From 8ae260322e9b1fdba9b076dd2c0ece9c4cd1b8fe Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Mon, 15 Jan 2024 19:08:31 +0530 Subject: [PATCH 01/16] Initialized project folder --- Facial-Emotion-Detection/Dataset/README.md | 0 Facial-Emotion-Detection/Model/README.md | 0 Facial-Emotion-Detection/README.md | 0 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Facial-Emotion-Detection/Dataset/README.md create mode 100644 Facial-Emotion-Detection/Model/README.md create mode 100644 Facial-Emotion-Detection/README.md diff --git a/Facial-Emotion-Detection/Dataset/README.md b/Facial-Emotion-Detection/Dataset/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/Facial-Emotion-Detection/Model/README.md b/Facial-Emotion-Detection/Model/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/Facial-Emotion-Detection/README.md b/Facial-Emotion-Detection/README.md new file mode 100644 index 000000000..e69de29bb From 16ce5b1fd3808d2590a7f2fcacfb3644b895154e Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Mon, 15 Jan 2024 19:18:55 +0530 Subject: [PATCH 02/16] Added dataset link --- Facial-Emotion-Detection/Dataset/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/Facial-Emotion-Detection/Dataset/README.md b/Facial-Emotion-Detection/Dataset/README.md index e69de29bb..976482bfa 100644 --- a/Facial-Emotion-Detection/Dataset/README.md +++ b/Facial-Emotion-Detection/Dataset/README.md @@ -0,0 +1 @@ +# Data source: [FER-DS - Kaggle](https://www.kaggle.com/datasets/mhantor/facial-expression) \ No newline at end of file From 78e0c66366bd5f07d18d48084a9e286f13e41b67 Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Mon, 15 Jan 2024 19:42:16 +0530 Subject: [PATCH 03/16] Added baseline CNN model notebook --- .../Model/00_baseline_cnn.ipynb | 810 ++++++++++++++++++ 1 file changed, 810 insertions(+) create mode 100644 Facial-Emotion-Detection/Model/00_baseline_cnn.ipynb diff --git a/Facial-Emotion-Detection/Model/00_baseline_cnn.ipynb b/Facial-Emotion-Detection/Model/00_baseline_cnn.ipynb new file mode 100644 index 000000000..7e95eda8b --- /dev/null +++ b/Facial-Emotion-Detection/Model/00_baseline_cnn.ipynb @@ -0,0 +1,810 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "collapsed_sections": [ + "o2Q64Wb2IMCv", + "Un-UbpZ8IQI4", + "yt8gM6cXlv1g" + ] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Setup" + ], + "metadata": { + "id": "o2Q64Wb2IMCv" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from keras.layers import (\n", + " Rescaling, Conv2D, MaxPooling2D, Flatten, Dense)\n", + "from keras.utils import set_random_seed\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "SEED = 2024\n", + "set_random_seed(SEED)" + ], + "metadata": { + "id": "iUqy4Y-OEJMT" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**<-- Mount Drive manually**" + ], + "metadata": { + "id": "Uo-tDnNdLvNC" + } + }, + { + "cell_type": "code", + "source": [ + "DATA_PATH = '/content/drive/MyDrive/notebooks/swoc_s4/facial_emotion_detection/'\n", + "images = np.load(f'{DATA_PATH}/images.npy')\n", + "labels = np.load(f'{DATA_PATH}/labels.npy')" + ], + "metadata": { + "id": "WG7_wuH3_lvg" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Data overview" + ], + "metadata": { + "id": "Un-UbpZ8IQI4" + } + }, + { + "cell_type": "code", + "source": [ + "images.shape, labels.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "js6QMLwiL_sr", + "outputId": "892accaa-be48-42b1-e4d1-8bcb015174bf" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "((19950, 48, 48, 3), (19950, 4))" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "labels[:3]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R1z2vRMyMWsv", + "outputId": "43016f2f-fb65-4780-e448-b82cba5b3e2c" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0., 0., 0., 1.],\n", + " [0., 0., 0., 1.],\n", + " [0., 0., 1., 0.]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "* Number of samples: 19950\n", + "* Image resolution: 48 x 48 (x 3 channels for RGB)\n", + "* Labels: one-hot encoded\n" + ], + "metadata": { + "id": "cdAse2r0MFtp" + } + }, + { + "cell_type": "code", + "source": [ + "# converting to one-hot encoded to ordinal labels\n", + "ord_labels = np.argmax(labels, axis=1)\n", + "ord_labels[:3]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SJuyeU_QM9CI", + "outputId": "3f4065c6-e80d-48e3-c154-a4c3f4202933" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([3, 3, 2])" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Proportion of classes in dataset**" + ], + "metadata": { + "id": "NWzaoT57Koze" + } + }, + { + "cell_type": "code", + "source": [ + "values, counts = np.unique(ord_labels, return_counts=True)\n", + "dict(zip(values, counts))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c7QouSnMRWxm", + "outputId": "a76d9ffc-43b4-4c00-d8fc-617e12986b06" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{0: 3850, 1: 7200, 2: 5100, 3: 3800}" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Label mapping from data source:\n" + ], + "metadata": { + "id": "21SAWrHHNQor" + } + }, + { + "cell_type": "code", + "source": [ + "label_mapping = {0: 'angry', 1: 'happy', 2: 'neutral', 3: 'surprised'}" + ], + "metadata": { + "id": "K_hoBjeYNw74" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Visualizing some samples**" + ], + "metadata": { + "id": "7dmxUpG6K0qL" + } + }, + { + "cell_type": "code", + "source": [ + "plt.subplots(3, 5, figsize=(10, 6))\n", + "for i in range(15):\n", + " plt.subplot(3, 5, i+1)\n", + " plt.title(f'{ord_labels[i]}: {label_mapping[ord_labels[i]]}')\n", + " plt.imshow(images[i])\n", + " plt.axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "jlx06qCqEDYE", + "outputId": "4305dcd9-c03e-430f-db3a-46614eafe675" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Issues** (and possible solutions):\n", + "* Image quality is poor due to low resolution (image upscaling: using interpolation algorithms as well as deep learning methods)\n", + "* Some samples in the visualized images have watermarks, which may interfere with the learning process (preprocessing techniques for watermark removal)\n", + "* Imbalance in labels (resampling/class-weights) [low priority issue]" + ], + "metadata": { + "id": "xi3ArBbIPtmD" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Data preparation" + ], + "metadata": { + "id": "yt8gM6cXlv1g" + } + }, + { + "cell_type": "markdown", + "source": [ + "Creating test dataset for checking model performance on unseen data:" + ], + "metadata": { + "id": "FQ602ljjnSg5" + } + }, + { + "cell_type": "code", + "source": [ + "train_images, test_images, train_labels, test_labels = train_test_split(\n", + " images, ord_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=ord_labels, # to maintain proportion of classes in test data\n", + " random_state=SEED)" + ], + "metadata": { + "id": "mzUBrLzXl7ij" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "len(train_labels), len(test_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LkWisykILDCF", + "outputId": "4543849b-ed05-4af4-9a11-7eb3c0bed1f8" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(17955, 1995)" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Creating validation dataset for early stopping mechanism of model training:" + ], + "metadata": { + "id": "EOQ2D_Qrz26C" + } + }, + { + "cell_type": "code", + "source": [ + "train_images, val_images, train_labels, val_labels = train_test_split(\n", + " train_images, train_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=train_labels, # to maintain proportion of classes in val data\n", + " random_state=SEED)" + ], + "metadata": { + "id": "mPljtL5qyrXY" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "len(train_labels), len(val_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3qLtuF-dLLdx", + "outputId": "017ab5ff-a1f2-4f42-c728-9122e625a7b8" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(16159, 1796)" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Model training:" + ], + "metadata": { + "id": "9gJyORNM9QvE" + } + }, + { + "cell_type": "markdown", + "source": [ + "We will build a convolutional neural network as our baseline model." + ], + "metadata": { + "id": "njK-BWRl91R_" + } + }, + { + "cell_type": "code", + "source": [ + "def build_model():\n", + " model = tf.keras.Sequential([\n", + " Rescaling(scale=1./255, input_shape=(48, 48, 3)),\n", + " Conv2D(48, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " MaxPooling2D(2, 2),\n", + " Flatten(),\n", + " Dense(64, activation='relu'),\n", + " Dense(4, activation='softmax') # number of classes\n", + " ])\n", + "\n", + " model.compile(\n", + " optimizer='adam',\n", + " loss='sparse_categorical_crossentropy', # for ordinal labels\n", + " metrics=['accuracy']\n", + " )\n", + "\n", + " return model" + ], + "metadata": { + "id": "VuNlMAvx9S_8" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Model summary**" + ], + "metadata": { + "id": "fZek1_XDMZ4z" + } + }, + { + "cell_type": "code", + "source": [ + "model = build_model()\n", + "\n", + "model.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IcMi8xhL0J5y", + "outputId": "7613feb1-642f-46f2-c1f7-b4e07cc9310b" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " rescaling (Rescaling) (None, 48, 48, 3) 0 \n", + " \n", + " conv2d (Conv2D) (None, 46, 46, 48) 1344 \n", + " \n", + " max_pooling2d (MaxPooling2 (None, 23, 23, 48) 0 \n", + " D) \n", + " \n", + " conv2d_1 (Conv2D) (None, 21, 21, 64) 27712 \n", + " \n", + " max_pooling2d_1 (MaxPoolin (None, 10, 10, 64) 0 \n", + " g2D) \n", + " \n", + " conv2d_2 (Conv2D) (None, 8, 8, 64) 36928 \n", + " \n", + " max_pooling2d_2 (MaxPoolin (None, 4, 4, 64) 0 \n", + " g2D) \n", + " \n", + " flatten (Flatten) (None, 1024) 0 \n", + " \n", + " dense (Dense) (None, 64) 65600 \n", + " \n", + " dense_1 (Dense) (None, 4) 260 \n", + " \n", + "=================================================================\n", + "Total params: 131844 (515.02 KB)\n", + "Trainable params: 131844 (515.02 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Fit model**\n", + "* High number of epochs to avoid underfitting\n", + "* Early stopping mechanism to avoid overfitting" + ], + "metadata": { + "id": "_DKCjFL4MBFx" + } + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "early_stopping = EarlyStopping(\n", + " monitor='val_accuracy',\n", + " patience=5,\n", + " min_delta=2e-4,\n", + " restore_best_weights=True)\n", + "\n", + "history = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=100,\n", + " batch_size=512,\n", + " callbacks=[early_stopping],\n", + " verbose=0)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g7nyJrZyBziy", + "outputId": "e75b29c9-9d66-4779-c252-b01e452015c7" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 27.7 s, sys: 1.19 s, total: 28.9 s\n", + "Wall time: 42.9 s\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Validation accuracy** (using best weights from early stopping)" + ], + "metadata": { + "id": "63q1iB4mL108" + } + }, + { + "cell_type": "code", + "source": [ + "model.evaluate(val_images, val_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N6ArOABtGaL4", + "outputId": "48f91ba4-cb1c-4ba1-c3d5-e9ab71b22f6e" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "57/57 [==============================] - 0s 5ms/step - loss: 0.7887 - accuracy: 0.7105\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.7886693477630615, 0.7104676961898804]" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Visualizing training curves**" + ], + "metadata": { + "id": "TOg8v0XB-F5y" + } + }, + { + "cell_type": "code", + "source": [ + "def plot_training_curve(history):\n", + " train_loss = history.history['loss']\n", + " train_accuracy = history.history['accuracy']\n", + " val_loss = history.history['val_loss']\n", + " val_accuracy = history.history['val_accuracy']\n", + " num_epochs = len(train_loss)\n", + " epochs = range(num_epochs)\n", + "\n", + " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4), sharex=True)\n", + " ax[0].plot(epochs, train_loss, label='train_loss')\n", + " ax[0].plot(epochs, val_loss, label='val_loss')\n", + " ax[0].set_title('Loss')\n", + "\n", + " ax[1].plot(epochs, train_accuracy, label='train_accuracy')\n", + " ax[1].plot(epochs, val_accuracy, label='val_accuracy')\n", + " ax[1].set_title('Accuracy')\n", + "\n", + " train_stop = (num_epochs-6, val_accuracy[num_epochs-6])\n", + " ax[1].annotate(f'Early stopping\\ntriggered',\n", + " xy=train_stop, xycoords='data',\n", + " xytext=(0, -100), textcoords='offset points',\n", + " arrowprops=dict(facecolor='black', shrink=0.05),\n", + " horizontalalignment='center', verticalalignment='bottom')\n", + "\n", + " ax[0].minorticks_on(); ax[1].minorticks_on()\n", + " ax[0].set_xlabel('Epochs'); ax[1].set_xlabel('Epochs')\n", + " ax[0].legend(); ax[1].legend()\n", + " fig.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "id": "3rlgg-Ah-KnQ" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "plot_training_curve(history)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "id": "F-B3-vly-b6f", + "outputId": "ce99e4e2-0326-43b8-effc-54047726f4ec" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Performance on test (unseen) data" + ], + "metadata": { + "id": "Oslm046H-eZq" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Test accuracy**" + ], + "metadata": { + "id": "E0wZpoPHNA64" + } + }, + { + "cell_type": "code", + "source": [ + "model.evaluate(test_images, test_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UEEyoDOkCJSV", + "outputId": "6263f6d4-fc7b-488d-d0bc-fded365cfb46" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 0s 5ms/step - loss: 0.7930 - accuracy: 0.7123\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.7929964065551758, 0.7122806906700134]" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model is able to replicate its validation accuracy (71.05%) on the unseen test set (71.23%). \n", + "This shows that the model has generalized well and not simply overfit the training data." + ], + "metadata": { + "id": "RO2HFu8lyfZz" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Confusion matrix**" + ], + "metadata": { + "id": "9HCeLAJ6M0MX" + } + }, + { + "cell_type": "code", + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BrZ3OTlxHxKk", + "outputId": "fc1229cf-2211-4eac-8297-48a39979d35e" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 0s 2ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "cjGeHYLuJlCW", + "outputId": "35567719-64be-4b9a-e2ab-5a44a9bc02c4" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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"text": [ + "63/63 [==============================] - 0s 5ms/step - loss: 0.6889 - accuracy: 0.7499\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.6888747811317444, 0.7498747110366821]" + ] + }, + "metadata": {}, + "execution_count": 110 + } + ], + "source": [ + "model_1.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "id": "L8lTPJRXrtEs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ea1bb7d3-46cc-4886-d251-f4d558ee64be" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 1s 5ms/step\n" + ] + } + ], + "source": [ + "predictions_1 = np.argmax(model_1.predict(test_images), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "id": "rBMBfOZCrx9i", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": 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" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions_1,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (Model 1)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6GL_QvFDsDrh" + }, + "source": [ + "### Model 2 (Data augmentation configuration - 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "id": "bpFpSTQSsDri", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fb0ed83f-9f6d-4706-e081-8f6ecdf61545" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1min 32s, sys: 4.76 s, total: 1min 37s\n", + "Wall time: 1min 56s\n" + ] + } + ], + "source": [ + "%%time\n", + "keras.backend.clear_session()\n", + "\n", + "model_2 = build_model(augment_config_2)\n", + "\n", + "history_2 = model_2.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "id": "0OkzIUVNsDri", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "outputId": "17412501-4d27-452e-d80a-5de72b3ededf" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_training_curve(history_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "id": "FblaKLmtgirB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "435df406-9eb7-48b3-cf21-5aec3cc9e52d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "57/57 [==============================] - 0s 4ms/step - loss: 0.8072 - accuracy: 0.6782\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.8071913719177246, 0.6781737208366394]" + ] + }, + "metadata": {}, + "execution_count": 115 + } + ], + "source": [ + "model_2.evaluate(val_images, val_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "id": "l9f7vLzjsDrj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "241da724-eefa-45f6-d26b-e981238aab6f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 0s 4ms/step - loss: 0.7943 - accuracy: 0.6792\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.7942925095558167, 0.6791979670524597]" + ] + }, + "metadata": {}, + "execution_count": 116 + } + ], + "source": [ + "model_2.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "id": "IBIg7j_bsDsC", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "714bdfa0-5eff-4e98-ff6c-27c1d5e2b97a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 0s 3ms/step\n" + ] + } + ], + "source": [ + "predictions_2 = np.argmax(model_2.predict(test_images), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "id": "H8Jsh_bssDsD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": 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" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions_2,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (Model 2)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_1KawlmjsOrI" + }, + "source": [ + "### Model 3 (Data augmentation configuration - 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "id": "CdfoCDDTsOrJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "439dae0b-4d16-406d-c200-ad9ebd7de4be" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 2h 22min 35s, sys: 18min 23s, total: 2h 40min 59s\n", + "Wall time: 1h 45min 56s\n" + ] + } + ], + "source": [ + "%%time\n", + "keras.backend.clear_session()\n", + "\n", + "model_3 = build_model(augment_config_3)\n", + "\n", + "history_3 = model_3.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "id": "bRFgwzf9sOrK", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "outputId": "94288974-45e3-450a-a885-08fc76225e1d" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_training_curve(history_3)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "id": "Z2OUT9pUhcYQ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "11c7c33b-8d42-4a6e-9425-0f2c393b00f1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "57/57 [==============================] - 9s 151ms/step - loss: 0.9387 - accuracy: 0.6047\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.938670814037323, 0.6046770811080933]" + ] + }, + "metadata": {}, + "execution_count": 121 + } + ], + "source": [ + "model_3.evaluate(val_images, val_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "id": "ZP8qnyQdsOrK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "34a0190d-0967-4b33-dbd6-13c5e94c848c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 11s 175ms/step - loss: 0.9424 - accuracy: 0.6015\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.9424149394035339, 0.6015037298202515]" + ] + }, + "metadata": {}, + "execution_count": 122 + } + ], + "source": [ + "model_3.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": { + "id": "OYfl_p03sOsM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "19b4c168-acea-4c9d-df2d-fddf70394f05" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 15s 175ms/step\n" + ] + } + ], + "source": [ + "predictions_3 = np.argmax(model_3.predict(test_images), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "id": "PasmprDlsOsN", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "outputId": "f3bad4e0-316e-4f79-bff7-41f0a68df833" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ], + "source": [ + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions_3,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (Model 3)')\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 6b9cf9592108d62c957dad9fc16045775595a3a7 Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Sat, 20 Jan 2024 18:28:20 +0530 Subject: [PATCH 06/16] Added images from notebook 01 --- .../00_config1_training_curves.png | Bin 0 -> 51121 bytes .../01_config1_confusion_matrix.png | Bin 0 -> 32640 bytes .../02_config2_training_curves.png | Bin 0 -> 55989 bytes .../03_config2_confusion_matrix.png | Bin 0 -> 32024 bytes .../04_config3_training_curves.png | Bin 0 -> 55057 bytes .../05_config3_confusion_matrix.png | Bin 0 -> 32291 bytes 6 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/00_config1_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/01_config1_confusion_matrix.png create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/02_config2_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/03_config2_confusion_matrix.png create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/04_config3_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/01_data_augmentation_cnn/05_config3_confusion_matrix.png diff --git a/Facial-Emotion-Detection/Images/01_data_augmentation_cnn/00_config1_training_curves.png 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z28m3S`yaim^5J^xfbDuRRTJ@EoazwV6U-`S5I%Ns;xU*dU0oM2dC2=YR9+CpAkao| zPnfMomV2~V-=4zr0oZQ1G9)-A?d~TeVEJm-1|QQ|}Bh#y5z zfypN9>{l=v2{tpIP9P_ZYuy6{&yjwMX)TB^T}sNznX`iJ%#4hT;M<2WiH6EdyhSMc z1Y&o0>$*LsOCckxt7PVBi-98bfXYz7I8Rjf@t*rNLNO z0(+nsU`+&R@OhvcIkxAdHUvl7h!6&N!wS)ck<*lYp*VmVAt5IX+S(dOL&za!k?f&E8SSI~MS)w$*>9=#dBOhNH`dn>^252>*xJ6-fA!?))13_| zX#Xg=)XEFU7AX_XTx7Z~#ldj+M-1|9pg9YsEK13Uwn*6k(EqJ-V(>BE4_@NxDu}DN zF7SWM*u`SMOi$ko)*F$T<1SHHL}s3gSkCd=5&-`zfF`b8`_|F|VSwLra~38mxOIFC z{4ZuEre`QXUcP*Qf#IeOxS}Kj1kPO_so*9Vh?1DVTiasoPk_j)Qkd vko{Lz{@*_)`{pw}e%nXA3BmR8Z&2<17HpHHp%sGPZc#a@u9zin8t}gWB?rFW literal 0 HcmV?d00001 From 7fb4636ca11ce70f7d8f964107f129dbbb2ef67b Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Tue, 30 Jan 2024 23:17:19 +0530 Subject: [PATCH 07/16] Added notebook for transfer learning with MobileNetV2 --- .../02_transfer_learning_mobilenetv2.ipynb | 1247 +++++++++++++++++ 1 file changed, 1247 insertions(+) create mode 100644 Facial-Emotion-Detection/Model/02_transfer_learning_mobilenetv2.ipynb diff --git a/Facial-Emotion-Detection/Model/02_transfer_learning_mobilenetv2.ipynb b/Facial-Emotion-Detection/Model/02_transfer_learning_mobilenetv2.ipynb new file mode 100644 index 000000000..887694167 --- /dev/null +++ b/Facial-Emotion-Detection/Model/02_transfer_learning_mobilenetv2.ipynb @@ -0,0 +1,1247 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "uupI5Ue6kkCM" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "iwt6OlFmi6fx" + }, + "outputs": [], + "source": [ + "import gc\n", + "gc.enable()\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "from tensorflow import keras\n", + "from keras.applications import (\n", + " mobilenet_v2, efficientnet_v2, vgg19)\n", + "from keras.layers import (\n", + " Resizing, RandomFlip, RandomRotation,\n", + " GlobalAveragePooling2D, Dropout, Dense, Input)\n", + "\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.utils import set_random_seed\n", + "\n", + "SEED = 2024\n", + "set_random_seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Uo-tDnNdLvNC" + }, + "source": [ + "**<-- Mount Drive manually**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "WG7_wuH3_lvg" + }, + "outputs": [], + "source": [ + "DATA_PATH = '/content/drive/MyDrive/notebooks/swoc_s4/facial_emotion_detection/'\n", + "images = np.load(f'{DATA_PATH}/images.npy')\n", + "labels = np.load(f'{DATA_PATH}/labels.npy')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l_HUMU2vlDpC" + }, + "source": [ + "# Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "wZlTC6NelMAK" + }, + "outputs": [], + "source": [ + "label_mapping = {0: 'angry', 1: 'happy', 2: 'neutral', 3: 'surprised'}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "VnG7rpIHlGPh" + }, + "outputs": [], + "source": [ + "ord_labels = np.argmax(labels, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HhMmnVPrlSsi" + }, + "source": [ + "**Test dataset:** (using the same split as the baseline notebook)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "opsxI8l-lWIt" + }, + "outputs": [], + "source": [ + "train_images, test_images, train_labels, test_labels = train_test_split(\n", + " images, ord_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=ord_labels, # to maintain proportion of classes in test data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7qi9ZyJ3lnfc" + }, + "source": [ + "**Train and validation datasets:**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "FDrOWPbulrIj" + }, + "outputs": [], + "source": [ + "train_images, val_images, train_labels, val_labels = train_test_split(\n", + " train_images, train_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=train_labels, # to maintain proportion of classes in val data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "9WvgUlwX5fEY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0146d1cb-68f1-49d7-b940-a9464514604e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(16159, 1796, 1995)" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "len(train_labels), len(val_labels), len(test_labels)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Data-augmentation (best config chosen from notebook 01):**" + ], + "metadata": { + "id": "217hZZ1Hdi1o" + } + }, + { + "cell_type": "code", + "source": [ + "data_augmentation = keras.Sequential([\n", + " RandomRotation(factor=(-0.1, 0.2), fill_mode='constant', seed=SEED),\n", + " RandomFlip(mode='horizontal', seed=SEED),\n", + "])" + ], + "metadata": { + "id": "iMjYlvkVdcS3" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Model training" + ], + "metadata": { + "id": "GVLCqy8M_0oX" + } + }, + { + "cell_type": "code", + "source": [ + "# common parameters\n", + "BASE_LR = 1e-4\n", + "DROPOUT = 0.2\n", + "PATIENCE = 10\n", + "BATCH_SIZE = 32\n", + "EPOCHS = 200\n", + "VERBOSE = 0" + ], + "metadata": { + "id": "_tfQ4DvpXIhY" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# overfitting detection\n", + "early_stopping = EarlyStopping(\n", + " monitor='val_accuracy',\n", + " patience=PATIENCE,\n", + " min_delta=2e-4,\n", + " restore_best_weights=True)" + ], + "metadata": { + "id": "eOjhiIcdXEMW" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "6NHSuHRurGbN" + }, + "outputs": [], + "source": [ + "# visualizing training curves\n", + "def plot_training_curve(history):\n", + " train_loss = history.history['loss']\n", + " train_accuracy = history.history['accuracy']\n", + " val_loss = history.history['val_loss']\n", + " val_accuracy = history.history['val_accuracy']\n", + " num_epochs = len(train_loss)\n", + " epochs = range(num_epochs)\n", + "\n", + " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4), sharex=True)\n", + " ax[0].plot(epochs, train_loss, label='train_loss')\n", + " ax[0].plot(epochs, val_loss, label='val_loss')\n", + " ax[0].set_title('Loss')\n", + " ax[1].plot(epochs, train_accuracy, label='train_accuracy')\n", + " ax[1].plot(epochs, val_accuracy, label='val_accuracy')\n", + " ax[1].set_title('Accuracy')\n", + "\n", + " train_stop = (num_epochs-1-PATIENCE, val_accuracy[num_epochs-1-PATIENCE])\n", + " ax[1].annotate(f'Early stopping\\ntriggered',\n", + " xy=train_stop, xycoords='data',\n", + " xytext=(-5, -75), textcoords='offset points',\n", + " arrowprops=dict(facecolor='black', shrink=0.05),\n", + " horizontalalignment='center', verticalalignment='bottom')\n", + "\n", + " ax[0].minorticks_on(); ax[1].minorticks_on()\n", + " ax[0].set_xlabel('Epochs'); ax[1].set_xlabel('Epochs')\n", + " ax[0].legend(); ax[1].legend()\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 1. MobileNetV2, no data augmentation" + ], + "metadata": { + "id": "Q5uOnDEC_3G6" + } + }, + { + "cell_type": "code", + "source": [ + "img_size = 224" + ], + "metadata": { + "id": "3MLp69xyclCb" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Feature extraction phase:**" + ], + "metadata": { + "id": "FDAYLXRHWv1b" + } + }, + { + "cell_type": "code", + "source": [ + "keras.backend.clear_session()\n", + "preprocess_input = mobilenet_v2.preprocess_input\n", + "base_model = mobilenet_v2.MobileNetV2(input_shape=(img_size, img_size, 3), include_top=False)\n", + "base_model.trainable = False\n", + "\n", + "inputs = Input(shape=(48, 48, 3))\n", + "x = Resizing(img_size, img_size, interpolation='lanczos5', crop_to_aspect_ratio=True)(inputs)\n", + "x = preprocess_input(x)\n", + "x = base_model(x, training=False)\n", + "x = GlobalAveragePooling2D()(x)\n", + "# x = Dropout(DROPOUT)(x)\n", + "# x = Dense(64, activation='relu')(x)\n", + "x = Dropout(DROPOUT)(x)\n", + "outputs = Dense(4, activation='softmax')(x)\n", + "\n", + "model = keras.Model(inputs, outputs)\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M6skADuC_5dE", + "outputId": "2405f2ee-8ddb-455a-b742-45d8c5f29428" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n", + "9406464/9406464 [==============================] - 0s 0us/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "history = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "DnQIoem5WPiA", + "outputId": "9e3665ef-d5db-40f5-d593-189dfa511132" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "505/505 [==============================] - 94s 160ms/step - loss: 1.5501 - accuracy: 0.3243 - val_loss: 1.3957 - val_accuracy: 0.3614\n", + "Epoch 2/100\n", + "505/505 [==============================] - 79s 156ms/step - loss: 1.4817 - accuracy: 0.3446 - val_loss: 1.3379 - val_accuracy: 0.3909\n", + "Epoch 3/100\n", + "505/505 [==============================] - 78s 155ms/step - loss: 1.4248 - accuracy: 0.3706 - val_loss: 1.2928 - val_accuracy: 0.4254\n", + "Epoch 4/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.3833 - accuracy: 0.3886 - val_loss: 1.2565 - val_accuracy: 0.4438\n", + "Epoch 5/100\n", + "505/505 [==============================] - 77s 153ms/step - loss: 1.3447 - accuracy: 0.4047 - val_loss: 1.2268 - val_accuracy: 0.4605\n", + "Epoch 6/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.3134 - accuracy: 0.4253 - val_loss: 1.2025 - val_accuracy: 0.4710\n", + "Epoch 7/100\n", + "505/505 [==============================] - 78s 155ms/step - loss: 1.2857 - accuracy: 0.4370 - val_loss: 1.1810 - val_accuracy: 0.4839\n", + "Epoch 8/100\n", + "505/505 [==============================] - 77s 153ms/step - loss: 1.2620 - accuracy: 0.4495 - val_loss: 1.1631 - val_accuracy: 0.4944\n", + "Epoch 9/100\n", + "505/505 [==============================] - 76s 150ms/step - loss: 1.2418 - accuracy: 0.4633 - val_loss: 1.1466 - val_accuracy: 0.4994\n", + "Epoch 10/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.2277 - accuracy: 0.4705 - val_loss: 1.1324 - val_accuracy: 0.5106\n", + "Epoch 11/100\n", + "505/505 [==============================] - 75s 149ms/step - loss: 1.2092 - accuracy: 0.4743 - val_loss: 1.1201 - val_accuracy: 0.5117\n", + "Epoch 12/100\n", + "505/505 [==============================] - 74s 147ms/step - loss: 1.1946 - accuracy: 0.4819 - val_loss: 1.1087 - val_accuracy: 0.5178\n", + "Epoch 13/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.1849 - accuracy: 0.4917 - val_loss: 1.0983 - val_accuracy: 0.5200\n", + "Epoch 14/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.1708 - accuracy: 0.4974 - val_loss: 1.0889 - val_accuracy: 0.5245\n", + "Epoch 15/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.1609 - accuracy: 0.5028 - val_loss: 1.0806 - val_accuracy: 0.5317\n", + "Epoch 16/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.1574 - accuracy: 0.5067 - val_loss: 1.0734 - val_accuracy: 0.5390\n", + "Epoch 17/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.1451 - accuracy: 0.5068 - val_loss: 1.0657 - val_accuracy: 0.5418\n", + "Epoch 18/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.1317 - accuracy: 0.5214 - val_loss: 1.0589 - val_accuracy: 0.5440\n", + "Epoch 19/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.1217 - accuracy: 0.5255 - val_loss: 1.0525 - val_accuracy: 0.5484\n", + "Epoch 20/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.1159 - accuracy: 0.5270 - val_loss: 1.0467 - val_accuracy: 0.5501\n", + "Epoch 21/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 1.1112 - accuracy: 0.5296 - val_loss: 1.0413 - val_accuracy: 0.5579\n", + "Epoch 22/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 1.0966 - accuracy: 0.5363 - 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val_loss: 0.9387 - val_accuracy: 0.6013\n", + "Epoch 62/100\n", + "505/505 [==============================] - 76s 151ms/step - loss: 0.9581 - accuracy: 0.6035 - val_loss: 0.9374 - val_accuracy: 0.6013\n", + "Epoch 63/100\n", + "505/505 [==============================] - 77s 153ms/step - loss: 0.9563 - accuracy: 0.6052 - val_loss: 0.9362 - val_accuracy: 0.6002\n", + "Epoch 64/100\n", + "505/505 [==============================] - 76s 151ms/step - loss: 0.9540 - accuracy: 0.6049 - val_loss: 0.9353 - val_accuracy: 0.6036\n", + "Epoch 65/100\n", + "505/505 [==============================] - 77s 153ms/step - loss: 0.9556 - accuracy: 0.6036 - val_loss: 0.9338 - val_accuracy: 0.6075\n", + "Epoch 66/100\n", + "505/505 [==============================] - 80s 159ms/step - loss: 0.9523 - accuracy: 0.6078 - val_loss: 0.9328 - val_accuracy: 0.6075\n", + "Epoch 67/100\n", + "505/505 [==============================] - 79s 157ms/step - loss: 0.9494 - accuracy: 0.6107 - val_loss: 0.9319 - val_accuracy: 0.6041\n", + "Epoch 68/100\n", + "505/505 [==============================] - 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val_loss: 0.9243 - val_accuracy: 0.6069\n", + "Epoch 75/100\n", + "505/505 [==============================] - 78s 154ms/step - loss: 0.9361 - accuracy: 0.6185 - val_loss: 0.9234 - val_accuracy: 0.6091\n", + "Epoch 76/100\n", + "505/505 [==============================] - 76s 151ms/step - loss: 0.9344 - accuracy: 0.6165 - val_loss: 0.9223 - val_accuracy: 0.6091\n", + "Epoch 77/100\n", + "505/505 [==============================] - 75s 148ms/step - loss: 0.9304 - accuracy: 0.6198 - val_loss: 0.9216 - val_accuracy: 0.6141\n", + "Epoch 78/100\n", + "505/505 [==============================] - 73s 144ms/step - loss: 0.9306 - accuracy: 0.6173 - val_loss: 0.9205 - val_accuracy: 0.6102\n", + "Epoch 79/100\n", + "505/505 [==============================] - 79s 156ms/step - loss: 0.9266 - accuracy: 0.6238 - val_loss: 0.9195 - val_accuracy: 0.6102\n", + "Epoch 80/100\n", + "505/505 [==============================] - 77s 152ms/step - loss: 0.9267 - accuracy: 0.6211 - val_loss: 0.9187 - val_accuracy: 0.6102\n", + "Epoch 81/100\n", + "505/505 [==============================] - 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val_loss: 0.9044 - val_accuracy: 0.6214\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1h 30min 20s, sys: 2min 21s, total: 1h 32min 41s\n", + "Wall time: 2h 9min 29s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zu2WC7KkX-NB", + "outputId": "88631cd7-97ce-426b-d974-a219a86ecd66" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 8s 138ms/step - loss: 0.9044 - accuracy: 0.6214\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 8s 133ms/step - loss: 0.9166 - accuracy: 0.6311\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.9165595769882202, 0.6310777068138123]" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (MobileNetV2, no aug, feature-extraction)')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "_mFchi4AYPNm", + "outputId": "836c58a2-9ccb-477b-abaf-214db7b134be" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "63/63 [==============================] - 8s 115ms/step\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Fine-tuning phase:**" + ], + "metadata": { + "id": "0toDQNcRY1po" + } + }, + { + "cell_type": "code", + "source": [ + "base_model.trainable = True\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR/10),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ], + "metadata": { + "id": "_ZLN8ThoY5P7" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "history_ft = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history_ft)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "9G9hsJcMZ2aQ", + "outputId": "ff922b5e-f8ca-4909-d2fb-b2e2d3e99f50" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "505/505 [==============================] - 153s 251ms/step - loss: 0.8711 - accuracy: 0.6500 - val_loss: 0.8540 - val_accuracy: 0.6487\n", + "Epoch 2/100\n", + "505/505 [==============================] - 121s 239ms/step - loss: 0.8171 - accuracy: 0.6718 - val_loss: 0.8279 - val_accuracy: 0.6620\n", + "Epoch 3/100\n", + "505/505 [==============================] - 119s 236ms/step - loss: 0.7858 - accuracy: 0.6888 - val_loss: 0.8087 - val_accuracy: 0.6726\n", + "Epoch 4/100\n", + "505/505 [==============================] - 121s 239ms/step - loss: 0.7607 - accuracy: 0.6975 - val_loss: 0.7858 - val_accuracy: 0.6860\n", + "Epoch 5/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.7297 - accuracy: 0.7139 - val_loss: 0.7680 - val_accuracy: 0.6965\n", + "Epoch 6/100\n", + "505/505 [==============================] - 124s 246ms/step - loss: 0.7151 - accuracy: 0.7202 - val_loss: 0.7544 - val_accuracy: 0.6982\n", + "Epoch 7/100\n", + "505/505 [==============================] - 124s 246ms/step - loss: 0.6937 - accuracy: 0.7286 - val_loss: 0.7470 - val_accuracy: 0.7082\n", + "Epoch 8/100\n", + "505/505 [==============================] - 119s 236ms/step - loss: 0.6767 - accuracy: 0.7382 - val_loss: 0.7410 - val_accuracy: 0.7077\n", + "Epoch 9/100\n", + "505/505 [==============================] - 124s 246ms/step - loss: 0.6601 - accuracy: 0.7450 - val_loss: 0.7221 - val_accuracy: 0.7222\n", + "Epoch 10/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.6429 - accuracy: 0.7520 - val_loss: 0.7186 - val_accuracy: 0.7171\n", + "Epoch 11/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.6328 - accuracy: 0.7544 - val_loss: 0.7102 - val_accuracy: 0.7227\n", + "Epoch 12/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.6195 - accuracy: 0.7609 - val_loss: 0.7016 - val_accuracy: 0.7316\n", + "Epoch 13/100\n", + "505/505 [==============================] - 123s 244ms/step - loss: 0.6098 - accuracy: 0.7673 - val_loss: 0.6938 - val_accuracy: 0.7350\n", + "Epoch 14/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5979 - accuracy: 0.7736 - val_loss: 0.6883 - val_accuracy: 0.7400\n", + "Epoch 15/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5852 - accuracy: 0.7752 - val_loss: 0.6826 - val_accuracy: 0.7400\n", + "Epoch 16/100\n", + "505/505 [==============================] - 121s 239ms/step - loss: 0.5757 - accuracy: 0.7825 - val_loss: 0.6778 - val_accuracy: 0.7483\n", + "Epoch 17/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5657 - accuracy: 0.7874 - val_loss: 0.6744 - val_accuracy: 0.7467\n", + "Epoch 18/100\n", + "505/505 [==============================] - 120s 237ms/step - loss: 0.5551 - accuracy: 0.7912 - val_loss: 0.6727 - val_accuracy: 0.7461\n", + "Epoch 19/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5466 - accuracy: 0.7952 - val_loss: 0.6640 - val_accuracy: 0.7528\n", + "Epoch 20/100\n", + "505/505 [==============================] - 120s 238ms/step - loss: 0.5393 - accuracy: 0.7945 - val_loss: 0.6579 - val_accuracy: 0.7600\n", + "Epoch 21/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5320 - accuracy: 0.8002 - val_loss: 0.6628 - val_accuracy: 0.7528\n", + "Epoch 22/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.5195 - accuracy: 0.8067 - val_loss: 0.6566 - val_accuracy: 0.7600\n", + "Epoch 23/100\n", + "505/505 [==============================] - 123s 243ms/step - loss: 0.5128 - accuracy: 0.8079 - val_loss: 0.6492 - val_accuracy: 0.7650\n", + "Epoch 24/100\n", + "505/505 [==============================] - 123s 243ms/step - loss: 0.5056 - accuracy: 0.8122 - val_loss: 0.6597 - val_accuracy: 0.7556\n", + "Epoch 25/100\n", + "505/505 [==============================] - 123s 245ms/step - loss: 0.4997 - accuracy: 0.8127 - val_loss: 0.6466 - val_accuracy: 0.7634\n", + "Epoch 26/100\n", + "505/505 [==============================] - 123s 244ms/step - loss: 0.4916 - accuracy: 0.8174 - val_loss: 0.6429 - val_accuracy: 0.7645\n", + "Epoch 27/100\n", + "505/505 [==============================] - 124s 245ms/step - loss: 0.4822 - accuracy: 0.8238 - val_loss: 0.6425 - val_accuracy: 0.7661\n", + "Epoch 28/100\n", + "505/505 [==============================] - 125s 248ms/step - loss: 0.4804 - accuracy: 0.8218 - val_loss: 0.6379 - val_accuracy: 0.7667\n", + "Epoch 29/100\n", + "505/505 [==============================] - 127s 251ms/step - loss: 0.4735 - accuracy: 0.8257 - val_loss: 0.6413 - val_accuracy: 0.7706\n", + "Epoch 30/100\n", + "505/505 [==============================] - 126s 250ms/step - loss: 0.4645 - accuracy: 0.8298 - val_loss: 0.6332 - val_accuracy: 0.7695\n", + "Epoch 31/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.4561 - accuracy: 0.8317 - val_loss: 0.6334 - val_accuracy: 0.7678\n", + "Epoch 32/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.4488 - accuracy: 0.8350 - val_loss: 0.6358 - val_accuracy: 0.7700\n", + "Epoch 33/100\n", + "505/505 [==============================] - 123s 244ms/step - loss: 0.4435 - accuracy: 0.8361 - val_loss: 0.6312 - val_accuracy: 0.7678\n", + "Epoch 34/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.4382 - accuracy: 0.8374 - val_loss: 0.6290 - val_accuracy: 0.7684\n", + "Epoch 35/100\n", + "505/505 [==============================] - 126s 249ms/step - loss: 0.4307 - accuracy: 0.8419 - val_loss: 0.6276 - val_accuracy: 0.7728\n", + "Epoch 36/100\n", + "505/505 [==============================] - 129s 254ms/step - loss: 0.4253 - accuracy: 0.8423 - val_loss: 0.6367 - val_accuracy: 0.7728\n", + "Epoch 37/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.4170 - accuracy: 0.8473 - val_loss: 0.6260 - val_accuracy: 0.7728\n", + "Epoch 38/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.4107 - accuracy: 0.8517 - val_loss: 0.6292 - val_accuracy: 0.7700\n", + "Epoch 39/100\n", + "505/505 [==============================] - 128s 254ms/step - loss: 0.4081 - accuracy: 0.8498 - val_loss: 0.6236 - val_accuracy: 0.7739\n", + "Epoch 40/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.4010 - accuracy: 0.8533 - val_loss: 0.6219 - val_accuracy: 0.7717\n", + "Epoch 41/100\n", + "505/505 [==============================] - 125s 248ms/step - loss: 0.3935 - accuracy: 0.8562 - val_loss: 0.6229 - val_accuracy: 0.7756\n", + "Epoch 42/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.3927 - accuracy: 0.8579 - val_loss: 0.6218 - val_accuracy: 0.7756\n", + "Epoch 43/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.3850 - accuracy: 0.8615 - val_loss: 0.6206 - val_accuracy: 0.7745\n", + "Epoch 44/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.3767 - accuracy: 0.8630 - val_loss: 0.6240 - val_accuracy: 0.7790\n", + "Epoch 45/100\n", + "505/505 [==============================] - 129s 256ms/step - loss: 0.3730 - accuracy: 0.8664 - val_loss: 0.6266 - val_accuracy: 0.7767\n", + "Epoch 46/100\n", + "505/505 [==============================] - 130s 257ms/step - loss: 0.3659 - accuracy: 0.8695 - val_loss: 0.6190 - val_accuracy: 0.7717\n", + "Epoch 47/100\n", + "505/505 [==============================] - 129s 256ms/step - loss: 0.3609 - accuracy: 0.8684 - val_loss: 0.6216 - val_accuracy: 0.7745\n", + "Epoch 48/100\n", + "505/505 [==============================] - 129s 256ms/step - loss: 0.3564 - accuracy: 0.8746 - val_loss: 0.6236 - val_accuracy: 0.7762\n", + "Epoch 49/100\n", + "505/505 [==============================] - 129s 256ms/step - loss: 0.3493 - accuracy: 0.8760 - val_loss: 0.6210 - val_accuracy: 0.7778\n", + "Epoch 50/100\n", + "505/505 [==============================] - 129s 256ms/step - loss: 0.3460 - accuracy: 0.8762 - val_loss: 0.6239 - val_accuracy: 0.7751\n", + "Epoch 51/100\n", + "505/505 [==============================] - 127s 251ms/step - loss: 0.3422 - accuracy: 0.8786 - val_loss: 0.6219 - val_accuracy: 0.7784\n", + "Epoch 52/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.3369 - accuracy: 0.8800 - val_loss: 0.6209 - val_accuracy: 0.7784\n", + "Epoch 53/100\n", + "505/505 [==============================] - 129s 255ms/step - loss: 0.3309 - accuracy: 0.8819 - val_loss: 0.6218 - val_accuracy: 0.7812\n", + "Epoch 54/100\n", + "505/505 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bCxYs4MqVK7Rq1Yo7d+48NJa0tDQSExOzLUIIIURplq+k28nJCa1WS2RkZLb1kZGRD2165uzszKZNm0hOTubatWuEhIRga2tL1apVH3oeCwuLrDfi8mZcCCGEKHh5eSneuXNn+vTpQ4MGDejUqRNbt24lPj6eNWvWPPS4M2bMwMHBIWtxd3cvissRQgghiq18Jd3m5ub4+PgQGBiYtU6v1xMYGEizZs0eua+lpSVubm5kZmayfv16XnzxxSeLWAghhBDZFNVLcUdHR2rUqMGlS5ceWkZaqwkhhBDZ5XvKsAkTJjB06FCaNGmCn58fs2fPJjk5OWvgliFDhuDm5saMGTMAOHToEDdv3qRhw4bcvHmTqVOnotfree+99wr2SoQQwkjpdDoyMjIMHYZ4QmZmZmi12kI9x4Mvxbt37w7cfyk+fvz4R+5776V4RkYG69evp2/fvg8tm5SURGhoKIMHD35oGQsLCywsLPIVv16vJz09PV/7CPEoRfH/Tggh8irfSXe/fv2Ijo5m8uTJRERE0LBhQ7Zt25bVjywsLCxb07TU1FQ+/PBDLl++jK2tLV26dGHZsmU4OjoW2EUIIYQxUhSFiIgI4uPjDR2KeEqOjo6UL1++UOepLoyX4u+88w7dunXDw8ODW7duMWXKFLRaLQMGDCiwuNPT07ly5Qp6vb7AjikEFM3/OyGEyIt8J90A48ePf+ib8127dmX7vk2bNgQHBz/JaYQQolS7l3C7uLhgbW0tHxxLIEVRSElJISoqCoAKFSoU2rkK46X4jRs3GDBgALGxsTg7O9OyZUsOHjyIs7NzgcSsKArh4eFotVrc3d0fOciqEHlVlP/vhBAiLzTKvUmwi7HExEQcHBxISEiQQdWEEKWCTqfjwoULuLi4UK5cOUOHI55SbGwsUVFR1KhRI0eTV2N/xj3q+jIyMrh06RIVK1bEwcHBQBEKY/Wo/3dCCFEQ8voMl1fKQghRDN3rw21tbW3gSERBuPd7lL752el0OkDtky5EQZP/d0KI4qJYJ90BAQHUqVMHX19fQ4cihBAGIU3KjYP8Hh9Nfj6iMMh9JYQoLop10j1u3DiCg4M5cuRIgR87UycDtgghhBBCCCGEKFzFOukuDHsuRtP+612MXhZk6FCEEEI8hqenJ7Nnzy6QY+3atQuNRiOjwRcRaa2WNwV5jwshhFClZ+qZ9lswz36zm0E/HuSdtSf5+q/zrDwUxs6QKDKKuAL2iUYvL8nsLM0IjU4mLjkdRVGk6ZEQQhSwtm3b0rBhwwJJJI4cOYKNjc3TByWK3Lhx4xg3blzWIDPGRO5xIYQovpLSMhm7PIg9F2MAuBCZlG27iQbOf9K5SGMqdUl37Qp2mGtNuJ2SQVhcCh7l5EEnhBBFSVEUdDodpqaPfwQV1NRUQhQlucfvS09Pl4HyhBBFJiYpjeFLjnD6ZgJWZlqmvlAHUxMTIhJTuRV/l4iEVNIy9Zhpi7bBd6lrXm5hqqWumzqc+/GweMMGI4QQRmbYsGHs3r2bOXPmoNFo0Gg0LF26FI1Gwx9//IGPjw8WFhbs3buX0NBQXnzxRVxdXbG1tcXX15cdO3ZkO95/m95qNBp+/PFHevTogbW1NdWrV2fz5s1PHO/69eupW7cuFhYWeHp68vXXX2fbPn/+fKpXr46lpSWurq707t07a9u6deuoX78+VlZWlCtXjg4dOpCcnPzEsYiSoTjf4zqdjpdffpkqVapgZWVFzZo1mTNnTo5yixcvzrrvK1SowPjx47O2xcfHM3r0aFxdXbG0tKRevXr8/vvvAEydOpWGDRtmO9bs2bPx9PTM9vPp3r07n376KRUrVqRmzZoALFu2jCZNmmBnZ0f58uUZOHBg1jza95w9e5auXbtib2+PnZ0drVq1IjQ0lH/++QczMzMiIiKylX/zzTdp1apVnn42Qgjjdy02mV4L9nP6ZgJlbcz55ZWm9POtTC+fSoxrV41Pe9Rn0TBflo/0L/LYSl3SDdDIvQwAx8NuGzgSIYTIO0VRSEnPLPJFUZQ8xzhnzhyaNWvGqFGjCA8PJzw8HHd3dwAmTpzI559/zrlz52jQoAFJSUl06dKFwMBAjh8/znPPPUe3bt0ICwt75Dk+/vhj+vbty6lTp+jSpQuDBg0iLi4u3z/PoKAg+vbtS//+/Tl9+jRTp07lo48+YunSpQAcPXqU119/nWnTpnH+/Hm2bdtG69atAQgPD2fAgAGMGDGCc+fOsWvXLnr27Jmvn5XIyVD3eH7u8+J8j+v1eipVqsTatWsJDg5m8uTJvP/++6xZsyarzIIFCxg3bhyvvPIKp0+fZvPmzVSrVi1r/86dO7Nv3z6WL19OcHAwn3/+eb7nuA4MDOT8+fNs3749K2HPyMhg+vTpnDx5kk2bNnH16lWGDRuWtc/Nmzdp3bo1FhYW/P333wQFBTFixAgyMzNp3bo1VatWZdmyZVnlMzIyWLFiBSNGjMhXbEII46MoCnsuRtNrwX6uxaZQqYwV68Y0o6G7o6FDy1LqmpcDNKzsCPvgxPV4Q4cihBB5djdDR53Jfxb5eYOndcLaPG+PCwcHB8zNzbG2tqZ8+fIAhISEADBt2jQ6duyYVbZs2bJ4e3tnfT99+nQ2btzI5s2bs9W8/dewYcMYMGAAAJ999hnffvsthw8f5rnnnsvXdc2aNYv27dvz0UcfAVCjRg2Cg4OZOXMmw4YNIywsDBsbG7p27YqdnR0eHh40atQIUJPuzMxMevbsiYeHBwD169fP1/lFToa6xyHv93lxvsfNzMz4+OOPs76vUqUKBw4cYM2aNfTt2xeATz75hLfffps33ngjq9y9we527NjB4cOHOXfuHDVq1ACgatWqj/2Z/JeNjQ0//vhjtmblDybHVatW5dtvv8XX15ekpCRsbW0JCAjAwcGBVatWYWZmBpAVA8DLL7/MkiVLePfddwH47bffSE1NzbouIUTpoygKgeeimLfzUlZeV6eCPUuH++Jib2nY4P6jWNd0F9bIp43cbHEgieDwRFIzdAV6bCGEELlr0qRJtu+TkpJ45513qF27No6Ojtja2nLu3LnH1gI2aNAg62sbGxvs7e1zNFPNi3PnztGiRYts61q0aMHFixfR6XR07NgRDw8PqlatyuDBg1mxYgUpKSkAeHt70759e+rXr0+fPn344YcfuH1bWk+VdsXhHg8ICMDHxwdnZ2dsbW35/vvvs84XFRXFrVu3aN++fa77njhxgkqVKmVLdp9E/fr1c/TjDgoKolu3blSuXBk7OzvatGkDkBXbiRMnaNWqVVbC/V/Dhg3j0qVLHDx4EIClS5fSt29fGYROiFJGURSiElP59cRNOs/Zw8ifj3LiejwWpiYMa+7J6tFNi13CDcW8prtQRj69tINKv7/FDCsPXr07lrO3EvHxKFMwxxZCiEJkZaYleFong5y3IPz3w/E777zD9u3b+eqrr6hWrRpWVlb07t2b9PT0Rx7nvx/KNRoNen3BT/1hZ2fHsWPH2LVrF3/99ReTJ09m6tSpHDlyBEdHR7Zv387+/fv566+/mDt3Lh988AGHDh2iSpUqBR5LaWGoe/zeuZ+Woe/xVatW8c477/D111/TrFkz7OzsmDlzJocOHQLAysrqkfs/bruJiUmOZvgZGRk5yv3355CcnEynTp3o1KkTK1aswNnZmbCwMDp16pT1s3jcuV1cXOjWrRtLliyhSpUq/PHHH+zateuR+wghSr7ktEz+OBPBqRvxnI+4w/nIO8Sn3P+7Y2OuZXAzT15uWQVnOwsDRvpoxTrpLhTWTmjiw3iWm5SnL8fDbkvSLYQoETQaTZ6beRuSubk5Ot3jWxHt27ePYcOG0aNHD0CtFbx69WohR3df7dq12bdvX46YatSokdWH1dTUlA4dOtChQwemTJmCo6Mjf//9Nz179kSj0dCiRQtatGjB5MmT8fDwYOPGjUyYMKHIrsHYyD3+dPbt20fz5s159dVXs9aFhoZmfW1nZ4enpyeBgYG0a9cux/4NGjTgxo0bXLhwIdfabmdnZyIiIrJNuXrixInHxhUSEkJsbCyff/55Vv/3o0eP5jj3Tz/9REZGxkNru0eOHMmAAQOoVKkSXl5eOVqqCCGMx7nwRFYcusam47dISsvMts1EA55ONnRrUJHhLTxxtC7+MyQU/ydbQavYEDxbYXp1D8NM/+TE9XqGjkgIIYyKp6cnhw4d4urVq9ja2j60hq569eps2LCBbt26odFo+Oijjwqlxvph3n77bXx9fZk+fTr9+vXjwIEDzJs3j/nz5wPw+++/c/nyZVq3bk2ZMmXYunUrer2emjVrcujQIQIDA3n22WdxcXHh0KFDREdHU7t27SKLXxhOcb3Hq1evzs8//8yff/5JlSpVWLZsGUeOHMnW+mLq1KmMGTMGFxcXOnfuzJ07d9i3bx+vvfYabdq0oXXr1vTq1YtZs2ZRrVo1QkJC0Gg0PPfcc7Rt25bo6Gi+/PJLevfuzbZt2/jjjz+wt7d/ZFyVK1fG3NycuXPnMmbMGM6cOcP06dOzlRk/fjxz586lf//+TJo0CQcHBw4ePIifn1/WCOidOnXC3t6eTz75hGnTphX8D1AIYTB3UjMIibjD2ZsJ/HYqnKBr97tsVXGyoWMdV2qVt6OGqx3VXGyxLKBWeEWlWPfpLjTN1MFLBmr/5vy1cAMHI4QQxuWdd95Bq9VSp06drGakuZk1axZlypShefPmdOvWjU6dOtG4ceMii7Nx48asWbOGVatWUa9ePSZPnsy0adOyRlR2dHRkw4YNPPPMM9SuXZuFCxfyyy+/ULduXezt7fnnn3/o0qULNWrU4MMPP+Trr7+mc+fORRZ/cVdY47IUB8X1Hh89ejQ9e/akX79++Pv7Exsbm63WG2Do0KHMnj2b+fPnU7duXbp27crFixeztq9fvx5fX18GDBhAnTp1eO+997Jq9WvXrs38+fMJCAjA29ubw4cP88477zw2LmdnZ5YuXcratWupU6cOn3/+OV999VW2MuXKlePvv/8mKSmJNm3a4OPjww8//JCt1tvExIRhw4ah0+kYMmTI0/yohBAGEJuUxsnr8Ww7E8GSfVeYsfUco5cdpfWXO6k/9S/6LDzA1N+CCbp2G1MTDV3ql2fFSH8CJ7Th/S616dm4EvXcHEpcwg2gUUrA/Cb3+nQnJCQ89m1qnuj16Ob5oo27xLSMwYyZ+BUudsWvw70QovRKTU3lypUrVKlSBUtL+ftU0j3q91ngz7hi5lHXJ/e5yK+XX36Z6OjoPM1dLveXEIaXmqHjjzPhrDgYxtFrjx5wtIKDJbUr2OPjUYY+PpWK5YBo/5XXZ3jpa14OYGKCtvl4+P1NRpj+wcmrE+lYv5KhoxJCCCGEELlISEjg9OnTrFy5Mk8JtxDCsK7GJLPycBhrj17n9r8Dn2k04GpnSXkHSyo4WFLBwQq3MlbULm9H7Qr2lLEp/n2zn1TpTLoBvPuT9McUKuliCDq1Ceo/fL5MIYQQxd+YMWNYvnx5rtteeuklFi5cWMQRCVGwSvM9/uKLL3L48GHGjBmTbS50IUTxoSgKR6/d5rvdl9lxLjJrfUUHSwb4VaavrzuuJaD2ujAU66Q7ICCAgICAPI0Qmm9mVlytMoB6lxZS9+rPoIxTX78IIYQokaZNm/bQ/qXG2GxblD6l+R6X6cGEKL50eoW/zkbw/Z7LHA+Lz1rftqYzL/l70K6WC1qT0p1nFeuku1Dm6X6ARYvRpF1cRLWM8+iuHUDr2bzAzyGEEKJouLi44OLiYugwhCg0co8LIYoLRVEIibjDbydvsfnkLW7cvguAudaEno3dGNmqKtVcbA0cZfFRrJPuwlbVowobaUVv/iZl9xzsJOkWQgghhBBClHKKorD26A1+PXkTOwszXOwtcLGzwMXekoiEVDafvMWlqKSs8g5WZgxu6sGQ5h4yQHUuSnXSrTXRcNClP72j/8b2yp8QGwrlvAwdlhBCCCGEEEIYRNSdVCatP01gSNQjy5mbmtCupjPdvCvSvpYrVuYlbyqvolKqk24AFy9vdkZ40057EvZ8Dd3nGzokIYQQQgghhChy286EM2nDaW6nZGCuNeHVdl6UtTEnKjGNqDupRCamYaY14bl65Xm2riv2lmaGDrlEKPVJd0N3R+Zm9lCT7hMroPEQqNzU0GEJIYQQJVqhDoYqhBCiQF2IvMPCXaFsOH4TgNoV7JndryE1y9sZODLjIEl3ZUeOKTVYrWtLP+0u+H0CjP4HtKX+RyOEEEI8scIeDFUIIcTTuRB5hy2nwtlyOjyrf7aJBsa08eLNDjUwNzUxcITGo9T/JF3sLKlUxorPM/qTYe4IUWfhkPHOcymEEMWdp6cns2fPzlNZjUbDpk2bCjUeIQpafu5xIYQoSNfjUgjYeYmOs3bz7Df/MCfwIpeikjDXmtC+lgtrxzTjvedqScJdwIp1dW5RNU1r6O7I77fv8o/HeNpf/AR2zYC6PcDBrVDPK4QQQgghhBCFJTVDR2RiKnsvxbDp+E2OXL2dtc1ca0Kr6k4836ACHepI/+zCVKyT7qJqmubrWZbfT4Uz93ZT2lfygxuH4c9J0PfnQjunEEIIIURJo9Pp0Gg0mJhILZgQxc3ddB0rDl1j1/loIhNTiUxMJTE1M1sZjQaaVS1H94ZudKpXHgcrSbSLgvzFBJ5vUAFTEw0nbiRyrfknoNFC8K9wcYehQxNCiBLl+++/p2LFiuj1+mzrX3zxRUaMGEFoaCgvvvgirq6u2Nra4uvry44dBfe39vTp0zzzzDNYWVlRrlw5XnnlFZKS7s8jumvXLvz8/LCxscHR0ZEWLVpw7do1AE6ePEm7du2ws7PD3t4eHx8fjh49WmCxCeNQ1Pf4rFmzqF+/PjY2Nri7u/Pqq69mu6cB9u3bR9u2bbG2tqZMmTJ06tSJ27fV2iy9Xs+XX35JtWrVsLCwoHLlynz66aeA+v9Bo9EQHx+fdawTJ06g0Wi4evUqAEuXLsXR0ZHNmzdTp04dLCwsCAsL48iRI3Ts2BEnJyccHBxo06YNx44dyxZXfHw8o0ePxtXVFUtLS+rVq8fvv/9OcnIy9vb2rFu3Llv5TZs2YWNjw507d5745yVEaZSclsl3u0Np9eXffLLlHHsvxXAxKikr4TY3NaGemz3vd6nFgYntWTmqKX193SXhLkLFuqa7qDjZWtC2pgs7zkWy8po9k/zHwMEA2PoOvHoQzGSCdyFEMaAokJFS9Oc1s1ZfjedBnz59eO2119i5cyft27cHIC4ujm3btrF161aSkpLo0qULn376KRYWFvz8889069aN8+fPU7ly5acKMzk5mU6dOtGsWTOOHDlCVFQUI0eOZPz48SxdupTMzEy6d+/OqFGj+OWXX0hPT+fw4cNo/r22QYMG0ahRIxYsWIBWq+XEiROYmckHkiJlqHsc8nyfF/U9bmJiwrfffkuVKlW4fPkyr776Ku+99x7z56tTnJ44cYL27dszYsQI5syZg6mpKTt37szqmjdp0iR++OEHvvnmG1q2bEl4eDghISH5iiElJYUvvviCH3/8kXLlyuHi4sLly5cZOnQoc+fORVEUvv76a7p06cLFixexs7NDr9fTuXNn7ty5w/Lly/Hy8iI4OBitVouNjQ39+/dnyZIl9O7dO+s89763s5PRkoV4GEVRuJOWSVRiKlGJaRy/Hs+ivVeIS04HwL2sFS+3qEJ1Vztc7CxwsbPE3so061knDOOJku6AgABmzpxJREQE3t7ezJ07Fz8/v4eWnz17NgsWLCAsLAwnJyd69+7NjBkzsLQsPslsb59K7DgXycZjN3n3rf9henYD3L6iDqrW8k1DhyeEEGoy8lnFoj/v+7fA3CZPRcuUKUPnzp1ZuXJlVkKybt06nJycaNeuHSYmJnh7e2eVnz59Ohs3bmTz5s2MHz/+qcJcuXIlqamp/Pzzz9jYqPHOmzePbt268cUXX2BmZkZCQgJdu3bFy8sLgNq1a2ftHxYWxrvvvkutWrUAqF69+lPFI56Aoe5xyPN9XtT3+Jtvvpn1taenJ5988gljxozJSrq//PJLmjRpkvU9QN26dQG4c+cOc+bMYd68eQwdOhQALy8vWrZsma8YMjIymD9/frbreuaZZ7KV+f7773F0dGT37t107dqVHTt2cPjwYc6dO0eNGjUAqFq1alb5kSNH0rx5c8LDw6lQoQJRUVFs3bq1QFu+CGEMFEXh7K1EtpwOJ/BcJGFxKaRm6HOU8yhnzfh21ejeyA0zrTRmLm7y/RtZvXo1EyZMYMqUKRw7dgxvb286depEVFRUruVXrlzJxIkTmTJlCufOnWPRokWsXr2a999//6mDL0jP1HKhjLUZUXfS2HM9DdpPVjfsmwNp0sxJCCHyatCgQaxfv560tDQAVqxYQf/+/TExMSEpKYl33nmH2rVr4+joiK2tLefOnSMsLOypz3vu3Dm8vb2zEm6AFi1aoNfrOX/+PGXLlmXYsGF06tSJbt26MWfOHMLDw7PKTpgwgZEjR9KhQwc+//xzQkNDnzomYZyK8h7fsWMH7du3x83NDTs7OwYPHkxsbCwpKWqLgHs13bk5d+4caWlpD92eV+bm5jRo0CDbusjISEaNGkX16tVxcHDA3t6epKSkrOs8ceIElSpVykq4/8vPz4+6devy008/AbB8+XI8PDxo3br1U8UqhLG4FJXEF9tCaPvVLrrO3cuCXaFciEzKSrjtLE3xcrahVXUnZvX1JnBCG/o0cZeEu5jKd033rFmzGDVqFMOHDwdg4cKFbNmyhcWLFzNx4sQc5ffv30+LFi0YOHAgoL6lHTBgAIcOHXrK0AuWuakJLzZ0Y+n+q6wLukG7fn1hz9cQe0mt7W79rqFDFEKUdmbWam2cIc6bD926dUNRFLZs2YKvry979uzhm2++AeCdd95h+/btfPXVV1SrVg0rKyt69+5Nenp6YUSew5IlS3j99dfZtm0bq1ev5sMPP2T79u00bdqUqVOnMnDgQLZs2cIff/zBlClTWLVqFT169CiS2ASGu8fvnTuPiuoev3r1Kl27dmXs2LF8+umnlC1blr179/Lyyy+Tnp6OtbU1VlZWD93/UduArMHQFEXJWpeRkZHrcf7bNHXo0KHExsYyZ84cPDw8sLCwoFmzZlnX+bhzg1rbHRAQwMSJE1myZAnDhw+XJrBCADtDonhl2VEydOr/TUszE9rVdKFL/Qo0qOSAi50lVuZaA0cp8iNfr0LS09MJCgqiQ4cO9w9gYkKHDh04cOBArvs0b96coKAgDh8+DMDly5fZunUrXbp0eeh50tLSSExMzLYUhd4+lQDYfjaShDQF2vz7EmH/XLgbXyQxCCHEQ2k0avPXol7y+SHY0tKSnj17smLFCn755Rdq1qxJ48aNAXXAp2HDhtGjRw/q169P+fLlswZselq1a9fm5MmTJCcnZ63bt28fJiYm1KxZM2tdo0aNmDRpEvv376devXqsXLkya1uNGjV46623+Ouvv+jZsydLliwpkNhKo4CAAOrUqYOvr2/edzLUPZ7P+7yo7vGgoCD0ej1ff/01TZs2pUaNGty6lf2lRIMGDQgMDMx1/+rVq2NlZfXQ7c7OzgDZWnycOHEiT7Ht27eP119/nS5dulC3bl0sLCyIiYnJFteNGze4cOHCQ4/x0ksvce3aNb799luCg4OzmsALUZr9cyGa0cuDyNApNK1alnkDGxH0YUcWvORDN++KeJSzkYS7BMpX0h0TE4NOp8PV1TXbeldXVyIiInLdZ+DAgUybNo2WLVtiZmaGl5cXbdu2fWTz8hkzZuDg4JC1uLu75yfMJ1a3oj21ytuRrtOz+dQtqNcTnGtBagIcnP/4AwghhADU5rf3WkENGjQoa3316tXZsGEDJ06c4OTJkwwcODDHKNBPc05LS0uGDh3KmTNn2LlzJ6+99hqDBw/G1dWVK1euMGnSJA4cOMC1a9f466+/uHjxIrVr1+bu3buMHz+eXbt2ce3aNfbt28eRI0ey9fkW+TNu3DiCg4M5cuSIoUMpFEVxj1erVo2MjAzmzp3L5cuXWbZsGQsXLsxWZtKkSRw5coRXX32VU6dOERISwoIFC4iJicHS0pL//e9/vPfee/z888+EhoZy8OBBFi1alHV8d3d3pk6dysWLF9myZQtff/11nmKrXr06y5Yt49y5cxw6dIhBgwZlq91u06YNrVu3plevXmzfvp0rV67wxx9/sG3btqwyZcqUoWfPnrz77rs8++yzVKpU6Yl+TkIYi/2XYhj181HSM/U8W8eVZS/707VBRWwsZOzrkq7QG/3v2rWLzz77jPnz53Ps2DE2bNjAli1bmD59+kP3mTRpEgkJCVnL9evXCztMADQaTVZt97qgG2CihbaT1I0H5kNKXJHEIYQQJd0zzzxD2bJlOX/+fFb3IlC7KJUpU4bmzZvTrVs3OnXqlFVD+LSsra35888/iYuLw9fXl969e9O+fXvmzZuXtT0kJIRevXpRo0YNXnnlFcaNG8fo0aPRarXExsYyZMgQatSoQd++fencuTMff/xxgcQmjE9R3OPe3t7MmjWLL774gnr16rFixQpmzJiRrUyNGjX466+/OHnyJH5+fjRr1oxff/0VU1P1Q/pHH33E22+/zeTJk6lduzb9+vXLGofHzMyMX375hZCQEBo0aMAXX3zBJ598kqfYFi1axO3bt2ncuDGDBw/m9ddfx8XFJVuZ9evX4+vry4ABA6hTpw7vvfde1qjq99xrKj9ixIgn+hkJYSwOX4nj5Z+Okpap55laLswb2Fj6ZxsRjfJgR57HuNd/aN26dXTv3j1r/dChQ4mPj+fXX3/NsU+rVq1o2rQpM2fOzFq3fPnyrLlT7/UnepTExEQcHBxISEjA3t4+r+E+kZikNJp+FkimXmH7W62p7mwD37WGyNPQ8i3oMLVQzy+EEACpqalcuXKFKlWqFKuZHsSTedTvsyifcYbwqOuT+1wsW7aMt956i1u3bmFubl6gx5b7S5QEyWmZ7LkYzdtrTpKcrqNVdSd+GNIESzNpQl4S5PUZnq+2Cubm5vj4+BAYGJiVdOv1egIDAx86DUZKSkqOxFqrVW+ifOT7RebBObvXHbvBpM61od37sGoAHPoemo4DW2dDhymEEEIIUWKlpKQQHh7O559/zujRows84RbCkHR6hRPX4/k7JJIjV25jZa6loqMl5e2tqOBoiYWpCSeux3P06m2CwxPR6dWcqLlXOUm4jVS+2yxMmDCBH374gZ9++olz584xduxYkpOTs0YzHzJkCJMmTcoq361bNxYsWMCqVau4cuUK27dv56OPPqJbt25ZyXdxc6+J+cZjN8nU6aFmZ6jYGDKSYd9swwYnhBClxIoVK7C1tc11uTcPscguICAAT09PLC0t8ff3zxrENDcZGRlMmzYNLy8vLC0t8fb2ztbf9kmOKfKnNN/jX375JbVq1aJ8+fLZPjcKUVIpisLfIZFMWHMC30930GvBfgJ2hnL4ahy7L0Tzy+HrfLPjAu+tO8Ubq06wZN9VTt9MQKdXcHO04qWmlflxqCTcxirfvfL79etHdHQ0kydPJiIigoYNG7Jt27aswdXCwsKy1Wx/+OGHaDQaPvzwQ27evImzszPdunXj008/LbirKGAPztm9+0I07Wu7QrsPYEUvOPIj+L0CZTwMHaYQQhi1F154AX9//1y3mZmZFXE0xd/q1auZMGECCxcuxN/fn9mzZ9OpUyfOnz+fo68tqM/n5cuX88MPP1CrVi3+/PNPevTowf79+2nUqNETHVPkT2m+x6dOncrUqVMNHYYQBSJDp2fK5rOsPBSWtc7O0pS2NV1oXd0JRYFbCXeJSEjlVkIqSakZ1HNzoIlnWZp4lKGi4+On2BMlW776dBuKIfq7fbolmB/2XMG/SllWj24GigJLu8K1vVC1HQzemO9pdIQQIq+kL6JxKYo+3f7+/vj6+mYNHKfX63F3d+e1115j4sSJOcpXrFiRDz74gHHjxmWt69WrF1ZWVixfvvyJjpkb6dMtDEXuL1EUbienM3ZFEAcvx6HRwOCmHnSpXwEfjzIyEFopkNdnuNwJDzGiZRXMtBoOXYnjWNhtNcHuNgdMLeHyTji+zNAhCiGEEIA60GlQUBAdOnTIWmdiYkKHDh04cOBArvukpaXlSESsrKzYu3fvEx/z3nETExOzLUIIYYwuRN7hxYB9HLwch425lh+HNGHai/VoWrWcJNwim2J9NwQEBFCnTh18fX2L/NwVHKzo3tANgIW7QtWVTtXUZuYAf34ACTeLPC4hROlSAhojiTwo7N9jTEwMOp0uq6vXPa6urkREROS6T6dOnZg1axYXL15Er9ezfft2NmzYQHh4+BMfE2DGjBk4ODhkLe7u7o+NX+5zURjkvhIF5fSNBOYGXmRu4EUCdl5i4e5Q5gZepOf8/YTFpVC5rDUbx7VQu6QKkYtiPdP6uHHjGDduXFa1fVEb3aYqa4Nu8FdwJJei7lDNxQ6ajYPgX+HmUfj9TRi4RpqZCyEK3L3+nCkpKVhZSV+vki4lJQUoXv1058yZw6hRo6hVqxYajQYvLy+GDx/O4sWLn+q4kyZNYsKECVnfJyYmPjTxvjeganp6utznosAVx/93omS5nZzOl3+eZ9WRMB72Dqdp1bLMH+RDWRsZgV88XLFOug2tmosdHeu4sj04ku92X2ZmH28w0cKLAfBdK7j4F5xaDd79DR2qEMLIaLVaHB0diYqKAsDa2hqNvOArcRRFISUlhaioKBwdHQtt1g4nJye0Wi2RkZHZ1kdGRlK+fPlc93F2dmbTpk2kpqYSGxtLxYoVmThxIlWrVn3iYwJYWFhgYWGRp7hNTU2xtrYmOjoaMzOzHFOMCvEkiur/nTBeer3C2qDrfP5HCLdTMgDoUNsVZztzdHoFnR70ikJ1V1tGtaoqTcnFY0nS/Rhj23qxPTiSTSduMuHZGlRwsAKXWtDmf/D3dPjjf1C1Ldg9/AOIEEI8iXuJzb3EW5Rcjo6Oj0xUn5a5uTk+Pj4EBgbSvXt3QB30LDAwkPHjxz9yX0tLS9zc3MjIyGD9+vX07dv3qY+ZVxqNhgoVKnDlyhWuXbtWIMcU4p7C/n8njI9er7DnUgxzdlzgWFg8ADVd7Zj2Yl38q5YzbHCiRJOk+zEaVy6DX5WyHL4Sx+K9V/jg+TrqhhZvwLnNEH4SfnsD+v8C8oZeCFGA7iUkLi4uZGRkGDoc8YTMzMyKpKZtwoQJDB06lCZNmuDn58fs2bNJTk5m+PDhAAwZMgQ3NzdmzJgBwKFDh7h58yYNGzbk5s2bTJ06Fb1ez3vvvZfnYxYEc3NzqlevTnp6eoEdU4ii+n8njEPC3QzWBd1g+cFrXIlJBsDGXMtbHWswtLmn1GSLpyZJdx6MbevF4StxrDwUxvh21XGwNgOtGbw4H75vCxe2wd/ToMNUQ4cqhDBCWq1WPjyKx+rXrx/R0dFMnjyZiIgIGjZsyLZt27IGQgsLC8vWfDs1NZUPP/yQy5cvY2trS5cuXVi2bBmOjo55PmZBMTExkSmdhBBFLjktkxl/nGN90E3uZugAsLMwpZdPJca08aK8g/xdEgVD5unOA0VR6DxnDyERd3jn2RqMf6b6/Y0nV8HG0erX3b4Fn6FFHp8QQoiSy9DPuMJm7NcnhCiZUjN0DF9yhAOXYwG1GfmQ5h50b+iGjYXUS4q8kXm6C5BGo2FsWy8Aluy7Suq/b8IAdRC1NhPVr39/C0L/NkCEQgghhBBCiLxIz9Tz6opjHLgci425lqXDfdn2ZisG+XtIwi0KRbFOug05T/d/PV+/Am6OVsQmp/Pn2f/MT9p2IjToB4oO1gyFyGDDBCmEEEIIIYR4qEydnrdWn+DvkCgszUxYPMyXtjVdZIYQUaiKddI9btw4goODOXLkiKFDwVRrQm+fSgCsPXoj+0aNBl6YC5WbQ1oirOwLdyJzOYoQQgghhBDiaej0T9Y7Vq9X+N/602w5HY6ZVsN3g5vIqOSiSEj7iXzo7VOJOYEX2Rcaw834u7g5Wt3faGoB/VfAjx0gLlSdx7vzF1Cnu5qUCyGEEKVIQEAAAQEB6HS6xxcWQog82nzyFu+uPUnjymV4p1MNfDzK5ihzMfIOP+y5zOmbiViba7GxMMXGXEtiagb7LsWiNdEwd0Bj2tRwNsAViNJIBlLLp/7fH+Dg5Tje7liD19pXz1kgNlSt6Y69pH5fvRM8/xU4Vi7aQIUQQpQIxekZVxiM/fqEEEVnf2gMQxcfJkN3P31pW9OZd56tST03B46F3WbBrlC2Bz+8xalGA7P7NeTFhm5FEbIwcnl9xklNdz718XHn4OU41h27wfhnquXs/1HOC8bsg72zYM8suPgnBOyBdu+D/xh1qjEhhBBCCCFEnp2PuMPoZUFk6BQ61XWljLU5a4NusOt8NLvOR+PlbENotDrHtkYDz9ZxpWfjSiiKQnKajuT0TJLSMvH1LIuvZ87acSEKk9R051NKeiZ+nwaSlJbJ6leaProfSPR5dUTza/vU78t4Qpv/Qf2+oJX3HUIIIYrXM64wGPv1CSEKX0RCKj3m7yM8IRVfzzIse9kfSzMtV2OSmb3jAr+evIWigJlWQ/eGboxu40U1F1tDhy1Kgbw+4yTpfgL/W3eK1Uev09unEl/18X50Yb0eTiyHwOmQHKWuK1ddHfG8bk8wKdZj2QkhhChkxe0ZV9CM/fqEEIXrTmoGfRYeICTiDl7ONqwf2xxHa/NsZS5E3uFEWDwtqztR8cExl4QoZDJPdyHq00QdxXzr6XCS0zIfXdjEBBoPgTdOQIePwaosxF6E9S/D920gOabwAxZCCCGEEKIESbibwbYzEYxYeoSQiDs421mwdLhfjoQboIarHX193SXhFsWWtHF+Aj4eZajqZMPlmGS2ng6nTxP3x+9kbgMt34QmI+DQQtg/DyJOwZ/vQ8/vCz1mIYQQQgghiitFUThzM5Ht5yLZezGaE9fjuTczmLW5liXDfHEva23YIIV4QsU66S6u041oNBp6+VRi5p/nWRt0I29J9z2W9tDmPfBqDz+2h1OroeFAqNq20OIVQgghhBCiOIpNSmPj8ZusC7pBSMSdbNuqOtvQqpoT/XwrU6eidE8RJZf06X5C4Ql3afH53+gV2P1uWzzK2eT/IFvegSM/QFkvGLsfzCwLPlAhhBDFWnF8xhUkY78+IUT+xSalsS80lq2nwgkMicyaAszc1IQOtV1oU8OZltWdcZPm4qKYkynDClkFBytaVnfmnwvRrAu6wdvP1sz/Qdp/BOd+g7hQ2PsNtJtU8IEKIYQQQghhQDq9wqErsey5GMOei9GcuZmYbXuDSg70aeLOCw0q4mAt0+sK4yNJ91Po41OJfy5E88vh6wxp5omznUX+DmDpAM/NgHXD1Xm96/cGp+qFE6wQQghRhIprFzEhRNHR6RV+P3WLuX9f4lJUUrZttcrb0aaGM90buVG7grSCEcZNmpc/hbRMHV3m7CE0OhlfzzKsGNkUc9N8DgivKLCiN1zaAVVaw5DNoNEUTsBCCCGKneL6jCsoxn59QoicMnV6Np+8xby/L3E5JhkAO0tTOtZ2pVUNJ1pUc8LFTrpVipJPmpcXAQtTLd8PaUL3efs4cvU2038PZnr3evk7iEYDXb6C+U3hyj/qwGre/QsnYCGEEEIIIQpAfEo6+0Nj2Xsphlvxd7mbriM1U09quo7Y5HRiktIAcLQ2Y2TLKgxp7om9pTQdF6WTJN1PycvZltn9GzLy56MsO3iNem729POtnL+DlK2ijmgeOA02vQrHl0PNLlCzs7pNCCGEEEIIA7sel8LqI9fZcymGUzfieVR72TLWZoxqXZUhzTyxtZCUQ5Ru8j+gALSv7cqEDjX4evsFPtp0luqudjSuXCZ/B2n2Glw7AJe2w9U96vLnJHCpA36vgM8waXYuhBBCCCEM4sbtFF4M2EdccnrWuhqutrSs5kytCnZYmWmxMtNiaabFytyE2hXssTaXVEMIKOZJd0kahGVcu2qcuZXAn2cjGbMsiN9fa4mLfT76qpiaw0vrIO4ynN8G57fCtf0QFQy/vwkXtsEL88DWudCuQQghhBBCiP+6m67jlZ+DiEtOp6arHSNbVaFVdWfKO0i/bCHyQgZSK0BJaZn0CNjHxagk2tZ0Zulwv6c74N3bcGwZ/P0J6NLAxgW6L4DqHQomYCGEEAZXUp5xT8rYr08IY6coCq/9cpzfT4VTzsacza+1lPmzhfhXXp9x+RxqWzyKrYUpC17ywdREw67z0ey7FPN0B7QqAy1eh1d2gnNtSI6CFb3gj4mQkVowQQshhBBCCPEQC3df5vdT4ZiaaFjwko8k3EI8gSdKugMCAvD09MTS0hJ/f38OHz780LJt27ZFo9HkWJ5//vknDro4q+Ziy0tNPQD4bOs59PoCaEjgWldNvP1Gq98fWgA/tofo809/bCGEEEIIUarFp6Sz7OA1fj91i+txKdxrCLszJIov/wwBYOoLdfGrUtaQYQpRYuW7T/fq1auZMGECCxcuxN/fn9mzZ9OpUyfOnz+Pi4tLjvIbNmwgPf3+gAuxsbF4e3vTp0+fp4u8GHvtmWqsD7rB2VuJbD55i+6N3J7+oGZW0OVLqNYBNo2FyDPwfVvo/CU0ekkGWRNCCFGslKRxWYQorRRFYcOxm3y69Vy2AdLK2pjjXcmBo9duoygw0L9yVqWSECL/8t2n29/fH19fX+bNmweAXq/H3d2d1157jYkTJz52/9mzZzN58mTCw8OxsbHJ0zlLYn+wgJ2XmPnnedwcrQh8uw2WZtqCO/idCNjwClzZrX5frxd0/QYsHQruHEIIIYpESXzG5YexX58QJdWlqDt8sPEMh67EAVDV2QY7C1OCwxPJ0N1PD3w9y7BiZFPMTaVXqhD/lddnXL5qutPT0wkKCmLSpElZ60xMTOjQoQMHDhzI0zEWLVpE//7985xwl1QjWlRh2YFr3Iy/y7ID1xjVumrBHdyuPAzeBPu+gb8/hTPr4cZR6PEdeDQruPMIIYQQQgijkpapY27gJb77J5QMnYKlmQlvtK/Byy2rYG5qQlqmjnPhdzh1I56IhFRGtqoqCbcQTylfSXdMTAw6nQ5XV9ds611dXQkJCXns/ocPH+bMmTMsWrTokeXS0tJIS0vL+j4xMTE/YRYLVuZaJjxbg/fWnWLu3xfp06QSjtbmBXcCExNo9TZ4toJ1L0P8NVjSGZqOhWc+AnPrgjuXEEIIIYQo8c7cTGDCmhNciEwCoH0tF6a+UBf3svc/N1qYamno7khDd0cDRSmE8SnS11aLFi2ifv36+Pk9eiqtGTNm4ODgkLW4u7sXUYQFq1fjStR0tSMxNZP5u0IL5yTufjB2r9qvGwUOzoeFLeBa3loeCCGEEEII45ah0zN7xwW6B+zjQmQSTrbmLHypMT8ObZIt4RZCFI58Jd1OTk5otVoiIyOzrY+MjKR8+fKP3Dc5OZlVq1bx8ssvP/Y8kyZNIiEhIWu5fv16fsIsNrQmGiZ2qQXA0n1XuR6XUjgnsnSAFwNg0Hqwd4O4y2qt99Z31f7fQgghhBCi1FEUhRPX4+kxfx+zd1wkU6/QuV55/nyzNc/Vq4BGBuIVokjkK+k2NzfHx8eHwMDArHV6vZ7AwECaNXt0X+K1a9eSlpbGSy+99NjzWFhYYG9vn20pqdrWcKa5VznSdXo+2HSGfI5blz/VO8CrB+7Xeh/+HmbXh9/egNhCqmkXQgghhBDFxt10HYHnInl/42maf/433QP2ceZmIg5WZszp35D5gxpTztbC0GEKUarke8qwCRMmMHToUJo0aYKfnx+zZ88mOTmZ4cOHAzBkyBDc3NyYMWNGtv0WLVpE9+7dKVeuXMFEXkJoNBqmvViXLt/u5Z8L0Sw/FMbgwpxy4V6td73esGsGXD8EQUvh2M9Q50Vo+RZU8C688wshhBBCiCKXlqlj5rbzLDt4jbRMfdZ6SzMTOtYpz4fP18bV3tKAEQpReuU76e7Xrx/R0dFMnjyZiIgIGjZsyLZt27IGVwsLC8PEJHsF+vnz59m7dy9//fVXwURdwlRzseN/z9Vi+u/BfLblHC2rOVHFqZBHb/dqpy7XDsDeb+Din3B2o7rUeA5avweVfAo3BiGEEEIIUehCo5N4beVxgsPVwYfdHK14ppYLz9R2oVnVcgU7da0QIt/yPU+3IRjDHJ96vcJLiw6xPzSWRpUdWTu6GabaIhzHLuKMmnyf3QDKv28/vZ5Rk2+ZZkwIIQzGGJ5xj2Ls1yeEISmKwrqgG0zZfJaUdB1lbcz5olcDOtR2kf7aQhSBvD7jZNK9ImJiomFmH2/sLEw5HhbPwt1F3Me6fD3ovQjGHYGGg0CjhdC/Yclz8GMHOPELZKQWbUxCCCGMVkBAAHXq1MHX19fQoQhhlMJiU3hj1QneXXeKlHQdzb3K8ccbrehYx1USbiGKGanpLmIbjt1gwpqTmJpo2DSuBfXcHAwTSNwVteb7xErQZ6jrrMqoCXmTEVDOyzBxCSFEKWNMz7jcGPv1CVGUrsUms+V0OFtPh3PmptqUXGui4e1nazC6tRdaE0m2hShKeX3GFeukOyAggICAAHQ6HRcuXDCKB7aiKLy64hh/nImguostv73W0rD9bO5EwvFl6mBrCQ9MzeZaH6p3hBqdoJIvmEhfICGEKAzGnpQa+/UJURR2X4hm1l/nOXkjIWudiQaaeZXj7Wdr0rhyGQNGJ0TpZRRJ9z3G9sCOS07n2W/+ISYpjeEtPJnSra6hQwK9Di5uh6OL1H954LawKgNVWoNTDShbVV3KVAFbF5DmS0II8VSM7Rn3X8Z+fUIUpktRd/hkyzl2nY8G7ifaz9evSKe6rjL1lxAGltdnXL5HLxdPr6yNOTP7NGD4kiMs2XeVdjVdaF3D2bBBmWih5nPqkhwLl3aoI55f2gF3b0Pwrzn3ca4NvX6A8vWLPl4hhBBCCCN1Ozmd2TsusPxQGDq9gqmJhqHNPRnb1gsnSbSFKHFkIDUDaVfThaHN1Pm63157krjkdANH9ACbcuDdD3ovhncvw/A/oON0ta931bbgWBk0JhB9Dn5oD0eXQPFvMCGEEEYvICAAT09PLC0t8ff35/Dhw48sP3v2bGrWrImVlRXu7u689dZbpKbeH1Rz6tSpaDSabEutWrUK+zKEKNW2nYngma938dOBa+j0Ch1qu/LXW635qGsdSbiFKKGkptuAJnWpzf7QWC5GJfG/9af4frBP8RttUmsKHs3V5UHJsbBprFob/vubcHUPdJ0NltJ0UAghDGH16tVMmDCBhQsX4u/vz+zZs+nUqRPnz5/HxcUlR/mVK1cyceJEFi9eTPPmzblw4QLDhg1Do9Ewa9asrHJ169Zlx44dWd+bmspHByGeVFhsClvPhNOquhN1K2YfTDc5LZOPfzvLmqM3AKjpasfkbnVoUc3JEKEKIQqQ1HQbkKWZltn9G2Km1bA9OJJVR64/fqfiwqYcDFil1oBrtHBmPXzfBm4cNXRkQghRKs2aNYtRo0YxfPhw6tSpw8KFC7G2tmbx4sW5lt+/fz8tWrRg4MCBeHp68uyzzzJgwIActeOmpqaUL18+a3FykgRAiCcRdC2OFwP28vkfITz/7V6em/0PP+65TNSdVI6F3abLt3tYc/QGGg2MaePFb6+1lIRbCCMhSbeB1a3owLudagIw7bdgLkcnGTiifDAxgRavq83P7StB3GX4sT2sGgSRZ7OX1evh4g74ZQDM84M9syA92TBxCyGEkUlPTycoKIgOHTpkrTMxMaFDhw4cOHAg132aN29OUFBQVpJ9+fJltm7dSpcuXbKVu3jxIhUrVqRq1aoMGjSIsLCwR8aSlpZGYmJitkWI0u6P0+EM/OEQt1MycHO0wlxrQkiEOkhasxl/03vBfq7FpuDmaMUvo5oysXMtzE3lY7oQxkLaiBUDI1tWZdf5aPaHxvLW6hOsG9scM20J+kNb2R/G7IE/34eTqyDkdwjZAvV6QtNX4epeCFoCt6/e3yfwYzi4AFq/Cz5DwfQxfZTirsCer9Vjej1TqJcjhBAAZKbBPzPhwjawdAQbZ3Wx/fffOt3BytHAQapiYmLQ6XS4urpmW+/q6kpISEiu+wwcOJCYmBhatmyJoihkZmYyZswY3n///awy/v7+LF26lJo1axIeHs7HH39Mq1atOHPmDHZ2drked8aMGXz88ccFd3FClHCL9l7hky3BKAp0qO3CtwMakZGp8NupW2w4doNjYfEA9Gjkxscv1sXe0sywAQshClyxnjLMGOfpfpjwhLt0+uYfElMzmdCxBq+3r27okJ5MVAjsmgHBm3Jus3CAhgPBuQbsnQ3x19T1DpWh3fvg3T/3KchunYAVfSA5CrQWMHgjeLYoxIsQQpR6t47DxrHqgJEP8+YZcHR/6lMVxJRat27dws3Njf3799OsWbOs9e+99x67d+/m0KFDOfbZtWsX/fv355NPPsHf359Lly7xxhtvMGrUKD766KNczxMfH4+HhwezZs3i5ZdfzrVMWloaaWlp2a7P3d3dqJ/hQuRGp1f4ZEswS/ZdBWBwUw+mvlAXrUn2zzpXYpKJS07Hx0Pm2haipJF5ukugX0/c5I1VJzA10bBpXAvquTk8fqfiKvyUmnyf3woVG0GTl6FeLzC3VrdnpsPxn2H3TEiKUNd5tIRus8HpgRcOoTth9UuQngSmVpB5FywdYMSf4FK7yC9LCGFgd+PBwk6d5jCvUuJg67ugS4cGfaH6sw9vXaPLgH++Umu4FZ1ao91+CphZQXI0JEWp/ybHQJ+lYGb51JdUEM+49PR0rK2tWbduHd27d89aP3ToUOLj4/n115zTPrZq1YqmTZsyc+bMrHXLly/nlVdeISkpCROT3Ftc+fr60qFDB2bMmJGn2ErLM1yIB+n0Cu+uPcmG4zcBmNS5Fq+0rlr8BswVQjwVmae7BHrBuyLbzkTwx5kI3l5zks2vtcDCNB8fLIuTCg1gwC9qX+7cPriZmoPvSPAeCIe/g91fwrW9sKC52uS8xZtwbjNsHAP6DPBsBb0WwZrBcP0QLO8NI7eDfcUivzQhhAFcPww7P4XLu6CMJzQbDw0H3X+R9zARZ2DVwPsta85tBqsyULcnNOinvsRLvAEJNyHxpvqiMOK0WrZuD+jytTpwZDFnbm6Oj48PgYGBWUm3Xq8nMDCQ8ePH57pPSkpKjsRaq1WfOQ97H5+UlERoaCiDBw8uuOCFMDKZOj3vrD3JphO30JpomNXXmxcbuhk6LCGEAUlNdzETm5TGs9/8Q2xyOmPbevG/50rJfKi3r8GWCXDp32lpHCpDwr+D9dTtAT2+U2umUuJg0bMQexFc6sKIP9QPzUII43QzCHbOgEvbc26zKgt+o8DvFbDJZYTfs5vUqQ0zUtREvVZXdaaFO+GPPqdVGXj+a7V1ThEoqGfc6tWrGTp0KN999x1+fn7Mnj2bNWvWEBISgqurK0OGDMHNzS2rhnrq1KnMmjWL77//Pqt5+dixY/Hx8WH16tUAvPPOO3Tr1g0PDw9u3brFlClTOHHiBMHBwTg7Oxfp9QlREmTq9Ly15iS/nbyFqYmGuQMa0bl+BUOHJYQoJFLTXUKVs7Xgs571Gb0siO92h9Khtmvp6ONTxgMGrVM/EG+beD/h9h8DnWbcry23LgsvrYdFHSHqrDpSet+f1fVCiOLnTqTalUSvV5NZqzLq4GP2bmoXkYc1tYw6B4HT1JpnUKcmbDRIreG+8g8cmKcOzrj7C9j7DVRoCJWagJuPuhxfDnu+Uvet2hZ6L1H/TnScBld2q4M+nvsdTEzBwU2Nx8FNTc69B4Kda+5xFWP9+vUjOjqayZMnExERQcOGDdm2bVvW4GphYWHZarY//PBDNBoNH374ITdv3sTZ2Zlu3brx6aefZpW5ceMGAwYMIDY2FmdnZ1q2bMnBgwfznHALUZpk6PS8ueoEW06HY6bVMG9gYzrVLW/osIQQxYDUdBdTE1afYMPxm1RxsmHr662wMi+hzcyfREoc7J8LZatAo8G5fygPPwlLuqh9vTVa8Giu1mLVer5ABjYSQhSAxHBY2kWdTjA3FRqqMxzU7aF2OQG1mfeuz+DESlD0oDGBBv2hzbtQtur9ffU6tan4vjnqoGcP02w8dPgYtLm8Y1aUhyf9RcjYn3HGfn2idIpNSuN4WDy3U9KJT8ngdko6x8Juc/ByHOZaE+YPakyHOiXv5Z0QIn9kILUSLiElg06z/yEiMZXePpX4oleDHKNdlnpX98G2/93vf3lPWS8wt1Gbo2stQGumDs7WcBBUbJj7sXQZkHgLHNxz74MuhMgpMlj9f1bOK+e2O5Gw9Hm1K4hDZfBqB6nxcPe2Ohha9HnQ/TvCta0r+I5Sm4EfnA+Zqer62t3gmcnqjAcPoyhqUn/jKNw8qv4bcRq05tD1G/DuV9BXXeCM/Rln7NcnSp8jV+N4eekRElMzc2wz15rw3WAf2tVyMUBkQoiiJkm3Edh9IZqhiw8D6ryOc/o3wsZCegTkcPuqOi/4ud8h7ADwiFu6gjc0Hgr1e0NGqtpP9OJf6ijpaYng7g89FmavURNCZBdxGnZ+pjb91pioL7TafQD2//ZbTI5RE+7oELCvBMO3ql1IHpQcA0FL4PCP92cwuKdyM7UZuLvfk8WXkaqObq4tGXPdGvszztivT5QuO4IjGbfyGGmZetzLWlHVyZYy1mY4WptTxtqc9rVdSvbsM0KIfDGKpLs0zdP9ML+fusWENSdJz9RTz82eRUN9cbV/+ilqjFZStDqvbma6WouWmQYZdyE0EM79pk4ZBGoNuC4t92OY28JzMx7etL0wRZ9X+5jmVnMoRH7FXVanuKrY6OFTZD0oIxVuX4HYUEiKBLsK4FhZ7bJh6QDRF9SpAM9u+HcHDVkvucysofnr0Ogl+KU/RJ5R9x++9dEvsTLTIfhXOLpIbTLe8k2o2aVYNPsuKsaelBr79YnSY13QDf63/hQ6vcIztVwIGNi4dHX/E0LkYBRJ9z2l/YEddO02r/x8lNjkdCo6WLJomC+1K5S+n8NTS46FU6vh2E9qDRyoyUj1Tuq8vdZlYdOrELZf3Vbzeeg2B2wfGDBIUdRm6LeOw61j6r/hp9SpyxoPgfp91EGi8ktRYN9sdeAoE1Po+b3az1WIJ5FwU51e68RKQFHnuK/cVB1QrGob9aXT7StqUn5vib0MCdd5aEsRSwdIu6P2swZ1yq22k9Tm4n99ADeO/Fvw30Tc1hWGbVG7dohHMtZnnLw4F8bku92hzPhD/ezQq3ElPu9VHzOtdEcTorSTpNvIhMWmMHzpYUKjk7G1MOX7IT4098plihzxeIoCMRfUUZRt/9PnSq9TB3H7+xN1fnAzazCzUvt869LVmvNHNV83tYK63dUm7O7+eesfnpoAG8fC+S0PrNTA81+pc5mL0i3uCgQtVVtsWNiBhe2//zqAQyV1tG1bV/VeS01QR/I+uOB+v2jrcpASm/fzWdirNdN25eFOBMSHwd24+9trdoF270P5+vfXKQoEb4LtU9T5sK2d1ITbpZRMefiUjP0ZZ+zXJ4xTaoaOE9fjOXQ5jn2hMRy+ov4dfKV1VSZ1roWmFLXGEUI8nCTdRighJYMxy4M4cDkWWwtT1o5pJjXehSXiNKwfpTZV/y+NFlzqqIOyVWwE5Ruocwkf+wmigu+XM7eD8vXU7eXrq1+X8QRLx/tNZyPOwJrBak2j1hye+xwiz6pNbQHaTIS2E7M3tU1NvD9PuZl0NShRMtPUlzf6THUKLUWnvtSxsMtZNjYU9nytTm2l6B59XFNLtRl4crRa8wzg0ULtF+3mo7bsuLwbLu+Cq3vVfthlq6jJddmq/37tBeWqqfNd//fDZFqSWguuNX9014fMNDj/h3pOmUUgz4z9GWfs1yeMw73RyI+F3ebotducuB5PeqY+W5lJnWsxuo10/xJC3CdJt5FKy9QxdPFhDl6Oo7y9JRvHNaeCg5WhwzJOukyIOa8mKFrz+4ulvZoo/ZeiqCMnH1sKZzZCRnLuxzWzUWso7StC2EHIvKuO7tz3J3BrrB5n1+ew+3O1fJOXwbu/Othb6N9qM15FB7bloflr0GS4Olp7UVIUtXnyvXmXSxJdpjpC9r7ZYFUWGg5Qp6RycMv/sWJD4dIOuLpHnefZ75Xck9JbJ9S+0Bf+JNeWEg6VwbWuurjUhkuBaleIe8m2V3v1BU/aHXWavLREdQTw+DBIuJE9KXeqCR0/hhrP5d4v+t6ffKmlKTaM/Rln7NcnSq6IhFTmBF5gf2gs12JTcmx3trPAv0pZ/KuWo2U1J6o4FfGzVghR7EnSbcQSUjLovXA/F6OSqOlqx9qxzbC3LBmj9JYauky1CXvEKbXWPPwkRJ2DlJicZb3aQ68f1T7lDzr8A2x9l1yTNDOb+0m9VVl1rmO/UU/Wnzwv0pPh+mH1pcKNw2rif/e22pz+mQ+h6Vh1tOiClHZH7d+e2wuOJxV+Eja/pv6bjUbt71yvp3qtsZfUJeaS2jTbvuL9AcUcK6sjb1/cDnGhOY9Tuys0fwPcfdWWDLtmQMjvTxZvtY5qS4dKTR5eRpehJt7x19Qa9Cptc5+TWhRbxv6MM/brEyWPoiisDbrB9N+DufPAtF/VXGxpXNmRxpXL4FelLFWcbKQZuRDikSTpNnI3bqfQY/5+ou+k0aJaOZYM88PcVAb0KPbSU9SB2BKuQ+JNtVlxra4PT1jPbIBNY9Xmw1XbgNczULWdOir0qdWwd5baNB3UMuWqqc2Fy3mpzYUt7NREP/neEq3W1tu5qv2AbV3Vfu225dV1DzZ9j7usJpYX/lSbJP93tHeN9n4Nq1sTeHGeWkv7IF2m2rQ5KlhtNh91Tv06JQ4qNFCTyUp+UMlXTbDD9sO1/eoc7JFn1FjKVlWb87vWBedaao2ydVm1r7Klg1pGUdT+zHdvq0tm2v2+z+Z2ahK6Z5baX1/Rqft1nK7+3E+shGv7nuz3aWKqTm9VtQ2EHVKnoLvHqYb64kX9YUGDvtDqHTVx12jVc2tM1LmrI//9+USeUX8+duWhxVtQyefJ4hIlirE/44z9+kTJEpGQyqQNp9h5PhoA70oOvNmhBo0rl8HBWiowhBD5I0l3KXDmZgL9vjtAcrqOHo3cmNXXW97IGqP0ZDWhzi0x12WqA1jt+Tp7f/InpbVQk2+NiTr/+YPsK0FlfzVJdvcF13pw8hf46yO1ubPWHFq/qybHN47A9SPqCO8ZOZvsFRgTU7VpfVrS4/s931OnO3T+Ur3Oe+KuqH2nL+9SR6svVw3KVb/fxznx1r9Nua+r/2rN/30B0lbtbnBPZDAcCFBfiOgzAI06Cn3bieBcs+CuWxgVY3/GGfv1iZIhKS2Tjcdu8OWf57mTmom51oS3OtZgVKsqmMoo5EKIJyRJdymx+0I0I5YeQadX6NqgAjN7e8uckaWRovw77VOo2iw6LlT9OiMFbJwfWJzUWuCkyPvLnUhIilBrih90rxa3+rNQo5Nac5vbS52Em7BlAlzYlnts5nb/9leuo9ZYu9RR+4HfOq4m5zeOqC8MFL26zaMFeLaAys0B5d9a8mC1ljw6RK2tT4lV+zb/l5m1emxTC/VlRdqd+0m/vRt0mQm1nn+qH3WeJIar/e8rNlKvW4hHMPZnnLFfnyi+dHqFA6GxrD92g21nIribob6c9XZ35KveDajumssglkIIkQ9GkXTLHJ95s/H4Dd5de4pMvUJ9Nwe+H+Ijg6uJ/Mu4+28iHqUmtG4+ajPsvFAUOLNe7b+sNf+32bivujjVeHx/77Q76nRt+emTnpGqTmWVdked5sqqTO6juesyIf3fMgXd71yIAmDsSamxX58ofhRFYdnBayzYFUp4QmrW+qpONrzU1IMhzTykdlsIUSCMIum+Rx7Yj3fwcixjlwdxOyUDJ1sLvhvsg49HCRtVWgghSiFjfcbJi3NhCKkZOt7fcJoNx28C4GBlRjfvCvRqXImG7o7SDU8IUaAk6S6FrselMOrno4RE3MFca8JnPevT26eSocMSQgjxCMb+jDP26xPFR0RCKqOXHeXkjQS0Jhomda7F4GYeWJhKKychROHI6zPuidrWBAQE4OnpiaWlJf7+/hw+fPiR5ePj4xk3bhwVKlTAwsKCGjVqsHXr1ic5tXgE97LWrB/bnE51XUnX6Xln7Ul+PnDV0GEJIYQQQhSqoGu36TZvLydvJFDG2oxlI/wY2aqqJNxCiGIh30n36tWrmTBhAlOmTOHYsWN4e3vTqVMnoqKici2fnp5Ox44duXr1KuvWreP8+fP88MMPuLm5PXXwIicbC1MWDPLhldZVAZj861mWH7xm4KiEEEIIIQrH6iNhDPj+INF30qhV3o7N41vSvJqTocMSQogspvndYdasWYwaNYrhw4cDsHDhQrZs2cLixYuZOHFijvKLFy8mLi6O/fv3Y2amzn/o6en5dFGLRzL5t0kVwPf/XObDTWcwNdHQ36+ygSMTQgghhCgYqRk6Jv96hjVHbwDQqa4rs/o2xMYi3x9vhRCiUOWrpjs9PZ2goCA6dOhw/wAmJnTo0IEDBw7kus/mzZtp1qwZ48aNw9XVlXr16vHZZ5+h0+VxTl3xRDQaNfEe3sITgEkbT7P26HXDBiWEEEIIkUdRd1LpHrCPPgv3s/pIGElpmVnbrsel0HvhftYcvYGJBt7tVJMFg3wk4RZCFEv5+ssUExODTqfD1dU123pXV1dCQkJy3efy5cv8/fffDBo0iK1bt3Lp0iVeffVVMjIymDJlSq77pKWlkZaWlvV9YmJifsIU/9JoNEzuWgedXuHnA9d4b/0pTLUaejSSwdWEEEIIUXylZ+oZt+IYJ67HA3Dk6m0+/i2Y5+tXoGFlR77cdp6EuxmUtTHn2/6NaFldmpMLIYqvQn8dqNfrcXFx4fvvv0er1eLj48PNmzeZOXPmQ5PuGTNm8PHHHxd2aKWCRqPh4xfqotMrrDgUxjtrT2FrYUbHOq6P31kIIYQQwgA+3RLMkau3sbMwZUTLKvx28haXY5JZG3SDtUFqc3Jvd0cWDGpMRUcrA0crhBCPlq/m5U5OTmi1WiIjI7Otj4yMpHz58rnuU6FCBWrUqIFWe3/0yNq1axMREUF6enqu+0yaNImEhISs5fp1aRb9NDQaDdNfrEevxpXQ6RXGrTzGwcuxhg5LCCGEECKHdUE3+OmAOgjsN/0a8lbHGgS+3Yb1Y5vRr4k75WzMGdrMgzWjm0rCLYQoEfKVdJubm+Pj40NgYGDWOr1eT2BgIM2aNct1nxYtWnDp0iX0en3WugsXLlChQgXMzc1z3cfCwgJ7e/tsi3g6JiYavuhVnw61XUnP1DPqp6OcuZlg6LCEEEIIIbKcvpHA+xtPA/BG++p0+LdlnkajwcejLF/0bkDQRx35+MV6Mh2YEKLEyPeUYRMmTOCHH37gp59+4ty5c4wdO5bk5OSs0cyHDBnCpEmTssqPHTuWuLg43njjDS5cuMCWLVv47LPPGDduXMFdhcgTU60J8wY2wq9KWe6kZTJsyWGuxCQbOiwhhBBGKCAggDp16uDr62voUEQJEZuUxuhlR0nP1NO+lgtvtK9u6JCEEKJA5Dvp7tevH1999RWTJ0+mYcOGnDhxgm3btmUNrhYWFkZ4eHhWeXd3d/7880+OHDlCgwYNeP3113njjTdynV5MFD5LMy0/Dm1C3Yr2xCSl89KPh4hISDV0WEIIIYzMuHHjCA4O5siRI4YORZQAyWmZjF1+jFsJqVRxsuGb/g0xMdEYOiwhhCgQGkVRFEMH8TiJiYk4ODiQkJAgTc0LSExSGn0WHuBKTDIVHSz5fkgT6rk5GDosIYQodYz9GWfs1yeeXsLdDEYsPULQtdvYmGvZOK4FNVztDB2WEEI8Vl6fcfmu6RbGwcnWgp9H+FHVyYZbCan0XrifzSdvGTosIYQQQpQisUlpDPzhIEHXbmNvacrykf6ScAshjI4k3aWYe1lrNo5rQduazqRm6Hn9l+N8sS0Enb7YN34QQgghRAkXmZhKv+8PcvZWIuVszFn1SjMaVS5j6LCEEKLAFeukWwZhKXwOVmYsGurLmDZeACzYFcrIn46QmJph4MiEEEIIYSwURSExNYMrMckcvRrH1tPh9Fl4gEtRSZS3t2TNmGbUqSjdD4QQxkn6dIssv564yXvrTpGWqae6iy2Lh/niXtba0GEJIYRRM/ZnnLFfn3i8ZQev8eW2EO6kZubYVrmsNStG+svnDSFEiZTXZ5xpEcYkirkXG7pR1cmWkT8f4WJUEj3m7+O7wU3w8ZCmXkIIIYTIH51eYcbWc/y490rWOlsLU8rZmlPOxpyqzra826kmrvaWBoxSCCEKnyTdIpv6lRz4dVxLXv7pCGdvJTLgh4N81cebF7wrGjo0IYQQQpQQd9N1vLn6OH+ejQTgnWdr8HLLqliZaw0cmRBCFL1i3adbGEZ5B0vWjG5Gh9qupGeqA6x9G3iREtATQQghRDEh47KUXtF30uj/w0H+PBuJudaEOf0bMv6Z6pJwCyFKLenTLR5Kp1f4/I9z/LBHbRY2wM+d6S/Ww1Qr72qEEKKgGPszztivT6jiktM5fCWWg5fj+ONMOJGJaTham/HDkCb4epY1dHhCCFEopE+3eGpaEw0fPF8Hj3I2TP71DL8cvk5sUjrfDmiEpZm8rRZCCCFKM0VR+O6fy2w4doMLkUnZtnmUs2bpcD+qONkYKDohhCg+JOkWj/VSUw+cbM15fdUJ/gqOZMjiw/wwpAkOVmaGDk0IIYQQBrJ0/1U+/yMk6/sarrb4VymHf9WytKvpgo2FfMwUQggo5kl3QEAAAQEB6HQ6Q4dS6j1XrwI/jzBn1E9HOXwljn7fHeDnEX64yIijQgghRKkTdC2OT7ecA+DNDtUZ3NSDcrYWBo5KCCGKp2LdOXfcuHEEBwdz5MgRQ4cigKZVy7F6dDOc7SwIibhD94B9HL0aZ+iwhBBC/CsgIABPT08sLS3x9/fn8OHDjyw/e/ZsatasiZWVFe7u7rz11lukpqY+1TGF8YtJSuPVFcfI1Ct0867IG+2rS8IthBCPUKyTblH81Kloz4axzanqZMOthFT6fneA2TsukKnTGzo0IYQo1VavXs2ECROYMmUKx44dw9vbm06dOhEVFZVr+ZUrVzJx4kSmTJnCuXPnWLRoEatXr+b9999/4mMK46fTK7z+y3EiE9Oo5mLL5z3ro9FoDB2WEEIUa5J0i3xzL2vN5tda0rOxG3oFZu+4yIAfDnIz/q6hQxNCiFJr1qxZjBo1iuHDh1OnTh0WLlyItbU1ixcvzrX8/v37adGiBQMHDsTT05Nnn32WAQMGZKvJzu8xhfGbtf08+0NjsTbXsvClxtJvWwgh8kCSbvFEbC1MmdW3IbP7NcTWwpQjV2/TefY//HripsznLYQQRSw9PZ2goCA6dOiQtc7ExIQOHTpw4MCBXPdp3rw5QUFBWUn25cuX2bp1K126dHniYwrjtiM4koCdoQB83qsB1VzsDByREEKUDPJ6UjyV7o3caFTZkddXneDk9XjeWHWCDcdu8kn3eriXtTZ0eEIIUSrExMSg0+lwdXXNtt7V1ZWQkJBc9xk4cCAxMTG0bNkSRVHIzMxkzJgxWc3Ln+SYAGlpaaSlpWV9n5iY+KSXJYqRs7cSeGPVcQCGNffkBe+KBo5ICCFKDqnpFk/No5wN68Y0480O1THXmrD7QjQdv9nNwt2hZEhfbyGEKJZ27drFZ599xvz58zl27BgbNmxgy5YtTJ8+/amOO2PGDBwcHLIWd3f3AopYGMrN+LsMX3KE5HQdTauW5f0utQ0dkhBClCiSdIsCYaY14c0ONfjjzVY0rVqW1Aw9n/8RQre5ezkWdtvQ4QkhhFFzcnJCq9USGRmZbX1kZCTly5fPdZ+PPvqIwYMHM3LkSOrXr0+PHj347LPPmDFjBnq9/omOCTBp0iQSEhKyluvXrz/9BQqDSUjJYNjiw0TdSaOGqy3fDW6Cual8fBRCiPwo1n81AwICqFOnDr6+voYOReSRl7Mtv4xqyszeDXC0NiMk4g69Fuzn/Y2nSUjJMHR4QghhlMzNzfHx8SEwMDBrnV6vJzAwkGbNmuW6T0pKCiYm2T8GaLVaABRFeaJjAlhYWGBvb59tESVTWqaOV5Yd5WJUEq72Fiwd7oeDlZmhwxJCiBKnWPfpHjduHOPGjSMxMREHBwdDhyPySKPR0KeJO8/UcuGzrSGsP3aDlYfC+OtsBB88X5vuDd1kehEhhChgEyZMYOjQoTRp0gQ/Pz9mz55NcnIyw4cPB2DIkCG4ubkxY8YMALp168asWbNo1KgR/v7+XLp0iY8++ohu3bplJd+PO6YwDldikvludygajQaPctZULqsu3/9zmUNX4rC1MGXJMD8qOloZOlQhhCiRinXSLUq2crYWfN3Xmz5NKvHhpjNcikrirdUnWXPkBnMHNsLJ1sLQIQohhNHo168f0dHRTJ48mYiICBo2bMi2bduyBkILCwvLVrP94YcfotFo+PDDD7l58ybOzs5069aNTz/9NM/HFCXfjuBI3lp9gjtpmbluNzXRsOClxtSpKC0WhBDiSWmUEjC/072a7oSEBGmmVkKlZ+r5Yc9lvg28SFqmnjoV7PnllabSTE0IUeoZ+zPO2K+vpNLrFWYHXuTbwIsANPEoQ3OvclyLS+FabAphcSkkpWbyRe/69GhUycDRCiFE8ZTXZ5zUdIsiYW5qwrh21ehUtzz9vz9IcHgiI5YeYdnLfliby20ohBBCFJWElAzeXH2cneejARjazIMPnq+TY4A0vV7BxES6gwkhxNMq1gOpCeNTzcWWZS/7YW9pStC124xeFkRaps7QYQkhhBClwtlbCbwQsJed56OxMDXh6z7efPxivVxHJJeEWwghCoYk3aLI1a5gz9IRfliba9lzMYY3fjlBpsznLYQQQhQaRVFYfSSMHvP3cy02BTdHK9aPbU4vH2k6LoTIh9vX4GaQoaMocSTpFgbRuHIZvh/cBHOtCdvORvDuulOkpOc+iIsQQoiSR6b9LD7uput4d90p/rf+NOmZetrVdOb311pSz01mhhFC5FFyLPzxP5jbGH54BnZ+BsV/aLBio1gPpBYQEEBAQAA6nY4LFy7IICxGaNuZCMatPIZOr+Bqb8H/nqtF94Zu0qRNCFFqGPtAY8Z+fcXd5egkXl1xjJCIO5ho4O1nazK2jZc8Z4UozdJT4K8PwNoJmo8Hy0e8gMtIhcPfwT9fQ1pC9m2NXoKus0Gby8DIcZfBvhKYmhdo6FkSb4FdBTDwNMR5fcYV66T7HnlgG7ed56OY/OsZrsfdBcDb3ZHJXevg41HGwJEJIUThM/ZnnLFfX3F26kY8gxcdJuFuBk625nw7oBHNvZwMHZYQwtC2vA1HflS/ti4HbSeBz7DsyXN8GAT/Coe/V78GcK0Pz06H21dhywRQ9FCtI/RZCha2oMuEkN/gQADcOAJuTWDoZjC3Kdj4d30Ou2ZAzeeh78+gfcigzMeXQ8RpaPoqlPEo2Bj+JUm3KFFSM3Qs2XeVeX9fJDldHVite8OKvP98bVzsLA0cnRBCFB5jf8YZ+/UVV0evxjF8yRHupGXS0N2R7wb74Govz1MhSr1LO2B5L/VrRw+Iv6Z+Xa6amnwn3oLgTdn7bdtVhPYfQYN+YKJV153/A9YOh8y7UKEh1OsFh3+AhLDs56vWEQb8kntt+IOSouD6Ybh5VK3BbjIi932OLFIT/nsavQQvzMtZ4733G9gxVf1aawFNx0DLCWDl+Og48kmSblEiRd1J5es/L7Am6DqKAnaWprzXqSYD/T3QSlM4IYQRMvZnnLFfX3G0/1IML/90lLsZOvyrlGXRMF9sLWR6TiFypSgGb6LM1X0QfhIaDXp0U+/MdLhzCxJuqslx4k24GwfOtaCSH5TzevS1pMTB/GaQFAF+r0Cnz+DYT7BzBqTE/KewBjxaQN3u0HAQmFvnPN6No7CyL6TE3l9nXQ58R4KbD6wdBhkp4D0Qus/PHpuiqC8ATq1Wk+17yf89lZupNeh25e+vC94Ma4YACtR5Ec79pta2t3pHfSlwz4MJt3NtiD6nfm1VFtpOBJ/hBdbsvVCT7oCAAGbOnElERATe3t7MnTsXPz+/XMsuXbqU4cOHZ1tnYWFBampqns8nD+zS5/SNBD7YdJpTN9S+I96VHPi0R30Z9EUIYXSM/Rln7NdX3Ow8H8WYZUGkZeppVd2J7wc3wcpca+iwhCh8eh0kRf6bkP67mFqoNbBWuXRZTEuC/d/CoYVQt4faN9kQyfftq2oinJHyb1I4CZoMz17LG3MRDs6HE7+oNcsPY1UGKvmqybLPsOy1uooC64bD2Y1QrjqM/ud+Ip2aCPtmq8cv56UmtLVfADvXHKc4f/48wcHB9OjRQ10RG6omwooC/q+oteFmVuq2C3/CLwNA0UHLt6DDVHV9+Cn460O4svuBI2vUlwcVG0HI75CWCDYuauLt2UJ9MbGsB+jS1KS56zdw7Gf47XV19y5fgd+o7Al3uw+g9btw8S/46yOIOa+uL+sFL62HslUe88t5vEJLulevXs2QIUNYuHAh/v7+zJ49m7Vr13L+/HlcXFxylF+6dClvvPEG58+fv39SjQZX15y/xKe9GGFcdHqFFYeuMXPbee6kZWKigb5N3Bn/TDUqlcnlbZsQQpRAxv6MM/brK04Cz0UyZnkQGTqFDrVdCRjUCAtTSbhFCaXXQfR5iDgFLnWgQoPcy+kyIPBjOPQd6NJzbjezUWuQ/ceoCaUuE04sV0ffToq8X67rN2qT5kdJTbhfw5xwU+3HXK0jWD7h3zZFURPJyzvBxAz0Ger6sl7QcRpY2Kn9oy/+eX8fU0uwrwj2bupiYaf2W751XE1I7/lvAn9qLWwYCRotjNyu1kTnO1yFxo0bc+rUKc6fP0+1atUev9PxFfDrq+rXbd9Xa7RPrAQU0JqrP/Pqz0KlJvdr+WMuwZrBEBWsxtv8NTi6RB3IrVZXtR/3vWbuu7+EnZ8CGqjXE86sV9e3+xDavHs/Dl0mHP9Z/b1blYGxBx7eFzwfCi3p9vf3x9fXl3nz5gGg1+txd3fntddeY+LEiTnKL126lDfffJP4+Pj8XcED5IFdukUlpjJ9yzl+O3kLADOthr5N3BnXrhoVHa0MHJ0QQjwdY3/GGfv1FRfhCXd59pt/uJOayfP1KzC7f0PMtDIzrChhbgbB+W1w4zDcPKbWdgKgUQfDeubD7M2c70SoTZjDDvxbTPtvQvpvUhp9HqLO3j9Gzc4Qd+V+c+MynuDRUk3CtRZqMlrBO3tMep2a1B9d8kA8D9BaQLX2am15jefUBDwt6X5ynpYIVdrk3pf4xErYNFZNpEfvgWt71aQwOfo/BTVQsws0e1Wtxc6tRj4zHSJPq021jy7JXqvb8i11tPLUBDXxbfu/h/0GHmnLli107doVjUbDkCFDWLp0ad523PM1BE7Lvq5eL2g/Wf0d5CY9GX57E06vub+ucjMYvPF+TTqoLy62vA1HF91f99+E+0Fpd9QXJi618hb7YxRK0p2eno61tTXr1q2je/fuWeuHDh1KfHw8v/76a459li5dysiRI3Fzc0Ov19O4cWM+++wz6tat+9DzpKWlkZZ2/01NYmIi7u7u8sAu5Y5ejeObHRfYd0ntN2KuNWGAnztvdayBo3UhTUcghBCFzNiTUmO/vuJAURSGLD7MnosxeFdyYN3Y5pJwi5Il7rLaJDj4P7mEmQ04VVP7O4OaQL4YAB7N1ObG64artdUW9vDiPLUW1OSB1h2KojZhPhCgNjG+x9IR2vxP7XusNVObQF/4A8pWhVd236+5zrgLG0apfYfvsSrzby1zRbVpeMyF+9u05mpCmPqfqbXKeMJLG9Sa9nuSomCeL6TGq82uW76lrk9NhH1z4MA80JioA4Xdq6XPqwdrdR9M4N18YMRfT1TDe6+W+/Tp0+h0OkxMTPJe260o8OcHcDBATZyf/USt2c7Lfkd+hD/fB6eaMOy33LsK6HWwfqTadP6ZD6H1O/m+vidVKEn3rVu3cHNzY//+/TRr1ixr/Xvvvcfu3bs5dOhQjn0OHDjAxYsXadCgAQkJCXz11Vf8888/nD17lkqVKuV6nqlTp/Lxxx/nWC8PbAFw8HIss7Zf4PCVOABc7Cz4sncD2tbM2b1BCCGKO2NPSo39+oqDZQeu8tGvZ7EwNWHrG63wcrY1dEiiOEmKUmv38pO0FZWUOPjnK3VaKn2GmmTWeRE8W4G7n9qs3EQLF/6C395QBxG7V+t7YZvaV9ilDvRb/vjriz4Px5eBuS34j86evKXEwXetIeE61O0JvRfD3dtqMn79oJpMvzAPanfNPv2VokDUOTXZC96UPQG3sFeT87u31YHLrMvBwDX3k821w9T9yjeAUTtzJsIZqYCSvVY3vx5M4LXmMOpvcKr+RIe6V8t9j1ar5aWXXsp7bTeo96KNc/77zqcmqC9gHvWyQFHUFxi5JeWFqNgk3f+VkZFB7dq1GTBgANOnT8+1jNR0i8dRFIX9obFM/vUModHJAAzyr8wHz9fG2lxGaBVClBzGmpQGBAQQEBCATqfjwoULRnd9xcWVmGQ6z/mH1Aw9U7rVYXiLpx8YSBiRK3tg1SC1L2zVttBsPHi1B5MCbAmRHKMOWGZh9/AylwLVGsuMlOzrb51QEyVQ43p2Org+pDXs3Xi1tvTE8vvrGvRT+2IXxDzQ1w/Dks6gz1Rrwc9uVJNoCwcYsBI8Wz56f0VRa+z1meqUV/dqy5Oi1BG+bx0HUyt1YDBFD6sGqM3hX9mZs0l7QUuJU2uDbZ2faPf/1nLfk6/abiNVbJqX56ZPnz6Ympryyy+/5Km8sX4gEU8vNUPH53+EsHT/VQA8y1kzq19DGlcu2rdcQgjxpIz9GWfs12dImTo9fb47wPGweJp7lWP5y/6YyPSa4p4z62HjmJyDiznVUPtHe/d/8lrUhBtqU/Czm9T+12bW0OINdcCrBxPg1ES1L/Gxnx9+LJe68Ow0qNYhb+e+8BccmKv2ofYZXrAjju+fq46qfY+9GwxaB651nu64aUlqzfal7WptvoW9+rKhxZvQMWfr3uLmv7Xc9zxRbbeRKdSB1Pz8/Jg7dy6gDqRWuXJlxo8fn+tAav+l0+moW7cuXbp0YdasWXk6pzywxePsuxTDO2tPEp6QitZEw6TOtXi5ZRU0hp53UQghHsPYn3HGfn2GFLDzEjP/PI+dhSnb3mqNmwwuatwyUtXBpR5kos19gK7989RkF6B2N3hmsjofc9BPkH5HXe9cC4ZsznVaqFz9N9HOjW15eOYDdV7ny7tg8+uQeEPd5jsS3JtmL29VBrzaZe+HbUiKcr9/t0tdGLQWHNwK5ti6DPj9TTj+b0192aowdv/TNR8vAg+r5b6ntNd2F+qUYUOHDuW7777Dz8+P2bNns2bNGkJCQnB1dWXIkCG4ubkxY8YMAKZNm0bTpk2pVq0a8fHxzJw5k02bNhEUFESdOnl7ayQPbJEXCXczmPzrGX49oY5y/oJ3Rb7o1UDmJxVCFGvG/owz9uszhIiEVH4/dYsvtoWQoVP4uo83vXxyHydHGAFFURPmbe9DRnLO7XYVwd0XKvmpczQH/6oOWAXgNxqem3E/qU1NVJO+vd9AcpQ6ONXQ3x6eeD800daoA2LV7a4m9dcPqQOh3b6qbravdD/ZLuOpDn72uObZxUVGKoT+DVVaPbrJ/JNQFHUk7zProdu36u+tmHtYLfc9pb22u9CSboB58+Yxc+ZMIiIiaNiwId9++y3+/v4AtG3bFk9Pz6wf/FtvvcWGDRuIiIigTJky+Pj48Mknn9CoUaMCvxghFEXh5wPXmP57MJl6hVrl7fh+cBMql5N5vYUQxZOxP+OM/fqKSmRiKltOhbP1dDhHr93OWt+prisLX/KRll0l2Y0gdaqnuj3uz1N8T8Zd2PJO9n7MedVxGjR/Pffm13GXYWlXdUqr3BLvXEcTfzDRfgHsK2Q/Zmaa2m9795f3+2n7jYYOUwqmz7Uoco+r5b6nNNd2F2rSXdTkgS3y69DlWMatPEZMUjoOVmZ81LUODd0dqFzWBnNTmUZFCFF8GPszztivryjsvxTD8KVHSMvUZ63z8SjD8/UrMNC/MpZm0qKryIWfgmv7H1/ORAvVn4UyHjm3KQrs/xZ2fKyOwm1uC40GqyNrl62izie9ZghEnFL7AT/zkdpvWvPA55iMFHWAruuH4cYR9d/0JHhhLjTo++jYcku8tWbwz0w4/IM6mnhWot1DrdH+b6Kdm7u34fgKdZTuyk0fX14UW4+r5b6nNNd2S9ItSr3whLuMWX6Mk9fjs9ZpTTR4lLWmqrMtXeqXp3tDNxl0RghhUMb+jDP26yts1+NSeGHeXm6nZFDPzZ6ejSrRuX55KjgU736gRu34crWvsvLwmr9stOZqIt3q7fvTGaUmwq+v3p//2ba8Oq0UqEl19U4Qtl+dKsnaCXovUkcffxxFUfsOm5rnLbYHE29HD7WG+t4c09U6qLXlDxtNXBi1vNZy31Naa7sl6RYCdXTzbwMvsvdSDKFRSSSnZ/+j0aiyIx+/UJcGlRwNE6AQotQz9mecsV9fYbqbrqPngv2cC0+kQSUH1oxuJrXauclIVWuDHT3yPijYk1AUtRZ456fq9x4twPYx50u8qfZ3BjXhbvM/teZ43QiICwUTM+j8BTQZofYjPhAAoYH396/kC31+KrjBvHLzYOIN/44mPh2qtS+8c4piL6+13PeU1tpuo0i6ZY5PUZAURSEyMY3Q6CSOXI3jh38uk5yuQ6OBfk3cebdTTcrZWhg6TCFEKWPsSamxX19hURSF11ed4LeTt3CyNWfz+JZUlNHJc4q5BKtfguhz6veOldVEtZIfeDSD8g1y79Os10HI73Bmg7qP3yvg6P7w8+gyYcsEdUAzUGutn/no8dNVKQpc2qFOQxUdkn2bfSXo+zNU8sm+PuocHFmkjkre+r2811o/jbgral9sj+bQcGDxGU1cGER+a7nvKY213UaRdN8jD2xRGCITU5mx9Ryb/h3t3N7SlOnd6/Fiw0J8myyEEP9hrM84eXH+dL7/J5TPtoZgaqJhxUh//KuWM3RIxU/wZtj0qjoFlpmN2r+Z/3ysdfSAOi+qfZIrNoK0O2rz8EMLIf7a/XIarVqu2fjsSbBeB3fCYcvbcGGb2vS7y0x1+qv80GWqg6H9/ak6anjVttBrMdjI71UUP/mt5b6nNNZ2S9ItRB4duRrHlF/PEhyeCMDgph582LU2FqbyllcIUfiM/Rln7NdXGPZcjGbo4sPoFfj4hboMbe5p6JCKF10mBE6F/XPV7z1aQO8l6nzHN4PgxlF1equre/9NxP/lWBnuxkOa+rzHqow6n3TEKbjyz/1yFRupzb4Tb6kJ972+26aW0GsR1M5/MpIlLQkiz6i18VKbLIqhJ63lvqe01XZL0i1EPuj0CnMCL/Jt4EUAvN0dmT+oMW7SlE8IUciM/Rln7NdX0K7HpdBt3l7iUzLo41OJL3s3KN3TgWWmqXNFJ96EhJvqvxf/ut9Puvlr0H6KOur2f6WnqGWDN8GFP+8n4OWqQ7NXoUF/MP93StHwU3BwAZxe+++o3Q/QaMGpBnSbA5X9C+1ShSgOnrSW+57SVtstSbcQT2BnSBRvrj5Bwt0MylibMbt/I9rUcDZ0WEIII2bszzhjv76ClJqho/fC/Zy5WcoHTktPVpPksxvh4nbIvJuzjLkddA9Qm4Tn6ZgpcHmXmmR7tgaTh0wfeidCPaelPdi7qYuti9RKi1KjQ4cOBAYGYmaW/UWWXq/Pteb7v+UyMzMxMTEhKiqKsmXLFmqsxUFen3GmRRiTEMVeu1ou/P5aS15dcYzTNxMYtuQwo1t7MaFjDZnfWwghRKGa8utZztxMpIy1GQte8imZCfe1A+pUWHV7QPvJedtHUSA2VK29vrAtZ6JtZq0mvw7/JsEOlaBBPyjnlfe4zK2hVpfHl7MrD40H5/24QhiZt99+mxYtWmRbFxISwpo1a3KU1Wq11KpVix49emRbX7ZsWezs7Ao1zpJGarqFyEVqho5pvwez8lAYAPXc7JnTvxFezrYGjkwIYWyM/Rln7NdXUFYdDmPihtNoNPDzCD9aVS+BrayuHYDlvSAjWf1+9B6o0CD3ssmxELQYrh+GG0fg7u3s28t4Qp3uavJewfvxo4QLIQrNr7/+Svfu3XOs12q1DBgwgGXLlhV9UMWE1HQL8RQszbR81qM+ras7MXHDac7cTOT5b/fwUdc6DPSrXLr71wkhhChQp28kMHnzWQDeebZmyU+4Ta3Umuq/p8OgtTnLZqbDT10hKvj+Oq2FOoCZR3O1ybgk2kIII1Ksk+4HpxsRwhCeq1eBhu5leGftSfZeiuGDjWfYGRLF9O71qOAgg6wJIYR4OreT0xmzPIj0TD0darsytk0+mkwXFw8m3FXbQqfP4LvW6iBm1/arifSD9n6jJtzW5aD1u+qc2uXrF8181EIIYQDFupPquHHjCA4O5siRI4YORZRi5R0s+XmEHx8+XxtzrQk7zkXR4evdLNl3BZ2+2PfOEEIIUUzp9ApvrD7Bzfi7eJSz5uu+3piYFJPaXb0eYi4+vtx/E+7+v4BrXWg8RN2+42O1z/Y9USHwz0z1685fQtOx6rzYknALIYxYsU66hSguTEw0jGxVlc2vtaBxZUeS03V8/FswPebv48zNBEOHJ4QQogSa+/dF/rkQjaWZCQtf8sHBKpdprwzlt9dgXhPYO/vhZW5fhRV9sifc96bgav2e2sz8+kF1JHJQE/nfXlen5KreCer1KuSLEEKI4kGSbiHyoVZ5e9aNac6nPephZ2nKqRsJvDBvLx9sPM212GRDhyeEEMVGQEAAderUwdfX19ChFEu7zkcxJ1CtSf6sR31qVyhGg8wFb4bjy9Wv/54O4SdzltHrYONYSL+jNg9/MOEGsK8A/qPVrwOnqQn3kR/VEcrNbaHrLOmzLYQoNSTpFiKfTEw0DPL3IPDtNnTzrohegRWHwmj31S5eXRHEievxhg5RCCEMTrqIPdyN2ym8ufoEigID/SvTs3ElQ4d0351I+O0N9WtrJ9BnwoZXICM1e7kD8yBsv5pA9/ohe8J9T8s3wdIBos7Cvm8g8GN1fYep6rRfQghRSkjSLcQTcrGzZO6ARqx6pSltajijV2Dr6Qi6B+yj73cH2BEciV76fAshhHhAWqaOcSuOEZ+SQYNKDkzuWsfQId2nKLB5PNyNUwc2G7MXbFwgOkStrb4n4gz8/Yn69XOfq9N75caqDLT4N4EPnAbpSeDeFJq8XKiXIYQQxY0k3UI8paZVy/HTCD+2vdmKno3dMDXRcPhKHCN/PkqHb3az4tA1UjNkBH4hhBDwye/nOHkjAQcrMwIGNsbSTGvokO4LWqqOOK61gJ4/qE3EX5ynbjsYAJd3Q2aaWvOtS4eaXaDRS48+pv8YsHVVv9aawwtzwUQ+fgohShf5qydEAalV3p5ZfRuy53/tGN26KnaWplyOTuaDjWdo/vnffLP9AndSMwwdphBCCAOIupPKJ78Hs+zgNTQamN2/Ie5lc2mSbSixofDnB+rXHaaAS2316xqdwGeY+vWmV2HbJLW5uLUTdPv28f2yzW2g43RAozYrd65RSBcghBDFV7FOumUQFlESVXCwYlKX2hyY1J6PutbBzdGKuOR05gRepOOsf/jrbIShQxRCGKmAgAA8PT2xtLTE39+fw4cPP7Rs27Zt0Wg0OZbnn38+q8ywYcNybH/uueeK4lKMxrXYZD7YeJqWX+zkx71XAHj9meq0q+li4MgeoMuEjWPUUcg9W4H/2Ozbn/0UylSBxBtwdJG67oVvwdY5b8f37gcfRkKzcQUbtxBClBCmhg7gUcaNG8e4ceNITEzEwcHB0OEIkS+2Fqa83LIKQ5t58OfZSGb+GcLV2BReWRZE53rl+fiFurjYWxo6TCGEkVi9ejUTJkxg4cKF+Pv7M3v2bDp16sT58+dxccmZ4G3YsIH09PSs72NjY/H29qZPnz7Zyj333HMsWbIk63sLC4vCuwgjknA3g8m/nuG3k7e4N7xHo8qOvNq2Gh1qF6OEG9R5s28cBgt76L4gZ/NvC1vo+T0s7gSKXm1SXuv53I/1MKZy3wghSq9inXQLYQxMtSY836AC7Wu7/L+9O4+Lutr/OP6aGXZEEJBVFFcU98QFl9Qkl8yyxaUszUp/uaWZZbZoO3or04r0Zi4tlmZl19K0xLQ0d8MFFXHFDXADBBUUvr8/uE7NdVdgWN7Px2Mezpzv+Z75zKE4fOZ7vucwOTaRT37fy8/bklm5+zijO9emV9MQHC3FetKJiJQAEydOZMCAAfTv3x+AqVOnsnDhQmbMmMELL7xwSX1vb2+b13PmzMHNze2SpNvZ2ZmAgIDCC7wUMgyD57/dzJL4FADahVVkUNvqNKvqjam4bZO1dzmsmJD/vOtE8Aq5fL2QZvnTyQ+uyV88TURErpv+0hcpIi6OFkZ3rs2PQ1vTsJInp89d4OUfttHuneXMXLWPMzkX7B2iiJRQOTk5bNy4kaioKGuZ2WwmKiqK1atXX1cb06dPp3fv3ri7u9uUL1++HD8/P8LCwhg0aBAnTpy4ajvZ2dlkZGTYPMqabzYcZEl8Co4WE3MHtmBW/2Y0r+ZT/BLu0ynw3QDAgNv6QYMeV69/26Nwbww4exRJeCIipYWSbpEiFh5Unu8Ht2Jct3B8yzlxOO0sr/24nVbjlzFp6S5OZeVcuxERkX84fvw4ubm5+Pv725T7+/uTnHztdSTWrVvHtm3bePLJJ23KO3fuzOeff05sbCwTJkxgxYoVdOnShdzcK+/IEB0djaenp/UREnKFK6el1N5jmby6YDsAozqG0byaT9G9uWHkry5+PfJy4bsnICsV/OpClwmFG5uISBmmpFvEDixmE/1bVWXl6Dt4s3s9Knu7cerMeSYtTaT9e8v5eetRe4coImXI9OnTqV+/Ps2aNbMp7927N/fccw/169ene/fu/PTTT6xfv57ly5dfsa0xY8aQnp5ufRw8eLCQoy8+zufmMWJuHGfP59Kyug8D2lQr2gAWPQdv+sOMLrD2Ezh9lS9cVkyA/X+Aozv0/AwcXYsuThGRMkb3dIvYkYujhUdaVOGhZpX5edtRPlq2m53Jpxk0exMPNqnEuG7heLg42jtMESnmfH19sVgspKSk2JSnpKRc837srKws5syZw+uvv37N96lWrRq+vr7s3r2bDh06XLaOs7NzmV1sbdLSXWz57x7c7/VsiNlchNPJD6yG9dPynyf9mf/4+XmoHAk1OoBXFSgfBJ7BcDwRVvwrv263yeBbs+jiFBEpg5R0ixQDFrOJuxsE0aluAJOXJvLx8t18u/EQa/ed4P2ejYgI9b52IyJSZjk5OdGkSRNiY2Pp3r07AHl5ecTGxjJ06NCrnjtv3jyys7N55JFHrvk+hw4d4sSJEwQGBhZE2KXK2r0n+Hj5HgDG31+fQM8ivHKcewEWjcp/Xr8HBDWG+PlwaP3fCfjlNHns2vdxi4jILSvWSXdMTAwxMTFXvXdMpDRxtJgZ1SmMtmEVeWZuHAdPnqXnv1fTv1VVht1RAy83J3uHKCLF1MiRI+nXrx8RERE0a9aMSZMmkZWVZV3NvG/fvgQHBxMdHW1z3vTp0+nevTs+Prb3HmdmZvLaa6/xwAMPEBAQwJ49e3j++eepUaMGnTp1KrLPVRKknznPyG82YxjQM6ISXeoX8ZcS6z+FlG3gWgE6TwB3n/w9sdMPwfb/wNEtkHH4v48jcOFcfmKuVchFRIpEsU66tU+3lFVNQ71ZNLwNry6I5/tNh5m+ch/zNhxkcPsaPNYyFBdHi71DFJFiplevXhw7doyxY8eSnJxMo0aNWLx4sXVxtaSkJMz/s/9yQkICK1eu5JdffrmkPYvFwpYtW/jss89IS0sjKCiIjh078sYbb5TZ6eOXYxgGo7/bwuG0s1TxcWNct7pFG8DpFPjtrfznHcblJ9wXeVbKT77/yTDg7Kn8PbktxfrPQBGRUkO/bUWKqfIujkzs2YhuDYMYv2gnCSmnGf/zTj77cz8j76xF1waBuDnpf2ER+dvQoUOvOJ38coufhYWFYRjGZeu7urqyZMmSggyvVPpyzQEWxyfjaDHx4UONcXcu4t/Lv46F7Iz8K9e39b12fZMJ3HTLkohIUdJf7CLFXPswP26vWZH5fx1m4i8JHEk/x3PfbuGF77dSJ9CD2ypX4LbKFWha1ZtgL60+KyJSVOKPpPPGwh0AvNClDg0qeRVtAPtXwZY5gAm6vgdmzYISESmObmrLsJiYGEJDQ3FxcaF58+asW7fuus6bM2cOJpPJusiLiFwfi9nEg00qsWxUO8Z0qU2gpwu5eQbbDmfw+eoDjJgbR6vxyxj97RZOap9vEZFCl5V9gWFf/0XOhTw61Pbj8VahhfuG59Lh5N6/Hyf25G8RBtCkHwQ3Kdz3FxGRm3bDV7rnzp3LyJEjmTp1Ks2bN2fSpEl06tSJhIQE/Pz8rnje/v37GTVqFG3atLmlgEXKMhdHC//XtjoDb6/G0fRzbEo6xaYDaWxMOsXmg2nM3XCQxfHJPN85jN5NK2Mpyu1qRETKkLH/iWfvsSwCyrvwTo+GmEyF+Pt2+3/g+4H5C6D9L9cK+fdyi4hIsXXDV7onTpzIgAED6N+/P+Hh4UydOhU3NzdmzJhxxXNyc3Pp06cPr732GtWqVbulgEUETCYTQV6u3N0giLHdwvnPkFZ8+1QktQM8SD97npfmb+P+j1ex5VCavUMVkTIqJiaG8PBwmjZtau9QCtz3mw7x3aZDmE0wuXcjvN0LcWeJ7f+Bef3zE24HV3Aq9/fD1Rvuelf3aIuIFHM3dKU7JyeHjRs3MmbMGGuZ2WwmKiqK1atXX/G8119/HT8/P5544gn++OOPa75PdnY22dnZ1tcZGRk3EqZImRQR6s1Pw1rzxZoDTPxlF5sPpXNvzCr6t6zKsx1rFf3iPiJSppXWHUjSzuQwbkE8AMM71KJ5NZ9rnHELLibcRi406A3dP9Z92yIiJdANXek+fvw4ubm51u1HLvL39yc5Ofmy56xcuZLp06czbdq0636f6OhoPD09rY+QkJAbCVOkzHKwmOnfqiqxo9rSvVEQhgEzVu2j4/u/szwh1d7hiYiUeFNW7OH0uQvUDvBg6B01Cu+NlHCLiJQaN7WQ2vU6ffo0jz76KNOmTcPX1/e6zxszZgzp6enWx8GDBwsxSpHSx8/DhUm9G/PZ480I9nLlcNpZHpu5nmfmxrHjaAYpGec4m5N7xa2CRETkUikZ55i1aj8Az3cOK7x1M+J/+DvhbviQEm4RkRLuhuab+vr6YrFYSElJsSlPSUkhICDgkvp79uxh//79dOvWzVqWl5eX/8YODiQkJFC9evVLznN2dsbZ2flGQhORy2hbqyK/PHM7E3/dxcxV+5j/12Hm/3XYetzJYsbLzZH7bgvmmahauDjqjzoRkSv5cFki2RfyiKhSgfZhV1489qZlZ8LScbD+0/zXDR+Ce2OUcIuIlHA3dKXbycmJJk2aEBsbay3Ly8sjNjaWyMjIS+rXrl2brVu3EhcXZ33cc889tG/fnri4OE0bFykC7s4OvHJ3ON8PbkXT0ApUcHO0Xp3Jyc0j9XQ2/16xl7sm/8HGAyftHK2ISPF04EQWc9blz7x7rlNYwa9WvncFTIn8O+Fu/pQSbhGRUuKGV1YaOXIk/fr1IyIigmbNmjFp0iSysrLo378/AH379iU4OJjo6GhcXFyoV6+ezfleXl4Al5SLSOFqFOLFvKdaAmAYBlk5uWScPc/mg2m8+mM8e49n8eDU1TzeqiqjOobh6qQ/9ERELnr/111cyDNoW6tiwS6elp0Jv46FDdPzX3tWhns/gmptC+49RETErm446e7VqxfHjh1j7NixJCcn06hRIxYvXmxdXC0pKQmzuVBvFReRW2QymSjn7EA5ZweCvFxpWd2XNxZu59uNh5i+ch+xO1J4r2dDmlTRNjQiIjuTM/jP5iNA/lXuApOXB7N7QNKf+a8jnoA7XwNnj4J7DxERsbub2kNo6NChDB069LLHli9fftVzZ82adTNvKSKFyNPNkXd7NKRrg0Be/H4r+0+cocfU1Qy9oyZP31EDB4u+SBORsuvdJbswDOhaP5B6wQW4/Vn89/kJt6M7PPS1rm6LiJRS+ktaRKzah/mx5Jnbub9xMHkGfBCbyINTV7P/eJa9QxOREiYmJobw8HCaNm1q71BuycYDp1i6IwWzCUZ2rFVwDZ8/C0tfzX/e5hkl3CIipVixTrpLy4AtUpKUd3FkYq9GfPBQYzxcHIg7mMZdH/zB7LUHOJuTa+/wRKSEGDJkCNu3b2f9+vX2DuWmGYZB9KIdADzYpBLVK5YruMbXfAzpB6F8JYi8/OxBEREpHYp10l0aBmyRkuqehkEsHnE7zap6cyYnl5fmb6PxG7/w1Bcbmf/XIdLPnrd3iCIihWr+X4fZcOAUro4WRkQV4FXuzFT4Y2L+86hx4OhacG2LiEixc1P3dItI2RDs5crXA1owfeVePvvzAIfTzrI4PpnF8ck4mE10rOvPmC51CPF2s3eoIiIF6vS587y9aCcAwzrUIMirABPj396CnEwIug3qPVhw7YqISLGkpFtErspiNjHw9uoMaFON+CMZLIlPZkl8MrtSMlm0NZnYHakMbV+DgW2r4eygbcZEpHSYtDSR45nZVPV154nWVQuu4ZR42PR5/vNOb4N2fBERKfX0m15ErovJZKJesCfPdgzjl2fa8vPwNrSs7kP2hTze+3UXnd7/nRW7jtk7TBGRW7Yr5TSz/twPwKv31C24LxQNA5a8CEYehN8LVSILpl0RESnWlHSLyE2pE1ie2U8254OHGuPn4cz+E2foN2Mdj05fy7KdKeTlGfYOUUTkhhmGwdj/bCM3z6BjuD9ta1W8vhPPpcOF7KvX2bUY9i4HixNEvXbLsYqISMmg6eUictNMJhP3NAyifVhFJi1NZNaf+/kj8Th/JB6nio8bj7aoQo+IEDxdHe0dqojIdflxy1HW7D2Js4OZV+4Ov76TTifDR83A3Rf6LQDPSpfWSYmH7/8v/3nzp8C7AKesi4hIsaYr3SJyyzxcHHnl7nB+e7YdA9pUpbyLAwdOnOHNhTuIjI7luXmbWb//JIahq98iUnxlZV/grYXbARjcrsb1LxK57TvIToeTe2BWV0g/ZHs8LQm+fCC/TuVIaP9iAUcuIiLFWbFOurVPt0jJUtnHjZe6hrPmxQ68fV99wvw9OJOTy7yNh+gxdTV3vLeCmN92k5x+zt6hiojYMAyD13/cTkpGNpW93fi/ttWu/+St3+b/a3GCU/ttE+8zJ+GL++H0UahYBx76WluEiYiUMcU66dY+3SIlk5uTAw83r8ziEW2Y91QkPSMq4eZkYd/xLN5ZkkCrCct486ftZGZfsHeoIlJIStoX51+tS2LuhoOYTfD2ffVxcbzOxdNO7IEjm8BkgSd+gQqhfyfexxPhq55wIhHKB8Mj34FrhcL8GCIiUgwV66RbREo2k8lE01Bv/vVgQ9a/FMU7DzagaWgFcvMMPl25jw7vLefHzUc07VykFCpJX5xvPHCKVxfEAzCqUxita/pe/8nbvs//t1pbCGoMjy38O/GOaQ6H1oOLFzzyPXgGF3ToIiJSAijpFpEi4e7sQI+IEOY91ZKZ/ZtSxceNlIxshn39F49OX8eulNP2DlFEyqDUjHMM+nIj53MN7qofwKC21W+sgW3f5f9b74H8fz0r/Z14G7ng4AIPzwW/2gUat4iIlBxavVxEilz7MD8iR/gwdcUePl6+h5W7j9Px/d+pXtGddmF+tA/zo2nVCgW3N66IyGXkXMhj8OxNpJ7OpqZfOf71YENMJtP1N5ASD8d25N/LXfvuv8s9K8Fji2Dl+1C3O1RuUeCxi4hIyaGkW0TswsXRwoioWtzXOJg3F+5g2c5U9hzLYs+xfUxfuQ83JwttavrSpV4gd9Txo7yLth0TkYL15sLtbDhwCg9nB/79aBPKOd/gn0UXF1Cr2RFcvWyPeQZD13cLJE4RESnZlHSLiF1V8XFnWt8I0s+eZ2XicZYnpLJ81zGOnc5mSXwKS+JTcLKYaVXDhy71AulSPwAPJeAicos2HjjJ56sPADCpdyOqVSx3Yw0Yxj+mlt9fwNGJiEhpoqRbRIoFT1dHujYIpGuDQPLyDOKPZLAkPpmftx1lz7Esfks4xm8Jx3j9p+30bhrCY61CqVThOvfQFRH5H4u2JgPQvVEQHer433gDhzdC2gFwdIdaXQo4OhERKU2UdItIsWM2m6hfyZP6lTwZ1SmMxJTTLN6WzA9xh9lzLItPV+5j5p/76VIvgCfbVKNRiJe9QxaREsQwDJbuSAGgc72Am2vk4tTy2neBk74AFBGRKyvWSXdMTAwxMTHk5ubaOxQRsaOa/h7U9PdgSPsarEg8xqd/7GXV7hP8tOUoP205yu21KjLyzlpKvkXkuuxOzeTAiTM4OZhpU7PijTeQlwvx/90q7OKq5SIiIldQrLcMK0l7fIpI4TObTbQP82P2ky1Y9HQb7r8tGAezid93HaN7zCqe/Gw98UfS7R2miJD/xXl4eDhNmza1dyiX+GV7/lXuVtV9cL/RxdMA9q+EzJT8/berdyjY4EREpNQp1le6RUSuJDyoPBN7NmJEh1pMjk1k/l+HWLojlaU7UmkfVpFGIRWo6V+Omn7lqOLjjpNDsf6OUaTUGTJkCEOGDCEjIwNPT097h2Pj4tTyqPDruJd7/afw54fg5gPlg/MfyVvyj4XfAw5OhRipiIiUBkq6RaREq+zjxns9GzK4fXUmLU3kpy1HrIuuXeRgNtGyhi9vda9HiLfuvRQpy1JPnyPuYBoAUddaQC3rBPwyFs5nwan9+Yun/ZOmlouIyHVQ0i0ipUL1iuX48KHGDLujBr/tTCUxNZPE1Ex2p5wmKyeX33cdo/Ok3xnbLZyeESGYTCZ7hywidrBsRyqGAQ0reeJf3uXqlVd/mJ9wB9SHtqMh4wikH4KMw+BVBUJvL5qgRUSkRFPSLSKlSi1/D2r5e1hfG4bB7tRMXpq/jXX7TzL6u60siU9h/AP18fO4xh/cIlLqWKeWX89V7rWf5D9v92L+KuUiIiI3QTc5ikipZjKZqOnvwdcDW/DiXbVxsphZtjOVTu//zsfLd7PtcDp5eYa9wxQpEDExMYSGhuLi4kLz5s1Zt27dFeu2a9cOk8l0yaNr167WOoZhMHbsWAIDA3F1dSUqKorExMSi+CiF4mxOLn8kHgeu435u61XuBhCmfbhFROTmKekWkTLBYjYx8Pbq/DisNeGB5Tl15jz/WpzA3R+upMmbvzLkq018tTaJbYfTOXde2xRKyTN37lxGjhzJuHHj2LRpEw0bNqRTp06kpqZetv7333/P0aNHrY9t27ZhsVjo0aOHtc6//vUvPvjgA6ZOncratWtxd3enU6dOnDt3rqg+VoH6I/EY2RfyqFTBldoBHleuaHOVewzodhQREbkFxXp6ufbpFpGCFhbgwQ9DWjF3w0GW70xlzd4TnDpznoVbjrJwy1EAzCao4uNOmL8HdYPK071xsBZgk2Jv4sSJDBgwgP79+wMwdepUFi5cyIwZM3jhhRcuqe/t7W3zes6cObi5uVmTbsMwmDRpEi+//DL33nsvAJ9//jn+/v788MMP9O7du5A/UcH759Tyq67rcPEqd2BDXeUWEZFbVqyT7uK83YiIlFxODmYebVGFR1tU4XxuHnEH01iZeJy1+06QkHyaU2fOs+94FvuOZ7E4Ppn3l+7ijtp+PBoZSpsavpjNuuolxUtOTg4bN25kzJgx1jKz2UxUVBSrV6++rjamT59O7969cXd3B2Dfvn0kJycTFRVlrePp6Unz5s1ZvXp1iUu6c/MMYnfkX/W/82pTy3WVW0REClixTrpFRAqbo8VM01BvmobmX/UzDINjmdnsSs4kIeU0v+1MZeXu49Y9wKv6utM3sgoPNauMi6PFztGL5Dt+/Di5ubn4+9smk/7+/uzcufOa569bt45t27Yxffp0a1lycrK1jf9t8+Kxy8nOziY7O9v6OiMj47o+Q2GLO5jGiawcPFwcaFbV+8oV/3mVu1bnogtQRERKrZu6p/tGFmr5/vvviYiIwMvLC3d3dxo1asQXX3xx0wGLiBQmk8mEn4cLrWv68kTrqnz5ZHOWjmzLYy1D8XB2YN/xLF77cTt3vLucbzYcJFeLsEkpMH36dOrXr0+zZs1uua3o6Gg8PT2tj5CQkAKI8Nb9uj1/ann7MD8cLVf48yf9kK5yi4hIgbvhpPtGF2rx9vbmpZdeYvXq1WzZsoX+/fvTv39/lixZcsvBi4gUhRp+5Xj1nrqsebEDb3SvR0B5F46kn+P5b7fQedLv/BKfjGEo+Rb78fX1xWKxkJKSYlOekpJCQEDAVc/Nyspizpw5PPHEEzblF8+70TbHjBlDenq69XHw4MEb+SiFwjCMv+/n/t+p5ZnHYMMM+KwbTKqvq9wiIlLgbjjp/udCLeHh4UydOhU3NzdmzJhx2frt2rXjvvvuo06dOlSvXp3hw4fToEEDVq5cecvBi4gUJXdnBx5tUYXlz7Xjxbtq4+nqSGJqJgO/2MiDU1ezbt9Je4coZZSTkxNNmjQhNjbWWpaXl0dsbCyRkZFXPXfevHlkZ2fzyCOP2JRXrVqVgIAAmzYzMjJYu3btVdt0dnamfPnyNg97+y0hld2pmThZzLStVTG/MPs0zOkD79WCn56Bfb+DkQdBjaH7FF3lFhGRAnNDSffFhVr+uajKjSzUYhgGsbGxJCQkcPvtt994tCIixYCLo4WBt1fn9+fbM6R9dVwczWw8cIqe/15N/5nr2HG0eNzDKmXLyJEjmTZtGp999hk7duxg0KBBZGVlWVcz79u3r81CaxdNnz6d7t274+PjY1NuMpkYMWIEb775JgsWLGDr1q307duXoKAgunfvXhQfqUDkXMjjjZ92ANC/dSiero75B1Z/DDt/+jvRjnoNno6DgcvBv67d4hURkdLnhhZSu9mFWtLT0wkODiY7OxuLxcLHH3/MnXfeecX6xXURFhGRf/J0deS5TrXpFxnKB8sSmbPuIL8lHGP5rmPc2zCIZzuGaasxKTK9evXi2LFjjB07luTkZBo1asTixYutY3ZSUhJms+137QkJCaxcuZJffvnlsm0+//zzZGVlMXDgQNLS0mjdujWLFy/GxcWl0D9PQZn15z72Hc/Ct5wzQ9vXyC/MzoS1U/Kf3/cJNOxlvwBFRKTUK5LVyz08PIiLiyMzM5PY2FhGjhxJtWrVaNeu3WXrR0dH89prrxVFaCIit8yvvAtvdq/Pk62r8d6vu/hx8xF+iDvCom3JPNG6KoPbVcfDxdHeYUoZMHToUIYOHXrZY8uXL7+kLCws7KrrEZhMJl5//XVef/31ggqxSB07nc0HsbsBeL5z2N//H276DM6eAu9qUP9BO0YoIiJlwQ1NL7/ZhVrMZjM1atSgUaNGPPvsszz44INER0dfsX5xXIRFRORaQn3d+fChxvw0rDWtaviQcyGPKcv30P7dFcxZl6SVzkWK2LtLEsjMvkCDSp48eFul/MIL2fDnR/nPW40As7b+ExGRwnVDSfetLNTyT3l5eTbTx/9XcVyERUTketUL9uTLJ5rzad8Iqvq6czwzmxe+38rdH67kq7VJnMzKsXeIIqXe1kPpfLMx/0v7cd3qYjb/d2G0zXPg9BHwCISGve0YoYiIlBU3PL185MiR9OvXj4iICJo1a8akSZMuWaglODjYeiU7OjqaiIgIqlevTnZ2NosWLeKLL75gypQpBftJRESKEZPJRFS4P7fXqsjnq/czOTaRHUczeHH+Vsb+ZxutavjSrWEQHev6U15Tz0UKlGEYvPZjPIYB3RsF0aRKhfwDebmwalL+88ih4OBstxhFRKTsuOGk+0YXasnKymLw4MEcOnQIV1dXateuzZdffkmvXlq0RERKPycHM0+2qcb9t1Ximw0H+XHzEeKPZLBi1zFW7DqG5TsT9YLK0zTUm2ZVvWka6k0Fdyd7hy1Soi3YfIQNB07h6mhhdJfafx/Y/gOc3AuuFaDJY/YKT0REyhiTcbUVVIqJjIwMPD09SU9P11RzESnx9hzL5KfNR1mw+TB7jmVdcrxZqDcvda1DwxCvog9OilxpHeNiYmKIiYkhNzeXXbt2Fennu3PiChJTM3n2zloM61Azv9AwYGobSNkK7cZAuxeKJBYRkZLuP//5z2W3irRYLDz00EN88cUXRR9UMXG9Y3iRrF4uIiJ/q16xHMOjajI8qiaH086yft9J1u0/yfp9J0lMzWTd/pPcG7OKB5tU4vnOYfh5lJztmUQuGjJkCEOGDLH+QVJUDpzIIjE1Ewezib4tQ/8+sHtpfsLt6A7NBhZZPCIiIkq6RUTsKNjLleDGwXRvHAzA0fSzvLMkge83HebbjYdYvC2ZYXfU4P7bKlHBzREHyw2tfylS5izbmQpARGgFPF3/sV7CH+/l/xvRH9y87RCZiIiUVcU66f7n1DQRkbIg0NOViT0b8UiLKry2IJ7Nh9KJ/nkn0T/vxGSCCm5O+Lg74VvOmVBfN6pXLEf1iuWoVtGdShXcsFxcoVmkjLqYdN9R2+/vwuStkLQazI75C6iJiIgUoWKddNtrapqIiL3dVrkC8we34rtNh/hw2W4OnjqDYcDJrBxOZuWQmJrJ6r0nbM7xcnPknQcbcme4v52iFrGvrOwLrN17EvifpDvu6/x/w7pA+UA7RCYiUvrk5eXZO4QSo1gn3SIiZZnZbKJHRAg9IkLIzTM4dSaHE5k5nMjMJuX0OfYdy2LP8Sz2pGay93gWaWfOM/CLDbzcNZzHW4ViMumqt5Qtq3YfJyc3jxBvV6pXLJdfmHsetszNf96oj/2CExEpoWrVqkX16tU5c+aMTbnJZKJFixZ2iqpkUdItIlICWMwmfMs541vOGfC45Pj53DzGLYjnq7VJvPHTdvYfz2Jct3DdAy5lym8J+VPLO9T2//tLp8Rf4cxxcPeDGh3sGJ2ISMlUp04ddu/ebe8wSjT9NSYiUgo4Wsy81b0eL91VB5MJvlhzgCc/38Dpc+ftHZpIkTAMw3o/d3ubqeWz8/9t0BMsjpc5U0REpHAp6RYRKSVMJhMDbq/G1Eea4OJoZnnCMe79aBXzNhwk+4IWpJTSLf5IBikZ2bg6Wmhe9b+rk2cdh12L8583eth+wYmISJmmpFtEpJTpVDeAb/4vkooezuw9nsVz326h1fjfmLw0keOZ2fYOT8qImJgYwsPDadq0aZG832//vcrdqoYvLo6W/MKt30LeBQhsBP51iyQOERGR/6V7ukVESqEGlbxY+kxbvlqXxGd/7ic54xzvL91FzPLdNArxwsfdiQru+duPebs70aZmRWr4lbN32FKKFPUOJMsSLrNVWNyX+f9qATUREbGjYp10a59uEZGb5+nmyKB21XmyTVV+3pbM9JX72HwwjXX7Tl62fotq3jzcvAqd6vrj7GAp4mhFbt6JzGziDqYB0L52xfzCo1vy9+c2O0L9B+0XnIiIlHnFOunWPt0iIrfO0WLmnoZBdGsQSPyRDPYdz+LUmRzrnt/7T5xhZeIx1uw9yZq9J/Fxd+LBiEo80rwKId5u9g5f5JpW7DqGYUB4YHkCPV3zCzf/Y29uN2/7BSciImVesU66RUSk4JhMJuoFe1Iv+NIvMY+knWXO+oPMXZ9ESkY2/16xl09+30v7MD8ejaxC25oVMZu177cUTxdXLbdOLb+Qo725RaTMeeyxx0hLS+OHH36wdygFbvny5bRv355Tp07h5eVl73BumBZSExERgrxcGXlnLVaNvoNPHm1Cm5q+GEZ+MtN/5nrav7ecqSv2cOBElr1DFbFxPjePFbuOAf/YKmz3r3DmxH/35o6yY3QiIrYee+wxTCbTJY/OnTvbO7RrMplMdkvoW7ZsydGjR0vs7Gdd6RYRESsHi5mOdQPoWDeAvccy+XJNEvM2HuTAiTOM/3kn43/eSS3/ctwZ7s+d4QE0CPbUFXCxq40HTnH63AW83Z1oFOKVX/jXfxdQa9gLLPpTR0SKl86dOzNz5kybMmdn55tuLzc3F5OpdI/FTk5OBAQE2DuMm6Yr3SIiclnVKpZjbLdw1r7Ygej769Oyug8Ws4ldKZnE/LaH7jGrqPfqEjpP+p2Bn2/g7UU7mL32ACkZ5+wdupQhv/131fK2tSpiMZvgeCIk/Jx/sNEjdoxMROTynJ2dCQgIsHlUqFDBenzixInUr18fd3d3QkJCGDx4MJmZmdbjs2bNwsvLiwULFhAeHo6zszNJSUk27/H555/j4+NDdrbtVqHdu3fn0UcfvWxcOTk5DB06lMDAQFxcXKhSpQrR0dEAhIaGAnDfffdhMpmsrwGmTJlC9erVcXJyIiwsjC+++MKmXZPJxJQpU+jSpQuurq5Uq1aNb7/91np8//79mEwm5syZQ8uWLXFxcaFevXqsWLHCWmf58uWYTCbS0tJs+mDJkiXUqVOHcuXK0blzZ44ePWo958KFCzz99NN4eXnh4+PD6NGj6devH927d7/CT6bwKOkWEZGrcnNy4KFmlflqQAs2vXwnk3o1omv9QNydLJzJyWVn8ml+2Z7CJ7/v5aX527hz4gqWxCfbO2wpIy6uxt+6hm9+wcpJgAG1uoBfbbvFJSJys8xmMx988AHx8fF89tlnLFu2jOeff96mzpkzZ5gwYQKffvop8fHx+Pn52Rzv0aMHubm5LFiwwFqWmprKwoULefzxxy/7vh988AELFizgm2++ISEhgdmzZ1uT6/Xr1wMwc+ZMjh49an09f/58hg8fzrPPPsu2bdv4v//7P/r3789vv/1m0/Yrr7zCAw88wObNm+nTpw+9e/dmx44dNnWee+45nn32Wf766y8iIyPp1q0bJ06cuGI/nTlzhnfffZcvvviC33//naSkJEaNGmU9PmHCBGbPns3MmTNZtWoVGRkZ9rvf3SgB0tPTDcBIT0+3dygiIvJfORdyjT2pp41lO1OMWav2Ga8u2GZ0en+FUWX0T0aV0T8Zry7YZpw7f8HeYRZ7pX2MK8zPdzbnglHjxYVGldE/GQeOZxlG2kHDeM3bMMaVN4yktQX+fiIit6pfv36GxWIx3N3dbR5vvfXWFc+ZN2+e4ePjY309c+ZMAzDi4uIuafvee++1vh40aJDRpUsX6+v33nvPqFatmpGXl3fZ9xk2bJhxxx13XPE4YMyfP9+mrGXLlsaAAQNsynr06GHcddddNuc99dRTNnWaN29uDBo0yDAMw9i3b58BGOPHj7ceP3/+vFGpUiVjwoQJhmEYxm+//WYAxqlTp2z6YPfu3dZzYmJiDH9/f+trf39/45133rG+vnDhglG5cmWbPrpV1zvGFesbnbRPt4hI8eVoMVOtYjmqVSxnLcu5kMc7S3Yy7Y99zFy1nw37T/HRw42p4uNux0jFHopiDN9yKJ3zuQYVPZwJ8XaFxR9B3gUIbQMhzQrtfUVEbkX79u2ZMmWKTZm3999bGy5dupTo6Gh27txJRkYGFy5c4Ny5c5w5cwY3t/ytPJ2cnGjQoMFV32fAgAE0bdqUw4cPExwczKxZs6wLuV3OY489xp133klYWBidO3fm7rvvpmPHjld9jx07djBw4ECbslatWjF58mSbssjIyEtex8XFXbGOg4MDERERl1wN/yc3NzeqV69ufR0YGEhqav4tR+np6aSkpNCs2d9jgcVioUmTJuTl5V31MxWGYp10a59uEZGSxcnBzEtdw2le1YdR325m6+F07pr8B7UCPHB2MOPiaMHZwYy3uzNd6wfSsrqPFmIrpYpiDN944BQAEVUqYDpzAjbOyj/Q+plCeT8RkYLg7u5OjRo1Lnts//793H333QwaNIi33noLb29vVq5cyRNPPEFOTo416XZ1db3m4mmNGzemYcOGfP7553Ts2JH4+HgWLlx4xfq33XYb+/bt4+eff2bp0qX07NmTqKgom/uvixNHR0eb1yaTCcMw7BTN1emebhERKXBR4f4seroNEVUqkJWTy19JaazZe5LlCcdYEp/C1+uSeGT6Wtr86zcm/rqLgyfP2DtkKYE2Hsi/n7tJlQqwdipcOAuBjaD6HfYNTETkJm3cuJG8vDzee+89WrRoQa1atThy5MhNt/fkk08ya9YsZs6cSVRUFCEhIVetX758eXr16sW0adOYO3cu3333HSdP5v+udXR0vGT2Up06dVi1apVN2apVqwgPD7cpW7NmzSWv69Spc8U6Fy5cYOPGjZfUuV6enp74+/tb7z2H/FXeN23adFPt3apifaVbRERKriAvV+YMbMGmpDTSz57n3Plcsi/kce58LjuTM1gQd4TDaWf5IDaRD2ITaV3Dl8HtqhNZ3afUb30it84wDOuV7qaBDvDtJ/kH2owE/fcjIsVYdnY2ycm2C446ODjg6+tLjRo1OH/+PB9++CHdunVj1apVTJ069abf6+GHH2bUqFFMmzaNzz///Kp1J06cSGBgII0bN8ZsNjNv3jwCAgLw8vIC8lcwj42NpVWrVjg7O1OhQgWee+45evbsSePGjYmKiuLHH3/k+++/Z+nSpTZtz5s3j4iICFq3bs3s2bNZt24d06dPt6kTExNDzZo1qVOnDu+//z6nTp264qJv12PYsGFER0dTo0YNateuzYcffsipU6fs8jeGkm4RESk0DhYzzap6X/bYy13D+WV7CvM2HGTl7uPWR5MqFRh2Rw3a1qqo5FuuaM+xLE6dOY+zg5m6R76Dc+ngUxNqd7N3aCIiV7V48WICAwNtysLCwti5cycNGzZk4sSJTJgwgTFjxnD77bcTHR1N3759b+q9PD09eeCBB1i4cOE1t8ry8PDgX//6F4mJiVgsFpo2bcqiRYswm/MnR7/33nuMHDmSadOmERwczP79++nevTuTJ0/m3XffZfjw4VStWpWZM2fSrl07m7Zfe+015syZw+DBgwkMDOTrr7++5Gr4+PHjGT9+PHFxcdSoUYMFCxbg6+t7U58bYPTo0SQnJ9O3b18sFgsDBw6kU6dOWCyWm27zZpmM4jrx/R8u3g+Wnp5O+fLl7R2OiIgUsIMnzzDtj73MWX+QnAv5C5w0qOTJ462q0qGOHx4ujtdooeQq7WNcYX2+ueuTGP3dVlqFlmN25gDITIF7P4bGfQrsPURESoMOHTpQt25dPvjgA7u8v8lkYv78+VdM+vfv30/VqlX566+/aNSoUaHFkZeXR506dejZsydvvPFGgbR5vWOcrnSLiIjdhXi78fq99Rjavgaf/L6X2WuT2HIonRFz43CymGld05fOdQO4M9yfCu5O9g5XioEN+/Onlvd1XQXJKVC+EtTvYeeoRESKj1OnTrF8+XKWL1/Oxx9/bO9wityBAwf45ZdfaNu2LdnZ2Xz00Ufs27ePhx9+uMhjUdItIiLFhl95F16+O5xB7arz+eoD/LTlCHuOZbFsZyrLdqZimW8iqo4fT7SuRtPQCpp+XoZdvJ+75akF+QWRQ8BBX8iIiFzUuHFjTp06xYQJEwgLC7N3OEXObDYza9YsRo0ahWEY1KtXj6VLl9704my3QtPLRUSkWEtMOc3ibcn8vC2Z7UczrOX1gz15vHUoXesH4eRQcjfjKO1jXGF8vhOZ2TR5cynhpv0scn4RLE7wbAK4XX79ABERkcJQKqaXx8TEEBMTc8nS9CIiUnbU9Pegpr8HwzrUZFfKaWau2s/3mw6x9XA6z8zdzNuLdtKhth+316pIq+q+eLqV3vu/S5LCHMMvXuV+wmMN5ABhXZRwi4hIsaUr3SIiUuKczMrhq7UH+Hz1AVJPZ1vLzSZoGOJF6xq+NK7sRcNKXviUc7ZjpNdW2se4wvh80Yt2MOP3XfzlPoxyuenw8DdQq1OBtC0iInK9SsWVbhERkcvxdndi6B01GXh7dVbtOc4fu47ze+Ixdqdm8ldSGn8lpVnrVvZ2o2GIF21q+nJPwyBcHIt+qxApWBsOnKKdOS4/4Xb3g+od7B2SiEix9Oqrr/LDDz8QFxdn71Bu2qxZsxgxYgRpaWn2DuWm3dRNcDExMYSGhuLi4kLz5s1Zt27dFetOmzaNNm3aUKFCBSpUqEBUVNRV64uIiFwvJwcz7cP8GNstnKUj27LqhTsYf3997r8tmGoV3QFIOnmGHzcf4flvt9AiOpboRTs4ePKMnSOXm3XufC5bD6XzoOX3/IKGvcCiawgiUra0a9eOESNGXLPeqFGjiI2NLfyA5KpueJSaO3cuI0eOZOrUqTRv3pxJkybRqVMnEhIS8PPzu6T+8uXLeeihh2jZsiUuLi5MmDCBjh07Eh8fT3BwcIF8CBEREYBgL1d6N6tM72aVAUg/c57Nh9LYeOAU3206xKFTZ/n373v55I+9dKjtz/23BdOqhi+erroPvKTYdjidcrlpdHD8K7+gYdFv/SIiUtwZhkFubi7lypWjXLly9g6H3NxcTCYTZnPJXfj0Vtzwp544cSIDBgygf//+hIeHM3XqVNzc3JgxY8Zl68+ePZvBgwfTqFEjateuzaeffkpeXp6+cRERkULn6ebI7bUq8sydtVjxXHum9Y2gTU1fDAOW7khh8OxN3PbGr/SY+icxv+1m2+F0SsBSJ1d0IzPRANLS0hgyZAiBgYE4OztTq1YtFi1aZD3+6quvYjKZbB61a9cu7I9xVRsPnOJeyyocyIWgxuAfbtd4RESK2mOPPcaKFSuYPHmy9XfzrFmzMJlM/PzzzzRp0gRnZ2dWrlzJq6++SqNGjaznXrhwgaeffhovLy98fHwYPXo0/fr1o3v37tY6p0+fpk+fPri7uxMYGMj7779/yZX17OxsRo0aRXBwMO7u7jRv3pzly5dbj8+aNQsvLy8WLFhAeHg4zs7OJCUlXfO8i+dWrlwZNzc37rvvPk6cOFE4HVmEbijpzsnJYePGjURFRf3dgNlMVFQUq1evvq42zpw5w/nz5/H21iqjIiJSdCxmE3eG+/PFE81ZOrItT7auSvWK7uTmGazff4p3liRw94craREdy4vzt7JsZwrnzpec3TMuzkQbN24cmzZtomHDhnTq1InU1NTL1s/JyeHOO+9k//79fPvttyQkJDBt2rRLZqHVrVuXo0ePWh8rV64sio9zRRsOnPp7anmjPnaNRUTEHiZPnkxkZCQDBgyw/m4OCQkB4IUXXmD8+PHs2LGDBg0aXHLuhAkTmD17NjNnzmTVqlVkZGTwww8/2NQZOXIkq1atYsGCBfz666/88ccfbNq0yabO0KFDWb16NXPmzGHLli306NGDzp07k5iYaK1z5swZJkyYwKeffkp8fDx+fn7XPG/t2rU88cQTDB06lLi4ONq3b8+bb75ZwD1Y9G5oevnx48fJzc3F39/fptzf35+dO3deVxujR48mKCjIJnH/X9nZ2WRn/70abUZGxhXrioiI3KgafuV4+e5wXr47nIMnz7Bi1zFW7DrGqt3HScnI5qu1SXy1NglXRwtta1VkcPvqNKjkZe+wr+qfM9EApk6dysKFC5kxYwYvvPDCJfVnzJjByZMn+fPPP3F0zJ9eHxoaekk9BwcHAgICCjX262UYBpn7N1HXfIA8sxPmeg/YOyQRkSLn6emJk5MTbm5u1t/PF3Ox119/nTvvvPOK53744YeMGTOG++67D4CPPvrIZobT6dOn+eyzz/jqq6/o0CF/kcqZM2cSFBRkrZOUlMTMmTNJSkqylo8aNYrFixczc+ZM3n77bQDOnz/Pxx9/TMOGDa/7vMmTJ9O5c2eef/55AGrVqsWff/7J4sWLb73j7KhIJ9WPHz+eOXPmMH/+fFxcXK5YLzo6Gk9PT+vj4jc3IiIiBS3E241HWlRhWt8I/hp7J5893oxHW1QhyNOFs+dzWRyfzD0frWLI7E3sPZZp73Av62Zmoi1YsIDIyEiGDBmCv78/9erV4+23375kX+3ExESCgoKoVq0affr0ISkpqVA/y9XsO55FVM6y/Bfam1tE5BIRERFXPJaenk5KSgrNmjWzllksFpo0aWJ9vXfvXs6fP29Tx9PTk7CwMOvrrVu3kpubS61ataz3jJcrV44VK1awZ88eaz0nJyebq+3Xc96OHTto3ry5TdyRkZE30RPFyw1d6fb19cVisZCSkmJTnpKScs1vwd99913Gjx/P0qVLLzvV4Z/GjBnDyJEjra8zMjKUeIuISKFzdsi/st22VkVev7cu249mMP2PfcyPO8zCrUdZHJ9Mz4gQRkTVxL/8lb88Lmo3MxNt7969LFu2jD59+rBo0SJ2797N4MGDOX/+POPGjQOgefPmzJo1i7CwMI4ePcprr71GmzZt2LZtGx4eHpdttzBnq23al0p3S/70dnNjTS0XEflf7u7uhf4emZmZWCwWNm7ciMViuw3nPxdtc3V1xWQy3fB5pdENXel2cnKiSZMmNougXVwU7WrfQPzrX//ijTfeYPHixVf99uUiZ2dnypcvb/MQEREpSiaTibpBnkzs1YhFT7ehQ20/cvMMvl6XRNt3fmPdvpP2DvGW5OXl4efnxyeffEKTJk3o1asXL730ElOnTrXW6dKlCz169KBBgwZ06tSJRYsWkZaWxjfffHPFdgtztlp45lp8TKfJcvLV3twiUqY5OTldMjPpWjw9PfH392f9+vXWstzcXJv7tatVq4ajo6NNnfT0dHbt2mV93bhxY3Jzc0lNTaVGjRo2j6tdiL2e8+rUqcPatWttzluzZs0Nfc7i6Ia3DBs5ciT9+vUjIiKCZs2aMWnSJLKysqz3kPXt25fg4GCio6OB/Jv1x44dy1dffUVoaCjJyckAxWb5ehERkWupE1ie6Y81Zd2+k0xYvJOjaWdpUMnT3mFZ3cxMtMDAQBwdHW2uNtSpU4fk5GRycnJwcnK65BwvLy9q1arF7t27rxhLYc5WC6+QC24+uDd6WHtzi0iZFhoaytq1a9m/fz/lypUjLy/vus4bNmwY0dHR1KhRg9q1a/Phhx9y6tQp6xVpDw8P+vXrx3PPPYe3tzd+fn6MGzcOs9lsrVOrVi369OlD3759ee+992jcuDHHjh0jNjaWBg0a0LVr18u+9/Wc9/TTT9OqVSveffdd7r33XpYsWVLi7+eGm7inu1evXrz77ruMHTuWRo0aERcXx+LFi61T2pKSkjh69Ki1/pQpU8jJyeHBBx8kMDDQ+nj33XcL7lOIiIgUgWZVvfn2qUi+H9wKF0fLtU8oIjczE61Vq1bs3r3b5g+1Xbt2ERgYeNmEG/KnBu7Zs4fAwMArxlKos9UaPwIjd8LtowquTRGREmjUqFFYLBbCw8OpWLHida+3MXr0aB566CH69u1LZGQk5cqVo1OnTjbrbU2cOJHIyEjuvvtuoqKiaNWqFXXq1LGpM3PmTPr27cuzzz5LWFgY3bt3Z/369VSuXPmq73+t81q0aMG0adOYPHkyDRs25JdffuHll1++iR4qXkxGCdiQNCMjA09PT9LT0zXVXERESpWCGuPmzp1Lv379+Pe//22difbNN9+wc+dO/P39L5mJdvDgQerWrUu/fv0YNmwYiYmJPP744zz99NO89NJLQP4fdd26daNKlSocOXKEcePGERcXx/bt26lYsWKRfj4RESl4eXl51KlTh549e/LGG29ctk5WVhbBwcG89957PPHEE0UcYfF2vWOc5maJiIiUAr169eLYsWOMHTuW5ORkGjVqdMlMNLP57wluISEhLFmyhGeeeYYGDRoQHBzM8OHDGT16tLXOoUOHeOihhzhx4gQVK1akdevWrFmz5roTbhERKV4OHDjAL7/8Qtu2bcnOzuajjz5i3759PPzww9Y6f/31Fzt37qRZs2akp6fz+uuvA3DvvffaK+wST1e6RURE7Ki0j3Gl/fOJiJQkBw8epHfv3mzbtg3DMKhXrx7jx4/n9ttvt9b566+/ePLJJ0lISLDevjRx4kTq169vx8iLp1JxpTsmJoaYmJgbXplPREREREREbIWEhLBq1aqr1mncuDEbN24soojKhhteSK0oDRkyhO3bt9ssWS8iIiIiIiJSUhTrpFtERERERESkJFPSLSIiIgUuJiaG8PBwmjZtau9QRERE7EpJt4iIiBQ43SImIiKST0m3iIiIiIiISCFR0i0iIiIiIiJSSJR0i4iIiIiIiBSSErFP94ULF4D8zcdFRERKk4tjm2EYdo6kcFz8XBrDRUSktLneMdxklIBR/tChQ4SEhNg7DBERkUJz8OBBKlWqZO8wCpzGcBERKe2uNYaXiKQ7Ly+PI0eO4OHhgclkuuX2MjIyCAkJ4eDBg5QvX74AIrxU06ZNC3XF1sJsvzDbVt/bt331v/3aV9/bt/3i3P+GYXD69GmCgoIwm0vfXV8aw4uu7cJuv6T3fUlvX/1vv7bV9/Ztvzj3//WO4cV6evlFZrO5UL79L1++fKH94CwWS6G1XdjtF3bsoL63Z/ug/rdn++p7+7UPxbf/PT09Czia4kNjeNG1XRTtQ8nt+9LQPqj/7dH2Rep7+7UPxbf/r2cML31fqRcTQ4YMKbHtF3bsha0k931RtF/YSnr/lOT+L+l9U5L7Hkp+/PK3kjzGlvT/Dkt6/6j/7de++r50t1/YCjv+EjG9vKBlZGTg6elJenp6oX8jI7bU9/al/rcf9b19qf9LD/0s7Ud9b1/qf/tR39tXaej/Mnml29nZmXHjxuHs7GzvUMoc9b19qf/tR31vX+r/0kM/S/tR39uX+t9+1Pf2VRr6v0xe6RYREREREREpCmXySreIiIiIiIhIUVDSLSIiIiIiIlJIlHSLiIiIiIiIFJIyl3THxMQQGhqKi4sLzZs3Z926dfYOqVSKjo6madOmeHh44OfnR/fu3UlISLCpc+7cOYYMGYKPjw/lypXjgQceICUlxU4Rl17jx4/HZDIxYsQIa5n6vnAdPnyYRx55BB8fH1xdXalfvz4bNmywHjcMg7FjxxIYGIirqytRUVEkJibaMeLSITc3l1deeYWqVavi6upK9erVeeONN/jn0iXq+5JNY3jh0/hdvGgML1oav+2n1I/hRhkyZ84cw8nJyZgxY4YRHx9vDBgwwPDy8jJSUlLsHVqp06lTJ2PmzJnGtm3bjLi4OOOuu+4yKleubGRmZlrrPPXUU0ZISIgRGxtrbNiwwWjRooXRsmVLO0Zd+qxbt84IDQ01GjRoYAwfPtxarr4vPCdPnjSqVKliPPbYY8batWuNvXv3GkuWLDF2795trTN+/HjD09PT+OGHH4zNmzcb99xzj1G1alXj7Nmzdoy85HvrrbcMHx8f46effjL27dtnzJs3zyhXrpwxefJkax31fcmlMbxoaPwuPjSGFy2N3/ZV2sfwMpV0N2vWzBgyZIj1dW5urhEUFGRER0fbMaqyITU11QCMFStWGIZhGGlpaYajo6Mxb948a50dO3YYgLF69Wp7hVmqnD592qhZs6bx66+/Gm3btrUO2Or7wjV69GijdevWVzyel5dnBAQEGO+88461LC0tzXB2dja+/vrrogix1Oratavx+OOP25Tdf//9Rp8+fQzDUN+XdBrD7UPjt31oDC96Gr/tq7SP4WVmenlOTg4bN24kKirKWmY2m4mKimL16tV2jKxsSE9PB8Db2xuAjRs3cv78eZufR+3atalcubJ+HgVkyJAhdO3a1aaPQX1f2BYsWEBERAQ9evTAz8+Pxo0bM23aNOvxffv2kZycbNP/np6eNG/eXP1/i1q2bElsbCy7du0CYPPmzaxcuZIuXboA6vuSTGO4/Wj8tg+N4UVP47d9lfYx3MHeARSV48ePk5ubi7+/v025v78/O3futFNUZUNeXh4jRoygVatW1KtXD4Dk5GScnJzw8vKyqevv709ycrIdoixd5syZw6ZNm1i/fv0lx9T3hWvv3r1MmTKFkSNH8uKLL7J+/XqefvppnJyc6Nevn7WPL/e7SP1/a1544QUyMjKoXbs2FouF3Nxc3nrrLfr06QOgvi/BNIbbh8Zv+9AYbh8av+2rtI/hZSbpFvsZMmQI27ZtY+XKlfYOpUw4ePAgw4cP59dff8XFxcXe4ZQ5eXl5RERE8PbbbwPQuHFjtm3bxtSpU+nXr5+doyvdvvnmG2bPns1XX31F3bp1iYuLY8SIEQQFBanvRW6Cxu+ipzHcfjR+21dpH8PLzPRyX19fLBbLJas7pqSkEBAQYKeoSr+hQ4fy008/8dtvv1GpUiVreUBAADk5OaSlpdnU18/j1m3cuJHU1FRuu+02HBwccHBwYMWKFXzwwQc4ODjg7++vvi9EgYGBhIeH25TVqVOHpKQkAGsf63dRwXvuued44YUX6N27N/Xr1+fRRx/lmWeeITo6GlDfl2Qaw4uexm/70BhuPxq/7au0j+FlJul2cnKiSZMmxMbGWsvy8vKIjY0lMjLSjpGVToZhMHToUObPn8+yZcuoWrWqzfEmTZrg6Oho8/NISEggKSlJP49b1KFDB7Zu3UpcXJz1ERERQZ8+fazP1feFp1WrVpdsr7Nr1y6qVKkCQNWqVQkICLDp/4yMDNauXav+v0VnzpzBbLYd1iwWC3l5eYD6viTTGF50NH7bl8Zw+9H4bV+lfgy390puRWnOnDmGs7OzMWvWLGP79u3GwIEDDS8vLyM5OdneoZU6gwYNMjw9PY3ly5cbR48etT7OnDljrfPUU08ZlStXNpYtW2Zs2LDBiIyMNCIjI+0Yden1z5VPDUN9X5jWrVtnODg4GG+99ZaRmJhozJ4923BzczO+/PJLa53x48cbXl5exn/+8x9jy5Ytxr333ltitrwozvr162cEBwdbtxv5/vvvDV9fX+P555+31lHfl1waw4uGxu/iR2N40dD4bV+lfQwvU0m3YRjGhx9+aFSuXNlwcnIymjVrZqxZs8beIZVKwGUfM2fOtNY5e/asMXjwYKNChQqGm5ubcd999xlHjx61X9Cl2P8O2Or7wvXjjz8a9erVM5ydnY3atWsbn3zyic3xvLw845VXXjH8/f0NZ2dno0OHDkZCQoKdoi09MjIyjOHDhxuVK1c2XFxcjGrVqhkvvfSSkZ2dba2jvi/ZNIYXPo3fxY/G8KKj8dt+SvsYbjIMw7DPNXYRERERERGR0q3M3NMtIiIiIiIiUtSUdIuIiIiIiIgUEiXdIiIiIiIiIoVESbeIiIiIiIhIIVHSLSIiIiIiIlJIlHSLiIiIiIiIFBIl3SIiIiIiIiKFREm3iIiIiIiISCFR0i0iN81kMvHDDz/YOwwRERG5QRrDRYqOkm6REuqxxx7DZDJd8ujcubO9QxMREZGr0BguUrY42DsAEbl5nTt3ZubMmTZlzs7OdopGRERErpfGcJGyQ1e6RUowZ2dnAgICbB4VKlQA8qeNTZkyhS5duuDq6kq1atX49ttvbc7funUrd9xxB66urvj4+DBw4EAyMzNt6syYMYO6devi7OxMYGAgQ4cOtTl+/Phx7rvvPtzc3KhZsyYLFiywHjt16hR9+vShYsWKuLq6UrNmzUv+wBARESmLNIaLlB1KukVKsVdeeYUHHniAzZs306dPH3r37s2OHTsAyMrKolOnTlSoUIH169czb948li5dajMgT5kyhSFDhjBw4EC2bt3KggULqFGjhs17vPbaa/Ts2ZMtW7Zw11130adPH06ePGl9/+3bt/Pzzz+zY8cOpkyZgq+vb9F1gIiISAmlMVykFDFEpETq16+fYbFYDHd3d5vHW2+9ZRiGYQDGU089ZXNO8+bNjUGDBhmGYRiffPKJUaFCBSMzM9N6fOHChYbZbDaSk5MNwzCMoKAg46WXXrpiDIDx8ssvW19nZmYagPHzzz8bhmEY3bp1M/r3718wH1hERKSU0BguUrbonm6REqx9+/ZMmTLFpszb29v6PDIy0uZYZGQkcXFxAOzYsYOGDRvi7u5uPd6qVSvy8vJISEjAZDJx5MgROnTocNUYGjRoYH3u7u5O+fLlSU1NBWDQoEE88MADbNq0iY4dO9K9e3datmx5U59VRESkNNEYLlJ2KOkWKcHc3d0vmSpWUFxdXa+rnqOjo81rk8lEXl4eAF26dOHAgQMsWrSIX3/9lQ4dOjBkyBDefffdAo9XRESkJNEYLlJ26J5ukVJszZo1l7yuU6cOAHXq1GHz5s1kZWVZj69atQqz2UxYWBgeHh6EhoYSGxt7SzFUrFiRfv368eWXXzJp0iQ++eSTW2pPRESkLNAYLlJ66Eq3SAmWnZ1NcnKyTZmDg4N1oZN58+YRERFB69atmT17NuvWrWP69OkA9OnTh3HjxtGvXz9effVVjh07xrBhw3j00Ufx9/cH4NVXX+Wpp57Cz8+PLl26cPr0aVatWsWwYcOuK76xY8fSpEkT6tatS3Z2Nj/99JP1DwYREZGyTGO4SNmhpFukBFu8eDGBgYE2ZWFhYezcuRPIX5V0zpw5DB48mMDAQL7++mvCw8MBcHNzY8mSJQwfPpymTZvi5ubGAw88wMSJE61t9evXj3PnzvH+++8zatQofH19efDBB687PicnJ8aMGcP+/ftxdXWlTZs2zJkzpwA+uYiISMmmMVyk7DAZhmHYOwgRKXgmk4n58+fTvXt3e4ciIiIiN0BjuEjponu6RURERERERAqJkm4RERERERGRQqLp5SIiIiIiIiKFRFe6RURERERERAqJkm4RERERERGRQqKkW0RERERERKSQKOkWERERERERKSRKukVEREREREQKiZJuERERERERkUKipFtERERERESkkCjpFhERERERESkkSrpFRERERERECsn/A3lqk/FfUIcDAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 2h 26min 30s, sys: 2min 7s, total: 2h 28min 38s\n", + "Wall time: 2h 59min 55s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ], + "metadata": { + "id": "RWIlphVaaPtL", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "73f6074b-627f-44e2-8cba-cc0c8f9cc7f5" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 8s 131ms/step - loss: 0.6463 - accuracy: 0.7895\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 10s 159ms/step - loss: 0.6539 - accuracy: 0.7779\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[0.6539287567138672, 0.7779448628425598]" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (MobileNetV2, no aug, fine-tuning)')\n", + "plt.show()" + ], + "metadata": { + "id": "p3uc_i5vaSbF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2. MobileNetV2, with data augmentation" + ], + "metadata": { + "id": "lYbrD1J0c1nr" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Feature extraction phase:**" + ], + "metadata": { + "id": "WKXyHFt_c1nt" + } + }, + { + "cell_type": "code", + "source": [ + "keras.backend.clear_session()\n", + "preprocess_input = mobilenet_v2.preprocess_input\n", + "base_model = mobilenet_v2.MobileNetV2(input_shape=(img_size, img_size, 3), include_top=False)\n", + "base_model.trainable = False\n", + "\n", + "inputs = Input(shape=(48, 48, 3))\n", + "x = Resizing(img_size, img_size, interpolation='lanczos5', crop_to_aspect_ratio=True)(inputs)\n", + "x = preprocess_input(x)\n", + "x = data_augmentation(x)\n", + "x = base_model(x, training=False)\n", + "x = GlobalAveragePooling2D()(x)\n", + "# x = Dropout(DROPOUT)(x)\n", + "# x = Dense(64, activation='relu')(x)\n", + "x = Dropout(DROPOUT)(x)\n", + "outputs = Dense(4, activation='softmax')(x)\n", + "\n", + "model = keras.Model(inputs, outputs)\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ], + "metadata": { + "id": "echjGpFrc1nt" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "history = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history)" + ], + "metadata": { + "id": "-3GjlXxWc1nu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1effd131-d20a-4288-838b-729fe5335fe2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "505/505 [==============================] - 85s 162ms/step - loss: 1.6282 - accuracy: 0.2969 - val_loss: 1.4093 - val_accuracy: 0.3185\n", + "Epoch 2/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.5143 - accuracy: 0.3308 - val_loss: 1.3624 - val_accuracy: 0.3502\n", + "Epoch 3/100\n", + "505/505 [==============================] - 83s 164ms/step - loss: 1.4588 - accuracy: 0.3478 - val_loss: 1.3199 - val_accuracy: 0.3847\n", + "Epoch 4/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.4205 - accuracy: 0.3724 - val_loss: 1.2889 - val_accuracy: 0.4126\n", + "Epoch 5/100\n", + "505/505 [==============================] - 81s 160ms/step - loss: 1.3824 - accuracy: 0.3902 - val_loss: 1.2643 - val_accuracy: 0.4371\n", + "Epoch 6/100\n", + "505/505 [==============================] - 84s 166ms/step - loss: 1.3467 - accuracy: 0.4029 - val_loss: 1.2441 - val_accuracy: 0.4465\n", + "Epoch 7/100\n", + "505/505 [==============================] - 80s 158ms/step - loss: 1.3248 - accuracy: 0.4157 - val_loss: 1.2300 - val_accuracy: 0.4521\n", + "Epoch 8/100\n", + "505/505 [==============================] - 83s 164ms/step - loss: 1.3010 - accuracy: 0.4296 - val_loss: 1.2088 - val_accuracy: 0.4710\n", + "Epoch 9/100\n", + "505/505 [==============================] - 82s 163ms/step - loss: 1.2780 - accuracy: 0.4422 - val_loss: 1.1934 - val_accuracy: 0.4800\n", + "Epoch 10/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.2631 - accuracy: 0.4497 - val_loss: 1.1848 - val_accuracy: 0.4861\n", + "Epoch 11/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.2464 - accuracy: 0.4591 - val_loss: 1.1750 - val_accuracy: 0.4900\n", + "Epoch 12/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.2406 - accuracy: 0.4661 - val_loss: 1.1675 - val_accuracy: 0.4955\n", + "Epoch 13/100\n", + "505/505 [==============================] - 84s 166ms/step - loss: 1.2188 - accuracy: 0.4744 - val_loss: 1.1565 - val_accuracy: 0.4955\n", + "Epoch 14/100\n", + "505/505 [==============================] - 82s 163ms/step - loss: 1.2147 - accuracy: 0.4746 - val_loss: 1.1471 - val_accuracy: 0.5033\n", + "Epoch 15/100\n", + "505/505 [==============================] - 80s 158ms/step - loss: 1.1880 - accuracy: 0.4870 - val_loss: 1.1454 - val_accuracy: 0.5028\n", + "Epoch 16/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.1922 - accuracy: 0.4896 - val_loss: 1.1373 - val_accuracy: 0.5050\n", + "Epoch 17/100\n", + "505/505 [==============================] - 82s 163ms/step - loss: 1.1806 - accuracy: 0.4918 - val_loss: 1.1296 - val_accuracy: 0.5111\n", + "Epoch 18/100\n", + "505/505 [==============================] - 83s 164ms/step - loss: 1.1726 - accuracy: 0.5000 - val_loss: 1.1249 - val_accuracy: 0.5145\n", + "Epoch 19/100\n", + "505/505 [==============================] - 82s 163ms/step - loss: 1.1662 - accuracy: 0.5047 - val_loss: 1.1187 - val_accuracy: 0.5200\n", + "Epoch 20/100\n", + "505/505 [==============================] - 82s 162ms/step - loss: 1.1570 - accuracy: 0.5024 - val_loss: 1.1165 - val_accuracy: 0.5206\n", + "Epoch 21/100\n", + "505/505 [==============================] - 79s 157ms/step - loss: 1.1544 - accuracy: 0.5070 - val_loss: 1.1117 - val_accuracy: 0.5212\n", + "Epoch 22/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.1506 - accuracy: 0.5101 - val_loss: 1.1061 - val_accuracy: 0.5267\n", + "Epoch 23/100\n", + "505/505 [==============================] - 82s 163ms/step - loss: 1.1374 - accuracy: 0.5159 - val_loss: 1.1030 - val_accuracy: 0.5273\n", + "Epoch 24/100\n", + "505/505 [==============================] - 81s 161ms/step - loss: 1.1336 - accuracy: 0.5162 - val_loss: 1.0963 - val_accuracy: 0.5334\n", + "Epoch 25/100\n", + " 73/505 [===>..........................] - ETA: 1:04 - loss: 1.1207 - accuracy: 0.5321" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ], + "metadata": { + "id": "yyrIOv73c1nw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (MobileNetV2, data aug, feature-extraction)')\n", + "plt.show()" + ], + "metadata": { + "id": "13YuE_Jtc1nw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Fine-tuning phase:**" + ], + "metadata": { + "id": "fK9kutDdc1oP" + } + }, + { + "cell_type": "code", + "source": [ + "base_model.trainable = True\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR/10),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ], + "metadata": { + "id": "luigrDpWc1oQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "history_ft = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history_ft)" + ], + "metadata": { + "id": "ieI5S1qCc1oQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ], + "metadata": { + "id": "zgYR9hsxc1oR" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (MobileNetV2, data aug, fine-tuning)')\n", + "plt.show()" + ], + "metadata": { + "id": "4IJUiuSbc1oR" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 2691ca51bc45e2e926717796f13c11c27496c5d4 Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Tue, 30 Jan 2024 23:18:47 +0530 Subject: [PATCH 08/16] Added images from notebook 02 --- .../00_noaug_featext_training_curves.png | Bin 0 -> 46026 bytes .../01_noaug_featext_confusion_matrix.png | Bin 0 -> 34917 bytes .../02_noaug_finetune_training_curves.png | Bin 0 -> 45019 bytes .../03_noaug_finetune_confusion_matrix.png | Bin 0 -> 35987 bytes .../04_aug_featext_training_curves.png | Bin 0 -> 46726 bytes .../05_aug_featext_confusion_matrix.png | Bin 0 -> 35472 bytes .../06_aug_finetune_training_curves.png | Bin 0 -> 51677 bytes .../07_aug_finetune_confusion_matrix.png | Bin 0 -> 36224 bytes 8 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/00_noaug_featext_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/01_noaug_featext_confusion_matrix.png create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/02_noaug_finetune_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/03_noaug_finetune_confusion_matrix.png create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/04_aug_featext_training_curves.png create mode 100644 Facial-Emotion-Detection/Images/02_transfer_learning_mobilenetv2/05_aug_featext_confusion_matrix.png create mode 100644 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.../03_transfer_learning_efficientnetv2.ipynb | 1838 +++++++++++++++++ 1 file changed, 1838 insertions(+) create mode 100644 Facial-Emotion-Detection/Model/03_transfer_learning_efficientnetv2.ipynb diff --git a/Facial-Emotion-Detection/Model/03_transfer_learning_efficientnetv2.ipynb b/Facial-Emotion-Detection/Model/03_transfer_learning_efficientnetv2.ipynb new file mode 100644 index 000000000..d2899cb51 --- /dev/null +++ b/Facial-Emotion-Detection/Model/03_transfer_learning_efficientnetv2.ipynb @@ -0,0 +1,1838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b32301b3", + "metadata": { + "id": "uupI5Ue6kkCM", + "papermill": { + "duration": 0.010311, + "end_time": "2024-01-29T16:46:15.254819", + "exception": false, + "start_time": "2024-01-29T16:46:15.244508", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3e35b3bf", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:15.275446Z", + "iopub.status.busy": "2024-01-29T16:46:15.275059Z", + "iopub.status.idle": "2024-01-29T16:46:27.501758Z", + "shell.execute_reply": "2024-01-29T16:46:27.501004Z" + }, + "id": "iwt6OlFmi6fx", + "papermill": { + "duration": 12.239568, + "end_time": "2024-01-29T16:46:27.504015", + "exception": false, + "start_time": "2024-01-29T16:46:15.264447", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], + "source": [ + "import gc\n", + "gc.enable()\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "from tensorflow import keras\n", + "from keras.applications import efficientnet_v2\n", + "from keras.layers import (\n", + " Resizing, RandomFlip, RandomRotation,\n", + " GlobalAveragePooling2D, Dropout, Dense, Input)\n", + "\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.utils import set_random_seed\n", + "\n", + "SEED = 2024\n", + "set_random_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dfbf3970", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:27.524574Z", + "iopub.status.busy": "2024-01-29T16:46:27.524041Z", + "iopub.status.idle": "2024-01-29T16:46:27.528163Z", + "shell.execute_reply": "2024-01-29T16:46:27.527315Z" + }, + "id": "WG7_wuH3_lvg", + "papermill": { + "duration": 0.016416, + "end_time": "2024-01-29T16:46:27.530146", + "exception": false, + "start_time": "2024-01-29T16:46:27.513730", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# DATA_PATH = '/content/drive/MyDrive/notebooks/swoc_s4/facial_emotion_detection/'\n", + "# images = np.load(f'{DATA_PATH}/images.npy')\n", + "# labels = np.load(f'{DATA_PATH}/labels.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5541f3b8", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:27.550588Z", + "iopub.status.busy": "2024-01-29T16:46:27.549879Z", + "iopub.status.idle": "2024-01-29T16:46:29.067649Z", + "shell.execute_reply": "2024-01-29T16:46:29.066837Z" + }, + "papermill": { + "duration": 1.530418, + "end_time": "2024-01-29T16:46:29.069971", + "exception": false, + "start_time": "2024-01-29T16:46:27.539553", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "DATA_PATH = '/kaggle/input/facial-expression'\n", + "images = np.load(f'{DATA_PATH}/Facial expression.npy')\n", + "labels = np.load(f'{DATA_PATH}/Facial expression label.npy')" + ] + }, + { + "cell_type": "markdown", + "id": "4a41c4ff", + "metadata": { + "id": "l_HUMU2vlDpC", + "papermill": { + "duration": 0.009644, + "end_time": "2024-01-29T16:46:29.099195", + "exception": false, + "start_time": "2024-01-29T16:46:29.089551", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "950fa74e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.119323Z", + "iopub.status.busy": "2024-01-29T16:46:29.118986Z", + "iopub.status.idle": "2024-01-29T16:46:29.123860Z", + "shell.execute_reply": "2024-01-29T16:46:29.122655Z" + }, + "id": "wZlTC6NelMAK", + "papermill": { + "duration": 0.017264, + "end_time": "2024-01-29T16:46:29.125895", + "exception": false, + "start_time": "2024-01-29T16:46:29.108631", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "label_mapping = {0: 'angry', 1: 'happy', 2: 'neutral', 3: 'surprised'}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "adf1d924", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.149896Z", + "iopub.status.busy": "2024-01-29T16:46:29.149580Z", + "iopub.status.idle": "2024-01-29T16:46:29.153629Z", + "shell.execute_reply": "2024-01-29T16:46:29.152915Z" + }, + "id": "VnG7rpIHlGPh", + "papermill": { + "duration": 0.016097, + "end_time": "2024-01-29T16:46:29.155479", + "exception": false, + "start_time": "2024-01-29T16:46:29.139382", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ord_labels = np.argmax(labels, axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "615da4b3", + "metadata": { + "id": "HhMmnVPrlSsi", + "papermill": { + "duration": 0.009168, + "end_time": "2024-01-29T16:46:29.174012", + "exception": false, + "start_time": "2024-01-29T16:46:29.164844", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Test dataset:** (using the same split as the baseline notebook)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1795eba5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.193630Z", + "iopub.status.busy": "2024-01-29T16:46:29.193367Z", + "iopub.status.idle": "2024-01-29T16:46:29.252758Z", + "shell.execute_reply": "2024-01-29T16:46:29.251836Z" + }, + "id": "opsxI8l-lWIt", + "papermill": { + "duration": 0.071876, + "end_time": "2024-01-29T16:46:29.255168", + "exception": false, + "start_time": "2024-01-29T16:46:29.183292", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "train_images, test_images, train_labels, test_labels = train_test_split(\n", + " images, ord_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=ord_labels, # to maintain proportion of classes in test data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "029184b7", + "metadata": { + "id": "7qi9ZyJ3lnfc", + "papermill": { + "duration": 0.010647, + "end_time": "2024-01-29T16:46:29.277584", + "exception": false, + "start_time": "2024-01-29T16:46:29.266937", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Train and validation datasets:**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a525bea5", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.304566Z", + "iopub.status.busy": "2024-01-29T16:46:29.304189Z", + "iopub.status.idle": "2024-01-29T16:46:29.360470Z", + "shell.execute_reply": "2024-01-29T16:46:29.359632Z" + }, + "id": "FDrOWPbulrIj", + "papermill": { + "duration": 0.072742, + "end_time": "2024-01-29T16:46:29.362847", + "exception": false, + "start_time": "2024-01-29T16:46:29.290105", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "train_images, val_images, train_labels, val_labels = train_test_split(\n", + " train_images, train_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=train_labels, # to maintain proportion of classes in val data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b7240732", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.383726Z", + "iopub.status.busy": "2024-01-29T16:46:29.383186Z", + "iopub.status.idle": "2024-01-29T16:46:29.389935Z", + "shell.execute_reply": "2024-01-29T16:46:29.389072Z" + }, + "id": "9WvgUlwX5fEY", + "outputId": "0146d1cb-68f1-49d7-b940-a9464514604e", + "papermill": { + "duration": 0.019189, + "end_time": "2024-01-29T16:46:29.391879", + "exception": false, + "start_time": "2024-01-29T16:46:29.372690", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(16159, 1796, 1995)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_labels), len(val_labels), len(test_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "fb0ccf56", + "metadata": { + "id": "217hZZ1Hdi1o", + "papermill": { + "duration": 0.009265, + "end_time": "2024-01-29T16:46:29.410584", + "exception": false, + "start_time": "2024-01-29T16:46:29.401319", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Data-augmentation (best config chosen from notebook 01):**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aa94e550", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:29.431182Z", + "iopub.status.busy": "2024-01-29T16:46:29.430915Z", + "iopub.status.idle": "2024-01-29T16:46:30.370166Z", + "shell.execute_reply": "2024-01-29T16:46:30.369174Z" + }, + "id": "iMjYlvkVdcS3", + "papermill": { + "duration": 0.951852, + "end_time": "2024-01-29T16:46:30.372546", + "exception": false, + "start_time": "2024-01-29T16:46:29.420694", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data_augmentation = keras.Sequential([\n", + " RandomRotation(factor=(-0.1, 0.2), fill_mode='constant', seed=SEED),\n", + " RandomFlip(mode='horizontal', seed=SEED),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "50553aa3", + "metadata": { + "id": "GVLCqy8M_0oX", + "papermill": { + "duration": 0.009643, + "end_time": "2024-01-29T16:46:30.392116", + "exception": false, + "start_time": "2024-01-29T16:46:30.382473", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Model training" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "26adc4d9", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:30.412744Z", + "iopub.status.busy": "2024-01-29T16:46:30.412430Z", + "iopub.status.idle": "2024-01-29T16:46:30.416835Z", + "shell.execute_reply": "2024-01-29T16:46:30.415982Z" + }, + "id": "_tfQ4DvpXIhY", + "papermill": { + "duration": 0.016844, + "end_time": "2024-01-29T16:46:30.418705", + "exception": false, + "start_time": "2024-01-29T16:46:30.401861", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# common parameters\n", + "BASE_LR = 1e-4\n", + "DROPOUT = 0.2\n", + "PATIENCE = 10\n", + "BATCH_SIZE = 32\n", + "EPOCHS = 200\n", + "VERBOSE = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cb8c0d57", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:30.439104Z", + "iopub.status.busy": "2024-01-29T16:46:30.438804Z", + "iopub.status.idle": "2024-01-29T16:46:30.443008Z", + "shell.execute_reply": "2024-01-29T16:46:30.442183Z" + }, + "id": "eOjhiIcdXEMW", + "papermill": { + "duration": 0.016203, + "end_time": "2024-01-29T16:46:30.444857", + "exception": false, + "start_time": "2024-01-29T16:46:30.428654", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# overfitting detection\n", + "early_stopping = EarlyStopping(\n", + " monitor='val_accuracy',\n", + " patience=PATIENCE,\n", + " min_delta=2e-4,\n", + " restore_best_weights=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "37cbfa0e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:30.464972Z", + "iopub.status.busy": "2024-01-29T16:46:30.464630Z", + "iopub.status.idle": "2024-01-29T16:46:30.473848Z", + "shell.execute_reply": "2024-01-29T16:46:30.473130Z" + }, + "id": "6NHSuHRurGbN", + "papermill": { + "duration": 0.021078, + "end_time": "2024-01-29T16:46:30.475620", + "exception": false, + "start_time": "2024-01-29T16:46:30.454542", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# visualizing training curves\n", + "def plot_training_curve(history):\n", + " train_loss = history.history['loss']\n", + " train_accuracy = history.history['accuracy']\n", + " val_loss = history.history['val_loss']\n", + " val_accuracy = history.history['val_accuracy']\n", + " num_epochs = len(train_loss)\n", + " epochs = range(num_epochs)\n", + "\n", + " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4), sharex=True)\n", + " ax[0].plot(epochs, train_loss, label='train_loss')\n", + " ax[0].plot(epochs, val_loss, label='val_loss')\n", + " ax[0].set_title('Loss')\n", + " ax[1].plot(epochs, train_accuracy, label='train_accuracy')\n", + " ax[1].plot(epochs, val_accuracy, label='val_accuracy')\n", + " ax[1].set_title('Accuracy')\n", + "\n", + " train_stop = (num_epochs-1-PATIENCE, val_accuracy[num_epochs-1-PATIENCE])\n", + " ax[1].annotate(f'Early stopping\\ntriggered',\n", + " xy=train_stop, xycoords='data',\n", + " xytext=(-5, -75), textcoords='offset points',\n", + " arrowprops=dict(facecolor='black', shrink=0.05),\n", + " horizontalalignment='center', verticalalignment='bottom')\n", + "\n", + " ax[0].minorticks_on(); ax[1].minorticks_on()\n", + " ax[0].set_xlabel('Epochs'); ax[1].set_xlabel('Epochs')\n", + " ax[0].legend(); ax[1].legend()\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c416b67", + "metadata": { + "id": "Q5uOnDEC_3G6", + "papermill": { + "duration": 0.009205, + "end_time": "2024-01-29T16:46:30.494276", + "exception": false, + "start_time": "2024-01-29T16:46:30.485071", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 1. EfficientNetV2S, no data augmentation" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ec42f08e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:30.556137Z", + "iopub.status.busy": "2024-01-29T16:46:30.555771Z", + "iopub.status.idle": "2024-01-29T16:46:30.559895Z", + "shell.execute_reply": "2024-01-29T16:46:30.559010Z" + }, + "id": "3MLp69xyclCb", + "papermill": { + "duration": 0.016558, + "end_time": "2024-01-29T16:46:30.561749", + "exception": false, + "start_time": "2024-01-29T16:46:30.545191", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "img_size = 224" + ] + }, + { + "cell_type": "markdown", + "id": "37614b1e", + "metadata": { + "id": "FDAYLXRHWv1b", + "papermill": { + "duration": 0.009345, + "end_time": "2024-01-29T16:46:30.580564", + "exception": false, + "start_time": "2024-01-29T16:46:30.571219", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Feature extraction phase:**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b9a8ac80", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:30.600728Z", + "iopub.status.busy": "2024-01-29T16:46:30.600449Z", + "iopub.status.idle": "2024-01-29T16:46:37.560446Z", + "shell.execute_reply": "2024-01-29T16:46:37.559515Z" + }, + "id": "M6skADuC_5dE", + "outputId": "2405f2ee-8ddb-455a-b742-45d8c5f29428", + "papermill": { + "duration": 6.972841, + "end_time": "2024-01-29T16:46:37.562861", + "exception": false, + "start_time": "2024-01-29T16:46:30.590020", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-s_notop.h5\n", + "82420632/82420632 [==============================] - 0s 0us/step\n" + ] + } + ], + "source": [ + "keras.backend.clear_session()\n", + "base_model = efficientnet_v2.EfficientNetV2S(input_shape=(img_size, img_size, 3), include_top=False)\n", + "base_model.trainable = False\n", + "\n", + "inputs = Input(shape=(48, 48, 3))\n", + "x = Resizing(img_size, img_size, interpolation='lanczos5', crop_to_aspect_ratio=True)(inputs)\n", + "x = base_model(x, training=False) # includes preprocessing layer\n", + "x = GlobalAveragePooling2D()(x)\n", + "# x = Dropout(DROPOUT)(x)\n", + "# x = Dense(64, activation='relu')(x)\n", + "x = Dropout(DROPOUT)(x)\n", + "outputs = Dense(4, activation='softmax')(x)\n", + "\n", + "model = keras.Model(inputs, outputs)\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "dcb13d78", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T16:46:37.585371Z", + "iopub.status.busy": "2024-01-29T16:46:37.584780Z", + "iopub.status.idle": "2024-01-29T17:51:13.407757Z", + "shell.execute_reply": "2024-01-29T17:51:13.406825Z" + }, + "id": "DnQIoem5WPiA", + "outputId": "9e3665ef-d5db-40f5-d593-189dfa511132", + "papermill": { + "duration": 3875.836397, + "end_time": "2024-01-29T17:51:13.409908", + "exception": false, + "start_time": "2024-01-29T16:46:37.573511", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n", + "505/505 [==============================] - 107s 182ms/step - loss: 1.1691 - accuracy: 0.4914 - val_loss: 1.0390 - val_accuracy: 0.5980\n", + "Epoch 2/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 1.0005 - accuracy: 0.5995 - val_loss: 0.9624 - val_accuracy: 0.6242\n", + "Epoch 3/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.9427 - accuracy: 0.6268 - val_loss: 0.9248 - val_accuracy: 0.6375\n", + "Epoch 4/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.9087 - accuracy: 0.6404 - val_loss: 0.8998 - val_accuracy: 0.6526\n", + "Epoch 5/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.8874 - accuracy: 0.6516 - val_loss: 0.8824 - val_accuracy: 0.6576\n", + "Epoch 6/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.8696 - accuracy: 0.6600 - val_loss: 0.8688 - val_accuracy: 0.6559\n", + "Epoch 7/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.8549 - accuracy: 0.6661 - val_loss: 0.8574 - val_accuracy: 0.6620\n", + "Epoch 8/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.8409 - accuracy: 0.6727 - val_loss: 0.8493 - val_accuracy: 0.6604\n", + "Epoch 9/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8338 - accuracy: 0.6736 - val_loss: 0.8396 - val_accuracy: 0.6720\n", + "Epoch 10/200\n", + "505/505 [==============================] - 88s 173ms/step - loss: 0.8229 - accuracy: 0.6789 - val_loss: 0.8323 - val_accuracy: 0.6765\n", + "Epoch 11/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8157 - accuracy: 0.6835 - val_loss: 0.8254 - val_accuracy: 0.6759\n", + "Epoch 12/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8080 - accuracy: 0.6860 - val_loss: 0.8207 - val_accuracy: 0.6754\n", + "Epoch 13/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8040 - accuracy: 0.6863 - val_loss: 0.8154 - val_accuracy: 0.6771\n", + "Epoch 14/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7959 - accuracy: 0.6885 - val_loss: 0.8106 - val_accuracy: 0.6821\n", + "Epoch 15/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.7935 - accuracy: 0.6914 - val_loss: 0.8072 - val_accuracy: 0.6804\n", + "Epoch 16/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.7897 - accuracy: 0.6939 - val_loss: 0.8030 - val_accuracy: 0.6843\n", + "Epoch 17/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7859 - accuracy: 0.6953 - val_loss: 0.7994 - val_accuracy: 0.6815\n", + "Epoch 18/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7813 - accuracy: 0.6966 - val_loss: 0.7966 - val_accuracy: 0.6865\n", + "Epoch 19/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7781 - accuracy: 0.6971 - val_loss: 0.7922 - val_accuracy: 0.6865\n", + "Epoch 20/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.7750 - accuracy: 0.6992 - val_loss: 0.7888 - val_accuracy: 0.6921\n", + "Epoch 21/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.7710 - accuracy: 0.7002 - val_loss: 0.7877 - val_accuracy: 0.6910\n", + "Epoch 22/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.7682 - accuracy: 0.7030 - val_loss: 0.7850 - val_accuracy: 0.6904\n", + "Epoch 23/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7644 - accuracy: 0.7024 - val_loss: 0.7828 - val_accuracy: 0.6910\n", + "Epoch 24/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7623 - accuracy: 0.7054 - val_loss: 0.7799 - val_accuracy: 0.6960\n", + "Epoch 25/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7583 - accuracy: 0.7052 - val_loss: 0.7780 - val_accuracy: 0.6943\n", + "Epoch 26/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7578 - accuracy: 0.7077 - val_loss: 0.7761 - val_accuracy: 0.6938\n", + "Epoch 27/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7550 - accuracy: 0.7058 - val_loss: 0.7732 - val_accuracy: 0.6965\n", + "Epoch 28/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7531 - accuracy: 0.7082 - val_loss: 0.7713 - val_accuracy: 0.6982\n", + "Epoch 29/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.7519 - accuracy: 0.7103 - val_loss: 0.7699 - val_accuracy: 0.6988\n", + "Epoch 30/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7510 - accuracy: 0.7093 - val_loss: 0.7686 - val_accuracy: 0.7021\n", + "Epoch 31/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7489 - accuracy: 0.7112 - val_loss: 0.7684 - val_accuracy: 0.6982\n", + "Epoch 32/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7460 - accuracy: 0.7123 - val_loss: 0.7657 - val_accuracy: 0.6988\n", + "Epoch 33/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7421 - accuracy: 0.7153 - val_loss: 0.7628 - val_accuracy: 0.7010\n", + "Epoch 34/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7437 - accuracy: 0.7117 - val_loss: 0.7618 - val_accuracy: 0.7066\n", + "Epoch 35/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7388 - accuracy: 0.7141 - val_loss: 0.7601 - val_accuracy: 0.7032\n", + "Epoch 36/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7379 - accuracy: 0.7151 - val_loss: 0.7612 - val_accuracy: 0.6999\n", + "Epoch 37/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7373 - accuracy: 0.7165 - val_loss: 0.7581 - val_accuracy: 0.7032\n", + "Epoch 38/200\n", + "505/505 [==============================] - 88s 173ms/step - loss: 0.7386 - accuracy: 0.7145 - val_loss: 0.7567 - val_accuracy: 0.7004\n", + "Epoch 39/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7391 - accuracy: 0.7126 - val_loss: 0.7563 - val_accuracy: 0.7010\n", + "Epoch 40/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7338 - accuracy: 0.7159 - val_loss: 0.7559 - val_accuracy: 0.7038\n", + "Epoch 41/200\n", + "505/505 [==============================] - 87s 171ms/step - loss: 0.7337 - accuracy: 0.7198 - val_loss: 0.7536 - val_accuracy: 0.7016\n", + "Epoch 42/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.7304 - accuracy: 0.7146 - val_loss: 0.7527 - val_accuracy: 0.7032\n", + "Epoch 43/200\n", + "505/505 [==============================] - 87s 172ms/step - loss: 0.7310 - accuracy: 0.7156 - val_loss: 0.7515 - val_accuracy: 0.7049\n", + "Epoch 44/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.7270 - accuracy: 0.7168 - val_loss: 0.7523 - val_accuracy: 0.7043\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 47min 2s, sys: 34.4 s, total: 47min 36s\n", + "Wall time: 1h 4min 35s\n" + ] + } + ], + "source": [ + "%%time\n", + "history = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6d647d98", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T17:51:17.022850Z", + "iopub.status.busy": "2024-01-29T17:51:17.022472Z", + "iopub.status.idle": "2024-01-29T17:51:36.071079Z", + "shell.execute_reply": "2024-01-29T17:51:36.070173Z" + }, + "id": "Zu2WC7KkX-NB", + "outputId": "88631cd7-97ce-426b-d974-a219a86ecd66", + "papermill": { + "duration": 20.885892, + "end_time": "2024-01-29T17:51:36.073426", + "exception": false, + "start_time": "2024-01-29T17:51:15.187534", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 9s 153ms/step - loss: 0.7618 - accuracy: 0.7066\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 10s 161ms/step - loss: 0.7589 - accuracy: 0.7053\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.7589461803436279, 0.7052631378173828]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "91ca9a95", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T17:51:39.895374Z", + "iopub.status.busy": "2024-01-29T17:51:39.894632Z", + "iopub.status.idle": "2024-01-29T17:51:52.755042Z", + "shell.execute_reply": "2024-01-29T17:51:52.754098Z" + }, + "id": "_mFchi4AYPNm", + "outputId": "836c58a2-9ccb-477b-abaf-214db7b134be", + "papermill": { + "duration": 14.799206, + "end_time": "2024-01-29T17:51:52.757026", + "exception": false, + "start_time": "2024-01-29T17:51:37.957820", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/63 [==============================] - 12s 149ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (EfficientNetV2S, no aug, feature-extraction)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c48c7849", + "metadata": { + "id": "0toDQNcRY1po", + "papermill": { + "duration": 1.863099, + "end_time": "2024-01-29T17:51:56.482946", + "exception": false, + "start_time": "2024-01-29T17:51:54.619847", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Fine-tuning phase:**" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "da10873a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T17:52:00.160083Z", + "iopub.status.busy": "2024-01-29T17:52:00.159639Z", + "iopub.status.idle": "2024-01-29T17:52:00.202031Z", + "shell.execute_reply": "2024-01-29T17:52:00.201132Z" + }, + "id": "_ZLN8ThoY5P7", + "papermill": { + "duration": 1.934582, + "end_time": "2024-01-29T17:52:00.204119", + "exception": false, + "start_time": "2024-01-29T17:51:58.269537", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "base_model.trainable = True\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR/10),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0ec13591", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T17:52:03.939573Z", + "iopub.status.busy": "2024-01-29T17:52:03.938727Z", + "iopub.status.idle": "2024-01-29T18:44:15.526466Z", + "shell.execute_reply": "2024-01-29T18:44:15.525550Z" + }, + "id": "9G9hsJcMZ2aQ", + "outputId": "ff922b5e-f8ca-4909-d2fb-b2e2d3e99f50", + "papermill": { + "duration": 3133.465211, + "end_time": "2024-01-29T18:44:15.529741", + "exception": false, + "start_time": "2024-01-29T17:52:02.064530", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n", + "505/505 [==============================] - 278s 385ms/step - loss: 0.6252 - accuracy: 0.7619 - val_loss: 0.5745 - val_accuracy: 0.7856\n", + "Epoch 2/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.4683 - accuracy: 0.8290 - val_loss: 0.5302 - val_accuracy: 0.8174\n", + "Epoch 3/200\n", + "505/505 [==============================] - 190s 377ms/step - loss: 0.3768 - accuracy: 0.8644 - val_loss: 0.5297 - val_accuracy: 0.8207\n", + "Epoch 4/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.2877 - accuracy: 0.8994 - val_loss: 0.5241 - val_accuracy: 0.8268\n", + "Epoch 5/200\n", + "505/505 [==============================] - 190s 377ms/step - loss: 0.2028 - accuracy: 0.9302 - val_loss: 0.5790 - val_accuracy: 0.8296\n", + "Epoch 6/200\n", + "505/505 [==============================] - 190s 377ms/step - loss: 0.1338 - accuracy: 0.9545 - val_loss: 0.6387 - val_accuracy: 0.8324\n", + "Epoch 7/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.0766 - accuracy: 0.9756 - val_loss: 0.7854 - val_accuracy: 0.8129\n", + "Epoch 8/200\n", + "505/505 [==============================] - 191s 378ms/step - loss: 0.0442 - accuracy: 0.9864 - val_loss: 0.9223 - val_accuracy: 0.8185\n", + "Epoch 9/200\n", + "505/505 [==============================] - 191s 378ms/step - loss: 0.0281 - accuracy: 0.9913 - val_loss: 0.9882 - val_accuracy: 0.8224\n", + "Epoch 10/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.0196 - accuracy: 0.9941 - val_loss: 1.1542 - val_accuracy: 0.8174\n", + "Epoch 11/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.0218 - accuracy: 0.9933 - val_loss: 1.1667 - val_accuracy: 0.8163\n", + "Epoch 12/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.0141 - accuracy: 0.9950 - val_loss: 1.2392 - val_accuracy: 0.8257\n", + "Epoch 13/200\n", + "505/505 [==============================] - 190s 377ms/step - loss: 0.0142 - accuracy: 0.9954 - val_loss: 1.1794 - val_accuracy: 0.8202\n", + "Epoch 14/200\n", + "505/505 [==============================] - 190s 375ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 1.3309 - val_accuracy: 0.8263\n", + "Epoch 15/200\n", + "505/505 [==============================] - 190s 377ms/step - loss: 0.0128 - accuracy: 0.9962 - val_loss: 1.1686 - val_accuracy: 0.8229\n", + "Epoch 16/200\n", + "505/505 [==============================] - 190s 376ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 1.4427 - val_accuracy: 0.8302\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 44min, sys: 3min 52s, total: 47min 53s\n", + "Wall time: 52min 11s\n" + ] + } + ], + "source": [ + "%%time\n", + "history_ft = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history_ft)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2c09585a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T18:44:20.664715Z", + "iopub.status.busy": "2024-01-29T18:44:20.664347Z", + "iopub.status.idle": "2024-01-29T18:44:39.532202Z", + "shell.execute_reply": "2024-01-29T18:44:39.531337Z" + }, + "id": "RWIlphVaaPtL", + "outputId": "73f6074b-627f-44e2-8cba-cc0c8f9cc7f5", + "papermill": { + "duration": 21.463165, + "end_time": "2024-01-29T18:44:39.534106", + "exception": false, + "start_time": "2024-01-29T18:44:18.070941", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 9s 159ms/step - loss: 0.6387 - accuracy: 0.8324\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 10s 153ms/step - loss: 0.6643 - accuracy: 0.8231\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.6643170714378357, 0.8230576515197754]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8f6b6800", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T18:44:44.717808Z", + "iopub.status.busy": "2024-01-29T18:44:44.716954Z", + "iopub.status.idle": "2024-01-29T18:44:57.540336Z", + "shell.execute_reply": "2024-01-29T18:44:57.539412Z" + }, + "id": "p3uc_i5vaSbF", + "papermill": { + "duration": 15.402556, + "end_time": "2024-01-29T18:44:57.542490", + "exception": false, + "start_time": "2024-01-29T18:44:42.139934", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/63 [==============================] - 12s 149ms/step\n" + ] + }, + { + "data": { + "image/png": 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57bffKt0P/BYzMzOevACYqVOn8qRV1itS/jipznHMr93Xrl1jAFTo7YyIiKiQXnZuuXr1KjctJSWFkZGR4Wn79+YAlZ3b/Pz8eHqB+LXV39+fUVBQYF68eMFTzoIFCxgJCQnm3bt3DMNUfRykp6czMjIyzMiRIyuUgX97Kavy8uVLRlZWlhk7dixP+vbt2xkAjLS0NNO1a1dmyZIlzLVr17ifoR9Rdrx8e4wzDMMMHDiQUVdX574W5DuBH37n8gMHDlT4+1a3BygmJoYBwMyaNYsnn4eHxw/3AJVtX0FBoUJ6ZT1Azs7OPMf77NmzGQkJCe53gyDfK5WpbA6QoD1AAJg9e/Zw0woKChgdHR1m8ODB3LTKeoCq8x1z5MgRBgATFBTETSspKWG6detW6fdxz549GXNz8+/sAV41ugze2NgYY8eORUhICD59+sQ3z9mzZwEA3t7ePOllPQdnzpzhSTcyMoKLiwvfstzc3Hh+0dnb24NhGIwbN44nn729PRITE1FcXMxN+3YeUVZWFtLS0uDo6Ig3b94gKyvre02tlL+/P06fPo1du3Zx58RcuHABmZmZGDlyJNLS0riLhIQE7O3tcfnyZQDAp0+fEBsbC3d3d55ejx49elRrfs2FCxeQk5ODBQsWVBhrLfsVdPfuXaSkpGDKlCk8efr06YMWLVpU2P9A6byBb3Xu3Blv3ryp5h75vrK2nj9/Hvn5+dVe7+zZs7Czs0OnTp24aYqKipgwYQISEhLw9OlTnvyenp484++dO3cGgGq3Zfz48dz/l5CQgI2NDRiGwa+//spNV1FRgZmZGU+ZEhIS3O1yOBykp6ejuLgYNjY2uHfvXrXbC4D7i+h7LC0t4efnh23btsHFxQVpaWnYvXs3JCWr7uRNT08HwzBQVVWtNM+RI0dw4cIFnmXnzp0CtaMq1TmO+fnrr7+grKyMHj168HzOrK2toaioyP2clbGwsOAeA0Bpr2X5v111zZw5E6qqqvDz86uyfp07d4aqqipP/ZydnVFSUoKrV69+dzuqqqro3bs3Tp48iby8PAClcyMPHjwIGxsbmJqaVrpufn4+hg4dCjk5OaxcuZLnvXHjxiEiIgJOTk64fv06/P390blzZzRv3hw3b96s5l7gj9/54/Pnz8jOzgYg+HdCed+ey79+/Yq0tDS0b98eAAT+fAFAREQEAGDKlCk86dOnTxe4rJqYMGECz/HeuXNnlJSU4O3btwCq/71SFxQVFTFmzBjua2lpadjZ2VX7s/S975iIiAhISUnBy8uLm8Zms6scrSn7nAnih4fAyixevBhhYWFYuXIl1q9fX+H9t2/fgs1mo1mzZjzpOjo6UFFR4f5xyxgZGVW6raZNm/K8Lvsi1dfXr5DO4XCQlZUFdXV1AMCNGzfg4+ODW7duVfjSzcrKqnTYpSoRERHw8/PDwoULMXjwYG76y5cvAQDdunXju56SkhIAcNvevHnzCnnMzMy++2F+/fo1AFR524GybXw7lFimRYsWuH79Ok+arKxsheEsVVVVZGRkVFkXQRgZGcHb2xuBgYHYt28fOnfujH79+mHMmDFV/h3evn3L934PZUMUb9++5dkX5Y+Xsi/56raF3/EmKytbYUKwsrIyPn/+zJO2e/du/PHHH3j+/DmKioq46VUd3+VJSkoKNDwxd+5cHDx4ENHR0VixYoVAk9SZKi617tKly3cnQddEdY5jfl6+fImsrCyeoedvlZ/MW/7vCfz4sa2srIxZs2bBx8cH9+/f5xtAvnz5Eg8fPqx0eLi6k41Hjx6NY8eO4cSJExg1ahRu3ryJhIQEzJw5s9J1SkpKMGLECDx9+hTnzp3jO0XBxcUFLi4uyM/PR0xMDMLDwxEcHIy+ffvi+fPnle7X76nqc6ekpCTwd0J56enp8PPzw8GDByvswx/5MVtWn/KfzfL1q23fO19V93vly5cvFfaDjo6OUOuqp6dX4ceJqqoqHj58+N11q/Md8/btWzRu3LjCsH9VfxOGYQQe/qxxAGRsbIwxY8YgJCQECxYsqDRfdStW1RVflV05Ull62Un99evX6N69O1q0aIHAwEDo6+tDWloaZ8+exbp168DhcKpVt2/Fx8dj9OjR6NGjB5YvX87zXll5YWFhfA+87/0qr08/cnVOmcr+xiUlJRXS/vjjD3h4eODEiRP4+++/MWPGDAQEBOD27dtCm5PwvePiR9avTpl79+6Fh4cHBgwYgLlz50JLSwsSEhIICAjgftlXh4yMDNjs6nfSvnnzhnuSfPToUbXWUVNTA4vFEmqAW1c4HE6VN28sf5Kt6fFQXtlcID8/P773VuFwOOjRowfmzZvHd/2qem++1bdvXygrK2P//v0YNWoU9u/fDwkJCZ57NpXn5eWF06dPY9++fZV+YZaRl5dH586d0blzZ2hoaMDPzw/nzp2rdL7F91R3P//IXB2gdI7RzZs3MXfuXFhZWUFRUREcDge//PILz7lckPNRQ/C9/Vbd75Xw8HCeK/6+LaMygu6rmnyWavIdU5WMjAyBf6gJ5Zt48eLF2Lt3L88ktzIGBgbgcDh4+fIlz2TC5ORkZGZmwsDAQBhVqNKpU6dQUFCAkydP8kTZP9pl+OXLFwwaNAgqKio4cOBAhS8pExMTAICWlhacnZ0rLaes7WVfWt+Ki4v7bj3KtvP48eNKI+OybcTFxVU4EcbFxQl1/5f9YsnMzORJr+wXXatWrdCqVSssXrwYN2/eRMeOHREcHFwhoCxjYGDAd788f/6c+35DcPjwYRgbG+Po0aM8JxYfHx+efD/6BcAPh8OBh4cHlJSUMGvWLKxYsQJDhgzhTqqtjKSkJExMTBAfHy+0ugiqOsdxZetdvHgRHTt2FNqtMgT5m5T1Avn6+vINFkxMTJCbm1vlOaA625SRkcGQIUOwZ88eJCcn46+//kK3bt0q/VU/d+5c7Ny5E0FBQRg5cmS12wOAe6PKyqY0CENNvhMyMjIQGRkJPz8/ngng/M6hqqqqFc5FQMXzUVl94uPjeXrjX716JUizal11v1dcXFxw4cIFvu9VdqwJeu6ubQYGBrh8+TLy8/N5eoGq+pvEx8d/94KP8oTyKAwTExOMGTMGW7du5bk7LAD07t0bACr8QgoMDASAGs20r66yiPPb6DQrK+uH5zFMmjQJL168wLFjx/h2fbu4uEBJSQkrVqzgGf4oU3Z/lMaNG8PKygq7d+/m6bK8cOFChfks/PTs2RONGjVCQEAAvn79yvNeWVttbGygpaWF4OBgFBQUcN8/d+4cnj17JtT9X/YB/XZuQ0lJCUJCQnjyZWdn88zPAkqDITabzVPH8nr37o3o6GjcunWLm5aXl4eQkBAYGhrW+n2Jqovf8Xbnzh2eegPgfrD5naQFFRgYiJs3byIkJAT+/v7o0KEDJk+eXK0xcQcHB9y9e7fGdfhR1TmO+Rk2bBhKSkrg7+9f4b3i4uIf2q8KCgoAqv83mTVrFlRUVLBs2TK+9bt16xbOnz9f4b3MzEzuZ6A6x8Ho0aNRVFSEiRMnIjU1lXvvn/LWrFmDtWvX4n//+1+VQ2SRkZF808vm5/AbMheWmnwn8Pts8SsLKD0fZWVl8QzLfPr0qcJVZmVzTv/880+e9I0bN1bRironyPeKs7Mzz1JGQUGB7zBhdc/ddcXFxQVFRUUIDQ3lpnE4HGzevJlv/qysLLx+/fq7V9qVJ7SxmEWLFiEsLAxxcXFo2bIlN71NmzZwd3dHSEgIMjMz4ejoiOjoaOzevRsDBgxA165dhVWFSvXs2RPS0tJwdXXFxIkTkZubi9DQUGhpaQn8S+fMmTPYs2cPBg8ejIcPH/J8uBQVFTFgwAAoKSlhy5YtGDt2LNq1a4cRI0ZAU1MT7969w5kzZ9CxY0ds2rQJQOml/X369EGnTp0wbtw4pKenY+PGjWjZsiVyc3OrrIuSkhLWrVuH8ePHw9bWFqNGjYKqqioePHiA/Px87N69G1JSUli1ahU8PT3h6OiIkSNHci+DNzQ0FOySwe9o2bIl2rdvj4ULFyI9PR1qamo4ePBghWDn0qVLmDZtGoYOHQpTU1MUFxcjLCwMEhISPHOpyluwYAEOHDiAXr16YcaMGVBTU8Pu3bsRHx+PI0eOCDRcVJv69u2Lo0ePYuDAgejTpw/i4+MRHBwMCwsLnr+pnJwcLCwsEB4eDlNTU6ipqcHS0lLguTDPnj3DkiVL4OHhwX1Mw65du2BlZYUpU6bg0KFDVa7fv39/hIWF4cWLF3yHZQ4fPsz3ctkePXpAW1tboLryU53jmB9HR0dMnDgRAQEBiI2NRc+ePSElJYWXL1/ir7/+wvr16zFkyBCB6mJlZQUJCQmsWrUKWVlZkJGRQbdu3SqdD6OsrIyZM2fynQw9d+5cnDx5En379uVebp+Xl4dHjx7h8OHDSEhIgIaGRrWOA0dHR+jp6eHEiROQk5Pj27N37NgxzJs3D82bN4e5uTn27t3L8/63f6/+/fvDyMgIrq6uMDExQV5eHi5evIhTp07B1taW53EfHh4e3M+ZMJ7FVpPvBCUlJXTp0gWrV69GUVERdHV18ffff/PtwRwxYgTmz5+PgQMHYsaMGdxLxk1NTXnmV1pbW2Pw4MEICgrC58+fuZfBv3jxAkDFXhMWiwVHR8c6f4SNIN8rlbG2tkZ4eDi8vb1ha2sLRUVFuLq6VvvcXVcGDBgAOzs7/Pbbb3j16hVatGiBkydPcm/JUf5vcvHiRTAMg/79+wu2IYGuGWN4L4Mvr+wSN343QvTz82OMjIwYKSkpRl9fv8obIZZXdile+cumK6sLvxu4nTx5kmndujUjKyvLGBoaMqtWrWJ27NhR4dK/710GX9nN0sDnsvXLly8zLi4ujLKyMiMrK8uYmJgwHh4eFW6jf+TIEcbc3JyRkZFhLCwsBL4R4smTJ5kOHTowcnJyjJKSEmNnZ8ccOHCAJ094eDjTtm1bRkZGhlFTU6vyRojl8btxWGV/q9evXzPOzs7cG5T973//Yy5cuMBzKeWbN2+YcePGMSYmJoysrCyjpqbGdO3atcKN/Kq6EaKKigojKyvL2NnZVXojxPLHy/duaVC+veVvAFjZ/il/6wcOh8OsWLGCMTAwYGRkZJi2bdsyp0+f5vs3vXnzJmNtbc1IS0vzXHJb2bbK3isrp7i4mLG1tWX09PR4bqXAMP9d9h8eHl5lewsKChgNDQ3G39+f736obPn20ljU4DL4Mt87jiv7TISEhDDW1taMnJwc06hRI6ZVq1bMvHnzmI8fP3LzVHa8lv+8MwzDhIaGMsbGxoyEhARPOyt7zE9GRgajrKzMt605OTnMwoULmWbNmjHS0tKMhoYG06FDB2bt2rU8t/So7Dj41ty5cxkAzLBhwyq8xzCC/b0OHDjAjBgxgjExMWHk5OQYWVlZxsLCglm0aBHPDRMZhmEGDx7MyMnJMRkZGXy3W3775T83/P7e1f1O4Of9+/fMwIEDGRUVFUZZWZkZOnQo8/HjR7777e+//2YsLS0ZaWlpxszMjNm7dy/f81leXh4zdepURk1NjVFUVGQGDBjAxMXFMQB4bnuRk5PDAGBGjBjx3XoKehl8+e8xfpegl6VX53uFn9zcXGbUqFGMiopKhe+s6py7Gabyz0H5z2dVN0Isj9/fJDU1lRk1ahT3RogeHh7MjRs3GADMwYMHefIOHz6c6dSp03fbXx6LYX5wBiAh5Kfh7++PnTt34uXLl7U2SZGIJm1tbbi5uWHNmjX1XZU6FRsbi7Zt22Lv3r3cIcezZ8+ib9++ePDgAVq1alXPNRQ/x48fx8CBA3H9+nV07NgRQOmNPo2MjHDw4EGBe4AaxpgBIaRezZ49G7m5uTh48GB9V4U0IE+ePMGXL18wf/78+q5Krfry5UuFtKCgILDZbJ7nsF2+fBkjRoyg4KcOlP+blJSUYOPGjVBSUuK5c31QUBBatWol+PAXAOoBIoQQItb8/PwQExODrl27QlJSEufOncO5c+cwYcIEbN26tb6rJ5bGjx+PL1++wMHBAQUFBTh69Chu3ryJFStWYOHChULZBgVAhBBCxNqFCxfg5+eHp0+fIjc3F02bNsXYsWOxaNGiBn3ftp/Z/v378ccff+DVq1f4+vUrmjVrhsmTJ2PatGlC2wYFQIQQQggROzQHiBBCCCFihwIgQgghhIgdGtxswDgcDj5+/IhGjRoJ9bEJhBBC6gbDMMjJyUGTJk1q7WatX79+RWFhoVDKkpaWhqysrFDKaugoAGrAPn78WOFJ94QQQkRPYmKi0B70/K2vX79CrpE6UJwvlPJ0dHQQHx8vFkEQBUANWKNGjQAA6iOCwZYWzsMeSdXurelX31UQO2+Sq37kCxEuE+2KjzUhtScnJxvmzQy453NhKywsBIrzIWPhDkhI16ywkkIkPd2NwsJCCoBI/Sob9mJLy4EtLf+d3EQYlJSU6rsKYkcxn6Yi1iUlJQqA6kOtT2OQlAWrhgEQwxKvzyIFQIQQQoioYwGoaZAlZlNNKQAihBBCRB2LXbrUtAwxIl6tJYQQQggB9QARQgghoo/FEsIQmHiNgVEARAghhIg6GgITmHi1lhBCCCEE1ANECCGEiD4aAhMYBUCEEEKIyBPCEJiYDQqJV2sJIYQQQkA9QIQQQojooyEwgVEARAghhIg6ugpMYOLVWkIIIYQQUA8QIYQQIvpoCExgFAARQgghoo6GwARGARAhhBAi6qgHSGDiFe4RQgghhIB6gAghhBDRR0NgAqMAiBBCCBF1LJYQAiAaAiOEEEII+alRDxAhhBAi6tis0qWmZYgRCoAIIYQQUUdzgAQmXq0lhBBCCAH1ABFCCCGij+4DJDAKgAghhBBRR0NgAhOv1hJCCCFEaDZv3gxDQ0PIysrC3t4e0dHRVeYPCgqCmZkZ5OTkoK+vj9mzZ+Pr1691VFteFAARQgghoq5sCKymiwDCw8Ph7e0NHx8f3Lt3D23atIGLiwtSUlL45t+/fz8WLFgAHx8fPHv2DNu3b0d4eDj+97//CWMPCIwCIEIIIUTUlQ2B1XQRQGBgILy8vODp6QkLCwsEBwdDXl4eO3bs4Jv/5s2b6NixI0aNGgVDQ0P07NkTI0eO/G6vUW2hAIgQQggRdXXcA1RYWIiYmBg4Oztz09hsNpydnXHr1i2+63To0AExMTHcgOfNmzc4e/YsevfuXbO2/yCaBE0IIYQQruzsbJ7XMjIykJGR4UlLS0tDSUkJtLW1edK1tbXx/PlzvuWOGjUKaWlp6NSpExiGQXFxMSZNmkRDYIQQQgj5QUIcAtPX14eysjJ3CQgIEEoVo6KisGLFCvz555+4d+8ejh49ijNnzsDf318o5QuKeoAIIYQQUSfE+wAlJiZCSUmJm1y+9wcANDQ0ICEhgeTkZJ705ORk6Ojo8C1+yZIlGDt2LMaPHw8AaNWqFfLy8jBhwgQsWrQIbHbd9slQDxAhhBBCuJSUlHgWfgGQtLQ0rK2tERkZyU3jcDiIjIyEg4MD33Lz8/MrBDkSEhIAAIZhhNiC6qEeIEIIIUTkCeFGiAL2iXh7e8Pd3R02Njaws7NDUFAQ8vLy4OnpCQBwc3ODrq4udwjN1dUVgYGBaNu2Lezt7fHq1SssWbIErq6u3ECoLlEARAghhIi6engUxvDhw5GamoqlS5ciKSkJVlZWiIiI4E6MfvfuHU+Pz+LFi8FisbB48WJ8+PABmpqacHV1xe+//16zev8gFlMf/U6kWrKzs6GsrAxNt91gS8vXd3XEwouNg+q7CmLnVVJufVdBrDTXUazvKoiV7Oxs6GmrIisri2dejTDLV1ZWhkyPVWBJydaoLKboKwouzK+1ujY01ANECCGEiDoWSwjPAqOHoRJCCCFElNDDUAUmXq0lhBBCCAH1AJEfNNbRBBN7mEJTSRbP3mfBJ/w+HrzN4Jv34GxHtDfVrJB+6dEnjPvzRm1XVSTsOHwVf+67hJT0bFg008UK7yFo19Kg0vwnI+9jVcgZJCalw0hPE0um9oNzh5Z8885dFY49x29g2cyBmDiiKzf9YVwi/DefROyzd5Bgs9CnqxWWzRgIBfmKl7z+7I6cvYX9x68hPTMXzQx1MHu8KyxM9fnmPfn3PzgXdQ/x70rvf2JmoouJo3vy5I+69RjHz0cj7vUHZOd+wc7AaTA1alInbWmItn9zfLes5vG98t/j2/g7x/ecf49v/3LHd5mCwiL8Mj4QT15+QOTueWhlqie0djUo9TAJWtRRDxARWF9rPSwe3BrrzzxFnxUX8fR9JvbM6Az1Rvy/OCduvQnb+ae4S49lf6O4hIOz997Xcc0bpuMX78FnwzH89usvuLBrLlo218WI2X8iNT2Hb/5/Hr7BJJ/dGOXqgIu756FXl9bwmL8Nz15/rJD3bNQDxDxJgI6GMk96UmoWhk7fDCM9DZzb5o0D6yYj7s0nzFi+t1ba2JBdvP4QG3eexbjh3bHjj6loZtgY3st2IiOT/+Tse0/eoEfnNtjgPx5bV06CloYyZvvtROrnLG6erwVFaG1ugMluv9RVMxqssuN7zq+/4OK/x/fwKo7v6IdvMPHf4zvy3+PbvZLj+0wlx/e3lm0+WeX7P416eBiqqBOv1taRwsLC+q5CrRrf3RQHb8Tjr1tv8SopB4sO3MOXwhIMczDkmz8rvwip2QXcpbO5Fr4UluAMBUAAgOADlzGmXweM7NseZkaNsWbeMMjJSOPA6dt884ccuoKu9uaYOqY7TA11sGBiH7Qy08OOw9d48n1KycT/Ag/jT183SEny3mPj7xuPISkpgZVzhqKZgTbaWhhg9bzhOH35AeITU2utrQ1R+MnrcO1hiz7drWGkr425k/pDRkYapyNj+Ob3nT0cg3q1h6lRExjoaWHBlEHgMAzuPnzNzfOLU1uMG94dtm2a1VUzGixBj+/QQ1fQzd4c0745vlub6WF7Jcf3Fj7Hd5nIW08Rdec5fKf3F3q7Gpw6fhjqz+CnD4AiIiLQqVMnqKioQF1dHX379sXr16UnqoSEBLBYLBw9ehRdu3aFvLw82rRpU+FJtqGhodDX14e8vDwGDhyIwMBAqKiocN/39fWFlZUVtm3bBiMjI8jKymLPnj1QV1dHQUEBT1kDBgzA2LFja73dtUVKggXLpiq48TyFm8YwwI3nyWhnrF6tMoZ1MMKpu4n4UlhSW9UUGYVFxXgYl4jOtmbcNDabjS62Zrj7OJ7vOjGPE9DF1pQnrau9OU9+DoeDqcvCMGV0d7Qwbsx3u9JSEjz36JCTkQIA3Hn4pkZtEiVFRcWIe/2RJ1Bhs9mwaW2Cx3HvqlXG18IiFJeUQEmRblVRXmFRMR7EJaKLAMf3XT7Ht1Mlx/fUSo5vAEhJz4Z3wAFs9hkLOVlpIbSG/Gx++gAoLy8P3t7euHv3LiIjI8FmszFw4EBwOBxunkWLFmHOnDmIjY2FqakpRo4cieLiYgDAjRs3MGnSJMycOROxsbHo0aMH35s2vXr1CkeOHMHRo0cRGxuLoUOHoqSkBCdPnuTmSUlJwZkzZzBu3Di+dS0oKEB2djbP0tCoKspAUoKNtOyvPOmp2QXQVPr+PSjaGKiiha4ywm/wP/mJm/TMPJSUcKCp1ognXVOtEVI+8x8iSPmcDU01pSrzbwy7CEkJNryGOfIto5O1KVI+Z2Pz3kgUFhUjMzsfy7eUHqvJaVl81/kZZebko4TDgZoy771x1FQUkZ7Jf/+Xt2VPBDRUlWDTxqQ2qijSavP4lqji+GYYBjP898F9YCdYmTetYStEBA2BCeynnwQ9ePBgntc7duyApqYmnj59CkXF0pPenDlz0KdPHwCAn58fWrZsiVevXqFFixbYuHEjevXqhTlz5gAATE1NcfPmTZw+fZqn3MLCQuzZsweamv9N9h01ahR27tyJoUOHAgD27t2Lpk2bwsnJiW9dAwIC4OfnJ5R2N1TDOxrh2fvMSidMk5p78PwdQg9dwcVd88CqpEu7hXFjbFgyBj4bjuH34FOQYLMwfqgjNNUagc0Wr27wmgg7cgUXrz/EJv/xkJGWqu/qiIUHz98h5NAVRFZxfG/76ypy8wsw061HHdeuHtEkaIH99AHQy5cvsXTpUty5cwdpaWncnp93797BwsICANC6dWtu/saNS7tTU1JS0KJFC8TFxWHgwIE8ZdrZ2VUIgAwMDHiCHwDw8vKCra0tPnz4AF1dXezatQseHh6VfmgXLlwIb29v7uvs7Gzo6/O/EqW+ZOQWoLiEA41yvT2aSjJILdcrVJ6ctAT62uhj3akntVlFkaKmogAJCXaFCaGp6TnQUm/Edx0tdSWkpmdXmv927GukZeSi3UAf7vslJRz4bjyO0PAruHvMFwAw2MUGg11skJKeDQVZGYAFBB+8DIMmGkJsYcOm0kgeEmw20rN4JzynZ+ZCTYX//i+z//g17D16BUF+49DMkP8wjLirzeO7bbnj22fjcYSEX0HMMV9ci3mBu4/joefozVNOz3FrMbinDTYtHSOM5hER99MHQK6urjAwMEBoaCiaNGkCDocDS0tLnonKUlL//XIrC06+HSKrDgUFhQppbdu2RZs2bbBnzx707NkTT548wZkzZyotQ0ZGhu9TdxuSohIGj99looOZFv5+UHpVBosFdDDTwp6o11Wu26edHmQk2TgWXb25FeJAWkoSrc30ce3uC/R2LA3EORwOrt2Nw7ghXfiuY21piGt3X/Bc8nsl+jlsLI0AAEN72fHMuQCAEbO2YEgvW4zsY1+hPK1/hxv2n7oFGWkpONqZVcjzs5KSkoSZSRPcffgKXexLfxBxOBzEPHqNwb34P9EaAPYdu4rdhy8jcKknzJv9pJdVC4G0lCTaVHJ8/1rJ8W3zA8f38FlbMPSb43vF7MFYOKEP9/2ktCwMn7UFIf4esK7i8ntRxmKxKv1xLUAhwqmMiPipA6DPnz8jLi4OoaGh6Ny5MwDg+vXrApVhZmaGf/75hyet/OuqjB8/HkFBQfjw4QOcnZ0bXI/Oj9gW+QJ/uNvi0bsMxCak49duzSEvI4m/biUAAP5wt0Vy5hesPvGYZ71hHY3w94OPyMz7ua+SE9SkkV0xw38vrFroo21LA4QcjEL+10KM6Ft6Mp/mFwYdTWUsntIPADBhmCMGTNmALfsvwblDSxy/GIMHzxOxdsEIAICasgLUlHkDcilJCWipNUIzA21u2va/rsK2tREU5GRwJfo5lm06gUVT+kG5kXhN5h3erxN+33AYLUz0YNFcD4dO38DXr4Xo070dAMB//V/QUFPC5LEuAIC9R69g24GL8PEejsZaqvicUdq7IScrDXm50h8w2Tn5SErLRNq/PR/vPqQBANRVGkFdteqepZ/NpJFdMd1/L9q00Ee7lgbYWu74nuoXhsbfHN9e/x7ff+6/hB4dWuLYv8f3HwIc33o6ajzvl93bylBXA020VGu1vfWFAiDB/dQBkKqqKtTV1RESEoLGjRvj3bt3WLBggUBlTJ8+HV26dEFgYCBcXV1x6dIlnDt3rtoH2qhRozBnzhyEhoZiz549P9KMBud0zHuoKcpgdl8L7o0Q3TdeR1pO6RVvumryKP+MXWNtRdg108CY9Vfro8oN2gDndvickYvV284i5XM2WjbXw4F1k7k9Mx+SM3jm5di2NsYWP3esDDmDFcGnYKSvhV2rxsPcRLAb7d1/+hZrtp1F3pcCNDPQxpr5wzG0l51Q2yYKnDu1RmZ2HrYdvIj0jBw0N2qMP5Z6cofAklMzeT7vxyLuoKi4BItX7+cpZ9zwbvh1hDMA4No/z7Bi4xHuez5/HKyQR1yUP74tm+vhYBXHt11rYwT7uSPg3+PbWF8Lu3/g+Cbke376p8FfvHgRM2bMwJs3b2BmZoYNGzbAyckJx44dg5WVFYyMjHD//n1YWVkBADIzM6GqqorLly9zJyuHhobCz88P6enpcHFxgY2NDTZt2oRPnz4BKL0M/vjx44iNjeVbBzc3N5w5cwYfP34UaIiLngZf9+hp8HWPngZft+hp8HWrrp4GL9d/M1hScjUqiyn6gi8nptLT4H8Wzs7OePr0KU/atzFf+fhPRUWlQpqXlxe8vLx4Xjdr9t99Q3x9feHr61tpHT58+IDRo0c3+Pk9hBBCRBMNgQnupw+AhGHt2rXo0aMHFBQUcO7cOezevRt//vnnd9fLyMhAVFQUoqKiqpWfEEIIIXWDAqBqiI6OxurVq5GTkwNjY2Ns2LAB48eP/+56bdu2RUZGBlatWgUzM/G5soYQQkjdoh4gwVEAVA2HDh36ofUSEhKEWxFCCCGEDwqABEcBECGEECLiKAASnHg9+IMQQgghBNQDRAghhIg+1r9LTcsQIxQAEUIIISKOhsAER0NghBBCCBE71ANECCGEiDgWC0LoARJOXUQFBUCEEEKIiGNBCENgYhYB0RAYIYQQQsQO9QARQgghIo4mQQuOAiBCCCFE1NFl8AKjITBCCCGEiB3qASKEEEJEnRCGwBgaAiOEEEKIKBHGHKCaX0UmWigAIoQQQkQcBUCCozlAhBBCCBE7FAARQgghoo4lpEVAmzdvhqGhIWRlZWFvb4/o6OhK8zo5OXF7qr5d+vTpI/iGhYACIEIIIUTE8QssfmQRRHh4OLy9veHj44N79+6hTZs2cHFxQUpKCt/8R48exadPn7jL48ePISEhgaFDhwpjFwiMAiBCCCGECCwwMBBeXl7w9PSEhYUFgoODIS8vjx07dvDNr6amBh0dHe5y4cIFyMvLUwBECCGEkB8jzB6g7OxsnqWgoKDC9goLCxETEwNnZ2duGpvNhrOzM27dulWtOm/fvh0jRoyAgoKCcHaCgCgAIoQQQkScMAMgfX19KCsrc5eAgIAK20tLS0NJSQm0tbV50rW1tZGUlPTd+kZHR+Px48cYP368cHbAD6DL4AkhhBDClZiYCCUlJe5rGRkZoW9j+/btaNWqFezs7IRednVRAEQIIYSIOGHeB0hJSYknAOJHQ0MDEhISSE5O5klPTk6Gjo5Olevm5eXh4MGDWLZsWY3qW1M0BEYIIYSIujq+DF5aWhrW1taIjIzkpnE4HERGRsLBwaHKdf/66y8UFBRgzJgx1d9gLaAeIEIIIYQIzNvbG+7u7rCxsYGdnR2CgoKQl5cHT09PAICbmxt0dXUrzCHavn07BgwYAHV19fqoNhcFQIQQQoiIq49HYQwfPhypqalYunQpkpKSYGVlhYiICO7E6Hfv3oHN5h1oiouLw/Xr1/H333/XqK7CQAEQIYQQIuLq61lg06ZNw7Rp0/i+FxUVVSHNzMwMDMMIvJ3aQAEQIYQQIuLoYaiCo0nQhBBCCBE71ANECCGEiLoffJhphTLECAVAhBBCiIijITDB0RAYIYQQQsQO9QARQgghIo56gARHARAhhBAi4lgQQgAkZpOAaAiMEEIIIWKHeoAIIYQQEUdDYIKjAIgQQggRdXQZvMAoABIBsWv7QUlJqb6rIRY028+o7yqInffXg+q7CmJFSpJmPtQl2t8NFwVAhBBCiIijITDBUQBECCGEiDgKgARHARAhhBAi4lis0qWmZYgTGpwkhBBCiNihHiBCCCFExJX2ANV0CExIlRERFAARQgghok4IQ2Didhk8DYERQgghROxQDxAhhBAi4ugqMMFRAEQIIYSIOLoKTHA0BEYIIYQQsUM9QIQQQoiIY7NZYLNr1oXD1HB9UUMBECGEECLiaAhMcDQERgghhBCxQz1AhBBCiIijq8AERwEQIYQQIuJoCExwFAARQgghIo56gARHc4AIIYQQInaoB4gQQggRcdQDJDgKgAghhBARR3OABEdDYIQQQggRO9QDRAghhIg4FoQwBAbx6gKiHiBCCCFExJUNgdV0EdTmzZthaGgIWVlZ2NvbIzo6usr8mZmZmDp1Kho3bgwZGRmYmpri7NmzP9jqmqEeIEIIIYQILDw8HN7e3ggODoa9vT2CgoLg4uKCuLg4aGlpVchfWFiIHj16QEtLC4cPH4auri7evn0LFRWVuq88KAAihBBCRF59XAUWGBgILy8veHp6AgCCg4Nx5swZ7NixAwsWLKiQf8eOHUhPT8fNmzchJSUFADA0NKxRnWuChsAIIYQQEVfXQ2CFhYWIiYmBs7MzN43NZsPZ2Rm3bt3iu87Jkyfh4OCAqVOnQltbG5aWllixYgVKSkpq2vwfQj1AhBBCCOHKzs7meS0jIwMZGRmetLS0NJSUlEBbW5snXVtbG8+fP+db7ps3b3Dp0iWMHj0aZ8+exatXrzBlyhQUFRXBx8dHuI2oBuoBIoQQQkRc2RBYTRcA0NfXh7KyMncJCAgQSh05HA60tLQQEhICa2trDB8+HIsWLUJwcLBQyhcU9QARQgghIk6YN0JMTEyEkpISN7187w8AaGhoQEJCAsnJyTzpycnJ0NHR4Vt+48aNISUlBQkJCW6aubk5kpKSUFhYCGlp6Zo1QEDUA0QIIYSIOGH2ACkpKfEs/AIgaWlpWFtbIzIykpvG4XAQGRkJBwcHvnXs2LEjXr16BQ6Hw0178eIFGjduXOfBD0ABECGEEEJ+gLe3N0JDQ7F79248e/YMkydPRl5eHveqMDc3NyxcuJCbf/LkyUhPT8fMmTPx4sULnDlzBitWrMDUqVPrpf40BEYIIYSIOiEMgQl6I+jhw4cjNTUVS5cuRVJSEqysrBAREcGdGP3u3Tuw2f/1s+jr6+P8+fOYPXs2WrduDV1dXcycORPz58+vYcV/DAVAhBBCiIirr6fBT5s2DdOmTeP7XlRUVIU0BwcH3L59W+Dt1AYaAiOEEEKI2KEeIEIIIUTECfMqMHFBARAhhBAi4uprCEyU0RAYIYQQQsQO9QARQgghIo6GwARHARAhhBAi4mgITHA0BEYIIYQQsUM9QIQQQoiIox4gwf10AZCTkxOsrKwQFBRU31URWdsPX8XmvZeQkp6Nls10EfDbELRraVBp/hOR97Ey5AwSP6XDWF8TS6b2Q48OLbnvrw49i2MX7+FjciakpCTQxkwf/5vUF9aWhgCAGzEvMWDqRr5l/73jN7S1qHzbP6vxQ7tg+pju0FJXwuOXHzB/zV+49/RtpfknjXTCuMGdoaetivSsPJyIvI9lm0+ioLAYAPDghB+aNlGvsN62v65i7upDtdaOhmrXkWsIPnAJqek5MDdpAv/Zg6s8zk5fisWabWfxPikdhnqa+N9kV3R3sODJ8zIhCSu2nMLt2NcoLuHA1FAbIcvHQVdHFYmfPsNhqD/fsoOXeaBvNythNq/BCT10BRv3RiLlczYsm+ti1dyhsG5pWGn+4xfvYUXwGbz79BnG+prwnT4APTuWnlOKikuwfMspXLjxBG8/fIaSoiwc7VrAZ1o/NNZUqVBWQWERnD3W4vHLD7i6dwFamenVUivrF80BEtxPFwCRmjl24R6Wrj+GNfOHw7qlAbYevIJhs/7ErfDF0FRrVCF/9MM3mLh0NxZPdkXPji1x5O8YuM/bhsjdc2Fu0gQAYNJUCyt/GwoDXXV8LShC8IHLGDrzT0QfXgIN1UawbW2Ex2eW85S7cusZXLv7AlbmTeuk3Q3JwB7tsHzWQHivDEfM4wRMGtkVRzZOhe2QZUjLyK2Qf4iLDXym9sd0/3248/ANmjXVwmafsWAYYHHQUQBAN/c1kJD47+xmbtIExzdPx/GL9+usXQ3Fych7WLbpOALmDENbCwNsO3QFY7yDceXA/6ChWvEYv/soHlP99mDBxL5w7mCB4xfuYfzC7Ti3Yw5aGDcGACR8SMPAKRswom97/PZrLygqyOJFfBJkZEpPsU20VHHvxDKecvedvIng/ZfRtb157Te6Hh39OwaLg44hcMFwWFsaIvjAZQyevhn/HF7K95xy58EbjF+8C0un9oNLJ0scjriLMXNCEBU2HxbNmiD/ayEePk/E3F97wbK5LjJz8rHwj8MY9dtWXN5T8ZEKPhtOQEdTGY9ffqiL5tYb6gESHM0BIjyCD1zGmP4dMKpve5gZNcba+cMgJyuN/af537o8JPwKurU3x7Qx3WFqpIOFE/ugtZketh++xs0z2MUGjnZmMNTVQAvjxvCfNRA5eV/x9NVHAIC0lCS01ZW4i5qyAiKuPcLIvvZi94EEgCmjumHP8ZvYf+o24uKT4B1wEPlfCzGmH/8nLNu1NsKdh29w+PxdJH5Kx+U7z3Hk77uw/qbX7nNmLlI+53AXl06WeJOYihv3XtZVsxqMkINRGOnqgOF97GFqpIOVc4dCVlYaB0/f4Zt/+19X4GTfApNHdUNzQx3M9eoNS1M97Dry3zG+OuQMujlYYPGUfrA01YOhrgZ6drLkBlQSEmxoqSvxLBFXH6FvNysoyFd80vbP5M/9l+A2oANG93NAC+PGCFw4AvKy0th78hbf/FsPRqG7gzlmjHWGmZEOFk3uizYt9BH61xUAgLKiHI5tno6BPdqhuaE2bFsZYfXcYYh9lojEpHSesi7ceILLd57Bf+bAWm8nET0/ZQDE4XAwb948qKmpQUdHB76+vtz3AgMD0apVKygoKEBfXx9TpkxBbu5/v6p37doFFRUVHD9+HM2bN4esrCxcXFyQmJjIzePr6wsrKyts3boV+vr6kJeXx7Bhw5CVlQUAuHr1KqSkpJCUlMRTr1mzZqFz58612/gaKCwqxoO4RDjamnHT2Gw2utia4e6jeL7r3H2cgC62pjxpXdubV5q/sKgYe47fhJKiHFo21+WbJ+LqI6Rn5WFkX/sfbInokpKUgFULfURFx3HTGIbBleg42LYy4rtO9MN4WLXQR7t/h3AMdNXRo0NLXLjxpNJtDOtli32VfAH9zAqLivHoxXt0tvnvmGWz2ehsY4p7TxL4rhPzOIEnPwA42rdAzOPS/BwOB5E3n8JYXxOjvbegTd/F6OsViIirDyutx8PniXjy8gNG9m1f4zY1ZIVFxYh9nggnO95ziqOdGf6p5BwR/SgeTrYteNK6tTfHP48SKt1Odu4XsFgsKCvKcdNSPmdj1ooDCPZzg7ysdM0aIgLKhsBquoiTnzIA2r17NxQUFHDnzh2sXr0ay5Ytw4ULFwCUfvg2bNiAJ0+eYPfu3bh06RLmzZvHs35+fj5+//137NmzBzdu3EBmZiZGjBjBk+fVq1c4dOgQTp06hYiICNy/fx9TpkwBAHTp0gXGxsYICwvj5i8qKsK+ffswbty4Wm79j0vPzENJCadCt7SWaiOkfM7hu07K52xoqSnxpGnyyf/39ccw6DoHel1+Q/DBKBzeMAXqKop8y9x36ja62pujiZZqDVojmtRVFCEpKYHUdN79l5qeDS11Jb7rHD5/Fyu2nsG5bbORcms9Yo/74UbMSwTu+ptv/j5OraGsKIf9lfR4/MzSs/gf4xpqjZDyOZvvOqnpORWGxjRVGyE1vTR/WkYu8r4UYPPeSDjZm2P/ukn4pUtreC3aiVv3X/Et8+Dp22huqA2bSoLan8XnzFy++1tTTanS/Z3yORua6uXzV/73+VpQBN9NJzC4pzWU/g2AGIbBFL+98BzUSWzmEJYNgdV0ESc/ZQDUunVr+Pj4oHnz5nBzc4ONjQ0iIyMBlPbCdO3aFYaGhujWrRuWL1+OQ4d4J4EWFRVh06ZNcHBwgLW1NXbv3o2bN28iOjqam+fr16/Ys2cPrKys0KVLF2zcuBEHDx7k9vr8+uuv2LlzJzf/qVOn8PXrVwwbNqzSehcUFCA7O5tn+Vl0tG6Oy3vm42zoLHRrb47xi3ZW+JIHgI8pGbh85xlGu/7cv4yFqWO75vD2dMGcVeFwGrMKY+aGoGenlpjz6y9884/p1wEXbz1FUlpWHdf058RhGABAz06W8BruhJbN9TBtrDOcO1hg7/EbFfJ/KSjE8YsxGNGHjvGaKiougefC7WAYBn8sGM5NDwm/gtz8r5jt0bMea0caup82APpW48aNkZKSAgC4ePEiunfvDl1dXTRq1Ahjx47F58+fkZ+fz80vKSkJW1tb7usWLVpARUUFz54946Y1bdoUurr/DeE4ODiAw+EgLq506MLDwwOvXr3C7dulc2d27dqFYcOGQUFBodJ6BwQEQFlZmbvo6+vXYC8ITk1FARIS7AqBSUpGDrTUK05WBAAtdSWkpPMGaql88ivIycBYXxM2lkZYv2gUJCQksO9UxSGYA6fvQE1ZAb90aVXD1oimz5m5KC4uEegX86JJfXDobDTCTtzC09cfcSbqIfz/PIXZHj0r/KLT11GFk50Z9hy/WWttaMjUlPkf42npOZX2sGmqNUJaRrkeuYwcaP7b86mmrABJCTZMDXV48jQz0MaHlMwK5Z25/ABfvhZhyC+2Fd772airKPLd31X1aGqpKyH1c/n8Ff8+ZcFPYlIGjm2axu39AYCrd1/gn0fx0O44CxrtZ6DdID8AQFf31Zjsu0cYTWtwWBDCEFh9N6KO/ZQBkJSUFM9rFosFDoeDhIQE9O3bF61bt8aRI0cQExODzZs3AwAKCwuFWgctLS24urpi586dSE5Oxrlz5747/LVw4UJkZWVxl2/nHdUFaSlJtDHTx9V/XnDTOBwOrv0TV2lXvY2lIa59kx8ArkQ//27XPsNwUPjvJdr/pTE4cPoOhvWyg5SkxA+2QrQVFZcg9jnvPCwWi4UutqaVzpmQk5UGh8PwpJWUcP5dlzfvKFcHpGbk4O9K5gf97KSlJNHKVA/XY/6b/M3hcHA95gXaVXJZtrWlIa7f5Z0sfu2fOO5tHKSlJNHGvCleJ6bw5HmTmApd7YrDuAdP30aPTpZQV+U/BPwzkZaShFULfVz55785bRwOB1f/eVHpnDa7VkY8+QHg8p3nsG1lyH1dFvy8fpeK45unQa3ccPrKOUNwbd9CXN27AFf3LsChoMkAgB0rPLF4squQWtewsFksoSzi5KcMgCoTExMDDoeDP/74A+3bt4epqSk+fvxYIV9xcTHu3r3LfR0XF4fMzEyYm/93ueq7d+941r19+zbYbDbMzP774ho/fjzCw8MREhICExMTdOzYscr6ycjIQElJiWepa5NGdsXekzdx8MwdvIhPwtzVh5D/tRAj+5ROSJ7qFwb/P09y808Y7ohLt5/hz32X8DIhGatDzyL2WSJ+HVI62TvvSwGWbzmFu4/jkfgpHQ+ev8OM5fvwKTUL/bq35dn2tbsv8Pbj50qvdhIXZVfNjOhjD1NDbQQuGA4FORnsO1Xam7jFdyyWTu3HzR9x7TE8B3fCoB7WaNpEHU52LfC/SX0Rce0RT2DEYrEw2rU9Dp65ww2QxNGEEU44cOoW/joXjZcJSVi49i98+VKI4f8e4zP99yIg+BQ3/69DHRF15xm2HriMV2+T8cf2c3j4PBEeg/+7oGHSyG44FXkf+07eQvz7VOw8cg0Xbz6B+8BOPNuOf5+KOw/e/PSTn79VdlXjgdP/XtW4Mhx5Xwq4w9yTfPbAb9MJbv6JI5wQeespNu2NxIuEJKwMOYPYZ+/gNdQRQGnw4z5/G+4/fYcQf3eUlDBITstGclo2CotKf1Tp66jBolkT7tKsqRYAwEhXk29QSsSTWN0HqFmzZigqKsLGjRvh6uqKGzduIDg4uEI+KSkpTJ8+HRs2bICkpCSmTZuG9u3bw87OjptHVlYW7u7uWLt2LbKzszFjxgwMGzYMOjr/dYO7uLhASUkJy5cvx7JlyypspyEa2KMdPmfmYlXo2X9vWqaH8HWTud3P75MyeIZV7FobI3iZOwK2nsHvwadgrK+F3avHc+8BJMFm41VCMjzPRiM9Mxeqygpoa94Up4Jncu+hUmbfqduwbWWE5obaddfgBujYhXvQUFHE/yb2gZZ6Izx68QFDZmzmDiPo6ahx550AwNodEWAYBosm90VjTWV8zsxFxLXH8P/zFE+5TnZm0G+shr0n+d/SQFz0694OnzPzsHbbOaSmZ8OimS7C/pjIHXb8kJwBNvu/Y9ymlRE2+bhhdegZrAo5DSM9TWwL+JXn+O3l2BoBc4Zi096LWBp0FCZNNRGy3BN2bYx5th1+5g4aayrD8Zuron52g3paIy0zFyu2nkHK5xy0MtXF4Q1TvzmnpPP0PNi3MUbocg/8vuU0/P88BWN9TexdOwEWzUrPKZ9SMnHu6iMAQJfRK3m2dSp4BjpZ816xJy7oRoiCYzEMw3w/m+jgdyfoAQMGQEVFBbt27cK6deuwZs0aZGZmokuXLhg9ejTc3NyQkZHBzTNr1izs2LEDc+fOxYcPH9C5c2ds374dTZuW3pTP19cXx48fx8SJE7F8+XKkp6ejb9++CAkJgaoq76+LpUuXYsWKFUhMTETjxrxf+N+TnZ0NZWVlfEjJqJfeIHGk2X5GfVdB7Ly/HlTfVRArCjJi9bu33mVnZ0NbXRlZWVm1ch4v+57otjYSknKVzzGtjuIvebg0p3ut1bWh+ek+CVFRURXSjh8/zv3/2bNnY/bs2Tzvjx07tsI6gwYNwqBBg6rc1uTJkzF58uQq83z48AG9e/cWOPghhBBCqovNKl1qWoY4+ekCoIYiKysLjx49wv79+3Hy5Mnvr0AIIYSQOkMBUC3p378/oqOjMWnSJPTo0aO+q0MIIeRnxhLCs7zErAdIrK4Cqw4PDw9kZmZWmcfX1xexsbFV5omKikJ+fj7WrVsnvMoRQgghfNCjMARHARAhhBBCxA4NgRFCCCEijvXvv5qWIU4oACKEEEJEHF0FJjgaAiOEEEKI2KEeIEIIIUTEsVisGl8FVuOryERMtQIgQe5j069fv+9nIoQQQojQ0KMwBFetAGjAgAHVKozFYqGkpKQm9SGEEEIIqXXVCoA4HPF9cjQhhBDS0LFZLJ6Hyv5oGeKkRnOAvn79CllZWWHVhRBCCCE/gIbABCfwVWAlJSXw9/eHrq4uFBUV8ebNGwDAkiVLsH37dqFXkBBCCCFVK5sEXdNFnAgcAP3+++/YtWsXVq9eDWlpaW66paUltm3bJtTKEUIIIaTh2rx5MwwNDSErKwt7e3tER0dXmnfXrl0VAq76HEUSOADas2cPQkJCMHr0aEhISHDT27Rpg+fPnwu1coQQQgj5vvp4Flh4eDi8vb3h4+ODe/fuoU2bNnBxcUFKSkql6ygpKeHTp0/c5e3btzVs+Y8TOAD68OEDmjVrViGdw+GgqKhIKJUihBBCSPWVTYKu6SKIwMBAeHl5wdPTExYWFggODoa8vDx27NhR6TosFgs6OjrcRVtbu6ZN/2ECB0AWFha4du1ahfTDhw+jbdu2QqkUIYQQQupHdnY2z1JQUFAhT2FhIWJiYuDs7MxNY7PZcHZ2xq1btyotOzc3FwYGBtDX10f//v3x5MmTWmlDdQh8FdjSpUvh7u6ODx8+gMPh4OjRo4iLi8OePXtw+vTp2qgjIYQQQqrA+nepaRkAoK+vz5Pu4+MDX19fnrS0tDSUlJRU6MHR1taudDqMmZkZduzYgdatWyMrKwtr165Fhw4d8OTJE+jp6dWw9oITOADq378/Tp06hWXLlkFBQQFLly5Fu3btcOrUKfTo0aM26kgIIYSQKgjzURiJiYlQUlLipsvIyNSo3DIODg5wcHDgvu7QoQPMzc2xdetW+Pv7C2Ubgvih+wB17twZFy5cEHZdCCGEEFLPlJSUeAIgfjQ0NCAhIYHk5GSe9OTkZOjo6FRrO1JSUmjbti1evXr1w3WtiR9+Gvzdu3cRFhaGsLAwxMTECLNOhBBCCBEAmyWcpbqkpaVhbW2NyMhIbhqHw0FkZCRPL09VSkpK8OjRIzRu3FjQ5gqFwD1A79+/x8iRI3Hjxg2oqKgAADIzM9GhQwccPHiwXsbxCCGEEHFWH0+D9/b2hru7O2xsbGBnZ4egoCDk5eXB09MTAODm5gZdXV0EBAQAAJYtW4b27dujWbNmyMzMxJo1a/D27VuMHz++RvX+UQIHQOPHj0dRURGePXsGMzMzAEBcXBw8PT0xfvx4RERECL2ShBBCCGlYhg8fjtTUVCxduhRJSUmwsrJCREQEd2L0u3fvwGb/N9CUkZEBLy8vJCUlQVVVFdbW1rh58yYsLCzqpf4shmEYQVaQk5PDzZs3K1zyHhMTg86dOyM/P1+oFRRn2dnZUFZWxoeUjO+OxxLh0Gw/o76rIHbeXw+q7yqIFQWZGj0CkggoOzsb2urKyMrKqpXzeNn3xLCQ65CWV6xRWYX5uTg0oVOt1bWhEfiToK+vz/eGhyUlJWjSpIlQKkUIIYSQ6quPITBRJ/Ak6DVr1mD69Om4e/cuN+3u3buYOXMm1q5dK9TKEUIIIeT76noS9M+gWj1AqqqqPJFhXl4e7O3tISlZunpxcTEkJSUxbtw4DBgwoFYqSgghhBAiLNUKgIKCgmq5GoQQQgj5UTQEJrhqBUDu7u61XQ9CCCGE/CBhPgpDXNTocoCvX7+isLCQJ00cZo4TQgghRLQJHADl5eVh/vz5OHToED5//lzh/ZKSEqFUjBBCCCHVw2axwK7hEFZN1xc1Al8FNm/ePFy6dAlbtmyBjIwMtm3bBj8/PzRp0gR79uypjToSQgghpAoslnAWcSJwD9CpU6ewZ88eODk5wdPTE507d0azZs1gYGCAffv2YfTo0bVRT0IIIYQQoRG4Byg9PR3GxsYASuf7pKenAwA6deqEq1evCrd2hBBCCPmusqvAarqIE4EDIGNjY8THxwMAWrRogUOHDgEo7RkqezgqIYQQQuoODYEJTuAAyNPTEw8ePAAALFiwAJs3b4asrCxmz56NuXPnCr2ChBBCCCHCJvAcoNmzZ3P/39nZGc+fP0dMTAyaNWuG1q1bC7VyhBBCCPk+ugpMcDV+LLCBgQEMDAyEURdCCCGE/ABhDGGJWfxTvQBow4YN1S5wxowZP1wZQgghhAiOHoUhuGoFQOvWratWYSwWiwIgQgghhDR41QqAyq76IvUjv6AEEgV0h+268P56UH1XQezo9Qmo7yqIlYyLS+q7CqQWsPEDVzXxKUOc1HgOECGEEELqFw2BCU7cAj5CCCGEEOoBIoQQQkQdiwWw6SowgVAARAghhIg4thACoJquL2poCIwQQgghYueHAqBr165hzJgxcHBwwIcPHwAAYWFhuH79ulArRwghhJDvo4ehCk7gAOjIkSNwcXGBnJwc7t+/j4KCAgBAVlYWVqxYIfQKEkIIIaRqZUNgNV3EicAB0PLlyxEcHIzQ0FBISUlx0zt27Ih79+4JtXKEEEIIIbVB4EnQcXFx6NKlS4V0ZWVlZGZmCqNOhBBCCBEAPQtMcAL3AOno6ODVq1cV0q9fvw5jY2OhVIoQQggh1Vf2NPiaLuJE4ADIy8sLM2fOxJ07d8BisfDx40fs27cPc+bMweTJk2ujjoQQQgipAltIizgReAhswYIF4HA46N69O/Lz89GlSxfIyMhgzpw5mD59em3UkRBCCCFEqAQOgFgsFhYtWoS5c+fi1atXyM3NhYWFBRQVFWujfoQQQgj5DpoDJLgfvhO0tLQ0LCwshFkXQgghhPwANmo+h4cN8YqABA6AunbtWuXNki5dulSjChFCCCGE1DaB5zxZWVmhTZs23MXCwgKFhYW4d+8eWrVqVRt1JIQQQkgVyobAaroIavPmzTA0NISsrCzs7e0RHR1drfUOHjwIFouFAQMGCL5RIRG4B2jdunV80319fZGbm1vjChFCCCFEMPXxMNTw8HB4e3sjODgY9vb2CAoKgouLC+Li4qClpVXpegkJCZgzZw46d+5cswrXkNCuehszZgx27NghrOIIIYQQ0oAFBgbCy8sLnp6esLCwQHBwMOTl5auMBUpKSjB69Gj4+fnV+70DhRYA3bp1C7KyssIqjhBCCCHVxGLV/GaIggyBFRYWIiYmBs7Oztw0NpsNZ2dn3Lp1q9L1li1bBi0tLfz66681aa5QCDwENmjQIJ7XDMPg06dPuHv3LpYsWSK0ihFCCCGkeoR5GXx2djZPuoyMDGRkZHjS0tLSUFJSAm1tbZ50bW1tPH/+nG/5169fx/bt2xEbG1uzigqJwAGQsrIyz2s2mw0zMzMsW7YMPXv2FFrFCCGEEFL39PX1eV77+PjA19e3RmXm5ORg7NixCA0NhYaGRo3KEhaBAqCSkhJ4enqiVatWUFVVra06EUIIIUQAwpwEnZiYCCUlJW56+d4fANDQ0ICEhASSk5N50pOTk6Gjo1Mh/+vXr5GQkABXV1duGofDAQBISkoiLi4OJiYmNWuAgASaAyQhIYGePXvSU98JIYSQBoQlpH8AoKSkxLPwC4CkpaVhbW2NyMhIbhqHw0FkZCQcHBwq5G/RogUePXqE2NhY7tKvXz907doVsbGxFXqd6oLAQ2CWlpZ48+YNjIyMaqM+hBBCCBFQfVwG7+3tDXd3d9jY2MDOzg5BQUHIy8uDp6cnAMDNzQ26uroICAiArKwsLC0tedZXUVEBgArpdUXgAGj58uWYM2cO/P39YW1tDQUFBZ73v+02I4QQQsjPafjw4UhNTcXSpUuRlJQEKysrREREcCdGv3v3Dmx2w33GfLUDoGXLluG3335D7969AQD9+vXjeSQGwzBgsVgoKSkRfi0JIYQQUqn66AECgGnTpmHatGl834uKiqpy3V27dgm+QSGqdgDk5+eHSZMm4fLly7VZH0IIIYQIiMViVfmczuqWIU6qHQAxDAMAcHR0rLXKEEIIIYTUBYHmAIlbdEgIIYSIgvoaAhNlAgVApqam3w2C0tPTa1QhQgghhAhGmHeCFhcCBUB+fn4V7gRNCCGEECJqBAqARowYUeUj7gkhhBBS98oeaFrTMsRJtQMgmv9DCCGENEw0B0hw1b5DUdlVYIQQQgghoq7aPUBlDy0jhBBCSAMjhEnQELMeIIEfhUEIIYSQhoUNFtg1jGBqur6ooQCIEEIIEXF0GbzgGu5TygghhBBCagn1ABFCCCEijq4CExwFQKSCPceuY+vBS0hNz4G5SRP4zRwEK3ODSvOfuRyLP3acw/ukdBjpamLBpL7o2t6C+35efgFWhZzG39cfISMrH/qN1eAxuDPG9O/IU07M4wSs3XYGsc/eQYLNgkUzXexZOxGyMtK11taGYteRawg+8N8+9589GG0tKt/npy/FYs22s3iflA5DPU38b7IrujtY8OR5mZCEFVtO4XbsaxSXcGBqqI2Q5eOgq6MKAEj4kAb/TSfwz6M3KCwshpO9OfxnD4amWqNabWtDNL6fDaYPc4CWmiIev07G/E0RuBf3sdL8kwbZYZyrDfS0lJCelY8T155h2bZLKCgqAQDMHtkRfTu1QHN9dXwtKEb00/fwDY3Eq/ef66pJDUrooSvYuDcSKZ+zYdlcF6vmDoV1S8NK8x+/eA8rgs/g3afPMNbXhO/0AejZsSUAoKi4BMu3nMKFG0/w9sNnKCnKwtGuBXym9UNjTZUKZRUUFsHZYy0ev/yAq3sXoJWZXi21sn7RfYAER0NgdcjQ0BBBQUH1XY0qnbp0H8s3H8dMdxecCf0NFiZN4DZnK9Iycvjmj3kcjxn+YRje2x5nQ+egZ2dLTFi0A3FvPnHzLN98HFein2PdojG4uGcBxg3pAp/1R3HhxuNvykmAx7yt6GxrhhPBs3BiqzfcBnYCi/XzH6InI+9h2abjmO35C85tnwOLZroY4x1c6T6/+ygeU/32YETf9ojYMQe/dG6F8Qu34/k3+zzhQxoGTtkAEwNt/LVxGi7snoeZHi6QkSn9zZP/pQCjZ28Bi8VC+PqpOLZlJoqKi+ExP1Tsrvgc6GSB5ZN6YFXYVThNCsXjN8k4snIUNFTk+eYf0s0SPuO7Y3XYVdiP24Lpf5zGQMeWWPJrN26eDq2bYtuJf9Bz+k4Mmr8PUpJsHF01CvKyUnXVrAbj6N8xWBx0DPPH90JU2HxYNtfF4OmbkZrO//i+8+ANxi/ehTH9HXBl7wL0cWyDMXNC8PRVaUCa/7UQD58nYu6vpeXtWe2FV2+TMeq3rXzL89lwAjqa9AQDUtHP/+1SA05OTpg1a1Z9V6NObTsUhRF9HTCstz2aG+rg99+GQk5WGofO3uGbf8fhq3C0a4GJI7uhmaE2fvu1N1qa6mH3sWvcPDFPEjDYxRYObZtBv7EaRvXrAHOTJnjw7B03j//m4/AY3BlTRjvD1KgxTJpqoW+3tpCR/vk7KUMORmGkqwOG97GHqZEOVs4dCllZaRw8zX+fb//rCpzsW2DyqG5obqiDuV69YWmqh11H/tvnq0POoJuDBRZP6QdLUz0Y6mqgZydLaKiW9u788ygeiUnpWLdoFMxNmsDcpAnWLRqNh88TcSPmZZ20u6GYMrg99py9j/3nHyDuXRq8g84gv6AIY36x4pvfzkIPdx4n4vClx0hMzsLlmDc4cvkxrFs04eYZuvAADvz9EM/fpuLxm2RMWX0S+toqsGreuI5a1XD8uf8S3AZ0wOh+Dmhh3BiBC0dAXlYae0/e4pt/68EodHcwx4yxzjAz0sGiyX3RpoU+Qv+6AgBQVpTDsc3TMbBHOzQ31IZtKyOsnjsMsc8SkZjE+yzKCzee4PKdZ/CfObDW21nfyiZB13QRJxQA1RDDMCguLq7vaghFYVExHr94j47Wptw0NpuNjtbNce/JW77r3H+SwJMfALrYmvHkt25piIs3HiMpNRMMw+DmvZeIT0xFZ1szAEBaRg5in76FuooiBk1ZD5sBSzBsxib88/BNLbSyYSksKsajF+/R2YZ3n3e2McW9Jwl814l5nMCTHwAc7Vsg5nFpfg6Hg8ibT2Gsr4nR3lvQpu9i9PUKRMTVh/9tt7AYLBYL0lL/BZgy0lJgs1mIFoP9XkZKkg0r08aIuhfPTWMY4Mq9eNha8B8qiX76HlamjdHOrDTgMWisgh52zXHhzqtKt6OkIAMAyMj5IsTaN3yFRcWIfZ4IJzszbhqbzYajnRn+eRTPd53oR/Fwsm3Bk9atvTn+eZRQ6Xayc7+AxWJBWVGOm5byORuzVhxAsJ8b5GV//mF0NljcYbAfXsTsMniRDYCcnJwwY8YMzJs3D2pqatDR0YGvry/3/czMTIwfPx6amppQUlJCt27d8ODBA+77Hh4eGDBgAE+Zs2bNgpOTE/f9K1euYP369WCxWGCxWEhISEBUVBRYLBbOnTsHa2tryMjI4Pr163j9+jX69+8PbW1tKCoqwtbWFhcvXqyDPSE8GVl5KCnhcHsJymiqNkJqejbfdVLTc/jmT/smv+/MwWhmqI32Q/zQvPsceMzbimWzBsO+jQkA4N3H0nkRQbvOY0Tf9ti1eiIsTXUx2vtPxL9PFWYTG5z0f/d5+Xk3GmqNkPJZsH1e9jdKy8hF3pcCbN4bCSd7c+xfNwm/dGkNr0U7cet+6Zd0u5aGkJeVxootJ/HlayHyvxTAf/MJlJRwKt3uz0hdWR6SEmykZuTypKdm5EFLVZHvOocvPcaKXVE4F+SBlIj/ITZsOm48TEDggRt887NYQMCUnrj9+B2eJfzcx3N5nzNz+R7fmmpKlR5nKZ+zoalePn/ln4evBUXw3XQCg3taQ+nfAIhhGEzx2wvPQZ2qnEtHxJvIBkAAsHv3bigoKODOnTtYvXo1li1bhgsXLgAAhg4dipSUFJw7dw4xMTFo164dunfvjvT09O+UWmr9+vVwcHCAl5cXPn36hE+fPkFfX5/7/oIFC7By5Uo8e/YMrVu3Rm5uLnr37o3IyEjcv38fv/zyC1xdXfHu3bsqtsKroKAA2dnZPMvPYPfRa4h9+hbbVvyKU6G/YdGU/lgadATX78YB+O8xK6NcO2BYb3tYmuph6bSBMNbXqnTojVSO8+/+7NnJEl7DndCyuR6mjXWGcwcL7D1e+iWtrqqIYH8PXLzxBKY95sP8l4XIzv2CVqZ6YIvbpSAC6tjGAN6jOmHOhrNwmrwNY3wOoad9c8wZ3Zlv/rUzesHcUAu/Lj9axzX9+RUVl8Bz4XYwDIM/FgznpoeEX0Fu/lfM9uhZj7WrWzQEJjiRnmDRunVr+Pj4AACaN2+OTZs2ITIyEnJycoiOjkZKSgpkZEq7nteuXYvjx4/j8OHDmDBhwnfLVlZWhrS0NOTl5aGjo1Ph/WXLlqFHjx7c12pqamjTpg33tb+/P44dO4aTJ09i2rRp1WpPQEAA/Pz8qpW3NqgqK0BCgl1h8m1qRg401ZT4rqOp1ohvfo1/838tKMSa0DPYutwT3RxKr+IwN2mCp68+ICQ8Cp1szKClXpq3uaE2TzkmBtr4mJwhlLY1VGr/7vPyE0LT0nO4+6W8yvZ52d9ITVkBkhJsmBryHrfNDLR5hh0c7VrgxqElSM/MhYQEG8qN5NG23xI0baIhjKaJhM9Z+Sgu4UCzXG+PpqoCUsr1CpVZ5OGEQxcfIuxcLADgaXwKFGSlsW52H/yx/xq+fWzi6mm/wMW+OXp778HHNP6Tfn9m6iqKfI/v1PTsSo9vLXUlpH4un7/i56Es+ElMysDJP6dze38A4OrdF/jnUTy0O87iWaer+2oM/cUGW3zdatCqhomNmvdoiHSPyA8Q6fa2bt2a53Xjxo2RkpKCBw8eIDc3F+rq6lBUVOQu8fHxeP36tVC2bWNjw/M6NzcXc+bMgbm5OVRUVKCoqIhnz54J1AO0cOFCZGVlcZfExESh1LW6pKUkYWmqh5sxL7hpHA4HN++9RLuW/LuR27Y05MkPANfvvuDmLyrmoKi4pMLVXGw2G8y/Vxvp6ahBW0MZbxJTePLEJ6ZCV1utxu1qyKSlJNHKVA/Xv5l4zOFwcD3mBdpVcpmwtaUhrt/lnah87Z84WFsacstsY94Ur8vtzzeJqdDVVq1QnpqKIpQbyeNGzAukZeSiZ6eWNWuUCCkq5iD2xSc4tjPkprFYQJe2Rvjn6Xu+68jJSKH8hXIl/yawvvkJvXraL+jTyQz95u7Fu6RMYVddJEhLScKqhT6u/BPHTeNwOLj6zwvYtjLiu45dKyOe/ABw+c5z2LYy5L4uC35ev0vF8c3ToKbCG8CunDME1/YtxNW9C3B17wIcCpoMANixwhOLJ7sKqXVE1Il0D5CUFO8lpSwWCxwOB7m5uWjcuDGioqIqrKOiogLg3y/gck+4Lyoqqva2FRQUeF7PmTMHFy5cwNq1a9GsWTPIyclhyJAhKCwsrHaZMjIy3B6r+jJ+mBN+C9iPVi30YdXCANsPX0H+l0IM7WUPAPD+fR+0NZUxf0JfAMC4IV0wfMYmhIZfRtf2Fjh16T4exSUiYM4wAEAjBVnYW5kgIPgkZGWkoKejituxr3H0/F0sntofQOnfbcKIrgjaGQFzkyawaKaLI+f/wet3KdiyzKNe9kNdmjDCCbN/3482LfRhZd4U2w5dwZcvhRjep3Sfz/TfCx1NZSycVHri/nWoI4ZM24itBy6jewcLnLh4Dw+fJ2LVvP+GACaN7IYpPrth38YEHdo1Q9Sd57h48wn+2vBfb2T4mTtoZqANdVVFxDxOgM/6o/Aa5giTprw9cT+7P4/cxp/z+uN+3Cfci/uIyYPsoCArhX0RpXMGt8zvj09pOVi2/RIAIOL2C0wZ3B4PXyXh7vMPMG6iiv95OCHi9gtwOKXnlLUzemFIN0uMWhqO3PwCaKmWni+y8wrwtfDnuGiiuqaM6oYpfmFoa94U7VoaYsuBy8j7UoDRru0BAJN89qCxpjJ8ppWeDyaOcELfiUHYtDcSPTu1xNG/YxD77B2C/jcSQGnw4z5/Gx48T8TBdZNQUsIgOa10uoCqsjykpSShr8P7w0lRvvS8aqSryfdHwM+gbK5qTcsQJyIdAFWmXbt2SEpKgqSkJAwNDfnm0dTUxOPHj3nSYmNjeYIqaWlplJSUVGubN27cgIeHBwYOLL3cMjc3FwkJCT9U//rk2q0t0jNzsW5HBFLTs2HeTBe710zkTmL8kJIB1jdzRKwtjbB+yVj8sf0s1oSegaGeJkJ+Hwcz4/8u99241A2rQ85g1vK9yMzOh66OKuaO740x/Ttw8/w61BEFhUXw33QCmTn5MDdpgr1/TIKB7s8/HNOvezt8zszD2m3nkJqeDYtmugj745t9npzBMy/HppURNvm4YXXoGawKOQ0jPU1sC/gVLb7Z570cWyNgzlBs2nsRS4OOwqSpJkKWe8KujTE3z+t3KVi59TQys/Ohp6OGGW494DXcqc7a3VAci3oKDWV5/M/DEVqqinj0OhlDFu5HamYeAEBPS4kb2ADA2r2lw1yLPJ3QWKMRPmflI+LWC/jvuMzN82u/0h7iM4HuPNuasvoEDvz9EOJkUE9rpGXmYsXWM0j5nINWpro4vGEqd0jrfVI6zw347NsYI3S5B37fchr+f56Csb4m9q6dAItmpVfdfUrJxLmrjwAAXUav5NnWqeAZ6FTuqlRxwULNH+YuXuEPwGLKd4OICCcnJ1hZWfHcWHDAgAFQUVHBzp070aVLF+Tk5GD16tUwNTXFx48fcebMGQwcOBA2NjY4f/48evXqhV27dsHBwQF79+5FUFAQ2rZty+05mjBhAmJjY3Ho0CEoKipCTU0NV69eRdeuXZGRkcHtTQKAQYMGIT4+Hjt37gSLxcKSJUsQFRWFcePGcetoaGiIWbNmVfveQtnZ2VBWVsbLxDQ0UuI/Xk6ES1JC3E4B9U+vT0B9V0GsZFxcUt9VECvZ2dnQVldGVlYWlGrhPF72PRES9RRyijW7i/uX3BxMcLKotbo2NCI9B6gyLBYLZ8+eRZcuXeDp6QlTU1OMGDECb9++hbZ2afe+i4sLlixZgnnz5sHW1hY5OTlwc+OdGDdnzhxISEjAwsICmpqaVc7nCQwMhKqqKjp06ABXV1e4uLigXbt2tdpOQgghhPwYke0BEgfUA1T3qAeo7lEPUN2iHqC6VZc9QPI17AHKF7MeoJ9yDhAhhBAiToRxHx8xmwP9cw6BEUIIIYRUhXqACCGEEBFHl8ELjgIgQgghRMTRnaAFJ27tJYQQQgihHiBCCCFE1NEQmOAoACKEEEJEHN0JWnA0BEYIIYSQH7J582YYGhpCVlYW9vb2iI6OrjTv0aNHYWNjAxUVFSgoKMDKygphYWF1WFteFAARQgghIq5sCKymiyDCw8Ph7e0NHx8f3Lt3D23atIGLiwtSUlL45ldTU8OiRYtw69YtPHz4EJ6envD09MT58+eFsQsERgEQIYQQIuLYQloEERgYCC8vL3h6esLCwgLBwcGQl5fHjh07+OZ3cnLCwIEDYW5uDhMTE8ycOROtW7fG9evXBW6vMFAARAghhIg4YfYAZWdn8ywFBQUVtldYWIiYmBg4Oztz09hsNpydnXHr1q3v1pdhGERGRiIuLg5dunQR3o4QAAVAhBBCCOHS19eHsrIydwkIqPi8vrS0NJSUlHAfMF5GW1sbSUlJlZadlZUFRUVFSEtLo0+fPti4cSN69Ogh9DZUB10FRgghhIg4YV4FlpiYyPMwVBkZmRqW/J9GjRohNjYWubm5iIyMhLe3N4yNjeHk5CS0bVQXBUCEEEKIiBPmw1CVlJS++zR4DQ0NSEhIIDk5mSc9OTkZOjo6la7HZrPRrFkzAICVlRWePXuGgICAegmAaAiMEEIIIQKRlpaGtbU1IiMjuWkcDgeRkZFwcHCodjkcDofvHKO6QD1AhBBCiIhjgwV2DQfBBF3f29sb7u7usLGxgZ2dHYKCgpCXlwdPT08AgJubG3R1dblziAICAmBjYwMTExMUFBTg7NmzCAsLw5YtW2pU7x9FARAhhBAi4oQ5BFZdw4cPR2pqKpYuXYqkpCRYWVkhIiKCOzH63bt3YLP/G2jKy8vDlClT8P79e8jJyaFFixbYu3cvhg8fXrOK/yAWwzBMvWyZfFd2djaUlZXxMjENjb4zHkuEQ1JC3G4GX//0+lS8woTUnoyLS+q7CmIlOzsb2urKyMrK+u68mh8tX1lZGeG3XkJesVGNysrPzcFwh+a1VteGhnqACCGEEBHH+vdfTcsQJxQAEUIIISKuPobARB1dBUYIIYQQsUM9QIQQQoiIYwnhKjAaAiOEEEKISKEhMMFRAEQIIYSIOAqABEdzgAghhBAidqgHiBBCCBFxdBm84CgAIoQQQkQcm1W61LQMcUJDYIQQQggRO9QDRAghhIg4GgITHAVAhBBCiIijq8AER0NghBBCCBE71ANECCGEiDgWaj6EJWYdQBQAEUIIIaKOrgITHA2BEUIIIUTsUA8QIYQQIuLoKjDBUQBECCGEiDi6CkxwFAARQgghIo6Fmk9iFrP4h+YAEUIIIUT8UA8QIYQQIuLYYIFdwzEstpj1AVEAJAIUZCWhKEt/qrpQVMKp7yqInU/n/lffVRArqh1+q+8qiBWmpKBOtkNDYIKjITBCCCGEiB3qViCEEEJEHXUBCYwCIEIIIUTE0X2ABEdDYIQQQggRO9QDRAghhIg6IdwIUcw6gCgAIoQQQkQdTQESHA2BEUIIIUTsUA8QIYQQIuqoC0hgFAARQgghIo6uAhMcBUCEEEKIiKOnwQuO5gARQgghROxQAEQIIYSIOJaQFkFt3rwZhoaGkJWVhb29PaKjoyvNGxoais6dO0NVVRWqqqpwdnauMn9towCIEEIIEXX1EAGFh4fD29sbPj4+uHfvHtq0aQMXFxekpKTwzR8VFYWRI0fi8uXLuHXrFvT19dGzZ098+PBB8PYKAQVAhBBCCBFYYGAgvLy84OnpCQsLCwQHB0NeXh47duzgm3/fvn2YMmUKrKys0KJFC2zbtg0cDgeRkZF1XPNSFAARQgghIo4lpH8AkJ2dzbMUFBRU2F5hYSFiYmLg7OzMTWOz2XB2dsatW7eqVef8/HwUFRVBTU1NODtBQBQAEUIIISKu7Cqwmi4AoK+vD2VlZe4SEBBQYXtpaWkoKSmBtrY2T7q2tjaSkpKqVef58+ejSZMmPEFUXaLL4AkhhBDClZiYCCUlJe5rGRkZoW9j5cqVOHjwIKKioiArKyv08quDAiBCCCFExAnzRtBKSko8ARA/GhoakJCQQHJyMk96cnIydHR0qlx37dq1WLlyJS5evIjWrVvXpMo1QkNghBBCiKir46vApKWlYW1tzTOBuWxCs4ODQ6XrrV69Gv7+/oiIiICNjY0ADRQ+6gEihBBCiMC8vb3h7u4OGxsb2NnZISgoCHl5efD09AQAuLm5QVdXlzuHaNWqVVi6dCn2798PQ0ND7lwhRUVFKCoq1nn9KQAihBBCRFx9PAts+PDhSE1NxdKlS5GUlAQrKytERERwJ0a/e/cObPZ/A01btmxBYWEhhgwZwlOOj48PfH19a1T3H0EBECGEECLi6utZYNOmTcO0adP4vhcVFcXzOiEhQfAN1CIKgAghhBARJ8xJ0OKCJkETQgghROxQDxAhhBAi6qgLSGAUABFCCCEirj4mQYs6GgIjhBBCiNihHiBCCCFExNXXVWCijAIgQgghRMTRFCDB0RAYIYQQQsQO9QARQgghoo66gARGARAhhBAi4ugqMMHREBghhBBCxA71ABFCCCEijq4CExwFQIQQQoiIoylAgqMAiBBCCBF1FAEJjOYAEUIIIUTsUA8QIYQQIuLoKjDBUQBECCGEiDohTIIWs/iHhsAIIYQQIn6oB+hfvr6+OH78OGJjY2ttGywWC8eOHcOAAQNqbRvCsP2vq9i0LxIpn7PRsrkuVv42BO1aGlaa/0TkfQRsPY3ET+kw1tfE0qn90aNjS+77q0LP4tiFGHxMzoSUlATatNDHokmusLasWGZBYRFcxv2Bxy8/4HLYfLQy1auFFjY8O49cw5/7LiE1PRsWzXTxu/dgtLUwqDT/qUv3sSrkLN4npcNITxOLp7iie4f/9vnM5ftw6Gw0zzpO9i1wYN1k7mvbQX54n5TOk+d/k/piulsPIbWq4aL9Xf/GD+qI6aOdoKXWCI9ffcT8wGO49yyx0vyThnXGuIEdoKejivTMPJy4/ADLgs+ioLCYm6exhhJ8p/aFc/sWkJOVRvz7NEz9/SBin7+viybVK5oDLTgKgP41Z84cTJ8+vb6rUe+OXYjBkvXHsHb+cFi3NEDwwSgMnfknbh9aAk21RhXyRz98gwlLdmHxZFe4dLLEkfN34TYvFJf2zIO5SRMAgElTLayaMxQGuhr4WlCELQcuY8iMzfjnyFJoqPKW6bfxBHQ0lPH45Yc6aW9DcOLiPfhuOIZVc4ehbUtDhIZHYeTsLbh+YBE0+Ozzfx7FY7LPHvxvUl84d2yJY3/HwHPBdvy9cw5a/LvPAaBre3MELRrFfS0tVfHjPterN8b0c+C+VpSXEXLrGh7a3/VvYHcrLJ/RD95rDiPmyTtMGt4ZR9ZNgO3IVUjLyK2Qf0iPtvCZ3AfTV4TjzqMENGuqic2LRoABsHjDSQCAciM5RGydjmv3XmGodyjSMvNgoq+BzJwvddy6ekIRkMB+miGwwsLCH1qPYRgUFxdDUVER6urqQq6V6Nly4DLG9nfAKNf2MDNujD8WDIecrDT2n7rFN//W8Ch0a2+O6WOdYWqkg4WT+qK1mT62/XWVm2eIiw0c7VrAUFcDLYwbY/nMgcjJ+4qnrz7ylHXx5hNcjn4OvxkDarOJDc7Wg1EY3a8DRvRtDzMjHayeNwxyMtI4cPo23/zbDl1BV/sWmDK6O0wNdTB/Qh+0MtPDjiPXePJJS0lCS12Ju6goyVcoS1FehiePvNzP/4VM+7v+TRnRBXtO3sb+M/8gLiEZ3quPIL+gCGP62vHNb9fKEHceJeDwhftITMrA5egXOHLxPqzNm3LzzBrTDR+SMzHt93Dce5aId5/ScTn6BRI+fK6rZhERU68B0OHDh9GqVSvIyclBXV0dzs7OyMvLg5OTE2bNmsWTd8CAAfDw8OC+NjQ0hL+/P9zc3KCkpIQJEyYgISEBLBYLBw8eRIcOHSArKwtLS0tcuXKFu15UVBRYLBbOnTsHa2tryMjI4Pr16/D19YWVlRVPPjs7OygoKEBFRQUdO3bE27dvue+fOHEC7dq1g6ysLIyNjeHn54fi4v+6Yl++fIkuXbpAVlYWFhYWuHDhgtD3n7AVFhXjwfNEONqZcdPYbDYcbc3wz6MEvuvcfZQAR1sznrSu7Vvg7qP4Srex+/hNKCnKoWVzXW56yudszF5xEH/6ukFOVrrmjRERhUXFeBiXiM42ptw0NpuNzramiHmcwHedu4/j0bncPneyb1Eh/637r2DZexE6jfgd89ccQnpWXoWyNoVdhMUvC9HDfTX+3BeJ4uKSGrepIaP9Xf+kJCVgZaaHqLsvuWkMw+DKPy9ga8l/GDL6UQKszPTQzlwfAGDQRA09HMxx4dYzbp5fOlng/vNE7FzuhhdnfHFllzfc+tnXbmMaEJaQ/omTehsC+/TpE0aOHInVq1dj4MCByMnJwbVr18AwTLXLWLt2LZYuXQofHx+e9Llz5yIoKAgWFhYIDAyEq6sr4uPjeXp4FixYgLVr18LY2BiqqqqIiorivldcXIwBAwbAy8sLBw4cQGFhIaKjo8H6d4r9tWvX4Obmhg0bNqBz5854/fo1JkyYAADw8fEBh8PBoEGDoK2tjTt37iArK6tCQNcQfc7MQ0kJB5pqSjzpmmqN8PJtMt91Uj5nVxga01JrhJTPOTxp568/xoTFO5H/tQjaGko4vHEq1FUUAZSe/Kb774XHoI5oa94U7z6Kzy+2dO4+592HmmqN8OptCt91Uj/nQLPc0KGmaiOkfM7mvu5qb47ejq3RtIk6Et6nIWDraYz2DsbpkNmQkCj93fPr0C5obaYHFSV5/PMoHgHBp5Gclg2/mQOF3MqGg/Z3/VNXUYCkpARS03nPEanpuWhuoMV3ncMX7kNNRQHngqeBxWJBSlICO47eROCeSG4ewybqGDewA/48eAWBeyLRzlwfK2cPRGFRCQ6eu1urbWoI6FEYgqvXAKi4uBiDBg2CgUFp1N+qVSuByujWrRt+++037uuEhAQAwLRp0zB48GAAwJYtWxAREYHt27dj3rx53LzLli1Djx78Jx9mZ2cjKysLffv2hYmJCQDA3Nyc+76fnx8WLFgAd3d3AICxsTH8/f0xb948+Pj44OLFi3j+/DnOnz+PJk1K5wisWLECvXr1qrI9BQUFKCgo4KnHz6KTdXNcDluA9MxchJ24ifH/24HzO+ZAU60RQg9dQW5eAWa596zvav40BvRox/1/c5MmsGjWBO2H+uPm/ZfobFPamzFpZFduHotmupCWksS8VeH432RXyEjT9EBB0P6uXR3bmsDbrTvmrD2KmCdvYaSngZWzBmCOhzPW7roIAGCzWYh9/h7+W88BAB69+ABzYx14DnQQiwCICK7ehsDatGmD7t27o1WrVhg6dChCQ0ORkZEhUBk2NjZ80x0c/ptkKCkpCRsbGzx79ownT2XrAoCamho8PDzg4uICV1dXrF+/Hp8+feK+/+DBAyxbtgyKiorcxcvLC58+fUJ+fj6ePXsGfX19bvBTvk6VCQgIgLKyMnfR19f/7jrCpK6iAAkJNlLTeQOv1PQcaJXrFSqjpa5U4ZdcSnoOtNR5fzEryMnAWF8TNq2MsH7xaEhISGDfydJ5RdfuvsA/j+PRpPNsaHeYCdshywAAzh5rMNUvTFjNa5DUuPu8/K/hHGjxmZALAJrqjZCaUS5/Rg601Pn/jQDAQFcDaioKiH+fVmmedhYGKC7hIPHTz9sDR/u7/n3OzENxcQmfXjhFpJT7u5RZ5PULDkXEIOzUHTx9k4QzVx/Df+tZzHbrzu2ZT/6cjefxvD3VLxKSoaetWjsNaWBYQlrESb0FQBISErhw4QLOnTsHCwsLbNy4EWZmZoiPjwebza4wFFZUVFShDAUFhR/e/vfW3blzJ27duoUOHTogPDwcpqamuH27dJJkbm4u/Pz8EBsby10ePXqEly9fQlZW9ofrtHDhQmRlZXGXxMTKLwmtDdJSkmjTQh9X/3nBTeNwOLj6zwvYtjLku45NK0NcvfuCJ+1KdBxsWhlVuS2GYVBQVDpnKuC3IbiydwGiwuYjKmw+DgZOAgBsW+6JRZP61qBFDZ+0lCRam+njegzvPr9+9wXf2wQAgI2lEa6X2+dXo+MqzQ8AH1MykZGVD+0qvrQfv/wANptV4cq8nwnt7/pXVFyC2Lj3cLRuzk1jsVjoYtMc/zx+y3cdOVkpcMp9J5RwmH/XLX1952ECmjfV5Mljoq+J90mC/bAWWRQBCaxeJ0GzWCx07NgRfn5+uH//PqSlpXHs2DFoamry9LiUlJTg8ePH1S63LFABSufzxMTE8AxhVVfbtm2xcOFC3Lx5E5aWlti/fz8AoF27doiLi0OzZs0qLGw2G+bm5khMTORpw7d1qoyMjAyUlJR4lro2eWRXhJ24iYNn7uBFfBLmrDqE/K8FGNm3PQBgiu8e+G8+yc0/cbgTLt16is37IvEyIQmrQs8i9tk7jB/aBQCQ96UAy/88ibuP4pH4KR2xz95hhv8+fErNRP/ubQEAejpqMDdpwl1MmpbOAzDU00ATMfj1NnGEE/advIVDZ6PxIiEJ89f8hfyvhRjRt3QC5/Rle/H7llPc/OOHOeLy7WcI3n8JLxOSsXbbOTx4nohxgzsDAPLyC7Bs0wnEPE5A4qfPuHY3Dh7zQ2GkpwEn+9LPwd1H8QgJj8KTlx/w9kMajpy/C5/1xzDYxYbv1Us/E9rf9e/Pg1fh1s8eI3rZwNRAC4FzB0NBVhr7TpfeS2nLkpFYOqk3N3/EjafwHNgBg5yt0LSxGpxsTfE/r18Qcf0pOP8GQn+GX4WNpQG83brDSFcdQ3q0hXv/9th25Ea9tLGu0SRowdXbwPOdO3cQGRmJnj17QktLC3fu3EFqairMzc2hoKAAb29vnDlzBiYmJggMDERmZma1y968eTOaN28Oc3NzrFu3DhkZGRg3bly114+Pj0dISAj69euHJk2aIC4uDi9fvoSbmxsAYOnSpejbty+aNm2KIUOGgM1m48GDB3j8+DGWL18OZ2dnmJqawt3dHWvWrEF2djYWLVok6C6qFwN7WONzZi5WhpxByuccWJrq4lDQFG53//vkDLDZ/31I7FobY6u/B1YEn8bvW07DWF8Te1Z7ce8BJMFm4+XbZBw8G430zDyoKsujrbkBTm2dhRbGjeuljQ1Nf+d2+JyZi9WhZ5Gano2WzfWwP3ASdzL6h3L73LaVEf70c8OqkLMI2HoaRnqa2LnyV+49adgSLDx99RGHzkYjO/cLtDWU4WhnhvkTenPnmkhLS+LExXv4Y3sECguLod9EDRNGOGHiiK4VK/iTof1d/45FxkJDRQH/83KBlpoSHr38gCHeoUj99x5Aetoq3MAGANbuugiGARZN6IXGmsr4nJGLiBtP4b/1LDfP/WeJGLtgJ5ZO7oO5nj3w9lM6/rf+BP76+16dt4+IBhYjyGVXQvTs2TPMnj0b9+7dQ3Z2NgwMDDB9+nRMmzYNRUVFmDlzJsLDwyEpKYnZs2fj9u3bUFFRwa5duwCUXgY/a9YsnqurEhISYGRkhP379yMoKAixsbFo1qwZNm3ahK5dS080UVFR6Nq1KzIyMqCiosJd99s7QScnJ2PSpEm4c+cOPn/+jMaNG8Pd3R0+Pj5gs0s7zc6fP49ly5bh/v37kJKSQosWLTB+/Hh4eXkBAF68eIFff/0V0dHRMDQ0xIYNG/DLL78IdCfo7OxsKCsr42NqZr30BomjohJOfVeBkFrV2HHe9zMRoWFKClBwbzOysrJq5Txe9j3xOD4FjWpYfk52NiyNtGqtrg1NvQVAtaEsALp//z7PPX1EFQVAdY8CIPKzowCobtVVAPRESAFQSzEKgH6aO0ETQgghhFQX3XyCEEIIEXF0I0TB/VQ9QIaGhmAY5qcY/iKEEEKqr36ug9+8eTMMDQ0hKysLe3t7REdHV5r3yZMnGDx4MAwNDcFisRAUFCTw9oTppwqACCGEEFI3wsPD4e3tDR8fH9y7dw9t2rSBi4sLUlL4P1YmPz8fxsbGWLlyJXR0dOq4thVRAEQIIYSIuLIhsJougggMDISXlxc8PT1hYWGB4OBgyMvLY8eOHXzz29raYs2aNRgxYgRkZGSE0OqaoQCIEEIIEXHCHADLzs7mWb59RmWZwsJCxMTEwNnZmZvGZrPh7OyMW7du1U4jhYwCIEIIIYRw6evr8zyXMiAgoEKetLQ0lJSUQFtbmyddW1sbSUlJdVXVGqGrwAghhBARJ8yrwBITE3nuA9QQhqtqAwVAhBBCiIgTxrO8ytavzrMoNTQ0ICEhgeTkZJ705OTkBjHBuTpoCIwQQggRdXV8Fby0tDSsra0RGRnJTeNwOIiMjISDg0PN21MHqAeIEEIIIQLz9vaGu7s7bGxsYGdnh6CgIOTl5cHT0xMA4ObmBl1dXe4cosLCQjx9+pT7/x8+fEBsbCwUFRXRrFmzOq8/BUCEEEKIiPux2xhWLEMQw4cPR2pqKpYuXYqkpCRYWVkhIiKCOzH63bt33AeIA8DHjx/Rtm1b7uu1a9di7dq1cHR0RFRUVA1rLzgKgAghhBARV1+Pwpg2bRqmTZvG973yQU3Z0xoaCpoDRAghhBCxQz1AhBBCiIgT5lVg4oICIEIIIUTU1cckIBFHQ2CEEEIIETvUA0QIIYSIOOoAEhwFQIQQQoiIq6+rwEQZDYERQgghROxQDxAhhBAi8mp+FZi4DYJRAEQIIYSIOBoCExwNgRFCCCFE7FAARAghhBCxQ0NghBBCiIijITDBUQBECCGEiDh6FIbgaAiMEEIIIWKHeoAIIYQQEUdDYIKjAIgQQggRcfQoDMHREBghhBBCxA71ABFCCCGijrqABEYBECGEECLi6CowwdEQGCGEEELEDvUAEUIIISKOrgITHAVAhBBCiIijKUCCowCIEEIIEXUUAQmM5gARQgghROxQDxAhhBAi4ugqMMFRAEQIIYSIOJoELTgKgBowhmEAADk52fVcE/FRVMKp7yoQUquYkoL6roJYYUoKS//77/m8tmRn1/x7QhhliBIKgBqwnJwcAICZcdN6rgkhhJCayMnJgbKystDLlZaWho6ODpob6QulPB0dHUhLSwulrIaOxdR2WEp+GIfDwcePH9GoUSOwRKhvMjs7G/r6+khMTISSklJ9V0cs0D6vW7S/65Yo72+GYZCTk4MmTZqAza6d646+fv2KwsJCoZQlLS0NWVlZoZTV0FEPUAPGZrOhp6dX39X4YUpKSiJ3shJ1tM/rFu3vuiWq+7s2en6+JSsrKzZBizDRZfCEEEIIETsUABFCCCFE7FAARIRORkYGPj4+kJGRqe+qiA3a53WL9nfdov1NagNNgiaEEEKI2KEeIEIIIYSIHQqACCGEECJ2KAAihBBCiNihAIiQeuDk5IRZs2bVdzVIA2NoaIigoKD6robI8PX1hZWVVa1ug8Vi4fjx47W6DVI/KAAihJAfRIFs/ZozZw4iIyPruxpERFEAROqdsG7hTkhDxDAMiouL67saDdKPfvbL9qmioiLU1dWFXCsiLigAIjwiIiLQqVMnqKioQF1dHX379sXr168BAAkJCWCxWDh69Ci6du0KeXl5tGnTBrdu3eIpIzQ0FPr6+pCXl8fAgQMRGBgIFRUV7vtl3dbbtm2DkZERZGVlsWfPHqirq6OggPdJ1QMGDMDYsWNrvd31gcPhYN68eVBTU4OOjg58fX257wUGBqJVq1ZQUFCAvr4+pkyZgtzcXO77u3btgoqKCo4fP47mzZtDVlYWLi4uSExM5OYp289bt27l/j2GDRuGrKwsAMDVq1chJSWFpKQknnrNmjULnTt3rt3G1wEnJyfMmDGj0n2cmZmJ8ePHQ1NTE0pKSujWrRsePHjAfd/DwwMDBgzgKXPWrFlwcnLivn/lyhWsX78eLBYLLBYLCQkJiIqKAovFwrlz52BtbQ0ZGRlcv34dr1+/Rv/+/aGtrQ1FRUXY2tri4sWLdbAnhOvw4cNo1aoV5OTkoK6uDmdnZ+Tl5fHtDRswYAA8PDy4rw0NDeHv7w83NzcoKSlhwoQJ3PPKwYMH0aFDB8jKysLS0hJXrlzhrlfZPi0/BBYVFQU7OzsoKChARUUFHTt2xNu3b7nvnzhxAu3atYOsrCyMjY3h5+fHE5y+fPkSXbp0gaysLCwsLHDhwgWh7z/ScFAARHjk5eXB29sbd+/eRWRkJNhsNgYOHAgOh8PNs2jRIsyZMwexsbEwNTXFyJEjuSeRGzduYNKkSZg5cyZiY2PRo0cP/P777xW28+rVKxw5cgRHjx5FbGwshg4dipKSEpw8eZKbJyUlBWfOnMG4ceNqv+H1YPfu3VBQUMCdO3ewevVqLFu2jHvCZbPZ2LBhA548eYLdu3fj0qVLmDdvHs/6+fn5+P3337Fnzx7cuHEDmZmZGDFiBE+eV69e4dChQzh16hQiIiJw//59TJkyBQDQpUsXGBsbIywsjJu/qKgI+/bt+2n2eVX7eOjQoUhJScG5c+cQExODdu3aoXv37khPT69W2evXr4eDgwO8vLzw6dMnfPr0Cfr6/z2Re8GCBVi5ciWePXuG1q1bIzc3F71790ZkZCTu37+PX375Ba6urnj37l2ttL02fPr0CSNHjsS4cePw7NkzREVFYdCgQRDkdnJr165FmzZtcP/+fSxZsoSbPnfuXPz222+4f/8+HBwc4Orqis+fP/OsW36ffqu4uBgDBgyAo6MjHj58iFu3bmHChAncB0lfu3YNbm5umDlzJp4+fYqtW7di165d3PMTh8PBoEGDIC0tjTt37iA4OBjz58//0V1FRAFDSBVSU1MZAMyjR4+Y+Ph4BgCzbds27vtPnjxhADDPnj1jGIZhhg8fzvTp04enjNGjRzPKysrc1z4+PoyUlBSTkpLCk2/y5MlMr169uK//+OMPxtjYmOFwOLXQsvrl6OjIdOrUiSfN1taWmT9/Pt/8f/31F6Ours59vXPnTgYAc/v2bW7as2fPGADMnTt3GIYp3c8SEhLM+/fvuXnOnTvHsNls5tOnTwzDMMyqVasYc3Nz7vtHjhxhFBUVmdzc3Jo3sp5VtY+vXbvGKCkpMV+/fuV538TEhNm6dSvDMAzj7u7O9O/fn+f9mTNnMo6OjjzbmDlzJk+ey5cvMwCY48ePf7eOLVu2ZDZu3Mh9bWBgwKxbt+77jasnMTExDAAmISGhwnv89kX//v0Zd3d37msDAwNmwIABPHnKzisrV67kphUVFTF6enrMqlWrGIapfJ/6+Pgwbdq0YRiGYT5//swAYKKiovjWvXv37syKFSt40sLCwpjGjRszDMMw58+fZyQlJZkPHz5w3z937hwDgDl27BjfMoloox4gwuPly5cYOXIkjI2NoaSkBENDQwDg+ZX67S+vxo0bAyjtrQGAuLg42NnZ8ZRZ/jUAGBgYQFNT8//t3XtQVFUcB/DvLshj2VUeIgIioiuwJm6gFhszkopiTkSiMYkpq0D5RDESGURFUnyBiVNiWDwcCJgUpngY9EBBBIOEUVs2QFZ80IwZOm4mr739wXCndZEWQwX395lxhnvv755z7mEXf/ecs3fV9oWEhKC4uBg3b94E0DPNI5VK2Tu4F82jd7DW1tZsP37//feYO3cubG1tIRAIsHz5cty5cwcPHjxg4/X19TFz5kx229nZGaamppDJZOy+8ePHw9bWlt2WSCRQqVSQy+UAeqZxGhsbUVlZCaCnz/39/WFiYjL4F/wcPK6P6+rqoFQqYWFhAT6fz/5rbm5mp3z/rxkzZqhtK5VKhIeHQyQSwdTUFHw+HzKZbFiNAInFYsydOxcuLi545513kJycjLa2tgGV8Wi/9JJIJOzP+vr6mDFjhtprub9zAcDc3BxSqRTe3t7w8fHB4cOH0drayh6vq6vDrl271H7fvaN3Dx48gEwmg52dHWxsbPpsE3nx6D/vBpChxcfHB/b29khOToaNjQ1UKhWmTp2qtlhxxIgR7M+9ycm/p8i00dd/sK6urhCLxUhPT8f8+fNx5coVFBQUPOGVDH3/7kegpy9VKhUUCgXefPNNrFmzBrt374a5uTnKy8sRFBSEjo4O8Hi8QWvDmDFj4OPjg5SUFDg4OKCoqAilpaWDVv7z9rg+ViqVsLa27vNae9ercblcjamdzs5Oret+9DUeHh6OkpISHDx4EEKhEMbGxliyZMmw+hCAnp4eSkpKUFFRgeLiYhw5cgRRUVGoqqrSur/+T3L9X+empKQgNDQUp0+fRnZ2NrZt24aSkhK4u7tDqVQiJiYGfn5+GucZGRk9cZvI8EUJEGHduXMHcrkcycnJ7CLY8vLyAZXh5OSEn3/+WW3fo9v9CQ4OxieffIKbN2/Cy8tLbU2FrqipqYFKpUJ8fDy43J5B2pycHI24rq4uVFdXsyNscrkcd+/ehUgkYmNaWlpw69Yt9q62srISXC4XTk5ObExwcDCWLl2KcePGYdKkSfDw8HialzckuLm54ffff4e+vj47yvkoS0tLXL58WW1fbW2tWlJlYGCA7u5ureo8d+4cpFIpFi1aBKBnREihUDxR+58nDocDDw8PeHh4YPv27bC3t0dubi4sLS3VRly6u7tx+fJlzJ49W6tyKysrMWvWLAA9r+2amhqsX79+wO1zdXWFq6srIiMjIZFIkJmZCXd3d7i5uUEul0MoFPZ5nkgkwvXr19Ha2sqObPeOjJIXE02BEZaZmRksLCzw+eefo7GxET/++CM2b948oDI2bNiAwsJCJCQkoKGhAceOHUNRUZHW01gBAQG4ceMGkpOTX5iFuAMlFArR2dmJI0eO4OrVqzhx4gSSkpI04kaMGIENGzagqqoKNTU1kEqlcHd3V5tyNDIyQmBgIOrq6lBWVobQ0FD4+/tj7NixbIy3tzdGjhyJjz/+GCtXrnwm1/i8eXl5QSKR4O2330ZxcTEUCgUqKioQFRWF6upqAMCcOXNQXV2N9PR0NDQ0YMeOHRoJ0YQJE1BVVQWFQoE//vij35HQyZMns4v+6+rqEBAQMOCR0+etqqoKe/bsQXV1NVpaWnDq1Cncvn0bIpEIc+bMQUFBAQoKClBfX481a9bg7t27Wpf96aefIjc3F/X19Vi3bh3a2toG9DegubkZkZGROH/+PK5du4bi4mI0NDSwNwTbt29Heno6YmJicOXKFchkMmRlZWHbtm0Ael4Tjo6Oau+XqKioAfUPGV4oASIsLpeLrKws1NTUYOrUqQgLC8OBAwcGVIaHhweSkpKQkJAAsViM06dPIywsTOsh5lGjRmHx4sXg8/kaH0HWFWKxGAkJCdi3bx+mTp2KjIwMxMXFacTxeDxEREQgICAAHh4e4PP5yM7OVosRCoXw8/PDwoULMX/+fEybNg2fffaZWgyXy4VUKkV3dzdWrFjxVK9tqOBwOCgsLMSsWbOwcuVKODo64t1338W1a9dgZWUFoCcxjI6OxpYtWzBz5kzcv39fo3/Cw8Ohp6eHKVOmwNLSst/1PAkJCTAzM8Nrr70GHx8feHt7w83N7ale52AbOXIkzp49i4ULF8LR0RHbtm1DfHw83njjDaxatQqBgYFYsWIFPD09MXHiRK1HfwBg79692Lt3L8RiMcrLy/HNN99g9OjRWp/P4/FQX1+PxYsXw9HREe+//z7WrVuHDz74AEDP7zM/Px/FxcWYOXMm3N3dcejQIdjb2wPoeR/k5ubi77//xiuvvILg4OA+P8FKXhwc5tFJW0IGWUhICOrr61FWVqZV/Ny5c/HSSy8hMTHxKbds+EpNTcWmTZv6vcPeuXMn8vLyUFtb+5/lBQUF4fbt22qPISDkWVAoFHBwcMDFixef+tdaEPJvtAaIDLqDBw9i3rx5MDExQVFREdLS0jRGHfrS1taG0tJSlJaWahVP/r979+7h0qVLyMzMpOSHEKJTKAEig+7ChQvYv38/7t+/j4kTJyIxMRHBwcH/eZ6rqyva2tqwb98+tUW65Onx9fXFhQsXsHr1asybN+95N4cQQp4ZmgIjhBBCiM6hRdCEEEII0TmUABFCCCFE51ACRAghhBCdQwkQIYQQQnQOJUCEkH5JpVK1h1K+/vrr2LRp0zNvR2lpKTgcTr/PPuJwOMjLy9O6zJ07d/7vZ88oFApwOBytnrdECBk6KAEiZBiSSqXgcDjgcDgwMDCAUCjErl270NXV9dTrPnXqFGJjY7WK1SZpIYSQ54GeA0TIMLVgwQKkpKSgvb0dhYWFWLduHUaMGIHIyEiN2I6ODhgYGAxKvebm5oNSDiGEPE80AkTIMGVoaIixY8fC3t4ea9asgZeXF/s0595pq927d8PGxoZ9sOT169fh7+8PU1NTmJubw9fXV+0bybu7u7F582aYmprCwsICW7ZswaOPCnt0Cqy9vR0RERGws7ODoaEhhEIhvvjiCygUCva7oMzMzMDhcCCVSgEAKpUKcXFxcHBwgLGxMcRiMb7++mu1egoLC+Ho6AhjY2PMnj37ib45PSIiAo6OjuDxeJg4cSKio6PR2dmpEXfs2DHY2dmBx+PB398f9+7dUzt+/PhxiEQiGBkZwdnZmZ5UTsgLgBIgQl4QxsbG6OjoYLd/+OEHyOVylJSUID8/H52dnfD29oZAIEBZWRnOnTsHPp+PBQsWsOfFx8cjNTUVX375JcrLy/Hnn38iNze333pXrFiBr776ComJiZDJZDh27Bj4fD7s7Oxw8uRJAIBcLkdraysOHz4MAIiLi0N6ejqSkpJw5coVhIWF4b333sOZM2cA9CRqfn5+8PHxQW1tLYKDg7F169YB94lAIEBqaip+/fVXHD58GMnJyTh06JBaTGNjI3JycvDtt9/i9OnTuHjxItauXcsez8jIwPbt27F7927IZDLs2bMH0dHRSEtLG3B7CCFDCEMIGXYCAwMZX19fhmEYRqVSMSUlJYyhoSETHh7OHreysmLa29vZc06cOME4OTkxKpWK3dfe3s4YGxsz3333HcMwDGNtbc3s37+fPd7Z2cmMGzeOrYthGMbT05PZuHEjwzAMI5fLGQBMSUlJn+386aefGABMW1sbu+/hw4cMj8djKioq1GKDgoKYpUuXMgzDMJGRkcyUKVPUjkdERGiU9SgATG5u7mOPHzhwgJk+fTq7vWPHDkZPT4+5ceMGu6+oqIjhcrlMa2srwzAMM2nSJCYzM1OtnNjYWEYikTAMwzDNzc0MAObixYuPrZcQMvTQGiBChqn8/Hzw+Xx0dnZCpVIhICAAO3fuZI+7uLiorfupq6tDY2MjBAKBWjkPHz5EU1MT7t27h9bWVrz66qvsMX19fcyYMUNjGqxXbW0t9PT04OnpqXW7Gxsb8eDBA43vHuvo6ICrqysAQCaTqbUDACQSidZ19MrOzkZiYiKampqgVCrR1dWFkSNHqsWMHz8etra2avWoVCrI5XIIBAI0NTUhKCgIISEhbExXVxdGjRo14PYQQoYOSoAIGaZmz56No0ePwsDAADY2NtDXV387m5iYqG0rlUpMnz4dGRkZGmVZWlo+URuMjY0HfI5SqQQAFBQUqCUeQM+6psFy/vx5LFu2DDExMfD29saoUaOQlZWF+Pj4Abc1OTlZIyHT09MbtLYSQp49SoAIGaZMTEwgFAq1jndzc0N2djbGjBmjMQrSy9raGlVVVZg1axaAnpGOmpoauLm59Rnv4uIClUqFM2fOwMvLS+N47whUd3c3u2/KlCkwNDRES0vLY0eORCIRu6C7V2Vl5X9f5L9UVFTA3t4eUVFR7L5r165pxLW0tODWrVuwsbFh6+FyuXBycoKVlRVsbGxw9epVLFu2bED1E0KGNloETYiOWLZsGUaPHg1fX1+UlZWhubkZpaWlCA0NxY0bNwAAGzduxN69e5GXl4f6+nqsXbu232f4TJgwAYGBgVi1ahXy8vLYMnNycgAA9vb24HA4yM/Px+3bt6FUKiEQCBAeHo6wsDCkpaWhqakJv/zyC44cOcIuLF69ejUaGhrw0UcfQS6XIzMzE6mpqQO63smTJ6OlpQVZWVloampCYmJinwu6jYyMEBgYiLq6OpSVlSE0NBT+/v4YO3YsACAmJgZxcXFITEzEb7/9hkuXLiElJQUJCQkDag8hZGihBIgQHcHj8XD27FmMHz8efn5+EIlECAoKwsOHD9kRoQ8//BDLly9HYGAgJBIJBAIBFi1a1G+5R48exZIlS7B27Vo4OzsjJCQEf/31FwDA1tYWMTEx2Lp1K6ysrLB+/XoAQGxsLKKjoxEXFweRSIQFCxagoKAADg4OAHrW5Zw8eRJ5eXkQi8VISkrCnj17BnS9b731FsLCwrB+/Xq8/PLLqKioQHR0tEacUCiEn58fFi5ciPnz52PatGlqH3MPDg7G8ePHkZKSAhcXF3h6eiI1NZVtKyFkeOIwj1vdSAghhBDygqIRIEIIIYToHEqACCGEEKJzKAEihBBCiM6hBIgQQgghOocSIEIIIYToHEqACCGEEKJzKAEihBBCiM6hBIgQQgghOocSIEIIIYToHEqACCGEEKJzKAEihBBCiM6hBIgQQgghOucfrhXayN0SsB0AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (EfficientNetV2S, no aug, fine-tuning)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aecde1fb", + "metadata": { + "id": "lYbrD1J0c1nr", + "papermill": { + "duration": 2.532154, + "end_time": "2024-01-29T18:45:02.653174", + "exception": false, + "start_time": "2024-01-29T18:45:00.121020", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### 2. EfficientNetV2S, with data augmentation" + ] + }, + { + "cell_type": "markdown", + "id": "3786f979", + "metadata": { + "id": "WKXyHFt_c1nt", + "papermill": { + "duration": 2.52829, + "end_time": "2024-01-29T18:45:07.721299", + "exception": false, + "start_time": "2024-01-29T18:45:05.193009", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Feature extraction phase:**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c1c27f26", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T18:45:12.857231Z", + "iopub.status.busy": "2024-01-29T18:45:12.856856Z", + "iopub.status.idle": "2024-01-29T18:45:19.491299Z", + "shell.execute_reply": "2024-01-29T18:45:19.490498Z" + }, + "id": "echjGpFrc1nt", + "papermill": { + "duration": 9.200009, + "end_time": "2024-01-29T18:45:19.493618", + "exception": false, + "start_time": "2024-01-29T18:45:10.293609", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "keras.backend.clear_session()\n", + "base_model = efficientnet_v2.EfficientNetV2S(input_shape=(img_size, img_size, 3), include_top=False)\n", + "base_model.trainable = False\n", + "\n", + "inputs = Input(shape=(48, 48, 3))\n", + "x = Resizing(img_size, img_size, interpolation='lanczos5', crop_to_aspect_ratio=True)(inputs)\n", + "x = data_augmentation(x)\n", + "x = base_model(x, training=False)\n", + "x = GlobalAveragePooling2D()(x)\n", + "# x = Dropout(DROPOUT)(x)\n", + "# x = Dense(64, activation='relu')(x)\n", + "x = Dropout(DROPOUT)(x)\n", + "outputs = Dense(4, activation='softmax')(x)\n", + "\n", + "model = keras.Model(inputs, outputs)\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e766e937", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T18:45:24.562309Z", + "iopub.status.busy": "2024-01-29T18:45:24.561548Z", + "iopub.status.idle": "2024-01-29T20:06:30.136006Z", + "shell.execute_reply": "2024-01-29T20:06:30.135047Z" + }, + "id": "-3GjlXxWc1nu", + "outputId": "1effd131-d20a-4288-838b-729fe5335fe2", + "papermill": { + "duration": 4872.025212, + "end_time": "2024-01-29T20:06:34.133058", + "exception": false, + "start_time": "2024-01-29T18:45:22.107846", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n", + "505/505 [==============================] - 105s 181ms/step - loss: 1.2593 - accuracy: 0.4250 - val_loss: 1.1119 - val_accuracy: 0.5278\n", + "Epoch 2/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 1.1112 - accuracy: 0.5334 - val_loss: 1.0240 - val_accuracy: 0.5802\n", + "Epoch 3/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 1.0521 - accuracy: 0.5673 - val_loss: 0.9807 - val_accuracy: 0.6013\n", + "Epoch 4/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 1.0155 - accuracy: 0.5832 - val_loss: 0.9569 - val_accuracy: 0.6114\n", + "Epoch 5/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9886 - accuracy: 0.5969 - val_loss: 0.9379 - val_accuracy: 0.6203\n", + "Epoch 6/200\n", + "505/505 [==============================] - 89s 175ms/step - loss: 0.9772 - accuracy: 0.5998 - val_loss: 0.9241 - val_accuracy: 0.6253\n", + "Epoch 7/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9643 - accuracy: 0.6036 - val_loss: 0.9125 - val_accuracy: 0.6286\n", + "Epoch 8/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9545 - accuracy: 0.6104 - val_loss: 0.9072 - val_accuracy: 0.6336\n", + "Epoch 9/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9392 - accuracy: 0.6158 - val_loss: 0.8941 - val_accuracy: 0.6431\n", + "Epoch 10/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9346 - accuracy: 0.6179 - val_loss: 0.8877 - val_accuracy: 0.6409\n", + "Epoch 11/200\n", + "505/505 [==============================] - 88s 173ms/step - loss: 0.9250 - accuracy: 0.6206 - val_loss: 0.8825 - val_accuracy: 0.6425\n", + "Epoch 12/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9213 - accuracy: 0.6264 - val_loss: 0.8780 - val_accuracy: 0.6448\n", + "Epoch 13/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9088 - accuracy: 0.6329 - val_loss: 0.8737 - val_accuracy: 0.6503\n", + "Epoch 14/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9061 - accuracy: 0.6358 - val_loss: 0.8677 - val_accuracy: 0.6487\n", + "Epoch 15/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9071 - accuracy: 0.6329 - val_loss: 0.8645 - val_accuracy: 0.6509\n", + "Epoch 16/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.9017 - accuracy: 0.6346 - val_loss: 0.8610 - val_accuracy: 0.6509\n", + "Epoch 17/200\n", + "505/505 [==============================] - 90s 178ms/step - loss: 0.9015 - accuracy: 0.6387 - val_loss: 0.8583 - val_accuracy: 0.6548\n", + "Epoch 18/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.8916 - accuracy: 0.6370 - val_loss: 0.8542 - val_accuracy: 0.6598\n", + "Epoch 19/200\n", + "505/505 [==============================] - 88s 173ms/step - loss: 0.8871 - accuracy: 0.6467 - val_loss: 0.8532 - val_accuracy: 0.6565\n", + "Epoch 20/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8846 - accuracy: 0.6420 - val_loss: 0.8485 - val_accuracy: 0.6643\n", + "Epoch 21/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8867 - accuracy: 0.6426 - val_loss: 0.8475 - val_accuracy: 0.6604\n", + "Epoch 22/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8843 - accuracy: 0.6439 - val_loss: 0.8461 - val_accuracy: 0.6609\n", + "Epoch 23/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8844 - accuracy: 0.6413 - val_loss: 0.8431 - val_accuracy: 0.6620\n", + "Epoch 24/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8838 - accuracy: 0.6417 - val_loss: 0.8400 - val_accuracy: 0.6665\n", + "Epoch 25/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8738 - accuracy: 0.6504 - val_loss: 0.8391 - val_accuracy: 0.6665\n", + "Epoch 26/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8743 - accuracy: 0.6463 - val_loss: 0.8402 - val_accuracy: 0.6620\n", + "Epoch 27/200\n", + "505/505 [==============================] - 89s 175ms/step - loss: 0.8730 - accuracy: 0.6503 - val_loss: 0.8348 - val_accuracy: 0.6676\n", + "Epoch 28/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8702 - accuracy: 0.6473 - val_loss: 0.8335 - val_accuracy: 0.6704\n", + "Epoch 29/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8692 - accuracy: 0.6542 - val_loss: 0.8325 - val_accuracy: 0.6682\n", + "Epoch 30/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8664 - accuracy: 0.6538 - val_loss: 0.8306 - val_accuracy: 0.6704\n", + "Epoch 31/200\n", + "505/505 [==============================] - 87s 173ms/step - loss: 0.8672 - accuracy: 0.6477 - val_loss: 0.8296 - val_accuracy: 0.6687\n", + "Epoch 32/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8625 - accuracy: 0.6531 - val_loss: 0.8273 - val_accuracy: 0.6720\n", + "Epoch 33/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8688 - accuracy: 0.6483 - val_loss: 0.8260 - val_accuracy: 0.6682\n", + "Epoch 34/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8633 - accuracy: 0.6525 - val_loss: 0.8257 - val_accuracy: 0.6709\n", + "Epoch 35/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8626 - accuracy: 0.6546 - val_loss: 0.8229 - val_accuracy: 0.6776\n", + "Epoch 36/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8586 - accuracy: 0.6580 - val_loss: 0.8245 - val_accuracy: 0.6715\n", + "Epoch 37/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8620 - accuracy: 0.6533 - val_loss: 0.8228 - val_accuracy: 0.6704\n", + "Epoch 38/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8594 - accuracy: 0.6534 - val_loss: 0.8223 - val_accuracy: 0.6704\n", + "Epoch 39/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8577 - accuracy: 0.6574 - val_loss: 0.8218 - val_accuracy: 0.6715\n", + "Epoch 40/200\n", + "505/505 [==============================] - 89s 175ms/step - loss: 0.8529 - accuracy: 0.6565 - val_loss: 0.8204 - val_accuracy: 0.6737\n", + "Epoch 41/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.8547 - accuracy: 0.6545 - val_loss: 0.8177 - val_accuracy: 0.6782\n", + "Epoch 42/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8583 - accuracy: 0.6513 - val_loss: 0.8180 - val_accuracy: 0.6754\n", + "Epoch 43/200\n", + "505/505 [==============================] - 89s 177ms/step - loss: 0.8555 - accuracy: 0.6583 - val_loss: 0.8178 - val_accuracy: 0.6765\n", + "Epoch 44/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8495 - accuracy: 0.6556 - val_loss: 0.8154 - val_accuracy: 0.6726\n", + "Epoch 45/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8496 - accuracy: 0.6558 - val_loss: 0.8153 - val_accuracy: 0.6793\n", + "Epoch 46/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8515 - accuracy: 0.6573 - val_loss: 0.8134 - val_accuracy: 0.6793\n", + "Epoch 47/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8511 - accuracy: 0.6573 - val_loss: 0.8132 - val_accuracy: 0.6765\n", + "Epoch 48/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8496 - accuracy: 0.6620 - val_loss: 0.8127 - val_accuracy: 0.6782\n", + "Epoch 49/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.8478 - accuracy: 0.6622 - val_loss: 0.8109 - val_accuracy: 0.6782\n", + "Epoch 50/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.8521 - accuracy: 0.6593 - val_loss: 0.8097 - val_accuracy: 0.6759\n", + "Epoch 51/200\n", + "505/505 [==============================] - 90s 178ms/step - loss: 0.8508 - accuracy: 0.6531 - val_loss: 0.8090 - val_accuracy: 0.6793\n", + "Epoch 52/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8511 - accuracy: 0.6547 - val_loss: 0.8097 - val_accuracy: 0.6793\n", + "Epoch 53/200\n", + "505/505 [==============================] - 88s 174ms/step - loss: 0.8487 - accuracy: 0.6590 - val_loss: 0.8098 - val_accuracy: 0.6754\n", + "Epoch 54/200\n", + "505/505 [==============================] - 88s 175ms/step - loss: 0.8460 - accuracy: 0.6586 - val_loss: 0.8093 - val_accuracy: 0.6771\n", + "Epoch 55/200\n", + "505/505 [==============================] - 89s 176ms/step - loss: 0.8438 - accuracy: 0.6587 - val_loss: 0.8080 - val_accuracy: 0.6776\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 59min 33s, sys: 49.8 s, total: 1h 22s\n", + "Wall time: 1h 21min 5s\n" + ] + } + ], + "source": [ + "%%time\n", + "history = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "cb898319", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T20:06:43.828231Z", + "iopub.status.busy": "2024-01-29T20:06:43.827860Z", + "iopub.status.idle": "2024-01-29T20:07:02.659025Z", + "shell.execute_reply": "2024-01-29T20:07:02.658122Z" + }, + "id": "yyrIOv73c1nw", + "papermill": { + "duration": 23.683692, + "end_time": "2024-01-29T20:07:02.660924", + "exception": false, + "start_time": "2024-01-29T20:06:38.977232", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 9s 155ms/step - loss: 0.8153 - accuracy: 0.6793\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 10s 156ms/step - loss: 0.8342 - accuracy: 0.6747\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.8341718316078186, 0.6746867299079895]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f843fa84", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T20:07:12.552402Z", + "iopub.status.busy": "2024-01-29T20:07:12.552007Z", + "iopub.status.idle": "2024-01-29T20:07:25.514420Z", + "shell.execute_reply": "2024-01-29T20:07:25.513488Z" + }, + "id": "13YuE_Jtc1nw", + "papermill": { + "duration": 17.847197, + "end_time": "2024-01-29T20:07:25.516552", + "exception": false, + "start_time": "2024-01-29T20:07:07.669355", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/63 [==============================] - 13s 149ms/step\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (EfficientNetV2S, data aug, feature-extraction)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "993b4b0e", + "metadata": { + "id": "fK9kutDdc1oP", + "papermill": { + "duration": 4.910361, + "end_time": "2024-01-29T20:07:35.333648", + "exception": false, + "start_time": "2024-01-29T20:07:30.423287", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "**Fine-tuning phase:**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4b67e6dc", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T20:07:45.175741Z", + "iopub.status.busy": "2024-01-29T20:07:45.175384Z", + "iopub.status.idle": "2024-01-29T20:07:45.218620Z", + "shell.execute_reply": "2024-01-29T20:07:45.217805Z" + }, + "id": "luigrDpWc1oQ", + "papermill": { + "duration": 5.027077, + "end_time": "2024-01-29T20:07:45.220625", + "exception": false, + "start_time": "2024-01-29T20:07:40.193548", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "base_model.trainable = True\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate=BASE_LR/10),\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f5a75b5b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T20:07:54.973303Z", + "iopub.status.busy": "2024-01-29T20:07:54.972921Z", + "iopub.status.idle": "2024-01-29T22:18:43.921969Z", + "shell.execute_reply": "2024-01-29T22:18:43.921053Z" + }, + "id": "ieI5S1qCc1oQ", + "papermill": { + "duration": 7859.636972, + "end_time": "2024-01-29T22:18:49.735271", + "exception": false, + "start_time": "2024-01-29T20:07:50.098299", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n", + "505/505 [==============================] - 279s 391ms/step - loss: 0.7269 - accuracy: 0.7117 - val_loss: 0.6256 - val_accuracy: 0.7589\n", + "Epoch 2/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.5873 - accuracy: 0.7798 - val_loss: 0.5640 - val_accuracy: 0.7884\n", + "Epoch 3/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.5118 - accuracy: 0.8057 - val_loss: 0.5602 - val_accuracy: 0.7962\n", + "Epoch 4/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.4637 - accuracy: 0.8262 - val_loss: 0.5100 - val_accuracy: 0.8224\n", + "Epoch 5/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.4155 - accuracy: 0.8464 - val_loss: 0.5131 - val_accuracy: 0.8213\n", + "Epoch 6/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.3738 - accuracy: 0.8617 - val_loss: 0.4901 - val_accuracy: 0.8302\n", + "Epoch 7/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.3424 - accuracy: 0.8752 - val_loss: 0.4932 - val_accuracy: 0.8280\n", + "Epoch 8/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.3039 - accuracy: 0.8877 - val_loss: 0.4976 - val_accuracy: 0.8268\n", + "Epoch 9/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.2653 - accuracy: 0.9032 - val_loss: 0.5257 - val_accuracy: 0.8313\n", + "Epoch 10/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.2349 - accuracy: 0.9152 - val_loss: 0.5351 - val_accuracy: 0.8318\n", + "Epoch 11/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.2053 - accuracy: 0.9262 - val_loss: 0.5607 - val_accuracy: 0.8307\n", + "Epoch 12/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.1777 - accuracy: 0.9356 - val_loss: 0.5945 - val_accuracy: 0.8291\n", + "Epoch 13/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.1556 - accuracy: 0.9441 - val_loss: 0.6091 - val_accuracy: 0.8296\n", + "Epoch 14/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.1299 - accuracy: 0.9534 - val_loss: 0.6551 - val_accuracy: 0.8352\n", + "Epoch 15/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.1171 - accuracy: 0.9586 - val_loss: 0.6470 - val_accuracy: 0.8252\n", + "Epoch 16/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0999 - accuracy: 0.9637 - val_loss: 0.7119 - val_accuracy: 0.8224\n", + "Epoch 17/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0801 - accuracy: 0.9719 - val_loss: 0.7897 - val_accuracy: 0.8363\n", + "Epoch 18/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.0740 - accuracy: 0.9746 - val_loss: 0.7716 - val_accuracy: 0.8369\n", + "Epoch 19/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0647 - accuracy: 0.9776 - val_loss: 0.7940 - val_accuracy: 0.8274\n", + "Epoch 20/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.0587 - accuracy: 0.9802 - val_loss: 0.8941 - val_accuracy: 0.8374\n", + "Epoch 21/200\n", + "505/505 [==============================] - 195s 385ms/step - loss: 0.0529 - accuracy: 0.9814 - val_loss: 0.8654 - val_accuracy: 0.8369\n", + "Epoch 22/200\n", + "505/505 [==============================] - 195s 386ms/step - loss: 0.0441 - accuracy: 0.9860 - val_loss: 0.9011 - val_accuracy: 0.8352\n", + "Epoch 23/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0440 - accuracy: 0.9844 - val_loss: 0.9080 - val_accuracy: 0.8268\n", + "Epoch 24/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0388 - accuracy: 0.9864 - val_loss: 0.9585 - val_accuracy: 0.8324\n", + "Epoch 25/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0300 - accuracy: 0.9899 - val_loss: 0.9725 - val_accuracy: 0.8335\n", + "Epoch 26/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0333 - accuracy: 0.9889 - val_loss: 1.0196 - val_accuracy: 0.8274\n", + "Epoch 27/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0334 - accuracy: 0.9884 - val_loss: 0.9881 - val_accuracy: 0.8324\n", + "Epoch 28/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.0277 - accuracy: 0.9897 - val_loss: 1.0601 - val_accuracy: 0.8380\n", + "Epoch 29/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0225 - accuracy: 0.9924 - val_loss: 1.1169 - val_accuracy: 0.8324\n", + "Epoch 30/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0276 - accuracy: 0.9916 - val_loss: 1.0766 - val_accuracy: 0.8424\n", + "Epoch 31/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0240 - accuracy: 0.9920 - val_loss: 1.0603 - val_accuracy: 0.8313\n", + "Epoch 32/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0244 - accuracy: 0.9919 - val_loss: 1.0989 - val_accuracy: 0.8202\n", + "Epoch 33/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0201 - accuracy: 0.9930 - val_loss: 1.1914 - val_accuracy: 0.8413\n", + "Epoch 34/200\n", + "505/505 [==============================] - 194s 383ms/step - loss: 0.0212 - accuracy: 0.9933 - val_loss: 1.1336 - val_accuracy: 0.8252\n", + "Epoch 35/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0188 - accuracy: 0.9935 - val_loss: 1.1875 - val_accuracy: 0.8352\n", + "Epoch 36/200\n", + "505/505 [==============================] - 194s 385ms/step - loss: 0.0187 - accuracy: 0.9936 - val_loss: 1.2048 - val_accuracy: 0.8402\n", + "Epoch 37/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0179 - accuracy: 0.9947 - val_loss: 1.1549 - val_accuracy: 0.8291\n", + "Epoch 38/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0198 - accuracy: 0.9934 - val_loss: 1.1871 - val_accuracy: 0.8302\n", + "Epoch 39/200\n", + "505/505 [==============================] - 193s 383ms/step - loss: 0.0169 - accuracy: 0.9944 - val_loss: 1.1625 - val_accuracy: 0.8402\n", + "Epoch 40/200\n", + "505/505 [==============================] - 194s 384ms/step - loss: 0.0208 - accuracy: 0.9924 - val_loss: 1.1511 - val_accuracy: 0.8413\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1h 49min 58s, sys: 10min 1s, total: 1h 59min 59s\n", + "Wall time: 2h 10min 48s\n" + ] + } + ], + "source": [ + "%%time\n", + "history_ft = model.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)\n", + "\n", + "plot_training_curve(history_ft)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "afec4180", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T22:19:02.969602Z", + "iopub.status.busy": "2024-01-29T22:19:02.968918Z", + "iopub.status.idle": "2024-01-29T22:19:21.673666Z", + "shell.execute_reply": "2024-01-29T22:19:21.672838Z" + }, + "id": "zgYR9hsxc1oR", + "papermill": { + "duration": 25.434568, + "end_time": "2024-01-29T22:19:21.675657", + "exception": false, + "start_time": "2024-01-29T22:18:56.241089", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VALIDATION:\n", + "57/57 [==============================] - 9s 153ms/step - loss: 1.0766 - accuracy: 0.8424\n", + "\n", + "TEST:\n", + "63/63 [==============================] - 10s 156ms/step - loss: 1.0805 - accuracy: 0.8286\n" + ] + }, + { + "data": { + "text/plain": [ + "[1.0805283784866333, 0.8285714387893677]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print('VALIDATION:')\n", + "model.evaluate(val_images, val_labels)\n", + "print('\\nTEST:')\n", + "model.evaluate(test_images, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ac2893d4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-01-29T22:19:34.728004Z", + "iopub.status.busy": "2024-01-29T22:19:34.727285Z", + "iopub.status.idle": "2024-01-29T22:19:47.609922Z", + "shell.execute_reply": "2024-01-29T22:19:47.608969Z" + }, + "id": "4IJUiuSbc1oR", + "papermill": { + "duration": 19.44458, + "end_time": "2024-01-29T22:19:47.612197", + "exception": false, + "start_time": "2024-01-29T22:19:28.167617", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63/63 [==============================] - 13s 149ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predictions = np.argmax(model.predict(test_images), axis=1)\n", + "\n", + "disp = ConfusionMatrixDisplay.from_predictions(\n", + " test_labels, predictions,\n", + " display_labels=label_mapping.values(),\n", + " cmap=plt.cm.Blues,\n", + " normalize='true')\n", + "\n", + "disp.ax_.set_title('Normalized confusion matrix (EfficientNetV2S, data aug, fine-tuning)')\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kaggle": { + "accelerator": "gpu", + "dataSources": [ + { + "datasetId": 2366449, + "sourceId": 3988031, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30636, + "isGpuEnabled": true, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + 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Tiwari Date: Wed, 31 Jan 2024 10:32:23 +0530 Subject: [PATCH 11/16] Added project structure --- Facial-Emotion-Detection/README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/Facial-Emotion-Detection/README.md b/Facial-Emotion-Detection/README.md index e69de29bb..b7811c2b8 100644 --- a/Facial-Emotion-Detection/README.md +++ b/Facial-Emotion-Detection/README.md @@ -0,0 +1,21 @@ +# Facial Emotion Detection + +## Project Structure + +```text +. +├── Dataset +│   └── README.md +├── Images +│   ├── 00_baseline_cnn +│   ├── 01_data_augmentation_cnn +│   ├── 02_transfer_learning_mobilenetv2 +│   └── 03_transfer_learning_efficientnetv2 +├── Model +│   ├── 00_baseline_cnn.ipynb +│   ├── 01_data_augmentation_cnn.ipynb +│   ├── 02_transfer_learning_mobilenetv2.ipynb +│   ├── 03_transfer_learning_efficientnetv2.ipynb +│   └── README.md +└── README.md +``` From c56f739afbcfbecb2b1a3790c707595e64e99f1c Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Wed, 31 Jan 2024 10:55:46 +0530 Subject: [PATCH 12/16] Added requirements.txt --- Facial-Emotion-Detection/Model/requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 Facial-Emotion-Detection/Model/requirements.txt diff --git a/Facial-Emotion-Detection/Model/requirements.txt b/Facial-Emotion-Detection/Model/requirements.txt new file mode 100644 index 000000000..ed22cdbf6 --- /dev/null +++ b/Facial-Emotion-Detection/Model/requirements.txt @@ -0,0 +1,5 @@ +numpy==1.23.5 +matplotlib==3.7.1 +scikit-learn==1.2.2 +tensorflow==2.15.0 +keras_cv==0.8.1 \ No newline at end of file From 5293e4e57abc97d85e5fc3870cadec28bac4f19e Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Wed, 31 Jan 2024 10:57:37 +0530 Subject: [PATCH 13/16] Updated project structure --- Facial-Emotion-Detection/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Facial-Emotion-Detection/README.md b/Facial-Emotion-Detection/README.md index b7811c2b8..694b64b29 100644 --- a/Facial-Emotion-Detection/README.md +++ b/Facial-Emotion-Detection/README.md @@ -16,6 +16,7 @@ │   ├── 01_data_augmentation_cnn.ipynb │   ├── 02_transfer_learning_mobilenetv2.ipynb │   ├── 03_transfer_learning_efficientnetv2.ipynb -│   └── README.md +│   ├── README.md +│   └── requirements.txt └── README.md ``` From debfa40a3b20cf983ec433d897007f099cd9efd8 Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Wed, 31 Jan 2024 13:32:08 +0530 Subject: [PATCH 14/16] Added project summary --- Facial-Emotion-Detection/Model/README.md | 71 ++++++++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/Facial-Emotion-Detection/Model/README.md b/Facial-Emotion-Detection/Model/README.md index e69de29bb..e490d942a 100644 --- a/Facial-Emotion-Detection/Model/README.md +++ b/Facial-Emotion-Detection/Model/README.md @@ -0,0 +1,71 @@ +# Project Summary + +**PROJECT TITLE:** Facial Emotion Detection + +**GOAL:** Multiclass classification of facial emotions from grayscale images. + +**DATASET:** [FER-DS - Kaggle](https://www.kaggle.com/datasets/mhantor/facial-expression) + +**DESCRIPTION:** +We have 19950 images covering 4 facial emotions (angry, happy, neutral, surprised). +The images are in grayscale and have a low resolution (48x48). +Our aim is to use these images to train a deep learning model which can classify them accurately. + +**TASKS PERFORMED:** + +1. Data exploration and creation of custom validation and test sets using images sampled from the original dataset. +These will be fixed to ensure valid comparison of different models. +Final distribution of images: Train - 16159, Validation - 1796, Test - 1995 (Total: 19950) +2. Trained CNN model from scratch as a baseline, trying different configurations for number of layers and hidden units. +3. Added 3 configurations for data-augmentation to the data pipeline, and evaluated their effectiveness using the same CNN architecture from the previous step. +4. Applied transfer-learning techniques: feature-extraction and fine-tuning, using 2 backbone architectures: MobileNetV2 and EfficientNetV2S. + +**MODELS USED:** + +1. Convolutional Neural Network +2. MobileNetV2 +3. EfficientNetV2S + +(Data-augmentation utilized with each of these) + +**LIBRARIES USED:** + +1. Tensorflow +2. Keras +3. Keras_CV +4. Numpy +5. Matplotlib +6. Scikit-learn + +**VISUALIZATION:** + +1. [Baseline CNN](Images/00_baseline_cnn) ([Notebook 00](Model/00_baseline_cnn.ipynb)) +2. [CNN + Data augmentation](Images/01_data_augmentation_cnn) ([Notebook 01](Model/01_data_augmentation_cnn.ipynb)) +3. [Transfer-learning: MobileNetV2](Images/02_transfer_learning_mobilenetv2) ([Notebook 02](Model/02_transfer_learning_mobilenetv2.ipynb)) +4. [Transfer-learning: EfficientNetV2S](Images/03_transfer_learning_efficientnetv2) ([Notebook 03](Model/03_transfer_learning_efficientnetv2.ipynb)) + +**ACCURACIES:** + +| Model configuration | Val accuracy (%) | Test accuracy (%) | +|:-----:|:-----:|:-----:| +| Baseline CNN | 71.05 | 71.23 | +| CNN + Simple data-augment | 75.45 | 74.99 | +| CNN + Complex data-augment | 67.82 | 67.92 | +| CNN + Keras-CV RandAugment | 60.47 | 60.15 | +| MobileNetV2 | 80.01 | 79.30 | +| MobileNetV2 + Simple data-augment | 82.41 | 81.35 | +| EfficientNetV2S | 83.24 | 82.31 | +| EfficientNetV2S + Simple data-augment | **84.24** | **82.86** | + +**CONCLUSION:** +We used low-resolution, grayscale images to train a deep learning model to detect four facial emotions. +Starting with a solid baseline using a CNN, we setup a framework for comparison of different model configurations. +Combining data-augmentation with transfer-learning techniques improved performance significantly. +Our final model consists of: +Preprocessing - Lanczos5 interpolation for resizing +Data augmentation - Rotation + Horizontal flipping +Model (transfer-learning) - EfficientNetV2S backbone for feature-extraction and fine-tuning. + +**AUTHOR**: +Siddhant Tiwari +([Github](https://www.github.com/siddhant4ds) - [Kaggle](https://www.kaggle.com/sid4ds) - [LinkedIn](https://www.linkedin.com/in/siddhant4ds/)) From fc0235eb35823a0cf95e5c95bc846391e7a1dad9 Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Thu, 1 Feb 2024 10:29:34 +0530 Subject: [PATCH 15/16] Renamed project folder to remove hyphens --- .../Dataset/README.md | 0 .../Images/00_baseline_cnn/00_data_samples.png | Bin .../Images/00_baseline_cnn/01_model_summary.png | Bin .../Images/00_baseline_cnn/02_training_curves.png | Bin .../Images/00_baseline_cnn/03_confusion_matrix.png | Bin .../00_config1_training_curves.png | Bin .../01_config1_confusion_matrix.png | Bin .../02_config2_training_curves.png | Bin .../03_config2_confusion_matrix.png | Bin .../04_config3_training_curves.png | Bin .../05_config3_confusion_matrix.png | Bin .../00_noaug_featext_training_curves.png | Bin .../01_noaug_featext_confusion_matrix.png | Bin .../02_noaug_finetune_training_curves.png | Bin .../03_noaug_finetune_confusion_matrix.png | Bin .../04_aug_featext_training_curves.png | Bin .../05_aug_featext_confusion_matrix.png | Bin .../06_aug_finetune_training_curves.png | Bin .../07_aug_finetune_confusion_matrix.png | Bin .../00_noaug_featext_training_curves.png | Bin .../01_noaug_featext_confusion_matrix.png | Bin .../02_noaug_finetune_training_curves.png | Bin .../03_noaug_finetune_confusion_matrix.png | Bin .../04_aug_featext_training_curves.png | Bin .../05_aug_featext_confusion_matrix.png | Bin .../06_aug_finetune_training_curves.png | Bin .../07_aug_finetune_confusion_matrix.png | Bin .../Model/00_baseline_cnn.ipynb | 0 .../Model/01_data_augmentation_cnn.ipynb | 0 .../Model/02_transfer_learning_mobilenetv2.ipynb | 0 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a/Facial-Emotion-Detection/Model/README.md b/Facial Emotion Detection/Model/README.md similarity index 100% rename from Facial-Emotion-Detection/Model/README.md rename to Facial Emotion Detection/Model/README.md diff --git a/Facial-Emotion-Detection/Model/requirements.txt b/Facial Emotion Detection/Model/requirements.txt similarity index 100% rename from Facial-Emotion-Detection/Model/requirements.txt rename to Facial Emotion Detection/Model/requirements.txt diff --git a/Facial-Emotion-Detection/README.md b/Facial Emotion Detection/README.md similarity index 100% rename from Facial-Emotion-Detection/README.md rename to Facial Emotion Detection/README.md From e6836bed843ec24f71021cec621ade8620dcacfc Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Thu, 1 Feb 2024 12:49:49 +0530 Subject: [PATCH 16/16] Updated formatting in project summary --- Facial Emotion Detection/Model/README.md | 63 ++++++++++++++---------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/Facial Emotion Detection/Model/README.md b/Facial Emotion Detection/Model/README.md index e490d942a..2ed6dc69e 100644 --- a/Facial Emotion Detection/Model/README.md +++ b/Facial Emotion Detection/Model/README.md @@ -1,34 +1,41 @@ # Project Summary -**PROJECT TITLE:** Facial Emotion Detection +## PROJECT TITLE: Facial Emotion Detection -**GOAL:** Multiclass classification of facial emotions from grayscale images. +### GOAL -**DATASET:** [FER-DS - Kaggle](https://www.kaggle.com/datasets/mhantor/facial-expression) +Multiclass classification of facial emotions from grayscale images. -**DESCRIPTION:** -We have 19950 images covering 4 facial emotions (angry, happy, neutral, surprised). -The images are in grayscale and have a low resolution (48x48). -Our aim is to use these images to train a deep learning model which can classify them accurately. +### DATASET -**TASKS PERFORMED:** +[FER-DS - Kaggle](https://www.kaggle.com/datasets/mhantor/facial-expression) + +### DESCRIPTION + +* We have 19950 images covering 4 facial emotions (angry, happy, neutral, surprised). +* The images are in grayscale and have a low resolution (48x48). +* Our aim is to use these images to train a deep learning model which can classify them accurately. + +### TASKS PERFORMED 1. Data exploration and creation of custom validation and test sets using images sampled from the original dataset. These will be fixed to ensure valid comparison of different models. Final distribution of images: Train - 16159, Validation - 1796, Test - 1995 (Total: 19950) 2. Trained CNN model from scratch as a baseline, trying different configurations for number of layers and hidden units. -3. Added 3 configurations for data-augmentation to the data pipeline, and evaluated their effectiveness using the same CNN architecture from the previous step. -4. Applied transfer-learning techniques: feature-extraction and fine-tuning, using 2 backbone architectures: MobileNetV2 and EfficientNetV2S. +3. Added 3 configurations for data-augmentation to the data pipeline, and evaluated their effectiveness using the +same CNN architecture from the previous step. +4. Applied transfer-learning techniques: feature-extraction and fine-tuning, using 2 backbone architectures: +MobileNetV2 and EfficientNetV2S. -**MODELS USED:** +### MODELS IMPLEMENTED -1. Convolutional Neural Network -2. MobileNetV2 -3. EfficientNetV2S +1. Convolutional Neural Network (for baseline model) +2. MobileNetV2 (for transfer-learning backbone) +3. EfficientNetV2S (for transfer-learning backbone) -(Data-augmentation utilized with each of these) +(Data-augmentation utilized with each of these models) -**LIBRARIES USED:** +### LIBRARIES NEEDED 1. Tensorflow 2. Keras @@ -37,14 +44,14 @@ Final distribution of images: Train - 16159, Validation - 1796, Test - 1995 (Tot 5. Matplotlib 6. Scikit-learn -**VISUALIZATION:** +### VISUALIZATION 1. [Baseline CNN](Images/00_baseline_cnn) ([Notebook 00](Model/00_baseline_cnn.ipynb)) 2. [CNN + Data augmentation](Images/01_data_augmentation_cnn) ([Notebook 01](Model/01_data_augmentation_cnn.ipynb)) 3. [Transfer-learning: MobileNetV2](Images/02_transfer_learning_mobilenetv2) ([Notebook 02](Model/02_transfer_learning_mobilenetv2.ipynb)) 4. [Transfer-learning: EfficientNetV2S](Images/03_transfer_learning_efficientnetv2) ([Notebook 03](Model/03_transfer_learning_efficientnetv2.ipynb)) -**ACCURACIES:** +### MODEL PERFORMANCE (BASED ON ACCURACY SCORES) | Model configuration | Val accuracy (%) | Test accuracy (%) | |:-----:|:-----:|:-----:| @@ -57,15 +64,17 @@ Final distribution of images: Train - 16159, Validation - 1796, Test - 1995 (Tot | EfficientNetV2S | 83.24 | 82.31 | | EfficientNetV2S + Simple data-augment | **84.24** | **82.86** | -**CONCLUSION:** -We used low-resolution, grayscale images to train a deep learning model to detect four facial emotions. -Starting with a solid baseline using a CNN, we setup a framework for comparison of different model configurations. -Combining data-augmentation with transfer-learning techniques improved performance significantly. -Our final model consists of: -Preprocessing - Lanczos5 interpolation for resizing -Data augmentation - Rotation + Horizontal flipping -Model (transfer-learning) - EfficientNetV2S backbone for feature-extraction and fine-tuning. +### CONCLUSION + +* We used low-resolution, grayscale images to train a deep learning model to detect four facial emotions. +* Starting with a solid baseline using a CNN, we setup a framework for comparison of different model configurations. +* Combining data-augmentation with transfer-learning techniques improved performance significantly. +* Our final model consists of: + 1. Preprocessing - Lanczos5 interpolation for resizing and upscaling + 2. Data augmentation - Rotation + Horizontal flipping + 3. Model (transfer-learning) - EfficientNetV2S backbone for feature-extraction and fine-tuning. + +### SIGNATURE -**AUTHOR**: Siddhant Tiwari ([Github](https://www.github.com/siddhant4ds) - [Kaggle](https://www.kaggle.com/sid4ds) - [LinkedIn](https://www.linkedin.com/in/siddhant4ds/))

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zE^GBx8l8I+%$)}^ZdQdMq6%rHQQGjZx>tIGEp`O;3VjUzqFc8Pk*~mU;s!!mo=m0U zyYn1-_F#)C0@KSx=A355%G6AYFtky4L5k%93Rkf$S&K;UiPV6oI#KZ6aDOeW+1F}JP~!4icT z1hr2D*XbMLQt1h-S#uARX1J(fC1c2~J!bPk4R%Fmu#Dgiu5A(|QnXPJk&u%h*9EZ@25ijo$S_u0v#8bW?Cnp|BU(5)J1;CQg0%(#_Fi;UAo$@DN8W&( z0Omx9F2Fe`Nr9gec{F#-VYF>va1e`aX1q%Q?B0gFl_U0f>FHAdI}x3LQlR)kc9y!C zrm$C^Or>_N?WK~&MyZIM{wWH22!QSX6TR`h{|nEdAVvS%uhkY%!CjgrItl~%Kkrt% avSyz1^&kEX4(0eN&6$&Giplc&p8p5CiN#(3 literal 0 HcmV?d00001 From d87d0b1914c236ba07cc67952976c1f9885f24ea Mon Sep 17 00:00:00 2001 From: Siddhant Tiwari Date: Sat, 20 Jan 2024 18:27:35 +0530 Subject: [PATCH 05/16] Added data-augmentation CNN model notebook --- .../Model/01_data_augmentation_cnn.ipynb | 965 ++++++++++++++++++ 1 file changed, 965 insertions(+) create mode 100644 Facial-Emotion-Detection/Model/01_data_augmentation_cnn.ipynb diff --git a/Facial-Emotion-Detection/Model/01_data_augmentation_cnn.ipynb b/Facial-Emotion-Detection/Model/01_data_augmentation_cnn.ipynb new file mode 100644 index 000000000..a0cf8e245 --- /dev/null +++ b/Facial-Emotion-Detection/Model/01_data_augmentation_cnn.ipynb @@ -0,0 +1,965 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "uupI5Ue6kkCM" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "id": "iwt6OlFmi6fx" + }, + "outputs": [], + "source": [ + "import gc\n", + "gc.enable()\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "from tensorflow import keras\n", + "from keras.layers import (\n", + " Rescaling, RandomFlip, RandomContrast, RandomTranslation,\n", + " RandomRotation, Conv2D, MaxPooling2D, Flatten, Dense)\n", + "\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.utils import set_random_seed\n", + "\n", + "SEED = 2024\n", + "set_random_seed(SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "id": "RyXOY1dWsvB6" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install --upgrade keras-cv" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "id": "3cbwrwfJtWYN" + }, + "outputs": [], + "source": [ + "from keras_cv.layers import RandAugment" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Uo-tDnNdLvNC" + }, + "source": [ + "**<-- Mount Drive manually**" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "id": "WG7_wuH3_lvg" + }, + "outputs": [], + "source": [ + "DATA_PATH = '/content/drive/MyDrive/notebooks/swoc_s4/facial_emotion_detection/'\n", + "images = np.load(f'{DATA_PATH}/images.npy')\n", + "labels = np.load(f'{DATA_PATH}/labels.npy')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l_HUMU2vlDpC" + }, + "source": [ + "# Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "id": "wZlTC6NelMAK" + }, + "outputs": [], + "source": [ + "label_mapping = {0: 'angry', 1: 'happy', 2: 'neutral', 3: 'surprised'}" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "id": "VnG7rpIHlGPh" + }, + "outputs": [], + "source": [ + "ord_labels = np.argmax(labels, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HhMmnVPrlSsi" + }, + "source": [ + "**Test dataset:** (using the same split as the baseline notebook)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "id": "opsxI8l-lWIt" + }, + "outputs": [], + "source": [ + "train_images, test_images, train_labels, test_labels = train_test_split(\n", + " images, ord_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=ord_labels, # to maintain proportion of classes in test data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7qi9ZyJ3lnfc" + }, + "source": [ + "**Train and validation datasets:**" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "id": "FDrOWPbulrIj" + }, + "outputs": [], + "source": [ + "train_images, val_images, train_labels, val_labels = train_test_split(\n", + " train_images, train_labels,\n", + " test_size=0.1,\n", + " shuffle=True,\n", + " stratify=train_labels, # to maintain proportion of classes in val data\n", + " random_state=SEED)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "id": "9WvgUlwX5fEY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "67032eaa-38cf-40ef-ac31-a218507dd447" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(16159, 1796, 1995)" + ] + }, + "metadata": {}, + "execution_count": 101 + } + ], + "source": [ + "len(train_labels), len(val_labels), len(test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xAwu-f5w5lzs" + }, + "source": [ + "# Model training" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "id": "jj9StfpSn6Wy" + }, + "outputs": [], + "source": [ + "# common parameters\n", + "PATIENCE = 10\n", + "BATCH_SIZE = 128\n", + "EPOCHS = 100\n", + "VERBOSE = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l2j7eMyk6BZz" + }, + "source": [ + "Similar model architecture to our baseline CNN in the previous notebook. \n", + "We include data augmentation layers between Rescaling and Convolutional layers (in the form of a Sequential model)." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "id": "Aooph36S5n7z" + }, + "outputs": [], + "source": [ + "def build_model(data_augmentation_layers):\n", + " model = keras.Sequential([\n", + " Rescaling(scale=1./255, input_shape=(48, 48, 3)),\n", + " data_augmentation_layers,\n", + " Conv2D(48, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " MaxPooling2D((2, 2)),\n", + " Conv2D(64, (3, 3), activation='relu'),\n", + " MaxPooling2D(2, 2),\n", + " Flatten(),\n", + " Dense(64, activation='relu'),\n", + " Dense(4, activation='softmax') # number of classes\n", + " ])\n", + "\n", + " model.compile(\n", + " optimizer='adam',\n", + " loss='sparse_categorical_crossentropy', # for ordinal labels\n", + " metrics=['accuracy']\n", + " )\n", + "\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "umqEFaYK6W2y" + }, + "source": [ + "We will test three configurations with different types of data augmentation:\n", + "1. Manual - few types (and low magnitude of changes)\n", + "2. Manual - more types (and higher magnitude of changes)\n", + "3. Automatic - even more types, all-in-one (KerasCV - RandAugment)\n", + "\n", + "(Configuration numbers correspond to the listed order here)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "id": "24Em1Z4o57dX" + }, + "outputs": [], + "source": [ + "augment_config_1 = keras.Sequential([\n", + " RandomRotation(factor=(-0.1, 0.2), fill_mode='constant', seed=SEED),\n", + " RandomFlip(mode='horizontal', seed=SEED),\n", + "])\n", + "\n", + "augment_config_2 = keras.Sequential([\n", + " RandomContrast(factor=0.2, seed=SEED),\n", + " RandomTranslation(\n", + " height_factor=0.15, width_factor=0.15, fill_mode='constant', seed=SEED),\n", + " RandomRotation(factor=(-0.15, 0.25), fill_mode='constant', seed=SEED),\n", + " RandomFlip(mode='horizontal_and_vertical', seed=SEED),\n", + "])\n", + "\n", + "augment_config_3 = RandAugment(value_range=(0, 1), seed=SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_-Rjp1cXrJMd" + }, + "source": [ + "**Overfitting detection:**" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "id": "zTqIA4ggoht1" + }, + "outputs": [], + "source": [ + "early_stopping = EarlyStopping(\n", + " monitor='val_accuracy',\n", + " patience=PATIENCE,\n", + " min_delta=2e-4,\n", + " restore_best_weights=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C5AYqnU3rMhn" + }, + "source": [ + "**Visualizing training curves:**" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "id": "6NHSuHRurGbN" + }, + "outputs": [], + "source": [ + "def plot_training_curve(history):\n", + " train_loss = history.history['loss']\n", + " train_accuracy = history.history['accuracy']\n", + " val_loss = history.history['val_loss']\n", + " val_accuracy = history.history['val_accuracy']\n", + " num_epochs = len(train_loss)\n", + " epochs = range(num_epochs)\n", + "\n", + " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4), sharex=True)\n", + " ax[0].plot(epochs, train_loss, label='train_loss')\n", + " ax[0].plot(epochs, val_loss, label='val_loss')\n", + " ax[0].set_title('Loss')\n", + " ax[1].plot(epochs, train_accuracy, label='train_accuracy')\n", + " ax[1].plot(epochs, val_accuracy, label='val_accuracy')\n", + " ax[1].set_title('Accuracy')\n", + "\n", + " train_stop = (num_epochs-1-PATIENCE, val_accuracy[num_epochs-1-PATIENCE])\n", + " ax[1].annotate(f'Early stopping\\ntriggered',\n", + " xy=train_stop, xycoords='data',\n", + " xytext=(0, -100), textcoords='offset points',\n", + " arrowprops=dict(facecolor='black', shrink=0.05),\n", + " horizontalalignment='center', verticalalignment='bottom')\n", + "\n", + " ax[0].minorticks_on(); ax[1].minorticks_on()\n", + " ax[0].set_xlabel('Epochs'); ax[1].set_xlabel('Epochs')\n", + " ax[0].legend(); ax[1].legend()\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EMzA7FQJooRa" + }, + "source": [ + "### Model 1 (Data augmentation configuration - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "id": "zrqDFvOmq18t", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a0d5fe1d-2418-4158-d351-e72f962f3ed4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 1min 57s, sys: 4.57 s, total: 2min 2s\n", + "Wall time: 2min 17s\n" + ] + } + ], + "source": [ + "%%time\n", + "keras.backend.clear_session()\n", + "\n", + "model_1 = build_model(augment_config_1)\n", + "\n", + "history_1 = model_1.fit(\n", + " train_images, train_labels,\n", + " validation_data=(val_images, val_labels),\n", + " epochs=EPOCHS,\n", + " batch_size=BATCH_SIZE,\n", + " callbacks=[early_stopping],\n", + " verbose=VERBOSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "id": "ye0reXgWrXRD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "outputId": "108387d6-a74d-4d0c-d51c-c17d9d3af242" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "