2Pct/s5fTkLDwdrlk/qVuFFoYWoKLtu3Yz/0xSFyzPfQims/EqfisFA3CuvoE9JwbpJE7xeeaXSH0MIIUTlk+RKlMvVxYNvPSTweioLCxrMeRceGQ7AuB1FvHY4gFc7GxfgzdHB3K0R9PrwT37cewmdXqFHIzdWT+nOt090rtb1n3IPHSL2vy+CwYDzmDG4T59WbY8N4Olow4JxHbBQq9h0LJ5v/r55tcX6LDNfx/jv9nMyLhM3OyuWP9WVEI/qScCF8HzpRTSurhScPUfKt99W+vFTvvmWnNC9qGxs8P3kY9Q2UphFCCFqA0muxC0pBgN5R4oXD769eUeXcy4zvdm/LOtjfMq13xpJ3Juz+HzHGWYf0fDNnovk6wx0CHBm+aSuLHuqGx0Dy78IcWUoOHeO6ClTUQoKsL/nHrxnvnXH62Pdjo6Brrw1tAUA7/8eTuj55GqPoabLytfx+Hf/EhaTgaudFcsndaOxl4O5wxL1iIWLC16vvQZA8tcLKIisvC9Cco8cIemzzwDw/t8bVVJIRwghRNUwe3L11VdfERQUhI2NDV27duXff0tXlSum0+l4++23CQkJwcbGhrZt27Jly5YS++j1et58800aNmyIVqslJCSEd955R4ZX3YHCixfRZ2SgsrbGpnnzCt8/PT+dyTsmk5iXyMn7mmD7xisoKjU5a1bj8OE7GAr1NPd24LsnOrF6Sne6h9x8nauqoLt8mahJT2PIzETbrh2+8z8ya0Wux7oFMrKDLwYFnl1+hLj0PLPFUtPkFBQx4fsDHIlKx9nWkqUTu9LUWxIrUf0c7x+CXc+eKDodl2fORDEY7viY+sxM4v77Iuj1OA6+D6dRoyohUiGEENXFrMnVr7/+ygsvvMDMmTM5fPgwbdu2ZeDAgSQmJpa5///+9z8WLVrEF198walTp3jmmWcYMWIER45cnVA8d+5cFixYwJdffkl4eDhz585l3rx5fPHFF9V1WnWOaUhg69aorKwqdN/8onye/eNZLmRcwFPryT3OrzM6yo85ncahU2voGXeMxce+Yc3jbbinmZdZeor0mZlET3qaovh4rIKD8VvwNWqteUt4q1Qq3hvRmhYNHEnJKWTK0kPk6/RmjakmyC0sYsIPBzh4KQ1HGwuWTuxKC587X9BZiNuhUqmMPdxaLbkHDpCxZs0dHU9RFOLfmokuLg5LPz+8Z882y/9EIYQQt8+sydXHH3/MpEmTmDBhAi1atGDhwoXY2try3Xfflbn/Tz/9xOuvv87gwYMJDg5mypQpDB48mPnz55v2CQ0NZdiwYQwZMoSgoCBGjx7NgAEDbtojJm4utxyLB5elyFDES7tf4mjSUWzU9uREPcn83xNJyirgUquuxL7yHiqtFu9L54h/ehJFaWlVEf5NGQoKiJk6jYKzZ7Hw8CBgyWIsXKp3OOKN2FhqWPRYR5y0loTFZDB7Y9mLMdcXeYV6nvrxIP9eSMXB2oKfJnallW/1zccToixWfn54PPssAAnzPqQoKem2j5W+YiVZW7aAhQW+H89H4yA9skIIUduYbdxTYWEhhw4d4rUrY9YB1Go1/fv3Z+/evWXep6CgAJvrJvVqtVr27Nlj+r179+4sXryYiIgImjRpQlhYGHv27OHjjz++YSwFBQUUFBSYfs/MzASMwxB1Ot1tnV9dknv4EABWbduUuz0UReHdf+fwV/RfoFiQGvko+jxnvBysmdInmDEdfLGyUJPd3JvYZ6ZQcPwEFx8dh++ihVh4e1fdyVwbo17P5ZdeIvfgQdT29jRYsAA8PWvU39zbwZJPxrRm4k+H+fnfaFo1cODBTuUrhX8jxedXk87zVgp0eiYvO0ro+RTsrDR8O74DLbztasU51Mb2ru2qu80dHh5LxsaNFISHEz/nPbw/nFfhYxScO0fCe+8B4DbjWSyaN69Vzxl5nlc/afPqJe1d/WpSm1ckBpVipslIcXFx+Pr6Ehoayl133WXa/vLLL7Nr1y72799f6j6PPPIIYWFhrFu3jpCQEHbu3MmwYcPQ6/Wm5MhgMPD6668zb948NBoNer2eOXPmlEjirjdr1ixmX1m35FrLly/H1rZ+r5ejyc4m5J13ATg38y0M5WgPRYFf0/7khHoniqIiP/ZRrPNa0t/XwN1eClaakvtbJSTi++23WGZkoHN2Juapieg8PKridEoE6bl+A85792LQaIid+CR5NXjS+LYYFZuiNWhUCs+11BNYj77QLjLAN2fUhKersVIrPNNcT4iMBBQ1jHVsLAFffoXKYCD2iSfIad6s3PdVFRYS8OVXWCckkNOkCbETngC12adECyGEuCI3N5dHHnmEjIwMHB1v/iHEfDP2b8Nnn33GpEmTaNasGSqVipCQECZMmFBiGOGKFStYtmwZy5cvp2XLlhw9epTnn38eHx8fHn/88TKP+9prr/HCCy+Yfs/MzMTf358BAwbcsgHruuw//uQyYBUSwqDRo2+6r6IohEamMmvX96TY7DRuTBnOtM7DeKJ7IPbWpZ9uOp2O7du3E7R8GYnTpsHFSzT67jsaLFiATYsWVXBGRqnffEPq3r2gUtHggw9oMmhglT1WZRhkUCj4+Sg7TiexPMqOdVO64WZvfVvHKm7ze++9F0tLy0qOtHIVFhmY/stRwtOTsbFU881jHeja0NXcYVVIbWrvusJcbZ6cmUn6Dz8SuHUrAVOeQV3OL+cS336HzIQENG5utFy0iLbublUcaeWT53n1kzavXtLe1a8mtXnxqLbyMFty5e7ujkajISEhocT2hIQEvG8wLMzDw4N169aRn59PSkoKPj4+vPrqqwQHB5v2eemll3j11VcZO3YsAK1bt+bSpUu8//77N0yurK2tsbYu/UHV0tLS7H9Mcys8FgaAbYcON22LgxdT+XDrGQ4m7UHr9zMqoJXdSBaMeQNn21sXwdAGBBC0fDnRk54m/+RJ4p6ciN/XX2PXtUtlnYpJ+pq1pH72OQBer72G69D7K/0xqsLHY9sz/Mt/iEzO4T8rT/DTxC5YaG7/2+2a/vzW6Q28sCqMP88kY22h5rvHO9O9UfVXkqwsNb2966LqbnOvGTPI2b4DXWws6V9/bSrVfjOZW7aQuXIlAD7z5qJtUD3DoquKPM+rn7R59ZL2rn41oc0r8vhmG3dgZWVFx44d2blzp2mbwWBg586dJYYJlsXGxgZfX1+KiopYvXo1w4YNM92Wm5uL+rrhFBqNBkMllMitj/JuUcziRGwGT3z/L6MX7uXA5SNofX9GpVIYFDiU5aNmlSuxKmbh6krAjz9g27UrhpwcoidNIuua50dlyN69m/g33wTA7amJuI5/rFKPX5UcbSxZ9FhHbK007I1M4cOtZ8wdUpUp0ht4/pejbD2ZgJWFmiXjO9XqxErUD2pbW7xnzQIg9ael5B0/ftP9C2NiiX/zLQDcJk3CvkePqg5RCCFEFTProO4XXniBJUuW8OOPPxIeHs6UKVPIyclhwoQJAIwfP77EXKn9+/ezZs0aIiMj+fvvvxk0aBAGg4GXX37ZtM/QoUOZM2cOmzZt4uLFi6xdu5aPP/6YESNGVPv51XaGggLyT5wAwLZ9uxK3nU3IYsrSQ9z/xR7+OpOEhU0SzkE/oVLr6Onbk/d63V4JYY29Pf6LF2Hfvx9KYSExz84gfc3ayjgd8o4dI+a550Gvx2nYA3hcMxS0tmjs5cCHo9sCsGh3JJuOxZs5osqnNyi8sCKMTcfjsdKoWTSuI72aVPEcPCEqiX3Pu3EcOhQMBuLffAvlBpOgFZ2OuP/+F0NWFtq2bfGY8Ww1RyqEEKIqmHXO1UMPPURSUhJvvfUWly9fpl27dmzZsgUvLy8AoqKiSvRC5efn87///Y/IyEjs7e0ZPHgwP/30E87OzqZ9vvjiC958802mTp1KYmIiPj4+TJ48mbfeequ6T6/Wyz95EkWnQ+PqimVgIIqisPd8Cj/uvcj2UwkYFFCpYFAbLWc0y0jKy6a1e2s+6v0Rlurb775VW1vj9+mnxL81k4w1a4h//XX0GRm4TXjito9ZePEi0ZOfQcnLw65HDxq8+y6qWjphfEibBhyLCWbR7kheWhVGYy97mnjVjQoXeoPCSyvD2BAWh4VaxdePdqBvM09zhyVEhXi9+go5u3dTcPo0qT/+iNtTT5XaJ+mLL8kLC0Pt4IDP/I9QyTAjIYSoE8xe0GL69OlMnz69zNv++uuvEr/37t2bU6dO3fR4Dg4OfPrpp3z66aeVFGH9lXdlcWartu1Yuu8SP+69xLnEbNPtA1t6MbmPL3OOTCcp7TKBjoF82e9LbC3vvMKiysKCBnPeRePsTOp335E4dy76tDQ8/vN8hXvEipKSiHpqEvq0NGxatsT3s89q/QeZlwY25XhsBqHnU5j80yHWT++Bo03tPieDQeGV1cdYcyQWjVrFl490oH8LL3OHJUSFWbi54fnKK8S//jpJX36Fw4ABWAUEmG7PCQ0lZckSABq88zZWfne2vIIQQoiaw+zJlbi5uf/O5e/Yv3GydsLF2gVna2fjxcbZ+LuN8ffi645WjlioK+fPmrz3AACLU+1Zvt64gK2tlYZRHfwYf1cgge7WPLPjGSLSInCzcWNh/4W42lReJTeVSoXXyy+hcXEmaf7HpCxejD4tDe9ZM1FpNLc+AKDPziF68jPoYmKwDAjAf9FCNPZ2lRajuVho1HzxcHuGfrGHC8k5/HdFGIvGdUStrvhQzJrAYFB4Y91xVh2KQaNW8fnY9gxqVbsn9ov6zWnEcDI2bCB33z4uz5qF/7ffolKpKEpOJvaVV0BRcH7wQRwHDTJ3qEIIISqRJFc1XEx2DJcyL1XoPo5WjrjYuJRIyEr8fl1C5mTlhEZtTFb0BoW/ziTyY+hFpvx7EGfgsFMAwe52jL8rkJEd/XC0scSgGHh598scuHwAO0s7FvRfgJ9D1Xz76j5pEhonJy7Pmk36ypXoMzPx+XAeaqubF8tQCguJnTGD/FOn0Li6EvDNEizc605RBDd7axaM68iYhXvZfiqBr/86x/R7Gps7rArLyNPxwe+n+fnfaNQq+PjBtgxp08DcYQlxR1QqFQ1mzyLygWHkhO4lc8MGHIcOJe7V19AnJWPduBFer71q7jCFEEJUMkmuarhXOr/CEy2fID0/nfSCdNIK0kjPN/7MKMgw/Z5ekE5mobEGf2Zhpul6eahQYW/pgEaxJyvXioICGxqkqHEuzEGnUdN3rDUdg/W4atNIzlcowpklx5aw9eJWLNQWfNLnE5q7Na+qJgDA5cEH0Tg5E/fii2Rt3UpMViZ+X3yB2q7sXijFYCDujf+RExqKytYW/0WLSgzLqSva+jvzzvCWvLL6OPO3R9DK14k+TWv+HCWd3sCuM0msPRLL9vAECosMqFTw0Zi2DGvna+7whKgUVoGBuE+dStInn5Dw/gcUnDtPzp49qKyt8Zk/H7VWa+4QhRBCVDJJrmo4Pwe/cvcIFRmKyCjIIL3AmGwVJ2HXXi8rIVNQyNJlAplgBRZW0PKCsXT9uQYGlkZ/xtLosh/z3R7vcpfPzUvnVxbHgQPQOCwkevqz5ITu5dKEJ/FftBALF5dS+ybOn0/mxo1gYYHfZ5+hbd2qWmI0h4c6B3A0OoOf/43iuV+OsnH63QS43fm8t8qmKArHYzNYcziWjWFxpOQUmm5r6uXAc/0bM7i19FiJusXtyQlkbtpEQUSEaZ6V12uvYdOkiZkjE0IIURUkuapDLNQWuGndcNO63XQ/nd7A1pOX+b/QS8ReTEKlyUOlySHIU0WvZlqa+qhpsH8lcAJ9qyb09vMv0WOWVZiFtcaa/3T8D0OCh1TPyV1h1707gT98T/TTk8k/doxL4x4j4NtvsLxm4enUH38k9dvvAPCZ8y72Pe+u1hjNYdYDLTgVn0lYdDrPLD3E6ind0VqVb15aVYtNz2PdkVjWHI7hfFKOabu7vTXD2vkwsoMvLRo43lbpfiFqOpWlJQ3eeZuLYx8GRcFh4ECcH3rQ3GEJIYSoIpJc1SOJWfn88m80y/ZfIiGzADAmZINaNubx7kF0CnQxfcA9f/FHCoFBDzzPmHv6ljhOkaGIIkMRNhY21X0KAGjbtCFw2VKiJj5F4fnzXHzkEQK+/Rbrhg3J2LSJhPc/AMDjvy/gdM0C03WZtYWGBY92YOgXezgVn8kba48z/8G2ZktYsguK+P14PGsOx7LvQgqKUhynmoEtvRnRwZeejdyx0NTOcvhCVIS2bVu8Xn+dvCOH8Z41S75IEEKIOkySqzpOURQOR6Xzf3svsvl4PDq98VOuu701j3QN4JEuAXg7lUySitLSKIyMBEB73eLBYEzIKqsi4e2yDgkhaPkyop6cSOHFi1x6dBzuU6aQMG8eAC6PPVbm2jJ1mY+zli8eac9j3/7LmiOxtPV35vHuQdX2+EV6A3vOJbP2SCxbT14mX2cw3dYt2JWR7f24r7U3DrW8ZLwQt8P1sXHw2DhzhyGEEKKKSXJVR+Xr9GwMi+P/9l7ieGyGaXuHAOMH7vtaNcDKouxeg7wjRwGwatiwzPlMNYWljw+By5cR/dQk8k+dImHOHAAcBg3C67VX6+W3w91D3Hl1UDPmbA7nnd9O0dLHkU5BlVcevyyn4jJZcziG9WFxJGUVmLYHe9gxqoMfw9r54OdS8+aACSGEEEJUNkmu6piYtFyW7Y/il3+jSMvVAWBloWZYWx/G3xVEaz+nWx4j78hhALQd2ldprJXBwtWVgP/7kZip08j9919su3TBZ+4HqNT1d7jZUz0bcjQmnU3H4pmy7DCbnr0bT8fKHcKZkJnP+qOxrDkcy+nLWabtrnZWDG3TgJEd/Gjj51QvE1whhBBC1F+SXNUBiqKw93wKP4ReZEd4AoYr81t8nbWM6xbIQ539cbW7+ZpQ18o9cgQA2w4dqiLcSqextyfgmyXkHj6CtkP7W65/VdepVCrmjWrD2YQsIhKymbrsMMsndbthT2V55RYWsfXkZdYcjuWfc8mm55mVRk3/Fp6MaO9H7yYed/w4QgghhBC1lSRXtVhOQRFrDsfw495LnEvMNm3v0ciN8XcF0b+5Fxp1xXoOlMJC8o+fAEDbvnYkVwAqKyvsunU1dxg1hp21BYse68QDX+zh4KU05mw6xexhFS9Hrzco7ItMYfXhGLacuExuod50W6dAF0Z28GNI6wY42co8KiGEEEIISa5qofNJ2fy09xKrD8WQVVAEgK2VhlEd/Bh/VyCNvRxu+9j5p06hFBSgcXHBqmFQJUUszKGhux2fPNSOp/7vID/uvURbf2dGdijfmmkRCVmsORzL+qOxxGfkm7YHutkyor0vI9r7EuhW9gLOQgghhBD1lSRXtYTeoPDXmUR+CL3I32eTTduD3e0Yf1cgIzv64VgJVdhyDxuHBGrbt5f5MnVA/xZezLinEZ//cY7X1hyniZcDTT3LLi6RnF3AhqNxrDkSw4nYTNN2RxsLhrY1rkfVIcBFnhdCCCGEEDcgyVUNl55byIqD0fy07xLRqXkAqFTQr5kn4+8K4u5G7qgrOPTvZoqLWdjWgmIWonye69+EY7EZ/HUmiWeWHmLtM91Mt+Xr9Gw/lcDaI7HsikhCf2UilYVaRd9mnozq4EvfZp5YW9SMBYmFEEIIIWoySa5quEeW7OdUvLEXwUlryUOd/RnXNZAAt8ovba0oytWeq1pSzELcmkat4tOH2jH0yz1Ep+bxwspjtLOC19edZMuJBNPQUoC2/s6M6uDL/W18KlQERQghhBBCSHJV443p5MeKgzE8flcgw9r5orWquh4EXVQU+pQUVJaW2LRsWWWPI6qfs60Vi8Z1YuSCf/j7XAp/YwHEAsaqkiPa+zKigy8hHvbmDVQIIYQQohaT5KqGe6xbIE90D6qWeS7FvVY2rVqhtrau8scT1auFjyNzR7Xh+V+PYq1WGNrOj1Ed/ekS5FqpQ0uFEEIIIeorSa5qOAtN9a0ZlHe49iweLG7PsHa+tPdzZP/uPxg+tCWWllJCXQghhBCisshqn8Ikt7iYRXtJruqyBk42VOHoUiGEEEKIekuSKwGAPj2dwnPnAWMZdiGEEEIIIUTFSHIlAMgLCwPAKjAQCzc3M0cjhBBCCCFE7SPJlQCQEuxCCCGEEELcIUmuBCDFLIQQt0lRwGAwdxRCCCFEjSDVAgWKTkfe8eMA2ErPlRCivAqy4YfBkHQGPJqCZwvwbA6eLY0/HX2gGpaREEIIIWoKSa4E+eHhKPn5aJycsGrY0NzhCCFqiy2vQLxxvibxYVevF7NxuibhuuanrWv1xyqEEEJUA0muBLnFQwLbt0ellpGiQohyOLEGjiwFVDDme1BbQuKpK5dwSD4L+RkQtdd4uZa9tzHR8rrSw+XZHDyagZWdWU5FCCGEqCySXAnypJiFEKIi0i7BxueN13v+F1qOMF5vfv/VfYoKjAlWYvg1SdcpSI+C7MvGS+Sf1xxUBS5BV3u4vFoYr7s1Ao0sdi2EEKJ2kOSqnlMU5eriwVLMQghxK/oiWDMJCjLArzP0ebXs/SyswbuV8XKtgixIPH21hyvxpPFnThKkXTBezmy6ur/aEtybXO3hKu7tcgoA6WkXQghRw0hyVc/pYmLQJyWDpSU2rVrd+g5CiPpt9zyI3g/WjjDqm4r3Klk7gH9n4+Va2UnXJFzXDC8szL6SgJ0sub+lHXg2u9LT1eJq4mXncWfnJ4QQQtwBSa7queIS7DYtmqO2sTFzNEKIGu1SKOz+0Hj9/k+Mw/gqi70H2PeG4N5XtykKZERDwqmSiVdyBOhyIPaQ8XItWzc0Hs1on6VCs/F36d2qJhqDgfYxMdLm1UjavHpJe1c/jcFA29g4YLC5Q6kQSa7qudwjxvlWtu1lvpUQ4iby0mD1JFAM0PYRaD266h9TpQLnAOOl6aCr2/U6SI2EhJMle7pSL0BuCupL/xAAkFr1IQojNUibVzNp8+ol7V391IC/ypLatpKiJFf13NViFjLfSghxA4oCG2ZAZgy4BsPgeeaNR2NpXFfLo2nJ7YW5kHyGorjjnDn8N82aNkOjkW+Yq4Neb+D0mdPS5tVI2rx6SXtXP73eQPiZCJreetcaRZKrekyfmUnB2bOALB4shLiJwz9C+AZjcYlR3xrnTdVEVrbg0x7FoxXnYp1o0n0wGkupNFgdDDod59I3S5tXI2nz6iXtXf0MOh3n0zfXuuRKUu96LC8sDBQFy4AALNzdzR2OEKImSjoDv1+pCNjvTfCVL2KEEEKIG5Hkqh4rXjzYtr0MCRRClKGoAFZNhKI8CO4Ldz1r7oiEEEKIGk2Sq3pMFg8WQtzUjlmQcBxs3WDEQqmQJYQQQtyCvFPWU4pOR96xY4AsHiyEKMPZ7bDva+P1YV+Dg7d54xFCCCFqAUmu6qn802dQ8vJQOzpiFRJi7nCEEDVJVgKsfcZ4vcvkkmXQhRBCCHFDklzVU3lHjPOttO3boZKhPkKIYgYDrHsGcpPBqxXc+7a5IxJCCCFqDflUXU/lHpbFg4UQZdj3FZz/Ayy0xrLrljbmjkgIIYSoNSS5qocURSHvcHHPlcy3EkJcEXcEdsw2Xh/0Hng2M288QgghRC0jyVU9VBQXR1FiIlhYoG3T2tzhCCFqgoJsY9l1gw6aD4WOE8wdkRBCCFHrSHJVDxUPCbRp3hy1VmvmaIQQNcLvr0DqeXD0haGfg0pl7oiEEEKIWkeSq3qouJiFlGAXQgBwYjUcXQqoYORisHU1d0RCCCFErSTJVT1U3HOllWIWQoi0S7DxeeP1Xi9C0N1mDUcIIYSozSS5qmf02dkUREQAoJWeKyHqN30RrH4KCjLBrwv0ftXcEQkhhBC1miRX9Uze0TAwGLD088PS09Pc4QghzGn3PIj5F6wdYdQS0FiYOyIhhBCiVpPkqp4xlWCXXish6rdLobD7Q+P1+z8BlyCzhiOEEELUBZJc1TO5pmIWMt9KiHorLw1WTwLFAG0fgdajzR2REEIIUSdIclWPKEVF5IUdA6SYhRD1lqLAhhmQGQOuwTB4nrkjEkIIIeoMSa7qkYKICJTcXNT29lg3CjF3OEIIczj8I4RvALUljPoWrB3MHZEQQghRZ0hyVY+YSrC3a4dKozFzNEKIapd0Bn6/UhGw35vgKz3YQgghRGWS5KoekWIWQtRjunxYNRGK8iC4L9z1rLkjEkIIIeocSa7qkdwjxp4rKWYhRD20YxYkHAdbNxixENTy718IIYSobPLuWk/o4uMpio8HjQZtmzbmDkcIUZ0itsH+BcbrwxeAg7d54xFCCCHqKEmu6oncK0MCbZo1Q21ra+ZohBDVJusyrJtivN71GWgy0LzxCCGEEHWYhbkDENUjr7iYhQwJFOZWVADZCZCdaPzgn30ZcpLBrRE0HgA2juaOsO4wGGDtM5CbDF6toP9sc0ckhBBC1GmSXNUTVxcPlmIWogooCuRnXEmaEiDrys/sy9dcv3LJS7vxcTTWEHIPtBgGTe8DrXO1nUKdtO8riPwTLLTGsuuWNuaOSAghhKjTJLmqB/TZORScPgNIz5WoIH0R5CRdkzRdNvY4ZV++Jom6sq0ov/zHVVuCvRc4eIG9N2hdIGovpJ6HiN+NF7UlBPcxJlrNhoCta5WdZp0UdwR2XOmpGvQeeDYzbzxCCCFEPSDJVT2QfywMDAYsfXyw9PIydziiBtDoCyA1EvJTryZHWZdL9zzlJAFK+Q9s7QT2nsaCCfZe1yRQxdevbNe6gEpV8r6KAomn4NR6OLUBksLh3HbjZeNz0LDnlUTrfuNjiBsryDaWXTfooPlQ6DjB3BEJIYQQ9YIkV/VAcQl26bUSXD6Oxa+PcX/aBThWzvuo1GDnWTJJMiVKnsaeJwcv4z5Wd1AsRaUCr5bGS9/XjQventoA4evh8nGI/Mt42fRfCOhuTLSaDwXHBrf/mHXV768YewEdfWHo56UTWSGEEEJUCUmu6gFTMYv27cwbiDCvgixY8TiqtAsAKJa2qK5Pkky9Ttdct3UDtab64/VoCr1fMl5SzkP4BmOyFXcYLu0xXn5/Cfy7Xkm0HgBn/+qPs6Y5sRqOLgVUMHKxDKcUQgghqpEkV3WcoteTd/QoIIsH13ubX4LU8ygOPmwNfJV+wx7B0tLS3FGVj1sI3P0f4yXtEoRvNCZb0fuvXra+Dj4djIlWiwfANdjcUVe/tEuw8Xnj9V4vQtDdZg1HCCGEqG8kuarjCs6exZCTg9rODusmTcwdjjCXsF8g7GdQqdEPX0TBiZtU7KvpXAKh+3TjJTPOmGid2gCX/jH2asUdhh0zwbv1lURrOLg3NnfUVU9fBKufgoJM8OsCvV81d0RCCCFEvSPJVR1XvHiwtm1bVBozDO0S5pdyHn57wXi996soAXfBic3mjamyOPpA18nGS1YCnP7N2KN14W/jPK3Lx+GPd8Gj+ZVEaxh4Nq+bc5B2zYWYf8HaEUZ9Axr59y6EEEJUN3n3reNk8eB6rqgAVk0AXQ4E3m0cKqY3mDuqquHgBZ0nGi85KXBmk7FHK/IvY+XBXeGw6wNwa2wcNthiGHi3qRuJ1sV/4O+PjNfv/8TYuyeEEEKIaifJVR2Xd1gWD67XdsyC+DDQusKoJcbCFHU1ubqWnRt0GG+85KXBmS3GHq1zOyHlLPw933hxCTIWwmgxHHw71M5EKzcV1kwCxQBtH4HWo80dkRBCCFFvSXJVh+kSEtDFxYFajU2btuYOR1S3M1tg39fG68MXGIfQ1UdaF2j3sPGSnwlntxnX0jq7HdIuQujnxouTv7G0e4thxjlLarW5I781RYGNMyAz1ljAY/A8c0ckhBBC1GuSXNVhxb1W1s2aorG3M3M0olplxsG6KcbrXadA00HmjaemsHE09uy0Hg2FOcYEK3wDRGyFjGhjMrrva2Mp+uZDIbi3cRiha0OwsDZ39KUd/tFY0ENtCaO+BWsHc0ckhBBC1GuSXNVhuVfmW9m2l/lW9YpBD2uehrxU45yie2ebO6KaycoOWg43XnR5cP4P4xytM79D9mU4sMR4AeNCyi5BxkTL/crFrTG4NwE7d/MMJ0w6A79fqQjY703jsEYhhBBCmJUkV3VY3pHiYhYy36pe+Xs+XPwbLO1g9Pc1s8elprHUQrMhxktRAUTugtMbjdUGk89BYRakRhovZ7eWvK+N09VEy72R8WdV93bp8mHVRCjKg+C+cNezVfM4QgghhKgQSa7qKENuLvnh4YAsHlyvXAqFv943Xh8y3/hhX1SMhTU0GWC8gHFeU9ZlYyGM5CuXlLOQHAHp0ZCfAbEHjZdrXent0riG0DJDg+pIMng2q5zerh2zIOE42LrBiIW1Y36YEEIIUQ9IclVH5R07Dno9Ft7eWDZoYO5wRHXITYXVV6rGtRlrLOAg7pxKBY4NjJeGvUrepssz9mYlRxh7uIqTrmt6u9SpkTQC2Lzl6v2u7+0qvl6e3q6IbbB/gfH68AXg4F2ZZyuEEEKIOyDJVR2Vd0RKsNcrigIbnoXMGGPVuCEfmTui+sFSC14tjZdrXdPbpU84zcVD22nooEedeg7So27Z23XDuV3ZCdcUKnkGmgysltMUQgghRPlIclVHFRez0Eoxi/rhwDdw+jdj1bjR30vVOHO7prfL4HcXJxK8CBg8GLWlZbl6u244t8tCC7nJ4NUa+kuhEiGEEKKmMftA/a+++oqgoCBsbGzo2rUr//777w331el0vP3224SEhGBjY0Pbtm3ZsmVLqf1iY2MZN24cbm5uaLVaWrduzcGDB8s4Yt2kGAzkHT0KSDGLeuHycdj6hvH6vW+DTzuzhiNuobi3q+UI6P0SjFwMT/8Fr0XDf8/A4xthyMfGEvqN+oNzIKAy9nZlXzYmWKO+AUsbc5+JEEIIIa5j1p6rX3/9lRdeeIGFCxfStWtXPv30UwYOHMiZM2fw9PQstf///vc/li5dypIlS2jWrBlbt25lxIgRhIaG0r69MYlIS0ujR48e9O3bl99//x0PDw/Onj2Li4tLdZ+e2RScPYchKwuVrS02TZuaOxxRlQpzYOUE0BdAk0HQbYq5IxK3S6Uyzp9y8L7x3K6Uc+DWyFgYQwghhBA1jlmTq48//phJkyYxYcIEABYuXMimTZv47rvvePXVV0vt/9NPP/HGG28wePBgAKZMmcKOHTuYP38+S5cuBWDu3Ln4+/vz/fffm+7XsGHDajibmqN4vpW2bRtUFjLys077/WXjsDKHBjDsa/OstySq3o3mdgkhhBCiRjHbJ+/CwkIOHTrEa6+9ZtqmVqvp378/e/fuLfM+BQUF2NiUHAqj1WrZs2eP6fcNGzYwcOBAxowZw65du/D19WXq1KlMmjTphrEUFBRQUFBg+j0zMxMwDkPU6XS3dX7mlHPwEADWbdvW+PiL46vpcdZEqpOrsTiyFAUV+mELUKwcoRztKG1evaS9q5+0efWTNq9+0ubVS9q7+tWkNq9IDCpFUZQqjOWG4uLi8PX1JTQ0lLvuusu0/eWXX2bXrl3s37+/1H0eeeQRwsLCWLduHSEhIezcuZNhw4ah1+tNyVFx8vXCCy8wZswYDhw4wHPPPcfChQt5/PHHy4xl1qxZzJ5denL48uXLsbW1rYzTrVZBc+dhlZpKzMQnyW3SxNzhiCpgW5BAn9NvYmnI54z3ME43GGXukIQQQggh6qTc3FweeeQRMjIycHR0vOm+tSq5SkpKYtKkSWzcuBGVSkVISAj9+/fnu+++Iy8vDwArKys6depEaGio6X4zZszgwIEDN+0Ru77nyt/fn+Tk5Fs2YE1TlJTExXv6gVpN8D97UNvbmzukm9LpdGzfvp17770XS0tLc4dTO+gL0fw4GHX8UQz+3dCPWwfq8ndCS5tXL2nv6idtXv2kzauftHn1kvaufjWpzTMzM3F3dy9XcmW2YYHu7u5oNBoSEhJKbE9ISMDbu+xFMT08PFi3bh35+fmkpKTg4+PDq6++SnBwsGmfBg0a0KJFixL3a968OatXr75hLNbW1lhbl16409LS0ux/zIrKO34CAOsmTbCuRUU8amNbm82fsyH+KNg4ox79LWpr7W0dRtq8ekl7Vz9p8+onbV79pM2rl7R39asJbV6RxzdbKXYrKys6duzIzp07TdsMBgM7d+4s0ZNVFhsbG3x9fSkqKmL16tUMGzbMdFuPHj04c+ZMif0jIiIIDAys3BOoofIOy+LBddrZ7RD6hfH6sK/Ayc+88QghhBBCCBOzlpJ74YUXePzxx+nUqRNdunTh008/JScnx1Q9cPz48fj6+vL+++8DsH//fmJjY2nXrh2xsbHMmjULg8HAyy+/bDrmf/7zH7p37857773Hgw8+yL///svixYtZvHixWc6xuuUeKV48WJKrOifrMqx9xni98yRofr954xFCCCGEECWYNbl66KGHSEpK4q233uLy5cu0a9eOLVu24OXlBUBUVBRq9dXOtfz8fP73v/8RGRmJvb09gwcP5qeffsLZ2dm0T+fOnVm7di2vvfYab7/9Ng0bNuTTTz/l0Ucfre7Tq3aGvDzyT50CQNu+g5mjEZXKYIA1T0NuMni1ggHvmjsiIYQQQghxHbMvgjR9+nSmT59e5m1//fVXid979+7NqSvJw83cf//93H9//ftWP+/4cSgqwsLTE0tfH3OHIyrTP5/AhV1gaQujvwdLm1vfRwghhBBCVCuzzbkSlS/v8JUhgR06oJLFZOuOqP3wxxzj9cEfgoeU1xdCCCGEqIkkuapDco9IMYs6Jy8dVj8Fih5ajYZ2dX94qxBCCCFEbSXJVR2hGAzkHTkKyHyrOkNRYMOzkBEFLkFw/ycgPZJCCCGEEDWWJFd1ROH58xgyM1Fptdg0a2rucERlOPQ9hG8wLhA8+juwqV0LWgshhBBC1DeSXNURucXzrdq0QSWL29V+Cadgy2vG6/1ngW9Hs4YjhBBCCCFuTZKrOiKveH0rmW9V+xXmwqoJUJQPjfpDt2nmjkgIIYQQQpSDJFd1xNViFjLfqtbb+hoknQZ7Lxi+ENTyMhVCCCGEqA3kU1sdUJScjO5SFKhUaNu2NXc44k6cXAuHfgBUMHIx2HuYOyIhhBBCCFFOklzVAblXhgRaN2qExlGKHtRaaRdhw3PG6z1fgOA+5oxGCCGEEEJUkCRXdcC1iweLWkqvg1UToSAD/LpAn9fMHZEQQgghhKggSa7qgLzDsnhwrffnHIg9CDZOMOob0EjFRyGEEEKI2kaSq1rOkJ9P3qlTgPRc1Vrn/4A9nxivP/AFuASaNx4hhBBCCHFbJLmq5fJPnACdDo2HO5Z+fuYOR1RUdiKsmWy83ulJaDHMvPEIIYQQQojbJslVLVe8eLBt+w6oVCozRyMqxGCAtZMhJxE8W8DA98wdkRBCCCGEuAOSXNVyxfOtZPHgWmjvF8YhgRZaGP0dWGrNHZEQQgghhLgDklzVYoqikHelDLssHlzLxByEnW8br9/3AXg2N288QgghhBDijlU4uZo5cyaXLl2qilhEBRVeuIA+IwOVjQ02zeXDea2RnwGrngRDEbQcAR0eN3dEQgghhBCiElQ4uVq/fj0hISH069eP5cuXU1BQUBVxiXIwDQls3RqVpZTurhUUBTY+D+mXwDkAhn4GMldOCCGEEKJOqHBydfToUQ4cOEDLli157rnn8Pb2ZsqUKRw4cKAq4hM3UVzMQtte5lvVGkd+gpNrQG0Bo74zrmslhBBCCCHqhNuac9W+fXs+//xz4uLi+Pbbb4mJiaFHjx60adOGzz77jIyMjMqOU5RBilnUMomnYfPLxuv3/A/8O5s3HiGEEEIIUanuqKCFoijodDoKCwtRFAUXFxe+/PJL/P39+fXXXysrRlGGotRUCi9eBMC2XTuzxiLKQZdnnGdVlAfBfaH7c+aOSAghhBBCVLLbSq4OHTrE9OnTadCgAf/5z39o37494eHh7Nq1i7NnzzJnzhxmzJhR2bGKaxRXCbRqFILG2dm8wYhb2/oGJJ4EOw8YsQjUUqhTCCGEEKKuqfAnvNatW9OtWzcuXLjAt99+S3R0NB988AGNGjUy7fPwww+TlJRUqYGKknKvDAm0bS8l2Gu8Uxvg4LfG6yMWgYOXeeMRQgghhBBVwqKid3jwwQd58skn8fX1veE+7u7uGAyGOwpM3FxecTELWd+qZkuPgg3Tjdd7PAeN+pk3HiGEEEIIUWUqnFy9+eabVRGHqABDQQH5J04AYCvFLGoufRGsfsq4rpVvR7hHXjtCCCGEEHVZhYcFjho1irlz55baPm/ePMaMGVMpQYmbyz95CkWnQ+PmhmVAgLnDEWXJSYZloyB6P1g7wqhvQSNrkQkhhBBC1GUVTq52797N4MGDS22/77772L17d6UEJW4u78iV+VYd2qOSBWhrnqj9sLAnRP4FlrYwcgm4NjR3VEIIIYQQoopVeFhgdnY2VlZWpbZbWlqSmZlZKUGJm7u6eLDMt6pRFAX2LYDtb4KhCNwaw0M/gWdzc0cmhBBCCCGqwW1VCyxrDatffvmFFi1aVEpQ4sYURTGVYZf5VjVIfiasfAK2vmZMrFqOhKf/lMRKCCGEEKIeua2CFiNHjuT8+fPcc889AOzcuZOff/6ZlStXVnqAoqTCixfRp6aisrLCWpLZmiHhFKx4DFLOgdoCBr4HXZ4GGbIphBBCCFGvVDi5Gjp0KOvWreO9995j1apVaLVa2rRpw44dO+jdu3dVxCiuUVyC3aZ1a9RlDM8U1SzsV/jtedDlgqMvjPkB/LuYOyohhBBCCGEGFU6uAIYMGcKQIUMqOxZRDrnXFLMQZlRUAFtehYPfGX8P7gujvgE7d/PGJYQQQgghzOa2kithPnlSzML80i7Bysch7giggt4vQ+9XQK0xd2RCCCGEEMKMKpxc6fV6PvnkE1asWEFUVBSFhYUlbk9NTa204ERJRWlpFEZGAqBt3868wdRXEdtgzSTITwetC4z8Bhr3N3dUQggh/r+9O4+P6d7/OP6e7CshSMQWlFCCopSWuqWCXhd1qyWtpUUpSlO/KrV20+Uq6lp6tZa6VOtqtbfa2HVB7VSvUmtSa0pFJESSmfP7Y5ppp1lkmMxkeT0fj3nknDPnfOcznxyT+Tjf8/0CQBHg8GiBU6ZM0VtvvaWHH35Yly9fVlxcnB588EF5eHho8uTJhRAisl3bu0+S5FOrlrzKlXNvMKWNxSxtfFla9pC1sIpoKj35NYUVAAAAbBwurpYuXar58+fr2WeflZeXl3r37q13331XEydO1HfffVcYMeI32UOw+3O/lWulXZD+/aD09ZvW9TsHSo/HSyHV3RsXAAAAihSHi6tz584pOjpakhQUFKTLly9Lkv76179q9erVzo0OdmyDWXC/leskbpfmtZGOb5a8A6zdAB+YJnn5ujsyAAAAFDEOF1dVq1bV2bNnJUm1a9fW2rVrJUk7d+6Ury9fOAuLkZGh9AM/SOLKlUsYhrRtjrSoi3TljBRaRxq0UWr0kLsjAwAAQBHlcHHVo0cPbdiwQZI0YsQITZgwQXXq1FHfvn31+OOPOz1AWKUfPCjj+nV5li8vn8hId4dTsqWnSCv6S2vGSpYsqcGD0uBNUqX67o4MAAAARZjDowW+9tprtuWHH35YNWrU0NatW1WnTh117drVqcHhd1dtQ7DfIZPJ5OZoSrDzB6WPHpMuHpU8vKWYV6QWgyVyDgAAgBtwqLjKzMzUk08+qQkTJqhmzZqSpLvuukt33XVXoQSH312z3W/VxL2BlGT7P5Q+HyVlXpXKVJEeWixVu9PdUQEAAKCYcKhboLe3t1auXFlYsSAPhmH8fuWqKYNZOF3WdenzZ6RPBlsLq1p/sQ6zTmEFAAAABzh8z1X37t21atWqQggFeclMTJT54kWZvL3l16CBu8MpWS4lSAtipF0LJJmke8dIj66UAiu4OzIAAAAUMw7fc1WnTh29+OKL2rJli5o1a6bAwEC7559++mmnBQer7KtWfg0byoMRGZ3np7XSx4OskwL7l7MOs86kwAAAALhJDhdX7733nkJCQrR7927t3r3b7jmTyURxVQiu7bHeb8UQ7E5iMUubXpW++Yd1PaKp1GsxkwIDAADgljhcXJ04caIw4kA+bJMHc7/VrUu7IP3ncenEV9b1OwdZRwRkUmAAAADcIoeLK7iW+fJlZRw9Jsk6DDtuQeJ26/xVV85I3gFS17eZFBgAAABO43BxdaOJghcsWHDTwSCna/v2SZJ8IiPlVb68e4MprgxD+m6utG6CdVLg0DrSw0uYFBgAAABO5XBxdenSJbv1zMxM/fDDD0pOTtZ9993ntMBgxRDstyg9RfpshHRwlXW9wYPS396WfIPdGhYAAABKHoeLq08++STHNovFoqFDh6p27dpOCQq/yx7MIoDBLBx3/qD00WPSxaOSh7f13qoWgyWTyd2RAQAAoARyyj1XHh4eiouLU7t27fTcc885o0n8JuLNN3Rtzx75N2vm7lCKl/0fSp+Psk4KXKaK9NBiJgUGAABAoXLagBbHjh1TVlaWs5rDb7zDw+XdpYu7wyg+sq5L8c//NimwpFp/kXq+y6TAAAAAKHQOF1dxcXF264Zh6OzZs1q9erX69evntMAAh11KkFb0k87slWSS7n1OuneM5OHp7sgAAABQCjhcXO3du9du3cPDQxUrVtS0adNuOJIgUGjO7JXe7y6lJ0v+5aQH35XqdHB3VAAAAChFHC6uNm3aVBhxADfPYpH+O9JaWEU0lXotlkKquzsqAAAAlDIejh5w4sQJHTlyJMf2I0eO6OTJk86ICXDMgY+ks/sln2Cpz0cUVgAAAHALh4ur/v37a+vWrTm2b9++Xf3793dGTEDBZVyVNrxoXW4TJwVVdG88AAAAKLUcLq727t2ru+++O8f2u+66S/v27XNGTEDBfTdbSjktla0m3fWUu6MBAABAKeZwcWUymXTlypUc2y9fviyz2eyUoIACuXJe+naGdbn9JMnbz63hAAAAoHRzuLhq27atpk6daldImc1mTZ06Vffcc49TgwPytflVKSPVOohFw57ujgYAAAClnMOjBb7++utq27atoqKi1KZNG0nSN998o5SUFG3cuNHpAQK5On9Q2vO+dTnmVcnD4f8nAAAAAJzK4W+kt99+u77//nv16tVLSUlJunLlivr27atDhw6pYcOGhREjkNPa8ZJhker/TarRyt3RAAAAAI5fuZKkiIgIvfrqq86OBSiYo+ulYxskD2/p/inujgYAAACQdBNXrhYuXKgVK1bk2L5ixQotXrzYKUEBebKYpbUTrMstBkvla7k3HgAAAOA3DhdXU6dOVYUKFXJsr1SpElezUPj2LpGSDkp+IVLb0e6OBgAAALBxuLhKTExUzZo1c2yvUaOGEhMTnRIUkKvrV6SNr1iX7x0jBZR3bzwAAADAHzhcXFWqVEnff/99ju379+9XaGioU4ICcrVlppSWZO0KeOdAd0cDAAAA2HG4uOrdu7eefvppbdq0SWazWWazWRs3btTIkSP1yCOPFEaMgHT5lLR1lnX5/hclLx/3xgMAAAD8icOjBb700ks6efKk2rdvLy8v6+EWi0V9+/bVK6+84vQAAUnShpekrHSpemup3l/dHQ0AAACQg8PFlY+Pjz788EO9/PLL2rdvn/z9/RUdHa0aNWoURnyAdGav9P1y63LMK5LJ5N54AAAAgFzc1DxXklSnTh3VqVNHkpSSkqK5c+fqvffe065du5wWHCDDkNaMty5H95KqNHVvPAAAAEAebrq4kqRNmzZpwYIF+vjjj1W2bFn16NHDWXEBVoe/kBK+lbz8pPYT3R0NAAAAkCeHi6vTp09r0aJFWrhwoZKTk3Xp0iUtW7ZMvXr1konuWnAmc6a07reC6q6npJBq7o0HAAAAyEeBRwtcuXKlunTpoqioKO3bt0/Tpk3TmTNn5OHhoejoaAorON+uBdLFo1JgRemeZ9wdDQAAAJCvAl+5evjhhzVmzBh9+OGHCg4OLsyYAOnaJWnzVOvyX8ZJfmXcGw8AAABwAwW+cvXEE09o9uzZ6tSpk+bNm6dLly45LYjZs2crMjJSfn5+atmypXbs2JHnvpmZmXrxxRdVu3Zt+fn5qXHjxoqPj89z/9dee00mk0mjRo1yWrxwga//YS2wKtaT7ujr7mgAAACAGypwcfXOO+/o7NmzGjx4sD744ANVrlxZ3bp1k2EYslgsNx3Ahx9+qLi4OE2aNEl79uxR48aNFRMTo6SkpFz3Hz9+vN555x3NmjVLBw8e1JAhQ9SjRw/t3bs3x747d+7UO++8o0aNGt10fHCDX09IO/5lXe74suR5S+OuAAAAAC7h0LdWf39/9evXT/369dORI0e0cOFC7dq1S3fffbceeOAB/f3vf9eDDz7oUABvvfWWBg0apAEDBkiS5s2bp9WrV2vBggV6/vnnc+y/ZMkSvfDCC+rSpYskaejQoVq/fr2mTZumf//737b9UlNTFRsbq/nz5+vll1/ON4br16/r+vXrtvWUlBRJ1qtkmZmZDr0fOCY7v3/Ms+e6SfIwZ8hSs53MNe6V+B04VW45R+Eh365Hzl2PnLseOXct8u16RSnnjsRgMgzDuJUXs1gsWr16td577z19+eWXdkXKjWRkZCggIED/+c9/1L17d9v2fv36KTk5WZ9++mmOY0JDQ/XGG2/oiSeesG179NFH9e233+rkyZN2bZQvX17Tp09Xu3bt1KRJE82YMSPXOCZPnqwpU6bk2L5s2TIFBAQU+P3g1pVLPaK2R16SIZM213tJKf7V3R0SAAAASrGrV6+qT58+unz5ssqUyX8cgFvub+Xh4aGuXbuqa9eueXbly8uFCxdkNpsVFhZmtz0sLEyHDh3K9ZiYmBi99dZbatu2rWrXrq0NGzbo448/ltlstu2zfPly7dmzRzt37ixQHGPHjlVcXJxtPSUlRdWqVVPHjh1vmEDcmszMTK1bt07333+/vL285Ln4bUmS0biP7vnrEDdHVzLZ5dzb293hlHjk2/XIueuRc9cj565Fvl2vKOU8u1dbQTj1ZpZKlSo5s7lczZw5U4MGDVK9evVkMplUu3ZtDRgwQAsWLJAk/fzzzxo5cqTWrVsnPz+/ArXp6+srX1/fHNu9vb3d/sssLby9veV9+DPp9C7JO1AeHSbKg9wXKs5v1yLfrkfOXY+cux45dy3y7XpFIeeOvH6BB7QoDBUqVJCnp6fOnz9vt/38+fMKDw/P9ZiKFStq1apVSktLU0JCgg4dOqSgoCDVqlVLkrR7924lJSWpadOm8vLykpeXl7766iu9/fbb8vLysrvChSIkK11aN9m6fM8oKTj33z8AAABQVLm1uPLx8VGzZs20YcMG2zaLxaINGzaoVatW+R7r5+enKlWqKCsrSytXrlS3bt0kSe3bt9eBAwe0b98+26N58+aKjY3Vvn375OnpWajvCTfHY+e/pMuJUnCE1Gq4u8MBAAAAHOb2Ma7j4uLUr18/NW/eXC1atNCMGTOUlpZmGz2wb9++qlKliqZOtU4ou337dp0+fVpNmjTR6dOnNXnyZFksFj333HOSpODgYDVs2NDuNQIDAxUaGppjO4oGn8wUeWyZbl1pP0HyYRARAAAAFD8OF1e1atXSzp07FRoaarc9OTlZTZs21fHjxx1q7+GHH9Yvv/yiiRMn6ty5c2rSpIni4+Ntg1wkJibKw+P3C2zp6ekaP368jh8/rqCgIHXp0kVLlixRSEiIo28FRUTUuVUyXb8ihTeSGj3i7nAAAACAm+JwcXXy5Mlc71u6fv26Tp8+fVNBDB8+XMOH594VbPPmzXbr9957rw4ePOhQ+39uA0XIhSOKvLDRuhzziuTh1p6qAAAAwE0rcHH12Wef2ZbXrFmjsmXL2tbNZrM2bNigyMhIpwaHks9z42R5yCJLnRh51Gzr7nAAAACAm1bg4ip7kl+TyaR+/frZPeft7a3IyEhNmzbNqcGhhDv+lTyOrJFFnjK3n+ze0VUAAACAW1Tg4spisUiSatasqZ07d6pChQqFFhRKAYtZWvuCJOlkhb+oWmgdNwcEAAAA3BqHLxacOHEiR2GVnJzsrHhQWuxfLp07IMO3jA5X7uHuaAAAAIBb5nBx9frrr+vDDz+0rT/00EMqX768qlSpov379zs1OJRQGWnSxpckSZa7n1GGV7CbAwIAAABuncPF1bx581StWjVJ0rp167R+/XrFx8erc+fO+r//+z+nB4gSaOs/pStnpZDqstw5yN3RAAAAAE7h8FDs586dsxVXn3/+uXr16qWOHTsqMjJSLVu2dHqAKGGunJO2zLQud5gsefm5NRwAAADAWRy+clWuXDn9/PPPkqT4+Hh16NBBkmQYRq7zXwF2Nr4sZaZJVe+UGjzo7mgAAAAAp3H4ytWDDz6oPn36qE6dOrp48aI6d+4sSdq7d69uu+02pweIEuTcAWnvv63LMa9KJpN74wEAAACcyOHiavr06YqMjNTPP/+sN954Q0FBQZKks2fP6qmnnnJ6gCghDENaO16SITXoIVVr4e6IAAAAAKdyuLjy9vbW6NGjc2x/5plnnBIQSqgj66TjmyVPH+u9VgAAAEAJ4/A9V5K0ZMkS3XPPPYqIiFBCQoIkacaMGfr000+dGhxKCHPWb1etJLV8UioX6dZwAAAAgMLgcHE1d+5cxcXFqXPnzkpOTrYNYhESEqIZM2Y4Oz6UBHsWSxcOS/7lpTY5r3oCAAAAJYHDxdWsWbM0f/58vfDCC/L09LRtb968uQ4cOODU4FACpKdIm161Lrd7XvIPcWs4AAAAQGFxuLg6ceKE7rjjjhzbfX19lZaW5pSgUIJ8O126ekEKvU1q/ri7owEAAAAKjcPFVc2aNbVv374c2+Pj41W/fn1nxISSIjlR2jbbunz/S5Knt3vjAQAAAApRgUcLfPHFFzV69GjFxcVp2LBhSk9Pl2EY2rFjhz744ANNnTpV7777bmHGiuJmw4uS+boU2UaK6uzuaAAAAIBCVeDiasqUKRoyZIgGDhwof39/jR8/XlevXlWfPn0UERGhmTNn6pFHHinMWFGcnNotHVghySR1fJkJgwEAAFDiFbi4MgzDthwbG6vY2FhdvXpVqampqlSpUqEEh2LKMKS1L1iXGz8iRTRxazgAAACAKzg0ibDpT1cfAgICFBAQ4NSAUAL8+F8pcZvk5S/dN8Hd0QAAAAAu4VBxVbdu3RwF1p/9+uuvtxQQirmsDGn9JOty6+FS2SrujQcAAABwEYeKqylTpqhs2bKFFQtKgp3vSr8el4LCpLtHuTsaAAAAwGUcKq4eeeQR7q9C3q7+Kn31unX5Ly9IvkHujQcAAABwoQLPc3Wj7oCAvn5TSk+WKjWQ7njU3dEAAAAALlXg4uqPowUCOVw8Ju2Yb13u+JLk4eneeAAAAAAXK3C3QIvFUphxoLhbP0myZEq3dZBua+/uaAAAAACXK/CVKyBPCVutw6+bPKwTBgMAAAClEMUVbo3FIq35bcLgpn2lSvXdGw8AAADgJhRXuDU/rJTO7JF8gqwjBAIAAAClFMUVbl7mNWn9ZOvyPc9IQQzTDwAAgNKL4go377s5UsopqUxVqdUwd0cDAAAAuBXFFW5O6i/SN9Oty+0nSt7+7o0HAAAAcDOKK9ycza9KGVekiDuk6IfcHQ0AAADgdhRXcFzSIWn3Iutyx1ckD04jAAAAgG/FcNz2eZJhker9VYq8293RAAAAAEUCxRUcd/Ib6887HnVvHAAAAEARQnEFx1w5J108KskkVW/l7mgAAACAIoPiCo5J2GL9Gd5Q8g9xaygAAABAUUJxBcec/K24qnGPe+MAAAAAihiKKzgmYav1JwNZAAAAAHYorlBwaRelX360Lldv7d5YAAAAgCKG4goFl32/VcX6UmCoe2MBAAAAihiKKxRcdnFFl0AAAAAgB4orFJxtMAuKKwAAAODPKK5QMNcuSed/sC5TXAEAAAA5UFyhYBK/k2RIobdJwWHujgYAAAAociiuUDAnv7X+5KoVAAAAkCuKKxSMbTALJg8GAAAAckNxhRtLT5HO7rcuc+UKAAAAyBXFFW7s5x2SYZHKRUplq7g7GgAAAKBIorjCjSVwvxUAAABwIxRXuDHmtwIAAABuiOIK+ctIk87ssS5HUlwBAAAAeaG4Qv5O7ZQsWVKZqlJIDXdHAwAAABRZFFfIn61LYGvJZHJvLAAAAEARRnGF/Nnmt6JLIAAAAJAfiivkLTNdOrXLulyDyYMBAACA/FBcIW+nd0nm61JQmBRa293RAAAAAEUaxRXylrDV+rPG3dxvBQAAANwAxRXydjJ78uDW7o0DAAAAKAYorpC7rAzp5x3W5UjutwIAAABuhOIKuTuzV8q6JgWEShXruTsaAAAAoMijuELuEv7QJZD7rQAAAIAborhC7myDWdAlEAAAACgIiivkZM6SEr+zLjOYBQAAAFAgFFfI6dx+KSNV8isrhTVwdzQAAABAsUBxhZxObrH+rN5a8vB0bywAAABAMUFxhZwSfiuuIu92bxwAAABAMUJxBXsWs5Swzbpcg+IKAAAAKCiKK9g7/z/p+mXJJ1gKb+TuaAAAAIBig+IK9rK7BFZvKXl6uTcWAAAAoBihuIK9k9mTB9MlEAAAAHAExRV+Z7H8PnlwJJMHAwAAAI6guMLvLhyWrv0qeflLlZu4OxoAAACgWCkSxdXs2bMVGRkpPz8/tWzZUjt27Mhz38zMTL344ouqXbu2/Pz81LhxY8XHx9vtM3XqVN15550KDg5WpUqV1L17dx0+fLiw30bxl90lsFoLycvHvbEAAAAAxYzbi6sPP/xQcXFxmjRpkvbs2aPGjRsrJiZGSUlJue4/fvx4vfPOO5o1a5YOHjyoIUOGqEePHtq7d69tn6+++krDhg3Td999p3Xr1ikzM1MdO3ZUWlqaq95W8WSb34ougQAAAICj3F5cvfXWWxo0aJAGDBig22+/XfPmzVNAQIAWLFiQ6/5LlizRuHHj1KVLF9WqVUtDhw5Vly5dNG3aNNs+8fHx6t+/vxo0aKDGjRtr0aJFSkxM1O7du131toofw5BO/lZcMZgFAAAA4DC3jrWdkZGh3bt3a+zYsbZtHh4e6tChg7Zt25brMdevX5efn5/dNn9/f3377bd5vs7ly5clSeXLl8+zzevXr9vWU1JSJFm7IGZmZhbszRR3F4/KOy1JhqevssIaSS5639n5LTV5LgLIuWuRb9cj565Hzl2PnLsW+Xa9opRzR2IwGYZhFGIs+Tpz5oyqVKmirVu3qlWrVrbtzz33nL766itt3749xzF9+vTR/v37tWrVKtWuXVsbNmxQt27dZDab7QqkbBaLRX/729+UnJycZwE2efJkTZkyJcf2ZcuWKSAg4BbeYfFR48ImNfl5oS4ERWlLnRfcHQ4AAABQJFy9elV9+vTR5cuXVaZMmXz3LXazxM6cOVODBg1SvXr1ZDKZVLt2bQ0YMCDPboTDhg3TDz/8kO+VrbFjxyouLs62npKSomrVqqljx443TGBJ4fnpZ9LPUrkmf1WXe7u47HUzMzO1bt063X///fL29nbZ65Zm5Ny1yLfrkXPXI+euR85di3y7XlHKeXavtoJwa3FVoUIFeXp66vz583bbz58/r/Dw8FyPqVixolatWqX09HRdvHhRERERev7551WrVq0c+w4fPlyff/65vv76a1WtWjXPOHx9feXr65tju7e3t9t/mS5hGFKitRumZ6228nTDey41uS5CyLlrkW/XI+euR85dj5y7Fvl2vaKQc0de360DWvj4+KhZs2basGGDbZvFYtGGDRvsugnmxs/PT1WqVFFWVpZWrlypbt262Z4zDEPDhw/XJ598oo0bN6pmzZqF9h5KhEsnpZTTkoe3VLWFu6MBAAAAiiW3dwuMi4tTv3791Lx5c7Vo0UIzZsxQWlqaBgwYIEnq27evqlSpoqlTp0qStm/frtOnT6tJkyY6ffq0Jk+eLIvFoueee87W5rBhw7Rs2TJ9+umnCg4O1rlz5yRJZcuWlb+/v+vfZFGXsNX6s0pTyad03GMGAAAAOJvbi6uHH35Yv/zyiyZOnKhz586pSZMmio+PV1hYmCQpMTFRHh6/X2BLT0/X+PHjdfz4cQUFBalLly5asmSJQkJCbPvMnTtXktSuXTu711q4cKH69+9f2G+p+Mme36pGa/fGAQAAABRjbi+uJOu9UcOHD8/1uc2bN9ut33vvvTp48GC+7blxAMTi6eRvg33UYPJgAAAA4Ga5fRJhuNnlU1JygmTylKq3dHc0AAAAQLFFcVXanfytS2DlxpJvsHtjAQAAAIoxiqvSLvt+q8i73RsHAAAAUMxRXJV2tsEsKK4AAACAW0FxVZpdOSddPCrJJFXPf14xAAAAAPmjuCrNsq9ahTeU/EPcGgoAAABQ3FFclWbZg1kwBDsAAABwyyiuSrOErdafDGYBAAAA3DKKq9Iq7aL0y4/W5eqt3RsLAAAAUAJQXJVW2fdbVawvBYa6NxYAAACgBKC4Kq2Y3woAAABwKoqr0uok81sBAAAAzkRxVRpduySd/8G6THEFAAAAOAXFVWmU+J0kQwq9TQoOc3c0AAAAQIlAcVUanfzW+pOrVgAAAIDTUFyVRrbBLJg8GAAAAHAWiqvSJj1FOrvfulyD+a0AAAAAZ6G4Km1+3iEZFimkhlS2qrujAQAAAEoMiqvSJuG3+63oEggAAAA4FcVVacP8VgAAAEChoLgqTTLSpDN7rMuRFFcAAACAM1FclSandkqWLKlMVes9VwAAAACchuKqNLF1CWwtmUzujQUAAAAoYSiuShPb/FZ0CQQAAACcjeKqtMhMl07tsi7XYKRAAAAAwNkorkqL07sk83UpKEwKre3uaAAAAIASx8vdAcBFErZaf9a4m/utAABAiWY2m5WZmem09jIzM+Xl5aX09HSZzWantYu8uTLn3t7e8vT0dEpbFFelxcnfJg+u0dq9cQAAABQSwzB07tw5JScnO73d8PBw/fzzzzLxn9Qu4eqch4SEKDw8/JZfi+KqNMjKkH7eYV2O5H4rAABQMmUXVpUqVVJAQIDTvpRbLBalpqYqKChIHh7cVeMKrsq5YRi6evWqkpKSJEmVK1e+pfYorkqDM3ulrGtSQKhUsZ67owEAAHA6s9lsK6xCQ0Od2rbFYlFGRob8/PworlzElTn39/eXJCUlJalSpUq31EWQs6M0SPhDl0AuZQMAgBIo+x6rgIAAN0eC4ij7vLnVe/UorkoD22AWdAkEAAAlG/dE4WY467yhuCrpzFlS4nfWZQazAAAAAAoNxVVJd26/lJEq+ZWVwhq4OxoAAACgxKK4KulObrH+rN5a8nDO+P0AAAAoWTZv3iyTyZTvY/Pmze4Os8hjtMCSLuG34irybvfGAQAAgCKrdevWOnv2rG195MiRSklJ0cKFC23bypcvb1vOyMiQj4+PS2MsDrhyVZJZzFLCNusy91sBAAAUSe3atdOIESM0atQolStXTmFhYZo/f77S0tI0YMAABQcH67bbbtOXX35pO8ZsNuuJJ55QzZo15e/vr6ioKM2cOdP2fHp6uho0aKDBgwfbth07dkzBwcFasGBBjhh8fHwUHh5ue/j7+8vX19e2Pm/ePLVo0ULvvvuuatasKT8/P0lSZGSkZsyYYddWkyZNNHnyZNt6cnKyBg4cqIoVK6pMmTK67777tH//fidlr2ihuCrJzv9Pun5Z8gmWwhu7OxoAAACXMgxDVzOynPK4lmF2aH/DMByKdfHixapQoYJ27NihESNGaOjQoXrooYfUunVr7dmzRx07dtRjjz2mq1evSrLOA1W1alWtWLFCBw8e1MSJEzVu3Dh99NFHkiQ/Pz8tXbpUixcv1qeffiqz2axHH31U999/vx5//PGbyufRo0e1cuVKffzxx9q3b1+Bj3vooYeUlJSkL7/8Urt371bTpk3Vvn17/frrrzcVR1FGt8CSLLtLYPWWkie/agAAULpcyzTr9olr3PLaB1+MUYBPwb9/NW7cWOPHj5ckjR07Vq+99poqVKigQYMGSZImTpyouXPn6vvvv9ddd90lb29vTZkyxXZ8zZo1tW3bNn300Ufq1auXJOsVpJdfflkDBw7UI488ooSEBH3++ec3/Z4yMjL0/vvvq2LFigU+5ttvv9WOHTuUlJQkX19fSdI//vEPrVq1Sv/5z3/srqyVBHzjLslOZk8ezP1WAAAARVmjRo1sy56engoNDVV0dLRtW1hYmCQpKSnJtm327NlasGCBEhMTde3aNWVkZKhJkyZ27T777LNatWqV/vnPf+rLL79UaGjoTcdYo0YNhworSdq/f79SU1NzvO61a9d07Nixm46lqKK4Kqkslt8nD45k8mAAAFD6+Ht76uCLMbfcjsVi0ZWUKwouEywPj4LdVePv7dgozd7e3nbrJpPJblv2JLcWi0WStHz5co0ePVrTpk1Tq1atFBwcrDfffFPbt2+3aycpKUk//fSTPD09deTIEXXq1MmhuP4oMDAwxzYPD48cXSAzMzNty6mpqapcuXKuIw2GhITcdCxFFcVVSXXhsHTtV8nLX6rcxN3RAAAAuJzJZHKoa15eLBaLsnw8FeDjVeDiqrBt2bJFrVu31lNPPWXbltuVoMcff1zR0dF64oknNGjQIHXo0EH169d3WhwVK1a0G2UwJSVFJ06csK03bdpU586dk5eXlyIjI532ukVV0Tg74HzZXQKrtZC8GCYTAACgJKlTp4527dqlNWvW6KefftKECRO0c+dOu31mz56tbdu2afHixYqNjVX37t0VGxurjIwMp8Vx3333acmSJfrmm2904MAB9evXT56ev1+169Chg1q1aqXu3btr7dq1OnnypLZu3aoXXnhBu3btclocRQXFVUllm9+KLoEAAAAlzZNPPqkHH3xQDz/8sFq2bKmLFy/aXcU6dOiQ/u///k9z5sxRtWrVJElz5szRhQsXNGHCBKfFMXbsWN17773661//qgceeEDdu3dX7dq1bc+bTCZ98cUXatu2rQYMGKC6devaBtfIvo+sJKFbYElkGNLJ34orBrMAAAAo0nK7H+nkyZM5tv3x3iZfX18tXLjQbpJfSZo6daokqV69erZh27OFhIQoMTGxQDEtWrTIbn3y5Ml2c1dlK1OmjJYvX263rV+/fnbrwcHBevvtt/X2228X6LWLM65clUQXj0lpSZKnr1SlmbujAQAAAEoFiquSKOG3+62qNpe8/dwbCwAAAFBKUFyVRHQJBAAAAFyO4qqkMYw/DGZBcQUAAAC4CsVVSXPppJRyWvLwlqq2cHc0AAAAQKlBcVXSJGy1/qzSVPIJcG8sAAAAQClCcVXSZHcJrNHavXEAAAAApQzFVUlz8reRAmsweTAAAADgShRXJcnlU1JygmTylKq3dHc0AAAAQKlCcVWSZA/BXrmx5Bvs3lgAAABQYm3evFkmk0nJycnuDqVIobgqSRiCHQAAAAU0efJkmUymHI/169e7O7Riy8vdAcCJEpg8GAAAAAXXoEGDHMVU+fLl3RRN8ceVq5Liyjnp4lFJJql6K3dHAwAA4H6GIWWkOeeRedWx/Q2jwGHGx8frnnvuUUhIiEJDQ/XXv/5Vx44dsz3funVrjRkzxu6YX375Rd7e3vr6668lSWfPntUDDzwgf39/1axZU8uWLVNkZKRmzJiR72t7eXkpPDzc7uHj46MlS5aoefPmCg4OVnh4uPr06aOkpKQ820lISFDXrl1Vrlw5BQYGqkGDBvriiy9sz//www/q3LmzgoKCFBYWpscee0wXLlwocI6KC65clRTZV63CG0r+IW4NBQAAoEjIvCq9GnHLzXhICnH0oHFnJJ/AAu2alpamuLg4NWrUSKmpqZo4caJ69Oihffv2ycPDQ7GxsXrjjTf02muvyWQySZI+/PBDRUREqE2bNpKkvn376sKFC9q8ebO8vb0VFxeXbzF0I5mZmXrppZcUFRWlpKQkxcXFqX///nYF0x8NGzZMGRkZ+vrrrxUYGKiDBw8qKChIkpScnKz77rtPAwcO1PTp03Xt2jWNGTNGvXr10saNG286xqKI4qqkyB7MgiHYAQAAipWePXvarS9YsEAVK1bUwYMH1bBhQ/Xq1UujRo3St99+ayumli1bpt69e8tkMunQoUNav369du7cqebNm0uS3n33XdWpU+eGr33gwAFbESRJt99+u3bs2KHHH3/ctq1WrVp6++23deeddyo1NdVu/2yJiYnq2bOnoqOjbcdk++c//6k77rhDr776qt17rFatmn766SfVrVu3IGkqFiiuSoqErdafTB4MAABg5R1gvYJ0iywWi1KuXFGZ4GB5eBTwrhrvgAK3f+TIEU2cOFHbt2/XhQsXZLFYJFkLloYNG6pixYrq2LGjli5dqjZt2ujEiRPatm2b3nnnHUnS4cOH5eXlpaZNm9ravO2221SuXLkbvnZUVJQ+++wz27qvr68kaffu3Zo8ebL279+vS5cu2cV0++2352jn6aef1tChQ7V27Vp16NBBPXv2VKNGjSRJ+/fv16ZNm3Ityo4dO0ZxhSIm7aL0y4/WZQazAAAAsDKZCtw1L18Wi+RttrZV0OLKAV27dlWNGjU0f/58RUREyGKxqGHDhsrIyLDtExsbq6efflqzZs3SsmXLFB0dbbtKdCt8fHx022232W1LS0tTTEyMYmJitHTpUlWsWFGJiYmKiYmxi+mPBg4cqJiYGK1evVpr167V1KlTNW3aNI0YMUKpqanq2rWrXn/99RzHVa5c+ZbfQ1HCgBYlQfb9VhXrS4Gh7o0FAAAABXbx4kUdPnxY48ePV/v27VW/fn1dunQpx37dunVTenq64uPjtWzZMsXGxtqei4qKUlZWlvbu3WvbdvTo0VzbKYhDhw7p4sWLeu2119SmTRvVq1evQPdvVatWTUOGDNHHH3+sZ599VvPnz5ckNW3aVP/73/8UGRmp2267ze4RGOiE4rcIobgqCZjfCgAAoFgqV66cQkND9a9//UtHjx7Vxo0bFRcXl2O/wMBAde/eXRMmTNCPP/6o3r17256rV6+eOnTooMGDB2vHjh3au3evBg8eLH9/f9sAGI6oXr26fHx8NGvWLB0/flyfffaZXnrppXyPGTVqlNasWaMTJ05oz5492rRpk+rXry/JOtjFr7/+qt69e2vnzp06duyY1qxZowEDBshsNjscX1FGcVUS2AazoLgCAAAoTjw8PLR8+XLt3r1bDRs21DPPPKM333wz131jY2O1f/9+tWnTRtWrV7d77v3331dYWJjatm2rHj16aNCgQQoODpafn5/DMVWsWFGLFi3SihUrdPvtt+u1117TP/7xj3yPMZvNGjZsmOrXr69OnTqpbt26mjNnjiQpIiJCW7ZskdlsVseOHRUdHa1Ro0YpJCSk4PewFRPcc1XcXbsknf/BukxxBQAAUOx06NBBBw8etNtm5DJPVufOnXPdLlnvXfrjMOmnTp1SUlJSjvup/mjy5MmaPHlyrs/17t3b7urYn2Nq166d3fqsWbPyfB1JqlOnjj7++ON89ykJKK6Ku8TvJBlS6G1ScJi7owEAAIAbbNy4UampqYqOjtbZs2f13HPPKTIyUm3btnV3aKUKxVVxd/Jb60+uWgEAAJRamZmZGjdunI4fP67g4GC1bt1aS5culbe3t7tDK1Uoroo722AWTB4MAABQWmUPnQ73Kll3kJU26SnS2f3WZSYPBgAAANyK4qo4+3mHZFikkBpS2arujgYAAAAo1SiuirOE3+63oksgAAAA4HYUV8UZ81sBAAAARQbFVXGVkSad2WNdjqS4AgAAANyN4qq4OrVTsmRJZapa77kCAAAA4FZForiaPXu2IiMj5efnp5YtW2rHjh157puZmakXX3xRtWvXlp+fnxo3bqz4+PhbarNYsnUJbC2ZTO6NBQAAAID7i6sPP/xQcXFxmjRpkvbs2aPGjRsrJiZGSUlJue4/fvx4vfPOO5o1a5YOHjyoIUOGqEePHtq7d+9Nt1ks2ea3oksgAAAAHBMZGSmTyZTno3///u4OsVhye3H11ltvadCgQRowYIBuv/12zZs3TwEBAVqwYEGu+y9ZskTjxo1Tly5dVKtWLQ0dOlRdunTRtGnTbrrNYiczXTq1y7pcg5ECAQAA4JidO3fq7NmzOnv2rFauXClJOnz4sG3bzJkz7fbPzMx0R5jFjpc7XzwjI0O7d+/W2LFjbds8PDzUoUMHbdu2Lddjrl+/Lj8/P7tt/v7++vbbb2+pzevXr9vWU1JSJFlPoqJ4IpkSvpOX+bqMwErKKlNdKoIxFlR2fotinksqcu5a5Nv1yLnrkXPXI+c5ZWZmyjAMWSwWWSwWSZJhGLqWdc0p7V/LuiavzIJ/dfb38pepgLduxMfH69VXX9UPP/wgT09P3XXXXZoxY4Zq164tSbrnnnt0zz336LXXXrMd88svv6hq1apat26d2rZtq7Nnz2rQoEHatGmTwsPD9dJLL2n8+PEaOXKkRo4cmeM1Q0NDbcshISGSpAoVKigkJEQnT55U5cqVtWzZMs2bN0/bt2/XnDlzlJCQoE8//VR79uyxHTtz5kzNnDlTx48ft2179913NX36dJ04cUKRkZEaMWKEhg4dWuDcSdbfXfbP7N9nYbJYLDIMQ5mZmfL09LR7zpF/Z24tri5cuCCz2aywsDC77WFhYTp06FCux8TExOitt95S27ZtVbt2bW3YsEEff/yxzGbzTbc5depUTZkyJcf2tWvXKiAg4GbeWqGqe26V6ks67V1Tu7/80t3hOMW6devcHUKpQ85di3y7Hjl3PXLueuT8d15eXgoPD1dqaqoyMjIkWQuijqs7uiWetQ+slb+Xf4H2vXDhgp588kk1aNBAaWlpevXVV9W9e3d988038vDwUI8ePfT2229r7NixtoJt8eLFCg8PV+PGjZWSkqJHH31UFy9e1H//+195e3vrhRdeUFJSktLT020XDvJy9epVSdKVK1fk4eGh1NRUSdLzzz+vl19+WW+//bZ8fX31008/yWw227WXnp4ui8Vi2/bRRx9p0qRJeuONN9SoUSN9//33GjlypDw8PNS7d2+H83jlyhWHj7kZGRkZunbtmr7++mtlZWXZPZedn4Jwa3F1M2bOnKlBgwapXr16MplMql27tgYMGHBLXf7Gjh2ruLg423pKSoqqVaumjh07qkyZMs4I26k8l74rSarcsqe6NO/i5mhuTWZmptatW6f7779f3t7e7g6nVCDnrkW+XY+cux45dz1ynlN6erp+/vlnBQUF2Xo5OXKlydmCg4MV4F2w/6R/9NFH7dYXL16ssLAwnTp1Sg0bNlTfvn01btw4ff/992rTpo0kadWqVerTp4/Kli2rQ4cOafPmzdq+fbuaN28uSVqwYIGioqLk5+d3w++z2RcTgoODVaZMGQUFBUmSnnnmGcXGxtr28/X1laenp117fn5+8vDwsG1744039I9//MNWSEVHR+vkyZNasmSJnnzyyQLlQ7Jesbpy5YqCg4MLfAXwVqSnp8vf319t27bN0UvuRsXpH7m1uKpQoYI8PT11/vx5u+3nz59XeHh4rsdUrFhRq1atUnp6ui5evKiIiAg9//zzqlWr1k236evrK19f3xzbvb29i94HVlaGdRh2SZ6128qzqMV3k4pkrks4cu5a5Nv1yLnrkXPXI+e/M5vNMplM8vDwkIeHdViBQJ9Abe+z/Zbbtlgsti/62W3fiCPdAo8cOaKJEydq+/btunDhgq0b3KlTp9SoUSOFhYWpY8eO+uCDD3TvvffqxIkT2rZtm9555x15eHjoyJEj8vLyUvPmzW3x1a1bV+XKlbPlJD/Zz2fnLnv9zjvvtDs2+/3ktS0tLU3Hjh3ToEGD7AqprKwslS1btsC5k2TLQUHidwYPDw+ZTKZc/0058m/MrcWVj4+PmjVrpg0bNqh79+6SrIncsGGDhg8fnu+xfn5+qlKlijIzM7Vy5Ur16tXrltssFs7slbKuSQGhUsV67o4GAACgyDKZTAW+epQfi8WiLK8sBXgHFMoX/a5du6pGjRqaP3++IiIiZLFY1LBhQ1v3RkmKjY3V008/rVmzZmnZsmWKjo5WdHS002P5o8DAQLt1Dw8P271Q2f54P1J2d8L58+erZcuWdvv9+T6mksrt3QLj4uLUr18/NW/eXC1atNCMGTOUlpamAQMGSJL69u2rKlWqaOrUqZKk7du36/Tp02rSpIlOnz6tyZMny2Kx6Lnnnitwm8VagnXgDua3AgAAKP4uXryow4cPa/78+bYuf9kDtf1Rt27dNHjwYMXHx2vZsmXq27ev7bmoqChlZWVp7969atasmSTp6NGjunTpklNjrVixos6dOyfDMGxXrPbt22d7PiwsTBERETp+/Lhdd8LSxO3F1cMPP6xffvlFEydO1Llz59SkSRPFx8fbBqRITEy0+x+C9PR0jR8/XsePH1dQUJC6dOmiJUuW2EY5KUibxVrCVuvPGsxvBQAAUNyVK1dOoaGh+te//qXKlSsrMTFRzz//fI79AgMD1b17d02YMEE//vij3eAQ9erVU4cOHTR48GDNnTtX3t7eevbZZ+XvX/CuiQXRrl07/fLLL3rjjTf097//XfHx8fryyy/t7sGaMmWKnn76aZUtW1adOnXS9evXtWvXLl26dMlujIOSyu3zXEnS8OHDlZCQoOvXr2v79u12lxE3b96sRYsW2dbvvfdeHTx4UOnp6bpw4YLef/99RUREONRmsWXOkhK/sy5TXAEAABR7Hh4eWr58uXbv3q2GDRvqmWee0ZtvvpnrvrGxsdq/f7/atGmj6tWr2z33/vvvKywsTG3btlWPHj00aNAgBQcH5xic4VbUr19fc+bM0ezZs9W4cWPt2LFDo0ePtttn4MCBevfdd7Vw4UJFR0fr3nvv1aJFi1SzZk2nxVGUuf3KFRxwbr+UkSr5lZXCGrg7GgAAADhBhw4ddPDgQbttf763SZI6d+6c63ZJqly5sr744gvb+qlTp5SUlKTbbrvthq/frl07u3YjIyPzfJ0hQ4ZoyJAhdtvGjRtnt96nTx/16dPnhq9bElFcFScnt1h/Vm8teZSOmwIBAABwYxs3blRqaqqio6N19uxZPffcc4qMjFTbtm3dHVqpQnFVnCT8VlxF0iUQAAAAv8vMzNS4ceN0/PhxBQcHq3Xr1lq6dClD9bsYxVVxYTFLCdusyzVauzcWAAAAFCkxMTGKiYlxdxilXpEY0AIFcP5/0vXLkk+wFN7Y3dEAAAAA+BOKq+Iiu0tg9ZaSJxccAQAAgKKG4qq4OJk9eTD3WwEAAABFEcVVcWCx/D55cOQ97o0FAAAAQK4oroqDC4ela79KXv5S5SbujgYAAABALiiuioPsLoHVWkhePu6NBQAAAECuKK6KA9v8VnQJBAAAKGnatWunUaNGuTsMOAHFVVFnGNLJ34orBrMAAACAgzZv3iyTyaTk5GR3h5LD5MmTZTKZcjw8PT1Vrlw5eXp6ujtEh1BcFXUXj0lpSZKnr1SlmbujAQAAAJxm9OjROnv2rO1RtWpVvfjiizp9+rQOHTqk06dP2+2fkZHhpkgLhuKqqEv47X6rqs0lbz/3xgIAAFCMGIYhy9Wrznlcu+bQ/oZhOBRrVlaWhg8frrJly6pChQqaMGGCXRvXr1/X6NGjVaVKFQUGBqply5bavHmz7fmEhAR17dpV5cqVU2BgoBo0aKAvvvhCJ0+e1F/+8hdJUrly5WQymdS/f/9cY7h48aJ69+6tKlWqKCAgQNHR0frggw9sz//rX/9SRESELBaL3XHdunXT448/blt/+eWXValSJQUHB2vgwIF6/vnn1aRJk1xfMygoSOHh4baHp6engoODFR4errCwMPXp00fDhw/XqFGjVKFCBcXExOjkyZMymUzat2+frZ3k5GSZTCa7nPzwww/q3LmzgoKCFBYWpscee0wXLly4wW/i1jAbbVFHl0AAAICbYly7psNNndfz57wD+0bt2S1TQECB91+8eLGeeOIJ7dixQ7t27dLgwYNVvXp1DRo0SJI0fPhwHTx4UMuXL1dERIQ++eQTderUSQcOHFCdOnU0bNgwZWRk6Ouvv1ZgYKAOHjyooKAgVatWTStXrlTPnj11+PBhlSlTRv7+/rnGkJ6ermbNmmnMmDEqU6aMVq9erccee0y1a9dWixYt9NBDD2nEiBHatGmT2rdvL0n69ddfFR8fry+++EKStHTpUr3yyiuaM2eO7r77bi1fvlzTpk1TzZo1HcheztwMHTpUW7ZsKfAxycnJuu+++zRw4EBNnz5d165d05gxY9SrVy9t3LjxpmO5EYqroi70NqlSAwazAAAAKMGqVaum6dOny2QyKSoqSgcOHND06dM1aNAgJSYmauHChUpMTFRERIQka3e6+Ph4LVy4UK+++qoSExPVs2dPRUdHS5Jq1apla7t8+fKSpEqVKikkJCTPGKpUqaLRo0fb1keMGKE1a9boo48+UosWLVSuXDl17txZy5YtsxVX//nPf1ShQgXb1bFZs2bpiSee0IABAyRJEydO1Nq1a5WamnrTualTp47eeOMN2/rJkydveMw///lP3XHHHXr11Vdt2xYsWKBq1arpp59+Ut26dW86nvxQXBV17cZYHwAAAHCIyd9fUXt233I7FotFKVeuqExwsDw8CnZXjSmPq0N5ueuuu2QymWzrrVq10rRp02Q2m3XgwAGZzeYcBcH169cVGhoqSXr66ac1dOhQrV27Vh06dFDPnj3VqFEjh2Iwm8169dVX9dFHH+n06dPKyMjQ9evXFfCHK3CxsbEaNGiQ5syZI19fXy1dulSPPPKILS+HDx/WU089ZdduixYtbulqUbNmjl993L9/vzZt2qSgoKAczx07doziCgAAAHCEyWRyqGteniwWeWRlySMgoMDFlTOlpqbK09NTu3fvzjF6XnbxMHDgQMXExGj16tVau3atpk6dqmnTpmnEiBEFfp0333xTM2fO1IwZMxQdHa3AwECNGjXKbhCJrl27yjAMrV69Wnfeeae++eYbTZ8+3TlvNA+BgYF269m/gz/ek5aZmWm3T2pqqrp27arXX389R3uVK1cuhCitKK4AAAAAN9u+fbvd+nfffac6derI09NTd9xxh8xms5KSktSmTZs826hWrZqGDBmiIUOGaOzYsZo/f75GjBghHx8fSdYrU/nZsmWLunXrpkcffVSS9YrdTz/9pNtvv922j5+fnx588EEtXbpUR48eVVRUlJo2bWp7PioqSjt37lTfvn1t23bu3FnwRBRAxYoVJUlnz57VHXfcIUl2g1tIUtOmTbVy5UpFRkbKy8t1JQ+jBQIAAABulpiYqLi4OB0+fFgffPCBZs2apZEjR0qS6tatq9jYWPXt21cff/yxTpw4oR07dmjq1KlavXq1JGnUqFFas2aNTpw4oT179mjTpk2qX7++JKlGjRoymUz6/PPP9csvv+R5/1OdOnW0bt06bd26VT/++KOefPJJnT+fcxiP2NhYrV69WgsWLFBsbKzdcyNGjNB7772nxYsX68iRI3r55Zf1/fff23V5vFX+/v6666679Nprr+nHH3/UV199pfHjx9vtM2zYMP3666/q3bu3du7cqWPHjmnNmjUaMGDADYvMW0FxBQAAALhZ3759de3aNbVo0ULDhg3TyJEjNXjwYNvzCxcuVN++ffXss88qKipK3bt3186dO1W9enVJ1qtSw4YNU/369dWpUyfVrVtXc+bMkWQdqGLKlCl6/vnnFRYWpuHDh+caw/jx49W0aVPFxMSoXbt2Cg8PV/fu3XPsd99996l8+fI6fPiw+vTpY/dcbGysxo4dq9GjR6tp06Y6ceKE+vfvLz8/504ptGDBAmVlZalZs2YaNWqUXn75ZbvnIyIitGXLFpnNZnXs2FHR0dEaNWqUQkJCCrVrp8lwdBD+UiAlJUVly5bV5cuXVaZMGXeHU6JlZmbqiy++UJcuXeTt7e3ucEoFcu5a5Nv1yLnrkXPXI+c5paen68SJE6pZs6bTv8hbLBalpKSoTJkybrnnqri7//77FR4eriVLlhT4GFfnPL/zx5HagHuuAAAAADjF1atXNW/ePMXExMjT01MffPCB1q9fr3Xr1rk7NJeguAIAAADgFCaTSV988YVeeeUVpaenKyoqSitXrlSHDh3cHZpLUFwBAAAAcAp/f3+tX7/e3WG4DZ1GAQAAAMAJKK4AAABQYjBWG26Gs84biisAAAAUe9mjJl69etXNkaA4yj5vbnX0Te65AgAAQLHn6empkJAQJSUlSZICAgKcNnGtxWJRRkaG0tPTGYrdRVyVc8MwdPXqVSUlJSkkJESenp631B7FFQAAAEqE8PBwSbIVWM5iGIauXbsmf39/pxVsyJ+rcx4SEmI7f24FxRUAAABKBJPJpMqVK6tSpUrKzMx0WruZmZn6+uuv1bZtWyZtdhFX5tzb2/uWr1hlo7gCAABAieLp6em0L8vZ7WVlZcnPz4/iykWKa87pNAoAAAAATkBxBQAAAABOQHEFAAAAAE7APVe5yJ5ELCUlxc2RlHyZmZm6evWqUlJSilV/2uKMnLsW+XY9cu565Nz1yLlrkW/XK0o5z64JCjLRMMVVLq5cuSJJqlatmpsjAQAAAFAUXLlyRWXLls13H5NRkBKslLFYLDpz5oyCg4OZy6CQpaSkqFq1avr5559VpkwZd4dTKpBz1yLfrkfOXY+cux45dy3y7XpFKeeGYejKlSuKiIi44YTGXLnKhYeHh6pWreruMEqVMmXKuP0fTmlDzl2LfLseOXc9cu565Ny1yLfrFZWc3+iKVTYGtAAAAAAAJ6C4AgAAAAAnoLiCW/n6+mrSpEny9fV1dyilBjl3LfLteuTc9ci565Fz1yLfrldcc86AFgAAAADgBFy5AgAAAAAnoLgCAAAAACeguAIAAAAAJ6C4AgAAAAAnoLhCoZk6daruvPNOBQcHq1KlSurevbsOHz6c7zGLFi2SyWSye/j5+bko4uJv8uTJOfJXr169fI9ZsWKF6tWrJz8/P0VHR+uLL75wUbQlQ2RkZI6cm0wmDRs2LNf9Occd8/XXX6tr166KiIiQyWTSqlWr7J43DEMTJ05U5cqV5e/vrw4dOujIkSM3bHf27NmKjIyUn5+fWrZsqR07dhTSOyh+8st5ZmamxowZo+joaAUGBioiIkJ9+/bVmTNn8m3zZj6bSpMbnef9+/fPkb9OnTrdsF3O89zdKN+5faabTCa9+eabebbJOZ6/gnwnTE9P17BhwxQaGqqgoCD17NlT58+fz7fdm/0bUJgorlBovvrqKw0bNkzfffed1q1bp8zMTHXs2FFpaWn5HlemTBmdPXvW9khISHBRxCVDgwYN7PL37bff5rnv1q1b1bt3bz3xxBPau3evunfvru7du+uHH35wYcTF286dO+3yvW7dOknSQw89lOcxnOMFl5aWpsaNG2v27Nm5Pv/GG2/o7bff1rx587R9+3YFBgYqJiZG6enpebb54YcfKi4uTpMmTdKePXvUuHFjxcTEKCkpqbDeRrGSX86vXr2qPXv2aMKECdqzZ48+/vhjHT58WH/7299u2K4jn02lzY3Oc0nq1KmTXf4++OCDfNvkPM/bjfL9xzyfPXtWCxYskMlkUs+ePfNtl3M8bwX5TvjMM8/ov//9r1asWKGvvvpKZ86c0YMPPphvuzfzN6DQGYCLJCUlGZKMr776Ks99Fi5caJQtW9Z1QZUwkyZNMho3blzg/Xv16mU88MADdttatmxpPPnkk06OrPQYOXKkUbt2bcNiseT6POf4zZNkfPLJJ7Z1i8VihIeHG2+++aZtW3JysuHr62t88MEHebbTokULY9iwYbZ1s9lsREREGFOnTi2UuIuzP+c8Nzt27DAkGQkJCXnu4+hnU2mWW8779etndOvWzaF2OM8LpiDneLdu3Yz77rsv3304xx3z5++EycnJhre3t7FixQrbPj/++KMhydi2bVuubdzs34DCxpUruMzly5clSeXLl893v9TUVNWoUUPVqlVTt27d9L///c8V4ZUYR44cUUREhGrVqqXY2FglJibmue+2bdvUoUMHu20xMTHatm1bYYdZImVkZOjf//63Hn/8cZlMpjz34xx3jhMnTujcuXN253DZsmXVsmXLPM/hjIwM7d692+4YDw8PdejQgfP+Jl2+fFkmk0khISH57ufIZxNy2rx5sypVqqSoqCgNHTpUFy9ezHNfznPnOX/+vFavXq0nnnjihvtyjhfcn78T7t69W5mZmXbnbL169VS9evU8z9mb+RvgChRXcAmLxaJRo0bp7rvvVsOGDfPcLyoqSgsWLNCnn36qf//737JYLGrdurVOnTrlwmiLr5YtW2rRokWKj4/X3LlzdeLECbVp00ZXrlzJdf9z584pLCzMbltYWJjOnTvninBLnFWrVik5OVn9+/fPcx/OcefJPk8dOYcvXLggs9nMee8k6enpGjNmjHr37q0yZcrkuZ+jn02w16lTJ73//vvasGGDXn/9dX311Vfq3LmzzGZzrvtznjvP4sWLFRwcfMPuaZzjBZfbd8Jz587Jx8cnx3/S5HfO3szfAFfwctsro1QZNmyYfvjhhxv2P27VqpVatWplW2/durXq16+vd955Ry+99FJhh1nsde7c2bbcqFEjtWzZUjVq1NBHH31UoP91w61577331LlzZ0VEROS5D+c4SorMzEz16tVLhmFo7ty5+e7LZ9OteeSRR2zL0dHRatSokWrXrq3Nmzerffv2boys5FuwYIFiY2NvOPAQ53jBFfQ7YXHFlSsUuuHDh+vzzz/Xpk2bVLVqVYeO9fb21h133KGjR48WUnQlW0hIiOrWrZtn/sLDw3OMxHP+/HmFh4e7IrwSJSEhQevXr9fAgQMdOo5z/OZln6eOnMMVKlSQp6cn5/0tyi6sEhIStG7dunyvWuXmRp9NyF+tWrVUoUKFPPPHee4c33zzjQ4fPuzw57rEOZ6XvL4ThoeHKyMjQ8nJyXb753fO3szfAFeguEKhMQxDw4cP1yeffKKNGzeqZs2aDrdhNpt14MABVa5cuRAiLPlSU1N17NixPPPXqlUrbdiwwW7bunXr7K6soGAWLlyoSpUq6YEHHnDoOM7xm1ezZk2Fh4fbncMpKSnavn17nuewj4+PmjVrZneMxWLRhg0bOO8LKLuwOnLkiNavX6/Q0FCH27jRZxPyd+rUKV28eDHP/HGeO8d7772nZs2aqXHjxg4fyzlu70bfCZs1ayZvb2+7c/bw4cNKTEzM85y9mb8BLuG2oTRQ4g0dOtQoW7assXnzZuPs2bO2x9WrV237PPbYY8bzzz9vW58yZYqxZs0a49ixY8bu3buNRx55xPDz8zP+97//ueMtFDvPPvussXnzZuPEiRPGli1bjA4dOhgVKlQwkpKSDMPIme8tW7YYXl5exj/+8Q/jxx9/NCZNmmR4e3sbBw4ccNdbKJbMZrNRvXp1Y8yYMTme4xy/NVeuXDH27t1r7N2715BkvPXWW8bevXttI9O99tprRkhIiPHpp58a33//vdGtWzejZs2axrVr12xt3HfffcasWbNs68uXLzd8fX2NRYsWGQcPHjQGDx5shISEGOfOnXP5+yuK8st5RkaG8be//c2oWrWqsW/fPrvP9uvXr9va+HPOb/TZVNrll/MrV64Yo0ePNrZt22acOHHCWL9+vdG0aVOjTp06Rnp6uq0NzvOCu9HnimEYxuXLl42AgABj7ty5ubbBOe6YgnwnHDJkiFG9enVj48aNxq5du4xWrVoZrVq1smsnKirK+Pjjj23rBfkb4GoUVyg0knJ9LFy40LbPvffea/Tr18+2PmrUKKN69eqGj4+PERYWZnTp0sXYs2eP64Mvph5++GGjcuXKho+Pj1GlShXj4YcfNo4ePWp7/s/5NgzD+Oijj4y6desaPj4+RoMGDYzVq1e7OOrib82aNYYk4/Dhwzme4xy/NZs2bcr1cyQ7pxaLxZgwYYIRFhZm+Pr6Gu3bt8/xe6hRo4YxadIku22zZs2y/R5atGhhfPfddy56R0Vffjk/ceJEnp/tmzZtsrXx55zf6LOptMsv51evXjU6duxoVKxY0fD29jZq1KhhDBo0KEeRxHlecDf6XDEMw3jnnXcMf39/Izk5Odc2OMcdU5DvhNeuXTOeeuopo1y5ckZAQIDRo0cP4+zZszna+eMxBfkb4GomwzCMwrkmBgAAAAClB/dcAQAAAIATUFwBAAAAgBNQXAEAAACAE1BcAQAAAIATUFwBAAAAgBNQXAEAAACAE1BcAQAAAIATUFwBAAAAgBNQXAEA4GQmk0mrVq1ydxgAABejuAIAlCj9+/eXyWTK8ejUqZO7QwMAlHBe7g4AAABn69SpkxYuXGi3zdfX103RAABKC65cAQBKHF9fX4WHh9s9ypUrJ8naZW/u3Lnq3Lmz/P39VatWLf3nP/+xO/7AgQO677775O/vr9DQUA0ePFipqal2+yxYsEANGjSQr6+vKleurOHDh9s9f+HCBfXo0UMBAQGqU6eOPvvss8J90wAAt6O4AgCUOhMmTFDPnj21f/9+xcbG6pFHHtGPP/4oSUpLS1NMTIzKlSunnTt3asWKFVq/fr1d8TR37lwNGzZMgwcP1oEDB/TZZ5/ptttus3uNKVOmqFevXvr+++/VpUsXxcbG6tdff3Xp+wQAuJbJMAzD3UEAAOAs/fv317///W/5+fnZbR83bpzGjRsnk8mkIUOGaO7cubbn7rrrLjVt2lRz5szR/PnzNWbMGP38888KDAyUJH3xxRfq2rWrzpw5o7CwMFWpUkUDBgzQyy+/nGsMJpNJ48eP10svvSTJWrAFBQXpyy+/5N4vACjBuOcKAFDi/OUvf7ErniSpfPnytuVWrVrZPdeqVSvt27dPkvTjjz+qcePGtsJKku6++25ZLBYdPnxYJpNJZ86cUfv27fONoVGjRrblwMBAlSlTRklJSTf7lgAAxQDFFQCgxAkMDMzRTc9Z/P39C7Sft7e33brJZJLFYimMkAAARQT3XAEASp3vvvsux3r9+vUlSfXr19f+/fuVlpZme37Lli3y8PBQVFSUgoODFRkZqQ0bNrg0ZgBA0ceVKwBAiXP9+nWdO3fObpuXl5cqVKggSVqxYoWaN2+ue+65R0uXLtWOHTv03nvvSZJiY2M1adIk9evXT5MnT9Yvv/yiESNG6LHHHlNYWJgkafLkyRoyZIgqVaqkzp0768qVK9qyZYtGjBjh2jcKAChSKK4AACVOfHy8KleubLctKipKhw4dkmQdyW/58uV66qmnVLlyZX3wwQe6/fbbJUkBAQFas2aNRo4cqTvvvFMBAQHq2bOn3nrrLVtb/fr1U3p6uqZPn67Ro0erQoUK+vvf/+66NwgAKJIYLRAAUKqYTCZ98skn6t69u7tDAQCUMNxzBQAAAABOQHEFAAAAAE7APVcAgFKF3vAAgMLClSsAAAAAcAKKKwAAAABwAoorAAAAAHACiisAAAAAcAKKKwAAAABwAoorAAAAAHACiisAAAAAcAKKKwAAAABwgv8HSW6YSeHK2qMAAAAASUVORK5CYII=", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read the test CSV files\n", + "test_accuracy_df = pd.read_csv('test_accuracy.csv')\n", + "test_loss_df = pd.read_csv('test_loss.csv')\n", + "x_values = np.arange(1, 21)\n", + "# Test Accuracy vs Epoch plot\n", + "plt.figure(figsize=(10, 6))\n", + "for technique in test_accuracy_df.columns[1:]: # Skip the 'epoch' column\n", + " plt.plot(x_values, test_accuracy_df[technique], label=technique)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Test Accuracy')\n", + "plt.title('Test Accuracy vs Epoch for Different Augmentation Techniques')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "# Save the test accuracy plot\n", + "plt.savefig('test_augmentation_accuracy.png')\n", + "plt.close() # Close the current figure to start fresh for the next plot\n", + "\n", + "# Test Loss vs Epoch plot\n", + "plt.figure(figsize=(10, 6))\n", + "for technique in test_loss_df.columns[1:]: # Skip the 'epoch' column\n", + " plt.plot(x_values, test_loss_df[technique], label=technique)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Test Loss')\n", + "plt.title('Test Loss vs Epoch for Different Augmentation Techniques')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "# Save the test loss plot\n", + "plt.savefig('test_augmentation_loss.png')\n", + "plt.close() # Close the current figure to start fresh for the next plot\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GPU_TF", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.20" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/testing/hyperparameters.json b/testing/hyperparameters.json new file mode 100644 index 0000000..6a8adf2 --- /dev/null +++ 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vbl/XvXt3du7cmWw3rZRYunQpdevWdSiMkCtXLh5//PFkty9fvjx169a1Lye2Kjdr1owiRYokWZ/4Hrx8+TK//vorXbt25dq1a/ZzeOnSJUJDQzl06BCnTp1yOFb//v0d3usNGzYkPj4+2a65/+Xp6Wn//YqPj+fSpUv4+flRpkyZJNc7pRYvXoyrqyvPPfecw/phw4ZhGAZLlixxWN+iRQuHVsTKlSsTEBCQ5t9LMFuhw8PD6d69u8N72dXVlTp16rB69WrAHO/322+/8eSTTzpcF0j534/k3q9peU2Jf/MGDx7scOz/tvSmRkrPw738bxFxduqqJ5KJXLhwgcjIyGS7ZZUrV46EhAROnDhBhQoVeOONN+jQoQOlS5emYsWKPPjgg/To0cNeGcrT05N3332XYcOGkT9/fh544AHatm1Lz549CQoKumMcjzzyCEOHDmXu3Lm89NJLGIbB/Pnz7eOuwPxgP3ToUCZMmMCcOXNo2LAh7du354knnkhRNz1XV1datGiRovNSqlQph2U/Pz+Cg4Pt/ekTP/T997x5eHhQvHhx++OJiWDFihXvekwvLy/y5s3rsC5nzpxp/qBw7NgxXFxcKFmypMP6oKAgAgMDk3xwLVasWJqOk5zr168D4O/vny77u3DhAuHh4UybNo1p06Ylu8358+cdlv/7ehIT/l69et32OFevXiVnzpz25f9+WE187MqVK0m6d6bUV199RbFixfD09OTw4cOA2b3Px8eHOXPmpKmU/LFjxxwSoUT/vfaJ/vu6En9/ChcunOz6xPfg4cOHMQyDV199lVdffTXZfZ8/f56CBQve9li3nsO7SUhI4MMPP+STTz7h6NGjDuP90tpN7tixYxQoUCDJezOx2t1/fy/+Gz/c2+8l/PtebNasWbKPJ763EhOZlPz9gJS/X9PymhLPy3//NubNm9fhdyY1Unoe7uV/i4izU+IkkkU1atSII0eOsGjRIpYvX87nn3/OBx98wJQpU+wVwYYMGUK7du1YuHAhy5Yt49VXX2Xs2LH8+uuvVKtW7bb7LlCgAA0bNmTevHm89NJL/PHHHxw/fjxJ3/zx48fTu3dvewzPPfccY8eO5Y8//qBQoUIZ+vozWkZVsErum+nk3KmCXmr99ddfwO0/uKdWYkvQE088cdvE57+lnf/7ehL38f777992guD/TkJ8u2ti/KeIQEpFRETw008/ERUVleQDKMDXX3/NW2+9dddrdreCIXdzu9d1t9ebeA6HDx9+29bJ/17zezmHb7/9Nq+++ipPPvkkb775Jrly5cLFxYUhQ4bctxLj6f0egH/P4+zZs5P94O/mlraPUimNNSNe061u9/797/s2Nechrf9bRJydEieRTCRv3rz4+Phw4MCBJI/t378fFxcXh2+hc+XKRZ8+fejTpw/Xr1+nUaNGjBo1yqGUcokSJRg2bBjDhg3j0KFDVK1alfHjx/PVV1/dMZZu3boxYMAADhw4wNy5c/Hx8aFdu3ZJtqtUqRKVKlXilVdeYcOGDdSvX58pU6YwZsyYezgTjg4dOkTTpk3ty9evX+fMmTO0bt0awF757cCBAw5dHGNiYjh69Ki9ZSuxO8xff/2V4tau9FK0aFESEhI4dOiQw9wx586dIzw83P4aMsLs2bOx2WxJyjKnVd68efH39yc+Pj7N5zHxWgQEBKTrtUhpYgpmxbqoqCg+/fRT8uTJ4/DYgQMHeOWVV1i/fj0NGjQAzFaA/1ZpjImJ4cyZMw7rihYtam+9ulVy6+5F4nvd3d39vpzDBQsW0LRpU7744guH9eHh4Q7nLzXXoGjRoqxcuZJr1645tDrt37/f/nhGS3wv5suX747nMfF8J34RYaXE83Lo0CGHv3kXLlxI0lKV2AIVHh7u0EX5v615KT0Pt26flv8tIs5MY5xEMhFXV1datWrFokWLHMq6njt3jq+//poGDRrYu0tcunTJ4bl+fn6ULFnSXto6MjKSqKgoh21KlCiBv79/kvLXyencuTOurq588803zJ8/n7Zt2+Lr62t/PCIigri4OIfnVKpUCRcXlxTtPzWmTZtGbGysffnTTz8lLi6Ohx56CDDHCHh4eDBp0iSHb2m/+OILrl69Sps2bQCoXr06xYoVY+LEiUk+AKfXt7u3k5jkTZw40WH9hAkTAOwxprd33nmH5cuX061bt2RbVdLC1dWVzp0789133yX7IfLW8ta3U6NGDUqUKMG4cePsXQlTu4/k+Pr6JluCPjlfffUVxYsX55lnnqFLly4Ot+HDh+Pn58ecOXPs25coUYLffvvNYR/Tpk1L8s19aGgoGzduZMeOHfZ1ly9fdthXesiXLx9NmjRh6tSpSZI3SP9z6OrqmuT3ZP78+UnGUSX+nUjJdWjdujXx8fFMnjzZYf0HH3yAzWaz/45npNDQUAICAnj77bcd/s4kSjyPefPmpVGjRkyfPp3jx487bJPRfz/+q0WLFri7u/PRRx85HPu/f1/g34To1vduYtn+W6X0PNzr/xYRZ6YWJxEnNH369GTnHvnf//7HmDFjWLFiBQ0aNGDAgAG4ubkxdepUoqOjee+99+zbli9fniZNmlCjRg1y5crFn3/+yYIFCxg0aBAABw8epHnz5nTt2pXy5cvj5ubGDz/8wLlz53j00UfvGmO+fPlo2rQpEyZM4Nq1a3Tr1s3h8V9//ZVBgwbxyCOPULp0aeLi4pg9e7b9Q/XdxMXF3fabyU6dOjkkaTExMfbXcuDAAT755BMaNGhA+/btAfMDzciRIxk9ejQPPvgg7du3t29Xq1Yt+8SwLi4ufPrpp7Rr146qVavSp08fgoOD2b9/P3v27GHZsmV3jTutqlSpQq9evZg2bRrh4eE0btyYzZs3M2vWLDp27OjQopYWt57PqKgojh07xo8//siuXbto2rTpbccipdU777zD6tWrqVOnDv369aN8+fJcvnyZbdu2sXLlSi5fvnzH57u4uPD555/z0EMPUaFCBfr06UPBggU5deoUq1evJiAggJ9++inVcdWoUYOVK1cyYcIEChQoQLFixexFFW51+vRpVq9enaQoQSJPT09CQ0OZP38+kyZNwt3dnaeeeopnnnmGzp0707JlS3bu3MmyZcuStFaNGDGCr776ipYtWzJ48GB7OfIiRYpw+fLlVLXI3M3HH39MgwYNqFSpEv369aN48eKcO3eOjRs3cvLkSYc5llKqRo0afPrpp4wZM4aSJUuSL18+mjVrRtu2bXnjjTfo06cP9erVY/fu3cyZM8ehxQPMD9GBgYFMmTIFf39/fH19qVOnTrLj9tq1a0fTpk15+eWXCQsLo0qVKixfvpxFixYxZMiQFJWTv1cBAQF8+umn9OjRg+rVq/Poo4+SN29ejh8/zi+//EL9+vXtid2kSZNo0KAB1atXp3///hQrVoywsDB++eUXh0Q5oyXOKzd27Fjatm1L69at2b59O0uWLEnyfmzVqhVFihShb9++vPDCC7i6ujJ9+nT7a0ztebjX/y0iTu3+F/ITkdtJLP96u9uJEycMwzCMbdu2GaGhoYafn5/h4+NjNG3a1KFcsWEYxpgxY4zatWsbgYGBhre3t1G2bFnjrbfespftvnjxojFw4ECjbNmyhq+vr5EjRw6jTp06xrx581Ic72effWYAhr+/v3Hz5k2Hx/7++2/jySefNEqUKGF4eXkZuXLlMpo2bWqsXLnyrvu9UzlybimPm3i+1q5da/Tv39/ImTOn4efnZzz++OPGpUuXkux38uTJRtmyZQ13d3cjf/78xrPPPpuk7LhhGMa6deuMli1bGv7+/oavr69RuXJlh9K/tyvvfbuyvsm9vuSeHxsba4wePdooVqyY4e7ubhQuXNgYOXKkQylpwzBLVN+p1Hxyx7v1/Pn4+BghISFG586djQULFiQpIW0Y916O3DAM49y5c8bAgQONwoULG+7u7kZQUJDRvHlzY9q0afZtEsuR365s/fbt242HH37YyJ07t+Hp6WkULVrU6Nq1q7Fq1Sr7Nonn/cKFCw7P/W85ZcMwjP379xuNGjUyvL29k5RlvtX48eMNwOE4/zVz5kyHsvfx8fHG//3f/xl58uQxfHx8jNDQUOPw4cNJypEnvq6GDRsanp6eRqFChYyxY8cakyZNMgDj7Nmz9u1ud61Tcy2OHDli9OzZ0wgKCjLc3d2NggULGm3btjUWLFiQ5Fz9dyqExOtza4nzs2fPGm3atDH8/f0NwP4+iYqKMoYNG2YEBwcb3t7eRv369Y2NGzcm+15atGiRUb58ecPNzc2hNPl/y5EbhlkG+/nnnzcKFChguLu7G6VKlTLef/99hzLbtzsniecwNSXo/1uO/NZzERoaauTIkcPw8vIySpQoYfTu3dv4888/Hbb766+/jE6dOhmBgYGGl5eXUaZMGePVV1+1P56a92tKX1Nyz42PjzdGjx5tvx5NmjQx/vrrr2TPx9atW406deoYHh4eRpEiRYwJEyYku8+UnIf0+N8i4qxshnGf249FRNLJzJkz6dOnD1u2bKFmzZpWhyNyT4YMGcLUqVO5fv16hhUfEQkJCaFJkybMnDnT6lBEMh2NcRIREbnPbt686bB86dIlZs+eTYMGDZQ0iYg4KY1xEhERuc/q1q1LkyZNKFeuHOfOneOLL74gIiLitvMtiYiI9ZQ4iYiI3GetW7dmwYIFTJs2DZvNRvXq1fniiy9o1KiR1aGJiMhtaIyTiIiIiIjIXWiMk4iIiIiIyF0ocRIREREREbmLbDfGKSEhgdOnT+Pv75+ukwyKiIiIiEjmYhgG165do0CBAri43LlNKdslTqdPn6Zw4cJWhyEiIiIiIk7ixIkTFCpU6I7bZLvEyd/fHzBPTkBAgMXRZB2xsbEsX76cVq1a4e7ubnU42Z6uh/PRNXE+uibORdfD+eiaOB9dk/QXERFB4cKF7TnCnWS7xCmxe15AQIASp3QUGxuLj48PAQEB+kV2AroezkfXxPnomjgXXQ/no2vifHRNMk5KhvCoOISIiIiIiMhdKHESERERERG5CyVOIiIiIiIid5HtxjiJiIiISPqKj48nNjbW6jCyvNjYWNzc3IiKiiI+Pt7qcDINd3d3XF1d73k/SpxEREREJM2uX7/OyZMnMQzD6lCyPMMwCAoK4sSJE5qPNBVsNhuFChXCz8/vnvajxElERERE0iQ+Pp6TJ0/i4+ND3rx59WE+gyUkJHD9+nX8/PzuOlmrmAzD4MKFC5w8eZJSpUrdU8uTEicRERERSZPY2FgMwyBv3rx4e3tbHU6Wl5CQQExMDF5eXkqcUiFv3ryEhYURGxt7T4mTzriIiIiI3BO1NIkzS6/3pxInERERERGRu1DiJCIiIiIichdKnERERERE0mDmzJkEBgbal0eNGkXVqlVTtQ+bzcbChQvTNa47CQkJYeLEiffteFmJikOIiIiIiKSD4cOHM3jwYKvDuKMtW7bg6+trdRiZkhInEREREcnWYmJi8PDwuOf9+Pn53fNcQRktb968VoeQaamrnoiIiIikC8MwiIyJs+SWmgl4mzRpwqBBgxgyZAh58uQhNDSUCRMmUKlSJXx9fSlcuDADBgzg+vXrDs+bOXMmRYoUwcfHh06dOnHp0iWHx//bVW/Lli20bNmSPHnykCNHDho3bsy2bdvSdG7DwsJwdXXlp59+onnz5vj4+FClShU2btzosN13331HhQoV8PT0JCQkhPHjxzs8fmtXPcMwGDVqFEWKFMHT05MCBQrw3HPP2beNjo5m+PDhFCxYEF9fX+rUqcOaNWvSFH9WoBYnEREREUkXN2PjKf/aMkuOvfeNUHw8Uv7RdtasWTz77LOsX78egCVLljBp0iSKFSvG33//zYABAxgxYgSffPIJAJs2baJv376MHTuWjh07snTpUl5//fU7HuPatWv06tWLjz76CMMwGD9+PK1bt+bQoUP4+/un6XWOGTOGcePGUaZMGV5++WW6d+/O4cOHcXNzY+vWrXTt2pVRo0bRrVs3NmzYwIABA8idOze9e/dOsq/vvvuODz74gG+//ZYKFSpw9uxZdu7caX980KBB7N27l2+//ZYCBQrwww8/8OCDD7J7925KlSqVpvgzMyVOIiIiIpLtlCpVivfee8++XKZMGfv9kJAQxowZwzPPPGNPnD788EMefPBBRowYAUDp0qXZsGEDS5cuve0xmjVr5rA8bdo0AgMDWbt2LW3btk1T3IMGDaJNmza4uLgwevRoKlSowOHDhylbtiwTJkygefPmvPrqq/YY9+7dy/vvv59s4nT8+HGCgoJo0aIF7u7uFClShNq1a9sfmzFjBsePH6dAgQKAOYZr6dKlzJgxg7fffjtN8WdmSpysFHEGwtZB/gqQv7zV0YiIiIjcE293V/a+EWrZsVOjRo0aDssrV65k7Nix7N+/n4iICOLi4oiKiiIyMhIfHx/27dtHp06dHJ5Tt27dOyZO586d45VXXmHNmjWcP3+e+Ph4IiMjOX78eKpivVWFChXs94ODgwE4f/48ZcuWZd++fXTo0MFh+/r16zNx4kTi4+NxdXU8R4888ggTJ06kePHiPPjgg7Ru3Zp27drh5ubG7t27iY+Pp3Tp0g7PiY6OJnfu3GmOPzNT4mSlVaNh5zfQYCjkv3NTr4iIiIizs9lsqeouZ6VbK8uFhYXRtm1bnn32Wd566y1y5crFunXr6Nu3LzExMfj4+KTpGL169eLSpUt8+OGHFC1aFE9PT+rWrUtMTEya43Z3d7fft9lsACQkJKRpX4ULF+bAgQOsXLmSFStWMGDAAN5//33Wrl3L9evXcXV1ZevWrUkSLmcvgJFRMsc7O6sqWt9MnI6ttzoSERERkWxr69atJCQkMH78eFxczNpp8+bNc9imXLlybNq0yWHdH3/8ccf9rl+/nk8++YTWrVsDcOLECS5evJiOkTsqV66cfczWrTGULl06SfKTyNvbm3bt2tGuXTsGDhxI2bJl2b17N9WqVSM+Pp7z58/TsGHDDIs5M1HiZKWQBubPU9sgJhI80vZthoiIiIikXcmSJYmNjeWjjz6iXbt2rF+/nilTpjhs89xzz1G/fn3GjRtHhw4dWLZs2R276YE5jmr27NnUrFmTiIgIXnjhBby9vTPsdQwbNoxatWrx5ptv0q1bNzZu3MjkyZPt47T+a+bMmcTHx1OnTh18fHz46quv8Pb2pmjRouTOnZvHH3+cnj17Mn78eKpVq8aFCxdYtWoVlStXpk2bNhn2OpyVypFbKWcIBBSEhFg4udnqaERERESypSpVqjBhwgTeffddKlasyJw5cxg7dqzDNg888ACfffYZH374IVWqVGH58uW88sord9zvF198wZUrV6hevTo9evTgueeeI1++fBn2OqpXr868efP49ttvqVixIq+99hpvvPFGsoUhAAIDA/nss8+oX78+lStXZuXKlfz000/2MUwzZsygZ8+eDBs2jDJlytCxY0e2bNlCkSJFMuw1ODObkZqi91lAREQEOXLk4OrVqwQEBFgdDnzXD3bPg0YjoNnLVkeTZrGxsSxevJjWrVs79L0Va+h6OB9dE+eja+JcdD2cT0quSVRUFEePHqVYsWJ4eXnd5wizn4SEBCIiIggICLB3KZS7u9P7NDW5gc641ULqmz/D1lkbh4iIiIiI3JYSJ6uF/DPY7tSfEHvT2lhERERExBJvv/02fn5+yd4eeughq8MTVBzCermKg18QXD8LJ/+EYqpaIiIiIpLdPPPMM3Tt2jXZxzKyoISknBInq9lsZne9v74zy5IrcRIRERHJdnLlykWuXLmsDkPuQF31nEFRjXMSEREREXFmSpycQeJ8Tie3QFy0tbGIiIiIiEgSSpycQZ7S4JsX4qLg1FaroxERERERkf9Q4uQMbLZbuuuttzYWERERERFJQomTs0jsrhf2u7VxiIiIiIhIEkqcnEVii9OJzRAXY20sIiIiIlmcYRj079+fXLlyYbPZCAwMZMiQIfbHQ0JCmDhxomXxifNROXJnkbcs+OSGyEtwejsUqWN1RCIiIiJZ1tKlS5k5cyZr1qyhePHidOnSxeHxLVu24Ovra1F04ozU4uQsXFygaD3z/jGVJRcRERHJSEeOHCE4OJh69eoRFBSEm5tje0LevHnx8fGxKDpxRkqcnEnRxHFOKhAhIiIimZBhQMwNa26GkeIwe/fuzeDBgzl+/Dg2m42QkJAk2/y3q57NZuPTTz/loYcewtvbm+LFi7NgwYJ0OGmSWairnjMJ+Wec0/E/ID4WXN2tjUdEREQkNWIj4e0C1hz7pdPgkbKudR9++CElSpRg2rRpbNmyBVdXVx555JG7Pu/VV1/lnXfe4cMPP2T27Nk8+uij7N69m3Llyt1r9JIJqMXJmeSrAF6BEHsDzuy0OhoRERGRLClHjhz4+/vj6upKUFAQefPmTdHzHnnkEZ566ilKly7Nm2++Sc2aNfnoo48yOFpxFmpxciYuLmZ1vQO/QNg6KFTT6ohEREREUs7dx2z5serYGaxu3bpJlnfs2JHhxxXnoMTJ2YT8kzgdWw8NhlgdjYiIiEjK2Wwp7i4nktmoq56zSZzP6dhGiI+zNhYRERERsfvjjz+SLGt8U/ahFidnE1QJPHNA9FU4uwsKVrc6IhEREREB5s+fT82aNWnQoAFz5sxh8+bNfPHFF1aHJfeJWpycjYsrFP2n/+wxlSUXERERcRajR4/m22+/pXLlynz55Zd88803lC9f3uqw5D5Ri5MzKlofDi4153OqN9jqaERERESynCFDhjBkyBD78po1axweDwsLS/KcAgUKsHz58owNTJyWWpyckX0+pw2QEG9tLCIiIiIiosTJKQVVAQ9/iLoK5/6yOhoRERERkWxPXfWckasbFHkADq8wu+sFV7E6IhEREZFszTAMq0MQi6nFyVkldtdTgQgREREREcspcXJWRRuYP4+th4QEa2MREREREcnmlDg5qwJVwd0Xbl6B83utjkZEREREJFtT4uSsXN2hSB3zvrrriYiIiIhYSomTMyv6zzinsHXWxiEiIiIiks0pcXJmIYnjnDaAKrmIiIiIiFhGiZMzK1Ad3Lwh8iJcOGB1NCIiIiIi2ZYSJ2fm5gGFa5n3w363NhYRERERkWxMiZOzC2lo/lSBCBERERERyyhxcnb2AhHrNc5JREREnJphGETGRlpyM1LxOSksLAybzZbk1qRJEwC+++47KlSogKenJyEhIYwfP97h+VeuXKFnz57kzJkTHx8fHnroIQ4dOmR/fObMmQQGBvLzzz9TpkwZfHx86NKlC5GRkcyaNYuQkBBy5szJc889R3x8fLqce8l4blYHIHdRsAa4esKN83DpMOQpZXVEIiIiIsm6GXeTOl/XseTYmx7bhI+7T4q2LVy4MGfOnLEvnz17lhYtWtCoUSO2bt1K165dGTVqFN26dWPDhg0MGDCA3Llz07t3bwB69+7NoUOH+PHHHwkICOD//u//aN26NXv37sXd3R2AyMhIJk2axLfffsu1a9d4+OGH6dSpE4GBgSxevJi///6bzp07U79+fbp165bu50PSnxInZ+fuBYVqwbF15jgnJU4iIiIi98TV1ZWgoCAAoqKi6NixI3Xr1mXUqFH06NGD5s2b8+qrrwJQunRp9u7dy/vvv++QMK1fv5569eoBMGfOHAoXLszChQt55JFHAIiNjeXTTz+lRIkSAHTp0oXZs2dz7tw5/Pz8KF++PE2bNmX16tVKnDIJJU6ZQUiDfxKn9VDzSaujEREREUmWt5s3mx7bZNmx0+LJJ5/k2rVrrFixAhcXF/bt20eHDh0ctqlfvz4TJ04kPj6effv24ebmRp06/7as5c6dmzJlyrBv3z77Oh8fH3vSBJA/f35CQkLw8/NzWHf+/Pk0xS33nxKnzCCkPqzFLBBhGGCzWR2RiIiISBI2my3F3eWcwZgxY1i2bBmbN2/G398/Xfed2GUvkc1mS3ZdQkJCuh5XMo6KQ2QGhWqBqwdcOwOX/7Y6GhEREZFM77vvvuONN95g3rx5Di1D5cqVY/16x2rG69evp3Tp0ri6ulKuXDni4uLYtOnflrVLly5x4MABypcvf9/il/tPiVNm4O5tFokACFtnbSwiIiIimdxff/1Fz549+b//+z8qVKjA2bNnOXv2LJcvX2bYsGGsWrWKN998k4MHDzJr1iwmT57M8OHDAShVqhQdOnSgX79+rFu3jp07d/LEE09QsGDBJF38JGtR4pRZJJYl13xOIiIiIvfkzz//JDIykjFjxhAcHGy/Pfzww1SvXp158+bx7bffUrFiRV577TXeeOMNe0U9gBkzZlCjRg3atm1L3bp1MQyDxYsXJ+mKJ1mLxjhlFiEN4Pdx/87npHFOIiIiImnSu3dvh0Tovzp37kznzp1v+3jOnDn58ssvU7X/UaNGMWrUKId1M2fOTEG04izU4pRZFK4NLm4QcRLCj1kdjYiIiIhItqLEKbPw8IUC1c37YequJyIiIiJyPylxykxC/hnnpAIRIiIiIiL3lRKnzCSkgfnzmBInEREREZH7SYlTZlK4DthcIfw4hJ+wOhoRERERkWxDiVNm4ukPBaqa91WWXERERETkvlHilNkkzucU9ru1cYiIiIiIZCNKnDKbkIbmT1XWExERERG5b5Q4ZTZFHgCbC1w5ChGnrY5GRERERCRbUOKU2XgFQFBl875anURERESc3qhRo6hatarVYcg9UuKUGaksuYiIiIjIfaXEKTMqqolwRURERETuJyVOmVHRuoANLh2Ga2etjkZEREQEAMMwSIiMtORmGEaK42zSpAmDBw9myJAh5MyZk/z58/PZZ59x48YN+vTpg7+/PyVLlmTJkiUAxMfH07dvX4oVK4a3tzdlypThww8/dNjnmjVrqF27Nr6+vgQGBlK/fn2OHTuW7PGPHDlC8eLFGTRoUKriFmu5WR2ApIF3TgiqCGd3m/M5VexsdUQiIiIiGDdvcqB6DUuOXWbbVmw+PineftasWYwYMYLNmzczd+5cnn32WX744Qc6derESy+9xAcffECPHj04fvw47u7uFCpUiPnz55M7d242bNhA//79CQ4OpmvXrsTFxdGxY0f69evHN998Q0xMDJs3b8ZmsyU57q5duwgNDaVv376MGTMmPU+BZDC1OGVWRf8Z56QCESIiIiKpVqVKFV555RVKlSrFyJEj8fLyIk+ePPTr149SpUrx2muvcenSJXbt2oW7uzujR4+mZs2aFCtWjMcff5w+ffowb948ACIiIrh69Spt27alRIkSlCtXjl69elGkSBGHY27YsIEmTZowfPhwJU2ZkFqcMquQ+rDpU41zEhEREadh8/amzLatlh07NSpXrmy/7+rqSu7cualUqZJ9Xf78+QE4f/48AB9//DHTp0/n+PHj3Lx5k5iYGHulvFy5ctG7d29CQ0Np2bIlLVq0oGvXrgQHB9v3d/z4cVq2bMlbb73FkCFD0vgqxUpqccqsEgtEXDwA1y9YG4uIiIgIYLPZcPHxseSWXLe4O3F3d08S+63rEveXkJDAt99+y/Dhw+nbty/Lly9nx44d9OnTh5iYGPv2M2bMYOPGjdSrV4+5c+dSunRp/vjjD/vjefPmpXbt2nzzzTdERESk5fSKxZQ4ZVY+uSBfBfP+MXXXExEREcko69evp169egwYMIBq1apRsmRJjhw5kmS7atWqMXLkSDZs2EDFihX5+uuv7Y95e3vz888/4+XlRWhoKNeuXbufL0HSgRKnzCzkn1YnJU4iIiIiGaZUqVL8+eefLFu2jIMHD/Lqq6+yZcsW++NHjx5l5MiRbNy4kWPHjrF8+XIOHTpEuXLlHPbj6+vLL7/8gpubGw899BDXr1+/3y9F7oESp8zMPp+TEicRERGRjPL000/z8MMP061bN+rUqcOlS5cYMGCA/XEfHx/2799P586dKV26NP3792fgwIE8/fTTSfbl5+fHkiVLMAyDNm3acOPGjfv5UuQeqDhEZpaYOJ3fAzcugW9ua+MRERERyQTWrFmTZF1YWFiSdbfOsTRjxgxmzJjh8PjYsWMBs5DEDz/8cNvjjRo1ilGjRtmX/fz8WL9eX3xnNk7R4vTxxx8TEhKCl5cXderUYfPmzSl63rfffovNZqNjx44ZG6Cz8ssLecua949vsDYWEREREZEszPLEae7cuQwdOpTXX3+dbdu2UaVKFUJDQ+2lH28nLCyM4cOH07Bhw/sUqZNSdz0RERERkQxneeI0YcIE+vXrR58+fShfvjxTpkzBx8eH6dOn3/Y58fHxPP7444wePZrixYvfx2idkL1AhOZzEhERERHJKJaOcYqJiWHr1q2MHDnSvs7FxYUWLVqwcePG2z7vjTfeIF++fPTt25fff//9jseIjo4mOjravpxYNz82NpbY2Nh7fAVOoGAd3AHj7F/ERVwA70BLwkg8l1ninGYBuh7OR9fE+eiaOBddD+eTkmsSGxuLYRgkJCSQkJBwv0LLthLHXCWec0mZhIQEDMMgNjYWV1dXh8dS8zfH0sTp4sWLxMfH22dmTpQ/f37279+f7HPWrVvHF198wY4dO1J0jLFjxzJ69Ogk65cvX46Pj0+qY3ZGzTyD8Y8+w7aFkzmbo7qlsaxYscLS44sjXQ/no2vifHRNnIuuh/O50zVxc3MjKCiI69evO0wGKxlLc0ClTkxMDDdv3uS3334jLi7O4bHIyMgU7ydTVdW7du0aPXr04LPPPiNPnjwpes7IkSMZOnSofTkiIoLChQvTqlUrAgICMirU+8qFlbD9S2rmjSahRWtLYoiNjWXFihW0bNkyyUzccv/pejgfXRPno2viXHQ9nE9KrklUVBQnTpzAz88PLy+v+xxh9mMYBteuXcPf3x+bzWZ1OJlGVFQU3t7eNGrUKMn7NLE3WkpYmjjlyZMHV1dXzp0757D+3LlzBAUFJdn+yJEjhIWF0a5dO/u6xGZKNzc3Dhw4QIkSJRye4+npiaenZ5J9ubu7Z50/zMUawfYvcT2+EVeLX1OWOq9ZgK6H89E1cT66Js5F18P53OmaxMfHY7PZcHFxwcXF8qHzWV7i597Ecy4p4+Ligs1mS/a9nJq/N5aecQ8PD2rUqMGqVavs6xISEli1ahV169ZNsn3ZsmXZvXs3O3bssN/at29P06ZN2bFjB4ULF76f4TuPxAIRZ3dB1FVrYxERERERyYIs76o3dOhQevXqRc2aNalduzYTJ07kxo0b9OnTB4CePXtSsGBBxo4di5eXFxUrVnR4fmBgIECS9dlKQAHIVRwu/w3H/4DSoVZHJCIiIiKSpVjextetWzfGjRvHa6+9RtWqVdmxYwdLly61F4w4fvw4Z86csTjKTMA+n5PKkouIiIg4i969e9OxY8f7drxRo0ZRtWrV+3a87MTyFieAQYMGMWjQoGQfW7NmzR2fO3PmzPQPKDMKaQDbZ8MxTYQrIiIikl0NHz6cwYMHWx1GlmR5i5Okk8QWp9M7IFolKkVERERSKiuVUvfz8yN37txWh5ElKXHKKgILQ2ARMOLh+CaroxEREZFsyDAMYqPjLbklTg6bEk2aNGHQoEEMGTKEPHnyEBoayoQJE6hUqRK+vr4ULlyYAQMGcP36dftzZs6cSWBgIMuWLaNcuXL4+fnx4IMPOgwpiY+PZ+jQoQQGBpI7d25GjBiR6riee+45RowYQa5cuQgKCmLUqFEO25w4cYKOHTvi5+dHQEAAXbt2dahQ/d+uemvWrKF27dr4+voSGBhI/fr1OXbsmP3xRYsWUb16dby8vChevDijR49OMtdRcmbOnInNZktyS4w3ISGBN954g0KFCuHp6UnVqlVZunSpwz52795Ns2bN8Pb2Jnfu3PTv39/hnCd2c3z77bfJnz8/gYGBvPHGG8TFxfHCCy+QK1cuChUqxIwZM1J8ju+FU3TVk3QS0hB2zIFj66BUC6ujERERkWwmLiaBaf9ba8mx+3/YGHdP1xRvP2vWLJ599lnWrzeHOSxZsoRJkyZRrFgx/v77bwYMGMCIESP45JNP7M+JjIxk3LhxzJ49GxcXF5544gmGDx/OnDlzABg/fjwzZ85k+vTplCtXjvHjx/PDDz/QrFmzVMU1dOhQNm3axMaNG+nduzf169enZcuWJCQk8Pjjj5MjRw7Wrl1LXFwcAwcOpFu3bskOb4mLi6Njx47069ePb775hpiYGDZv3myfA+r333+nZ8+eTJo0iYYNG3LkyBH69+8PwOuvv37HOLt168aDDz5oX16zZg09evSgfn2zF9SHH37I+PHjmTp1KtWqVWP69Om0b9+ePXv2UKpUKW7cuEFoaCh169Zly5YtnD9/nqeeeopBgwY5DMX59ddfKVSoEL/99hvr16+nb9++bNiwgUaNGrFp0ybmzp3L008/TcuWLSlUqFCKz3NaKHHKSorWNxOnMI1zEhEREbmTUqVK8d5779mXy5QpY78fEhLCmDFjeOaZZxwSp9jYWKZMmWKfN3TQoEG88cYb9scnTpzIyJEjefjhhwGYMmUKy5YtS1VclStXtictpUqVYvLkyaxatYqWLVuyatUq9u7dy5EjRyhatCgAX375JRUqVGDLli3UqlXLYV8RERFcvXqVtm3b2mMuV66c/fHRo0fz4osv0qtXLwCKFy/Om2++yYgRI+6aOHl7e+Pt7Q2Yc60OHDiQt99+m5YtWwIwbtw4/u///o9HH30UgHfffZfVq1czceJEPv74Y77++muioqL48ssv8fX1BWDy5Mm0a9eOd999114oLleuXEyaNAkXFxfKlCnDe++9R2RkJC+99BIAI0eO5J133mHdunX2Y2UUJU5ZSeJ8Tqe3QcwN8PC1Nh4RERHJVtw8XOj/YWPLjp0aNWrUcFheuXIlY8eOZf/+/URERBAXF0dUVBSRkZH4+PgA4OPjY09AAIKDgzl//jwAV69e5cyZM9SpU+ffmNzcqFmzZqq661WuXNlh+dZj7N+/n4IFCzrMXVq+fHkCAwPZt29fksQpV65c9O7dm9DQUFq2bEmLFi3o2rUrwcHBAOzcuZP169fz1ltv2Z8THx+f5HXfSWJi1qZNG1544QXATNhOnz5tb31KVL9+fXbu3AnAvn37qFKlij1pSnw8ISGBAwcO2BOnChUqOEz2mz9/fodpiFxdXcmdO7f9HGUkjXHKSgKLQkAhSIiDExrnJCIiIveXzWbD3dPVklti97OUuvUDe1hYGG3btqVy5cp89913bN26lY8//hhwLBzh7u6e5PWmJilKieSOkZCQkOb9zZgxg40bN1KvXj3mzp1L6dKl+eOPPwC4fv06o0ePZseOHfbb7t27OXToEF5eXnfdd3x8PN26dSMgIIBp06alOcY7Se58pPc5SiklTlmJzWaWJQd11xMRERFJoa1bt5KQkMD48eN54IEHKF26NKdPn07VPnLkyEFwcDCbNv375XVcXBxbt25NtzjLli3LqVOnOHHihH3d3r17CQ8Pp3z58rd9XrVq1Rg5ciQbNmygYsWKfP311wBUr16dAwcOULJkySS3W1t5buf5559n9+7dLFy40CHRCggIoECBAvbxY4nWr19vj7NcuXLs3LmTGzduODye2CXPGamrXlYTUh92fav5nERERERSqGTJksTGxvLRRx/Rrl071q9fz5QpU1K9n//973+88847lCpVirJlyzJhwgTCw8PTLc4WLVpQvnx5evTowcSJE4mLi2PAgAE0btyYmjVrJtn+6NGjTJs2jfbt21OgQAEOHDjAoUOH6NmzJwCvvfYabdu2pUiRInTp0gUXFxd27tzJX3/9xZgxY+4Yy4wZM/jkk0/44YcfsNlsnD17FjDLofv5+fHCCy/w+uuvU6JECapWrcqMGTPYsWOHvZDG448/zuuvv06vXr0YNWoUFy5cYPDgwfTo0cPeTc/ZqMUpq0mcz+nUVoi9aW0sIiIiIplAlSpVmDBhAu+++y4VK1Zkzpw5jB07NtX7GTZsGD169KBXr17UrVsXf39/OnXqlG5x2mw25syZQ2BgII0aNaJFixYUL16cuXPnJru9j48P+/fvp3PnzpQuXZr+/fszcOBAnn76aQBCQ0P5+eefWb58ObVq1eKBBx7ggw8+sBeeuJO1a9cSHx9P+/btCQ4Ott/GjRsHwHPPPcfQoUMZNmwYlSpVYunSpfz444+UKlXKHtuyZcu4fPkytWrVokuXLjRv3pzJkyen09lKfzYjvTtmOrmIiAhy5MjB1atXCQgIsDqc9GcYMKEcXDsDvX6CYo3uy2FjY2NZvHgxrVu3TtLvVO4/XQ/no2vifHRNnIuuh/NJyTWJiori6NGjFCtWLEVjYuTeJCQkEBERQUBAQIq60onpTu/T1OQGOuNZjcM4p3XWxiIiIiIikkUoccqKErvrqUCEiIiIiFM4fvy4ffxPcrfjx49bHaKDOXPm3DbWChUqWB2eJVQcIitKbHE6uQVio8BdTeciIiIiVipQoAA7duy44+POpH379g5zUt0qu3anVeKUFeUuCb754MZ5s0hESP27P0dEREREMoybmxslS5a0OowU8/f3x9/f3+ownIq66mVFNtu/yZLGOYmIiIiI3DMlTllVYne9Y0qcRERERETulRKnrKroP4nTiS0QF2NtLCIiIiIimZwSp6wqbxnwyQNxN+H0NqujERERERHJ1JQ4ZVU2GxStZ97XOCcRERERkXuixCkrC2lo/lTiJCIiIpJprFmzBpvNRnh4uNWhyC2UOGVliZX1TmyG+FhrYxERERGRFKlXrx5nzpwhR44cVocit1DilJXlLQfeOSH2BpzeYXU0IiIiIpICHh4eBAUFYbPZrA5FbqHEKStzcYGi/7Q6qSy5iIiIZDDDMIiNirLkZhhGiuNs0qQJzz33HCNGjCBXrlwEBQUxatQo++PHjx+nQ4cO+Pn5ERAQQNeuXTl37lyK9j1q1CiqVq3K7NmzCQkJIUeOHDz66KNcu3bNvk10dDTPPfcc+fLlw8vLiwYNGrBlyxb74//tqnfs2DHatWtH7ty5KViwIJUqVWLx4sX27f/66y8eeugh/Pz8yJ8/Pz169ODixYspPh+SMm5WByAZrGh92P+zOc6pwfNWRyMiIiJZWFx0NJN6dbHk2M/NWoC7l1eKt581axZDhw5l06ZNbNy4kd69e1O/fn2aN29uT5rWrl1LXFwcAwcOpFu3bqxZsyZF+z5y5AgLFy7k559/5sqVK3Tt2pV33nmHt956C4ARI0bw3XffMWvWLIoWLcp7771HaGgohw8fJleuXEn2N3DgQGJiYlizZg2GYXD8+HH8/PwACA8Pp1mzZjz11FN88MEH3Lx5k//7v/+ja9eu/Prrryk+H3J3SpyyusSJcI//AfFx4KpLLiIiIlK5cmVef/11AEqVKsXkyZNZtWoVALt37+bo0aMULlwYgC+//JIKFSqwZcsWatWqddd9JyQkMHPmTPz9/QHo0aMHq1at4q233uLGjRt8+umnzJw5k4ceegiAzz77jBUrVvDFF1/wwgsvJNnf8ePH6dy5M5UqVSIiIoLKlSvj4mJ2HJs8eTLVqlXj7bfftm8/ffp0ChcuzMGDBylduvQ9nCW5lT5FZ3X5K4BXDoi6Cmd3QsEaVkckIiIiWZSbpyfPzVpg2bFTo3Llyg7LwcHBnD9/nn379lG4cGF70gRQvnx5AgMD2bdvX4oSp5CQEHvSdOu+wWyNio2NpX79+vbH3d3dqV27Nvv27Ut2f8899xzPPvssy5cvp0GDBnTv3p2qVasCsHPnTlavXm1vgbrVkSNHlDilIyVOWZ2LKxSpBweXQNh6JU4iIiKSYWw2W6q6y1nJ3d3dYdlms5GQkOCU+37qqacIDQ3lp59+YsmSJdSuXZvx48czePBgrl+/Trt27Xj33XeTPC84ODjNx5SkVBwiO0gsS675nERERETuqFy5cpw4cYITJ07Y1+3du5fw8HDKly9/z/svUaIEHh4erF+/3r4uNjaWLVu23HH/hQsX5plnnmH27NkMHTqUzz77DIDq1auzZ88eQkJCKFmypMPN19f3nuOVfylxyg7s45w2QkK8tbGIiIiIOLEWLVpQqVIlHn/8cbZt28bmzZvp2bMnjRs3pmbNmve8f19fX5599lleeOEFli5dyt69e+nXrx+RkZH07ds32ecMGTKEZcuWcfToUXbu3MmaNWsoV64cYBaOuHz5Mt27d2fLli0cOXKEZcuW0adPH+Lj9bkvPSlxyg6CKoNnAERHwNndVkcjIiIi4rRsNhuLFi0iZ86cNGrUiBYtWlC8eHHmzp2bbsd455136Ny5Mz169KB69eocPnyYZcuWkTNnzmS3j4+PZ+DAgVSoUIEuXbpQqlQpPvnkEwAKFCjA+vXriY+Pp1WrVlSqVIkhQ4YQGBhoLyAh6UNjnLIDF1co8gAcWg7H1kOBqlZHJCIiImKZ5MqKL1y40H6/SJEiLFq0KE37HjVqlMOcUGC2GA0ZMsS+7OXlxaRJk5g0aVKy+2jSpInDvFQfffQRYFbri4iIICAgwCEpKlWqFN9//32a4pWUUxqaXSROhBu2/s7biYiIiIhIEkqcsouQhubPY+shnSrGiIiIiGQ3FSpUwM/PL9nbnDlzrA5PMpC66mUXwVXAww+iwuH8HgiqZHVEIiIiIpnO4sWLiY2NTfax/Pnz3+do5H5S4pRduLpB4TpwZJXZXU+Jk4iIiEiqFS1a1OoQxCLqqpedJM7ndEzzOYmIiEj6ubWQgYizSa/3pxKn7KToP/M5hWmck4iIiNw7V1dXAGJiYiyOROT2Et+fie/XtFJXveykQDVw94Gbl+HCfsh/77Nfi4iISPbl5uaGj48PFy5cwN3dXfMGZbCEhARiYmKIiorSuU6hhIQELly4gI+PD25u95b6KHHKTtw8oHBt+HuNWV1PiZOIiIjcA5vNRnBwMEePHuXYsWNWh5PlGYbBzZs38fb2xmazWR1OpuHi4kKRIkXu+ZwpccpuijYwE6ewdVC7n9XRiIiISCbn4eFBqVKl1F3vPoiNjeW3336jUaNGuLu7Wx1OpuHh4ZEuLXRKnLIbe4GI9WAYoG8rRERE5B65uLjg5eVldRhZnqurK3FxcXh5eSlxsoA6R2Y3BWuAmxfcuAAXD1odjYiIiIhIpqDEKbtx84RCtcz7YSpLLiIiIiKSEkqcsqOQf8qSH1tvbRwiIiIiIpmEEqfsqOg/45zC/hnnJCIiIiIid6TEKTsqVAtcPeH6Wbh0xOpoREREREScnhKn7MjdCwrVNO8f0zgnEREREZG7UeKUXd3aXU9ERERERO5IiVN29d/5nERERERE5LaUOGVXhWqDiztEnIIrYVZHIyIiIiLi1JQ4ZVcePuZkuKD5nERERERE7kKJU3Z2a3c9ERERERG5LSVO2ZkKRIiIiIiIpIgSp+yscB2wucLV43DlmNXRiIiIiIg4LSVO2ZmnHxSsbt5Xdz0RERERkdtS4pTdqbueiIiIiMhdKXHK7kIamD+PqbKeiIiIiMjtKHHK7grXAZuLOZfT1ZNWRyMiIiIi4pSUOGV3XgEQXMW8r+56IiIiIiLJUuIk6q4nIiIiInIXSpwEiv6TOKnFSUREREQkWUqcBIo8ANjg8hG4dtbqaEREREREnI4SJwHvQAiqZN4PU3c9EREREZH/UuIkppCG5k8lTiIiIiIiSShxElPIPxPhHtM4JxERERGR/1LiJKYidQEbXDwI189bHY2IiIiIiFNR4iQmn1yQv4J5X61OIiIiIiIOlDjJvxLnc9I4JxERERERB0qc5F9F/xnnpPmcREREREQcKHGSfyUmThf2wY1L1sYiIiIiIuJElDjJv3xzQ95y5n2NcxIRERERsVPiJI5UllxEREREJAklTuJIBSJERERERJJQ4iSOEsc5ndsDkZetjUVERERExEkocRJHfvkgT2nAgOMbrY5GRERERMQpKHGSpFSWXERERETEgRInSco+zul3a+MQEREREXESSpwkqcTE6exuuBluaSgiIiIiIs5AiZMk5R8EuUpgjnP6w+poREREREQsp8RJkmefz0llyUVERERElDhJ8kIamj9VIEJERERERImT3EZiZb0zOyAqwtJQRERERESspsRJkpejIOQMASMBTmyyOhoREREREUspcZLbK5pYllzjnEREREQke3OKxOnjjz8mJCQELy8v6tSpw+bNm2+77ffff0/NmjUJDAzE19eXqlWrMnv27PsYbTZiLxChcU4iIiIikr1ZnjjNnTuXoUOH8vrrr7Nt2zaqVKlCaGgo58+fT3b7XLly8fLLL7Nx40Z27dpFnz596NOnD8uWLbvPkWcDieOcTm2D6OvWxiIiIiIiYiE3qwOYMGEC/fr1o0+fPgBMmTKFX375henTp/Piiy8m2b5JkyYOy//73/+YNWsW69atIzQ0NMn20dHRREdH25cjIsxCB7GxscTGxqbjK8mC/ArglqMwtqsniAvbgFG86W03TTyXOqfOQdfD+eiaOB9dE+ei6+F8dE2cj65J+kvNubQZhmFkYCx3FBMTg4+PDwsWLKBjx4729b169SI8PJxFixbd8fmGYfDrr7/Svn17Fi5cSMuWLZNsM2rUKEaPHp1k/ddff42Pj889v4asrtqxqRS5vJ6D+duxr8AjVocjIiIiIpJuIiMjeeyxx7h69SoBAQF33NbSFqeLFy8SHx9P/vz5Hdbnz5+f/fv33/Z5V69epWDBgkRHR+Pq6sonn3ySbNIEMHLkSIYOHWpfjoiIoHDhwrRq1equJ0fAtuMK/LKeku7nKda69W23i42NZcWKFbRs2RJ3d/f7GKEkR9fD+eiaOB9dE+ei6+F8dE2cj65J+kvsjZYSlnfVSwt/f3927NjB9evXWbVqFUOHDqV48eJJuvEBeHp64unpmWS9u7u73nApUaIxAC6nt+FixILHnVvpdF6di66H89E1cT66Js5F18P56Jo4H12T9JOa82hp4pQnTx5cXV05d+6cw/pz584RFBR02+e5uLhQsmRJAKpWrcq+ffsYO3ZssomT3KOcIRBQECJOwcnNULyJ1RGJiIiIiNx3llbV8/DwoEaNGqxatcq+LiEhgVWrVlG3bt0U7ychIcGhAISkI5vt3+p6YSpLLiIiIiLZk+Vd9YYOHUqvXr2oWbMmtWvXZuLEidy4ccNeZa9nz54ULFiQsWPHAjB27Fhq1qxJiRIliI6OZvHixcyePZtPP/3UypeRtYXUh93zNJ+TiIiIiGRblidO3bp148KFC7z22mucPXuWqlWrsnTpUnvBiOPHj+Pi8m/D2I0bNxgwYAAnT57E29ubsmXL8tVXX9GtWzerXkLWV7SB+fPknxAbBe5e1sYjIiIiInKfWZ44AQwaNIhBgwYl+9iaNWsclseMGcOYMWPuQ1Ril7sE+AXB9bNwcgsUa2h1RCIiIiIi95WlY5wkk7DZzO56oO56IiIiIpItKXGSlLEXiFhnbRwiIiIiIhZQ4iQpE5I4zmkLxKmCoYiIiIhkL0qcJGXylAbfvBAXBae2Wh2NiIiIiMh9pcRJUkbzOYmIiIhINqbESVIusbveMY1zEhEREZHsRYmTpFxii9OJzRAfa20sIiIiIiL3kRInSbm8ZcEnN8RGwuntVkcjIiIiInLfKHGSlHNxgaL1zPthv1sbi4iIiIjIfaTESVKn6D/jnFQgQkRERESyESVOkjohieOcNkF8nLWxiIiIiIjcJ0qcJHXyVQCvQIi5Dmd2Wh2NiIiIiMh9ocRJUsfF5Zb5nDTOSURERESyByVOknqJ3fWOaZyTiIiIiGQPSpwk9RJbnI7/AQnx1sYiIiIiInIfKHGS1AuqBJ45IDoCzu6yOhoRERERkQynxElSz8UVitY176ssuYiIiIhkA0qcJG3sBSLWWRuHiIiIiMh9oMRJ0iaxQMTxDRrnJCIiIiJZnhInSZugKuDhD1FX4dweq6MREREREclQSpwkbVzdoMgD5n2VJRcRERGRLE6Jk6RdiMY5iYiIiEj2oMRJ0q5oA/PnsfVgJFgbi4iIiIhIBlLiJGlXoCq4+8LNK3Bhv9XRiIiIiIhkGCVOknau7lC4NgAuxzZYHIyIiIiISMZJU+J04sQJTp48aV/evHkzQ4YMYdq0aekWmGQSIWZ3PdtxJU4iIiIiknWlKXF67LHHWL16NQBnz56lZcuWbN68mZdffpk33ngjXQMUJ3dr4mQYFgcjIiIiIpIx0pQ4/fXXX9SubXbRmjdvHhUrVmTDhg3MmTOHmTNnpmd84uwKVAc3b2yRF/GPOm11NCIiIiIiGSJNiVNsbCyenp4ArFy5kvbt2wNQtmxZzpw5k37RifNz84DCtQDIfV0FIkREREQka0pT4lShQgWmTJnC77//zooVK3jwwQcBOH36NLlz507XACUTCGkIQN5reywOREREREQkY6QpcXr33XeZOnUqTZo0oXv37lSpUgWAH3/80d6FT7KRYo0BKHD1T1wX9IZrZ62NR0REREQknbml5UlNmjTh4sWLREREkDNnTvv6/v374+Pjk27BSSZRpA7xjf4P22/jcDnwMxz7HVqNgWo9wGazOjoRERERkXuWphanmzdvEh0dbU+ajh07xsSJEzlw4AD58uVL1wAlc0ho+AJry4wmIagKRF2FHwfDl+3h8t9WhyYiIiIics/SlDh16NCBL7/8EoDw8HDq1KnD+PHj6dixI59++mm6BiiZR4RPEeL7LDNbm9y84ehv8Ek9WD8J4uOsDk9EREREJM3SlDht27aNhg3NggALFiwgf/78HDt2jC+//JJJkyala4CSybi4Qb3BMGADFGsEcTdhxavweXM4u9vq6ERERERE0iRNiVNkZCT+/v4ALF++nIcffhgXFxceeOABjh07lq4BSiaVqzj0/BHaTwavHHBmB0xrAqvegNgoq6MTEREREUmVNCVOJUuWZOHChZw4cYJly5bRqlUrAM6fP09AQEC6BiiZmM0G1XvAwM1Qrj0kxMHv42FKfTi2weroRERERERSLE2J02uvvcbw4cMJCQmhdu3a1K1bFzBbn6pVq5auAUoW4B8E3WZDt6/ALwguHYYZD8HPz0NUhNXRiYiIiIjcVZoSpy5dunD8+HH+/PNPli1bZl/fvHlzPvjgg3QLTrKYcu1g4Cao3tNc/nM6fFwHDiyxNi4RERERkbtIU+IEEBQURLVq1Th9+jQnT54EoHbt2pQtWzbdgpMsyDsQ2n8EvX6CnMXg2mn45lGY3weuX7A6OhERERGRZKUpcUpISOCNN94gR44cFC1alKJFixIYGMibb75JQkJCescoWVGxRjBgI9T/H9hcYc/38HEt2PENGIbV0YmIiIiIOEhT4vTyyy8zefJk3nnnHbZv38727dt5++23+eijj3j11VfTO0bJqty9oeUb0O9XCKoEN6/Awmfgq4fhiqozioiIiIjzcEvLk2bNmsXnn39O+/bt7esqV65MwYIFGTBgAG+99Va6BSjZQIGq0G81bPgI1rwDR36FT+pC81ehdn9wcbU6QhERERHJ5tLU4nT58uVkxzKVLVuWy5cv33NQkg25ukPDofDsBihaH2JvwNIX4YtWcG6v1dGJiIiISDaXpsSpSpUqTJ48Ocn6yZMnU7ly5XsOSrKxPCWh18/QdiJ4BsCpP2FqI1j9NsRFWx2diIiIiGRTaeqq995779GmTRtWrlxpn8Np48aNnDhxgsWLF6drgJINubhAzT5QOhR+GQ4HfoG178KehdBhMhSubXWEIiIiIpLNpKnFqXHjxhw8eJBOnToRHh5OeHg4Dz/8MHv27GH27NnpHaNkVwEF4NE58MhM8M0LFw+YXfcWj4Doa1ZHJyIiIiLZSJpanAAKFCiQpAjEzp07+eKLL5g2bdo9ByYCgM0GFTpBscaw/BXYMQc2T4UDi6HtB1CqpdURioiIiEg2kOYJcEXuK59c0PET6PEDBBaBqydgThf4vj/cuGR1dCIiIiKSxSlxksylRDMY8AfUHQQ2F9g115w4d9d8TZwrIiIiIhlGiZNkPh6+EPoW9F0J+SpA5CX4/in4uitcPWl1dCIiIiKSBaVqjNPDDz98x8fDw8PvJRaR1ClUA/qvgfUfwm/vwaHl8HEdaDEKavY1q/OJiIiIiKSDVCVOOXLkuOvjPXv2vKeARFLFzQMavwDl28OPz8GJP2DxcNi9ANp/BHlLWx2hiIiIiGQBqUqcZsyYkVFxiNybvGWgzxL48wtYOcpMoKbUh0YjoP7/zARLRERERCSN1JdJsg4XF6jdzyweUaoVxMfA6jEwrQmc3Gp1dCIiIiKSiSlxkqwnsDA8Ng8e/hx8csP5PfBFC1j6EsTcsDo6EREREcmElDhJ1mSzQeVHYOAWqNwNjAT442P4pC4cWW11dCIiIiKSyShxkqzNNzc8PA0eXwA5CkP4MZjdERYOgMjLVkcnIiIiIpmEEifJHkq1hAEbofbTgA12zDFLl+/5QRPnioiIiMhdKXGS7MPTH1q/B32XQ54ycOM8zO8N3z4OEaetjk5EREREnJgSJ8l+CteGZ36Hxi+Cizsc+MVsffpzBiQkWB2diIiIiDghJU6SPbl5QtOR8PRvULAmREfAz0NgVju4eNjq6ERERETEyShxkuwtf3mz696D74C7DxxbZ06cu/NbqyMTERERESeixEnExRUeeNacOLd4E4iLgh+ehsUvQFyM1dGJiIiIiBNQ4iSSKGdReOIHc+wTwOZpMKstRJyxNi4RERERsZwSJ5FbubiYY5+6zwXPHHBiE0xrDMc2WB2ZiIiIiFhIiZNIcso8CP1XQ77ycP2cWTTijyma80lEREQkm1LiJHI7uUvAUyuhYhdIiIOl/wff94OYSKsjExEREZH7TImTyJ14+ELnz82qezZX2D0fvmgJl/+2OjIRERERuY+UOIncjc1mVt3r9RP45oNzf8G0JnBwudWRiYiIiMh9osRJJKVC6sPTa6FQLYi6Cl93hTXvQEKC1ZGJiIiISAZT4iSSGgEFoPdiqPUUYMCasfDNo3DzitWRiYiIiEgGUuJksbNXo7h6M9bqMCQ13DygzXjo+Cm4ecGhZTCtKZz9y+rIRERERCSDKHGy0Kgf91D3nVV8v+2k1aFIWlR9DPouh8AicOUofN4Cds23OioRERERyQBKnCwUktsHw4CFO05bHYqkVXAV6L8WSjSHuJvw/VOw5EWIVyuiiIiISFaixMlCbasUwNXFxs4T4Ry9eMPqcCStfHLB4/Oh4XBzedOnMKs9XDtnbVwiIiIikm6UOFkoj58nDUrmAWDRjlMWRyP3xMUVmr8Kj34NngFwfANMbQTHN1kdmYiIiIikAyVOFutYrQAAi3acxjAMi6ORe1a2DfRbDXnLwvWzMLMNbP4MdG1FREREMjUlThZrVT4Ib3dXjl68wa6TV60OR9JDnpLw1Coo3xESYmHxcFj4LMTetDoyEREREUkjJU4W8/V0o2X5/AAsVHe9rMPTDx6ZCa3GgM0Vdn4DX7SEK2FWRyYiIiIiaaDEyQkkdtf7aecZ4uITLI5G0o3NBvUGQ8+F4JMHzu6GqY3h8EqrIxMRERGRVFLi5AQalspLLl8PLl6PZsORS1aHI+mtWCN4+jcoWAOiwuGrLrD2fUhQkiwiIiKSWShxcgLuri60qRQMqLtelpWjIPRZAjX6AAasHgNzH4cojWsTERERyQycInH6+OOPCQkJwcvLizp16rB58+bbbvvZZ5/RsGFDcubMSc6cOWnRosUdt88sErvrLfvrLDdj4i2ORjKEmye0mwjtJ4OrJxxYDNOawrm9VkcmIiIiIndheeI0d+5chg4dyuuvv862bduoUqUKoaGhnD9/Ptnt16xZQ/fu3Vm9ejUbN26kcOHCtGrVilOnMndLTfUiOSmcy5sbMfGs2KeJU7O06j3gyaWQozBcPgKfN4e/vrM6KhERERG5A8sTpwkTJtCvXz/69OlD+fLlmTJlCj4+PkyfPj3Z7efMmcOAAQOoWrUqZcuW5fPPPychIYFVq1bd58jTl81mo0OVggAs2p65k0BJgYLVof9aKN4EYiNhwZOw7GWIj7M6MhERERFJhpuVB4+JiWHr1q2MHDnSvs7FxYUWLVqwcePGFO0jMjKS2NhYcuXKlezj0dHRREdH25cjIiIAiI2NJTY29h6iT39tKuZj8urDrD14gXPhN8jl62F1SCmWeC6d7Zw6NY8A6DYXl7Vv47rhQ9g4mYTT24nv9Dn45r2nXet6OB9dE+eja+JcdD2cj66J89E1SX+pOZc2wzCMDIzljk6fPk3BggXZsGEDdevWta8fMWIEa9euZdOmTXfdx4ABA1i2bBl79uzBy8sryeOjRo1i9OjRSdZ//fXX+Pj43NsLyADv73Ll5A0bjxSLp0GQZZdG7rPg8C1UP/YZbglR3HTPyZZig7niW9LqsERERESytMjISB577DGuXr1KQEDAHbe1tMXpXr3zzjt8++23rFmzJtmkCWDkyJEMHTrUvhwREWEfF3W3k2OFsznCGLv0IH/H5+bt1rWtDifFYmNjWbFiBS1btsTd3d3qcDKh1hgXu2Ms6IX3pUM0PDyWhNCxJFTrZc4HlUq6Hs5H18T56Jo4F10P56Nr4nx0TdJfYm+0lLA0ccqTJw+urq6cO+dYDOHcuXMEBQXd8bnjxo3jnXfeYeXKlVSuXPm223l6euLp6Zlkvbu7u1O+4TpWL8w7yw6y9Xg4Z6/FUjiX87WK3YmzntdMIbgC9F8NC5/Ftu8nXJcMx/XMDmgzDty907RLXQ/no2vifHRNnIuuh/PRNXE+uibpJzXn0dLiEB4eHtSoUcOhsENioYdbu+7913vvvcebb77J0qVLqVmz5v0I9b7JH+BFvRK5Afhx52mLo5H7ztMfus6GFqPA5gI7voLpoRB+3OrIRERERLI1y6vqDR06lM8++4xZs2axb98+nn32WW7cuEGfPn0A6Nmzp0PxiHfffZdXX32V6dOnExISwtmzZzl79izXr1+36iWkuw5Vzep6C7efwsIhaGIVmw0aPA89fgDvXHBmJ0xtDEd+tToyERERkWzL8sSpW7dujBs3jtdee42qVauyY8cOli5dSv78+QE4fvw4Z86csW//6aefEhMTQ5cuXQgODrbfxo0bZ9VLSHcPVgzCw82FQ+evs/dMyvtdShZTvAk8vRaCq8LNy/BVZ/h9AiiZFhEREbnvnKI4xKBBgxg0aFCyj61Zs8ZhOSwsLOMDsliAlzstyuVj8e6zLNpxmgoFclgdklglsAg8uQwWD4fts2HVaDi1FTp+Cl7OV9xEREREJKuyvMVJkpfYXW/RjlPEJ6iFIVtz94IOk6Hdh+DqAft/hs+awfn9VkcmIiIikm0ocXJSTcrkJcDLjXMR0Wz6+5LV4YgzqNEb+iyFgIJw6RB83hz2LrI6KhEREZFsQYmTk/J0c6VN5WAAFu44ZXE04jQK1YD+ayGkIcRch3k9YfmrEB9ndWQiIiIiWZoSJyeW2F1vye6zRMXGWxyNOA2/vNBjIdQbbC5vmARfdYIbFy0NS0RERCQrU+LkxGqH5KJADi+uRcexev95q8MRZ+LqBq3GwCMzwd0Xjv5mliw/tdXqyERERESyJCVOTszFxUb7xDmd1F1PklOhE/RbBblLQsRJmP4gbJ1ldVQiIiIiWY4SJyfXsVoBAFbvv8DVyFiLoxGnlK8c9PsVyrSB+Bj46TlcfxmCS0KM1ZGJiIiIZBlKnJxc2aAAygb5ExOfwJK/ztz9CZI9eeWAbl9Bs1cBGy47vqLBobfg2lmrIxMRERHJEpQ4ZQId1F1PUsLFBRoNhycWYHjnJGfkUVwXPQMJCVZHJiIiIpLpKXHKBNpXNbvrbTp6mTNXb1ocjTi9ki2I67WYOBcPXI6tg02fWh2RiIiISKanxCkTKBjoTe1iuTAM+HHHaavDkcwgdyn+KviYeX/laDi/z9p4RERERDI5JU6ZRMd/uuv9sF3d9SRljuVuSkKJFhAfDd/3hzgVixARERFJKyVOmUTrSkG4u9rYf/YaB85eszocyQxsNuLbTATvXHB2F6x91+qIRERERDItJU6ZRKCPB03K5ANUJEJSwT8I2n5g3l83AY5vsjYeERERkUxKiVMmkthd78cdp0lIMCyORjKNCh2hcjcwEuCHpyH6utURiYiIiGQ6Spwykebl8uHn6cap8Jv8eeyK1eFIZvLQexBQCK4cheWvWB2NiIiISKajxCkT8XJ35aGKQYC660kqeQdCx0/M+1tnwMHlloYjIiIiktkoccpkOlYzu+st3n2GmDhNbCqpULwxPDDAvP/jILhxydp4RERERDIRJU6ZzAPFc5PP35PwyFjWHrxgdTiS2TR/DfKUgevn4OchYGisnIiIiEhKKHHKZFxdbLSvUgBQdz1JA3dveHgauLjBvh9h11yrIxIRERHJFJQ4ZUKJ3fVW7j3HtahYi6ORTKdAVWj8onl/8QsQfsLScEREREQyAyVOmVCFAgGUyOtLdFwCy/acszocyYwaPA+FakF0BCx8FhI0Xk5ERETkTpQ4ZUI2m80+p9PC7equJ2ng6gadpoK7D4T9DpumWB2RiIiIiFNT4pRJdfgncdpw5CLnI6IsjkYypdwloNUY8/7KUXB+v6XhiIiIiDgzJU6ZVJHcPlQvEkiCAT/uPG11OJJZ1XwSSraE+Gj4vh/ExVgdkYiIiIhTUuKUiSUWiVi0Q4mTpJHNBh0mg3dOOLsL1r5rdUQiIiIiTkmJUybWplIwri42dp+6ypEL160ORzIr/yBo+4F5f90EOLHZ2nhEREREnJASp0wst58njUrlAWCRikTIvajQCSp3AyMBfngaYm5YHZGIiIiIU1HilMkldtdbuOM0hmFYHI1kag+9BwEF4fLfsPwVq6MRkdTQlAIiIhlOiVMm17J8fnw8XDl+OZLtJ8KtDkcyM+9A6PiJef/P6XBohaXhiEgKXDoC05rCpCpwbq/V0YiIZGlKnDI5Hw83QisEAequJ+mgeBOo86x5f9FAuHHJ0nBE5A72LISpjeH0Ngg/DrPawtm/rI5KRCTLUuKUBXSoWgCAn3edITZe3TXkHrV4HfKUgevn4JfnQV1ARZxLXAws+T+Y3wtirkGRehBcFSIvmcnTmV1WRygikiUpccoCGpTMQx4/Dy7diGHd4YtWhyOZnbs3PDwVXNxg7yLYNc/qiEQkUfgJmPEQbJpiLtcfAr1+gp6LoEB1uHkFZrWD0zusjFJEJEtS4pQFuLm60Lay2eq0UN31JD0UqAaNXzTvL34Brp60Nh4RMccdTm0Ip/4ErxzQ/VtoORpc3cwxij0XQqFaEBUOX7aHU1stDlhEJGtR4pRFJHbXW77nHDei4yyORrKEBs+bH8Kir8LCZ1W1S8QqCfGw6k2Y08VsUQquCk//BmUectzOKwc88T0UrgNRV+HLTnDyT0tCFhHJipQ4ZRFVCwdSNLcPN2PjWbH3nNXhSFbg6gadpoK7Dxz9DTZPtToikezn2jn4sgP8Ps5crvUU9F0OOUOS394rAJ74DorUNb/0+LKjJrUWEUknSpyyCJvNRoeqiXM6qbuepJPcJaDVm+b9Fa/D+f3WxiOSnYStN7vmhf0O7r7Q+QtoMx7cPO/8PE9/eHwBFK1vFo+Y3QmObbw/MYuIZGFKnLKQjv901/v90EUuXo+2OBrJMmr2hZItID4afuhvVvQSkYyTkADrPjCLPFw/B3nLQv/VUKlLyvfh6QePz4eQhhBzHb7qbCZiIiKSZkqcspDief2oXCgH8QkGv+w6Y3U4klXYbNB+MnjnhDM74bf3rI5IJOuKvAzfdoeVo8CIh8qPQr9fIW+Z1O/Lwxcem2fOzxZ7wxwjdfT39I5YRCTbUOKUxai7nmSIgGBo+4F5//fxcGKLtfGIZEWntpkT2h5cCq6e0O5D6DTFTIDSysPHrL5XohnERsKcR+DvNekWsohIdqLEKYtpVyUYFxtsPx7OsUs3rA5HspIKnaBSVzASzC57MXp/iaQLw4DNn8H0ULh63Cz88NQKqNHbbPG9V+7e8Og3ULIlxN2Er7vB4VX3vl8RkWxGiVMWk8/fi/ol8wCwaMdpi6ORLKf1+xBQEC7/DctfsToakcwv+hp81xcWD4f4GCjbFvqvheAq6Xscdy94dA6UCoW4KPimOxxamb7HEBHJ4pQ4ZUEdb+muZxiGxdFIluIdCB0/Me//Od2ckFNE0ub8PpjWFP76DlzcoNVb0O0r8/csI7h5QrfZUKa1Wezl2+5wcHnGHEtEJAtS4pQFhVYMwsvdhb8v3GD3qatWhyNZTfEmUOcZ8/6igeZgdhFJnZ3fwmfN4NIh8C8AvX+BeoPSp2venbh5wiOzzJat+Bj49jE4sCRjjykikkUoccqC/DzdaFEuPwALt6u7nmSAFqMgT2mzVPLPz5tjNETk7mKj4Mfn4IenzWINxZvCM79DkQfuXwxuHvDITCjfARJiYW4P2Pfz/Tu+iEgmpcQpi0rsrvfTrtPEJ+hDraQzd294eJrZvWjvQtg93+qIRJzf5b/hixawbRZggyYj4YnvwDfP/Y/F1d2cULfCw2byNL8X7F10/+MQEclElDhlUY1K5yXQx50L16LZcOSi1eFIVlSgGjT+P/P+L8Ph6klr45GMZRhw9DezItvYImYrRdg6tTam1L6fYGoTOLsbfHJDj++hyYvg4mpdTK7u8PBnUOkRSIiD+X1gzw/WxSMi4uSUOGVRHm4utKkUDKi7nmSgBkOhYE2IvgoLn4WEBKsjkvQWFwM7voEpDWFWO3OOoeirsO9HmNkGPq0Pf85QefrbiY+FZS/D3CfM81a4Djz9uzmvkjNwdYNOU82Jdo14WNAXdi+wOioREaekxCkL61jN7K63bM9ZomLjLY5GsqTED11u3mZrxOapVkck6SXyMvw2DiZWgoXPwLnd4O4DtfpBz0VQo4+5fH4P/DwEJpQzE4TLR62O3HlcPWUmlxsnm8t1B5lFIHIUtDau/3JxNatlVn3cTJ6+7we75lkdlYiI03GzOgDJODWK5KRgoDenwm+yct852lYuYHVIkhXlKQmt3jTnoVk5yvwmPW8Zq6OStLp4GP74BHZ8bU6WCuAfDLX7mxOy+uQy1xVvAi1eN7fb/BlcOWomCBs/hlKtoE5/KN4MXLLp93OHV5kJSOQl8MxhJibl2lod1e25uEL7yWBzge2zzeIVCfFQtbvVkYmIOI1s+h8te3BxsdGhqpksqbueZKhaT0GJ5ubEmt/3N7snSeZx6/ilyTXgzy/MpCmosjkG5n+7oOHQf5OmRN45oe5AGLwNHpsHJVsABhxaBl91ho9rwR9TICrCkpdliYR4WD3WfP2Rl8xz+PQa506aErm4QLtJZoJsJJjdb7d/ZXVUIiJOQ4lTFpfYXW/twfOER8ZYHI1kWTYbdPjY/CB9Zgesfc/qiCQl4mLM+YSm3jJ+CZs5QWrvX+Dp36ByV7N89Z24uEDpULNC3KCt5jxfHv5w6TAs/T+zG98vw+HCwfvysixz/QJ89TCsfQcwzASk7wrIVdzqyFLOxQXafAA1+wIGLBoEW2dZHZWIiFNQ4pTFlc7vT/ngAGLjDX7ZfcbqcCQrCwiGNhPM+7+PhxNbrI1Hbi/ysnmNJlYyu2SdTRy/9BQM+hO6fwMhDdI2GWuekvDQuzBsH7QeB3nKQMx12PKZ2QL1ZQfYv9hsmclKjv9hJqB/rzHPZadp0O5DcPeyOrLUc3GBNuPN7pkY8NNz8Od0q6MSEbGcEicLxUTFsW7BIaJuZGy3po7VzO56i9RdTzJaxYfN0sZGPPzQX5XWnM3Fw/DLMPigAqx6A66fNccvNX8dnt9jfljOUzJ9juXpD7X7wcBNZjGJMm3M8TN/r4Fvu8OkqrD+QzOJy8wMAzZ8BDNaw7Uz5sTQ/X6FKt2sjuze2Gzw0HtQ51lz+efnzbFsIiLZmBInCy3/fA87V55g9Vf7MTJwLpT2VQpis8HmsMucvBKZYccRAaD1++BfwJzsc/mrVkcjhgFHf4evH4XJNWHL5xAbaY696TTt9uOX0ovNZhaS6P41PLcD6v/P7NIZfhxWvAYTysOPg81Wr8zmZrhZZnz5K+aXBRW7QL/VkK+c1ZGlD5sNHhxrVgMEswDMJlXOFJHsS4mThWq3K4aLq42/t19gz2+nMuw4QTm8eKBYbgAW7VCrk2Qw75xmBTEwiwwcWmltPNlVXAzsnAtTG8GstnBwCWBA6Yeg18/m+KUq3e4+fik95SwKLd+AofvMCm5BlcwiFNu+hCkNYPpD5gSsmaG4yOkdMK0x7P8ZXD3M1rrOn4Onn9WRpS+bDVqNMRNegCUjzMqJIiLZkBInC+UrGkDdTiUAWDf/MBdPXs+wY9m76+04laGtWyIAlGhqFggAWDQw83fHykwSxy99WNnsLnl2lznPVq2nzMINj30LxRqmbfxSenH3huo9zIlg+yyFCg+Dixsc3wDze8PEyrD2fbh+3roYb8cwzAl/v2gFV8IgsAg8ucw8v1ae04xks0GL0dBwmLm87CVYP8namERELKDEyWJVmhemaKXcxMclsPzzv4iNzpgB0w9WDMbD1YWD566z78y1DDmGiIMWo8zxHtfPwi9DzQ+cknEuHXEcv3TtDPgFQfPXYOje9B2/lF5sNihaFx6ZAUN2Q6MR4JsXrp2G1WPM1/J9fzi51epITTE3zGIaPw+B+Giz9e7p36Bgdasjy3g2GzR71bxGACtehXUfWBuTiMh9psTJYjabjeY9y+Gbw4MrZyP5fW7GlOvN4e1Os7L5ALPVSSTDuXtDp6lmS8KeH2D3AqsjynoMA8LWwTfd4aMat4xfqmSe+yG7zVaCjBq/lJ4CCkCzl80iFQ9/BgVrQnwM7JoLnzeDaU3N0ulx0dbEd+EAfNbMjMfmarbAPPq12TU1u7DZzGvUZKS5vHIU/Pa+pSGJiNxPSpycgLe/By2frAA22LfhDAc3n82Q4yR21/tx52kSEvTtv9wHBav/+w31L8Pg6klr48kqEscvTWsMM9vAgcX8O37pJ7MLXJVH7+/4pfTi5mnOHdVv1T/V6bqbY4hObzNbeyaUh1VvwtX7+AXQ7gVm4nZhv9mK1+snaDDELNudHTV5EZq+Yt7/dQysedfaeERE7pNs+lff+RQsk5OarUMAWPP1AcLPp3/1uyZl8uHv5caZq1FsOqoxJ3KfNBwGBWtA9FVYOAASEqyOKPOKvAy/T/h3/NKZneb4pZp9zfmXHvsWijXKOmNtCtaATlPg+b1mN7GAghB5EX4fZ85BNa8XhK3PuG6gcdHw81D4ri/E3jDP7TO/Q0j9jDleZtL4BbOMPcCat2H12+qOKyJZnhInJ1KrdQjBJXMQGxXPii/2EB+Xvh8wvdxdaV0xGFB3PbmPXN3Mstdu3nB0LWyeZnVEmc+lI/DL8H/GL43+d/xSs1fN8UttJ0CeUlZHmXH88kKj4Wbp9K5fQtEGZvnvvQthZmuY0hC2zoKYdPzC6UqYWQDizy/M5UYvQI+F4Jcv/Y6R2TUcalZJBFj7rtn6pORJRLIwJU5OxMXVhZZPVsDTx43zx67xx8Ij6X6MDv9011u8+wzRcRlTiEIkiTwlodWb5v2Vr5vjReTODMNsTfnmsX/GL31mjl/Kf8v4pUbDM8f4pfTi6gblO0CfX+CZ9VC9l5mQn9sNPz0HE8qZcypdCbu34xxYYpZxP7MDvHPB499Bs1fAxTU9XkXWUv9/EPq2ef/3cea4JyVPIpJFKXFyMv65vGjW05w8ccfKE4Ttvpiu+3+gWG6CAryIiIpj9f4L6bpvkTuq9RSUaA5xUWaltMwwV48V4mNh17x/xi+1hgO/YI5fetAcW/NMJh6/lJ6CKkL7STBsnznPUGBRiAqHDR/Bh1XNghlHfk3dh/j4OHNS3m8ehairUKiWeb5LtcioV5E11B0ID/4zzmn9RLPinpInEcmClDg5oeJV81KpaSEAVs3ax43w9Ksi5eJio0PVf+d0ErlvbDbo8DF4BZrf5K99z+qInMvNK2Z554mV4ft+t4xfevKf8Utzs9b4pfTinRPqDYbntkP3uVCiGWCYBTNmd4KPa8OmaRB9l2kYIs7ArHaw/kNzuc6z0Hsx5CiU4S8hS3jgGWg9zry/4SNzriclTyKSxShxclL1Hi5BnsJ+RF2PZcWMPelaBa9D1YIArNp3nqs39a2/3EcBweZ4HDAnaT35p7XxOINLR2DxC2a1uJWjzDmM/PKbXcOe3wNtP8ja45fSi4srlHkQevxgJpq1nwYPf7h4EJa8AOPLweIRcPFQkqfawn6DqQ3NCXg9/OGRWfDQO2rVS63a/aDNP7/ff3wCS/5PyZOIZClKnJyUm7srrfpWwM3TlVMHwtm2NCzd9l0u2J/S+f2IiU9g6V9n0m2/IilSsTNU7GIO7v++vzmpaHbz3/FLm6f9M36pInSc8s/4pRfAN7fVkWZOeUpB6/fMwhmtx5kTMcdcg81TYXJNsyXqwFJIiKP02UW4ft0Fblwwz//Ta6FCR6tfQeZVqy+0mwTYzPO9eLgqaYpIlqHEyYnlDPKlcffSAGz+6SinD4Wny35tNpu91Wnh9tPpsk+RVGkzDvwLwOUj5piSbMJmxGH7awFMa+I4fqlUKPT8EZ5ZB1W7m3MZyb3zCjBbQQZuNluiyrQGbObYp2+64TahFOXOfIfNSIBqPeCplZC7hNVRZ341ekGHyYDNnJT5l6FKnkQkS1Di5OTKPhBM6Tr5MQxYMX0PUTfSp2td4jinP45e4uzVqHTZp0iKeeeEjh+b97d8DodXWhtPRroZDgeX47L6TVruGYbbomfMMV5uXlCjDwzcAo/Pg+KNNX4po9hs5tin7t/A/3aYY6K8ArFFXyPO5kFc24/MD/ru3lZHmnVUewI6fgrYYOsM+Pl/Sp5EJNNzszoAubvG3ctw7u8Irl64ya9f7uOhZyphu8cPWIVy+lArJCdbwq7w485T9G+kb1nlPivRzByHsnkqLBwIAzZmjdLa4Sfg+B9wfKP58/xewMAV8AYM33zY6vSHGk+qK54VcoaYVfiavETc/sWs2XeRxlW6Wx1V1lS1uzn27IenYduXkBAP7T9SWXcRybTU4pQJeHi5EdqvIi6uNo7uvMhfa9OnGp6664nlWoyC3KXg+lmzO09mG0iekADn9pitZt89BRMqwMSK8P1T5sSp5/cABuQqQULlx/iz6DPEDdqu8UvOwMMHo1wHbngFWx1J1la5Kzz8GdhcYcccWDjATKBERDIhtThlEnmL+FPv4ZKsm3+IdQsOEVQiB3kL+9/TPttUCmbUj3vYeyaCQ+euUSr/ve1PJNU8fODhqfB5S9jzA5RpA5UfsTqq24uNgtPb/m1NOr4Joq86bmNzheAqUKQuFHnAvPnlIz42llOLF1NF45cku6nUxWxlWtAXdn1rFobpOMWc0FhEsqcLB81pN5z5f34y9FcrE6ncrBAn918mbPclln++h0dG1sTDK+2XMKevB03K5GXlvvMs3HGKF0LLpmO0IilUsAY0HgFrxsLiYVC0HuQoaHVUpsjLcGLTv4nS6e0QH+O4jYefOVFqYqJUqCZ4+FoTr4izqtAJbC6w4EnYPR+MBOg0TcmTSHYTHwsbJsGafybNDq4CeUtbG1Mq6C9WJmKz2WjWqxxzx2wh/Fwkv889SPNe5e9pnx2qFmTlvvMs2nGa4a3K3PPYKZE0aTgMDi4zW3MWDYAnfgCX+9yT2DAg/Ljj+KQL+5Ju55f/nyTpn0Qpf0V9+BNJifIdzDmy5veGv74zu+x1/hxc3a2OTETuhzO7YNFAOLvLXC7ZMtN90aj/9pmMt58HLZ8sz6IPtrN/41kKlc1FmTpBad5fi3L58fVw5eSVm2w9doWaIVlgcL5kPq7u8PA0mNIQ/l4DWz6DOk9n7DET4s3xSbcmSteSGe+Xp/Q/Xe7+SZZyhqj6nUhalWsL3WbDvJ6wd6HZba/zdE02LJKVxUXDb+/Dug8gIQ68AuGhd6Fyt0z3/1SJUyZUsHROarYOYcsvYaz9+gD5QwIIzO+Tpn15e7gSWjGI77edYuGOU0qcxDp5SkGrN80JM1e8BsWbpm/zfUwknNr6b6J0YrM5KeqtXNygQLV/E6XCdcA3T/rFICJQ5iHoNgfmPg77fjJboB6ZCWSuD1AikgIn/zRbmS7sN5fLtTcnJvfPb21caaTEKZOq2TqEUwfDOX0onOVf7KHzCzVwdU9b16ZO1Qry/bZT/LzrDK+1rYCHm4otikVqPQUHFpsTlH7fz5yQNK3deG5cghP/JEnHNppzJyXEOW7jGQCFa/+bKBWobhasEJGMVboVPPoNfPuYORH0vB7Q6QuroxKR9BITCavfgj8+Mcc0+uY1E6YKHa2O7J4occqkXFxdaPlkeb4ds5kLx6+xceERGjxSKk37qlciD3n9PblwLZrfDl6gRfnM+S2AZAE2G3T4GD6payY6v70PTV+6+/MMA64cdex2d/Fg0u38g80EqWg9M1nKV15zyohYpVQLeOxb+KY7HFyK64JeuPg9anVUInKvwtbBj4Ph8t/mcuVu8OA7WWKuRiVOFjIMg3OR5wjyTdsYJb+cXjTvVZ7Fn+xi56oTFCqTk5DKqe9W5Opio13lAkxff5SFO04pcRJrBRSANuPhu77w2zgoFQqFajhuEx8H53Y7JkrXzyXdV95yt4xPegACi2S6/tQiWVqJZvDYXPj6UVyOrKS2/zm42Rjc81kdmYikVvQ1WPG6OY8hQEBBaDvRbGHOIpQ4WWjZsWW8/PvLPFPlGXpX7I27S+q7JBWrnIfKzQqx69eTrJq1j26v1MYvZ+rnielYzUycVu47x/XoOPw89dYQC1XqYnbZ++s7+KE/PLnMsZDDyS0Qc93xOa4eZlc7+/ik2lni2y2RLK94E3h8PsbXXcl/bTfGpMpQpRvUeQbylbM6OhFJicMr4achcPWEuVyjN7R8A7xyWBlVutOnYwv9fvJ3YhJimLR9EsvCljG6/mgq5K6Q6v3U61SS04fCuXjiOium76HD89VwcUndt+qVCuageB5f/r54g2V/naVzjUKper5hGKnaXuSuWo8zxyZdOgzvl0j6uFcOKPzALeOTqoG71/2PU0TuXbGGxD+2gBvzniHHzeOwdaZ5K94E6jwLpVrd/ykKROTuIi/Dspdh59fmcmBRaP8RFG9sbVwZRImThcbUH8MDwQ/w7pZ3OXDlAI//8jg9y/fk2arP4u3mneL9uLq7EPpURea9vYXTh8LZuiSMWm2KpSoWm81Gh6oF+WDlQRbuOHXHxMmIiSHq0CGi9u4las8eovbsJfrwYQoWKUJ83bq451MXC0kHPrmg48fwVWdzYGmOwv8kSf8kSnnL6YOUSBZiFKrNmjJv0qZiIG5/fma2Ov+9xrzlLGZOUVD1cfAKsDpUEQGzKuYvw/7pKm+DB56FZq9kurmZUkOJk4VsNhvtSrSjXoF6vLv5XZaELWHGnhmsOr6KUfVGUSuoVor3FZjfh8bdS7Ny5j62/HyUgqUDKVAqZ6ri6VC1AB+sPMj6wxc5fy2KfP5eJERHE33w0D8J0h6i9u4l+uBBjNjYJM/3PXCAk48/QeGpU/AsXjxVxxZJVolmMHgruLhDYGGroxGRjGazYRStDyWbwJVj5pxu2740i78sfRF+fQuqPQ61+0PuZFqiRSTjXb8AS16APT+Yy3lKQ/vJUKSOtXHdB5Z/Xfvxxx8TEhKCl5cXderUYfPmzbfdds+ePXTu3JmQkBBsNhsTJ068f4FmoNzeuXmv8Xt81Owj8vnk4/i14zy57ElGbxzNtf/OM3MHZR4IpswDQRgGrJi+l6jrSZObOyni50o798s8dGQ9+57/P/7u9DAHatQk7JFHODtqFOHz5xO1Zw9GbCwuOXLgU/cBcj/Vl4IfTKDAF58TkysXsSdOEPZod2788UdqT4NI8nIVV9Ikkh3lLAqtxsDze82CMXlKm3OvbZoCH9WAr7uZUxeoq7jI/WEYsGs+fFzbTJpsrtBgKDz9e7ZImsDiFqe5c+cydOhQpkyZQp06dZg4cSKhoaEcOHCAfMl094qMjKR48eI88sgjPP/88xZEnLGaFG5Cjfw1mLh1IvMOzmPBwQX8duI3XnngFZoWaZqifTR6tDRn/77K1fM3WfXlPlo/WwlbMlXEEiIjidp/wN6KFLVnD9FHjjAgPt6+TfQ/P10DA/GqUMG8lS+PV8UKuBcs6LDf2NhYTgwcQIUffyJq506OP9WPoNdfI+cjj9zTORERkWzO08+c463Gk/D3ajNxOrQcDi41b3nLmt34KnfL0l2ERCwVcRp+ft78nQPIXwk6TIYCVS0N636zNHGaMGEC/fr1o0+fPgBMmTKFX375henTp/Piiy8m2b5WrVrUqmV2X0vu8azA38OfV+u+yoPFHmT0xtEcizjGc6ufIzQklBdrv0ge7zuXG/fwciP0qYoseO9PwnZdZPeak1SsnYuoffvsCdLNPXuI+fsoJCQkeb4tVy62uOfjUI6C9Oz1IEXrVsctODjZ5CvR+bC/2bFiMZeuRBA0bSqX33iTiF9+4eyrrxETFka+YcOwaSyKiIjcCxcXKNncvF08DJunwo6v4cJ+8wPdytFQvSfU7mdOPSAi984wzO6yy1+B6Aiz63zj/4MGQ9I+QX0mZlniFBMTw9atWxk5cqR9nYuLCy1atGDjxo3pdpzo6Giio6PtyxEREYDZQhKbzDgdZ1E1d1W+efAbpv01jdn7ZrMsbBl/nP6DYdWH0aZYm9smMgnXr+N5cj+Vgy+y40Ru1n27j6gR4/C/diLJtq558+JZvhye5crhVb48nuXK4Zo/P2/P3sZvhy6Rw684z+XNS1xcXJLnGgkJhO3cxvYlP3Jy7277+vlvn6X5MwPJVbQIlz/5lMtfTCc67Bj5334LFx+f9DtBckeJ721nfo9nN7omzkfXxLmk6nrkKAot34aGL+Ky62tctnyOLTwMNkzC2DgZo0wbEmr1xyj8gOZuuwf6HXE+9/WahB/DdfFQXI6uBSChQHXi204yW3kTgISs8b5Izbm0GRbVkT59+jQFCxZkw4YN1K1b175+xIgRrF27lk2bNt3x+SEhIQwZMoQhQ4bccbtRo0YxevToJOu//vprfDLJB/nTcaf54eYPnIk/A0BJt5J08O5A7hgvPE+dwuvUKTxPncbr1Ck8Ll4EwAB2V3yai3kq4x15jqqHPiU+OA9RBQsSXbAQUQULEB+QfGWiPy/YmH3YlTyeBq9Ui3f4n5MQF8e1sEOE7/+L2Ihwc6XNhm/BItw8d4aE2Biw2QgsV5mi8a4Ef/c9LvHxRBUsyKnevW57TBERkTQzEgiK2EHx88vJe32vfXW4d1H+ztuKUznrkODiYWGAIpmIkUCxiyspf3oebgkxxNvc2RfchSP5QsGW9XoQRUZG8thjj3H16lUC7vI5NcsnTsm1OBUuXJiLFy/e9eQ4k6jLF1i6/BP2bPiRImfiKHEO8l1J/tK5BQfjWb48lK7IqrDSRN6EUrXz0bRHmRQd60Z0HHXfXcPN2ATm969N1cKB3Ai/wu6VS9i1ahlR18xWOw9vHyo2bUmV0LZ4BeRgyY+LcD15hCNbzMIQOfIFUb9RC1wmTCLhyhVc8+WjwMeT8SxbNn1OitxWbGwsK1asoGXLlri7Z7+mdGeka+J8dE2cS7pdj/P7cN0yDdtf87HFRQFg+OQhoXovEqr3Af+gdIo4C4v5//buO76N+v7j+OtOW/Lee8Z2HGfvxd6QsOcPKN2lQAuFUvYos6WsFlooHXSwoeyEESCQvXfi2I4d771tyZp3vz/Olq3YiTPs2Em+z8fjIul0kr7OWda99f1+P2dHqtqIUraKisJtJOdOQw5JgKBY1KBYCIrVThlxHB5Ej3bD/nerqQjdp7chV2rH4UrKXHwXPK8VajpOtbe3ExUVdVDBacSG6kVFRaHT6airqwtYX1dXR1zc0P1RM5lMmEymfusNBsOo/aD0Njf7z4/UUwbcU13NeGD8Ptu2RBiJmDyDqEkzuws4jEMf3luG/JyiVj58dhNF6+pJGRfJ2Nnxg75+mMHA2XlxfLSlms9WbKPJvp3dK77F1z1kLyQ6lmnnX8j4087CaNF67TweD3qLlfNvvYvybZv46h8v0VZfy+L3XiP3sgtIX7oaX3ExlTd8n8Snnyb49IMrdiEcmdH8e36iEvtk9BH7ZHQ54v2ROBESX4SzH9FOorv+70jtVehWPINu1R8h7xLtpLpJ04aszcc8RzOUr4ayVdplzVZQtM/8TICGL/s/RtaDLQaCYyEorv9lUGz39dgTci7McBvyv1s+L6x+AZY+CT4XGIPgrN8iT/sh8nE+T/1Q/h9HLDgZjUamTZvG119/zcUXXwyAoih8/fXX3HLLLSPVrKPO29CAc9cuunqC0q5deGtqBtzWkJqizUUaN451oU082/E+9fouDPImfjJxGj8ePxP9Pn+cErLCmLEgnXWf7OW7NwuJSw8lLPbAQxRVVeX0oBbU2k+x7a1gZ/f6+OyxTF9wCWOmz0bW6fb7+Mxps0jKncCKt/7Nli8Xk79+NWXxIUyInELEus1U3nwzMXf9hogbbjhg0QlBEARBOGzWCDjpdpj7S9j9Cax5GSrWwPZ3tSVpBsy6EcZddOId2LdWBAalht39twlJQkmeRXGDk8y4YGR7g3ai045acDRqwaqjWlsGY43cT7iK0XoAg2K1S1EVcWTU7oCPboaaLdrtzDNg4fOiyMoARrSq3u23384NN9zA9OnTmTlzJs8//zx2u91fZe973/seiYmJPPnkk4BWUGLXrl3+61VVVWzZsoWgoCDGjBkzYj/H4ap56GFa3367/x2ShDEtTSv93VMCfFwuuj7dhxcA0+zf57E1j/Fd5Xf8Zctf+LL0Sx6Z+wgToicEPN2089Ko3N1CdVErX/x9B5f/Zjo6Q/9vD7xuN/krv2Xjpx/SVFlOKqAgEZk3jXOvvoqE7NyD/tlMVitn/PDn5M4/lS//+gJNleWsBRLmTiFn/Xbqf/d73KWlxN13H5L4plcQBEEYLjq91suUdwlUb9YC1I7/QeV6bfnyfpjxI5j2A7AduHLtMUlVoaEAyldB2WotKLX1LxhFVA6kzoGUudplWAo+j4ddixeTdv75yH0/q30e6KyHzlroqNMuO+u1UNUTrjrrtEXxgqNJW+p39n/dvozB+4Sr2N5Q1ffSEi6KfgwFrxuWPwPLn9b2kzkUzv0dTLpG/P/ux4gGp6uuuoqGhgYefPBBamtrmTx5Mp9//jmxsbEAlJeXB3QPVldXM2XKFP/tp59+mqeffppTTjmFb7/99mg3/4gZU5K1kJSRgTlvHJbukGTKzUUXFDTo4+Nscbxw+gt8Xvo5v1v3O/a07uG6z67j2txruWXyLVgNWs+SLEuc9cM83n5sHY0Vnaz6YA8nXZntfx5Hextbv1zMli8X4WhrBcBgttCZOpU37WmcmZPLDw8hNPWVkJ3L9b//I+s+eo+1779Ntb2d+gkZZJXXkfbW23jKK0h8/rmAUCgIgiAIwyJhClz6VzjrEdj4Kqz/B3TUwDePwXd/gIlXaMP44vYdGH8M8XmgZltgUOpqDtxG0kH8JEidCylztMUWefCvoTNAaKK2HIiiaK/dUbv/cNVz6XFoJzhu6oCmPYO8vqk7VMX0D1V9L61RWnAW+qvaCB/dAvXdxVTGLtBONC3mAB7QiP823XLLLfsdmrdvGEpLS2OEalkMi7ArryT86quRbYffNS1JEueln8fs+Nk8tf4pPi35lP/u+i/flH/Dw3MfZnb8bACCwk2c8f1cFv15G9u+qSQpJ5yQSCcbF39I/rKleD1uAIIjo5l63kImnHEOOxrcvPLSKr7YWYvD7cVqPLxfF53ewJzLriF79nyWvPICVbt3kZ8YRU1kCOM3bcBzzf+R/NeXMSYlHfb/gyAIgiActOBYOPVumP8r2PkBrHlJG6a0+TVtSZ0Ps2+EnPNB3v/Q9FHBbdd6zspWa2GpcoMWQvrSWyBpem9QSpqhnVh4uMmy1otni6L/LO0+VBVcHX16sfYTrjrroKtFm4PTVq4tByLJWngKjoWQREiYCskzIHE6mE/QL2w9XbD0CVj9IqiK9v9z/h+0HlnRyzSoEQ9OJzJdcPCQPVe4OZwnT3qS89PP55E1j1DVWcVPvvwJl4y5hDum30GoKZS0CVFMPD2JzV+s4uNnP8TrLPE/Pi4zi2kLLiFr5lx0eu3XYmqKlZQIK+XNDpbsquOiyYN8szSIyMRkrnrod2z7+guWvf4qrcDK7CQy6ltwX3kVqS++iHXqlMGeRhAEQRCGht4Ek66GiVdBxTpY+xLs+hjKVmhLWArM/ClMuR4sYSPdWs0BCjn4WcK7e5Jma0Pv4ieBfhSXY5ckLciYQyBqkKkXXld3mKobIGT1GT5or9eCgb1eW2q3Q+HnPS8IsXmQPBOSZ2lBMiLj+A8OZau0XqbmYu32hCvg3N8fWm/jCU4Ep+PMSUkn8eFFH/LHTX/krd1v8cGeD1hetZy7p91FUqWBkvUf4Oks7d5aYsyMWUxbcAmJOeP6FWqQJImLJifwwjd7+GhL9REHJwBJlpl01nlkTpvJN6/+laJ1qyiODafG5WbiTT9j/P0PEbrggiN+HUEQBEE4aJIEKbO0pa1SG8K38V/QWq7NgVr6hDbvY9aNEJ096NMNqYMs5KDNT5qj9SpF5Wi9PccjvUkLtIMVLlB8YG/sDVIte7WeuYq12n6t26EtG/6pbW+N0kJU8kxtSZgCBsvw/zxHg6sDvvotrP+bdjs4HhY8BznnjWy7jkEiOB2HbAYb9866V+t9WvoA5u3NrPv0eXa4tN2tN5qQ9OOQ9JOJz5lC0tj91+a/aHIiL3yzh2WFDTTb3UTYhuYbq6CISC68416K1q3i63+8hL21hTWpMVQ9/3vmFhWReNutouKeIAiCcPSFJsGZD8Epv4Ft78Dal7V5IBv+oS2ZZ8Dsn2uXQx1OjqCQw3DweT3aie2PRbJOG6IXHAs9Z2KZ9TPtsqNW62GsWKtd1mzRKgUWLNIW0Mqtx0/qDVNJMwef0zUaFX8DH9/aO6xx6vfgrEdHTw/qMUYEp+NUc3UVjYtXccp3Brxu7bxOdpOXkkw3Cy65jgldp/DVq/lsWFxKYk44idnhAz7PmJggxieGsKOqnUXbqrl+TtqQtjNr5lxSxk9i2euvsu2rz6mIDOGD5Z8zY89uZjzzPDqzeUhfTxAEQRAOisEC027QDjT3LoO1f4WCxVD8tbZEjoGZP4PJ14DpMIfeH41CDoepvrSE93/3W7o6O6gZN5aUcRMGf9CxIjgOxl2oLaAN/6vZ2h2kusNUZ51WQKFqI6z5i7ZdSFLv8L7kGRA3cfSWsu9qhS/v0+bsgRauF/4JMsV5NI+ECE7HEVVVqczfwcZFH1K8cZ32zRUQk5ZJwmmz+LvrI3a07mLj5seZFf8VF07/GRUbOljyz11cdf8MLEED9yZdPDmRHVXtfLC5asiDE4DJauOsn9xC7vxT+fyZJ2nraGNlYyUlN1zNeU88TXjmsVdqXhAEQThOSBJknKItzXth3d9g83+1ym+f3QnfPKrNgZr5E4hIP/BzBRRyWK1dHy2FHPrYu2Ujnzz3OzzOLgA+eupRrrj/MeLGHOVhikeL3tQ7RI9faMdPreW9vVKV67RzHbVXws5K2Pl+9+MskDi1t0cqeeboKGm/exF8ers2TBFJm6d3xoNH/ffoeCSC03HA5/VSuGYFGz79gPq9xf71GdNmMv2Ci0kaNwFJkjhFuZLX81/nxc0vsrZmLTsN+Vwf9iD2Vvjm3/mcf9PEAYfHLZyUwBOL89lU3kp5k4OUyAOfQPdwJeWO5/sv/YsVLzzDpjXLqZG8/OfuW5l70RVM/7/vIR2v47UFQRCEY0NEOpz7BJx2D2x5E9b9VQtQa/6s9UrknKfNg0o/WQtch1TIoXt+UtzEES3ksO3rL/jq739GVRSSxo2nsaERZ0Mt7z3xAFc88ASx6Zkj1rajRpIgPFVbJl6hrXN1QvWm3h6pinXgbIWyldrSIyKzt0cqeRZEjz16lRntjfDZb7TzlIHWK3rhi9pwTmFIiOB0DHN2drLt68/Z/PkndDY3Adr8pbxTTmfq+RcRkRBY3lsv67kh7wZOTz6dh1c/zLradbyT8hyXtd9O6fYmtn1TyaQzkvu9TmyImbmZUazY08hHW6r4xRlZw/Yz6Q0GTr39brJXn8Tnf3icFoPMsk/eo2DDas69416iklOH7bUFQRAE4aCYgmHWT2HGj7Vhe2te0i4LFmtLdC6gHlOFHFRVZeXbr7H2g7cBGHfSaZz2o5/z2aJFdG1ZS03Rbt577H6ufPAJolMH6Vk7HpmCtECcfrJ2W1G00NzTI1WxTtvfzcXasvWN7seFaD2IPT1SSdO1E80OJVXVwtJnv9FONCzJMPeXWsn946XAxSghgtMxqLWulk2LP2LH0iV4XE4AbGHhTD5nARPPPBdryIHfkMkhyfz97L/zwZ4PeHr906xM/YCT9l7B8v8VEpVhIzE9ot9jLpqcwIo9jXy4pYpbTh8z7IUbEubM4/q//Zdlt9zIDo+dupoq/nvnL5h56VXMuuRK9IZROqZYEARBOHHIMmSdpS0NhVoP1JY3oCG/d5ujVMjhSPi8Hr54+U/kL18KwOzLrmbuFdfi9XqRDUYuvPN+PnrqEWr3FPLuY/dz1UNPEpk0+n6Oo0qWtQqL0dkw9XptXVeLdh6tnl6pyg3gatcKNBR/0/1ACWLG9fZIJc86slLo7TWw6HYtsAPE5MFFL2pDCIUhJ4LTMUJVVaoL8tm46EOK1q/2z1+KSklj2gUXM3beKYcUJiRJ4tKsS5mfOJ/HVz/O3tZtpLdM5PUXvuX0X6UzPTnwfErnjo/j/g93UNxgZ2d1O+MTh/jbkgEYIiM57d+vk3L3XazZtYX6UBtr/vcmhauXc9bPfkHS2Lxhb4MgCIIgHJTobLjgGTj9AW2OiTn0qBVyOBJOeycfP/MEFTu3IckyZ/30FiacdnbANiarjcvueYR3H7uP+r3FvPPIvVz18O/6jWw54VnCe4M0gM+rVWSsWNtbCr2lFOp3asvGf2nbWSN7zyeVPEsrhW4cZFqEqsKm/8IX94GrDWQDnPxrmH/76D5n1zFOBKdRTvH5KFq3ig2ffkDtnkL/+vTJ05h2wSWkTJh0RL0/MdYYnj/9eT5PWMKOl9uwOcJ47a9LWLLgc26dditWg/bGDTYbODM3lkXba/hwc9VRCU4AstHImGeeJfQvf2HHf15lV2IUzdWVvP3QXUw881xOvvYHmKy2o9IWQRAEQRiUJQymXDvSrTgo7Q31vP+7h2mqLMdgtnDhr+4mbfK0Abc1BwVx+X2P8u4j99JQXsq7j9zLlQ//jvC4hKPc6mOITg/xE7Vl5k+0dR11vUP7KtZB9WZteF3PME/QSqHHTewtWJE8SyuT383ibkT35hWw91ttRcIUuOjP2kl9hWElgtMo5XLY2f7Nl2z67GM6GhsA0BkMjDvpNKaef9GQzvWRJInzcs9mzM+q+OKF3WQ1Tueb5a9xSeUlPDjnQeYlzgO04XqLttfw8dZq7jk/F518dM6zJEkSMTffzLS0NKLvu4/8qBAqIkPY9tXnFG9cxxk/vJGsmXOPSlsEQRAE4XhQV7KHD37/W+ytLQSFR3DJ3Q8Tk7b/8zoCWIJDuPyBx3nnt/fQVFnOu4/cx1UPP0loTNxRavVxIDgWchdqC3SXQt/WHabWQvlarRpe9SZtWfuytl1IIiTNQA5J4vT8vyMrTtCb4bR7YfbNWkgThp34Xx5l2hvq2fTZR2z/5kvcXVoZUEtIKJPPvoDJZ5+PNTRs2F47a1wibQvdrP14LyeXXsm7wX/gxq9uZGHGQn4z4zecmhNDqMVAfYeLNSVNzBtzdEtuhl5wAZkJCZhuvoWElip2pMVjb2nm42eeYMyMOZzxwxsJihjdQyIEQRAEYaSVbF7Pp8/9Ho/LSVRKGpfc9RAhUdEH9VhrSChXPPA4b//2HlqqK3mnOzyFRMUMc6uPU3pT93ynGTDnZm0IXltFb49UxVqo3Q7tVbCrip76fErybOSL/gxR4pQtR5MITqNETVEBGxZ9SNGalaiqAkBEYjLTLriYcSedht54dMarTj03jcqCFqoKWrmy/DZezXqIT0o+YWX1Su6ZeQ/njU/grfUVfLi56qgHJwDrlCmkvfMO+p/fSNiuYooToymOCmXP+tWU79jKydd+n4lnnCtKlwuCIAjCALZ99Tlf/eMvqIpCyoTJXHj7PYc85N0WFs6VDzzO27+9m9baGt595D6ufPhJgiNGwTmMjnWSpBUQCUuBCZdr69x2bUhfxVqU2h1saw8h7/qnkI2mkW3rCUgcXY4gRfFRtHYVbz5wJ2/cfweFq5ejqgqpE6dw6T2/5ftP/5mJZ5xz1EITgCxLnPWDPMxBBnTNNh6UXmRM2Bianc3cuexOqox/QdK38fmOWpwe31FrV1/GpERS33iDkLlzya6sZ15BGVGh4bi7HHz197/w9m/vpqmqYkTaJgiCIAijkaooLH/z3yz524uoikLeKWdy6d0PH/Y84aCISK544AlCY2JprdPCk721ZYhbLQBgtEHafDjpDnwXv0JZ1OlayXHhqBP/6yNo0fNP8fGzT1BdmI+s05N3ypl876kXuPy+R0mfPG3Eek1sYSbOuCEXgOo1Tp5KfombJt+EXtazpXklQZnP4bSs5Kv82hFpH4AuOJjkv75M2DVXE9LlZsayDUyLTsJgMlO1exf//c0vWP3em/i8nhFroyAIgiCMBl6Ph8UvPsO6D98FYO4V13LOz29Fpz+ygUchUdFc8cATBEdF01JTxbuP3oejrXUIWiwIo5MITiMoe858zEHBzL70Kn7y539y7k23jZqTyqVNiGLymdrJcL97rZDrkn/AuwveZWL0RJCdmOM/4InNt1LWXjZibZT0euIefJDYe+9BkmViv/qOsyQraeMn4/N6WfXu6/z3rlupKsgf9Llq25x8tr2GJxbn84cvdtPY6ToKP4EgCIIgDK+uzg7+9/gD7F75HbJOx7k3/Yo5l18zZOdjDI2J5coHniAoIlIrGPHY/XR1tA/JcwvCaCPmOI2grJlzyZg6A4PJPNJNGdDsizOpLmqlvqyDJf/cycW/msJ/zv0Pz69/lVd3/YUOuYBLP7qMm6fcxDVZ14xIGyVJIuJ738OQnEzVHb9GXbueSQ1N5Nz4I5Z/+j5NleW89dBvmHz2+cy/+gZMVitOj4+d1W1sLm9lc3krm8pbqGlzBjzva2vKufu8sVw1PRn5KFUPFARBEISh1FZfy/tPPkxzdSVGi5ULb7+X1ImTh/x1wuLiueKBJ3jnkXtoLC/lvcce4IoHHsccFDTkryUII0n0OI0gWacbtaEJQKeXOetHeRjMOmr2tLF+USk6Wccds35MXMf9eDuzcCsuntv4HN/74nvscu+ixl6D2n1y3qMp+LTTSHvjdfRxcXhKSjA++TRX3XAjeaeeCarKli8W8aebfswPHvkXEx7+gsteWs1ji/JZtL2GmjYnsgS58SFcMzOF3PgQ2ro83PP+dq7462p214pvzgRBEIRjS21xEW/c/2uaqysJiozi6keeGpbQ1CMiIZEr7n8ca2gY9aXFvPf4A7gc9mF7PUEYCaLHSTigsBgrp16bw5J/7GLDZ6Uk5oSTlBPO5ZMm8bvPjGRnFmAPep/dLbvZzW7e+OgNgo3BZIdnkxOeQ05EDjnhOWSGZWLWD29INI8dS+zrb1B2401QtJuqn/6cxbP/j01xCzmt8TvCutoZv/M9TNYMtiefTm5mElNSwpiaEs7EpFBsJu3t4PUp/GtVKc8uKWRjWQsL/rSCH52Uzq1nZGE1ireMIAiCMLoVb1zLp398Cq/LRXRqOpfc/dBRqXgXmZTMFfc/xtuP3EtdSRH/e+JBLr/vUYwW67C/tiAcDeIoUBhU9ow4KvNbyF9Vw1f/3MlV98/kwkkJ/P7z3RQWj+XT297m/ZJ/sqx4GU1qEx3uDjbWbWRj3Ub/c8iSTGpIqj9MZYdnkx2eTaw19rDHWauqyt5GuzbkrqKFTWWtFNR1oM/5Hr/ufJP5Ndv5+cr/8ObYs9h+6o+Z0bYJU/4yshwljK+s4+RTfsiEU6f1e329TubHJ2Vw/oR4Hv54J1/uquOv35Xw6dYaHrkojzNyY4/o/1MQBEEQhsuWLxbxzat/RVUV0iZNZcFtd2OyHr3gEpWSxhX3P8a7j9xLTVEB7//uYS675xEM5tE7wkYQDpYITsJBOemqbGpL2mipdfD1v/O54OaJzEyLYO3eZpbvdnHPvHuY1DCJM885k0pHJQUtBRQ0F1DQUkBhcyEtrhb2tu1lb9tePi/93P+8oaZQf+9Udng2ORFa75RJ1//cBO1OD1srWrvnJrWwuaKVVkf/qnnRESFs/tGdpG74mOTP3+Oa3Uv4WaaR+Ccep7HmUr786wvUlRSx5JUXyF+xlLN+8gsiEhL7PU9CmIVXvjedJbvqePjjnVS1dvGjf2/g3Lw4HrpwHPGhlqH9TxYEQRCEw6QqCsve+BcbPnkfgPGnnc2ZP77piCvnHY6YtAwuv/8x3n30Pqp27+KDpx7hkrseHNXTEwThYIjgJBwUg0nH2T8ez3u/20DZjia2fl3BxVMSWbu3mY+2VPHjeSkAGHVGbXheRA5kao9VVZXGrkZ/mCpsKaSwpZC9bXtpc7WxvnY962vX+19LJ+lIDUkjwZqBwZtIR3s05bWhlNTpUNXA3iGjXmZCYihTU8KYkhLOlJSw3kBz3Qxa/zeFmoceon3RIjxVVST9+UX+77Gn2fz5J6x4+79U7trBf35zC7MvvZoZF16KTm/o97OfNS6WeWMi+eNXRfx9xV4+31nL8qIGbj87hxvmpKLXiamCgiAIwsjxut189pfnKFy9HIB5V17HrEuvGrLKeYcjNmMMl937CO89fj8VO7fx0dOPc/GdDxzVc1MKwlATwUk4aFFJQcy/YgzfvVnI6g+KOeeXkzDqZHbXdlBQ27Hfx0mSRLQ1mmhrNPMT5/vXu3wuiluLKWwpZFtdPlvqd1HesQeX2klJWzElbcW9TxIO1mAbRl8CcZYMxkeN5aTUSZyeOYGgA3yDFXbZpRgSE6m89Va6tmyh9MqrSH75JaZdcDFjZszmq7//hdKtm1j59n8pWL2cs3/6C+Kzcvo9j9Wo557zc7l4SiL3fbCdTeWtPPrpLt7fVMnjl0xgcnLYYf2fCoIgCMKR6Opo56OnH6Nq9y5knZ5zbvwl404+faSbBUB8Vg6X3v1b/vfEg5Rt28zHzzzOhb++H72h/5eUgnAsEMFJOCR5JydSsbuFks0NrHytgNMzo/i8sJ6Pt9WQd5DP4fUp7K7tYHNFK5vL3WwuD2Nv40RgIqAi6duRTTWYbHWEhzUiGatp99Ug6+149UVUKkVU1n/B5/Wg36AnLTTNX4SiZ7hflKV3Eqxt9izS3nqTihtvxFNWTuk1/0fi888TOn8el97zW3av+Jal//4bjeWlvPHAr8mdfyoR8YlYQkKxhoZqlyFhWENCGRsXzHs3zuXtDRU8uTifndXtXPKXlVw3K5U7z80hxCw+DARBEISjo7WulveffIiWmipMVhsX3nEvKeMnjXSzAiSOHccldz/E+08+zN4tG/nkuSe58PZ7BhzhIQijnQhOwiGRJInTrhtLfVk77Q1dzA4L4XMVPtlWS27uwI+p73D6z5m0ubyFbZVtdHl8/bbLiLYxJVkbbjclJYyc2GD/MDin1+nvneo7f6rD3cGe1j3sad3DIhb5nyvCHBFQ2S87PJvUN16j7tbbcWzYQMXPfkbcA/cTfvXV5J50GqmTpvLdf/7OruVLyV++dL8/v6zTaUEqOIS7g0Io9kBhu0r+5xv5ybJgLpqdzSkT0/2By2S1jehQCUEQBOH4VLOngA9+/whd7W0ER0Zz6T0PE5WcOtLNGlDyuAlc/JsH+PD3j1CycR2L/vgHLrj1NyMy/0oQjoT4jRUOmdlm4Owf5vHBs5uxF7UzPcTAhjYnJR3g9irsrNUC0qbuoFTZ0tXvOYLNeiYn985LmpwURrht/+OezXozeVF55EX19mupqkqdo84fonrmT5W1l9HsbGZNzRrW1Kzxb6+X9WRdmsb3DClkri6n9uHf0laUT+q9D2INCeW8W+5g/OlnU7plI472dro62nC0t9HVpl26uxwoPh/2lmbsLc0AWIHJPS/QAjX/+5q3/tfbblmnxxoSgiVU67GyhvT0YPVe9u3VMlosImh1a3W4eWt9BR9vqSYmxMQ1M1M4Y2yMmFMmCPvw+Dx4FA9Wgyj5fKLYs34Ni/70B7xuFzFpmVxy14MERUSOdLMOKHXCZC789X189IdHKVq3is/+/Czn33IHsk430k0ThIMmgpNwWOLHhDFzYTprPyrhlE49JTYv/yrU8dfHv8HtVQK2lSTIjglmamqYv0cpMzoIWT6ygCBJEnG2OOJscZySfIp/fZe3iz0te7SKfi2F/kDV6ekkv2MP95yicolR5prvFLpef4d313/E8p/OID0ul+yIbPIuOIW0kLR+AcbrdtPV0Y6jrZWudi1MOdrb6Gpvo6O1ld17a6itb8Ts7cLi68KoelB8XjpbmunsDlqD0en1WsgK7huo9glZwaFYQ8OwhoRgMB9/QWtXdTv/XlXKh1uqcHX/Lu2qgW8LGogLMXP1zGSunpFCXKioziSc2EpaS3in8B0+3vMxXd4u5iXOY2HmQk5NPnXAyqTC8WHz55/wzb9eAVUlbfI0Ft5217CdJ8nj8+BW3UP2fOmTp7Hw9nv5+JknKFi1DJ1Oxzk33YYsi/AkHBtEcBIO29RzUqkqaKFydwsL7UZek134JIVwq4EpKeH+SncTk0IJPopzfyx6CxOiJzAheoJ/naqqVNurKWzWhvoVphXyn4SNXP1uPRMKXQQ/tYLfX7GaphAthMTb4pmbMJf5ifOZFT+LYGMweqOR4MgogiMHPong+cDeRjsPfLiDFXsa0SlexoZJ3DY/gTEh0oCBy9HehqNNu+5xOfF5vXQ2NdLZ1HhQP6veYAycixWs9W6ZbEG0l5XTVj+VqMTkI/4/HW4en8KXO+v496pS1pX2hsxx8SFcNzuV8mYH726ooLbdyfNfFfHCN3s4KzeWa2enMC8z6ohDuCAcKzw+D19XfM07Be8EVCMF+K7yO76r/I5gQzBnp53NwsyFTImZgiyJXtrjgaoofPf6q2z89AMAJpxxDmf+6KZh6bHxKB4+KPqAv2z5Cx3ODhp3NPL9Cd/Hoj/y03BkTpvJgl/dxafP/Y5dy5ci6/Wc/dNfIMni91QY/SRVVdWRbsTR1N7eTmhoKG1tbYSEhIx0c4559jYXbz+2jq4OD16zQkxcKMFBRvRGHXqj7L809Nw29K437LNN4LY69AYZaZgPiFs2rqXmF7ciNbfRFWbhrR+m87WlFLfS+w2bTtIxKXqSP0jlRuYe8EBEVVU+3lrNo5/uorFTe57LpiZx7/ljiQza/7fAHpeTrvb27mDVql1vaw0IWX3DltftOqifMTw+gdSJU0mbNJXkvAkYzaPn/FNNnS7eXFfOa2vKqW13AqCTJc4dH8f356YxPTXc36Pm8vr4fEctr68tZ93e3nCVFmnl/2alcMW05AMO9xxpHo+HxYsXc/7552MQFaVGhWNpn9Taa3m38F3eL3qfxi7tixVZkjkl6RSuyrmKOFsci0oW8UnJJ9Taa/2PSwxKZEHGAhZmLiQ1ZHTOf+lxLO2Po83jdvH5i89SuHYlAPOv/h4zL75iyEccqKrKNxXf8PzG5yltLw24L9Yayy+n/pIFGQuGJIwXrF7Boj8+haoqTDzzXM788c3H3QiK4SDeJ0PvULKBCE7CESvb2cSiP29FVQbf9lDp9HK/cGXw3+6zztA/gOn7hLWBQlrPOqW+hsqbbsZVWIhkNhP54L0U5NhYad/OiqoV/T48wk3hzEmYw7zEecxNmBtQwa+vNoeHp77YzRvrylFVCLMauOe8sVwxLXlIekg8TmdgyGpv03q1OtrpbGmmtCAfd3Mjiq+3EIes05OUO84fpKJT00fkg2pbZSv/WlXKp1trcPu0X5yoICPXzEzh2lmpgw7DK6zr4I215fxvYyUdLi+gndPrggnxXDc7hakp4aPuA1h82I0+o32fKKrCqupVvF3wNssql6F0/5GNskRxWdZlXJ59OXG2uH6P2Vi3kY+LP2ZJ2RLsHrv/vonRE1mYsZBz084lzBx2NH+UgzLa98dIcbS38dEfHqO6MB9Zp+fcm24jd/6pQ/46W+q38OzGZ9lcvxnQPut+Mv4nlOSXsFJaSY29BoDciFzumH4Hs+JnHfFr5q/4lsUvPgOqyuRzFnD6D3426v52jzbifTL0RHA6ABGchkdTTTtLPlnGpIlTQJHwuhU8bh9et4I34NKHp+86T+99Pdv7PMOQwAahN8jIbgeSy4HJ1UpU03biXMWEJ4XhTYmlPNzHZksDS+Uiym0O1D5/2MdGjGVewjzmJc5jcvRkDLrAP2Sbylu474Md5Ne0AzAjLZzHLp5ATlzwsP08PX9Yzzj1VGoL8yndupHSrZtoq68L2M4WHkHaxCmkTppK6oTJWENCh61Nbq/CZztq+NeqUjaXt/rXT0oK5Ya5aVwwMR6T/tCGnDjcXj7ZWs1ra8rZXtXmXz82LphrZ6dy8eSEozpM9EDEh93oM1r3SYuzhQ/3fMi7he9S0VHhXz8zbiZX5lzJ6SmnY5AHb2+Xt4tvK77l4+KPWV29Gp+qfYmil/WcnHgyCzMXcnLSyRh1o6OndrTuj5HUWlvD+797iJaaakw2GxfdcR/JeROH9DVK20r50+Y/saRsCQBmnZnrx13PD8f/EJNk0j5LzjmDd/a8w9+2/Y1OTycApySdwu3TbicjLOOIXn/nd1/z+UvPg6oy7YKLOOX6H4vwdADifTL0RHA6ABGchsdQvpFVRcXr6Q5Xnt6Q5ekOXn3D2EDr/OHM0/e+3uDm9WiPU7yD/+rbOquIadhCdMNmbI4aJEA1GemIC6Es3MOu4A6qIqEqUqImAoxmGzPjZzI/YT5zE+eSHKzNL/L6FP61qpRnlxTicPvQyxI/PimDW8/IwmIchvHpA+wPVVVpra2mdOsmSrduonznNryuPsP9JIm4jDGkTZpK6qSpJGSNHZKx8/XtTl5fW84b68pp6NBez6CTuGBCPDfMTWNKSvgRvwZovVivrSnj463VOLvDt82o46IpiVw7K4W8hOELhQdDfNiNPqNpn6iqytaGrbxT8A5flH7hHy4cbAjmojEXcUXOFWSEHv4BamNXI4tLFvNpyafkN+f714cYQzg37VwWZi5kUvSkET1gHU37YzSoLtzNh089QldHOyHRMVx692+JTBq6OatNXU28vPVl3it8D6/qRZZkLh5zMTdNuolYWyzQf580O5t5eevLvFvwLl7Vi07ScVnWZdw0+SYiLYdf1W/b15+z5JUXAZhx0eWcdM0NIjzth3ifDD0RnA5ABKfhcSy+kRWf0h3M+vR4uRQaKzso3lBLZWEbfd8dNk8z0bUbiK7bRFBnBfv+SVckqA+FqiiJykiojpTwpcSTPvEkZmafxvTY6bTYJX778U6+3KX1/CSFW3jkojxOHxs7pD/bwewPr8dDdcEu9m7ZSNnWTTSUlwbcb7LaSBk/ibRJ2rC+kOiYg359VVXZVN7Kv1eVsnh7DV5F+4+MCTZx7axUrpmVTEzw8FTFa+vy8P6mSl5fW86e+k7/+ikpYVw7K5UFE+MxG45+BSdXWxtLPv2Usy+9FKNl9MwzO5GNhr9bDo+DT0s+5Z2CdyhoKfCvHxc5jqtyruLctHOHvMx4UUsRn5R8wqKSRdQ76v3rk4OTWZixkAUZC0gOOfpFZUbD/hgtitatYvGfnsbrcRObMYZL7noIW9jQfMnk8Dj4z67/8OqOV3F4HQCcnHQyt029jazwrIBt97dP9rbt5fmNz/NNxTcA2Aw2fjT+R1w37rrDLiCx5YtFfP3PlwCYfdk1zLvy2sN6nuOdeJ8MPRGcDkAEp+FxPL6RnXYPe7c2UrK5nvL85oAeqiAbJAW1EusoxFa+BXdJCUpHx36fq9UG1VEy7uQYwrLycMZM5m/FoezyWECSOG98HA8uHEd86NAcUB/O/uhsbqJ022ZKt26ibPsWnB3tAfdHJCT5Q1TSuPEYTP2Dj9Pj49NtNfx7VWnA0LnpqeF8b24a5+bFYdQf2qRiZ6cHg1mH7hAfp6oqa/c289qaMr7YWYvHp+2/UIuBy6clce2sFDKigw7pOQd8HY8Hb0MD3vp6PHX1eOvr8dbXBd6uq0Ox9841kYOC0IWGaktYKHLP9dCw3vWhIehCe+4L07YziRLTQ2kk/27tadnD2wVv80nJJ/55SCadifPSz+OqnKsYHzV+0Ofw+hQ6XV7au7yYDDIxwaZD+pbep/hYV7uOT0s+ZUnZErq8vefcmxIzhQUZCzgn7RxCTUent/Z4/Bw5HJsWf8TS//wdVJWMqTO44NbfDElRH6/i5cM9H/KXLX+hoasB0AL6HdPuYGb8zAEfM9g+WV+7nmc2PMPOpp3AkReQ2LT4I5b++28AzLvyOmZfdvUhP8fxTrxPhp4ITgcggtPwON7fyO4uL6XbGyne3ED5jia8feZh2cJMZEyJJi3DSLinFk9pCe6SvTj2FOLYU4iusXW/z+sy6igPDqPcmkRdaCLTTp7K2efOxJKehnQE/49Huj8UxUddyZ7uYX2bqSnajar0/sw6g4Gk3PGkTZxC2uRpuIOjeX1tOW+uq6DZrg0xMuplLpqUwA1z0xifePAHXvZWF1WFLVQVtFBV2EpbQxfmIAN58xMYf0oiQeGH3lPV0OHinQ0VvLG2nKrW3oPDuZmRXDc7lbPGxWLY58S6qqLga23FW9cTgurw1jf03q7XQpGvqemQ23O4JLO5T7A6QOgKCw3YTrJaxbCXARztv1tun5uvyr7i7YK32VS/yb8+0ZbCqfEXMiX8LHw+C+1dHtqdHtq7vLQ7PXQ4vf3WtXd5sLt9Ac8fFWRkfGIoExJD/ZfxoeaD2vcOj4Ovy7/m05JPWVOzxl+IwiAbODX5VBZmLGR+4vx+cziH0vH+OTIYRfHx3X//yabFHwEw6azzOf0HPzviIdOqqvJd5Xc8t/E5StpKAK3a4q1Tb+WctHMOGHAOZp8oqsJnez/jj5v+GFBA4tfTf73fQHYg6z95n2Wv/ROAk/7v+8y86PJDfo7j2Yn+PhkOIjgdgAhOw+NEeiN7XD7KdjRRsrme0u1NeFy9By+WECMZk6PJnBJNQnYYOp2Mr9OOq6SY6p3rqNixGntRIZaqJmJaVPT7qYOh6nSYUlIwZmZgysjEmJGOKTMTY3oGuiDb4G0c4v3htHdSvmOrf35UR2NDwP2dOhtllmTKLcl4Ysdw5fwcrpmZQsRBlAe3t3UHpcJWqgtbaa1z7HdbSZbImBzFxNOSiR8TeshhwKeoLCts4J1lu9mxrZiIrjYinO2kKnZmhihk6ZwYmhu1YNTQAB7PwT2xwYAhOhp9TAz62FjtMiYaQ/d1XVQ0DoOOpStXcsGZZyHZ7Shtbfh6ltY+19va8LW3oeyzDuUIiqYYDIGBq88id/ds+YNXn9AlBwcf1+dWOdL3idPj84eZDqeH9gECTofTQ52jhlL31zSwHJ+k9Uyrqoy3Yxyeltn4HJnQb/DvwbMYdLh9Cj6l/8d5pG2fMJUUSsIgYareUc/iksV8XPIxRS1F/vVhpjDOTTuXCzMvZHzU+CEN4x6Xk9qSYpZ99SVnLryImJS04/p3b18et4vPXniGonWrAC0wzLjwsiP+P97esJ1nNj7DxrqNAISaQvnZxJ9xVc5VB1UU5FDeIy6fi9d2vcbft//9iAtIrP3gHVa89R8ATv3ej5l2wcWH9Pjj2Yl0vHW0iOB0ACI4DY8T9Y3s9fio2NVM8eYGSrc14nJ4/feZbHrSJ2khKnlsBDpD70GAy+diU+U6tm7+goqda9CV15DYqJLQpJLYDJYDnKhdHxuLKTMDY3pGQLDSR0f7P2SHc384XF7+981Gln+7EnNdEYnOavRqb3iUJJm4MVndw/qmETcmK+Cs8I52tz8oVRW09AtKkgRRycEkZoeRmBNOXEYoVYUtbF9aSVVhq3+7yKQgJp6WRPaMWPTdBTZUtxtvQ4PWG9QzTK6hf29R32Fzg9FFRqKPjcEQHdMnGPWGIn1sLF6jkc7mRjqaGmlvbKCjqZGOxnrtdlMDnU2N+Lza70ZwVDQJ2bkkZOeSmJNLVEoaOv2Bz0WuKgqK3d4nZLUeOHi1teJr08KXerDhbyCShC4kBDkssEdLHxmBZfp0bHPmHlSQH606HU7eX/QF0+eeTJdXDejNaXd6+/XwtDu7A1L3Orf3QGFWQRdUiDFsDbqgAiRJ+6hVPCF4WmfiaZ2B6tV6YoNMekLMekIsBkLMBoL91wdaZyDEovevCzYbMOplnB4fu2ra2VHVxvbKNrZXtVFU3zlgmIqwGclLCGFCn0CVFG4Z8CC9oLmAT4o/YdHeRf7zRwGkhaSxIGMBCzIXkBiUeND/56qqYm9ppr6shIbSvdSX7aWhbC+tNdWofc5pYbYFEZeVQ0LWWOKzxxI/JhuT9dj9XTsQR3sbHz71CDVFBej0es69+XbGzj35iJ6zor2CP27+I1+UfgFow0Cvzb2WH034ESHGgz/2OZzPkp4CEu8UvINP9aGTdFyefTk/n/TzQyogserd11n93psAnP7DG5lyzoKDfuzx7EQ93hpOIjgdgAhOw0O8kcHnVagqaKF4Uz0lWxtxdvYesBrNOtImRpE5JYaUvAj/gX6PWnstq6tX83XZMlZWriS0w0FCk0pSIyQ0qWS1WUhsUjG1de37sn5ycDCmjAyMGRno01LZ3tjItDlz0BuMIEvat7eS3HtdlkHqe737hMP7uV7T7uLDrTV8vL2WNqcPVZIwGnScOy6a02LceGuKKdu5jeaaqoB2maw2IpPGYrRm4OiMp71pn5AgQVRSEIk54SRmh5MwJhSTNfB3SFUUfC0t1O+sYseaRkpKVXyKdpBnUF0kOXaQWL4UY93eg95fclCQ1hsUHU29KYRtXQa2OY00mUNosoRijI3hvFPyuHRaMiZXBx1NDX2CUQMdjb233V377yXr/TklGODPrd5kIj4zm4QcLUzFZ+VgCR6av02qqqJ2dfUJVO29oWq/oUtbVMdB/EwGA9Zp0wg6+WSCTj4JY2bmqBkS6FNUGjpcVLV2UdPWRXVrF9WtTu2yTbveM6z0SEgSBJt6Q43V4sBhWkOD9B1OtbdnNsM2hfmxFzI7/iTCLWZCLVrwCTLp0euGp2fF6fGR3xOmqtrYXtVOUV2Hv1hLX+FWA+P7DPGbsE+Y8ipe1tas5ZOST/i67GucPqf/sdNip3Fh5oWclXoWwcbe0yz4vF6aqytpKC3RAlJpCQ1le+naZ/5kD2toGD6dHl9nO173PvtGkohMTCYhWwtSCVm5RCQkHvO9Ui01Vbz/5MO01tVgtgVx0Z33k5Q7+Py2/T6fs4W/bvsrbxe8jVfxIiFxYeaF3DLlln7n/DoYR/LZvrdtL89tfI6lFUsBrYDEjyf8mOtyr8OsH3zItaqqrHjrP6z78F0AzvrJLUw889xD/hmON+J4a+iJ4HQAIjgND/FGDqT4FKr3tFGyqZ7iLQ042noPAvQmHal5kWROjSZ1fCRGc2CQ8Cpe3tyykhfWLKJT3oFsrvR/W23rUklvMzHXncIEezjxjT505bV4KiuPbCjXEGq3BFMRnUxjsAWH3o5KYI+HJEdiIpIIh4/41noi2/diUF0gy9pBWneQkyQJtftEwUp7B3h7e/M8eis18XOoTDgZZ88JiFWFqMZtJNWtJMbY2qdHKAZDT09RtHZbHx2DLsiGqijY21q7Q1ADe0ur2Jy/l+qqGsyuDoJ8Hdh8+w+rfZmDggmOjCI4KprgyGhCoqL9t0MiozEGBfPZok+ZlJlBXUkh1YW7qSncjcvRv/crIiHJH6QSskfmAFFxuwN7tfqELE9lJfYVK3CXlQU8xpCQgO2Ukwk6+WRss2YhW4e2GlwPVdV6h6p7AlFbdyBq7aKm1UlVaxd17c4BA8K+ZFRCrcaA3pxgU2+vTk/PT3Cf6yGW3us2ox5Jgk31m3i74G2WlC3Bq2i/qyHGEC4eczFX5lxJakjqsPxfHCqnx8fu2g52VLX5A1VB7cBhKsxqYHxCYJhKjrDg8Dr4quwrPin+hHW161BRMXpkYjttTJfHkumOQWpw0FxZ7u9p7UuSZSISkohOTSc6NZ2Y1HSi0zIw2oJYvHgx55x9Nq3VldQU7dbeJ0W7+52DDsBksxE/Jof4rLEkZI8lbkw2ZtuRF3w5WqoK8vnwD4/i7GgnNCaWS+5+mMjEw6tm2OXt4vX81/nH9n/4h8nNS5zHr6b+ipyInMNu41B8tq+vXc/TG55mV9MuAOJscfxyyi+5IOOCQQtIqKrKstdfZcMn7wNwzo23Mv60sw6rHccLcbw19ERwOgARnIaHeCPvn6qo1Ja0Uby5geLN9XQ29547SWeQSRkXQeaUaNImRgX0tDg9Pl7+rpi/fLcNxVyIMbiI4PBiHEpLwPMnBiVyUvRsTlIzye0IgbJKnHv20LBjJ2FB3QcRqqoFK1VBVbqvKwqqOvB1RVFwub243F5URUFSVWRU9BLoJe1gs+f5PLKVltBMWsOyaQnLwt5n2I6qKqi+WnT2bSieUlyyI2Aqh6woRHY6iepwEN3hwOby7HemhyqBLzQIc1wC5rgE9DExyDEx1MuJFNWFUlPX+6csIsHG+FMSSZ8QTFdniz8Y9Q6j6+41ampC8fU/sNuXV9LRoQ/CZwklISmBvKwUouPiAoKRwXzgb1AHPLeWotBUVUF1YT7VBbupLsynZZ8eO9AOEBOyxmpBKieXuDHZQ1Jl60i5S0vpXLaczmXLcKxbh9qnl0AyGrHOmEFQd5AypqUd9PM6PT5q25z+nqGa7l6iqp7rrV39CiMMRCdLxIWYSQgzkxBm0ZbQ3uvRNj0rvlnCBRcc3t+tTncnn5Z8ytsFb7OndY9//cSoiVyZcyXnpJ1zUN+sjzSX10dBbQfb9wlTPZUo+wo165kW4SPb0EGcpxlaymiuLEZtH/gLBr3ZRGzaGGLSMvxBKTI5BYOxf4XIA32O2FtbqCkqoLpI+8KhtrgIr9sV+ATdvVI9QSoheywRCUmjsleqcO1KFr/wND6Ph7jMLC7+zYOHVW7cp/j4uPhjXtzyor+0fG5ELr+a9ivmJMw54nYO1We7oios3ruYP2360yEXkFBVlaX/foXNn32iVaG9+XbGnXTaYbflWCeOt4aeCE4HIILT8BBv5IOjqir1ZR2UbK6neFMDbQ29BxuyTiJpbASZU6NJnxSFJUibuFvS0MkDH+1g5Z4mQCU1vp3Tp7RQ4dzMprpNeJTeHh29pGdyzGTmxM3BXezm5Pkn48WL0+uky9eF0+vUFl/gZZdXu6/ZYWdPYws1be34cIHsQZY9WE0KOr0Xj+ICp474jkwS2saQ0J5FlKP//IZmSzW1wUXUBhVRF1SMW2dHVsHololtMRPfbCGm2YLFHdjb1mX00hjWRWNoF63BXfh0KpIKDrNW0t2nk5CQGBc5jnmxc5hmHU+CEoG9uZm6vVVU7CqnpaYOxduOqnQAgw/FkiQZW0QEIZHRA/YYVXnMvLezmQ+3VOPoPlg3G2QunJTAdbNTmZgUdlD7/mDfI472Nu0AsTCf6sJ8avf0P0CUJJno1HQScsb6e6VComNGdJic0tWFfe1a7MuW0fndMjxVgQHQkJKi9USddBKO3IlUO1Vq/D1FgUPoGjtd+3mVQBE2IwlhZuJDLSSGWfzXE8K029HBJnTy/v9PDvfvVkFzAW8XvM2ikkX+8+BY9BbOTz+fK3OuZFzkuIN+rtHK5fWRX97Elm35lBbtob2yFF1rDRGuJozqwPPmvLZgnBEyFdZ6amxtNId46LR4yQjLYGGmdn6oAw0XO5T94fN6aSwv9Qep6qLdtNXV9tvOZLURNyZbC1JZY4nLyhnRXilVVdm0+CO+/e8/tHLj02ay4Je/GfSLl4GeZ0XVCp7d+Kw/tCfYEvjF1F9wfvr5h1UKfCBDXmjI6+T1/NcDCkicmnQqv5r+qwOe4FlVVb7+x0tsXbIYSZK54NY7yZlz0hG351gkjreGnghOByCC0/AQb+RDp6oqTVV2ijfVU7y5gZaa3iFbkiyRkBXGmKnRpE+Oxhpi5OOt1Tz66S4aO7UwcNnUJG4/J43ijq2sqFrBqupVlLWX7e/lDpvRayG+PZOE9jEktmUR6UhAIvBDud3WQEtEFR1RtXTFNKG3Spj1Zm3R7XOpN2PRWzDJJqQmB+49NdiLKmgvrkD19SkyIcvEjskideIUGr3NlFTspqm+EqW9C1uXHov7IEv0SmYkORhbWCSxGYkkZCX7e4mCo6IICo88qHK/HU4PH26u4rU15RTU9Z6za0JiKNfNTmHhpASsxv0XeTjc94jP66WhbG93r1Q+1YW76Whq6LedLSy8O0SNJSEnl5j0MehH4L3Y4fRQ1dJF/c7duFauwLRxLRHFO9H12bdOnYFtUZmsj81lQ+xYam39J4ybDbI/AMX39BJ1h6KegGQxHlmZ5kOtGPZl6Ze8U/AOWxq2+Nenh6ZzVc5VLMxceEiT7keTfQs2NJRpRRv2Ldjgp9PjDY2lwRBJsS+UOkMEjcZI3HJPL5KP4PBiQqK3Yddtxdc9XFdCYmbcTBZkLuCs1LOwGQKLPRzp54i9tYWaPYXUFOZTXdTdK+XqH8IjeuZKdfdMRSYmH5VeKUXx8e2//87mzz8BYPI5F3Da938aUDznYOxs2slzG55jbe1aQBsS+tOJP+XqsVdj0g3tud6G67O92dnMS1te4t3Cdw+6gISqKHz5yovsWPolkiyz8La7yZo1d8jadKwQx1tDTwSnAxDBaXiIN/KRa6m1U7xJG87XWNHZe4cE8ZmhZE6JIXpsOH9et5c31pWjqtochHvPy+XyaUnIskRFRwWrqlaxvHI5G6s3YrPYsOgtmPVmTDqTFlp02m2dZKSyyUthrYtWO6AaUBUDeRHRnBySQlxnGM4Kma46Ffb5KxEaayYxO4Kk7oIO1pDBy9oOxuN0UpG/3X/uqJbqykEf49Wp2M1e7GYvnRYfDrOXkOgYslMmMC1zHjHuXApWN1G+q9n/mLBYKxNOTWLsnLh+88sOhqqqbCxr4fW15SzaVoPbpx1YBpv1XDZVO7FuVmxwv8cN5Xuko6mR6sLd/l6p+r3FKL7AoWs6vZ7YjKzuuVJaz9ThDAUC7USrdpdWervT5aXV4aG2Xesdqmrt6h4+p/Uadbj6D320eJxMatzDjLrdzKjLJ7qrLeD+jphE7JNnIs+eR+ScmSTGhBJmNQxrD5qqqrTUd/LNV99y2umnYDIb0ellZL2MTich62XtPdVewbuF7/LBng9odbUCWs/uGalncFXOVUyPnT5qCmIcDH/BhrK91HcXa2goLTlgwYa+w+xi0jIIj0/0f9ng9ioU1nX4h/jtqGojv7ajt+Kg7MQQvB196Cb0tt7iLQbJxJz4U7h67MXMSZyDXtYP+eeI4vPRUF7q75GqKdxNa11Nv+2MFivxWb1zpeLH5GAOGtpeKY/LyaI/PU3xhjUAnHLdD5m24JJD+t2p7Kjkhc0vsHjvYq3dstFfKW+4TlI83J/te9v28uzGZ/m24ltg8AISqqLw+UvPs2vZN8g6HRfecS+Z02YNebtGs9F4vKWqKk6fNnqly9uFw+PwXx9o6Xv/XTPvwqIf2aHnIjgdgAhOw2M0vpGPZW0NDm1O1KYG6ksDD2hi0kIwZwTxamkdm5q1gDUzLYLHLhlPdvcB+4H2x576Dv69qoz3N1Vid/swqpAlGTgtIoQ4p0R7jb1f8bewWKtWHjw7nITsMGyhQ/ut5kDa6uso3bqJ8p3bkCSpT7GFGIIjowiJikZvtbC9cTsrqlawomoF+c35Ac8RYgxhbsJcZllOIqQoldL1rXicWsAwmHXkzolnwqlJhMUeXhGDZrub9zZW8PracsqaeqvQzUyP4NpZKZw7Pg6TXjvAHM73iMftoq64qDdMFeQPeCBsjojBmpSBISEDNSYVZ1AMnW6VTpdWZlu77Fm0E692urz+IYoHK9Ri6O4t6h061zPPKD7UTHhtOc6VK+j8bhmOTZugb0+j1Ypt9mx/pT5DQsIR//+A9sHe1tBFdWGrvxy+vfXAQwJVScEreVEkLz7ZB7KK1WQm2ByM0WhAp5O6w1b3pU5G13O9TwDTBVwfaFsJnU4OuB64rXbZb1udpFW/3A9nZycNZSXdIUnrSWqqLDukgg2HE7Y9vsAwtb2qnfyadjxSI4aQLehDN6Mz9faa6tQQMi0ncVrCOegq2rjsgrOIDLYecJjl4XK0tVKzp8BfnKWmuHDgXqmEJK16X/cQv8iklMPulXK0tfLBU49Qu6cQncHAeTffQc6c+Qf9+FZnK69sf4W3dr+FR/EgIbEgYwG3TLmFhKCheX/sz9H6bD+UAhKK4uOzF59l98rv0On1XPTr+0mfMn3Y2jbaHO4+2TfcdHm6cHj3H3D6hpuBtut7v9PrRN33G9aDtPTKpUT1FHkaISI4HYAITsNDBKfh09HspKS7sERNcVtA748UbmS128FO2Uu7AX5ycga/PD0LvaQE7A+fovLN7nr+vaqUtUWNJHllkr0yWRiIcNO/RynGQmJ2OIk5YSRmhWMLG/6gNBQauxr9IWpV9So63B0B948PnsjczvOwFSbR1dg7BCklL4IJpyaRmhd5wAPR/VEUlZXFjby2poyv8uv9586JtBm5Ynoy185KIS7YcEjvEbdX8QeanjAzWMDxb9vlQepsIqS9kpiuWuKdtUR6mvsV3nBLBupMMdSY46gxxVFrisV9gKE+Jr2snU/IbCAutGduUXcg6hOUbKaD78nzdXRgX7WazmXf0blsGb6GxoD7TVlZ2E4+iaCTT8E6dQrSQf59UVWVtvquPidXbsHeFjjnTdZJICvIkg7Fp6IMUAxhtJNlCUkHJmsXoZGdGIwtuLtqaaos7Xei6h5Gi9XfezRYwYah4vEpFNV1doepVjbUbqXMtRwpaAuyvvdLB58zFl9XOoozEYuSRrghiXCrhTCrgXCrkTCrgTCLkXCbgTCrkfDu22FWA+E2Izaj7pB6cRSfj8aKsu4gpQ3xa60duFeq71yp+Kyx/XqlPDU12Fevwb5mNV0bN6ELC8OdNYbv6svosHdgDgrm4jsfIHHswc2Bc3qdvLH7Df6+7e90eLS/ZbPjZ3P7tNvJjcw96J/xSLhcbhZ/9hkLDrOAyqHoKSDxx01/pNauzVfLjcjlzhl3MiNuRuC2Ph+L/vgUhWtXojMYuOQ3D5E6cfKwtm+k+RQfpe2l7Kjfweotq0nPTseluA4YbvqGoyMJN4fCrNOG4wcsht7rVr014L7/y/2/gNMYjAQRnA5ABKfhIYLT0WFvc7F3SwPFmxuoKmzVKuR1a5QVCg0+2qIM3HpJDo7iDcw77Sz+t76Kr5ZXYGnxkOzVEeeTkPc5hA6Jtvh7lBKzwwgKH/2VwAbjVbxsb9zO8srl/XujVIks+2TmNJ2PtTqanlJ/odEWbRjf3HhMlkMfxgdQ2+bkrfXlvLWugtp27Vw3kgQnjYkkxltPZnYuDo/SJwz1DT694cd1wJOrHhpZgnC9jxRfI/GuWiLtNQS3V6Hz9S+eYYhKIChlDJHp2cRl5RCbnEyIxYjNpPP3ng0XVVVx5ef7K/V1bdkSUGZfDgrCNmcOQaecjO2kkzHExgQ89mCCUmx6iP/3PDLZyhdLPid2eiz/K/4fX5d+jaKo6FQ94YYILkhdwAUpC4i1xOHzKig+FZ9X0a57u6/7uq/7FHye3m0G3lZF6X6Mz6Oi+BR83p7LPtc9fbfVLr0eDz53Gz5vC6qvDVVp1RZfK6rSBgzcK2gLjyYuM5OYNK0HKSY1nZDo2FExvNDrU8iva+Gj3d+wovYLqtwbQQrsDVMVPYozAZ8zEZ8zCcWZiOKKAQbu/THoJEItWqAKtxoJtRr818O6g1e4tSd0dQcxqyHgd7unQEtPOfTaPYV4XM5+rxUel0B0cChhnV3YCosxl5QG/GVttpnZmBaHR6/D6vIwo6qZqDFZmMePxzJhPObx4zGmpfXryfIpPhbtXcQLm1/wB4js8GzumHYHcxMPPKdHVVVc3V+62F3e7ktfn+t91rl71+1vW7vbh05SSQizkhRuJTFc60FOCrOQGN49BzHMPKR/G5xeJ6/lv8bft/8du0eb+3tq8qn8alpgAQmf18snz/2O4g1r0BtNXHr3QyTnTRyydowkRVUoby9nZ9NObWncSX5zPl3egzs9xmBMOlO/AHOgcHOg+616q/8+s86M7hDn7I0GIjgdgAhOw0MEp6Ovq9PN3q2NFG9qoHJ3c8C35S2yQovFh8WlI8Yrods3KEWZ/QePCdnhBEcc+0FpMI1djaysWunvjWp3a0PZQpyR5NWeRF7DXPRe7Rt3vUlm7GxtGF9EvO1AT7tfXp/C17vreX1tOcsKB/7m/2BYjTqCTHrtZKlm7dxBPbeDzYY+1/d/2zrAt/CK4qOpsqK74IS2DPRNuzk4hISsnN5S6JlZGExH5/fF19pK58qV2Jctp3P5cnzNvXPVVMA3fg72vFNpCUqntkHC0b5PUNJLxKWHkpAdRmJWGLEZoaBTqOqsoryjnIKmAt7c9iYNSu/+mRw9mStzruTstLOHfKL9YDwuJ211tbTU1dBWW0NrXQ2tdbW01lbT3tiAeoBztck6PZaQeFQ1ErcrHFkXjaSPRpJM6E06ErPCSM6NICk3nIh426gITvtqsjfx0uKX0Ccb2dm0i8LW3Ti8/c9zpsOElRQM3hRUVyJdnQm0tYfhHvzMAvtlNeoIs3QHqu7erDBLd/gyywQ5mpBr9uAt2EJHdSl2d/8gpfcpRMh6YpNSCUrPYN3qb/H5fERIeqbtrcbQ1tHvMbLNhmncOOTcPHxZOWyOcPL35reocBQDEGKIZm74taSY5uNwq4GhxuXrH3zcPn+P99EUHWwisTtMJYX1VrZMDNeWEPOhHxc0dTXx8taX+xWQuGnyTUSYIwDwejx88uwTlGxaj8Fk5tJ7f0vS2Lyh/vGGlaqqVHVW+QPSzqad7Gra5a862JdFbyEnPAdvq5eM5AyCjEEnTLgZTiI4HYAITsNDBKeR5XJ4KN3WSMHGesp3NiHtc3wlB+nJzIskZWwECdlhhESO/DmARpJX8bKjcQfLq7TeqF1Nu9D7jGQ3zGB87UlEdMX7t43NDmLaGemkTohCPsw5F2VNdl5fU8qqHSVkpiQSajV2BxwDQWZ99wlW9QSZDN2X2nA4m0mHXnf0zkHjaGvtU3RiN3XFRXg9+/ba6IhKTiMkOhpbWDjW0DDtMiwcW2h49/WwIR/2pfh81K/cRum3u6gutdOkRuPeZ0K8jI+ocJX4iTEYsnS0h9dT1VVBRUcF5R3lVHRUUGOvQdmnUpxFb2FBxgKuyrnqiE4WejCcnZ3dgaiG1trey7a6Gjpbmg/4WL3RRFhcPGGxcYTFJRAWG0dobDzhcfEER0b7CzZ0trio3N1MRb62dHUElg+3hRq7Q1QEybkRQ1LcZSjs+znS91v3XU272Nm0k/ymfH8J+L6seivZ4WPJCMkhwZJFtHEMBiWGti4vrQ43rQ4PLQ4PrQ43LQ43rV0eWrtv7y9nyKrCmNZKJjcUMaW+iHHNpRi7T27s1sm0Ws2UR0TSGBKMV+dFVvv3+nmT82iafjl2r4SxpoqIyj1EV5eQWFdKclMFJl//0u7tFiiJ01EQksFu82wKw9Jothx68QerUYfNpP09sZl02Iw91/Xd6/ve32edUe9fb9KpfL7ka3KmzKGu0011q5PKli6qWruoanFQ1dqF0zN473iwWa8FqT49VQl9glZUkGm/f19L2kp4buNz+y0g4XW7+fAPj1K2bTNGi4XL7n2UhOyxh/z/dTSoqkqdo84fkHqWNldbv21NOhNjI8aSF5lHXlQeeZF5pIWkofgUcbw1xERwOgARnIaHCE6jh9vpZfm3ZSxbtodpM1I55aQUQqMOr/jBiaKxq5HV1atZXrWcVVWrsDXEMKHmZFJbxiN3DwlSg92kzgnh9LOnYAs69OB5LL5HfF4P9aUl/pPzVhfsGvTgvofJatPCVFhYn0ClXfZdLCEhA5ZjVlWV1jqHf9hdVWFrvx4lSVIIUusIr95GZH0+Ie2l6LrPa7YnDjZnSmzJlNkTD2qfgzKL3kJKcAqJQYnYGm3cueBOwq2HV3FwoHY72lppqa2mrbu3qLWu1h+QnJ39ex36MtlshMUmdAek+ICgZAsLP+SeIlVRaarupGJXCxW7m6kuasW3z4FuZFIQybkRJOeGkzAmDP0Rlnk/XAfzHvEpPsraywLC1O7m3QMOYQoyBJEbmasdeEbmMS5yHMnByQH/h4qi0uH00trlpsXupr1oD8qG9Ri2bCB49zYMXYE9Xi3WULbFZLM+IpMt0Vk0dQcaSVWI8LQQ56wl3lVHtKuBMmsKq8Nnoe7nnEqy4iPVUcQ471dkt5WTUaOQVg/6AXKIIzicluRMOtOzcWXkoGSPxRwVMUAY0kKS1agfkuIag+0TVVVpcWinIKhqdVDZ0lNt09EdrrpocQx83q++jDqZ+DBzv3DVcz0+1MLWxo38Yf0f/MOu+xaQ8Hk8fPj731K+YxtGi5UrHnicuMysI/75j1RjV6M/JO1o3MHOpp00O/v/DdXLenLCc/YJSen4FBm7y4fDrfUwdnm8tDvcrF+3jvlzZ2M1GzHqZIx6ufeyezHoJIw6eVT2Lo82IjgdgAhOw+NYPCg8non9cfh8io8dTTtYUbWC9YVbMOyOYWzdbMw+bcieV3ZjT69hzLwITp08hxhrzCDPqDke9omqqnQ0NVC/twR7azP21pbupRVHawv2Nu22zzP4gZKfJGENCcUaGobJGoKqWnG7TNjbdHjdZpCsSLINSbKh6vR0RNRTEVxAiXUHdUFl+HQeJEVlTDVMLVaYUqKSsc95UN1BZpzTx2KeP5f4088jOj4TSZIOe58oio+OxsYBe41a62oHnA/Tly0svDcYxcYTGhdPePelJWh4J0l7PT5q9rT5e6MCTn0A6PQy8WNCtSA1LoKoxKDDKphyOA77XGeKj71te/3f3u9q2sXu5t24fP2r5QUbgxkXOc4fpHKVWEK2leFYvRr7mjV46+oCtpeDg7HOmolt9hxsc+dgTE/Xfnd8Cq0OD21dblocHlrsPb1Y7u6eLQ8GndQbZoyBvTvIXXxZ9QaLy9/Fo2hfCJyffj635P2MqGo7zh076Nq+A+eOHbj27AmY69fDkJiIecIELOPzMI+fgHl8HrqhLqM+BH+3HG4v1a1dfXqquqhu7b1e2+7cb69fD0mCmGAT8WEmDCFbKFf/h0NpAiAjJIc7pv2aOTGTef93D1OZvwOTzcYVDzxBbHrmYbX5UPTMK6tqb2Rbww52Ne2ksDWfkvbdtLob+20vIRMsJxMkpWFWUtF7UlDccXS5JBxuX/dy6NVM96cnUBl0Um+w0skY9TqM+6wz9AlfJn337X0C2b5BLeAxOhnDviFugGCnl6VRFehEcDoAEZyGx/FwUHg8Eftj6DR1NbGibBVbV+1F3hlFuD3Of19VSBHNY/aQMyWB+cnzmRwzGYM88P/3ibJPVFXF5bBjb23pDlPdoao7YPWEK0drC462Vg7lI8gnqXSZfP5FtRowhQQTHB5JRGQccTEpJMeNId0ci3FzPp3LlmFfuRKlo08vjyRhmTgR2yknY547l29KSjh/4cJ++8Tr8dBWXztgr1FbfR2Kb/8TaiRJJjgqurunqG/PUTyhsXEYzaNnqKyj3U1lQTMV+S1U5jfT2RIYNizBBpLGar1RybkRw1o4ZijfI17FS3Frsb9XalfTLgqaC9A5XOSVq0wo1ZakpsDHqQY9hskTCJt3MkFz52IeNw5Jf3iFYgbi9rl5c/ebvLLtFf88y5lxM7l9+u3kRQ48N0dxOHDm5weEKXdp6YDbGtPTMU8Yj2X8eC1M5Y5Fthz+79vR+Lvl8SnUtTu7e626ei/7hKt+xXIkD8aIFRgjv0XSdf/O2vNIcF3E/IJlWFsqkMw2Uq//NRnZY0gMtxBlM+FRFBzdhTG63D7sbh+O7rlhPWHF7upzX5+enr49P3ZvB51qKS65DJ+hAtlcgWxs7fezqaqE4opB6S5u4utKQnHFg3rw/5eSBFaDDmt3ADfrZdo6OjBbbLh9Kh6fgtun4PZqi3cE5rgdKkkCg04LWl/dcQqxISM7z1oEpwMQwWl4nCgHhccKsT+Gh9fnZfWmrWxbWoG6NwhJ1YbgdBib2Rm3korEbUxJmcj8xPnMT5xPrC3W/9gTdZ/4FB+1jlrK2sooLauhrqgDV6UOY104RpcZ1C5UxYGq2vGpbXQYqujS1eGRWpEUJ0EeAxaXDp370D6qjBaLNjwwJAwzoG9tR1dZja62FpPHh8nrxej14TYYcMVE4YoIw2ExY9dJdHhc2B39ixL0pdPrCYmJIzxOC0Pa8DrtMjQmBp3+2NvHPcMjy3c1U5nfTFVhKx5X4Lfe4XHW7mF92nzJwzmJ9P4Mx3tEcbvp2rQZ+5rVdK5ahWvHzoDeGwXYGwfb0yS2p0kUJEm4DRLhpnDGRY0LGOYXaz38aoQ9pbZf3PwiVZ1VAIwJG8Pt025nfuL8Q35eX3s7zl276Nq+HeeOnTi3b8dTXd1/Q50O05gxmMfnYZkwQQtT2VlIxoOb1zYa/m6pqkqT3a31UnWHqsruXqvytnqqpY9QgtYgSQqqKkPDDM7b7iHO1YRDNvN+/EW0GCOQJPqdo/CgyC505ipkcyU6cxU6SyWysWnATVV3NDpPMkYlhSDSCZVTCTYFYTFoPY49886029qQSptJh8WgD7htNeixds83MxsCh9sNtk8URdWCVJ8w5em+7vL2rvf0ud+/bd913du4Bnged5/n23edtp3WC+f2+nD7tNs9RUskVcGgejAoXvSqh68euoTokJGdTnAo2WDo/uIJgiAc5/Q6PSfNmMZJM6bR0exkwzd72L2yjuCuCGaXL2R6xTkU7dnEC/F/42Hbw2SFZzE/cT4nJZ5EXvixVenpUHh8Hn+luoqOCsrby6lor6C51o5cE0RcWwbx7WOwesIxEk7PIZtXdlMXUkVnVD26JBeRKVbSwiaSHJxMSog2B8ms176J9LrdONpbA4cG9l3aunu4Wlrwety4u7pwd3XRUtPnYNIAJO9naKWvCzoD58nofApWt4cgSUewxUpIWARhcQlEpKYRmjEGU3IyhoR4ZPPxUZVSkiTC42yEx9mYdHoyPq9C3d42KvJbqMhvpr60nZZaBy21DrYtrUSWJeIyQ0nODScpN4KY1JDDLqAyVFSfD2f+bhxrVmNftRrHpk2ozsChk8bUVKxz52CbPQfD9ClAPY7GnbiaduFt2kVRSxEtrhZWVq1kZdVK/+MizZHkReX5h/rlReYRbY0etE1ratbw7IZn/XNzYqwx3DL5Fi7MvPCwq5vpQkKwzZ6NbfZs/zpvUxPOnTv9Yaprx3Z8DY24CgpwFRTQ9r/3AZAMBkxjx2phavwEzOPHYxqTiaQbnZXWJEkiKshEVJCJiUlhA2xxPjsbinh6/bNsaFgBMWtZfoqVc9emEtzm4LK6T/hf3EW0GHofa9TL2Ix9gotR680xGX2ohiqccimdaimtyl7avFX0O9khEGOJJztsHOMi85gYPZ5JMXmEmYf3C3lVVXHaPSge8HoU9Hq1X+iWZQmzrMNsOLz9qaoqXrcLj8uFx+nE49IWr8uFx+XWbvvXu/y3ve7u7RUXHo9TW5wu/+N7tlf2OQG3RV1w2P8fI0H0OAlDYjR8KyX0Evvj6PF6fBStr2fb0oqA+SI1wcXsiFvG3ohtKLKCTW8jWo0mJioGnayVB5ckCQkJWZK1IhQSyGjfLsrdE8plSUZC8q/r2b7vfX1vH+i+ntt9X9d/nyQFtKHvdj339dz2KFpQquio6K1UpyiEd8WR0D6GhLYxJLSPweINnK+jyD6U2E6C03QkZIeRk5NKUljCfoc3Hg5VVXF3dfUZKhgYsBxtrQHDCGWdnojIaELMFmzIWJ1uLG0dmOobkKuqwdV/nsy+dFFRGBITMCQkYExMRN99aUjQ1sm2wytpP9o47R6qClr886PaGwMDicmqJykn3F+tLzT60IaIHc7fLVVV8ZSVYV+zRgtKa9fiawusUKaLisI2Z44WNObMxpCQcMDndPlcFDYXBhSgKG4txjdA1bwYSwzjIsf5e6fGRY4jyhIFQEFzAc9tes4fvoIMQfxowo+4NvdaLPrhH66pqire+nqc27f7h/h17diB0ta/gptksWAeNy5gvpQxNRWvz3dMfZasq1nH0xueJr85H5Nb5oL1iYS0ydjCIznr178lOikRq0GrVur2uSloLgioblfcWtyv6iZohSh6wnLPfg4zhw1Zu71uH452t7a0ubG3ufrc7nO93Y3iU1FVH6gekLzodD5knQ+d3oes8yJJPmTZA5IPSfIg4QW8qKoHVA+q4kZRPKg+N4rPjc/nRvG68Xnd+NwuvF73YXbNHRpJkjGYTXz/mZcIjowa9tc7EDFU7wBEcBoe4kB9dBH74+hTVZXakna2L62geFMDSvewBK/FSX7cKjZGfo3T0P+8HMcsVdKCUtsYEtozSejIwuIJnJgu6yEy3Ura2BiSciKITQ1BZzh65dUH43a7+eyzzw5YMczX1ISnuhpPVVXvZVU1nuoq3FXVqI7+pbH3pQsLw9ATpHouk3pv64KHtyDEcGlrcPh7o6oKWnA5Ar9JDom2+Kv1JeWEY7Ie+G/Rwf7d8jY0YF+zFvvq1djXrMZbHXj+MdlmwzpzJrY5s7HNmYNxzJgjnoju9DopaClgR8NO8msLKK4rpb6lEYPHjMlnxeS1YPJaMXmthBFJqBROk6sRt86FT+dhbEwWM1NmEGoLwmDSYTDptUuzrvt276IzDF8lNFVV8VRWBoQp586dKAP8HsvBwZjG5VKjQkpqKrJOBqlnUr+kTVTxL/Sul+XAdf22Zz/P02f9APcd7HpVUtnZtIulFUvpcNmJao7H4DViNBowh1rp8jlx+Lpw+FwokooKqBKokoQK6HUGrAYrVqMNq8GGRW9FL+lRVQVUVZuX2X3Zs6Ao2vN0DwNVFQVV1U5orfgUFEW7VBXVf1tVVG1RVe25/b1aas/O6r6u7nNf39vDTQeSASQDEgYkWVtk2Yis1xad3ohOb0JvMKEzmNAbTehNJgwmMwazCaPJjNFiwWjpvTTbrBgsRgxGHbHpIegPs3dsqIjgdAAiOA0PcaA+uoj9MbLsrS52LKti5/Iq/zl0JB24QlsICQum55hI7fPhpwas6b3svV/d7/2961TthqQdIAXcK2n3BT5P7+MGfF5VHXBLnceAuTECyRk42ltvkInLDPWfXDkmLQSdfvQEpX0d6ftEVVV8ra37BKvqgNtKe/ugzyMHB/cPVokJGBISMSQmoAsLG1UVqAai+BTqyzuo2KX1RtWVtPu/PIDuqmhpIf75UbEZIej2OUfZ/vaHr9OOY/06HN29Sq6iosAXNxiwTp6MtTsoWcaPRzrA/lQVFVeXF5fDi8vh6b7se92D0+HFZQ9c53J4cXV5h/2YVZJAb+ofqAYMW0bdfgOY/zFmHQajrAWfgf4/fD7cpaUB86Wcu3ejHkRv67HApdexJjMBu3l0nK9sKEmyrAUVoxm9wYjOYNLCjM6ArDMiywZk2QCSHlnSo2JAQgeqHrXPoig6VMWAomjXFZ8Br0+HzycPVNBxSF3/wGRCEiOG90UGIeY4CYIgjCBbmIlZF2Yw/bw09myqZ9vSSupL2zE2hzPAKTyGRff3r8NKb5SJzwwlITucxKzRH5SGmiRJ6MPD0YeHY8kbeA6br6MjsKcqoOeqCl9rK0pHB67du3Ht3j3w61itGBMTAocA9rnURUaOeLCSdTJx6aHEpYcy44J03E4v1YWtlOdrhSZaah3U7W2nbm87GxaXYjDrSMwO91frC4vtnRyuejw4tmzBvnoN9tWr6dq+HfaZF2Ecl4txxnz0k2YgZ+bgVvS0ObzUd3pwfVM97OFHb5AxWfWYbAbt0qpdymZop4VWmoizxhGpi8bj8vVfnD3Xvf51Xnd3b4WKdr9zaMpR99Dp5d5ANWDQGothbB76Sddg0EtI7U34aiqoKSogKSEOnaydFFiSFCRUZBRkVKTuS1lSkFFA7b6Not2nKkhST09N9w/Ys9DbgxNw377rFWWf9ft7nv7rjV4X6S17qXa3YMSABQMm9BhVHTqfAl4fqteLR9HhxYAXI16dGa/eik9vxau34tFb8OqtePU2FJ0R/7df/r+0ff/iSt3XtH/13i4MHgcGrx2Dx47B48DosWPwdGJwd2Lw2DF6OtH5XPT89ZRUAFV7BlULSJLBgGQ0Iuv1eF0uzLIO2edD8nq1Hi6fT3ufDHF/iIqEIuvx6YwosgFFNvqv+2QDyj7Xff5ttEtFNvR5rKHfY3U+9+CNGEVEcBIEQRgmOoNMzqw4cmbFUV3czLefrWHChAnodLr9luEecHWflfv7TAxcv5/t9/vYg2hLn+uyTiImLYSY1OATKigdDl1wMLqcHMw5OQPer9jtWpCqrsZdVYW3+7Kn98rX2IjqcOAq2oOraA8D1fqTTCZ04eEga3PQAodQ9Rn61G9olTzw+oDhVn3WSfsMw9r3teTA4VeyJJEmSaTJEk6sNEhxNBBLA7G4nSZKtzVSuk07z40FB9FyA8amApb/+XPcGLoPVMfiyZ6KLygCJSQSrzEIt6LH7fJBGVDmAXYc1r7RG+Q+wUcLP+aeEGTTBwSi3ks9ZqthWIacqoqKxz1AyAoIWoFhq+/iHWCd2+lD7e7583kVfF4Fp/0QzrNGKDCT8ooj+9kkWULW9V1kdH2u91uv77Ne7r2u3Re4fc91nW5/6yUy9DIZKjg6tDlEjvbeeUP2NjfODvdB5Q0J0AF6PVjMYDapWAwKZoMXs86DWXZjklyY1S5MqgOjzwEeF6rLjepyobrdKG4XqsuH6tahusyobhnFbdS2cWvbKW43ePY55YHLDd1/AXr6Uw9p0J4kgV6PpNMhybL/Ojqdtk6n09bJMuh1SDo96GQk3T7b6XWg0+/zHP2363kOSSdr2+t02sg/nYKk94KsIul9WCKPraHKIjgJgiAcBdEpwdiSPYydGyeGTwp+ss2GKSsLU1bWgPcrTiee6pr+86y6L7319aguF97a2gEfP5pEdC/ZSHQGJdIcnktzxFjaQjPpkq2UK6kQngrh+3kCb/dCb0+M3igHhJqRDj9HQpIljGb9kJZ4V1UVxatqIWrfwNUnjHndAwU0Hy6nh4a6RsJCw1F8ap9FCbjt63NbHeA8QqqilaP2HUpmO8okCSzBRqyhRqwhRqyhJu0yxIitz3VrqHFI99H+qIriD1Kq243SHaw8DjvLV6zg5FNPRW8yDRB69L3r+l7Ko+v3/VglgpMgCIIgjFKy2YwpIx1TRvqA96tuN57aWq2SXJ8hSv5hS3Sv656s7h/K1L1eVZR+6/yPVwZYt9/13c8RMIGe3jbR81r92+DxNNLQZqSm2UBlg4eI5ASssWGYrcZ+4cfcd2icRT/qws9oI0kSOoOEziBjDjr0L2x6552ddvCVDhUVRekNWD7vAGFL0a5r9yn9Aplvn2DWdxuft//2/tfaz3qtEh0B4WffcGQJNo54Of2+JFlGMpthn9MdyB4P7uJijJmZ4ku4ESCCkyAIgiAcoySjEWNKykg344jFANndB+nnnT9THBAewyRZQidL6PSgDWwThOOH+KpGEARBEARBEARhECI4CYIgCIIgCIIgDEIEJ0EQBEEQBEEQhEGI4CQIgiAIgiAIgjAIEZwEQRAEQRAEQRAGIYKTIAiCIAiCIAjCIERwEgRBEARBEARBGIQIToIgCIIgCIIgCIMQwUkQBEEQBEEQBGEQIjgJgiAIgiAIgiAMQgQnQRAEQRAEQRCEQYjgJAiCIAiCIAiCMAgRnARBEARBEARBEAYhgpMgCIIgCIIgCMIgRHASBEEQBEEQBEEYhAhOgiAIgiAIgiAIgxDBSRAEQRAEQRAEYRAiOAmCIAiCIAiCIAxCP9INONpUVQWgvb19hFtyfPF4PDgcDtrb2zEYDCPdnBOe2B+jj9gno4/YJ6OL2B+jj9gno4/YJ0OvJxP0ZIQDOeGCU0dHBwDJyckj3BJBEARBEARBEEaDjo4OQkNDD7iNpB5MvDqOKIpCdXU1wcHBSJI00s05brS3t5OcnExFRQUhISEj3ZwTntgfo4/YJ6OP2Ceji9gfo4/YJ6OP2CdDT1VVOjo6SEhIQJYPPIvphOtxkmWZpKSkkW7GcSskJES8kUcRsT9GH7FPRh+xT0YXsT9GH7FPRh+xT4bWYD1NPURxCEEQBEEQBEEQhEGI4CQIgiAIgiAIgjAIEZyEIWEymXjooYcwmUwj3RQBsT9GI7FPRh+xT0YXsT9GH7FPRh+xT0bWCVccQhAEQRAEQRAE4VCJHidBEARBEARBEIRBiOAkCIIgCIIgCIIwCBGcBEEQBEEQBEEQBiGCkyAIgiAIgiAIwiBEcBIO25NPPsmMGTMIDg4mJiaGiy++mIKCgpFultDH7373OyRJ4rbbbhvpppzQqqqquO6664iMjMRisTBhwgQ2bNgw0s06Ifl8Ph544AHS09OxWCxkZmby6KOPIuokHT3Lli1j4cKFJCQkIEkSH374YcD9qqry4IMPEh8fj8Vi4cwzz6SoqGhkGnuCONA+8Xg83HXXXUyYMAGbzUZCQgLf+973qK6uHrkGH+cGe4/0deONNyJJEs8///xRa9+JTAQn4bB999133HzzzaxZs4YlS5bg8Xg4++yzsdvtI900AVi/fj1//etfmThx4kg35YTW0tLCvHnzMBgMfPbZZ+zatYtnnnmG8PDwkW7aCen3v/89L730Ei+++CL5+fn8/ve/56mnnuKFF14Y6aadMOx2O5MmTeLPf/7zgPc/9dRT/OlPf+Lll19m7dq12Gw2zjnnHJxO51Fu6YnjQPvE4XCwadMmHnjgATZt2sT7779PQUEBF1544Qi09MQw2HukxwcffMCaNWtISEg4Si0TUAVhiNTX16uA+t133410U054HR0dalZWlrpkyRL1lFNOUW+99daRbtIJ66677lLnz58/0s0Qul1wwQXqD3/4w4B1l156qXrttdeOUItObID6wQcf+G8riqLGxcWpf/jDH/zrWltbVZPJpL755psj0MITz777ZCDr1q1TAbWsrOzoNOoEtr/9UVlZqSYmJqo7duxQU1NT1eeee+6ot+1EJHqchCHT1tYGQERExAi3RLj55pu54IILOPPMM0e6KSe8jz/+mOnTp3PFFVcQExPDlClT+Nvf/jbSzTphzZ07l6+//prCwkIAtm7dyooVKzjvvPNGuGUCwN69e6mtrQ342xUaGsqsWbNYvXr1CLZM6KutrQ1JkggLCxvpppyQFEXh+uuv58477yQvL2+km3NC0Y90A4Tjg6Io3HbbbcybN4/x48ePdHNOaG+99RabNm1i/fr1I90UASgpKeGll17i9ttv595772X9+vX88pe/xGg0csMNN4x08044d999N+3t7YwdOxadTofP5+Pxxx/n2muvHemmCUBtbS0AsbGxAetjY2P99wkjy+l0ctddd3HNNdcQEhIy0s05If3+979Hr9fzy1/+cqSbcsIRwUkYEjfffDM7duxgxYoVI92UE1pFRQW33norS5YswWw2j3RzBLQvFaZPn84TTzwBwJQpU9ixYwcvv/yyCE4j4J133uH111/njTfeIC8vjy1btnDbbbeRkJAg9ocgDMLj8XDllVeiqiovvfTSSDfnhLRx40b++Mc/smnTJiRJGunmnHDEUD3hiN1yyy18+umnLF26lKSkpJFuzglt48aN1NfXM3XqVPR6PXq9nu+++44//elP6PV6fD7fSDfxhBMfH8+4ceMC1uXm5lJeXj5CLTqx3Xnnndx9991cffXVTJgwgeuvv55f/epXPPnkkyPdNAGIi4sDoK6uLmB9XV2d/z5hZPSEprKyMpYsWSJ6m0bI8uXLqa+vJyUlxf85X1ZWxh133EFaWtpIN++4J3qchMOmqiq/+MUv+OCDD/j2229JT08f6Sad8M444wy2b98esO4HP/gBY8eO5a677kKn041Qy05c8+bN61emv7CwkNTU1BFq0YnN4XAgy4HfGep0OhRFGaEWCX2lp6cTFxfH119/zeTJkwFob29n7dq1/PznPx/Zxp3AekJTUVERS5cuJTIycqSbdMK6/vrr+81fPuecc7j++uv5wQ9+MEKtOnGI4CQctptvvpk33niDjz76iODgYP/489DQUCwWywi37sQUHBzcb46ZzWYjMjJSzD0bIb/61a+YO3cuTzzxBFdeeSXr1q3jlVde4ZVXXhnppp2QFi5cyOOPP05KSgp5eXls3ryZZ599lh/+8Icj3bQTRmdnJ3v27PHf3rt3L1u2bCEiIoKUlBRuu+02HnvsMbKyskhPT+eBBx4gISGBiy++eOQafZw70D6Jj4/n8ssvZ9OmTXz66af4fD7/531ERARGo3Gkmn3cGuw9sm9wNRgMxMXFkZOTc7SbeuIZ6bJ+wrELGHB59dVXR7ppQh+iHPnI++STT9Tx48erJpNJHTt2rPrKK6+MdJNOWO3t7eqtt96qpqSkqGazWc3IyFDvu+8+1eVyjXTTThhLly4d8LPjhhtuUFVVK0n+wAMPqLGxsarJZFLPOOMMtaCgYGQbfZw70D7Zu3fvfj/vly5dOtJNPy4N9h7ZlyhHfvRIqipOly4IgiAIgiAIgnAgojiEIAiCIAiCIAjCIERwEgRBEARBEARBGIQIToIgCIIgCIIgCIMQwUkQBEEQBEEQBGEQIjgJgiAIgiAIgiAMQgQnQRAEQRAEQRCEQYjgJAiCIAiCIAiCMAgRnARBEARBEARBEAYhgpMgCIIgHAJJkvjwww9HuhmCIAjCUSaCkyAIgnDM+P73v48kSf2Wc889d6SbJgiCIBzn9CPdAEEQBEE4FOeeey6vvvpqwDqTyTRCrREEQRBOFKLHSRAEQTimmEwm4uLiApbw8HBAG0b30ksvcd5552GxWMjIyOC9994LePz27ds5/fTTsVgsREZG8tOf/pTOzs6Abf75z3+Sl5eHyWQiPj6eW265JeD+xsZGLrnkEqxWK1lZWXz88cfD+0MLgiAII04EJ0EQBOG48sADD3DZZZexdetWrr32Wq6++mry8/MBsNvtnHPOOYSHh7N+/Xreffddvvrqq4Bg9NJLL3HzzTfz05/+lO3bt/Pxxx8zZsyYgNf47W9/y5VXXsm2bds4//zzufbaa2lubj6qP6cgCIJwdEmqqqoj3QhBEARBOBjf//73ee211zCbzQHr7733Xu69914kSeLGG2/kpZde8t83e/Zspk6dyl/+8hf+9re/cdddd1FRUYHNZgNg8eLFLFy4kOrqamJjY0lMTOQHP/gBjz322IBtkCSJ+++/n0cffRTQwlhQUBCfffaZmGslCIJwHBNznARBEIRjymmnnRYQjAAiIiL81+fMmRNw35w5c9iyZQsA+fn5TJo0yR+aAObNm4eiKBQUFCBJEtXV1ZxxxhkHbMPEiRP91202GyEhIdTX1x/ujyQIgiAcA0RwEgRBEI4pNput39C5oWKxWA5qO4PBEHBbkiQURRmOJgmCIAijhJjjJAiCIBxX1qxZ0+92bm4uALm5uWzduhW73e6/f+XKlciyTE5ODsHBwaSlpfH1118f1TYLgiAIo5/ocRIEQRCOKS6Xi9ra2oB1er2eqKgoAN59912mT5/O/Pnzef3111m3bh3/+Mc/ALj22mt56KGHuOGGG3j44YdpaGjgF7/4Bddffz2xsbEAPPzww9x4443ExMRw3nnn0dHRwcqVK/nFL35xdH9QQRAEYVQRwUkQBEE4pnz++efEx8cHrMvJyWH37t2AVvHurbfe4qabbiI+Pp4333yTcePGAWC1Wvniiy+49dZbmTFjBlarlcsuu4xnn33W/1w33HADTqeT5557jl//+tdERUVx+eWXH70fUBAEQRiVRFU9QRAE4bghSRIffPABF1988Ug3RRAEQTjOiDlOgiAIgiAIgiAIgxDBSRAEQRAEQRAEYRBijpMgCIJw3BCjzwVBEIThInqcBEEQBEEQBEEQBiGCkyAIgiAIgiAIwiBEcBIEQRAEQRAEQRiECE6CIAiCIAiCIAiDEMFJEARBEARBEARhECI4CYIgCIIgCIIgDEIEJ0EQBEEQBEEQhEGI4CQIgiAIgiAIgjCI/wf1jfZp+z0qDwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -35,58 +14,145 @@ "loss_df = pd.read_csv('augmentation_loss.csv')\n", "x_values = np.arange(1, 16)\n", "# Accuracy vs Epoch plot\n", - "plt.figure(figsize=(10, 6))\n", + "plt.figure(figsize=(10, 5))\n", "for technique in accuracy_df.columns[1:]: # Skip the 'epoch' column\n", " plt.plot(x_values, accuracy_df[technique], label=technique)\n", - "plt.xlabel('Epoch')\n", + "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", - "plt.title('Accuracy vs Epoch for Different Augmentation Techniques')\n", + "plt.title(f'Accuracy vs Epoch for Different Augmentation Techniques')\n", "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()\n", "# Save the accuracy plot\n", - "plt.savefig('augmentation_accuracy.png')\n", + "plt.savefig(f'augmentation_accuracy.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n", "\n", "# Loss vs Epoch plot\n", "plt.figure(figsize=(10, 6))\n", - "for technique in loss_df.columns[1:]: # Skip the 'epoch' column\n", + "for technique in loss_df.columns: # Skip the 'epoch' column\n", " plt.plot(x_values, loss_df[technique], label=technique)\n", - "plt.xlabel('Epoch')\n", + "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", - "plt.title('Loss vs Epoch for Different Augmentation Techniques')\n", + "plt.title(f'Loss vs Epoch for Different Augmentation Techniques')\n", "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()\n", "# Save the accuracy plot\n", - "plt.savefig('augmentation_loss.png')\n", + "plt.savefig(f'augmentation_loss.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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", 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", 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0.991000\n", + "14 0.989167\n", + "15 0.991167\n", + "16 0.991833\n", + "17 0.991833\n", + "18 0.991333\n", + "19 0.993000\n", + "Name: best avg True, dtype: float64\n" + ] } ], "source": [ @@ -96,16 +162,16 @@ "x_values = np.arange(1, 21)\n", "# Test Accuracy vs Epoch plot\n", "plt.figure(figsize=(10, 6))\n", - "for technique in test_accuracy_df.columns[1:]: # Skip the 'epoch' column\n", + "for technique in test_accuracy_df.columns: # Skip the 'epoch' column\n", " plt.plot(x_values, test_accuracy_df[technique], label=technique)\n", + " print(test_accuracy_df[technique])\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Test Accuracy')\n", "plt.title('Test Accuracy vs Epoch for Different Augmentation Techniques')\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.show()\n", "# Save the test accuracy plot\n", - "plt.savefig('test_augmentation_accuracy.png')\n", + "plt.savefig(f'test_augmentation_accuracy.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n", "\n", "# Test Loss vs Epoch plot\n", @@ -117,9 +183,8 @@ "plt.title('Test Loss vs Epoch for Different Augmentation Techniques')\n", "plt.legend()\n", "plt.grid(True)\n", - "plt.show()\n", "# Save the test loss plot\n", - "plt.savefig('test_augmentation_loss.png')\n", + "plt.savefig(f'test_augmentation_loss.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n" ] } diff --git a/testing/test_augmentation_accuracy.png b/testing/test_augmentation_accuracy.png index 91b149c7687d2de2fa7ae617eaeb0c30ab11ef84..aad5c39694e80ab49b1c6debcddfe81c6032157f 100644 GIT binary patch literal 63117 zcmeFZ^l5$ztl_)7tT-wXJ`xlZ6sn}eCq*bI*j*?n81>h$z)#MIhseP%yjR~oSf_&xfvO)|K}GNY#mG) zNo8T@z?&f2NoY7iL7^Ey{z3l|C@_bDy1JMAB&6(?dbs53raV3Kdu?UPXu^{eG4O-n zZtH89Y*vL_99n&mZXc|{r?Pbw<-^uBECL!8_*}AoL_N^H21dV?IBEUNwL&8DN*D%C zA}R2Sf0VaBmCHDT&vYQU^=6(%Q~@*at?+A7$d~cVDO4S(e;-1=Ncb`Q_o=_|h+{$j z{WYHV&`tk+Y=H1V5a)lrob=ESZuh^B1Bs%cbpG?Uq=6N%=$Xj>eP0(r7^nX}{`2Pl 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z#nP=c2Ww~(PNF?bw&Of|0JYIolh(p!aot~d>34y|+F=A2fP7_uO(#ndV41Y+t>U5y zl1($o5x(L@u)6I6Iw1QP-o=hsTR>9^!~S#^{H@6BXl^uLrX%{$f5OdGn{X*J5&SK) z_t1o|?;4sQnz_|zW+QfJL?*X!L2NuA4el|t{=HNO=KKsLKL)y?6oByTCInRb%ptZC z0bf`KwnC-I=nBkjw$90yXyV_5qTn#*iNOrKG*Vb{zOa=Y+4cmn;un-PS8|1L;^rT#CEYTJ-Zi<^8)x=3SSx|PE21fO9$YDZe0@_S$( z5sf8ieQ`~RfJ|3|D1megdw3Vn{J&!1g(86=2Sz9QXEYgz8ybH|6Bhk1*bU-e5#i~p zY3k1roHO)*7Vi^GZLxmYiZ%BL*+@2UsQ-*DQUnP{gdfa;OZKDrI0|6hu@aA}JFyGhTn@7>*$p=`X^Rx9$I`fC7fNMOUzFe}+)u zZAv_%7-=4~m14PnL3z0C3)#T{^$*#j0XZ~=pdx(&3$pl1O%k02rADId2Pw`e^<;@QP$R z7NR0z!jLXd(y>`nv5LWid!btgHI`;GYR)mNVKt~~%w=evjSW5GZi_OoB z!u;e8Vu^TSr(Z2&u(;WClNoOQsiJwBg!fWS3c&KU2r~rLl02*Y*jV! zU6VqrO>n6ZP68;K_|Y)S9N(-VnaDK{bQ>?4en8EqOzwh%wv7I+cj5j%*q1q4u3~29 zud;&~gLxERhK|m;a|uJ63jGdNPGN_!kxz#SRZ$apVi}y*N@xInPHhG80vSyi{wvfC zJm^g4Q%kWiy2`+Cxr7Z9u<0x$4m7OvrhbH*kM#O*5)?_Se2_AM0y#*pniC>7r!+Q< zCR>9{PYzKLiGRRFPB&@etP@ChXDPeShb540K}c=VkeNhED~ zXh+0_{?um(F~r@*RWGHk1A`|q-X_4iNOVA0+V)5@ty=XNR3l++h?|Z4leij?f$IHh zq1~ZDp$`ImwbBK#IGDzJh>|lZ{s(5DuR_xjhT-2aN+b&c-Cwt3C7lywsPN}sDy_Usc`f?jAKIPa=K#{HYYl%QyZDb9@ON From 18e5e5d8f641e505be290f204cf8e26ec7967560 Mon Sep 17 00:00:00 2001 From: James Overmeyer Date: Fri, 25 Oct 2024 23:28:56 -0400 Subject: [PATCH 6/9] Fixed a figure and did 100 epochs for the best initialization --- testing/CNN-testing.ipynb | 221 ++++++++++-------- testing/augmentation_accuracy.png | Bin 61108 -> 65100 bytes .../confusion_matrix_avg_False_2024-10-25.png | Bin 0 -> 47573 bytes .../confusion_matrix_avg_True_2024-10-25.png | Bin 0 -> 42989 bytes ...fusion_matrix_best_avg_True_2024-10-25.png | Bin 0 -> 42153 bytes ...fusion_matrix_best_max_True_2024-10-25.png | Bin 0 -> 42654 bytes .../confusion_matrix_max_False_2024-10-25.png | Bin 0 -> 46820 bytes .../confusion_matrix_max_True_2024-10-25.png | Bin 0 -> 44064 bytes testing/figures.ipynb | 133 +---------- .../learning_curve_avg_False_2024-10-25.png | Bin 0 -> 33040 bytes .../learning_curve_avg_True_2024-10-25.png | Bin 0 -> 37330 bytes ...earning_curve_best_avg_True_2024-10-25.png | Bin 0 -> 33690 bytes ...earning_curve_best_max_True_2024-10-25.png | Bin 0 -> 35227 bytes .../learning_curve_max_False_2024-10-25.png | Bin 0 -> 35543 bytes .../learning_curve_max_True_2024-10-25.png | Bin 0 -> 36334 bytes testing/test_accuracy.csv | 122 ++++++++-- testing/test_accuracy.png | Bin 0 -> 45018 bytes testing/test_augmentation_accuracy.png | Bin 63117 -> 0 bytes testing/test_augmentation_loss.png | Bin 56091 -> 0 bytes testing/test_loss.csv | 122 ++++++++-- testing/test_loss.png | Bin 0 -> 44180 bytes 21 files changed, 337 insertions(+), 261 deletions(-) create mode 100644 testing/confusion_matrix_avg_False_2024-10-25.png create mode 100644 testing/confusion_matrix_avg_True_2024-10-25.png create mode 100644 testing/confusion_matrix_best_avg_True_2024-10-25.png create mode 100644 testing/confusion_matrix_best_max_True_2024-10-25.png create mode 100644 testing/confusion_matrix_max_False_2024-10-25.png create mode 100644 testing/confusion_matrix_max_True_2024-10-25.png create mode 100644 testing/learning_curve_avg_False_2024-10-25.png create mode 100644 testing/learning_curve_avg_True_2024-10-25.png create mode 100644 testing/learning_curve_best_avg_True_2024-10-25.png create mode 100644 testing/learning_curve_best_max_True_2024-10-25.png create mode 100644 testing/learning_curve_max_False_2024-10-25.png create mode 100644 testing/learning_curve_max_True_2024-10-25.png create mode 100644 testing/test_accuracy.png delete mode 100644 testing/test_augmentation_accuracy.png delete mode 100644 testing/test_augmentation_loss.png create mode 100644 testing/test_loss.png diff --git a/testing/CNN-testing.ipynb b/testing/CNN-testing.ipynb index a9541e1..01a41e4 100644 --- a/testing/CNN-testing.ipynb +++ b/testing/CNN-testing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -42,13 +42,13 @@ "Y_train = load('kmnist-train-labels.npz')\n", "y_test = load('kmnist-test-labels.npz')\n", "# Reshape the data for CNN input\n", - "X_train = x_train.reshape(-1, 28, 28, 1)\n", + "X_train = X_train.reshape(-1, 28, 28, 1)\n", "x_test = x_test.reshape(-1, 28, 28, 1)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -68,138 +68,162 @@ "text": [ "Testing configuration: {'batch_normalization': False, 'pooling': 'max'}\n", "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.4779 - accuracy: 0.8454 - val_loss: 0.2424 - val_accuracy: 0.9187\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.5087 - accuracy: 0.8361 - val_loss: 0.2364 - val_accuracy: 0.9225\n", "Epoch 2/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1746 - accuracy: 0.9455 - val_loss: 0.1632 - val_accuracy: 0.9475\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1852 - accuracy: 0.9436 - val_loss: 0.1487 - val_accuracy: 0.9517\n", "Epoch 3/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1203 - accuracy: 0.9623 - val_loss: 0.1420 - val_accuracy: 0.9543\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1260 - accuracy: 0.9624 - val_loss: 0.1391 - val_accuracy: 0.9530\n", "Epoch 4/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0896 - accuracy: 0.9717 - val_loss: 0.1250 - val_accuracy: 0.9615\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0960 - accuracy: 0.9707 - val_loss: 0.1136 - val_accuracy: 0.9642\n", "Epoch 5/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0714 - accuracy: 0.9772 - val_loss: 0.1177 - val_accuracy: 0.9675\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0759 - accuracy: 0.9762 - val_loss: 0.1117 - val_accuracy: 0.9642\n", "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0564 - accuracy: 0.9818 - val_loss: 0.1095 - val_accuracy: 0.9668\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0598 - accuracy: 0.9811 - val_loss: 0.0958 - val_accuracy: 0.9707\n", "Epoch 7/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0484 - accuracy: 0.9846 - val_loss: 0.1419 - val_accuracy: 0.9608\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0474 - accuracy: 0.9840 - val_loss: 0.1042 - val_accuracy: 0.9695\n", "Epoch 8/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0374 - accuracy: 0.9876 - val_loss: 0.1219 - val_accuracy: 0.9670\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0390 - accuracy: 0.9872 - val_loss: 0.1177 - val_accuracy: 0.9662\n", "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0343 - accuracy: 0.9882 - val_loss: 0.1108 - val_accuracy: 0.9715\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0339 - accuracy: 0.9890 - val_loss: 0.1213 - val_accuracy: 0.9657\n", "Epoch 10/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0294 - accuracy: 0.9903 - val_loss: 0.1394 - val_accuracy: 0.9638\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0287 - accuracy: 0.9905 - val_loss: 0.1091 - val_accuracy: 0.9697\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'max'}\n", "Epoch 1/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2068 - accuracy: 0.9444 - val_loss: 0.0855 - val_accuracy: 0.9750\n", + "1688/1688 [==============================] - 10s 5ms/step - loss: 0.2064 - accuracy: 0.9439 - val_loss: 0.1300 - val_accuracy: 0.9610\n", "Epoch 2/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0674 - accuracy: 0.9803 - val_loss: 0.0682 - val_accuracy: 0.9787\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0673 - accuracy: 0.9803 - val_loss: 0.0661 - val_accuracy: 0.9798\n", "Epoch 3/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0451 - accuracy: 0.9866 - val_loss: 0.0569 - val_accuracy: 0.9832\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0465 - accuracy: 0.9858 - val_loss: 0.0793 - val_accuracy: 0.9742\n", "Epoch 4/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0333 - accuracy: 0.9903 - val_loss: 0.0452 - val_accuracy: 0.9857\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0329 - accuracy: 0.9897 - val_loss: 0.0639 - val_accuracy: 0.9797\n", "Epoch 5/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0255 - accuracy: 0.9919 - val_loss: 0.0856 - val_accuracy: 0.9747\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0271 - accuracy: 0.9917 - val_loss: 0.0567 - val_accuracy: 0.9813\n", "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0211 - accuracy: 0.9936 - val_loss: 0.0746 - val_accuracy: 0.9778\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0185 - accuracy: 0.9946 - val_loss: 0.0678 - val_accuracy: 0.9783\n", "Epoch 7/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0180 - accuracy: 0.9942 - val_loss: 0.0435 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.0167 - accuracy: 0.9946 - val_loss: 0.0699 - val_accuracy: 0.9797\n", "Epoch 8/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0150 - accuracy: 0.9949 - val_loss: 0.0456 - val_accuracy: 0.9878\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 0.0589 - val_accuracy: 0.9830\n", "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0115 - accuracy: 0.9965 - val_loss: 0.0559 - val_accuracy: 0.9848\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0460 - val_accuracy: 0.9855\n", "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0580 - val_accuracy: 0.9837\n", - "313/313 [==============================] - 0s 1ms/step\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9962 - val_loss: 0.0527 - val_accuracy: 0.9868\n", + "313/313 [==============================] - 1s 1ms/step\n", "Testing configuration: {'batch_normalization': False, 'pooling': 'avg'}\n", "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6600 - accuracy: 0.7839 - val_loss: 0.3370 - val_accuracy: 0.8948\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6742 - accuracy: 0.7747 - val_loss: 0.3817 - val_accuracy: 0.8787\n", "Epoch 2/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.2646 - accuracy: 0.9176 - val_loss: 0.2276 - val_accuracy: 0.9302\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.2685 - accuracy: 0.9166 - val_loss: 0.2144 - val_accuracy: 0.9303\n", "Epoch 3/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1853 - accuracy: 0.9413 - val_loss: 0.1736 - val_accuracy: 0.9457\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1861 - accuracy: 0.9420 - val_loss: 0.1872 - val_accuracy: 0.9407\n", "Epoch 4/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1452 - accuracy: 0.9551 - val_loss: 0.1435 - val_accuracy: 0.9523\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1463 - accuracy: 0.9536 - val_loss: 0.1409 - val_accuracy: 0.9535\n", "Epoch 5/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1190 - accuracy: 0.9628 - val_loss: 0.1163 - val_accuracy: 0.9642\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1209 - accuracy: 0.9628 - val_loss: 0.1287 - val_accuracy: 0.9607\n", "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1007 - accuracy: 0.9691 - val_loss: 0.1121 - val_accuracy: 0.9650\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1026 - accuracy: 0.9670 - val_loss: 0.1261 - val_accuracy: 0.9610\n", "Epoch 7/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0852 - accuracy: 0.9731 - val_loss: 0.1128 - val_accuracy: 0.9622\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0865 - accuracy: 0.9727 - val_loss: 0.1401 - val_accuracy: 0.9558\n", "Epoch 8/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0734 - accuracy: 0.9764 - val_loss: 0.1235 - val_accuracy: 0.9608\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0764 - accuracy: 0.9754 - val_loss: 0.1028 - val_accuracy: 0.9703\n", "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0629 - accuracy: 0.9800 - val_loss: 0.0907 - val_accuracy: 0.9728\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0668 - accuracy: 0.9785 - val_loss: 0.1023 - val_accuracy: 0.9682\n", "Epoch 10/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0567 - accuracy: 0.9813 - val_loss: 0.0939 - val_accuracy: 0.9713\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0560 - accuracy: 0.9817 - val_loss: 0.1007 - val_accuracy: 0.9692\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", "Epoch 1/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1940 - accuracy: 0.9486 - val_loss: 0.0727 - val_accuracy: 0.9782\n", + "1688/1688 [==============================] - 10s 5ms/step - loss: 0.1956 - accuracy: 0.9483 - val_loss: 0.0812 - val_accuracy: 0.9755\n", "Epoch 2/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0668 - accuracy: 0.9805 - val_loss: 0.0744 - val_accuracy: 0.9775\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0649 - accuracy: 0.9810 - val_loss: 0.0657 - val_accuracy: 0.9805\n", "Epoch 3/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0457 - accuracy: 0.9863 - val_loss: 0.0427 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0455 - accuracy: 0.9863 - val_loss: 0.0954 - val_accuracy: 0.9680\n", "Epoch 4/10\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0346 - accuracy: 0.9895 - val_loss: 0.0598 - val_accuracy: 0.9837\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0353 - accuracy: 0.9891 - val_loss: 0.0501 - val_accuracy: 0.9840\n", "Epoch 5/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0263 - accuracy: 0.9916 - val_loss: 0.0419 - val_accuracy: 0.9882\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.0391 - val_accuracy: 0.9887\n", "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0209 - accuracy: 0.9935 - val_loss: 0.0416 - val_accuracy: 0.9860\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0219 - accuracy: 0.9933 - val_loss: 0.0438 - val_accuracy: 0.9873\n", "Epoch 7/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0171 - accuracy: 0.9947 - val_loss: 0.0428 - val_accuracy: 0.9877\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0189 - accuracy: 0.9943 - val_loss: 0.0352 - val_accuracy: 0.9895\n", "Epoch 8/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0168 - accuracy: 0.9944 - val_loss: 0.0531 - val_accuracy: 0.9843\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0158 - accuracy: 0.9951 - val_loss: 0.0346 - val_accuracy: 0.9898\n", "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0144 - accuracy: 0.9956 - val_loss: 0.0420 - val_accuracy: 0.9877\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0141 - accuracy: 0.9959 - val_loss: 0.0388 - val_accuracy: 0.9887\n", "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9963 - val_loss: 0.0449 - val_accuracy: 0.9888\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0121 - accuracy: 0.9962 - val_loss: 0.0522 - val_accuracy: 0.9848\n", "313/313 [==============================] - 0s 1ms/step\n", - "Best configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", - "Best accuracy: 0.9888333082199097\n", + "Best configuration: {'batch_normalization': True, 'pooling': 'max'}\n", + "Best accuracy: 0.9868333339691162\n", "Training best model on full training set...\n", - "Epoch 1/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1919 - accuracy: 0.9486 - val_loss: 0.1733 - val_accuracy: 0.9468\n", - "Epoch 2/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0663 - accuracy: 0.9811 - val_loss: 0.0569 - val_accuracy: 0.9845\n", - "Epoch 3/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0466 - accuracy: 0.9863 - val_loss: 0.0710 - val_accuracy: 0.9765\n", - "Epoch 4/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0345 - accuracy: 0.9895 - val_loss: 0.0448 - val_accuracy: 0.9862\n", - "Epoch 5/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0276 - accuracy: 0.9915 - val_loss: 0.0489 - val_accuracy: 0.9873\n", - "Epoch 6/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0232 - accuracy: 0.9925 - val_loss: 0.0357 - val_accuracy: 0.9907\n", - "Epoch 7/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0182 - accuracy: 0.9943 - val_loss: 0.0413 - val_accuracy: 0.9873\n", - "Epoch 8/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.0398 - val_accuracy: 0.9885\n", - "Epoch 9/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0136 - accuracy: 0.9958 - val_loss: 0.0454 - val_accuracy: 0.9862\n", - "Epoch 10/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0134 - accuracy: 0.9955 - val_loss: 0.0317 - val_accuracy: 0.9910\n", - "Epoch 11/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0099 - accuracy: 0.9970 - val_loss: 0.0791 - val_accuracy: 0.9773\n", - "Epoch 12/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0104 - accuracy: 0.9966 - val_loss: 0.0433 - val_accuracy: 0.9892\n", - "Epoch 13/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0095 - accuracy: 0.9969 - val_loss: 0.0440 - val_accuracy: 0.9878\n", - "Epoch 14/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0079 - accuracy: 0.9975 - val_loss: 0.0308 - val_accuracy: 0.9910\n", - "Epoch 15/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0087 - accuracy: 0.9972 - val_loss: 0.0360 - val_accuracy: 0.9892\n", - "Epoch 16/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.0318 - val_accuracy: 0.9912\n", - "Epoch 17/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0072 - accuracy: 0.9978 - val_loss: 0.0340 - val_accuracy: 0.9918\n", - "Epoch 18/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0353 - val_accuracy: 0.9918\n", - "Epoch 19/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0395 - val_accuracy: 0.9913\n", - "Epoch 20/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0060 - accuracy: 0.9980 - val_loss: 0.0248 - val_accuracy: 0.9930\n", - "Test accuracy: 0.9720\n", - "Test loss: 0.1486\n", - "313/313 [==============================] - 0s 1ms/step\n" + "Epoch 1/100\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.2136 - accuracy: 0.9405 - val_loss: 0.1286 - val_accuracy: 0.9587\n", + "Epoch 2/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0831 - accuracy: 0.9747 - val_loss: 0.0534 - val_accuracy: 0.9828\n", + "Epoch 3/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0577 - accuracy: 0.9826 - val_loss: 0.0527 - val_accuracy: 0.9832\n", + "Epoch 4/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0436 - accuracy: 0.9865 - val_loss: 0.0447 - val_accuracy: 0.9875\n", + "Epoch 5/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0357 - accuracy: 0.9884 - val_loss: 0.0406 - val_accuracy: 0.9870\n", + "Epoch 6/100\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0311 - accuracy: 0.9901 - val_loss: 0.0405 - val_accuracy: 0.9892\n", + "Epoch 7/100\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0256 - accuracy: 0.9919 - val_loss: 0.0463 - val_accuracy: 0.9852\n", + "Epoch 8/100\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0408 - val_accuracy: 0.9878\n", + "Epoch 9/100\n", + "3375/3375 [==============================] - 27s 8ms/step - loss: 0.0192 - accuracy: 0.9938 - val_loss: 0.0410 - val_accuracy: 0.9868\n", + "Epoch 10/100\n", + "3375/3375 [==============================] - 26s 8ms/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.0374 - val_accuracy: 0.9898\n", + "Epoch 11/100\n", + "3375/3375 [==============================] - 27s 8ms/step - loss: 0.0168 - accuracy: 0.9943 - val_loss: 0.0373 - val_accuracy: 0.9898\n", + "Epoch 12/100\n", + "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0143 - accuracy: 0.9954 - val_loss: 0.0344 - val_accuracy: 0.9908\n", + "Epoch 13/100\n", + "3375/3375 [==============================] - 25s 7ms/step - loss: 0.0122 - accuracy: 0.9960 - val_loss: 0.0375 - val_accuracy: 0.9893\n", + "Epoch 14/100\n", + "3375/3375 [==============================] - 24s 7ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 0.0341 - val_accuracy: 0.9913\n", + "Epoch 15/100\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0114 - accuracy: 0.9962 - val_loss: 0.0392 - val_accuracy: 0.9893\n", + "Epoch 16/100\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0112 - accuracy: 0.9965 - val_loss: 0.0400 - val_accuracy: 0.9890\n", + "Epoch 17/100\n", + "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0091 - accuracy: 0.9970 - val_loss: 0.0449 - val_accuracy: 0.9885\n", + "Epoch 18/100\n", + "3375/3375 [==============================] - 25s 7ms/step - loss: 0.0106 - accuracy: 0.9964 - val_loss: 0.0416 - val_accuracy: 0.9902\n", + "Epoch 19/100\n", + "3375/3375 [==============================] - 20s 6ms/step - loss: 0.0096 - accuracy: 0.9966 - val_loss: 0.0474 - val_accuracy: 0.9885\n", + "Epoch 20/100\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.0411 - val_accuracy: 0.9902\n", + "Epoch 21/100\n", + "3375/3375 [==============================] - 20s 6ms/step - loss: 0.0091 - accuracy: 0.9970 - val_loss: 0.0389 - val_accuracy: 0.9898\n", + "Epoch 22/100\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0085 - accuracy: 0.9967 - val_loss: 0.0333 - val_accuracy: 0.9912\n", + "Epoch 23/100\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0070 - accuracy: 0.9975 - val_loss: 0.0488 - val_accuracy: 0.9883\n", + "Epoch 24/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.0336 - val_accuracy: 0.9918\n", + "Epoch 25/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0076 - accuracy: 0.9974 - val_loss: 0.0532 - val_accuracy: 0.9878\n", + "Epoch 26/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.0495 - val_accuracy: 0.9875\n", + "Epoch 27/100\n", + "3375/3375 [==============================] - 15s 5ms/step - loss: 0.0079 - accuracy: 0.9972 - val_loss: 0.0466 - val_accuracy: 0.9890\n", + "Epoch 28/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 0.0361 - val_accuracy: 0.9915\n", + "Epoch 29/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0380 - val_accuracy: 0.9922\n", + "Epoch 30/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.0458 - val_accuracy: 0.9900\n", + "Epoch 31/100\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0057 - accuracy: 0.9980 - val_loss: 0.0418 - val_accuracy: 0.9910\n", + "Epoch 32/100\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0460 - val_accuracy: 0.9903\n", + "Test accuracy: 0.9692\n", + "Test loss: 0.1575\n", + "313/313 [==============================] - 1s 2ms/step\n" ] } ], @@ -219,7 +243,7 @@ " {'batch_normalization': True, 'pooling': 'avg'}\n", "]\n", "x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=42)\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", "best_config = None\n", "best_accuracy = 0\n", "histories = []\n", @@ -253,7 +277,7 @@ " # Append the last value of val_loss and val_accuracy an additional ten times\n", " last_val_loss = history.history['val_loss'][-1]\n", " last_val_accuracy = history.history['val_accuracy'][-1]\n", - " for _ in range(10):\n", + " for _ in range(len(history.history['val_loss']), 100):\n", " history.history['val_loss'].append(last_val_loss)\n", " history.history['val_accuracy'].append(last_val_accuracy)\n", " test_loss_df[f\"{config['pooling']} {config['batch_normalization']}\"] = history.history['val_loss']\n", @@ -289,11 +313,16 @@ "\n", "# Train on the full training set\n", "history = best_model.fit(x_train, y_train, \n", - " epochs=20, \n", - " batch_size=32, \n", + " epochs=100, \n", + " batch_size=16, \n", " validation_data=(x_val, y_val),\n", + " callbacks=[early_stopping],\n", " verbose=1)\n", - "\n", + "last_val_loss = history.history['val_loss'][-1]\n", + "last_val_accuracy = history.history['val_accuracy'][-1]\n", + "for _ in range(len(history.history['val_loss']), 100):\n", + " history.history['val_loss'].append(last_val_loss)\n", + " history.history['val_accuracy'].append(last_val_accuracy)\n", "# Evaluate on the test set\n", "test_loss, test_accuracy = best_model.evaluate(x_test, y_test, verbose=0)\n", "print(f\"Test accuracy: {test_accuracy:.4f}\")\n", diff --git a/testing/augmentation_accuracy.png b/testing/augmentation_accuracy.png index 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accuracy_df.columns: # Skip the 'epoch' column\n", " plt.plot(x_values, accuracy_df[technique], label=technique)\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", @@ -40,151 +40,38 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 0.918667\n", - "1 0.947500\n", - "2 0.954333\n", - "3 0.961500\n", - "4 0.967500\n", - "5 0.966833\n", - "6 0.960833\n", - "7 0.967000\n", - "8 0.971500\n", - "9 0.963833\n", - "10 0.963833\n", - "11 0.963833\n", - "12 0.963833\n", - "13 0.963833\n", - "14 0.963833\n", - "15 0.963833\n", - "16 0.963833\n", - "17 0.963833\n", - "18 0.963833\n", - "19 0.963833\n", - "Name: max False, dtype: float64\n", - "0 0.975000\n", - "1 0.978667\n", - "2 0.983167\n", - "3 0.985667\n", - "4 0.974667\n", - "5 0.977833\n", - "6 0.986667\n", - "7 0.987833\n", - "8 0.984833\n", - "9 0.983667\n", - "10 0.983667\n", - "11 0.983667\n", - "12 0.983667\n", - "13 0.983667\n", - "14 0.983667\n", - "15 0.983667\n", - "16 0.983667\n", - "17 0.983667\n", - "18 0.983667\n", - "19 0.983667\n", - "Name: max True, dtype: float64\n", - "0 0.894833\n", - "1 0.930167\n", - "2 0.945667\n", - "3 0.952333\n", - "4 0.964167\n", - "5 0.965000\n", - "6 0.962167\n", - "7 0.960833\n", - "8 0.972833\n", - "9 0.971333\n", - "10 0.971333\n", - "11 0.971333\n", - "12 0.971333\n", - "13 0.971333\n", - "14 0.971333\n", - "15 0.971333\n", - "16 0.971333\n", - "17 0.971333\n", - "18 0.971333\n", - "19 0.971333\n", - "Name: avg False, dtype: float64\n", - "0 0.978167\n", - "1 0.977500\n", - "2 0.987167\n", - "3 0.983667\n", - "4 0.988167\n", - "5 0.986000\n", - "6 0.987667\n", - "7 0.984333\n", - "8 0.987667\n", - "9 0.988833\n", - "10 0.988833\n", - "11 0.988833\n", - "12 0.988833\n", - "13 0.988833\n", - "14 0.988833\n", - "15 0.988833\n", - "16 0.988833\n", - "17 0.988833\n", - "18 0.988833\n", - "19 0.988833\n", - "Name: avg True, dtype: float64\n", - "0 0.946833\n", - "1 0.984500\n", - "2 0.976500\n", - "3 0.986167\n", - "4 0.987333\n", - "5 0.990667\n", - "6 0.987333\n", - "7 0.988500\n", - "8 0.986167\n", - "9 0.991000\n", - "10 0.977333\n", - "11 0.989167\n", - "12 0.987833\n", - "13 0.991000\n", - "14 0.989167\n", - "15 0.991167\n", - "16 0.991833\n", - "17 0.991833\n", - "18 0.991333\n", - "19 0.993000\n", - "Name: best avg True, dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "# Read the test CSV files\n", "test_accuracy_df = pd.read_csv('test_accuracy.csv')\n", "test_loss_df = pd.read_csv('test_loss.csv')\n", - "x_values = np.arange(1, 21)\n", + "x_values = np.arange(1, 101)\n", "# Test Accuracy vs Epoch plot\n", "plt.figure(figsize=(10, 6))\n", "for technique in test_accuracy_df.columns: # Skip the 'epoch' column\n", " plt.plot(x_values, test_accuracy_df[technique], label=technique)\n", - " print(test_accuracy_df[technique])\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Test Accuracy')\n", - "plt.title('Test Accuracy vs Epoch for Different Augmentation Techniques')\n", + "plt.title('Test Accuracy vs Epoch for Different Initializations')\n", "plt.legend()\n", "plt.grid(True)\n", "# Save the test accuracy plot\n", - "plt.savefig(f'test_augmentation_accuracy.png')\n", + "plt.savefig(f'test_accuracy.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n", "\n", "# Test Loss vs Epoch plot\n", "plt.figure(figsize=(10, 6))\n", - "for technique in test_loss_df.columns[1:]: # Skip the 'epoch' column\n", + "for technique in test_loss_df.columns: # Skip the 'epoch' column\n", " plt.plot(x_values, test_loss_df[technique], label=technique)\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Test Loss')\n", - "plt.title('Test Loss vs Epoch for Different Augmentation Techniques')\n", + "plt.title('Test Loss vs Epoch for Different Initializations')\n", "plt.legend()\n", "plt.grid(True)\n", "# Save the test loss plot\n", - "plt.savefig(f'test_augmentation_loss.png')\n", + "plt.savefig(f'test_loss.png')\n", "plt.close() # Close the current figure to start fresh for the next plot\n" ] } diff --git a/testing/learning_curve_avg_False_2024-10-25.png b/testing/learning_curve_avg_False_2024-10-25.png new file mode 100644 index 0000000000000000000000000000000000000000..a686f86938a085cf9bc8fa009afbf48baaf29227 GIT binary patch literal 33040 zcmeFZhgVct^fg#WD+ZLdm8gIUh=72KWQ!n@l`J_3O3oRIWC9dakf@R+r$TZpP*F)j zk%}T`BosLm8Rk5H-}lX$wPyZ<>D9K?Qt!R{?mhRMefHkh*J>*Ar%y7SM4?cp6&3Dj zpiq=EC=|u|@ni5O*lSe0@SCWctgf4;qlKHNsf#)4fvKC5ouiwbwb>;Pa~D@@M+X5u zQNCN(FIl;{Ik}4Q^V|QQFYr0KSn^+1q3na3oN!Xmb48(OOp#v{S(2I7DAZFw#e33P zUh(rop1ztJ2MsHp5sOCG?71%aym)&3?9qSHp1K@W|Cjy}c7(P&)R^`t1@3udXox{j z$njTMhUTx_lSQQ7uLqej z9Dy;QP#Hy^jQ)N=T}q}zK8&6{4(|*9c%hDn{r$l5^ywMo!&9pN{S}J;|0^&b|Ic}8 z*1};nZkzo$Ufh!9X6(BnD`?d&B^39-exOXSI{sF<@3u>+{eZT=zkiYLbus_F3b{87 zDVUA9k4E}|KX<86iNP?-v|r;zU7r-{uJ10@#Th9DMkXZWA0BMh%?(x--<6S>nx4+Q z`rwn*`bv=-GP3X#7;5SeqUEtXB@Ea?zc4-e3F{_dY3Dvhy{&lG%Jkq8mRTF 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create mode 100644 testing/augmentation_approaches_grayscale.png delete mode 100644 testing/confusion_matrix_avg_False_2024-10-24.png delete mode 100644 testing/confusion_matrix_avg_False_2024-10-25.png create mode 100644 testing/confusion_matrix_avg_False_2024-10-26.png delete mode 100644 testing/confusion_matrix_avg_True_2024-10-24.png delete mode 100644 testing/confusion_matrix_avg_True_2024-10-25.png create mode 100644 testing/confusion_matrix_avg_True_2024-10-26.png delete mode 100644 testing/confusion_matrix_best_avg_True_2024-10-24.png delete mode 100644 testing/confusion_matrix_best_avg_True_2024-10-25.png create mode 100644 testing/confusion_matrix_best_avg_True_2024-10-26.png delete mode 100644 testing/confusion_matrix_best_max_True_2024-10-24.png delete mode 100644 testing/confusion_matrix_best_max_True_2024-10-25.png create mode 100644 testing/confusion_matrix_best_max_True_2024-10-26.png delete mode 100644 testing/confusion_matrix_flip_2024-10-24.png create mode 100644 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"source": [ @@ -13,7 +13,7 @@ "import sys\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array\n", "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -37,8 +37,8 @@ " return np.load(file_path)['arr_0']\n", "\n", "# Load the data\n", - "X_train = load('kmnist-train-imgs.npz')/ 255.0\n", - "x_test = load('kmnist-test-imgs.npz')/ 255.0\n", + "X_train = load('kmnist-train-imgs.npz')/255.0\n", + "x_test = load('kmnist-test-imgs.npz')/255.0\n", "Y_train = load('kmnist-train-labels.npz')\n", "y_test = load('kmnist-test-labels.npz')\n", "# Reshape the data for CNN input\n", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -67,163 +67,137 @@ "output_type": "stream", "text": [ "Testing configuration: {'batch_normalization': False, 'pooling': 'max'}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.5087 - accuracy: 0.8361 - val_loss: 0.2364 - val_accuracy: 0.9225\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.1852 - accuracy: 0.9436 - val_loss: 0.1487 - val_accuracy: 0.9517\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1260 - accuracy: 0.9624 - val_loss: 0.1391 - val_accuracy: 0.9530\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0960 - accuracy: 0.9707 - val_loss: 0.1136 - val_accuracy: 0.9642\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0759 - accuracy: 0.9762 - val_loss: 0.1117 - val_accuracy: 0.9642\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0598 - accuracy: 0.9811 - val_loss: 0.0958 - val_accuracy: 0.9707\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0474 - accuracy: 0.9840 - val_loss: 0.1042 - val_accuracy: 0.9695\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0390 - accuracy: 0.9872 - val_loss: 0.1177 - val_accuracy: 0.9662\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0339 - accuracy: 0.9890 - val_loss: 0.1213 - val_accuracy: 0.9657\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0287 - accuracy: 0.9905 - val_loss: 0.1091 - val_accuracy: 0.9697\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.4911 - accuracy: 0.8421 - val_loss: 0.2453 - val_accuracy: 0.9215\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1757 - accuracy: 0.9455 - val_loss: 0.1625 - val_accuracy: 0.9467\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1200 - accuracy: 0.9631 - val_loss: 0.1319 - val_accuracy: 0.9593\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0912 - accuracy: 0.9715 - val_loss: 0.1222 - val_accuracy: 0.9615\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0696 - accuracy: 0.9779 - val_loss: 0.1241 - val_accuracy: 0.9622\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0546 - accuracy: 0.9818 - val_loss: 0.0990 - val_accuracy: 0.9707\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0427 - accuracy: 0.9861 - val_loss: 0.1041 - val_accuracy: 0.9692\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0345 - accuracy: 0.9890 - val_loss: 0.1223 - val_accuracy: 0.9695\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0301 - accuracy: 0.9904 - val_loss: 0.1018 - val_accuracy: 0.9740\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'max'}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 10s 5ms/step - loss: 0.2064 - accuracy: 0.9439 - val_loss: 0.1300 - val_accuracy: 0.9610\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0673 - accuracy: 0.9803 - val_loss: 0.0661 - val_accuracy: 0.9798\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0465 - accuracy: 0.9858 - val_loss: 0.0793 - val_accuracy: 0.9742\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0329 - accuracy: 0.9897 - val_loss: 0.0639 - val_accuracy: 0.9797\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0271 - accuracy: 0.9917 - val_loss: 0.0567 - val_accuracy: 0.9813\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0185 - accuracy: 0.9946 - val_loss: 0.0678 - val_accuracy: 0.9783\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 13s 7ms/step - loss: 0.0167 - accuracy: 0.9946 - val_loss: 0.0699 - val_accuracy: 0.9797\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 0.0589 - val_accuracy: 0.9830\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0460 - val_accuracy: 0.9855\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9962 - val_loss: 0.0527 - val_accuracy: 0.9868\n", - "313/313 [==============================] - 1s 1ms/step\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2146 - accuracy: 0.9421 - val_loss: 0.1013 - val_accuracy: 0.9692\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0702 - accuracy: 0.9795 - val_loss: 0.0660 - val_accuracy: 0.9787\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0478 - accuracy: 0.9854 - val_loss: 0.0700 - val_accuracy: 0.9773\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0338 - accuracy: 0.9899 - val_loss: 0.0664 - val_accuracy: 0.9808\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0278 - accuracy: 0.9914 - val_loss: 0.0831 - val_accuracy: 0.9752\n", + "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': False, 'pooling': 'avg'}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6742 - accuracy: 0.7747 - val_loss: 0.3817 - val_accuracy: 0.8787\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.2685 - accuracy: 0.9166 - val_loss: 0.2144 - val_accuracy: 0.9303\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1861 - accuracy: 0.9420 - val_loss: 0.1872 - val_accuracy: 0.9407\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1463 - accuracy: 0.9536 - val_loss: 0.1409 - val_accuracy: 0.9535\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1209 - accuracy: 0.9628 - val_loss: 0.1287 - val_accuracy: 0.9607\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1026 - accuracy: 0.9670 - val_loss: 0.1261 - val_accuracy: 0.9610\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0865 - accuracy: 0.9727 - val_loss: 0.1401 - val_accuracy: 0.9558\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0764 - accuracy: 0.9754 - val_loss: 0.1028 - val_accuracy: 0.9703\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0668 - accuracy: 0.9785 - val_loss: 0.1023 - val_accuracy: 0.9682\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0560 - accuracy: 0.9817 - val_loss: 0.1007 - val_accuracy: 0.9692\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6473 - accuracy: 0.7879 - val_loss: 0.3415 - val_accuracy: 0.8885\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.2587 - accuracy: 0.9183 - val_loss: 0.2265 - val_accuracy: 0.9290\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1791 - accuracy: 0.9437 - val_loss: 0.1768 - val_accuracy: 0.9430\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1445 - accuracy: 0.9542 - val_loss: 0.1378 - val_accuracy: 0.9567\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1182 - accuracy: 0.9638 - val_loss: 0.1264 - val_accuracy: 0.9600\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1018 - accuracy: 0.9682 - val_loss: 0.1197 - val_accuracy: 0.9620\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0879 - accuracy: 0.9728 - val_loss: 0.1182 - val_accuracy: 0.9615\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0769 - accuracy: 0.9754 - val_loss: 0.1300 - val_accuracy: 0.9580\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0638 - accuracy: 0.9797 - val_loss: 0.1016 - val_accuracy: 0.9677\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0584 - accuracy: 0.9808 - val_loss: 0.1068 - val_accuracy: 0.9673\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0498 - accuracy: 0.9840 - val_loss: 0.1049 - val_accuracy: 0.9672\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.1004 - val_accuracy: 0.9728\n", + "Epoch 13/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0390 - accuracy: 0.9872 - val_loss: 0.1034 - val_accuracy: 0.9700\n", + "Epoch 14/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0343 - accuracy: 0.9891 - val_loss: 0.1165 - val_accuracy: 0.9663\n", + "Epoch 15/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0312 - accuracy: 0.9897 - val_loss: 0.1113 - val_accuracy: 0.9688\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", - "Epoch 1/10\n", - "1688/1688 [==============================] - 10s 5ms/step - loss: 0.1956 - accuracy: 0.9483 - val_loss: 0.0812 - val_accuracy: 0.9755\n", - "Epoch 2/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0649 - accuracy: 0.9810 - val_loss: 0.0657 - val_accuracy: 0.9805\n", - "Epoch 3/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0455 - accuracy: 0.9863 - val_loss: 0.0954 - val_accuracy: 0.9680\n", - "Epoch 4/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0353 - accuracy: 0.9891 - val_loss: 0.0501 - val_accuracy: 0.9840\n", - "Epoch 5/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.0391 - val_accuracy: 0.9887\n", - "Epoch 6/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0219 - accuracy: 0.9933 - val_loss: 0.0438 - val_accuracy: 0.9873\n", - "Epoch 7/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0189 - accuracy: 0.9943 - val_loss: 0.0352 - val_accuracy: 0.9895\n", - "Epoch 8/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0158 - accuracy: 0.9951 - val_loss: 0.0346 - val_accuracy: 0.9898\n", - "Epoch 9/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0141 - accuracy: 0.9959 - val_loss: 0.0388 - val_accuracy: 0.9887\n", - "Epoch 10/10\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0121 - accuracy: 0.9962 - val_loss: 0.0522 - val_accuracy: 0.9848\n", + "Epoch 1/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1917 - accuracy: 0.9483 - val_loss: 0.1443 - val_accuracy: 0.9557\n", + "Epoch 2/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0661 - accuracy: 0.9809 - val_loss: 0.0630 - val_accuracy: 0.9822\n", + "Epoch 3/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0482 - accuracy: 0.9854 - val_loss: 0.0597 - val_accuracy: 0.9800\n", + "Epoch 4/20\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0348 - accuracy: 0.9892 - val_loss: 0.0559 - val_accuracy: 0.9823\n", + "Epoch 5/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0280 - accuracy: 0.9908 - val_loss: 0.0510 - val_accuracy: 0.9853\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0220 - accuracy: 0.9929 - val_loss: 0.0401 - val_accuracy: 0.9872\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0176 - accuracy: 0.9940 - val_loss: 0.0456 - val_accuracy: 0.9863\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0150 - accuracy: 0.9953 - val_loss: 0.0503 - val_accuracy: 0.9838\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0148 - accuracy: 0.9954 - val_loss: 0.0466 - val_accuracy: 0.9857\n", "313/313 [==============================] - 0s 1ms/step\n", - "Best configuration: {'batch_normalization': True, 'pooling': 'max'}\n", - "Best accuracy: 0.9868333339691162\n", + "Best configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", + "Best accuracy: 0.9871666431427002\n", "Training best model on full training set...\n", "Epoch 1/100\n", - "3375/3375 [==============================] - 17s 5ms/step - loss: 0.2136 - accuracy: 0.9405 - val_loss: 0.1286 - val_accuracy: 0.9587\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.2050 - accuracy: 0.9421 - val_loss: 0.1101 - val_accuracy: 0.9662\n", "Epoch 2/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0831 - accuracy: 0.9747 - val_loss: 0.0534 - val_accuracy: 0.9828\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0799 - accuracy: 0.9764 - val_loss: 0.0590 - val_accuracy: 0.9823\n", "Epoch 3/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0577 - accuracy: 0.9826 - val_loss: 0.0527 - val_accuracy: 0.9832\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0560 - accuracy: 0.9828 - val_loss: 0.0468 - val_accuracy: 0.9860\n", "Epoch 4/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0436 - accuracy: 0.9865 - val_loss: 0.0447 - val_accuracy: 0.9875\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0447 - accuracy: 0.9862 - val_loss: 0.0415 - val_accuracy: 0.9875\n", "Epoch 5/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0357 - accuracy: 0.9884 - val_loss: 0.0406 - val_accuracy: 0.9870\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0360 - accuracy: 0.9888 - val_loss: 0.0302 - val_accuracy: 0.9895\n", "Epoch 6/100\n", - "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0311 - accuracy: 0.9901 - val_loss: 0.0405 - val_accuracy: 0.9892\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0323 - accuracy: 0.9904 - val_loss: 0.0305 - val_accuracy: 0.9918\n", "Epoch 7/100\n", - "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0256 - accuracy: 0.9919 - val_loss: 0.0463 - val_accuracy: 0.9852\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0265 - accuracy: 0.9919 - val_loss: 0.0336 - val_accuracy: 0.9905\n", "Epoch 8/100\n", - "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0408 - val_accuracy: 0.9878\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0232 - accuracy: 0.9926 - val_loss: 0.0412 - val_accuracy: 0.9883\n", "Epoch 9/100\n", - "3375/3375 [==============================] - 27s 8ms/step - loss: 0.0192 - accuracy: 0.9938 - val_loss: 0.0410 - val_accuracy: 0.9868\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0194 - accuracy: 0.9936 - val_loss: 0.0433 - val_accuracy: 0.9877\n", "Epoch 10/100\n", - "3375/3375 [==============================] - 26s 8ms/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.0374 - val_accuracy: 0.9898\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0184 - accuracy: 0.9939 - val_loss: 0.0320 - val_accuracy: 0.9920\n", "Epoch 11/100\n", - "3375/3375 [==============================] - 27s 8ms/step - loss: 0.0168 - accuracy: 0.9943 - val_loss: 0.0373 - val_accuracy: 0.9898\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.0277 - val_accuracy: 0.9927\n", "Epoch 12/100\n", - "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0143 - accuracy: 0.9954 - val_loss: 0.0344 - val_accuracy: 0.9908\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0161 - accuracy: 0.9948 - val_loss: 0.0279 - val_accuracy: 0.9930\n", "Epoch 13/100\n", - "3375/3375 [==============================] - 25s 7ms/step - loss: 0.0122 - accuracy: 0.9960 - val_loss: 0.0375 - val_accuracy: 0.9893\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0125 - accuracy: 0.9962 - val_loss: 0.0331 - val_accuracy: 0.9922\n", "Epoch 14/100\n", - "3375/3375 [==============================] - 24s 7ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 0.0341 - val_accuracy: 0.9913\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0124 - accuracy: 0.9961 - val_loss: 0.0334 - val_accuracy: 0.9913\n", "Epoch 15/100\n", - "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0114 - accuracy: 0.9962 - val_loss: 0.0392 - val_accuracy: 0.9893\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0133 - accuracy: 0.9955 - val_loss: 0.0391 - val_accuracy: 0.9907\n", "Epoch 16/100\n", - "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0112 - accuracy: 0.9965 - val_loss: 0.0400 - val_accuracy: 0.9890\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0399 - val_accuracy: 0.9900\n", "Epoch 17/100\n", - "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0091 - accuracy: 0.9970 - val_loss: 0.0449 - val_accuracy: 0.9885\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0348 - val_accuracy: 0.9910\n", "Epoch 18/100\n", - "3375/3375 [==============================] - 25s 7ms/step - loss: 0.0106 - accuracy: 0.9964 - val_loss: 0.0416 - val_accuracy: 0.9902\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0101 - accuracy: 0.9968 - val_loss: 0.0294 - val_accuracy: 0.9928\n", "Epoch 19/100\n", - "3375/3375 [==============================] - 20s 6ms/step - loss: 0.0096 - accuracy: 0.9966 - val_loss: 0.0474 - val_accuracy: 0.9885\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0355 - val_accuracy: 0.9928\n", "Epoch 20/100\n", - "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.0411 - val_accuracy: 0.9902\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 0.0308 - val_accuracy: 0.9908\n", "Epoch 21/100\n", - "3375/3375 [==============================] - 20s 6ms/step - loss: 0.0091 - accuracy: 0.9970 - val_loss: 0.0389 - val_accuracy: 0.9898\n", - "Epoch 22/100\n", - "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0085 - accuracy: 0.9967 - val_loss: 0.0333 - val_accuracy: 0.9912\n", - "Epoch 23/100\n", - "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0070 - accuracy: 0.9975 - val_loss: 0.0488 - val_accuracy: 0.9883\n", - "Epoch 24/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0098 - accuracy: 0.9968 - val_loss: 0.0336 - val_accuracy: 0.9918\n", - "Epoch 25/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0076 - accuracy: 0.9974 - val_loss: 0.0532 - val_accuracy: 0.9878\n", - "Epoch 26/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.0495 - val_accuracy: 0.9875\n", - "Epoch 27/100\n", - "3375/3375 [==============================] - 15s 5ms/step - loss: 0.0079 - accuracy: 0.9972 - val_loss: 0.0466 - val_accuracy: 0.9890\n", - "Epoch 28/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 0.0361 - val_accuracy: 0.9915\n", - "Epoch 29/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0065 - accuracy: 0.9978 - val_loss: 0.0380 - val_accuracy: 0.9922\n", - "Epoch 30/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.0458 - val_accuracy: 0.9900\n", - "Epoch 31/100\n", - "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0057 - accuracy: 0.9980 - val_loss: 0.0418 - val_accuracy: 0.9910\n", - "Epoch 32/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0460 - val_accuracy: 0.9903\n", - "Test accuracy: 0.9692\n", - "Test loss: 0.1575\n", - "313/313 [==============================] - 1s 2ms/step\n" + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0359 - val_accuracy: 0.9907\n", + "Test accuracy: 0.9700\n", + "Test loss: 0.1298\n", + "313/313 [==============================] - 1s 1ms/step\n" ] } ], @@ -243,7 +217,7 @@ " {'batch_normalization': True, 'pooling': 'avg'}\n", "]\n", "x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=42)\n", - "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n", "best_config = None\n", "best_accuracy = 0\n", "histories = []\n", @@ -258,7 +232,7 @@ " hyperparameters.update(config)\n", " model = create_cnn_model(input_shape, num_classes, hyperparameters)\n", " history = model.fit(x_train, y_train,\n", - " epochs=10,\n", + " epochs=20,\n", " batch_size=32,\n", " validation_data=(x_val, y_val),\n", " verbose=1,\n", @@ -296,7 +270,7 @@ " plt.savefig(f'confusion_matrix_{config[\"pooling\"]}_{config[\"batch_normalization\"]}_{now.strftime(\"%Y-%m-%d\")}.png')\n", " plt.close()\n", " histories.append(history)\n", - " final_acc = history.history['val_accuracy'][-1]\n", + " final_acc = max(history.history['val_accuracy'])\n", "\n", " if final_acc > best_accuracy:\n", " best_accuracy = final_acc\n", @@ -310,7 +284,7 @@ "\n", "# Create and compile the best model\n", "best_model = create_cnn_model(input_shape, num_classes, hyperparameters)\n", - "\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", "# Train on the full training set\n", "history = best_model.fit(x_train, y_train, \n", " epochs=100, \n", @@ -318,16 +292,18 @@ " validation_data=(x_val, y_val),\n", " callbacks=[early_stopping],\n", " verbose=1)\n", + "# Append the last value of val_loss and val_accuracy an additional # of times so it can be plotted on same graph as other models\n", "last_val_loss = history.history['val_loss'][-1]\n", "last_val_accuracy = history.history['val_accuracy'][-1]\n", "for _ in range(len(history.history['val_loss']), 100):\n", " history.history['val_loss'].append(last_val_loss)\n", " history.history['val_accuracy'].append(last_val_accuracy)\n", - "# Evaluate on the test set\n", + "# Evaluate the best model on the test set\n", "test_loss, test_accuracy = best_model.evaluate(x_test, y_test, verbose=0)\n", "print(f\"Test accuracy: {test_accuracy:.4f}\")\n", "print(f\"Test loss: {test_loss:.4f}\")\n", "plt.figure(figsize=(10, 5))\n", + "# Plot the learnin curve of the best model\n", "plt.plot(history.history['accuracy'], label='Training Accuracy')\n", "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n", "plt.title(f'Learning Curve for Best Model: {best_config[\"pooling\"]} {best_config[\"batch_normalization\"]}')\n", @@ -362,7 +338,24 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9871666431427002\n" + ] + } + ], + "source": [ + "print(best_accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -372,32 +365,197 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "def cutout(image, mask_size=16):\n", + "def cutout(image, mask_size=16, num_cutouts=1):\n", " \"\"\"Applies Cutout augmentation to an image.\"\"\"\n", " h, w, _ = image.shape\n", - " x = np.random.randint(0, w)\n", - " y = np.random.randint(0, h)\n", + " image_copy = image.copy()\n", "\n", - " # Ensure the cutout does not go out of bounds\n", - " x1 = np.clip(x - mask_size // 2, 0, w)\n", - " x2 = np.clip(x + mask_size // 2, 0, w)\n", - " y1 = np.clip(y - mask_size // 2, 0, h)\n", - " y2 = np.clip(y + mask_size // 2, 0, h)\n", + " for _ in range(num_cutouts):\n", + " x = np.random.randint(0, w)\n", + " y = np.random.randint(0, h)\n", "\n", - " # Create a copy of the image to avoid modifying the original\n", - " image_copy = image.copy()\n", - " image_copy[y1:y2, x1:x2, :] = 0 # Set the cutout region to black\n", + " # Ensure the cutout does not go out of bounds\n", + " x1 = np.clip(x - mask_size // 2, 0, w)\n", + " x2 = np.clip(x + mask_size // 2, 0, w)\n", + " y1 = np.clip(y - mask_size // 2, 0, h)\n", + " y2 = np.clip(y + mask_size // 2, 0, h)\n", + "\n", + " # Set the cutout region to the mean value of the image\n", + " mask_color = np.mean(image_copy)\n", + " image_copy[y1:y2, x1:x2, :] = mask_color\n", "\n", " return image_copy" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# Random noise\n", + "def add_random_noise(image, noise_factor=0.1):\n", + " \"\"\"Adds random noise to an image.\"\"\"\n", + " noisy_image = image + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=image.shape)\n", + " return np.clip(noisy_image, 0.0, 1.0)\n", + "\n", + "def add_radial_noise(image, noise_factor=0.1):\n", + " \"\"\"Adds radial noise to an image.\"\"\"\n", + " h, w, _ = image.shape\n", + " center_y, center_x = h // 2, w // 2\n", + " y, x = np.ogrid[:h, :w]\n", + " dist_from_center = np.sqrt((x - center_x)**2 + (y - center_y)**2)\n", + " max_dist = np.sqrt(center_x**2 + center_y**2)\n", + " normalized_dist = dist_from_center / max_dist\n", + " noise = np.random.normal(loc=0.0, scale=1.0, size=image.shape)\n", + " radial_noise = noise * normalized_dist[:, :, np.newaxis] * noise_factor\n", + " noisy_image = image + radial_noise\n", + " return np.clip(noisy_image, 0.0, 1.0)\n", + "\n", + "def add_mask(image, mask_size=16, num_masks=1):\n", + " \"\"\"Adds random masks to an image.\"\"\"\n", + " masked_image = image.copy()\n", + " h, w, _ = image.shape\n", + " for _ in range(num_masks):\n", + " y = np.random.randint(0, h - mask_size)\n", + " x = np.random.randint(0, w - mask_size)\n", + " masked_image[y:y+mask_size, x:x+mask_size, :] = 0\n", + " return masked_image\n", + "\n", + "def add_random_noise_mask(image):\n", + " altered_image = add_random_noise(image)\n", + " altered_image = add_mask(altered_image)\n", + " return altered_image\n", + "\n", + "datagen_rand_noise = ImageDataGenerator(\n", + " preprocessing_function=add_random_noise # Noise injection\n", + ")\n", + "\n", + "# Radial noise\n", + "datagen_radial_noise = ImageDataGenerator(\n", + " preprocessing_function=add_radial_noise # Radial noise injection\n", + ")\n", + "\n", + "# Flip\n", + "datagen_flip = ImageDataGenerator(\n", + " horizontal_flip=True,\n", + " vertical_flip=True\n", + ")\n", + "\n", + "# Zoom\n", + "datagen_zoom = ImageDataGenerator(\n", + " zoom_range=0.5 # Zoom in on the image\n", + ")\n", + "\n", + "# Mask\n", + "datagen_mask = ImageDataGenerator(\n", + " preprocessing_function = cutout # Zoom in on the image\n", + ")\n", + "\n", + "# Random noise and zoom\n", + "datagen_rand_noise_zoom = ImageDataGenerator(\n", + " preprocessing_function=add_random_noise, # Noise injection\n", + " zoom_range=0.1 # Zoom in on the image\n", + ")\n", + "\n", + "# Random noise and mask\n", + "datagen_rand_noise_mask = ImageDataGenerator(\n", + " preprocessing_function=add_random_noise_mask, # Noise injection\n", + ")\n", + "\n", + "# No noise\n", + "datagen_no_noise = ImageDataGenerator()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Noise + Mask')" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get a sample image\n", + "sample_image = x_train[0]\n", + "img_arr = img_to_array(sample_image) \n", + "img_arr = img_arr.reshape((1,) + img_arr.shape)\n", + "# Apply different augmentations\n", + "augmented_images = []\n", + "# For the other augmentations, we can use the existing datagenerators\n", + "\n", + "# Apply each datagen separately to the same image\n", + "augmented_images.append(img_arr) # Original image\n", + "\n", + "# Random Noise\n", + "augmented_images.append(datagen_rand_noise.flow(img_arr, batch_size=1))\n", + "\n", + "# Radial Noise\n", + "augmented_images.append(datagen_radial_noise.flow(img_arr, batch_size=1))\n", + "\n", + "# Flip\n", + "augmented_images.append(datagen_flip.flow(img_arr, batch_size=1))\n", + "\n", + "# Zoom\n", + "augmented_images.append(datagen_zoom.flow(img_arr, batch_size=1))\n", + "\n", + "# Mask (Cutout)\n", + "augmented_images.append(datagen_mask.flow(img_arr, batch_size=1))\n", + "\n", + "# Random Noise + Zoom\n", + "augmented_images.append(datagen_rand_noise_zoom.flow(img_arr, batch_size=1))\n", + "\n", + "# No Augmentation\n", + "augmented_images.append(datagen_rand_noise_mask.flow(img_arr, batch_size=1))\n", + "\n", + "f, xyarr = plt.subplots(2, 4, figsize=(10, 5))\n", + "for ax in xyarr.flat:\n", + " ax.axis('off') # This removes all axes, including scale bars\n", + "plt.rcParams.update({'font.size': 12}) # Increase font size\n", + "xyarr[0,0].imshow(augmented_images[0][0].squeeze(), cmap='gray')\n", + "xyarr[0,0].set_title('Original')\n", + "xyarr[0,1].imshow(augmented_images[1][0].squeeze(), cmap='gray')\n", + "xyarr[0,1].set_title('Random Noise')\n", + "xyarr[0,2].imshow(augmented_images[2][0].squeeze(), cmap='gray')\n", + "xyarr[0,2].set_title('Radial Noise')\n", + "xyarr[0,3].imshow(augmented_images[3][0].squeeze(), cmap='gray')\n", + "xyarr[0,3].set_title('Flip') \n", + "xyarr[1,0].imshow(augmented_images[4][0].squeeze(), cmap='gray')\n", + "xyarr[1,0].set_title('Zoom')\n", + "xyarr[1,1].imshow(augmented_images[5][0].squeeze(), cmap='gray')\n", + "xyarr[1,1].set_title('Cutout')\n", + "xyarr[1,2].imshow(augmented_images[6][0].squeeze(), cmap='gray')\n", + "xyarr[1,2].set_title('Noise + Zoom')\n", + "xyarr[1,3].imshow(augmented_images[7][0].squeeze(), cmap='gray')\n", + "xyarr[1,3].set_title('Noise + Mask')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -406,215 +564,187 @@ "text": [ "{'num_layers': 3, 'filters': [32, 64, 64], 'kernel_sizes': [[3, 3], [3, 3], [3, 3]], 'activations': ['relu', 'relu', 'relu'], 'dense_units': 64, 'dense_activation': 'relu', 'batch_normalization': True, 'pooling': 'avg'}\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1963 - accuracy: 0.9473 - val_loss: 0.0956 - val_accuracy: 0.9685\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2032 - accuracy: 0.9444 - val_loss: 0.0979 - val_accuracy: 0.9705\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0700 - accuracy: 0.9792 - val_loss: 0.0603 - val_accuracy: 0.9827\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0710 - accuracy: 0.9791 - val_loss: 0.0918 - val_accuracy: 0.9717\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0469 - accuracy: 0.9859 - val_loss: 0.0517 - val_accuracy: 0.9817\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0485 - accuracy: 0.9853 - val_loss: 0.0865 - val_accuracy: 0.9708\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0405 - accuracy: 0.9873 - val_loss: 0.0437 - val_accuracy: 0.9868\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0383 - accuracy: 0.9884 - val_loss: 0.0981 - val_accuracy: 0.9743\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0312 - accuracy: 0.9905 - val_loss: 0.0539 - val_accuracy: 0.9830\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0314 - accuracy: 0.9902 - val_loss: 0.0525 - val_accuracy: 0.9838\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0270 - accuracy: 0.9918 - val_loss: 0.0376 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0269 - accuracy: 0.9917 - val_loss: 0.0475 - val_accuracy: 0.9862\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0234 - accuracy: 0.9923 - val_loss: 0.0404 - val_accuracy: 0.9880\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0207 - accuracy: 0.9935 - val_loss: 0.0507 - val_accuracy: 0.9850\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0185 - accuracy: 0.9938 - val_loss: 0.0423 - val_accuracy: 0.9880\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0472 - val_accuracy: 0.9852\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0173 - accuracy: 0.9944 - val_loss: 0.0383 - val_accuracy: 0.9900\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0168 - accuracy: 0.9946 - val_loss: 0.0444 - val_accuracy: 0.9867\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0396 - val_accuracy: 0.9880\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0411 - val_accuracy: 0.9885\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0138 - accuracy: 0.9956 - val_loss: 0.0468 - val_accuracy: 0.9885\n", + "1688/1688 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5ms/step - loss: 0.0355 - accuracy: 0.9891 - val_loss: 0.0419 - val_accuracy: 0.9880\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0353 - accuracy: 0.9892 - val_loss: 0.0363 - val_accuracy: 0.9882\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0289 - accuracy: 0.9912 - val_loss: 0.0646 - val_accuracy: 0.9788\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0280 - accuracy: 0.9911 - val_loss: 0.0498 - val_accuracy: 0.9847\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0233 - accuracy: 0.9925 - val_loss: 0.0428 - val_accuracy: 0.9883\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0217 - accuracy: 0.9932 - val_loss: 0.0551 - val_accuracy: 0.9840\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 0.0458 - val_accuracy: 0.9872\n", - "Epoch 8/15\n", - 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val_accuracy: 0.9650\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1066 - accuracy: 0.9674 - val_loss: 0.1680 - val_accuracy: 0.9498\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1042 - accuracy: 0.9673 - val_loss: 0.1422 - val_accuracy: 0.9568\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0990 - accuracy: 0.9691 - val_loss: 0.0990 - val_accuracy: 0.9698\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0946 - accuracy: 0.9709 - val_loss: 0.1196 - val_accuracy: 0.9647\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0921 - accuracy: 0.9714 - val_loss: 0.1025 - val_accuracy: 0.9683\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0861 - accuracy: 0.9734 - val_loss: 0.1603 - val_accuracy: 0.9517\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0846 - accuracy: 0.9738 - val_loss: 0.1349 - val_accuracy: 0.9568\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0822 - accuracy: 0.9736 - val_loss: 0.1076 - val_accuracy: 0.9672\n", - "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0785 - accuracy: 0.9749 - val_loss: 0.0845 - val_accuracy: 0.9753\n", - "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0723 - accuracy: 0.9776 - val_loss: 0.0972 - val_accuracy: 0.9693\n", - "Epoch 13/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0707 - accuracy: 0.9771 - val_loss: 0.0920 - val_accuracy: 0.9723\n", - "Epoch 14/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0695 - accuracy: 0.9779 - val_loss: 0.0799 - val_accuracy: 0.9777\n", - "Epoch 15/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0626 - accuracy: 0.9805 - val_loss: 0.1149 - val_accuracy: 0.9657\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0805 - accuracy: 0.9751 - val_loss: 0.1023 - val_accuracy: 0.9693\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2027 - accuracy: 0.9449 - val_loss: 0.1071 - val_accuracy: 0.9672\n", + "1688/1688 [==============================] - 14s 7ms/step - loss: 0.3872 - accuracy: 0.8849 - val_loss: 0.2624 - val_accuracy: 0.9168\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0743 - accuracy: 0.9780 - val_loss: 0.0726 - val_accuracy: 0.9788\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1758 - accuracy: 0.9457 - val_loss: 0.0840 - val_accuracy: 0.9747\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0526 - accuracy: 0.9843 - val_loss: 0.0628 - val_accuracy: 0.9818\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1351 - accuracy: 0.9580 - val_loss: 0.1059 - val_accuracy: 0.9705\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0440 - accuracy: 0.9863 - val_loss: 0.0479 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.1157 - accuracy: 0.9636 - val_loss: 0.0751 - val_accuracy: 0.9758\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0346 - accuracy: 0.9895 - val_loss: 0.0499 - val_accuracy: 0.9852\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.1014 - accuracy: 0.9688 - val_loss: 0.0811 - val_accuracy: 0.9757\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0283 - accuracy: 0.9911 - val_loss: 0.0347 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0906 - accuracy: 0.9719 - val_loss: 0.0554 - val_accuracy: 0.9830\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0258 - accuracy: 0.9915 - val_loss: 0.0344 - val_accuracy: 0.9890\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0846 - accuracy: 0.9737 - val_loss: 0.0519 - val_accuracy: 0.9848\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0223 - accuracy: 0.9923 - val_loss: 0.0418 - val_accuracy: 0.9875\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0769 - accuracy: 0.9768 - val_loss: 0.0555 - val_accuracy: 0.9817\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0181 - accuracy: 0.9945 - val_loss: 0.0638 - val_accuracy: 0.9815\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0722 - accuracy: 0.9776 - val_loss: 0.0927 - val_accuracy: 0.9713\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0169 - accuracy: 0.9946 - val_loss: 0.0443 - val_accuracy: 0.9882\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0685 - accuracy: 0.9781 - val_loss: 0.0403 - val_accuracy: 0.9863\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0158 - accuracy: 0.9950 - val_loss: 0.0335 - val_accuracy: 0.9903\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.0495 - val_accuracy: 0.9853\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 0.0627 - val_accuracy: 0.9838\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0618 - accuracy: 0.9804 - val_loss: 0.0312 - val_accuracy: 0.9903\n", "Epoch 13/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0129 - accuracy: 0.9956 - val_loss: 0.0330 - val_accuracy: 0.9908\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0568 - accuracy: 0.9820 - val_loss: 0.0455 - val_accuracy: 0.9857\n", "Epoch 14/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0439 - val_accuracy: 0.9888\n", + "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0557 - accuracy: 0.9824 - val_loss: 0.0470 - val_accuracy: 0.9872\n", "Epoch 15/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0104 - accuracy: 0.9969 - val_loss: 0.0462 - val_accuracy: 0.9885\n", + "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0501 - accuracy: 0.9841 - val_loss: 0.0431 - val_accuracy: 0.9863\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.4530 - accuracy: 0.8600 - val_loss: 0.0945 - val_accuracy: 0.9707\n", + "1688/1688 [==============================] - 10s 5ms/step - loss: 0.4048 - accuracy: 0.8751 - val_loss: 0.1133 - val_accuracy: 0.9643\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2316 - accuracy: 0.9260 - val_loss: 0.1051 - val_accuracy: 0.9693\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2047 - accuracy: 0.9341 - val_loss: 0.0577 - val_accuracy: 0.9818\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1934 - accuracy: 0.9377 - val_loss: 0.0597 - val_accuracy: 0.9792\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1676 - accuracy: 0.9458 - val_loss: 0.0736 - val_accuracy: 0.9777\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 6ms/step - loss: 0.1695 - accuracy: 0.9454 - val_loss: 0.0512 - val_accuracy: 0.9845\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1455 - accuracy: 0.9523 - val_loss: 0.0569 - val_accuracy: 0.9822\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1527 - accuracy: 0.9502 - val_loss: 0.0508 - val_accuracy: 0.9825\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1290 - accuracy: 0.9578 - val_loss: 0.0488 - val_accuracy: 0.9847\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1415 - accuracy: 0.9538 - val_loss: 0.0563 - val_accuracy: 0.9820\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1233 - accuracy: 0.9599 - val_loss: 0.1163 - val_accuracy: 0.9640\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1264 - accuracy: 0.9593 - val_loss: 0.0526 - val_accuracy: 0.9825\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1133 - accuracy: 0.9632 - val_loss: 0.0374 - val_accuracy: 0.9893\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1224 - accuracy: 0.9594 - val_loss: 0.0372 - val_accuracy: 0.9892\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1054 - accuracy: 0.9657 - val_loss: 0.0331 - val_accuracy: 0.9893\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1176 - accuracy: 0.9610 - val_loss: 0.0306 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0980 - accuracy: 0.9681 - val_loss: 0.0295 - val_accuracy: 0.9908\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1109 - accuracy: 0.9629 - val_loss: 0.0303 - val_accuracy: 0.9910\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0942 - accuracy: 0.9692 - val_loss: 0.0334 - val_accuracy: 0.9912\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1043 - accuracy: 0.9667 - val_loss: 0.0444 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0897 - accuracy: 0.9697 - val_loss: 0.0386 - val_accuracy: 0.9897\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1007 - accuracy: 0.9666 - val_loss: 0.0335 - val_accuracy: 0.9907\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0857 - accuracy: 0.9719 - val_loss: 0.0238 - val_accuracy: 0.9942\n", "Epoch 13/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0989 - accuracy: 0.9677 - val_loss: 0.0333 - val_accuracy: 0.9908\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0823 - accuracy: 0.9733 - val_loss: 0.0324 - val_accuracy: 0.9903\n", "Epoch 14/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0943 - accuracy: 0.9689 - val_loss: 0.0317 - val_accuracy: 0.9907\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0829 - accuracy: 0.9730 - val_loss: 0.0278 - val_accuracy: 0.9922\n", "Epoch 15/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0937 - accuracy: 0.9702 - val_loss: 0.0324 - val_accuracy: 0.9913\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0780 - accuracy: 0.9740 - val_loss: 0.0263 - val_accuracy: 0.9935\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.2142 - accuracy: 0.9411 - val_loss: 0.1341 - val_accuracy: 0.9570\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.2152 - accuracy: 0.9414 - val_loss: 0.1105 - val_accuracy: 0.9652\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0808 - accuracy: 0.9766 - val_loss: 0.0613 - val_accuracy: 0.9835\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0828 - accuracy: 0.9747 - val_loss: 0.0591 - val_accuracy: 0.9830\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0583 - accuracy: 0.9826 - val_loss: 0.0592 - val_accuracy: 0.9842\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0572 - accuracy: 0.9827 - val_loss: 0.0605 - val_accuracy: 0.9830\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0480 - accuracy: 0.9850 - val_loss: 0.0418 - val_accuracy: 0.9873\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0483 - accuracy: 0.9857 - val_loss: 0.0557 - val_accuracy: 0.9845\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0403 - accuracy: 0.9876 - val_loss: 0.0553 - val_accuracy: 0.9833\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.0477 - val_accuracy: 0.9850\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0322 - accuracy: 0.9900 - val_loss: 0.0325 - val_accuracy: 0.9913\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0318 - accuracy: 0.9898 - val_loss: 0.0597 - val_accuracy: 0.9820\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0282 - accuracy: 0.9912 - val_loss: 0.0324 - val_accuracy: 0.9918\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0284 - accuracy: 0.9909 - val_loss: 0.0447 - val_accuracy: 0.9872\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0249 - accuracy: 0.9919 - val_loss: 0.0458 - val_accuracy: 0.9863\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0247 - accuracy: 0.9922 - val_loss: 0.0392 - val_accuracy: 0.9898\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0221 - accuracy: 0.9931 - val_loss: 0.0445 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0228 - accuracy: 0.9925 - val_loss: 0.0378 - val_accuracy: 0.9897\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0216 - accuracy: 0.9932 - val_loss: 0.0300 - val_accuracy: 0.9908\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0197 - accuracy: 0.9938 - val_loss: 0.0415 - val_accuracy: 0.9885\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0157 - accuracy: 0.9949 - val_loss: 0.0336 - val_accuracy: 0.9910\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0172 - accuracy: 0.9941 - val_loss: 0.0371 - val_accuracy: 0.9910\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0181 - accuracy: 0.9942 - val_loss: 0.0311 - val_accuracy: 0.9920\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0520 - val_accuracy: 0.9867\n", "Epoch 13/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0142 - accuracy: 0.9953 - val_loss: 0.0300 - val_accuracy: 0.9913\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0143 - accuracy: 0.9950 - val_loss: 0.0434 - val_accuracy: 0.9888\n", "Epoch 14/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.0351 - val_accuracy: 0.9910\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0342 - val_accuracy: 0.9897\n", "Epoch 15/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0331 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0124 - accuracy: 0.9961 - val_loss: 0.0375 - val_accuracy: 0.9897\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.5823 - accuracy: 0.8147 - val_loss: 0.2716 - val_accuracy: 0.9188\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.3495 - accuracy: 0.8859 - val_loss: 0.6366 - val_accuracy: 0.8153\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2894 - accuracy: 0.9044 - val_loss: 0.3201 - val_accuracy: 0.8967\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2592 - accuracy: 0.9145 - val_loss: 0.5202 - val_accuracy: 0.8427\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 10s 5ms/step - loss: 0.1946 - accuracy: 0.9469 - val_loss: 0.0766 - val_accuracy: 0.9775\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1908 - accuracy: 0.9495 - val_loss: 0.0904 - val_accuracy: 0.9715\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0674 - accuracy: 0.9802 - val_loss: 0.1005 - val_accuracy: 0.9683\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0641 - accuracy: 0.9811 - val_loss: 0.0690 - val_accuracy: 0.9777\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0461 - accuracy: 0.9862 - val_loss: 0.0664 - val_accuracy: 0.9775\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0467 - accuracy: 0.9864 - val_loss: 0.0520 - val_accuracy: 0.9835\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0354 - accuracy: 0.9890 - val_loss: 0.0619 - val_accuracy: 0.9815\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0340 - accuracy: 0.9894 - val_loss: 0.0493 - val_accuracy: 0.9867\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0291 - accuracy: 0.9911 - val_loss: 0.0470 - val_accuracy: 0.9865\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0264 - accuracy: 0.9918 - val_loss: 0.0435 - val_accuracy: 0.9852\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0225 - accuracy: 0.9929 - val_loss: 0.0532 - val_accuracy: 0.9832\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0242 - accuracy: 0.9925 - val_loss: 0.0399 - val_accuracy: 0.9878\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0179 - accuracy: 0.9939 - val_loss: 0.0411 - val_accuracy: 0.9882\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0473 - val_accuracy: 0.9848\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0154 - accuracy: 0.9953 - val_loss: 0.0361 - val_accuracy: 0.9892\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0153 - accuracy: 0.9951 - val_loss: 0.0425 - val_accuracy: 0.9878\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0142 - accuracy: 0.9951 - val_loss: 0.0435 - val_accuracy: 0.9862\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0147 - accuracy: 0.9952 - val_loss: 0.0310 - val_accuracy: 0.9902\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0119 - accuracy: 0.9960 - val_loss: 0.0534 - val_accuracy: 0.9848\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0122 - accuracy: 0.9959 - val_loss: 0.0392 - val_accuracy: 0.9873\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0416 - val_accuracy: 0.9877\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9964 - val_loss: 0.0412 - val_accuracy: 0.9887\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0882 - val_accuracy: 0.9777\n", - "Epoch 13/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0097 - accuracy: 0.9968 - val_loss: 0.0335 - val_accuracy: 0.9907\n", - "Epoch 14/15\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0083 - accuracy: 0.9969 - val_loss: 0.0339 - val_accuracy: 0.9912\n", - "Epoch 15/15\n", - "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0074 - accuracy: 0.9974 - val_loss: 0.0362 - val_accuracy: 0.9903\n" + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 0.0389 - val_accuracy: 0.9897\n" ] } ], @@ -629,39 +759,10 @@ "best_model_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "best_model_mask = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "best_model_rand_noise_zoom = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "best_model_rand_noise_mask = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "best_model_no_noise = create_cnn_model(input_shape, num_classes, hyperparameters)\n", "\n", - "# Random noise\n", - "datagen_rand_noise = ImageDataGenerator(\n", - " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1) # Noise injection\n", - ")\n", - "\n", - "# Radial noise\n", - "datagen_radial_noise = ImageDataGenerator(\n", - " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape) * np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2)).reshape(1, x.shape[1], x.shape[2], 1) / np.max(np.sqrt(np.sum(np.square(np.indices(x.shape[1:3]).T - np.array(x.shape[1:3])/2), axis=2))), 0, 1) # Radial noise injection\n", - ")\n", - "\n", - "# Flip\n", - "datagen_flip = ImageDataGenerator(\n", - " horizontal_flip=True,\n", - " vertical_flip=True\n", - ")\n", - "\n", - "# Zoom\n", - "datagen_zoom = ImageDataGenerator(\n", - " zoom_range=0.1 # Zoom in on the image\n", - ")\n", "\n", - "# Mask\n", - "datagen_mask = ImageDataGenerator(\n", - " preprocessing_function = lambda x: cutout(x, mask_size=16) # Zoom in on the image\n", - ")\n", - "datagen_rand_noise_zoom = ImageDataGenerator(\n", - " preprocessing_function=lambda x: np.clip(x + np.random.normal(0, 0.1, x.shape), 0, 1), # Noise injection\n", - " zoom_range=0.1 # Zoom in on the image\n", - ")\n", - "# No noise\n", - "datagen_no_noise = ImageDataGenerator()\n", "\n", "datagen_val = ImageDataGenerator()\n", "# Wrap the datagen with our custom generator\n", @@ -671,18 +772,25 @@ "train_generator_zoom = datagen_zoom.flow(x_train, y_train, batch_size=32)\n", "train_generator_mask = datagen_mask.flow(x_train, y_train, batch_size=32)\n", "train_generator_rand_noise_zoom = datagen_rand_noise_zoom.flow(x_train, y_train, batch_size=32)\n", + "train_generator_rand_noise_mask = datagen_rand_noise_mask.flow(x_train, y_train, batch_size=32)\n", "train_generator_no_noise = datagen_no_noise.flow(x_train, y_train, batch_size=32)\n", "val_generator = datagen_val.flow(x_val, y_val, batch_size=32)\n", "# Train the model with custom augmentation\n", "augmentation_loss_df = pd.DataFrame()\n", "augmentation_accuracy_df = pd.DataFrame()\n", - "\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n", "# Random noise\n", "history_rand_noise = best_model_rand_noise.fit(train_generator_rand_noise,\n", " epochs=15,\n", " validation_data=val_generator, \n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_rand_noise.history['val_loss'][-1]\n", + "last_val_accuracy = history_rand_noise.history['val_accuracy'][-1]\n", + "for _ in range(len(history_rand_noise.history['val_loss']), 16):\n", + " history_rand_noise.history['val_loss'].append(last_val_loss)\n", + " history_rand_noise.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"rand_noise\"] = history_rand_noise.history['val_loss']\n", "augmentation_accuracy_df[f\"rand_noise\"] = history_rand_noise.history['val_accuracy']\n", "\n", @@ -690,8 +798,14 @@ "history_radial_noise = best_model_radial_noise.fit(train_generator_radial_noise,\n", " epochs=15,\n", " validation_data=val_generator, \n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_radial_noise.history['val_loss'][-1]\n", + "last_val_accuracy = history_radial_noise.history['val_accuracy'][-1]\n", + "for _ in range(len(history_radial_noise.history['val_loss']), 16):\n", + " history_radial_noise.history['val_loss'].append(last_val_loss)\n", + " history_radial_noise.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"radial_noise\"] = history_radial_noise.history['val_loss']\n", "augmentation_accuracy_df[f\"radial_noise\"] = history_radial_noise.history['val_accuracy']\n", "\n", @@ -700,8 +814,14 @@ "history_flip = best_model_flip.fit(train_generator_flip,\n", " epochs=15,\n", " validation_data=val_generator,\n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_flip.history['val_loss'][-1]\n", + "last_val_accuracy = history_flip.history['val_accuracy'][-1]\n", + "for _ in range(len(history_flip.history['val_loss']), 16):\n", + " history_flip.history['val_loss'].append(last_val_loss)\n", + " history_flip.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"flip\"] = history_flip.history['val_loss']\n", "augmentation_accuracy_df[f\"flip\"] = history_flip.history['val_accuracy']\n", "\n", @@ -709,8 +829,14 @@ "history_zoom = best_model_zoom.fit(train_generator_zoom,\n", " epochs=15,\n", " validation_data=val_generator,\n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_zoom.history['val_loss'][-1]\n", + "last_val_accuracy = history_zoom.history['val_accuracy'][-1]\n", + "for _ in range(len(history_zoom.history['val_loss']), 16):\n", + " history_zoom.history['val_loss'].append(last_val_loss)\n", + " history_zoom.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"zoom\"] = history_zoom.history['val_loss']\n", "augmentation_accuracy_df[f\"zoom\"] = history_zoom.history['val_accuracy']\n", "\n", @@ -718,8 +844,14 @@ "history_mask = best_model_mask.fit(train_generator_mask,\n", " epochs=15,\n", " validation_data=val_generator,\n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_mask.history['val_loss'][-1]\n", + "last_val_accuracy = history_mask.history['val_accuracy'][-1]\n", + "for _ in range(len(history_mask.history['val_loss']), 16):\n", + " history_mask.history['val_loss'].append(last_val_loss)\n", + " history_mask.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"mask\"] = history_mask.history['val_loss']\n", "augmentation_accuracy_df[f\"mask\"] = history_mask.history['val_accuracy']\n", "\n", @@ -727,20 +859,48 @@ "history_rand_noise_zoom = best_model_rand_noise_zoom.fit(train_generator_rand_noise_zoom,\n", " epochs=15,\n", " validation_data=val_generator,\n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_rand_noise_zoom.history['val_loss'][-1]\n", + "last_val_accuracy = history_rand_noise_zoom.history['val_accuracy'][-1]\n", + "for _ in range(len(history_rand_noise_zoom.history['val_loss']), 16):\n", + " history_rand_noise_zoom.history['val_loss'].append(last_val_loss)\n", + " history_rand_noise_zoom.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"rand_noise_zoom\"] = history_rand_noise_zoom.history['val_loss']\n", "augmentation_accuracy_df[f\"rand_noise_zoom\"] = history_rand_noise_zoom.history['val_accuracy']\n", "\n", + "# Random noise and mask\n", + "history_rand_noise_mask = best_model_rand_noise_mask.fit(train_generator_rand_noise_mask,\n", + " epochs=15,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", + " )\n", + "last_val_loss = history_rand_noise_mask.history['val_loss'][-1]\n", + "last_val_accuracy = history_rand_noise_mask.history['val_accuracy'][-1]\n", + "for _ in range(len(history_rand_noise_mask.history['val_loss']), 16):\n", + " history_rand_noise_mask.history['val_loss'].append(last_val_loss)\n", + " history_rand_noise_mask.history['val_accuracy'].append(last_val_accuracy)\n", + "augmentation_loss_df[f\"rand_noise_mask\"] = history_rand_noise_mask.history['val_loss']\n", + "augmentation_accuracy_df[f\"rand_noise_mask\"] = history_rand_noise_mask.history['val_accuracy']\n", + "\n", "# No noise\n", "history_no_noise = best_model_no_noise.fit(train_generator_no_noise,\n", " epochs=15,\n", " validation_data=val_generator,\n", - " verbose=1\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", " )\n", + "last_val_loss = history_no_noise.history['val_loss'][-1]\n", + "last_val_accuracy = history_no_noise.history['val_accuracy'][-1]\n", + "for _ in range(len(history_no_noise.history['val_loss']), 16):\n", + " history_no_noise.history['val_loss'].append(last_val_loss)\n", + " history_no_noise.history['val_accuracy'].append(last_val_accuracy)\n", "augmentation_loss_df[f\"no_noise\"] = history_no_noise.history['val_loss']\n", "augmentation_accuracy_df[f\"no_noise\"] = history_no_noise.history['val_accuracy']\n", "\n", + "# Save the dataframes to CSV files\n", "augmentation_loss_df.to_csv('augmentation_loss.csv', index=False)\n", "augmentation_accuracy_df.to_csv('augmentation_accuracy.csv', index=False)\n", "\n" @@ -748,27 +908,29 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Rand noise test accuracy: 0.9705\n", - "Rand noise test loss: 0.1497\n", - "Radial noise test accuracy: 0.9721\n", - "Radial noise test loss: 0.1365\n", - "Flip test accuracy: 0.9229\n", - "Flip test loss: 0.2834\n", - "Zoom test accuracy: 0.9672\n", - "Zoom test loss: 0.1553\n", - "Mask test accuracy: 0.9626\n", - "Mask test loss: 0.1412\n", - "Rand noise and zoom test accuracy: 0.9667\n", - "Rand noise and zoom test loss: 0.1692\n", - "No noise test accuracy: 0.9665\n", - "No noise test loss: 0.1730\n" + "Rand noise test accuracy: 0.9647\n", + "Rand noise test loss: 0.1557\n", + "Radial noise test accuracy: 0.9623\n", + "Radial noise test loss: 0.1362\n", + "Flip test accuracy: 0.9259\n", + "Flip test loss: 0.2598\n", + "Zoom test accuracy: 0.9647\n", + "Zoom test loss: 0.1349\n", + "Mask test accuracy: 0.9693\n", + "Mask test loss: 0.1327\n", + "Rand noise and zoom test accuracy: 0.9697\n", + "Rand noise and zoom test loss: 0.1519\n", + "Rand noise and mask test accuracy: 0.8253\n", + "Rand noise and mask test loss: 0.5927\n", + "No noise test accuracy: 0.9670\n", + "No noise test loss: 0.1402\n" ] } ], @@ -791,6 +953,9 @@ "rand_noise_zoom_loss, rand_noise_zoom_accuracy = best_model_rand_noise_zoom.evaluate(x_test, y_test, verbose=0)\n", "print(f\"Rand noise and zoom test accuracy: {rand_noise_zoom_accuracy:.4f}\")\n", "print(f\"Rand noise and zoom test loss: {rand_noise_zoom_loss:.4f}\")\n", + "rand_noise_mask_loss, rand_noise_mask_accuracy = best_model_rand_noise_mask.evaluate(x_test, y_test, verbose=0)\n", + "print(f\"Rand noise and mask test accuracy: {rand_noise_mask_accuracy:.4f}\")\n", + "print(f\"Rand noise and mask test loss: {rand_noise_mask_loss:.4f}\")\n", "no_noise_loss, no_noise_accuracy = best_model_no_noise.evaluate(x_test, y_test, verbose=0)\n", "print(f\"No noise test accuracy: {no_noise_accuracy:.4f}\")\n", "print(f\"No noise test loss: {no_noise_loss:.4f}\")\n" @@ -798,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -806,12 +971,13 @@ "output_type": "stream", "text": [ "313/313 [==============================] - 1s 2ms/step\n", - "313/313 [==============================] - 1s 2ms/step\n", - "313/313 [==============================] - 1s 2ms/step\n", - "313/313 [==============================] - 1s 1ms/step\n", - "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 0s 1ms/step\n", + "313/313 [==============================] - 0s 1ms/step\n", + "313/313 [==============================] - 0s 1ms/step\n", + "313/313 [==============================] - 0s 1ms/step\n", + "313/313 [==============================] - 0s 1ms/step\n", "313/313 [==============================] - 1s 1ms/step\n", - "313/313 [==============================] - 1s 2ms/step\n" + "313/313 [==============================] - 0s 1ms/step\n" ] } ], @@ -824,6 +990,7 @@ " (best_model_zoom, \"Zoom\"),\n", " (best_model_mask, \"Mask\"),\n", " (best_model_rand_noise_zoom, \"Random Noise + Zoom\"),\n", + " (best_model_rand_noise_mask, \"Random Noise + Mask\"),\n", " (best_model_no_noise, \"No Noise\")\n", "]\n", "\n", diff --git a/testing/augmentation_accuracy.csv b/testing/augmentation_accuracy.csv index 6aae28b..4ad65fb 100644 --- a/testing/augmentation_accuracy.csv +++ b/testing/augmentation_accuracy.csv @@ -1,16 +1,17 @@ -rand_noise,radial_noise,flip,zoom,mask,rand_noise_zoom,no_noise -0.968500018119812,0.9261666536331177,0.8453333377838135,0.9671666622161865,0.9706666469573975,0.9570000171661377,0.9775000214576721 -0.9826666712760925,0.9808333516120911,0.9150000214576721,0.9788333177566528,0.9693333506584167,0.9835000038146973,0.9683333039283752 -0.9816666841506958,0.9775000214576721,0.9265000224113464,0.9818333387374878,0.9791666865348816,0.98416668176651,0.9775000214576721 -0.9868333339691162,0.9879999756813049,0.9428333044052124,0.9866666793823242,0.984499990940094,0.987333357334137,0.9815000295639038 -0.9829999804496765,0.9788333177566528,0.9679999947547913,0.9851666688919067,0.9825000166893005,0.9833333492279053,0.9865000247955322 -0.9896666407585144,0.9883333444595337,0.9649999737739563,0.9901666641235352,0.9819999933242798,0.9913333058357239,0.9831666946411133 -0.9879999756813049,0.9871666431427002,0.9568333625793457,0.9890000224113464,0.9825000166893005,0.9918333292007446,0.9881666898727417 -0.9879999756813049,0.9856666922569275,0.9646666646003723,0.987500011920929,0.9891666769981384,0.9863333106040955,0.9891666769981384 -0.9900000095367432,0.9858333468437195,0.9516666531562805,0.9815000295639038,0.9901666641235352,0.9866666793823242,0.9861666560173035 -0.9879999756813049,0.9881666898727417,0.9671666622161865,0.9881666898727417,0.9909999966621399,0.9908333420753479,0.9848333597183228 -0.9884999990463257,0.9891666769981384,0.9753333330154419,0.9903333187103271,0.9871666431427002,0.9909999966621399,0.987666666507721 -0.9903333187103271,0.9878333210945129,0.9693333506584167,0.9838333129882812,0.9906666874885559,0.9919999837875366,0.9776666760444641 -0.9913333058357239,0.9901666641235352,0.9723333120346069,0.9908333420753479,0.9908333420753479,0.9913333058357239,0.9906666874885559 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zZ^U^v9t}R5PH^}g5P%66(vQB41`|LCF@}p5137110zBxOp*QPc`KVj<#JEE5d*|mf zV*${48Xi>IJ9iM83e2N*T8<<2K%~^L#D#1sZGzKFAcUXKpxf6deqIk7f(Jy}hZls%X3eRic&S!ffPG9gJV5~X%HYW*(F@qBz#UROp$(2>H`lR zRJq`iaxBH&-vpzb?JBqj5FCKfWpY6z6|~}*!bwkGS=-v;|JdxTV9iB#>A7*yz`~&! zYqV9lX6uy@_|Ar+`W-OJm~O#Vhi`Hg9y=`PuAk+$v`BLMpg&(HaKJ1LzT58JUMEzT z!4x@=d>@8__$7X1q?W%`R8$NCp^I@WOwJD;5O~23A~Y!IOVBM5ur9;@iFRf~eE6_n z*}n?NY66&bX97JB?*L?z58i;x(BKe$=68eOaWFt16Jc=%@TZ8J+!bK7&4Lf8*#Hh9 zd=4(i-;j?3QCb{uIC+M^EB2v;#Kk9Ub^B=N4X5!J*sv%}fh$}xEDTt{iT@1~4J+j1 zp%@RIe09W?{C%)9q+@2D&G$TF2;^J;2fSy8ohJS7Ile%~-u>1Ile?l_#bDf2X=#Pq94ZH@!r+!Vu9aN>t`i J-ovLa{~yQ83)}zz From 7a8dceb8be5bac3e330ee0cea58e8ee6342ab96f Mon Sep 17 00:00:00 2001 From: James Overmeyer Date: Sun, 27 Oct 2024 20:22:27 -0400 Subject: [PATCH 8/9] Pushing an updated CNN-testing.ipynb and the figures that I used in the paper --- testing/CNN-testing.ipynb | 687 ++++++++++++------ .../confusion_matrix_mask_long_2024-10-27.png | Bin 0 -> 55130 bytes ...trix_random_noise_zoom_long_2024-10-27.png | Bin 0 -> 56419 bytes testing/learning_curves_2024-10-27.png | Bin 0 -> 65474 bytes 4 files changed, 479 insertions(+), 208 deletions(-) create mode 100644 testing/confusion_matrix_mask_long_2024-10-27.png create mode 100644 testing/confusion_matrix_random_noise_zoom_long_2024-10-27.png create mode 100644 testing/learning_curves_2024-10-27.png diff --git a/testing/CNN-testing.ipynb b/testing/CNN-testing.ipynb index 499380f..0127519 100644 --- a/testing/CNN-testing.ipynb +++ b/testing/CNN-testing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 57, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -68,136 +68,170 @@ "text": [ "Testing configuration: {'batch_normalization': False, 'pooling': 'max'}\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 7s 4ms/step - loss: 0.4911 - accuracy: 0.8421 - val_loss: 0.2453 - val_accuracy: 0.9215\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.4720 - accuracy: 0.8490 - val_loss: 0.2665 - val_accuracy: 0.9190\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1757 - accuracy: 0.9455 - val_loss: 0.1625 - val_accuracy: 0.9467\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1775 - accuracy: 0.9446 - val_loss: 0.1600 - val_accuracy: 0.9487\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1200 - accuracy: 0.9631 - val_loss: 0.1319 - val_accuracy: 0.9593\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1211 - accuracy: 0.9625 - val_loss: 0.1345 - val_accuracy: 0.9548\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0912 - accuracy: 0.9715 - val_loss: 0.1222 - val_accuracy: 0.9615\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0912 - accuracy: 0.9716 - val_loss: 0.1098 - val_accuracy: 0.9640\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0696 - accuracy: 0.9779 - val_loss: 0.1241 - val_accuracy: 0.9622\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0718 - accuracy: 0.9772 - val_loss: 0.1086 - val_accuracy: 0.9653\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 5s 3ms/step - loss: 0.0546 - accuracy: 0.9818 - val_loss: 0.0990 - val_accuracy: 0.9707\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0550 - accuracy: 0.9820 - val_loss: 0.1061 - val_accuracy: 0.9682\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0427 - accuracy: 0.9861 - val_loss: 0.1041 - val_accuracy: 0.9692\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0466 - accuracy: 0.9845 - val_loss: 0.0994 - val_accuracy: 0.9687\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0345 - accuracy: 0.9890 - val_loss: 0.1223 - val_accuracy: 0.9695\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0365 - accuracy: 0.9878 - val_loss: 0.0914 - val_accuracy: 0.9742\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0301 - accuracy: 0.9904 - val_loss: 0.1018 - val_accuracy: 0.9740\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0297 - accuracy: 0.9903 - val_loss: 0.1214 - val_accuracy: 0.9680\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0270 - accuracy: 0.9906 - val_loss: 0.1207 - val_accuracy: 0.9690\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0235 - accuracy: 0.9921 - val_loss: 0.1119 - val_accuracy: 0.9720\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'max'}\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2146 - accuracy: 0.9421 - val_loss: 0.1013 - val_accuracy: 0.9692\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.2114 - accuracy: 0.9427 - val_loss: 0.1088 - val_accuracy: 0.9647\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0702 - accuracy: 0.9795 - val_loss: 0.0660 - val_accuracy: 0.9787\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0666 - accuracy: 0.9806 - val_loss: 0.0741 - val_accuracy: 0.9760\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0478 - accuracy: 0.9854 - val_loss: 0.0700 - val_accuracy: 0.9773\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0442 - accuracy: 0.9871 - val_loss: 0.0654 - val_accuracy: 0.9817\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0338 - accuracy: 0.9899 - val_loss: 0.0664 - val_accuracy: 0.9808\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0326 - accuracy: 0.9900 - val_loss: 0.1068 - val_accuracy: 0.9673\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0278 - accuracy: 0.9914 - val_loss: 0.0831 - val_accuracy: 0.9752\n", - "313/313 [==============================] - 0s 1ms/step\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0249 - accuracy: 0.9922 - val_loss: 0.0959 - val_accuracy: 0.9707\n", + "Epoch 6/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0205 - accuracy: 0.9936 - val_loss: 0.0579 - val_accuracy: 0.9830\n", + "Epoch 7/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0166 - accuracy: 0.9950 - val_loss: 0.0633 - val_accuracy: 0.9832\n", + "Epoch 8/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0150 - accuracy: 0.9951 - val_loss: 0.0374 - val_accuracy: 0.9890\n", + "Epoch 9/20\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0114 - accuracy: 0.9961 - val_loss: 0.0507 - val_accuracy: 0.9852\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0132 - accuracy: 0.9955 - val_loss: 0.0794 - val_accuracy: 0.9782\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0104 - accuracy: 0.9965 - val_loss: 0.0442 - val_accuracy: 0.9878\n", + "313/313 [==============================] - 1s 1ms/step\n", "Testing configuration: {'batch_normalization': False, 'pooling': 'avg'}\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.6473 - accuracy: 0.7879 - val_loss: 0.3415 - val_accuracy: 0.8885\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.6542 - accuracy: 0.7847 - val_loss: 0.3321 - val_accuracy: 0.8920\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.2587 - accuracy: 0.9183 - val_loss: 0.2265 - val_accuracy: 0.9290\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.2695 - accuracy: 0.9149 - val_loss: 0.2047 - val_accuracy: 0.9355\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1791 - accuracy: 0.9437 - val_loss: 0.1768 - val_accuracy: 0.9430\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.1872 - accuracy: 0.9405 - val_loss: 0.1597 - val_accuracy: 0.9510\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1445 - accuracy: 0.9542 - val_loss: 0.1378 - val_accuracy: 0.9567\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1454 - accuracy: 0.9540 - val_loss: 0.1529 - val_accuracy: 0.9512\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1182 - accuracy: 0.9638 - val_loss: 0.1264 - val_accuracy: 0.9600\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1213 - accuracy: 0.9620 - val_loss: 0.1280 - val_accuracy: 0.9600\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.1018 - accuracy: 0.9682 - val_loss: 0.1197 - val_accuracy: 0.9620\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.1034 - accuracy: 0.9671 - val_loss: 0.1133 - val_accuracy: 0.9650\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0879 - accuracy: 0.9728 - val_loss: 0.1182 - val_accuracy: 0.9615\n", + "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0880 - accuracy: 0.9724 - val_loss: 0.1141 - val_accuracy: 0.9635\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0769 - accuracy: 0.9754 - val_loss: 0.1300 - val_accuracy: 0.9580\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0758 - accuracy: 0.9755 - val_loss: 0.1198 - val_accuracy: 0.9622\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 7s 4ms/step - loss: 0.0638 - accuracy: 0.9797 - val_loss: 0.1016 - val_accuracy: 0.9677\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0656 - accuracy: 0.9785 - val_loss: 0.1044 - val_accuracy: 0.9677\n", "Epoch 10/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0584 - accuracy: 0.9808 - val_loss: 0.1068 - val_accuracy: 0.9673\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0557 - accuracy: 0.9820 - val_loss: 0.0965 - val_accuracy: 0.9697\n", "Epoch 11/20\n", - "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0498 - accuracy: 0.9840 - val_loss: 0.1049 - val_accuracy: 0.9672\n", + "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0486 - accuracy: 0.9845 - val_loss: 0.0977 - val_accuracy: 0.9720\n", "Epoch 12/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0452 - accuracy: 0.9849 - val_loss: 0.1004 - val_accuracy: 0.9728\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0429 - accuracy: 0.9858 - val_loss: 0.0941 - val_accuracy: 0.9722\n", "Epoch 13/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0390 - accuracy: 0.9872 - val_loss: 0.1034 - val_accuracy: 0.9700\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0361 - accuracy: 0.9883 - val_loss: 0.0955 - val_accuracy: 0.9750\n", "Epoch 14/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0343 - accuracy: 0.9891 - val_loss: 0.1165 - val_accuracy: 0.9663\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0321 - accuracy: 0.9892 - val_loss: 0.1099 - val_accuracy: 0.9708\n", "Epoch 15/20\n", - "1688/1688 [==============================] - 6s 3ms/step - loss: 0.0312 - accuracy: 0.9897 - val_loss: 0.1113 - val_accuracy: 0.9688\n", + "1688/1688 [==============================] - 6s 4ms/step - loss: 0.0301 - accuracy: 0.9900 - val_loss: 0.0990 - val_accuracy: 0.9725\n", "313/313 [==============================] - 0s 1ms/step\n", "Testing configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", "Epoch 1/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1917 - accuracy: 0.9483 - val_loss: 0.1443 - val_accuracy: 0.9557\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.1909 - accuracy: 0.9505 - val_loss: 0.0737 - val_accuracy: 0.9782\n", "Epoch 2/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0661 - accuracy: 0.9809 - val_loss: 0.0630 - val_accuracy: 0.9822\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0659 - accuracy: 0.9810 - val_loss: 0.0681 - val_accuracy: 0.9758\n", "Epoch 3/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0482 - accuracy: 0.9854 - val_loss: 0.0597 - val_accuracy: 0.9800\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0457 - accuracy: 0.9863 - val_loss: 0.0554 - val_accuracy: 0.9833\n", "Epoch 4/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0348 - accuracy: 0.9892 - val_loss: 0.0559 - val_accuracy: 0.9823\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0361 - accuracy: 0.9886 - val_loss: 0.0515 - val_accuracy: 0.9840\n", "Epoch 5/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0280 - accuracy: 0.9908 - val_loss: 0.0510 - val_accuracy: 0.9853\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0280 - accuracy: 0.9910 - val_loss: 0.0573 - val_accuracy: 0.9823\n", "Epoch 6/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0220 - accuracy: 0.9929 - val_loss: 0.0401 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 10s 6ms/step - loss: 0.0225 - accuracy: 0.9931 - val_loss: 0.0431 - val_accuracy: 0.9863\n", "Epoch 7/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0176 - accuracy: 0.9940 - val_loss: 0.0456 - val_accuracy: 0.9863\n", + "1688/1688 [==============================] - 9s 6ms/step - loss: 0.0175 - accuracy: 0.9946 - val_loss: 0.0397 - val_accuracy: 0.9897\n", "Epoch 8/20\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0150 - accuracy: 0.9953 - val_loss: 0.0503 - val_accuracy: 0.9838\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0168 - accuracy: 0.9944 - val_loss: 0.0394 - val_accuracy: 0.9885\n", "Epoch 9/20\n", - "1688/1688 [==============================] - 8s 5ms/step - loss: 0.0148 - accuracy: 0.9954 - val_loss: 0.0466 - val_accuracy: 0.9857\n", - "313/313 [==============================] - 0s 1ms/step\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0133 - accuracy: 0.9957 - val_loss: 0.0312 - val_accuracy: 0.9912\n", + "Epoch 10/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0536 - val_accuracy: 0.9865\n", + "Epoch 11/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 0.0429 - val_accuracy: 0.9885\n", + "Epoch 12/20\n", + "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0097 - accuracy: 0.9966 - val_loss: 0.0415 - val_accuracy: 0.9912\n", + "313/313 [==============================] - 1s 1ms/step\n", "Best configuration: {'batch_normalization': True, 'pooling': 'avg'}\n", - "Best accuracy: 0.9871666431427002\n", + "Best accuracy: 0.9911666512489319\n", "Training best model on full training set...\n", "Epoch 1/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.2050 - accuracy: 0.9421 - val_loss: 0.1101 - val_accuracy: 0.9662\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.2058 - accuracy: 0.9424 - val_loss: 0.1165 - val_accuracy: 0.9615\n", "Epoch 2/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0799 - accuracy: 0.9764 - val_loss: 0.0590 - val_accuracy: 0.9823\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0807 - accuracy: 0.9763 - val_loss: 0.0580 - val_accuracy: 0.9833\n", "Epoch 3/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0560 - accuracy: 0.9828 - val_loss: 0.0468 - val_accuracy: 0.9860\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0594 - accuracy: 0.9823 - val_loss: 0.0514 - val_accuracy: 0.9840\n", "Epoch 4/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0447 - accuracy: 0.9862 - val_loss: 0.0415 - val_accuracy: 0.9875\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0457 - accuracy: 0.9859 - val_loss: 0.0484 - val_accuracy: 0.9857\n", "Epoch 5/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0360 - accuracy: 0.9888 - val_loss: 0.0302 - val_accuracy: 0.9895\n", + "3375/3375 [==============================] - 23s 7ms/step - loss: 0.0365 - accuracy: 0.9886 - val_loss: 0.0329 - val_accuracy: 0.9900\n", "Epoch 6/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0323 - accuracy: 0.9904 - val_loss: 0.0305 - val_accuracy: 0.9918\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0295 - accuracy: 0.9907 - val_loss: 0.0375 - val_accuracy: 0.9905\n", "Epoch 7/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0265 - accuracy: 0.9919 - val_loss: 0.0336 - val_accuracy: 0.9905\n", + "3375/3375 [==============================] - 32s 9ms/step - loss: 0.0262 - accuracy: 0.9917 - val_loss: 0.0461 - val_accuracy: 0.9868\n", "Epoch 8/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0232 - accuracy: 0.9926 - val_loss: 0.0412 - val_accuracy: 0.9883\n", + "3375/3375 [==============================] - 31s 9ms/step - loss: 0.0227 - accuracy: 0.9929 - val_loss: 0.0393 - val_accuracy: 0.9898\n", "Epoch 9/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0194 - accuracy: 0.9936 - val_loss: 0.0433 - val_accuracy: 0.9877\n", + "3375/3375 [==============================] - 29s 8ms/step - loss: 0.0213 - accuracy: 0.9934 - val_loss: 0.0316 - val_accuracy: 0.9903\n", "Epoch 10/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0184 - accuracy: 0.9939 - val_loss: 0.0320 - val_accuracy: 0.9920\n", + "3375/3375 [==============================] - 33s 10ms/step - loss: 0.0174 - accuracy: 0.9944 - val_loss: 0.0324 - val_accuracy: 0.9910\n", "Epoch 11/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0163 - accuracy: 0.9952 - val_loss: 0.0277 - val_accuracy: 0.9927\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0176 - accuracy: 0.9941 - val_loss: 0.0515 - val_accuracy: 0.9860\n", "Epoch 12/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0161 - accuracy: 0.9948 - val_loss: 0.0279 - val_accuracy: 0.9930\n", + "3375/3375 [==============================] - 33s 10ms/step - loss: 0.0138 - accuracy: 0.9956 - val_loss: 0.0328 - val_accuracy: 0.9927\n", "Epoch 13/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0125 - accuracy: 0.9962 - val_loss: 0.0331 - val_accuracy: 0.9922\n", + "3375/3375 [==============================] - 28s 8ms/step - loss: 0.0147 - accuracy: 0.9953 - val_loss: 0.0300 - val_accuracy: 0.9923\n", "Epoch 14/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0124 - accuracy: 0.9961 - val_loss: 0.0334 - val_accuracy: 0.9913\n", + "3375/3375 [==============================] - 31s 9ms/step - loss: 0.0135 - accuracy: 0.9954 - val_loss: 0.0327 - val_accuracy: 0.9918\n", "Epoch 15/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0133 - accuracy: 0.9955 - val_loss: 0.0391 - val_accuracy: 0.9907\n", + "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0114 - accuracy: 0.9963 - val_loss: 0.0369 - val_accuracy: 0.9910\n", "Epoch 16/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0112 - accuracy: 0.9962 - val_loss: 0.0399 - val_accuracy: 0.9900\n", + "3375/3375 [==============================] - 34s 10ms/step - loss: 0.0126 - accuracy: 0.9956 - val_loss: 0.0382 - val_accuracy: 0.9897\n", "Epoch 17/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0348 - val_accuracy: 0.9910\n", + "3375/3375 [==============================] - 30s 9ms/step - loss: 0.0128 - accuracy: 0.9959 - val_loss: 0.0257 - val_accuracy: 0.9918\n", "Epoch 18/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0101 - accuracy: 0.9968 - val_loss: 0.0294 - val_accuracy: 0.9928\n", + "3375/3375 [==============================] - 31s 9ms/step - loss: 0.0094 - accuracy: 0.9969 - val_loss: 0.0324 - val_accuracy: 0.9917\n", "Epoch 19/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0103 - accuracy: 0.9966 - val_loss: 0.0355 - val_accuracy: 0.9928\n", + "3375/3375 [==============================] - 29s 8ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.0377 - val_accuracy: 0.9908\n", "Epoch 20/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 0.0308 - val_accuracy: 0.9908\n", + "3375/3375 [==============================] - 24s 7ms/step - loss: 0.0101 - accuracy: 0.9968 - val_loss: 0.0433 - val_accuracy: 0.9893\n", "Epoch 21/100\n", - "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0082 - accuracy: 0.9974 - val_loss: 0.0359 - val_accuracy: 0.9907\n", - "Test accuracy: 0.9700\n", - "Test loss: 0.1298\n", - "313/313 [==============================] - 1s 1ms/step\n" + "3375/3375 [==============================] - 24s 7ms/step - loss: 0.0096 - accuracy: 0.9968 - val_loss: 0.0344 - val_accuracy: 0.9907\n", + "Epoch 22/100\n", + "3375/3375 [==============================] - 23s 7ms/step - loss: 0.0083 - accuracy: 0.9973 - val_loss: 0.0297 - val_accuracy: 0.9938\n", + "Epoch 23/100\n", + "3375/3375 [==============================] - 23s 7ms/step - loss: 0.0091 - accuracy: 0.9970 - val_loss: 0.0340 - val_accuracy: 0.9925\n", + "Epoch 24/100\n", + "3375/3375 [==============================] - 23s 7ms/step - loss: 0.0087 - accuracy: 0.9971 - val_loss: 0.0327 - val_accuracy: 0.9913\n", + "Epoch 25/100\n", + "3375/3375 [==============================] - 25s 7ms/step - loss: 0.0075 - accuracy: 0.9974 - val_loss: 0.0315 - val_accuracy: 0.9923\n", + "Epoch 26/100\n", + "3375/3375 [==============================] - 22s 6ms/step - loss: 0.0062 - accuracy: 0.9979 - val_loss: 0.0386 - val_accuracy: 0.9923\n", + "Epoch 27/100\n", + "3375/3375 [==============================] - 22s 6ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.0324 - val_accuracy: 0.9928\n", + "Test accuracy: 0.9721\n", + "Test loss: 0.1304\n", + "313/313 [==============================] - 1s 2ms/step\n" ] } ], @@ -338,14 +372,14 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.9871666431427002\n" + "0.9911666512489319\n" ] } ], @@ -355,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -365,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -482,13 +516,13 @@ "Text(0.5, 1.0, 'Noise + Mask')" ] }, - "execution_count": 76, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -555,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -564,187 +598,181 @@ "text": [ "{'num_layers': 3, 'filters': [32, 64, 64], 'kernel_sizes': [[3, 3], [3, 3], [3, 3]], 'activations': ['relu', 'relu', 'relu'], 'dense_units': 64, 'dense_activation': 'relu', 'batch_normalization': True, 'pooling': 'avg'}\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2032 - accuracy: 0.9444 - val_loss: 0.0979 - val_accuracy: 0.9705\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.1970 - accuracy: 0.9482 - val_loss: 0.1445 - val_accuracy: 0.9525\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0710 - accuracy: 0.9791 - val_loss: 0.0918 - val_accuracy: 0.9717\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0730 - accuracy: 0.9787 - val_loss: 0.0934 - val_accuracy: 0.9720\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0485 - accuracy: 0.9853 - val_loss: 0.0865 - val_accuracy: 0.9708\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0527 - accuracy: 0.9840 - val_loss: 0.0658 - val_accuracy: 0.9813\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0383 - accuracy: 0.9884 - val_loss: 0.0981 - val_accuracy: 0.9743\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0392 - accuracy: 0.9882 - val_loss: 0.0511 - val_accuracy: 0.9847\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0314 - accuracy: 0.9902 - val_loss: 0.0525 - val_accuracy: 0.9838\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0320 - accuracy: 0.9898 - val_loss: 0.0485 - val_accuracy: 0.9847\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0269 - accuracy: 0.9917 - val_loss: 0.0475 - val_accuracy: 0.9862\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0273 - accuracy: 0.9914 - val_loss: 0.0498 - val_accuracy: 0.9847\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0207 - accuracy: 0.9935 - val_loss: 0.0507 - val_accuracy: 0.9850\n", + "1688/1688 [==============================] - 15s 9ms/step - loss: 0.0230 - accuracy: 0.9928 - val_loss: 0.0526 - val_accuracy: 0.9842\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0472 - val_accuracy: 0.9852\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0203 - accuracy: 0.9935 - val_loss: 0.0373 - val_accuracy: 0.9898\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0168 - accuracy: 0.9946 - val_loss: 0.0444 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.0398 - val_accuracy: 0.9885\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0411 - val_accuracy: 0.9885\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0153 - accuracy: 0.9950 - val_loss: 0.0300 - val_accuracy: 0.9920\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0124 - accuracy: 0.9958 - val_loss: 0.0485 - val_accuracy: 0.9877\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0151 - accuracy: 0.9951 - val_loss: 0.0389 - val_accuracy: 0.9882\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0126 - accuracy: 0.9960 - val_loss: 0.0511 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.0410 - val_accuracy: 0.9892\n", "Epoch 13/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 0.0445 - val_accuracy: 0.9868\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0567 - val_accuracy: 0.9860\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1833 - accuracy: 0.9520 - val_loss: 0.0756 - val_accuracy: 0.9763\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.1971 - accuracy: 0.9471 - val_loss: 0.1016 - val_accuracy: 0.9695\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0641 - accuracy: 0.9814 - val_loss: 0.0510 - val_accuracy: 0.9840\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0668 - accuracy: 0.9801 - val_loss: 0.0964 - val_accuracy: 0.9665\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0444 - accuracy: 0.9867 - val_loss: 0.0753 - val_accuracy: 0.9768\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0451 - accuracy: 0.9865 - val_loss: 0.0427 - val_accuracy: 0.9868\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0353 - accuracy: 0.9892 - val_loss: 0.0363 - val_accuracy: 0.9882\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0356 - accuracy: 0.9893 - val_loss: 0.0646 - val_accuracy: 0.9810\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0280 - accuracy: 0.9911 - val_loss: 0.0498 - val_accuracy: 0.9847\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0298 - accuracy: 0.9910 - val_loss: 0.0480 - val_accuracy: 0.9862\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0217 - accuracy: 0.9932 - val_loss: 0.0551 - val_accuracy: 0.9840\n", - "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - 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[==============================] - 13s 7ms/step - loss: 0.0845 - accuracy: 0.9736 - val_loss: 0.0438 - val_accuracy: 0.9862\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0769 - accuracy: 0.9768 - val_loss: 0.0555 - val_accuracy: 0.9817\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0770 - accuracy: 0.9766 - val_loss: 0.0415 - val_accuracy: 0.9882\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0722 - accuracy: 0.9776 - val_loss: 0.0927 - val_accuracy: 0.9713\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0709 - accuracy: 0.9777 - val_loss: 0.0506 - val_accuracy: 0.9857\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0685 - accuracy: 0.9781 - val_loss: 0.0403 - val_accuracy: 0.9863\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0664 - accuracy: 0.9789 - val_loss: 0.0570 - val_accuracy: 0.9833\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0654 - accuracy: 0.9800 - val_loss: 0.0495 - val_accuracy: 0.9853\n", - "Epoch 12/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0618 - accuracy: 0.9804 - val_loss: 0.0312 - val_accuracy: 0.9903\n", - "Epoch 13/15\n", - "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0568 - accuracy: 0.9820 - val_loss: 0.0455 - val_accuracy: 0.9857\n", - "Epoch 14/15\n", - "1688/1688 [==============================] - 11s 6ms/step - loss: 0.0557 - accuracy: 0.9824 - val_loss: 0.0470 - val_accuracy: 0.9872\n", - "Epoch 15/15\n", - "1688/1688 [==============================] - 11s 7ms/step - loss: 0.0501 - accuracy: 0.9841 - val_loss: 0.0431 - val_accuracy: 0.9863\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0648 - accuracy: 0.9794 - val_loss: 0.0441 - val_accuracy: 0.9872\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 10s 5ms/step - loss: 0.4048 - accuracy: 0.8751 - val_loss: 0.1133 - val_accuracy: 0.9643\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.4129 - accuracy: 0.8734 - val_loss: 0.1409 - val_accuracy: 0.9552\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2047 - accuracy: 0.9341 - val_loss: 0.0577 - val_accuracy: 0.9818\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2121 - accuracy: 0.9324 - val_loss: 0.0709 - val_accuracy: 0.9760\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1676 - accuracy: 0.9458 - val_loss: 0.0736 - val_accuracy: 0.9777\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1716 - accuracy: 0.9449 - val_loss: 0.0632 - val_accuracy: 0.9805\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1455 - accuracy: 0.9523 - val_loss: 0.0569 - val_accuracy: 0.9822\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1439 - accuracy: 0.9539 - val_loss: 0.0435 - val_accuracy: 0.9872\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1290 - accuracy: 0.9578 - val_loss: 0.0488 - val_accuracy: 0.9847\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1357 - accuracy: 0.9565 - val_loss: 0.0434 - val_accuracy: 0.9855\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1233 - accuracy: 0.9599 - val_loss: 0.1163 - val_accuracy: 0.9640\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1212 - accuracy: 0.9603 - val_loss: 0.0517 - val_accuracy: 0.9838\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1133 - accuracy: 0.9632 - val_loss: 0.0374 - val_accuracy: 0.9893\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.1151 - accuracy: 0.9624 - val_loss: 0.0378 - val_accuracy: 0.9898\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1054 - accuracy: 0.9657 - val_loss: 0.0331 - val_accuracy: 0.9893\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.1046 - accuracy: 0.9660 - val_loss: 0.0408 - val_accuracy: 0.9867\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0980 - accuracy: 0.9681 - val_loss: 0.0295 - val_accuracy: 0.9908\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0998 - accuracy: 0.9674 - val_loss: 0.0383 - val_accuracy: 0.9893\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0942 - accuracy: 0.9692 - val_loss: 0.0334 - val_accuracy: 0.9912\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.1008 - accuracy: 0.9668 - val_loss: 0.0365 - val_accuracy: 0.9900\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0897 - accuracy: 0.9697 - val_loss: 0.0386 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0934 - accuracy: 0.9699 - val_loss: 0.0343 - val_accuracy: 0.9895\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0857 - accuracy: 0.9719 - val_loss: 0.0238 - val_accuracy: 0.9942\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0889 - accuracy: 0.9706 - val_loss: 0.0371 - val_accuracy: 0.9897\n", "Epoch 13/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0823 - accuracy: 0.9733 - val_loss: 0.0324 - val_accuracy: 0.9903\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0842 - accuracy: 0.9724 - val_loss: 0.0325 - val_accuracy: 0.9913\n", "Epoch 14/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0829 - accuracy: 0.9730 - val_loss: 0.0278 - val_accuracy: 0.9922\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0819 - accuracy: 0.9731 - val_loss: 0.0281 - val_accuracy: 0.9922\n", "Epoch 15/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0780 - accuracy: 0.9740 - val_loss: 0.0263 - val_accuracy: 0.9935\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.0813 - accuracy: 0.9734 - val_loss: 0.0307 - val_accuracy: 0.9923\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.2152 - accuracy: 0.9414 - val_loss: 0.1105 - val_accuracy: 0.9652\n", + "1688/1688 [==============================] - 16s 9ms/step - loss: 0.2063 - accuracy: 0.9441 - val_loss: 0.0877 - val_accuracy: 0.9722\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0828 - accuracy: 0.9747 - val_loss: 0.0591 - val_accuracy: 0.9830\n", + "1688/1688 [==============================] - 15s 9ms/step - loss: 0.0796 - accuracy: 0.9763 - val_loss: 0.0535 - val_accuracy: 0.9820\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0572 - accuracy: 0.9827 - val_loss: 0.0605 - val_accuracy: 0.9830\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0586 - accuracy: 0.9823 - val_loss: 0.0574 - val_accuracy: 0.9820\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0483 - accuracy: 0.9857 - val_loss: 0.0557 - val_accuracy: 0.9845\n", + "1688/1688 [==============================] - 14s 9ms/step - loss: 0.0477 - accuracy: 0.9849 - val_loss: 0.0472 - val_accuracy: 0.9843\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0379 - accuracy: 0.9881 - val_loss: 0.0477 - val_accuracy: 0.9850\n", + "1688/1688 [==============================] - 15s 9ms/step - loss: 0.0382 - accuracy: 0.9883 - val_loss: 0.0497 - val_accuracy: 0.9845\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0318 - accuracy: 0.9898 - val_loss: 0.0597 - val_accuracy: 0.9820\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0344 - accuracy: 0.9887 - val_loss: 0.0694 - val_accuracy: 0.9792\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0284 - accuracy: 0.9909 - val_loss: 0.0447 - val_accuracy: 0.9872\n", + "1688/1688 [==============================] - 15s 9ms/step - loss: 0.0292 - accuracy: 0.9909 - val_loss: 0.0406 - val_accuracy: 0.9888\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0247 - accuracy: 0.9922 - val_loss: 0.0392 - val_accuracy: 0.9898\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0250 - accuracy: 0.9917 - val_loss: 0.0470 - val_accuracy: 0.9873\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0228 - accuracy: 0.9925 - val_loss: 0.0378 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0221 - accuracy: 0.9931 - val_loss: 0.0368 - val_accuracy: 0.9897\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0197 - accuracy: 0.9938 - val_loss: 0.0415 - val_accuracy: 0.9885\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0195 - accuracy: 0.9935 - val_loss: 0.0473 - val_accuracy: 0.9870\n", "Epoch 11/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0172 - accuracy: 0.9941 - val_loss: 0.0371 - val_accuracy: 0.9910\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0397 - val_accuracy: 0.9885\n", "Epoch 12/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0520 - val_accuracy: 0.9867\n", - "Epoch 13/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0143 - accuracy: 0.9950 - val_loss: 0.0434 - val_accuracy: 0.9888\n", - "Epoch 14/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0342 - val_accuracy: 0.9897\n", - "Epoch 15/15\n", - "1688/1688 [==============================] - 14s 8ms/step - loss: 0.0124 - accuracy: 0.9961 - val_loss: 0.0375 - val_accuracy: 0.9897\n", + "1688/1688 [==============================] - 15s 9ms/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.0494 - val_accuracy: 0.9883\n", "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.5823 - accuracy: 0.8147 - val_loss: 0.2716 - val_accuracy: 0.9188\n", + "1688/1688 [==============================] - 13s 7ms/step - loss: 0.5882 - accuracy: 0.8140 - val_loss: 0.2025 - val_accuracy: 0.9338\n", "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.3495 - accuracy: 0.8859 - val_loss: 0.6366 - val_accuracy: 0.8153\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.3471 - accuracy: 0.8865 - val_loss: 0.1390 - val_accuracy: 0.9545\n", "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2894 - accuracy: 0.9044 - val_loss: 0.3201 - val_accuracy: 0.8967\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2896 - accuracy: 0.9033 - val_loss: 0.1321 - val_accuracy: 0.9595\n", "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.2592 - accuracy: 0.9145 - val_loss: 0.5202 - val_accuracy: 0.8427\n", - "Epoch 1/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.1908 - accuracy: 0.9495 - val_loss: 0.0904 - val_accuracy: 0.9715\n", - "Epoch 2/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0641 - accuracy: 0.9811 - val_loss: 0.0690 - val_accuracy: 0.9777\n", - "Epoch 3/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0467 - accuracy: 0.9864 - val_loss: 0.0520 - val_accuracy: 0.9835\n", - "Epoch 4/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0340 - accuracy: 0.9894 - val_loss: 0.0493 - val_accuracy: 0.9867\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2546 - accuracy: 0.9166 - val_loss: 0.1869 - val_accuracy: 0.9428\n", "Epoch 5/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0264 - accuracy: 0.9918 - val_loss: 0.0435 - val_accuracy: 0.9852\n", + "1688/1688 [==============================] - 12s 7ms/step - loss: 0.2443 - accuracy: 0.9196 - val_loss: 0.1042 - val_accuracy: 0.9702\n", "Epoch 6/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0242 - accuracy: 0.9925 - val_loss: 0.0399 - val_accuracy: 0.9878\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.2241 - accuracy: 0.9263 - val_loss: 0.1004 - val_accuracy: 0.9692\n", "Epoch 7/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0473 - val_accuracy: 0.9848\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.2115 - accuracy: 0.9300 - val_loss: 0.0724 - val_accuracy: 0.9783\n", "Epoch 8/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0153 - accuracy: 0.9951 - val_loss: 0.0425 - val_accuracy: 0.9878\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.1996 - accuracy: 0.9348 - val_loss: 0.1087 - val_accuracy: 0.9678\n", "Epoch 9/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0147 - accuracy: 0.9952 - val_loss: 0.0310 - val_accuracy: 0.9902\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.1956 - accuracy: 0.9353 - val_loss: 0.1283 - val_accuracy: 0.9627\n", "Epoch 10/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0122 - accuracy: 0.9959 - val_loss: 0.0392 - val_accuracy: 0.9873\n", - "Epoch 11/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0113 - accuracy: 0.9964 - val_loss: 0.0412 - val_accuracy: 0.9887\n", - "Epoch 12/15\n", - "1688/1688 [==============================] - 9s 5ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 0.0389 - val_accuracy: 0.9897\n" + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.1851 - accuracy: 0.9394 - val_loss: 0.1120 - val_accuracy: 0.9655\n", + "Epoch 1/15\n", + "1688/1688 [==============================] - 14s 8ms/step - loss: 0.1967 - accuracy: 0.9476 - val_loss: 0.0793 - val_accuracy: 0.9765\n", + "Epoch 2/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0666 - accuracy: 0.9804 - val_loss: 0.1180 - val_accuracy: 0.9627\n", + "Epoch 3/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0462 - accuracy: 0.9865 - val_loss: 0.0538 - val_accuracy: 0.9833\n", + "Epoch 4/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0353 - accuracy: 0.9895 - val_loss: 0.0568 - val_accuracy: 0.9825\n", + "Epoch 5/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0293 - accuracy: 0.9911 - val_loss: 0.0680 - val_accuracy: 0.9803\n", + "Epoch 6/15\n", + "1688/1688 [==============================] - 13s 8ms/step - loss: 0.0210 - accuracy: 0.9934 - val_loss: 0.0554 - val_accuracy: 0.9833\n" ] } ], @@ -908,29 +936,29 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Rand noise test accuracy: 0.9647\n", - "Rand noise test loss: 0.1557\n", - "Radial noise test accuracy: 0.9623\n", - "Radial noise test loss: 0.1362\n", - "Flip test accuracy: 0.9259\n", - "Flip test loss: 0.2598\n", - "Zoom test accuracy: 0.9647\n", - "Zoom test loss: 0.1349\n", - "Mask test accuracy: 0.9693\n", - "Mask test loss: 0.1327\n", - "Rand noise and zoom test accuracy: 0.9697\n", - "Rand noise and zoom test loss: 0.1519\n", - "Rand noise and mask test accuracy: 0.8253\n", - "Rand noise and mask test loss: 0.5927\n", - "No noise test accuracy: 0.9670\n", - "No noise test loss: 0.1402\n" + "Rand noise test accuracy: 0.9676\n", + "Rand noise test loss: 0.1507\n", + "Radial noise test accuracy: 0.9590\n", + "Radial noise test loss: 0.1452\n", + "Flip test accuracy: 0.9489\n", + "Flip test loss: 0.1888\n", + "Zoom test accuracy: 0.9612\n", + "Zoom test loss: 0.1526\n", + "Mask test accuracy: 0.9679\n", + "Mask test loss: 0.1315\n", + "Rand noise and zoom test accuracy: 0.9663\n", + "Rand noise and zoom test loss: 0.1416\n", + "Rand noise and mask test accuracy: 0.9366\n", + "Rand noise and mask test loss: 0.2489\n", + "No noise test accuracy: 0.9481\n", + "No noise test loss: 0.1800\n" ] } ], @@ -963,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -971,13 +999,13 @@ "output_type": "stream", "text": [ "313/313 [==============================] - 1s 2ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n", - "313/313 [==============================] - 1s 1ms/step\n", - "313/313 [==============================] - 0s 1ms/step\n" + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n", + "313/313 [==============================] - 1s 2ms/step\n" ] } ], @@ -1016,6 +1044,249 @@ "\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.4245 - accuracy: 0.8686 - val_loss: 0.1133 - val_accuracy: 0.9658\n", + "Epoch 2/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.2301 - accuracy: 0.9258 - val_loss: 0.0722 - val_accuracy: 0.9775\n", + "Epoch 3/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1935 - accuracy: 0.9372 - val_loss: 0.0495 - val_accuracy: 0.9850\n", + "Epoch 4/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1692 - accuracy: 0.9451 - val_loss: 0.0347 - val_accuracy: 0.9903\n", + "Epoch 5/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1500 - accuracy: 0.9518 - val_loss: 0.0554 - val_accuracy: 0.9830\n", + "Epoch 6/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.1354 - accuracy: 0.9563 - val_loss: 0.0339 - val_accuracy: 0.9903\n", + "Epoch 7/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1296 - accuracy: 0.9583 - val_loss: 0.0342 - val_accuracy: 0.9893\n", + "Epoch 8/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1239 - accuracy: 0.9600 - val_loss: 0.0415 - val_accuracy: 0.9873\n", + "Epoch 9/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1180 - accuracy: 0.9619 - val_loss: 0.0337 - val_accuracy: 0.9897\n", + "Epoch 10/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.1093 - accuracy: 0.9646 - val_loss: 0.0273 - val_accuracy: 0.9918\n", + "Epoch 11/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.1066 - accuracy: 0.9657 - val_loss: 0.0330 - val_accuracy: 0.9890\n", + "Epoch 12/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0999 - accuracy: 0.9680 - val_loss: 0.0292 - val_accuracy: 0.9918\n", + "Epoch 13/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0983 - accuracy: 0.9681 - val_loss: 0.0271 - val_accuracy: 0.9912\n", + "Epoch 14/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0938 - accuracy: 0.9695 - val_loss: 0.0246 - val_accuracy: 0.9922\n", + "Epoch 15/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0948 - accuracy: 0.9688 - val_loss: 0.0271 - val_accuracy: 0.9923\n", + "Epoch 16/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0892 - accuracy: 0.9709 - val_loss: 0.0243 - val_accuracy: 0.9925\n", + "Epoch 17/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0892 - accuracy: 0.9714 - val_loss: 0.0290 - val_accuracy: 0.9910\n", + "Epoch 18/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0846 - accuracy: 0.9726 - val_loss: 0.0282 - val_accuracy: 0.9923\n", + "Epoch 19/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0827 - accuracy: 0.9731 - val_loss: 0.0235 - val_accuracy: 0.9935\n", + "Epoch 20/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0831 - accuracy: 0.9735 - val_loss: 0.0252 - val_accuracy: 0.9922\n", + "Epoch 21/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0792 - accuracy: 0.9744 - val_loss: 0.0257 - val_accuracy: 0.9927\n", + "Epoch 22/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0827 - accuracy: 0.9729 - val_loss: 0.0258 - val_accuracy: 0.9918\n", + "Epoch 23/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0778 - accuracy: 0.9745 - val_loss: 0.0242 - val_accuracy: 0.9932\n", + "Epoch 24/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0718 - accuracy: 0.9766 - val_loss: 0.0232 - val_accuracy: 0.9928\n", + "Epoch 25/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0744 - accuracy: 0.9762 - val_loss: 0.0279 - val_accuracy: 0.9920\n", + "Epoch 26/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0725 - accuracy: 0.9759 - val_loss: 0.0254 - val_accuracy: 0.9925\n", + "Epoch 27/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0715 - accuracy: 0.9766 - val_loss: 0.0251 - val_accuracy: 0.9938\n", + "Epoch 28/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0705 - accuracy: 0.9775 - val_loss: 0.0304 - val_accuracy: 0.9927\n", + "Epoch 29/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0654 - accuracy: 0.9786 - val_loss: 0.0227 - val_accuracy: 0.9937\n", + "Epoch 30/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0708 - accuracy: 0.9766 - val_loss: 0.0217 - val_accuracy: 0.9938\n", + "Epoch 31/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0651 - accuracy: 0.9781 - val_loss: 0.0250 - val_accuracy: 0.9932\n", + "Epoch 32/50\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0678 - accuracy: 0.9773 - val_loss: 0.0220 - val_accuracy: 0.9937\n", + "Epoch 33/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0654 - accuracy: 0.9790 - val_loss: 0.0222 - val_accuracy: 0.9952\n", + "Epoch 34/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0645 - accuracy: 0.9785 - val_loss: 0.0265 - val_accuracy: 0.9932\n", + "Epoch 35/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0619 - accuracy: 0.9802 - val_loss: 0.0269 - val_accuracy: 0.9932\n", + "Epoch 36/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0621 - accuracy: 0.9795 - val_loss: 0.0242 - val_accuracy: 0.9932\n", + "Epoch 37/50\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0622 - accuracy: 0.9794 - val_loss: 0.0223 - val_accuracy: 0.9937\n", + "Epoch 38/50\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0627 - accuracy: 0.9793 - val_loss: 0.0254 - val_accuracy: 0.9923\n", + "Epoch 39/50\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0626 - accuracy: 0.9793 - val_loss: 0.0248 - val_accuracy: 0.9930\n", + "Epoch 40/50\n", + "3375/3375 [==============================] - 16s 5ms/step - loss: 0.0582 - accuracy: 0.9808 - val_loss: 0.0235 - val_accuracy: 0.9932\n", + "Epoch 1/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.2352 - accuracy: 0.9322 - val_loss: 0.0827 - val_accuracy: 0.9745\n", + "Epoch 2/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0949 - accuracy: 0.9713 - val_loss: 0.1407 - val_accuracy: 0.9543\n", + "Epoch 3/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0719 - accuracy: 0.9781 - val_loss: 0.0414 - val_accuracy: 0.9865\n", + "Epoch 4/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0570 - accuracy: 0.9823 - val_loss: 0.0515 - val_accuracy: 0.9845\n", + "Epoch 5/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0454 - accuracy: 0.9860 - val_loss: 0.0375 - val_accuracy: 0.9890\n", + "Epoch 6/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0420 - accuracy: 0.9866 - val_loss: 0.0356 - val_accuracy: 0.9895\n", + "Epoch 7/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0373 - accuracy: 0.9884 - val_loss: 0.0377 - val_accuracy: 0.9890\n", + "Epoch 8/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0313 - accuracy: 0.9900 - val_loss: 0.0362 - val_accuracy: 0.9903\n", + "Epoch 9/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0273 - accuracy: 0.9909 - val_loss: 0.0420 - val_accuracy: 0.9887\n", + "Epoch 10/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0270 - accuracy: 0.9911 - val_loss: 0.0325 - val_accuracy: 0.9908\n", + "Epoch 11/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0330 - val_accuracy: 0.9900\n", + "Epoch 12/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0242 - accuracy: 0.9919 - val_loss: 0.0296 - val_accuracy: 0.9912\n", + "Epoch 13/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0217 - accuracy: 0.9928 - val_loss: 0.0349 - val_accuracy: 0.9910\n", + "Epoch 14/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0188 - accuracy: 0.9939 - val_loss: 0.0328 - val_accuracy: 0.9912\n", + "Epoch 15/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0208 - accuracy: 0.9934 - val_loss: 0.0336 - val_accuracy: 0.9908\n", + "Epoch 16/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0170 - accuracy: 0.9939 - val_loss: 0.0263 - val_accuracy: 0.9918\n", + "Epoch 17/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 0.0437 - val_accuracy: 0.9918\n", + "Epoch 18/50\n", + "3375/3375 [==============================] - 17s 5ms/step - loss: 0.0169 - accuracy: 0.9944 - val_loss: 0.0356 - val_accuracy: 0.9917\n", + "Epoch 19/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0155 - accuracy: 0.9947 - val_loss: 0.0453 - val_accuracy: 0.9898\n", + "Epoch 20/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0169 - accuracy: 0.9945 - val_loss: 0.0346 - val_accuracy: 0.9910\n", + "Epoch 21/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0127 - accuracy: 0.9957 - val_loss: 0.0384 - val_accuracy: 0.9907\n", + "Epoch 22/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0125 - accuracy: 0.9956 - val_loss: 0.0361 - val_accuracy: 0.9918\n", + "Epoch 23/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0133 - accuracy: 0.9957 - val_loss: 0.0306 - val_accuracy: 0.9928\n", + "Epoch 24/50\n", + "3375/3375 [==============================] - 19s 6ms/step - loss: 0.0135 - accuracy: 0.9954 - val_loss: 0.0314 - val_accuracy: 0.9923\n", + "Epoch 25/50\n", + "3375/3375 [==============================] - 18s 5ms/step - loss: 0.0117 - accuracy: 0.9961 - val_loss: 0.0353 - val_accuracy: 0.9922\n", + "Epoch 26/50\n", + "3375/3375 [==============================] - 19s 5ms/step - loss: 0.0119 - accuracy: 0.9958 - val_loss: 0.0290 - val_accuracy: 0.9933\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Mask Model Test Accuracy: 0.9780\n", + "313/313 [==============================] - 0s 1ms/step\n", + "Random Noise Zoom Model Test Accuracy: 0.9728\n" + ] + } + ], + "source": [ + "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n", + "best_model_mask_long = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "train_generator_mask_long = datagen_mask.flow(x_train, y_train, batch_size=16)\n", + "history_mask_long = best_model_mask_long.fit(train_generator_mask_long,\n", + " epochs=50,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", + " )\n", + "last_val_loss = history_mask_long.history['val_loss'][-1]\n", + "last_val_accuracy = history_mask_long.history['val_accuracy'][-1]\n", + "for _ in range(len(history_mask_long.history['val_loss']), 50):\n", + " history_mask_long.history['val_loss'].append(last_val_loss)\n", + " history_mask_long.history['val_accuracy'].append(last_val_accuracy)\n", + "\n", + "best_model_rand_noise_zoom_long = create_cnn_model(input_shape, num_classes, hyperparameters)\n", + "train_generator_rand_noise_zoom_long = datagen_rand_noise_zoom.flow(x_train, y_train, batch_size=16)\n", + "history_rand_noise_zoom_long = best_model_rand_noise_zoom_long.fit(train_generator_rand_noise_zoom_long,\n", + " epochs=50,\n", + " validation_data=val_generator,\n", + " verbose=1,\n", + " callbacks=[early_stopping]\n", + " )\n", + "last_val_loss = history_rand_noise_zoom_long.history['val_loss'][-1]\n", + "last_val_accuracy = history_rand_noise_zoom_long.history['val_accuracy'][-1]\n", + "for _ in range(len(history_rand_noise_zoom_long.history['val_loss']), 50):\n", + " history_rand_noise_zoom_long.history['val_loss'].append(last_val_loss)\n", + " history_rand_noise_zoom_long.history['val_accuracy'].append(last_val_accuracy)\n", + "\n", + "# Predictions on the test set for mask long model\n", + "y_pred_mask = best_model_mask.predict(x_test)\n", + "y_pred_mask_class = np.argmax(y_pred_mask, axis=1)\n", + "# Calculate and print accuracy for mask model\n", + "mask_accuracy = accuracy_score(y_test, y_pred_mask_class)\n", + "print(f\"Mask Model Test Accuracy: {mask_accuracy:.4f}\")\n", + "\n", + "\n", + "\n", + "# Confusion matrix for mask long\n", + "confusion_mask = confusion_matrix(y_test, y_pred_mask_class)\n", + "disp_mask = ConfusionMatrixDisplay(confusion_matrix=confusion_mask)\n", + "\n", + "# Plot mask long confusion matrix\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "disp_mask.plot(ax=ax)\n", + "plt.title('Confusion Matrix for Mask Long')\n", + "\n", + "# Save the mask long figure\n", + "now = datetime.datetime.now()\n", + "plt.savefig(f'confusion_matrix_mask_long_{now.strftime(\"%Y-%m-%d\")}.png')\n", + "plt.close()\n", + "\n", + "# Predictions on the test set for noise zoom long model \n", + "y_pred_noise = best_model_rand_noise_zoom.predict(x_test)\n", + "y_pred_noise_class = np.argmax(y_pred_noise, axis=1)\n", + "# Calculate and print accuracy for random noise zoom model \n", + "noise_accuracy = accuracy_score(y_test, y_pred_noise_class)\n", + "print(f\"Random Noise Zoom Model Test Accuracy: {noise_accuracy:.4f}\")\n", + "# Confusion matrix for noise zoom long\n", + "confusion_noise = confusion_matrix(y_test, y_pred_noise_class)\n", + "disp_noise = ConfusionMatrixDisplay(confusion_matrix=confusion_noise)\n", + "\n", + "# Plot noise zoom long confusion matrix\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "disp_noise.plot(ax=ax)\n", + "plt.title('Confusion Matrix for Random Noise Zoom Long')\n", + "\n", + "# Save the noise zoom long figure\n", + "plt.savefig(f'confusion_matrix_random_noise_zoom_long_{now.strftime(\"%Y-%m-%d\")}.png')\n", + "plt.close()\n", + "# Plot learning curves\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "\n", + "# Plot training & validation loss values for mask model\n", + "plt.plot(history_mask_long.history['val_accuracy'], label='Validation Accuracy Mask ')\n", + "plt.plot(history_rand_noise_zoom_long.history['val_accuracy'], label='Validation Accuracy Random Noise Zoom')\n", + "plt.title('Augmentation Learning Curves')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'learning_curves_{now.strftime(\"%Y-%m-%d\")}.png')\n", + "plt.close()\n" + ] } ], "metadata": { diff --git a/testing/confusion_matrix_mask_long_2024-10-27.png b/testing/confusion_matrix_mask_long_2024-10-27.png new file mode 100644 index 0000000000000000000000000000000000000000..8dec76d769a3a803f5c5c9a5a90a1b91837bb72d GIT binary patch literal 55130 zcmeFZcUV-*_AQDLMFABN1pxyf8AWmwP_W4YO_SM3&N)W|pnxD*a)zdxCMSUg1p&!9 z=Ny`xGq08&_c`ac?|t{Z|F7TYw|lp}R@bVkIp>&TjOvf_vJ!*@wlJ z?9BI!?Ch*;`PkUZ|Na20rHvumec5vZ;3k)?UaH#S;SuXVf6pL=GmP=@dh21&MUMIvaUtiYhfM#GCUHH&g( zsHANm2@LXPB?vjMxfmYnq=#l*rRBGPA?B5^xImVJfBU4Nm(ln%qIRi~pm^(aCd?xGv!151F4{A!AiCGD1c$t3L4b^em^GvkN;O9dn+e6g)lJ438fxGL4L2(_T^N z1M#aw3j>hCKJtJ 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..._matrix_random_noise_+_mask_2024-10-27.png | Bin 0 -> 63248 bytes ..._matrix_random_noise_+_zoom_2024-10-27.png | Bin 0 -> 60319 bytes ...nfusion_matrix_random_noise_2024-10-27.png | Bin 0 -> 57666 bytes testing/confusion_matrix_zoom_2024-10-27.png | Bin 0 -> 55960 bytes .../learning_curve_avg_False_2024-10-27.png | Bin 0 -> 34076 bytes .../learning_curve_avg_True_2024-10-27.png | Bin 0 -> 37141 bytes ...earning_curve_best_avg_True_2024-10-27.png | Bin 0 -> 34059 bytes .../learning_curve_max_False_2024-10-27.png | Bin 0 -> 36348 bytes .../learning_curve_max_True_2024-10-27.png | Bin 0 -> 38745 bytes testing/test_accuracy.csv | 200 +++++++++--------- testing/test_loss.csv | 200 +++++++++--------- 22 files changed, 232 insertions(+), 232 deletions(-) create mode 100644 testing/confusion_matrix_avg_False_2024-10-27.png create mode 100644 testing/confusion_matrix_avg_True_2024-10-27.png create mode 100644 testing/confusion_matrix_best_avg_True_2024-10-27.png create mode 100644 testing/confusion_matrix_flip_2024-10-27.png create mode 100644 testing/confusion_matrix_mask_2024-10-27.png create mode 100644 testing/confusion_matrix_max_False_2024-10-27.png create mode 100644 testing/confusion_matrix_max_True_2024-10-27.png create mode 100644 testing/confusion_matrix_no_noise_2024-10-27.png create mode 100644 testing/confusion_matrix_radial_noise_2024-10-27.png create mode 100644 testing/confusion_matrix_random_noise_+_mask_2024-10-27.png create mode 100644 testing/confusion_matrix_random_noise_+_zoom_2024-10-27.png create mode 100644 testing/confusion_matrix_random_noise_2024-10-27.png create mode 100644 testing/confusion_matrix_zoom_2024-10-27.png create mode 100644 testing/learning_curve_avg_False_2024-10-27.png create mode 100644 testing/learning_curve_avg_True_2024-10-27.png create mode 100644 testing/learning_curve_best_avg_True_2024-10-27.png create mode 100644 testing/learning_curve_max_False_2024-10-27.png create mode 100644 testing/learning_curve_max_True_2024-10-27.png diff --git a/testing/augmentation_accuracy.csv b/testing/augmentation_accuracy.csv index 4ad65fb..8f29478 100644 --- a/testing/augmentation_accuracy.csv +++ b/testing/augmentation_accuracy.csv @@ -1,17 +1,17 @@ rand_noise,radial_noise,flip,zoom,mask,rand_noise_zoom,rand_noise_mask,no_noise -0.9704999923706055,0.9763333201408386,0.8999999761581421,0.9168333411216736,0.9643333554267883,0.9651666879653931,0.918833315372467,0.9714999794960022 -0.971666693687439,0.984000027179718,0.924833357334137,0.9746666550636292,0.9818333387374878,0.9829999804496765,0.8153333067893982,0.9776666760444641 -0.9708333611488342,0.9768333435058594,0.9355000257492065,0.9704999923706055,0.9776666760444641,0.9829999804496765,0.8966666460037231,0.9835000038146973 -0.9743333458900452,0.9881666898727417,0.9608333110809326,0.9758333563804626,0.9821666479110718,0.984499990940094,0.8426666855812073,0.9866666793823242 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", 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