TP_CNN_VGG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "final of Copy of Copy of Copy of TP_CNN_VGG.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0aO1-q75fKc",
        "colab_type": "code",
        "outputId": "747f3fce-ff10-463b-cf56-f906fe00fc3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "# Ce code \"TP_CNN_TSP\"est basé sur tensorflow et keras pour construire un modele CNN\n",
        "# Ce code est basé sur le tutorial de CNN-tensorflow https://www.tensorflow.org/tutorials/images/cnn\n",
        "#Pour lier google Colab avec google drive \n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__dy5kAX5uAz",
        "colab_type": "code",
        "outputId": "272acd46-cd84-4508-8024-ea4d7bac1f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Pour importer les outils \n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import datasets, layers, models, Model\n",
        "from tensorflow.keras.models import load_model\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cv2\n",
        "from matplotlib import pyplot\n",
        "import h5py\n",
        "from keras.preprocessing.image import img_to_array"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrulpdtLfFPq",
        "colab_type": "text"
      },
      "source": [
        "# Nouvelle section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFgem5Ev55G0",
        "colab_type": "code",
        "outputId": "fec03db4-3f42-4a09-bd92-647648216b95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Pour telécharger la database\n",
        "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
        "num_classes =10"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 11s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSEoL4n8ZcGz",
        "colab_type": "code",
        "outputId": "5c903df7-2cbb-43d3-df40-5a44699769b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "\n",
        "from keras.utils import to_categorical\n",
        "trainY = to_categorical(train_labels)\n",
        "testY = to_categorical(test_labels)\n",
        "print(testY.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFxVyXIt5-b0",
        "colab_type": "code",
        "outputId": "c639e248-ae35-411d-b73d-6c7ad308887b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "#Pour afficher 25 images de l'ensemble d'apprentissage\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i])\n",
        "    # The CIFAR labels happen to be arrays, \n",
        "    # which is why you need the extra index\n",
        "    plt.xlabel(class_names[train_labels[i][0]])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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vv08aZbxy/Tqy0OT1lv3h9iJIh3a7RlMbkszGbE02xHdP98icphOuvfMS93feAaAcTWi2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EajMYtAHI40illtt0nSmG7XP0RKCbIs4ki7xVtXr5FkPq2qiiF5mXt6BCmwwjFVdrbC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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5_1aRXrXFfl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#VGG Model\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from tensorflow.keras.layers import Input, Dense, AveragePooling2D,Flatten,Dropout,BatchNormalization,Conv2D,MaxPooling2D\n",
        "from tensorflow.keras import Model,Sequential\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "# compile model\n",
        "opt = SGD(lr=0.001, momentum=0.9)\n",
        "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTK_O4_cY0KO",
        "colab_type": "code",
        "outputId": "1329e6a7-9fb5-441c-f79d-8f75e447ee29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_14 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_16 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_18 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_19 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_20 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               262272    \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 552,874\n",
            "Trainable params: 551,722\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hO9cu5m3Xlzv",
        "colab_type": "code",
        "outputId": "01b8c24b-3d36-43ee-97ba-f0aa926d357a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import *\n",
        "# prepare iterator\n",
        "it_train = datagen.flow(train_images, trainY, batch_size=64)\n",
        "# fit model\n",
        "steps = int(train_images.shape[0] / 64)\n",
        "filepath=\"/content/drive/My Drive/Models/VGG_CIFAR/cifar_vgglike_0803_epochs_{epoch:03d}_val_acc_{val_accuracy:.3f}.h5\"\n",
        "print(steps)\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=False, save_weights_only=False, mode='auto',period=10)\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "history = model.fit_generator(it_train, steps_per_epoch=steps, epochs=100, validation_data=(test_images, testY), callbacks=callbacks_list, verbose=1)\n",
        "\n",
        "\n",
        "# learning curves\n",
        "summarize_diagnostics(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "781\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 781 steps, validate on 10000 samples\n",
            "Epoch 1/100\n",
            "781/781 [==============================] - 26s 33ms/step - loss: 2.0813 - accuracy: 0.3042 - val_loss: 1.4934 - val_accuracy: 0.4466\n",
            "Epoch 2/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.5996 - accuracy: 0.4121 - val_loss: 1.4706 - val_accuracy: 0.4717\n",
            "Epoch 3/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.4830 - accuracy: 0.4553 - val_loss: 1.4965 - val_accuracy: 0.4634\n",
            "Epoch 4/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.4058 - accuracy: 0.4886 - val_loss: 1.4953 - val_accuracy: 0.4656\n",
            "Epoch 5/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.3454 - accuracy: 0.5113 - val_loss: 1.2295 - val_accuracy: 0.5558\n",
            "Epoch 6/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.3001 - accuracy: 0.5309 - val_loss: 1.2910 - val_accuracy: 0.5410\n",
            "Epoch 7/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.2521 - accuracy: 0.5474 - val_loss: 1.4350 - val_accuracy: 0.5028\n",
            "Epoch 8/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.2214 - accuracy: 0.5618 - val_loss: 1.1899 - val_accuracy: 0.5757\n",
            "Epoch 9/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.1852 - accuracy: 0.5763 - val_loss: 1.1643 - val_accuracy: 0.5898\n",
            "Epoch 10/100\n",
            "780/781 [============================>.] - ETA: 0s - loss: 1.1598 - accuracy: 0.5824\n",
            "Epoch 00010: saving model to /content/drive/My Drive/Models/VGG_CIFAR/cifar_vgglike_0803_epochs_010_val_acc_0.597.h5\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.1597 - accuracy: 0.5824 - val_loss: 1.1418 - val_accuracy: 0.5973\n",
            "Epoch 11/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.1342 - accuracy: 0.5916 - val_loss: 1.1336 - val_accuracy: 0.5984\n",
            "Epoch 12/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.1061 - accuracy: 0.6080 - val_loss: 1.0688 - val_accuracy: 0.6183\n",
            "Epoch 13/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.0861 - accuracy: 0.6112 - val_loss: 1.1630 - val_accuracy: 0.5926\n",
            "Epoch 14/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.0678 - accuracy: 0.6194 - val_loss: 1.0131 - val_accuracy: 0.6384\n",
            "Epoch 15/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.0424 - accuracy: 0.6287 - val_loss: 1.0940 - val_accuracy: 0.6171\n",
            "Epoch 16/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.0244 - accuracy: 0.6346 - val_loss: 0.9393 - val_accuracy: 0.6665\n",
            "Epoch 17/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 1.0017 - accuracy: 0.6437 - val_loss: 1.0357 - val_accuracy: 0.6389\n",
            "Epoch 18/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9865 - accuracy: 0.6516 - val_loss: 0.9816 - val_accuracy: 0.6553\n",
            "Epoch 19/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9631 - accuracy: 0.6581 - val_loss: 0.9251 - val_accuracy: 0.6697\n",
            "Epoch 20/100\n",
            "780/781 [============================>.] - ETA: 0s - loss: 0.9517 - accuracy: 0.6621\n",
            "Epoch 00020: saving model to /content/drive/My Drive/Models/VGG_CIFAR/cifar_vgglike_0803_epochs_020_val_acc_0.650.h5\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9518 - accuracy: 0.6621 - val_loss: 0.9971 - val_accuracy: 0.6497\n",
            "Epoch 21/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9456 - accuracy: 0.6665 - val_loss: 0.8274 - val_accuracy: 0.7051\n",
            "Epoch 22/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9284 - accuracy: 0.6694 - val_loss: 0.9248 - val_accuracy: 0.6745\n",
            "Epoch 23/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9144 - accuracy: 0.6750 - val_loss: 0.8684 - val_accuracy: 0.6937\n",
            "Epoch 24/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.9044 - accuracy: 0.6800 - val_loss: 0.8691 - val_accuracy: 0.6941\n",
            "Epoch 25/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8875 - accuracy: 0.6893 - val_loss: 0.8176 - val_accuracy: 0.7115\n",
            "Epoch 26/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8806 - accuracy: 0.6923 - val_loss: 0.8081 - val_accuracy: 0.7155\n",
            "Epoch 27/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8767 - accuracy: 0.6925 - val_loss: 0.8638 - val_accuracy: 0.6975\n",
            "Epoch 28/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8615 - accuracy: 0.6961 - val_loss: 0.8534 - val_accuracy: 0.7003\n",
            "Epoch 29/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8473 - accuracy: 0.7000 - val_loss: 0.8249 - val_accuracy: 0.7108\n",
            "Epoch 30/100\n",
            "780/781 [============================>.] - ETA: 0s - loss: 0.8382 - accuracy: 0.7035\n",
            "Epoch 00030: saving model to /content/drive/My Drive/Models/VGG_CIFAR/cifar_vgglike_0803_epochs_030_val_acc_0.723.h5\n",
            "781/781 [==============================] - 26s 33ms/step - loss: 0.8381 - accuracy: 0.7035 - val_loss: 0.7845 - val_accuracy: 0.7229\n",
            "Epoch 31/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8387 - accuracy: 0.7055 - val_loss: 0.8470 - val_accuracy: 0.7053\n",
            "Epoch 32/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8285 - accuracy: 0.7094 - val_loss: 0.8128 - val_accuracy: 0.7162\n",
            "Epoch 33/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8192 - accuracy: 0.7118 - val_loss: 0.7783 - val_accuracy: 0.7275\n",
            "Epoch 34/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8123 - accuracy: 0.7155 - val_loss: 0.7151 - val_accuracy: 0.7489\n",
            "Epoch 35/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.8093 - accuracy: 0.7158 - val_loss: 0.7487 - val_accuracy: 0.7355\n",
            "Epoch 36/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.7970 - accuracy: 0.7196 - val_loss: 0.7506 - val_accuracy: 0.7372\n",
            "Epoch 37/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.7868 - accuracy: 0.7247 - val_loss: 0.7232 - val_accuracy: 0.7498\n",
            "Epoch 38/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.7828 - accuracy: 0.7234 - val_loss: 0.7653 - val_accuracy: 0.7338\n",
            "Epoch 39/100\n",
            "781/781 [==============================] - 25s 32ms/step - loss: 0.7753 - accuracy: 0.7294 - val_loss: 0.7515 - val_accuracy: 0.7339\n",
            "Epoch 40/100\n",
            "467/781 [================>.............] - ETA: 9s - loss: 0.7640 - accuracy: 0.7356"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mon_epoch\u001b[0;34m(self, epoch, mode)\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 767\u001b[0;31m       \u001b[0;32myield\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    768\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m                 total_epochs=epochs)\n\u001b[0m\u001b[1;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m    127\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 98\u001b[0;31m                               distributed_function(input_fn))\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    567\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 568\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    598\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2362\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2363\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1610\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1611\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1692\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-32317c580dad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mcallbacks_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;31m# evaluate model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m               \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m               instructions)\n\u001b[0;32m--> 324\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[1;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1304\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m   @deprecation.deprecated(\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m                       total_epochs=1)\n\u001b[1;32m    396\u001b[0m                   cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,\n\u001b[0;32m--> 397\u001b[0;31m                                  prefix='val_')\n\u001b[0m\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mon_epoch\u001b[0;34m(self, epoch, mode)\u001b[0m\n\u001b[1;32m    769\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m         \u001b[0;31m# Epochs only apply to `fit`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 771\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    772\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m    300\u001b[0m     \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m       \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepochs_since_last_save\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperiod\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepochs_since_last_save\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             \u001b[0mfilepath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_best_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mcurrent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'val_accuracy'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptbQ6GUziGsL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#VGG Model\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from tensorflow.keras.layers import Input, Dense, AveragePooling2D,Flatten,Dropout,BatchNormalization\n",
        "from tensorflow.keras import Model\n",
        "VGG_input=Input(shape=train_images.shape[1:])\n",
        "VGG_model=keras.applications.vgg19.VGG19(include_top=False, weights=\"imagenet\" , input_tensor=VGG_input)\n",
        "#for layer in VGG_model.layers[:-4]:\n",
        "  #print(layer.name)\n",
        "  #layer.trainable = False\n",
        "vgg=VGG_model.get_layer('block5_pool').output\n",
        "\n",
        "x=Flatten()(vgg)\n",
        "\n",
        "x=Dense(128,activation='relu', kernel_initializer='he_uniform')(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=Dropout(0.5)(x)\n",
        "x=Dense(10,activation='softmax')(x)\n",
        "model=Model(inputs=VGG_model.input, outputs=x, name='vgg_cifar')\n",
        "model.summary()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YbnYEk_1zCO",
        "colab_type": "text"
      },
      "source": [
        "**Report assigments**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7eGKQHB0c67",
        "colab_type": "text"
      },
      "source": [
        "intro(R)\n",
        "database(R)\n",
        "\n",
        "model(M) test(M)\n",
        "\n",
        "training (performance confusion metrix)(D)\n",
        "result(D)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmeTSSNRSKH3",
        "colab_type": "code",
        "outputId": "46303e02-c869-441c-ace0-826799d009d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import *\n",
        "#v_optimizer = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "v_optimizer = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
        "datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "\t# prepare iterator\n",
        "it_train = datagen.flow(train_images, train_labels, batch_size=64)\n",
        "steps = int(train_images.shape[0] / 64)\n",
        "print(steps)\n",
        "filepath=\"/content/drive/My Drive/Models/VGG_CIFAR/cifar_0803_epochs_{epoch:03d}_val_acc_{val_accuracy:.3f}.h5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=False, save_weights_only=False, mode='auto',period=5)\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "model.compile(optimizer=v_optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              \n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(it_train, steps_per_epoch=steps, epochs=50, validation_data=(test_images, test_labels),callbacks=callbacks_list)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "781\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 781 steps, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "781/781 [==============================] - 85s 108ms/step - loss: 1.8573 - accuracy: 0.2162 - val_loss: 1.8360 - val_accuracy: 0.2356\n",
            "Epoch 2/50\n",
            "781/781 [==============================] - 83s 106ms/step - loss: 1.7275 - accuracy: 0.2830 - val_loss: 1.6069 - val_accuracy: 0.3236\n",
            "Epoch 3/50\n",
            "781/781 [==============================] - 83s 106ms/step - loss: 1.6515 - accuracy: 0.3228 - val_loss: 2.1954 - val_accuracy: 0.2200\n",
            "Epoch 4/50\n",
            "781/781 [==============================] - 83s 106ms/step - loss: 1.6066 - accuracy: 0.3429 - val_loss: 1.7716 - val_accuracy: 0.2504\n",
            "Epoch 5/50\n",
            "320/781 [===========>..................] - ETA: 46s - loss: 1.5857 - accuracy: 0.3472"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1KoJ0vU6hL5",
        "colab_type": "code",
        "outputId": "6b768221-7e75-4262-9d26-9d2eb7c4dc27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from keras.callbacks import *\n",
        "#v_optimizer = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "v_optimizer = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
        "\n",
        "filepath=\"/content/drive/My Drive/Models/VGG_CIFAR/cifar_0803_epochs_{epoch:03d}_val_acc_{val_accuracy:.3f}.h5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=False, save_weights_only=False, mode='auto',period=5)\n",
        "callbacks_list = [checkpoint]\n",
        "\n",
        "model.compile(optimizer=v_optimizer,\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=15, validation_data=(test_images, test_labels),batch_size=32,callbacks=callbacks_list)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/15\n",
            "50000/50000 [==============================] - 131s 3ms/sample - loss: 1.9429 - accuracy: 0.2249 - val_loss: 1.8864 - val_accuracy: 0.2173\n",
            "Epoch 2/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 1.6252 - accuracy: 0.3560 - val_loss: 1.4542 - val_accuracy: 0.4288\n",
            "Epoch 3/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 1.3506 - accuracy: 0.4969 - val_loss: 1.2016 - val_accuracy: 0.5656\n",
            "Epoch 4/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 1.1035 - accuracy: 0.6092 - val_loss: 1.0165 - val_accuracy: 0.6473\n",
            "Epoch 5/15\n",
            "49984/50000 [============================>.] - ETA: 0s - loss: 0.9556 - accuracy: 0.6660\n",
            "Epoch 00005: saving model to /content/drive/My Drive/Models/VGG_CIFAR/cifar_0803_epochs_005_val_acc_0.673.h5\n",
            "50000/50000 [==============================] - 132s 3ms/sample - loss: 0.9557 - accuracy: 0.6660 - val_loss: 0.9810 - val_accuracy: 0.6726\n",
            "Epoch 6/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 0.8610 - accuracy: 0.7037 - val_loss: 0.8765 - val_accuracy: 0.7060\n",
            "Epoch 7/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 0.7677 - accuracy: 0.7382 - val_loss: 0.8178 - val_accuracy: 0.7299\n",
            "Epoch 8/15\n",
            "50000/50000 [==============================] - 131s 3ms/sample - loss: 0.6891 - accuracy: 0.7675 - val_loss: 0.8022 - val_accuracy: 0.7380\n",
            "Epoch 9/15\n",
            "50000/50000 [==============================] - 132s 3ms/sample - loss: 0.6247 - accuracy: 0.7919 - val_loss: 0.7610 - val_accuracy: 0.7557\n",
            "Epoch 10/15\n",
            "49984/50000 [============================>.] - ETA: 0s - loss: 0.5693 - accuracy: 0.8101\n",
            "Epoch 00010: saving model to /content/drive/My Drive/Models/VGG_CIFAR/cifar_0803_epochs_010_val_acc_0.755.h5\n",
            "50000/50000 [==============================] - 132s 3ms/sample - loss: 0.5695 - accuracy: 0.8101 - val_loss: 0.7668 - val_accuracy: 0.7552\n",
            "Epoch 11/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 0.5161 - accuracy: 0.8304 - val_loss: 0.7856 - val_accuracy: 0.7552\n",
            "Epoch 12/15\n",
            "50000/50000 [==============================] - 130s 3ms/sample - loss: 0.4806 - accuracy: 0.8429 - val_loss: 0.8001 - val_accuracy: 0.7584\n",
            "Epoch 13/15\n",
            "32768/50000 [==================>...........] - ETA: 42s - loss: 0.4318 - accuracy: 0.8596"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-132e22c752f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m               metrics=['accuracy'])\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m   def evaluate(self,\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m                 total_epochs=epochs)\n\u001b[0m\u001b[1;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[1;32m    127\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 98\u001b[0;31m                               distributed_function(input_fn))\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    566\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 568\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    597\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2361\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2362\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2363\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1610\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1611\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1613\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1690\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1692\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1694\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    543\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujmv9YvFyUwW",
        "colab_type": "code",
        "outputId": "37be30ec-6027-4eb2-a9a2-ed9b58670d70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJJ-RAkqOqJx",
        "colab_type": "code",
        "outputId": "00942a49-a64e-4706-d7fd-994803e5aaa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#load the saved model*\n",
        "from tensorflow.keras.models import load_model\n",
        "saved_model=load_model(\"/content/drive/My Drive/Models/VGG_CIFAR/cifar_vgglike_0803_epochs_400_val_acc_0.882.h5\")\n",
        "saved_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_32 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_37 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_33 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_38 (Batc (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_34 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_39 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_35 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_40 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_36 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_41 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_37 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_42 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 128)               262272    \n",
            "_________________________________________________________________\n",
            "batch_normalization_43 (Batc (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 552,874\n",
            "Trainable params: 551,722\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7yP8P_Nv9RN",
        "colab_type": "text"
      },
      "source": [
        "fp, fn,tp ,tn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpntRs0k5x7I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model=load_model(\"/content/drive/My Drive/Colab Notebooks/my_model_5.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1nGYnel_8Cy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pour tracer la courbe accuracy en foctions de nombre des epochs\n",
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdCuIdiGyBdI",
        "colab_type": "code",
        "outputId": "632464fa-3246-45ec-bab2-a19bfdffe558",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'], label = 'val_loss')\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2O5e8029l7gltiIuOpLisnNioCKS4H7TpRUTPMXWPKybBkoAx9cASQThY+yG0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y7SxBACJCSnzQnkEYY0wESxCe1IQ4a2IyxpgIliA8KQlx1sRkjDERLEEAFG3i8dKbSSpZ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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhvmI54lwoOZ",
        "colab_type": "code",
        "outputId": "c320c716-5fdf-46fe-a465-5fdc7ba225bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "predicted_labels=saved_model.predict(test_images)\n",
        "\n",
        "class_labels = np.argmax(predicted_labels, axis=1)\n",
        "cm=confusion_matrix(test_labels, class_labels)\n",
        "\n",
        "print(cm)\n",
        "#[tn,fp,fn,tp] = confusion_matrix(test_labels, class_labels).ravel()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[888  10  21   5  12   0   9   7  34  14]\n",
            " [  4 959   0   0   0   1   2   0   8  26]\n",
            " [ 26   1 813  19  28  30  52  19   8   4]\n",
            " [  9   2  24 694  38 116  77  24   7   9]\n",
            " [  4   1  27  13 867   8  57  22   1   0]\n",
            " [  4   2  16  63  28 836  26  23   1   1]\n",
            " [  3   1   7   5   5   3 973   2   0   1]\n",
            " [  3   0   3   9  32  21  14 913   3   2]\n",
            " [ 26   7   5   0   3   1   4   1 941  12]\n",
            " [ 11  33   2   1   0   0   3   1  11 938]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cERlTk9FAKgK",
        "colab_type": "code",
        "outputId": "54ac1e40-e278-417d-c39c-031659ec1654",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(test_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7003\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQN0dyfaAPHy",
        "colab_type": "code",
        "outputId": "5726ff5c-96e2-4e0e-b681-59382794868f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "#pour importer l'image chien.jpg\n",
        "import os\n",
        "import numpy\n",
        "class_names = ['airpl', 'auto', 'bird', 'cat', 'deer',\n",
        "'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "imgs=[]#numpy.zeros(10,32,32,3)\n",
        "for i in numpy.arange(10):\n",
        "  img = cv2.imread(\"/content/sample_data/test/\"+class_names[i]+\".jpg\")\n",
        "  img_cvt=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  plt.subplot(2,5,i+1)\n",
        "  img = cv2.resize(img_cvt, (32,32))\n",
        "  img = img.astype('float32')\n",
        "  img= img / 255.0\n",
        "  imgs.append(img)\n",
        "  plt.imshow(imgs[i])\n",
        "  #plt.show()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AD4NqBOkeB4xofTQCNbxxjH5UrEYaNAQD4/pi48zuik0h6SpY4qsZDpJfITEABYWfylb5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\nPO741bZBl3Fysno38HPu6RMAAH35TsRtsg09JxER6ynHlQ9vlc45OOB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GzuE5/S04ZCxZ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\nRVGUJkUncEVRlCZFJ3BFUZQm5b8+QAV87QqfvQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67waQtibR0go",
        "colab_type": "code",
        "outputId": "fab5cfb0-6e25-4d34-cdd2-baf26e6b476a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "\n",
        "imgs=numpy.array(imgs)\n",
        "print(imgs.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 32, 32, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPOTRvr_APyT",
        "colab_type": "code",
        "outputId": "3c8c2362-1d83-4b10-87a7-062aef99540f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "\n",
        "Ypredicted=saved_model.predict(imgs)\n",
        "print(Ypredicted[7])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.6691507e-04 2.2489289e-06 1.4552403e-03 4.4525340e-03 4.5368597e-01\n",
            " 2.2377215e-02 8.1039284e-04 5.1692730e-01 1.5983755e-05 6.2308891e-06]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJQyWzJbSv2B",
        "colab_type": "code",
        "outputId": "a65bd6c9-30b6-4148-83e8-e0450bd4921f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "class_names = ['airpl', 'auto', 'bird', 'cat', 'deer',\n",
        "'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "plt.subplot(2,1,1)\n",
        "plt.bar(class_names,Ypredicted[3])\n",
        "plt.subplot(2,1,2)\n",
        "plt.imshow(imgs[3])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5b3275b898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v2DRzhk8t3JEtTDsQnpKorvYBU4cP+eLb/fBeKnv3+TVKKj1lkqGnQ1r27Uni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\n+//g67QtTORBxgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GB3Ji12F2aU7",
        "colab_type": "code",
        "outputId": "7b7209c6-54b4-450b-bf0b-5384cffb6d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "y = [cm[idx,idx]/1000 for idx in numpy.arange(10)]\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.888, 0.959, 0.813, 0.694, 0.867, 0.836, 0.973, 0.913, 0.941, 0.938]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5EFlNKGuqic",
        "colab_type": "code",
        "outputId": "91cd5668-4a6c-4f08-f35b-5dd8c52920d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "#plotting\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "idx=4\n",
        "x = class_names\n",
        "y=Ypredicted[1]\n",
        "#y = [cm[idx,idx]/1000 for idx in numpy.arange(10)]\n",
        "\n",
        "fig, ax = plt.subplots()    \n",
        "width = 0.75 # the width of the bars \n",
        "ind = np.arange(len(y))  # the x locations for the groups\n",
        "ax.barh(ind, y, width, color=\"blue\")\n",
        "ax.set_yticks(ind)\n",
        "ax.set_yticklabels(x, minor=False)\n",
        "fig.subplots_adjust(right=1.6)\n",
        "for i, v in enumerate(y):\n",
        "    ax.text(v, i,\"{:.3f}\".format(v),verticalalignment='center', color='red', fontweight='bold')\n",
        "plt.title('Classification output')\n",
        "plt.xlabel('Probability')\n",
        "plt.ylabel('Category')      \n",
        "#plt.show()\n",
        "plt.savefig(os.path.join('accPerCate'+str(idx)+'.png'), dpi=300, format='png', bbox_inches='tight')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S7f9+0v1/hwJLJTWN65G17wfOatb/Jkk9s+f3Zz+DfICIuIMUpKmurg6qslsK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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ApqGl-DShis",
        "colab_type": "code",
        "outputId": "bf318109-d595-4fa3-eea5-9bff3d318f89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "class_labels = np.argmax(Ypredicted, axis=1)\n",
        "print(class_names[class_labels[1]])\n",
        "plt.plot(class_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5006a63128>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LGHTY8kIgNTzdBVjt7svd3cPr/w93n+TuKe6e0qRJk3IpuqLLyy/ksRlLuOCp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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6ZH63KuoGZC",
        "colab_type": "code",
        "outputId": "63eadcc6-bbd8-43ee-adef-fd3cfe385525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        }
      },
      "source": [
        "#pour visualiser la couche i\n",
        "\n",
        "\n",
        "model2 = Model(inputs=model.inputs, outputs=model.layers[1].output)\n",
        "model2.summary()\n",
        "feature_maps = model2.predict(imgs)\n",
        "square = 6\n",
        "ix = 2\n",
        "for _ in range(square):\n",
        "\tfor _ in range(square):\n",
        "\t\t# specify subplot and turn of axis\n",
        "\t\tax = pyplot.subplot(square, square, ix)\n",
        "\t\tax.set_xticks([])\n",
        "\t\tax.set_yticks([])\n",
        "\t\t# plot filter channel in grayscale\n",
        "\t\tpyplot.imshow(feature_maps[0, :, :, ix-1])\n",
        "\t\tix += 1\n",
        "# show the figure\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_28\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9_input (InputLayer)  [(None, 32, 32, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_17 (Activation)   (None, 30, 30, 32)        0         \n",
            "=================================================================\n",
            "Total params: 896\n",
            "Trainable params: 896\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-86-4427fcd3ffec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                 \u001b[0;31m# plot filter channel in grayscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                 \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_maps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mix\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0mix\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# show the figure\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index 32 is out of bounds for axis 3 with size 32"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RzDlHFI8HMx5HLS7CGIkAYDggvLeG4tNZT21VOQwOoXitOp9abQ0YBugGaixdeMmqqGgV\nZeOFNcTiBncIzWGlJFaUofcN5LZPiQCYpKXpre24W8fzwCceLVwulIAfY2CCB7u3j8CjlrNtsZfu\nwuUk84odiMEk8uS4jVPx+TAj1r3ULqvoa2rQEhLHC2eR0kQLBFDrK/Gdj2OUF6Fc60U4ncjsPZqY\nAGCcv7S4k3qZYAvP25SJy1EzHkdoWl4eftmJCAM7gxiXWkht2ImrNZE7FkDv6rZiW9NpzDWVOLNO\npDxMwyo2sVTeaNNE8bjRe/vyBKTi8yHcLmBme57i8yE87jxBKVMpRHkZ4g7Lky1dKhw6c4smMDtS\nVfC0DGL2D+bFa05cqgq3i3TISemP29DH7rs0ofkahmGgxBIIpwM5GkP4fajSRLjd4LVqOEz01tuM\nYwvP2xStqjKv1JoSCCC8nlwKnnK1A3ZsAtMg7VcY2VmNb5JXfcy+52zuJbqzDs80SzpjcAjhct2i\nmcyAaY4LEUXNBcSbsRhMEC6TUUMhqC5HtnXmCc4x9M4u6Bwv16c2rYLwSMFjbzmJFDI8ghnLTxUV\nmobMOgWREk93DP16D+q6NVZEQFkJiaYyEKAlDNRYBrWle8ILcKRgnVabcez/ndsUY2AQtaw094OX\n6TQ4HahrrCpIRnMrpV+0apGWPHaGgXduRXnzbjzfPZ6XlQOWFuqPjhZeshbIPlo0FGGl0QBCEZa/\nKstYlafM+noU3UTrGsoJDiMchvBUp8p0GFeWom58Fiktp5/TgTmh1qY0jFx5PVQF06laGUjpjFXw\nuncY38lrmBUhy6uuG2CYaFWVyHgCIxJZdgkOyw1beN6mSF2HUBFkhacSKkYmk+C0StTF37IH77ez\nVcZjMUr+46CVTVNeiiwvgZZruSX8RCE8Ga22BqN38R0oQF6TtsmCYGy8youDDHxgH0O/4cbb0YD3\nuqTsm2fylr2TMe/eju63/p+0mL7kZdomz00NhZANVYQ3WLVkkyGBv9sgvDaAooN7QFJyqR0zGkXL\napcyFgeng8zKKrRIEkXUWNXlbabFFp63MWZrdhkuBDIShYpSRNKyicXLFbQHd+E925VbyhuRCEQi\nMLnxW2koJ4QnY/T2L11gvCTbFXNmyj5/kDJAXbeG4e1lDLxjK2pa4oqY+F64lGcLVoNBePEUzt1W\nZaahzX7KohsxT710a+YwRyYKUFEUIFXqRckWLi4/GbcC4n95D+6hNNpIitgDG/BfGSHWGMQ5kkG7\n1IHR14/D4bA01Yri3LLd1kALY+e229jY2MwDW/O8DRGaBsYEjUJKjEgE1aFB0KrtKYXVikLv6ib6\nrr0EHrF6v6ib1k0NXRkYQu7fhjhwOucYGnMmyUw6F6S+lKhlpZjDIzNqUcalZoIBd666ktixib63\nbiRdJKg4kUR9/gQiVIRY24DMxrUm7tvPxQ/48bXvp/bjS98pVGusR3pcuE634g74AZCJJAYQeLEF\nGY+jBPwEBkZINVXie6mXTG0JelMdmpSkV1cg0ibahTZEvVVOUM+GbWkrGuaWivsyQmusnzajbDZs\nzfM2ZEyAqMVFIMS4V9UwrHJzo3GKWzIM/K3lgCg+3pfrxWScv4S8a3teCwpjcAjDk13ipVKW4Ny7\ndcIFl6bj4kRvsTEwOOvyUy0uQh47h7J5vSWEjp+n9P8epPxUiq5Xuhl9x16r6tCE0nW1Hz/A2g8d\nIXBtabtKjt0P6dCsmqyDQ8ihYeTQMMLjRi0vx+jvz9m29c4u1J+cACHQBkZBEQy/ai3xCiejjR6E\n243e2p4TnMDLRnD2f3fdnI+dr+AEW3jOn91b6Pu9/TT/094ppcuWPdkMI2N4BKRErasZ/6wIUASO\nHx5D/0EZl7+0g0sfrCB63/pcNXnx81MMvn2bVZJtbbba/HPHx727AIfOMPCBbAvmRQ6OH0PqOsLh\nnLYJ32TGbJvmuYtW4Yws2o+P0/BXB/B/8xCK14saDNL8qb00f2ovyrYNAAQfXriujDeMEONB7cOR\n3GZjNIYxGkNvu5bLetI7uzCGR6x+XNVVyEgU6XYgfn4KU4NAyyjBK1FQVdTKiimlCl8OlP+StTIq\nVIh7ui4CcONNGe1l+3w5cpaKIxD5x738yw+/zDvOvJfAZ4twPXV0qUc2K4L8YgnmYNhqepZMYow5\ngxSVis8coHzfNvp3uBj81Qh9O6sxnNWs/tODhL5yEGVFA7KnH2XzeuSVVtKrKnFqVu663t5B2ecL\nt11eTGQmjTGcnv3ASYwVzlCLi/IcRmNCas0fWsJyOXQxV1wuRFEQo7cvL+pBLbJqjcpEYkol+Ika\nl759JQ6Xi6KvHxoPtG+sB082NreXlyX6hFjcMabtIoClANwItuZ5k6z5o0N8qPFuyt50mee/+IWl\nHs6ckNm4y7FlrRmNgqJYbXnLy8fzwYVAHDxNxWcO0PBxQfUBHceooOUbVqfM2MZKKx7QoaIE/Cgv\nnKT31XX0vrpu/GJCLLm982aYrvvpRJZDqxFjcgQEltddFFk2bMXrnTZRQXvu+JSCJnp7h9X9NfsS\n+UXgme5Tsx90A9jCcwF5bc3U9rvLFTMWQykuyhW8NePxqRWBJiy35dGzeDpHafirA4R+6EHduBYt\nbqCGQqh94wHl/i4df5eOstlqASGcS9v0bS6MNaabb0ZNevUSLm2FmLa/kEwkkYkkwulEqSxHqGou\nOUDx+RZzlMuChf592sLzNsbMalV5PyTTGM8gMo28zprm2ctojfWUPWm1Z3Bd6raycYRA1loCxPX0\nUVxPHyVZl/2esdyrXI7n+c8nnlFoGo6BJeyiWSB7SzicCE1DOJ3Wy0tVMQeGUMpLIVtmbqYkgNsN\nrb4Orb5u9gMnIaRdXj8PIUQ/0D7rgbeGRill+a2+yBLPEW6PedpzXECW4zxt4WljY2MzD+xlu42N\njc08sIWnjY2NzTywhaeNjY3NPLCFp42Njc08uKHANqdwSTe3Jj5MuCbEAxoGUs8PcUkSIy1Ttzza\nutAcZdCLiMQxSqztSkZiuARCWgU01LRErU6TGnGhZJNZtJEkpteFyPb3lkVeTM0avpKW6D6B8Os4\nrpnIbPjIUs5xMYkSHlgML63q80mtpCRvmzMqwQQllo2N1DRQFUhnkBPCqoTLiUzdWGaS8LgxXAq6\nR5AZGcKIxZbsXgq3C5mcppOnzwMxq61KutqHooMjqls96Sd0zBSKpVtNrIs6kcV6XmFuz6xwOq2i\n3mNk5zml4R8gHA5MrwNhSBhNFDxfqs6HMypJjfQXnOcNCU83PvaIBwruU8ut38K8Owdm56wEAgin\nY0pvmcPyufmd9waZPMfwb+wj9JWDIODyJ3cCUPd9lVRQQUtJgpei6EUu2l/vQosJiq6auAcNnJEM\nSjKD2jVgtbuIkMspb/7P7TR9Wie6wsv1v8zQ9O4TSzrHxeZZ+V+LEnKilZRQ+0f/DSlAGII1j0SQ\nXefzjok9tAfff1lFn/Nqf6YnfZ4LSbj8qd1oIyqd//Kpmxn6nJl8L5XN64mvCOJ+8kje+LVVKzB7\n+qxGf54SjLhVCavtTV48vYKMH8QdI6TaAqz+k8NodbXo1SHru30j6Ne6pnQQWKznFWZ4Zid2KsiQ\nm7PQNNi4HnnsHOr6ApXAdFBWrKfl7SHqn7NeMurzJ/KP6YJr39rC9bf9U8ExLdiy3ejvn5vgVGbu\n2z38pk2YI+PFDcaE8mKjbF6PuqHJEpxA29/uo/wFB+UvOIjWqYQujBI62EV0jR8tmsLbJVj5rQHC\nGwSucIrBzR7UrgHi2xtQtm+0TiolSImjxYPpUFAMqHjGSc8f7F+SOd4ulJ+A1X96EHl8XHBmXrWD\nzKt2jAvOBcLVp/HJt351Qc85VyK/spdLf+IjUZb/G1ObVnHxr0J0/c52wr+xL5fQIA6cBgFV/3SA\nFY/2EPqGn/of6iCl1ZLkyFk4chbp0FBLipdiStOSSzWVEv3+Hej35xf1kLqOErOEYrwhWPAc5pmL\nmA7ou9NN353ugseo6vTVCxZEeGorG+d0XORX9jL4vt3Ef3kPirvwYIPfOJSnYhsDAwWPu5Woa1ai\nDEcxLlwBYORX9yIViFcJ4lWCmq+cgyNnydSW4IyaSIeK6QKpKFS/qHPlQxrOiASXE+/pDq69rhi1\nuCj31/iXB0iWu3APWup2UbtdqftWUXxBUPyt/DYZg+/fh+PZ4zievbFCEHNBmJCWMysIt4Ir/7yX\nr/3d31NWHiH05ewL/2/20fY3+7j4++WYukLosk7JIycwz4y311j919nOn/2DBH5wDuczx6ac++Lv\nly+7ZnBjqcRqKISrawRXl5Utpzatyh1jXLiCWl6Ot32EwffvK3ietZ/txDMg8QxIrv3lVCVGVaYX\nnjf9P6LV1ebV/BtD8XpJvmITpkPguzSIcfkqwYcPWWljG9cgtzQhHQpKxkScvTJtfu5SNA+LbKvA\n99hhFK+XS5/YwupHU8RqPdR91qrIIspLIRLBdKq4+uN0/gVkLkj69oco+/xB1oxsQ7t0mf43rsMz\naFB81eTCx9ZR9yNrTRF4oQV3XwohJVIRuPumsU3Z3DSlZ0bzcvZTr99FoPPmX1Zj+fATWzgDCB36\n9SCLWYro+h/t5/X7j/Pqx/+Epj8YL4333oeeBeCrV3bT+Mej6K3tTP41KaFizFgMY3gEtbSkYFfR\nmrX9RPc04nli+ZVXGvil9ZT/rHvChjDqhqac4mP090N/P/Ff3k8p5KqHjaG3d1B6yLqX8arKKeev\nDkamTWu6Kc1Ta6wvWPZJq65C1Nfgvj6Kr2UE4/LV3D6p65hnLpKo9BBZ6SFR6UFuacr7vlpZMWdt\ndqERmorvscMk37Sb9j/aTsVBQde9HoJtJmY0ihmNkq4pRquvIxPQiDX4CT4aYNW3Rgh0WD9KceA0\nZHTKjgwQXqcxuEkBRRJpUIk0qJBJ07vHR7LcTTogUDLLobDZLx5qGmvpOXFbysR76uaL+spMeorg\nBNASUKqO3vT5b4REleT7R7ex/qPjfZS6/2w/I7qHEd1D8JuBggqOec8d9L+qgfBv7EOrrpq2h33s\nu1X4L8+9m+itwrx7amGP4XUgw8PI8DBgdT7tfnXW/3LfnbnjGj5+BGXbBvRdG/JPoKgYl69iXL6K\nLGDjfkVZ87TjuSnNc2JNwDHb5LX3NaGmIHQpM2NtS/eTR3DecwcAyrXe8ba1QqCvrgag+x216D5J\n8WUJX10c47TUDVKv30X/do1MQOKISzx9Ev83DzH0Xkv1H9osUdP1rH5khKvvLGLlfz+ICWTW7sVb\nXo5MJjEiEZR0mvJTQdrfZ+A74yVelS0FVxTEdEKsSqXi3w7w4Plhnt60vGxKvwiYjqnbHC+eQ9bX\nTN2xAKihEKk9o7zK2wks3tJdL8uw5kuG1aAvS/mJFD/utpahRY/kF2pOv3YnasokWu+i5OETKH4f\n5nSeeaDic4ehtGTa/YuFNpycUj/VqE9OKRvoGM3+zjImWnUV+vUeqzB2SyfOYAC5djVCN9Bb2vKc\nYO7Bcb1cuFzIVIqvPvYAULi9ih3naWNjYzMPblh4atVVBbf3/dIa+n5pDYnNCQwXeH46eytW5YWT\nOE5fRQiBumkd6qZ1ICXiwGni1W7iDQYX3/9Z3EOLW9ZMSxhkApLqnxsMblYpecmykQyvs/5Mr0nV\nHT1wtYMV343nWlH4v3kIGYtZxYUB1q3EdbyZ0h94rPlmrL+Ot9ZTdSBO6RcO0v1n+/mXE/ct6vxu\nF0QB06ZMpW5Zgd/YXU38f+uPUaYubgxt8TEnwjDznDqOZ49T9LVDFH1tansQRZc4jl5CKtmeU7UV\nM5eoMw2i96yafv8iIDSNkQKrMykFYteWvPYaxVes36vy4in06z0Mv3tf7hz69V7M1g5IZ/LbxgCV\nT7fnSjAqqy2zYcNfTd/U78Y1T00j8dBu5F3bUctK0WprULxeyh85Q/kjZ1j/0SEqj6SmvRlaY31e\n/UgjEkHv6UUMDiMGh1G2bWDgA/voepXk+2/4FPe997cWvbWF+vwJXnvfCYZXa6gpGFnlpu2v9xFo\ng0AbuHo1OntCRF+zEXHoDMblq6RetwsAEfCjNq1CW9mIMhJDhIopeewMSAi0W3/FLTrKi6do/bt9\nJLYlWPPrJ2ccj838MB2QePPuKdvVjWsX/FpafR0P/d2z/GnpKVY/8jsLfv6ZqP5JP717fLPXIxUC\n9m7FcCkMP7SF0XqBWl6e53K/41oAACAASURBVH2fDu+3Fzak60aRuk5vAYd5/bc0lHgaJZ5GDVoh\nScoL1u9prPBzsCUbBC9NMA3Umkqrl1NNtjC0243idiM9rpziI9s6Zx3TDds89Y5ORt7SQDqgUNJf\njD7BGQRgtsZwdF5HCYXAMDBTKVL3bsF0KozWqBhOQfnJUtRD5/Jutt5jefLUkiJ0j2Ddui7+4Fc+\niPPg0vQEOvM324i+KU3RaSej9YKyMwbOqGVxSZY4CB5z4+saxbx7u6VBx625JLc1IAWM1jgo+dJB\nlG0bEIMqDd8bIFFv3VzP8VYSD+5CWxOl/m3nph2Dzc0zltU1ETE0e2uNG+Wlv6jh26HvsOHbv4+y\nyAEixoUreLeXzniMWlmB0duHOH4RtzRxGQZBKVn+parHqTgCw+/eR/F/jvfGilWpuL9nBcCra1fD\nBLsvwmoto7x4CrW4CDObVWX2WvHo8uhZ60Xa2QNAdGsF3uyqRK5bCSfzEyomMy+bZ/XPRohVK/Te\nV7j9gMykMcJhjFFL+3Q+cwz3945Q9d1War/fje53IO/cgNy3bUq8p3HhCtUvjqC8ZRRx8PR8hrcg\neJ44wr/f+xVitRJza5Tg904TbnIQbnLg75QUtepgSrRIEnXdGpSfnkRduxrHD4/hOXKViue7Me67\nE8PnhLoqDL8L3aei+1TM+irUlMGKDy6/0I9fNHr2CuS+bXnbxl7UC4EaDDL4vn0UV0fY+OMPIDJL\n068p2BqfORYzmO3hnkkjdX3JolluhuA3DpF4y3DeNm+/kYufHkufBsvhY/T3QzaSJ7m7CWXtSoC8\nUCURjSPTaWQ6TeDceEy5nEVwwjyFpzx5nvTOURp/tZm2R7fm2RuEwwlCWH2kTSMvxk6/3oPe0obz\nB0fhyFmUExdRKsvzW8NKiTx+HuHz5bWAWAr+aed+9GKDi3d/leG3bCdRIUlUSBxxk1Sxgh5wogxF\nSdVa4++9twIlEMAIh9Fb21F/cgL1dDOm14np0vC1juJrHUVp7cR1uQc5Zhu1uXUISFa4QFFz5qKJ\njdCmS9aYDSUQsO71xhWoaYi0FuP2pjH9S6TLHToz47JdL7N+S+ra1bB3KzIWzy1XU2/YtVijvGlc\n3y3GfOUduc+ex48gV9QiV9SiXR8Pp8oF0XdZAtHxw2OWIiNEnrwa6yxqJpOWLRSmbZQ3mXl721e8\n8wxnDq/htasv0PwOP4mHdpN4aDdKSTHq+jUIZ36cyFgo08SByVTK6tJXoEOh3tU97nhZIozhEWqe\nE2z75Id4y//4Ec4RgXNEoCUlahqkKjBDflzXI2j1dVQ9dQ1RYwXadv4PK0xErl0BhkQ9eBYllUFJ\nZdA3rSS+pXb6xACbBaX7HgVx5wbLDr97C2L9Kti71fpT5xdSNBbzq77UhnvYQKqSxHU/Smzxs4vm\ngtafXc4OhlHONGP09uWEhuv7RzHuvRPhcJJ5zc6lHegslPzHQa7v9eRtk+ebrb94EjUUytunX+/J\nZR1p3UOWcnb07Hgm0oS001wvq1QqL1NpOm4qznP1nx7kalUlzt8SJEPWQ+MP+pHtXbn+1orPx7UP\nbyPeqFPVGKS3rwhlwMm6f7yG3tU95ZxqaQkiGEBGotMG7S4m/seO4TcNHrl/B7rX2ta/VaPxiSHi\nK4OIrj6MwSFib92DrytB9yv8mGoFWsxK6yz6+iHSr92Jp2klo2utG+U/2Ia+pR5513bEzxe2HarN\nJCQgBV33BilasQf/48dB04i8xQq4LuksLujcnJyJMh3J3U303akhHTooEiW1PIWnXlmEQ28AIQr+\nrtTnTyAB14svLYte9DNR+3HLA5551Q4czx7PCT2jv98SeuGsBqqoYBqIUUsW6Z1dqE2rMK60YAaz\nArh/0v9FttCI7Jm9TsdNp2fqPb2s+EwayrNBtAPhnOAEq0tf4+P9RDaWYDrKWTGQwfHsIWT2DaHV\n1oBhoGf7ThuDQ7AMhOYYWm01MuAl9A9+ht5pLYsqX1QQnddhRRBzVS1iVS1FhzvB6aD+iTjx1SFc\nTx1l5Nf2WjfLrRBdX4Lutuxh8R2NeC8P/EL1xF6uqAmBFhc4oxIlIzF3b8JxfZjQ09kqO1lb4GTm\nuipwH7lCXWY1HQ+4UTdFSGT8KMnl16de92kk7qwmeKCt4P4xh9LE3+5yx/HscZRAIG+Fmif0TIPY\nW/fgb4+hOR3o7R0kVpXgvNKSKxJjhPMzp1Kv24nrqaMIMfs9XJBsfyMcHpf2E09eXQWahn7hCr4L\n+fvMeBzz7u2YkSRSVRF12WX9hdalb4uafftojfWYAR+x1UEUXdL4pOVG7duuUFJdgbcjSnhLEdF6\nhaIWH4FvWZEBnvZOTMDfmab7wSpqnullYF8FGZ91Q3r3wtpeL1ptTUHt22ZhUHTwdVn1V10REzUl\nyQSdKOkAlGQdKMdndwzMhFxZy8hKN5VHDfpTRSjFkkV3t89C4qHduIYyOMOpPGeZWllB/xtWY7jA\nPSQJPNq3hKOcH5NNe5M/+x47bC0+1ljOIndvPE+z1upq81LM3T88aeX/V1eg6PqML5MFL5WilpZg\nDA6hlpYwfM8KXEMZ3EFfXj09rboKGfSjdg9jdveghooxqqxQi9HXbCbjFThHTXw/Orckb0K1uBhj\nXT1ycBS92I2WMDEdAi1uOQMMj4bpd9P1ygCZAEhN4owYRN++i9DRHmLrylEMyWi1A1+PQaY6SKAj\njSNqLS9cER+xBj9GU4Dip0bz0upsFg4lA75eE9dwhlTIgeESKBlJvM6Lp+fm7M2K17LhJGr8JMoE\nQ5sV3KtGMPp8KPHllbgXrdXwPHEEta6WiS4lo7ePkv/oszzVBfwOy5aJNTwnMKaMTCwMkvuKYYIQ\nmKdeyiuOHNlVh7fbClXCNHLbjUvNszoTF/wuG4NDIASx/Wvw9KdxDSSQbZ1ojfW5Y/SeXkQqDRkd\nVNXKPT15HnnyPN7vHCZ0dhg1aZK6e2OugPBiIdwuzFU1xGo9oCiki52ocR3HqE68wkm8wsmqj5+j\n7Y0BUiXWDfR2WcU9UkWCdH0I79UhlLRJ6MIoig5dr/DgOt+B7neg+x0UXxy15vl8KyJUNMuIbOaL\nkpb4r4yge63i1WOFcr3tMUxNwdTm//ib8ThmPI63bRipguE18TgzBKqWXwSFzKpIhYr4AHmCc7oM\nwmXFNJXWrr/JCr+Krxh3Ao1Vv9Jb23PhWUppSU6uOKK6FTwvrQwtZfP63HdnM93ckiJ9WkMdrqG0\nVcE7lsSIxfKX4lJiXO9F37eJ+P46io/3Ijuv5wZsnrmI88ytGNncCG8MUnQ1TrK+CDVpIB0KkQYX\nvh6rRYEZjeK7c4DAZ4tof7tJqkrB269RcSBMstaP5nGie1S00TSucIZUSBB+YFVu2V75owFMt3tB\n4w1tCqOHPCgZiZaQpH0KTmmSLnUjsmu3+YrPsfC6nnvLkAo4IioDXUWIjLLcVu0UX83MflAW/XrP\nLRzJrcWRXaQ6Rsf1a5lJ5zRrvS0biuR0IrZvRJ48n1fTVeo68txFFJ9vTqbDWyI89fYORHsHjsZ6\nkmsqcAQ3oXYP5m6M4naDw4F28DzBLU1kqoqQdZYDKRVyEC9XqPxO8/xbetwkwpTE6jw4ogaOuE7G\np6EYkkijFX5VWVpC6B/8pIsFJYecjNbD4BaIrCzB0ysBHxm/Qibgx98Wo+Sci0SZoPZJy76Zri+F\nxlKUn9ppmbcUBYRuoqZAKhqKDq7hDGosg3LV0sLmG5WZ3G0FX8erhPUiVU1620pQFqelzw3hbQ6/\nrDKJpkMtL59RJpR85xwm4OiNYN6xKRfontOssxWU9I5ORNn0K765+lxuaXlovb0Drb0DpbyczNpa\n1PJiRFu3Zcc0TCvE4Ni5vLd/oLICd1MNIuCDJRCeulcjdD5C9yuLCY0a6G6VWKUGAkyn9cPo+vX1\nhC5lcA7reB4/QtXKRvruq8E1YhBtUDEdDlwjJo6oweBWP1pCEqsWVgksQGnrsG7kNLYbm4VBKoJM\nkRNHJE2yVLGq+xsStXcYvYCD80ZwH28BQH/NWkxTobc/iPDrkHDO8s3FYyxWse1tFax8WCexpgxX\nzyhKX/hlueoxVlfPKBPGnEUiY2me6Qd3WQk5E8n+5sTlNiRWhtiYz2Hk1/ZS8v1LUzzw07HgNk+1\nuChnZxjD6O9H/PwU5pmLGJGIVVvPWaDYIpYRW3nxVE7QLDoCkBJvn2n9WwhcEZNUscDUwNQgXiWJ\nVWlEVrhIPLQbvbWdwLU0/idPUfetdvzdOp6BDIZbIV0kKH4pSv1zo3T8xX46/mL/eA1BW3DeWrLd\nTaWqWF1OUxIlbSBHbt5BJ9MZZDqDEdIZ7gmghjXEwPIRnADGlRaMKy34uyR6Sxtd9zgY2BnCqCuf\nNqtI7NwM3JriKTeLOlK4y6UaDOaKggCQTCFPnsfTPoxame3XlJ0XwhJ5SqUV3TPy4Mbc11JFCqk7\n5149anm5BW1sbGxeJiyY8BSahtA0jJEIQlXy89ULMNmuoK5ZiZqNxVpKpAIj64tQ0xLdo+Dqj6Ml\nTBQdDLf1p6asnu1FzQmUtETxenE8e5zk/VvRa0pwPXUUV9sg3u8cpv6xTq6/sggOncHwSAyPpOOj\ndrfMRUFYKbSpUitkzDOQQR1JzF66bS6nVhWEqhA65qDi5xqeXgWxTBcSqWLL3LTy8Qgl/3EQeewc\n3ksDsHcrAx/Ir/PWdZ+lwRkvXV70cc6G2Vy4m5ARiWBEIrlIgWu/ammPxoUrGCutbfKYVb1MbLO8\n6b33W90qevaOn6fiXw/gOtHC4PsKN4ubzIIJT6nrSF0n8+odsH7VDceNCVMiTEnq9UtbpEAqUHyy\nn1ilipKRJCu8ZHwKWlziiIIjCtUHMzz113+P8vPTeH72Epk91g1xPXMCtbXHqplY4kctLUFvu2b1\nxN65mYZnkjQ8kyRZ/otgvl/+qAlLSEphvewcQ0n0Uh9iRV3uZT9fhN+P8Psx3ILISkGiwizYA2c5\nUPt9y1F7/e4itFUrAKzstkNnKD9umTCG370PxevFPTD1DZB+cHkUDpnSM2pSG/PkxloAqg+Ox4ar\nrT154UciayqLvMpS3tb9n/F4ULl/G0Y4jPKWuXXsXfBlu/tEK5f/xEXbo1tp+cZ2uj6yPxdQLBxO\nUq/blVfVRC0rRQ0G0Vva0FvacD11lNjb9hB7256FHtqcUHTou6cCZ1TiuxphYJsDZ9TEM2jmBLxz\nOMWuZz/MwG/tZeuLMbr3uxl9+x4wDYz+fi6/14UajtH3y+usOSbh0nu9JCqcJCqcNH34MK6fvgzi\n6V7mSIeKtyOKc0THMWqipHVSpS5MrxO1qhJ1QrfEyXb62dA7u9A7u6j9RjMlF0wOvfMfcnGkyw3j\niuXcqvrnA3m+BHXNylyGVejRE6CqlHzp4JTvT3G6LDG54HUzXwlJFVsvw4n1IozePkQqzeg7LBXT\nPGV1uGj4nIa6aR3GwGDu2B/+11cA0J8sm9s45jf86TEGBln7wWbMVh8f3vYTXvHWE9x1aIgr/7oH\nZe1KfCevITIG3X+2n6uf3EfP29ZaZb1CoVxFlFilSuDy0mU8JMsFrqhBptyLYxRSxSppv4J7SOIe\nkgxs87Hhk1GG9qX57uP7SdYYdL9eZ9cpA7W8nIbvZj3rEq58eQfpIknRRY3wWpXwWuttefmnS2+i\n+EVHZAxMl0YmYLWI1os9mA6BEkuBYYBh5AKnC3XCnAll+0aU7Ru5/vY1jKxS+M+RLUzp67vMMbNx\nj4rXi0yllryK2VyZLnjd99hhEm/ePWVFYVxpwf/NQ7ne7WppCerzJxCxfAfU1r//EADln536AinE\nLQlVMqNRVn3kIE9+JMT1P74T7ZWD/PYrfsznHPfiaS9FSIg3ZnAOaAQ7dIY2+kjca7UEVRPWG3Ip\nK7s4hyWReg2tTCXYphMvV/H2G0QarP8uNS3peGMZFT+RFF2Nc+U3HZBS+db37sb7xTBln0xz+XO7\n8XRYqkjFCRPv40d4z8U2AL70sUYa/9fcbpDN/JGqZYd0D6RJl3oQusQ9mCZT4ceZyNaZ1Q20+jpw\naDcU4TGmwQx/YDf33/kSnzlx77ILjp+NMdvvy6kYyGx4Hj9ivcP2bkUdiGI0t6KuXW21yglllwYl\nxTA4hNHZbbVPz3YBrv7HA/R+eD+Vn56+b9FE5qd53kDKZPU/HKD8zc08/6s7ECmFZI1Bstyk7LDG\n6q8N4H7mJMVXkzlnTNU/z23gtwphWP2+pQLChHRQwRGXIED3Wn/+bh1TBS0p6dnjpfJ5FZEWrPm/\nXSiKydAmDygSZxTKnneixQ3UsjI++vi7+Ojj77rhJaLN/BCmJc0Ml4rpENay2pA4+kYx+wYw+waQ\nQR/oOjI8glpagraiofDJ9m7NLRfVtasZ+bW9VtWsmMrJvlqUfvueLisOnYFBq+p8+1st80zt85Zm\nPWbGkLqO3t7B6Dv25pb1o3vn/iKZn+Z5g/GJQhGYZy6y7s+9KEVBUBRwOizNYP0alIROw/9vCc25\n1lG8VZgaaAlrfu6wyWi1infARHcLfD2WPhyr1HDEIFVkvUSEhMBVlb77ajF/IFBNSekRDc+QgTAl\nw01ODPdKVv25pW1m7t+B9uPjhQdgs2BIRZAOuVBTBqYu0KJpS6CaEiWY7VIwMgqahvAqSL8Xs6Pb\nCpyORq3nXAgrnW80jcw+9x0PVVL3I+uHWfJMP/f9pIXPNb962Xrb58vlz1rN89Z+8MgSj2TuTNQk\nx4LdGz97HgNQe8K5wigTg+P/98e+AMAnvrmFUHDuwlPIGxCEQoh+oHC8wK2nUUpZfqsvYs9xUbgd\n5mnPcQFZjvO8IeFpY2NjY2NhZxjZ2NjYzANbeNrY2NjMA1t42tjY2MwDW3ja2NjYzIMbClVyCpd0\n47tVY5mRJDHS8tZXmrXnOAN+D4wWLguG143UFERk9lCPKOGBxfDSLsa9FJqKdDlhUrbKot1LzSvd\nrrG2EwKQEJ851E+oKtKYpb6C1410ZMu3JXRkemoGVlJNktbji5KUqvp8UispGd8gwRGTZPwCV+fN\nNYwUDksMmh5n3vMrHA6QklFnDCMWmzLPGxKebnzsEQ/c1EDny2H53KJc53afY9vfWClsK/7n1Awo\nobuQIlX4pGOyI/uIqWWleXnDE3lW/teihJws1L0Ud2xCSWUKVxoyIPHq3XQ8ZLL2vcdymxftXrqK\n2bvpt60PUlq56mM/87FklskRNSaz5+EnQKzfBIDS3Y/RO7Wz5mHXz+c97htFKymh9r/9ofVBSFZ9\nJ0nHq700PjGC7Lq5DqhawwoA9JY2lO0bME9nW/1mg0J/VnKo4PfsZbtNjvBv7mPF/zxYUHACc+pl\nPfj+fQy+fx8yla+pzFaicNkiBNGmAP7PD/JM9ymu//HUcoKeJ47g7HYw8L0lLCB8+vLUNspS3lTB\nbdF8DdF8jehdy6MOg5BQehaKLwp6d3lp+KuDuVYbc2Lv1oKbx4oSqU2rEO1zbwVuC08bAK78552E\nvjxzvv1Y5pe6bg3Ktg257VpjfS51sfSLByn94kHMaBTjvjtRfD4Ur3fZt7ZNPLSbkafW5Eq25ZCS\nwLeP0f7va9nyTx9Ce2Vhbdo5IvA4br5O6HxQrnbdcGGTMaLv2ou6oangPjMaxYxGCbzQYuX/LzHB\nZivltvxImKpPH0YtCeXtl/u3zXyCQ1ZXybH2JJMxrrRw+aMbCu4rhC08bbjyz3tpeveJgvtyD9oE\nrdO41EzL2yw7m3A40Tu6kcbUUi7qT6xzLvfCE8rW9XieOELR65sLFgeRuk7x14+guyF2pmTqCYDS\n8xm62uZWymxhERg30VYk8MghxPDM1ZSM/n70js55X2OhKD0XJ7JCofu+Eqv84+BQ3n7Do+W91CcT\n+RUrf1329E+pBTpGsHnuJlxbeN7miDs20fQHU206fR/aj1pZgXQ5ufa/9qPV1ebtX/HRg0TfuZe+\n9+1AbVqJuWvDlKW5vGu71TFgmgd12TCXQjemQeNTEaoPFtYutbhBycnFn6dIZxB3bkBrrJ+yT/H5\nUHyzO8zGutqqpYVfDDChhuYSoaah+x4fzghUfTp/hdT3e/vp+739dN/jytkrlUBgyjmCDx8i/eAu\n9DvXTKkFOkbZqbm/6G3heZsjLlwtuD10OYW+pgbz3EUa/veBgppH4NFDlF5IMrC3HO1y55SlebRh\nvGitum7Ngo99ocg5CCaRfONu1PLxoIB4nZf2Nwh6f3+q3dMxnKTs3xe/zKDpcRLeECCyo2bKPmN7\nE8b2JuRd2+d0rsma3ERE49Iu200HVJxMM3JHaoodt+r5IaqeH6LxifHnb6w2qf7ADoBctSznD46i\nHjg/bWUzR+vc+9bbwtPGxsZmHtjC8zZnYvk/xevF8Xw1juerSZQ7cFyb2stlrNp/7js/PUnoKweR\nNVPDNoMPHyL6zr18+dqLZMr9Cz/4OaJWVsyrla7nByeQE+y1wgAcEsfoVA+2yCxNXyrDJeh/dQo1\nOdXmPLjZw+Bmz/gK4Gauc6UVraoStWkVo+/YO+MS/1YgnSZdr3AQOOuask8kU4hsu+Exxp7TdFCD\nvVuR3vH/A5lJz+hg6/iLuTVotIWnTQ4zHidz73Uy916n5IWOgkt1Ixxm4AP7LBvYBFuheeYiytb1\nU47/5Mf+jffd/+soL56asm+xMHr76PjbGy9dK3UdpSSU68Hl6UlQfNJB6dmpDhbj/KWbHud80OIG\ndZVhrr3LmOJFLvv8Ico+f4ji88M3fZ3kG3YwurORTGWQoucuY66qnf1LC0lGYeX+a2QCEH9Lfn8z\no7nVamg3cVs4jLhjE97vHEbrGSa+Ym6hckZtGSu/1pH73PKJ6Ttp2sLzdmb3eCO+yV1L9c6uKYeb\nr7wDgECXbmmsk2xP5pmLeZ/3n07z7ic+NOXBXgr83wzmdVGcK3pHZy5aQB49i7/HQJ4obCNdKjov\nVfBv+79O3ysq83dk4zzNMxenaoo34MTTVjYytEHD1zJsvQQrSm8qfnS+lLljvPGhg1y/a2YH35id\nWh2KkH7tTsygF9dTc2tiF94YxByyXjZdH9k/Y0dUW3jezhw5y9B7rTfr2MNVyEOrrlmJcLlQfnqS\nro/sJx1QUTavR7imLqEmcmCbkzV/WDg7Y7EJPnwI9TM3H2vq/fbhaT21S0XTNxL8aGQT5kNDU+NU\nx8hMihK4gTnore3UfOJALsNKqksTPXHo4HoqnBFkZQq1smL6A0NB1A1NyHgS5zPHSNSNe97Hokam\ni0Io/qoVoyx2bUH3zDweW3je5piOSZ9jMSu8aAJGcysyZaVl1v7dAQKPHMI8d5HM3ZtJvHk36trV\nJB7avVhDnjeZe68v9RBuCUI3+fbRnZzY+Sg9/+wsqGGL4NTQnRu+jqZZXuulkhqmoDVRztX7v8Tw\nfYUD3QEy1UGMC1cw+vtR163J0zql24lWV4sZi00rQLW6WtIhF9osUUu28LzNKfv3g3z52otzOlYt\nK0Xs3Jz7rD13HM/jRxCpNJ4nXj59bmZiRo1mGbPui3FW//g9/Oumhwl8rh/j3jtz+5RAAL2udIqz\n70aRuo7edg2hL11v2x8+fwfvuXYPj3zi79Hv31HwmHTQgbaiAbW0BONSM2pZaW6f0dyaM0lNVhLG\n0Du70KIZTM1qAjkdtvC04Tcb7kY4nAz/+gzGcbcbioPIY+dQy8tR167O7dPbO5Y8iHqhKFQAA6ZG\nGSwrpARDsubTOv/9Q7/DiYNr6b7HjVZfl0urNDXF6lU/Aa2ultQbdlk539Pkfc94zUVGSCvi4fCT\nW3jwC39G5/1Okm/MX/FojfUkQyoYJmZDNQDGwOB4EoGizql7rVTFrNLxlvRtt3n5ITNpir96MJeZ\nMRZkPEbve+4gfKdOsNxN/GIxq79lpQSO9cTO7N2I+nx+iqe2asUN9UJfzox1YlyOKEkdEYmBrqO2\nmzT1lDGyPkiyyXIguU7GcFzpBiXf+yE9LlyDKcSxl5DmDQhD3QCx+K2W1YRAGOAekPh7DNx9KZSM\nycSRm/2DOEdrkF43ytUODCyBKh0aanERMp0hvXfDjN1r1bJSePEUru37ycyQoGULT5s8JgtNrboK\n6fNQ/tmDTIzkHHtg++6poDKRguen5sb/ogjOiWh1tQUjEZYKYZjI1g6MbESA4vVi9vZRdNmLUmWZ\nIPRwOK/V7hjGlRa4AjesQ/YPQXBxQ5UsoWm1Ba94oR+9zI84cHrK2CNv2IK/I4FxqTm3bawVMYCy\nfSNaPDPjtYyBQZSt63EPmYzWTe9ut5ftNjMiEwmIxgrmCgOUP3wavaMTsWvLogdOLwXLSXACyFR6\nvPCKEMiMjhoMWv92aEiHRup1u8BpeQbnWxpQLS/PhTcJ/+IXC1dTELqcpuLAICKeRHupcElYRSdX\nPakQ5qmXZtyfO+7MRXSXmLHuqa152kxL3+/up/JwBKW5I6eRqsVFGMMjCE1DrarMCRN59CwG5Izz\n6c2NOE+3Luvl7i8cUiLcLqRhIJxOZLuV5GBsKsVYU4sq5dSQpbmeurKU1O4VOMNp5MjMlepvBVos\ng7trFNlxHSMazYXJqaFQ7hlTvF5Gq1W8N3OdulrM4gCpGr8liG3haTMftLhEHjuHMbEcXbb4h9T1\nnOAULhdCCMxkMlc9Xn1+kOUVDXl7INNpFL8vr8iHmjBRRtMY4RG0miqITF/CTlvRgN52bcp289xF\nPC1eRE0lZtHNiKf5IVNpzHMXc9qvTKUQDmfey1k01lJ5JHLjZogJ6J1dDL1mH1JAdAV2kLzN/Chq\nS6G43QXLnU1EplIMvuuORRqVzWQmJisIIRBOZy6lFMD19FFL8JgGssg/Y+RAIcGJEGgrGqzrOB1z\nK+F3q5gpuD+j31AY1XRe95JzoyQqBMYsASS25mkzLepPTmACZts1Ir+yl8gKhcYvXCrYm2isCv2Y\n3TO+ezWup+eWEmdz0lzJDQAAIABJREFUcwinE6FpmLEYwufFHIlMW4B6Xjn4UpKuL0WuLMMxNE0D\nwCVgcnGPG00DHvv+WFaW3tKG2LmZjlcFUOdgmbCFp82cCD58iCBgYHks5bnLSH2q/Wxsueh6evra\nkMsZtbQEY3AI4XDOu7XFYmNGo6iVFfy/9t47Pq7ruvf97lOmF/ROAiAJVknsBbQVFzmSHTtyiRM7\ntpMbJ05eYseJHcfP9yZx7suzk+sU5/olN45jO8WxpLjI3VEcRZai2GIRSYmUWESwgQDR+wxmMOWc\ns98fBzPAcAZl0EGd7+eDD4lT98acWWfvvdb6LZFM2rGck+mTaigEgAiHFq0Er/zoecT+XUh9dYWt\n1cpKzIGBpbnW5Hrp9KgQtWuQdCBIqmTm8zI403YHBweHBeAYT4cZGfrV1oLhR9bZi7ZcW4HwpdQD\nB0g9cGAlmrcsZEbOMp1i4i2H6P/N+Wk7rjZmXz/SMDBHx7CiUTswfGMdcmMdqcaKeWXVzEVedc5V\nIDPqFFrhSXNGY6GQaM3t2wpFglz6H420fH5+4WiO8XSYkaofD2IODeekwEXfcST7/+kB9ZnSFELa\nP3cCgR9dpfzCyoflLAnJFCJtINIGY5u962YJYr7MlBGV0VhIvHYq3TSjx5ARt4HCxhVg+x9dKew0\nK4BjPB0Kou7aRtcDdk6Ra3Tqixf86gnGf/Yw3R/NHZHFGuyHWX/8NPrjpwFoOLF66vFLgTk0nK0A\nuh6YrhIk4xOIeAIRT2BpcPUzR0i86dC86xnNdN01wwxed9lqlx+e7qyUp88TfeeR3OOmGdIMaiiE\n2TL/Wk2O8XQoiHnhMlXPT/Dv3WcZ2+JFuN3ZAPjA10+ixUHb0MD1T7Wi7NnJpo/lFz97/kt3521z\nWFrEdDX/WCwr0GJGIlhDw1hDw1Q+/Dzlzws63mpx9R1uht/bmi2MNh+sWAx159Z1kUEmjp8j/rbD\neQa/5EV7ij7TdB/sv1k6oKN4PLPGd2ZwjKfDjChPP88DdXuoODXMvhMTBL6Nrb6jqFT9zTHa37OR\n8FVIVNsxhcZ9+7n28F6uPWzHfFb+7cpXk3y5IW9TN5KmZcdhCoEVj9s/iQSlXzrO1l85zYb/kIzs\nkvQcddPzkanZg9qyCXVHSzYNc+LNh1C3NKNuaQbAvNiWVSla6/i+edJ+kdyzPasqnwnRuj1CJKek\n9pF70J84w63f3Md8cEKVHObEvHCZM3sVYAg1lObPr/+Yj7z9V7F0KP/ilIHsuN/F5nc7BnOlUfx+\nZCqdU9hMuN0Fp6ae7z3L1uPlyIkEfb9wD70fPkrl8wksw8qpMxVoGyG50R5pamugjMpCmF4WZuzd\nRwg/XKCqgWtKDTxZ6sYNJCrmt2jvGE+HojAjET56+M3IvvNsPJ27b/Pvn8qmxpmv2beu1gvXM1lV\ndEVkDWYhw5k9PjqOEgxQ842rIC3M4dHsGqLWtBHSBsalK8gNt0VNqKuYWbRIChpOcpW/HvvC3/DW\nhkPzno+L24f9sx4sxABQWM5k+WmUUubXt11inD6uCC+Hfjp9XELWYj+LMp4ODg4ODjaOw8jBwcFh\nATjG08HBwWEBOMbTwcHBYQE4xtPBwcFhATjG08HBwWEBFBXn6RJu6WH2PFfhdiGTBUQI/F6I5Qqp\nCkUBMZkVcTsBL+mAghSgj0uS0QFSMrnsgWYz9THZ4MfTP1l1z7KQhoFQVVAUZNreLrweUkEVqYLU\n7KJVUgHXcBqZyv+bmGV+jJBEKBJ3e5IJK7oifVT9fqmX3JZqJ8HVE4OA1/59fAKha8jJmjfC5SrY\nhwzC5bK1HmMzi+WmNnkhoZDsvjW4EiEu83lel4yAF8OroI8kkIZJgtiqPq9muR9zUvvC1RNHqAog\nQNfs1MO4LXhiVvgx3NjDqMm66MICvS9W8H7pGj+W10IdVzAG+lekj7CwzzJV68fVE0MGfQhTQjz/\n2TQr/BgBO+LI3R4nXeNH783t+0yfZVHG04Ofw+K+gvvE3l32v5euYYkCSjRx8ospSZBHdqMYFvLU\ni7n7YtD/S0cZ3Z8CUzDyvk8V09QFU6iP1x/Zw4Z/UNG7pmo9q3dtY/TuUkyXoPRCBHnmAlr1Bobu\nrWd4l8DwW+y8pwNNWHRFw6j/UkbJV+2ocmkYIASqq5KB+zYTrxE0PdzJM+3/tCJ91EvK2PD+D9uf\nhwQ1KdjwyWP0f+AoVZ89Zh8kgOkFsNLkf37TSYPqD4PPfqQKqc1fe38ragKu/sFHViReb7bndcmJ\nweB7WvG/rRfvAzc4KX+4Irct1MfeDx3F90AfoTdcy25T7trB8O4SxusFvj5J1VPdyNEI469sYXiH\nRrJMYoQN9u5oJ5r2cO1CHVu+agfai2fszCOtuRHSBr0PbKT8/ATPDnx6Rfo4Uz8LoTU3YtywH6/4\n0cP4vnmSa/+zlepTksDX8gPlNb2akVY7BbXnYxZb3/9s3nM+02e5JBlG5mv2ocbs0ZdM5BtOoWk5\nOaXTf5+o9TC6SaXhgp0fPb18QNlLSZrf0cHOUC//ZykaWiSjv9jK43/yl+z5/iH0J57Nbk++4SA3\nf1qgliTQX/KR9oWoGd+CVAThh05Q6vMhNI3Y0W0M3q1j6eAJyNy8WinBMCj7h+OUYduplUaYgs1f\n6sa43o5WU03VZ49lNTpvr98+H8zRsax6TfArBUp1XITIpsW1ebUZ+PVWap7sx2y7lrev+tHLHHx/\nH//+7nvhoZUxntMxX7OPV3zmJH9U+Vl+8h3vzW4XB+7i2s8ECd41xERHKWpKZXxXNf4bHoIv9OH9\nzk1QVLTGBvoObCJWoxK2QElMfqcnryMjUcyhYSr+bm2VX5565k7Y36tJfN88iVoSZuN/pDn4p6c5\n+zWIvf0wobP92ZIdRm8faqoJABEwUHdtm3epkkUbT9m6m44H3DT/9/xUPDUUQpSGMWpLUc62YU0a\n1tuT840AjD1o6+8FvzL1dpCqIKAnScuVl/7v+chR/vk3/zc/feFdbPvgWSSgbt0MQMfPm+i6Sf2X\nXBheE/dIGkYjmH39wNQLwPOji9Q/ZaD4fEwc2gyHbJUhta0Dc3Qsp8LhSqMYgk2fv47R0wvYDxEs\nzGhOJ/P5Ga/dj/bkmZx96eDkUHcd445IOt5WTf2n8o2n8Hhodg/YJWtXGOO1+9n2pxf49dKTHP7Y\nRyn50ZTGwLWfC9J67wXOfO8uWp6IEq/3EbjQh9ndO5XGaZkYN24S6h8kEIuhNTeSaK4AQG3djX5r\naNGlPJYDobsYeqv9fQt+xS5gp9XWZJ9r2VSP92IP37y0B/G/vITbQIzH0Wqqs8+875snAbjnt6tJ\ntM2/VPaijefVd3vwd+TP58SBuyCeYnxzCYHzfRgFRqQA+riJHlELfqeUtEXccFGmFV5/WS60+jrG\nm0ze98kPUf73x7NN63hrNQB6h6T0ksT1gxNk9LkzNcuF243R1Q3YOccAZjKJ699PZ7UGE4da8LSP\nwEgEDGNVapu7Rsk+YEUjRM4bvhC3G04guwa3mrR/opWmjxcWL5lJTGM63kGDVEAvvFPX2OTqx5ph\n93IhNI2uV7npbm/h5oMllPTY/cvIzimNMZ77zl00fcl+WfqYnOkIgfnqfbgu3Zp68U8+s8aNm7h6\nbOMy8dq7SZXU4odVNaDqzq2YF9tynr/uDx7A6M71mZgNldDTy+CvtRJuT6OfvUjldzdQ+kwn1395\nI9aDm6j4fP4zcLGnmuZ037zbsyhvuzh4N1pUof6p20Yrh+5mZEcQTIu0X8muQRTC3TuOe0TiHjVw\nj94mFxVJEk15eKV/ARX/FsHYkQ1seSRB+d9P/YGte/eipEFJw+Yv9eUJDWiNGxi5v4Wr728kfX/h\nMhTKqQsopy7gfaETs+0a5sAA5sjIrBqDy4UeX8QIcIEpvdYakKEJFRAIMl5rG5m5DCeA68mz1DxW\nWGncaO/gJzwQaVzZIBaz9W7UJDR90sh5IXb8pIuOn3Thf8pP/aeO5b0sJx48SN9BDx3v3VKwvLSV\nSGAlEvgv9eP+t1OrPvIUE0n6P3AUtWSqOlt0R5qa41AzzRbefIO99OTvN1FStmENfuUEKIKGJyco\nuzhBz0eO2hq1u7ZlJfyMpJZdAlCrq+Zsz6IeZ7V3BG08iNrZn7tmpwgML1jXOwhd78gZVCrBYHZq\nqJaWQjxJ2aVYtt7y9GMH94T4vzd8n7/rezXwrcU0tSj83ziZ87taUU68wkXtX9rOlNs1rEd+qZXw\ntQShR05Q2rKJdF0Y2bobcfxcznGZ5YrMdOH27SuFsGD0NROUf2FFb0vjd4e49s7VFdQNdKXztrmG\nJxAV5QWdXLcjDQPjVuE1v/ZPtPL5sX7CNyy6F93S+aO3ddF4Pp0zg7Hu3Uvz92zvcsbhk0Hs38XA\ngRDVj17G/0QS854tTGyrxuNxY16+mnf92QY/K4n4xxQ1Hxol3roF92O2Unzgsk73a+3vT6juKDWf\nOUb9f9mzXO+3bT9Fz+8cpfYvj2Hc7ES52Ym6pZm6pw0Gf2Ef1T/oyJbpCD7noedVBsGvkB2Jz4YT\n5+ng4OCwABZlPGU8TmmbibExd4irvHCVmid6csRZs5gmwu22wx6khUgbqCNxUiVuUiW5i2INv3id\nKjXKi1++azHNXDDqlma0TU3ED27C962TBY9RfD4q/7MLFEi/bj9W2IcwJNrYhD2ynoHVmKpn7x0w\nMMfyqynKo7uzP0vJ+M8etv/dEl7S6y4EfTx/lG+dvTivUedsKMEgiiH49KNvJvRIYe3I5cLs68cc\nGUGrrUGrrUFtsUMaxDNn80adit+P2j9GyZUUkVe3kLh3J8K0cPfFYHB4xsJoAFptzbL2Yy6SpsbQ\n7jCex5/Pbiu/MDWTiB2Ko5aE0U9cBCD1+oMAVJ+ZHIFPVhCVfYPI0+cpuxAnvaECqQqkKojXS8IX\n7e9l+nVzlylZ1DdY6DpSEXT+ZJANU5E8tvT/NJHR6SjhENIwkZpqB8cHfJh+F2rCngxnFu3733+U\n51s+y94/eT9Vnz3GamhZZ8IZ3DP0RautIbW5hkSVG9eYwdhmF9X/cgUZiWBif1hafR1WJJrnxV7p\nqfp0rLjG/ruuM/rqfaj/ORUlIY6dm+WshaH4fAS+fpKJNx+i72Bhx+BKkqh04Z2Hw6toGutJb4uz\n+V1n5z52icmE/k1f01SuFD7W3L2FibALNWkhFezv3bMvknG5qCVhRG11wQqSC3YwLhHa7wT45vf+\nnF/50itR7tmO9cJLmF6Fxu/Zrb/5Jhdj9+8g/PglSCRw/eCUXXfradvYil1bkGcvolSUYUWjiOPn\n7DXPyWeh9IKf0i/ZS3P6E/kOz7z2LKYz5sgorqgJyvwGsNar9kJbD+bAACISQSaTqFKSrPSgJuw/\ngKuuBhJJrPtHaP7Or7Hj4Ut5a4yrhjIZMjWpum309KJMei8BKn6Yux6qVpQhU2lEXTXaeDDrhV8L\nfLj+cT780XdQ+p/Lc321JMzY/TsIfO0Eyj3b6TmqoqyB6rfBc30Yy6BhO3SglJY/G6NArtyyM9OL\nOFOfxxoeyYbPiWPnyIwtC9U2FYEAGCbq1s3IDntt15ohUmalsc5dYsxSUYJBxnaWEHwBgi+NQL89\naxD3tzBeqxAYHUPb1IRUFYyqEGJwCHl0N+PVHkJDDTlruNNjOsOVe1H8/mzEwVwsbtqeTOK7NkL5\neRO1umpOD1UqqGONRbLnAmBaJEMq6aD9k2wqZ+wVjfB0Kc3fsmAVp7cZlGCQofe1ogb8qIH5p4gZ\nPb2YAwPI7r41ZTgB3vvVD/AXO75O6oHCkQGLxRwdI/C1Ewi3m/4jpWgTa6OEg3HjJorPN/eBM6A1\nbbRLVdxG6T8dxzp7cTFNWxSZKWkGeXQ3kYMNRA42IFOpvD5nqmzejnGrC+NWF2bbtay3fS3xgff/\nFnc9PU742/YI37zYhqypRNZUEr6s4B2y0Jo2YoV8mFeuM9pipxuLY+eQih1qNfqLrQWvrTz9/LwN\nJyxBnKd5+SqhyDhDr7PXWcIPF/ZSCbcb/6l2zMk3oFoSxhwdA9Mk7Re4I/ZowPCoKIZESYFUxIz1\nmVcSKxql/IvHC46AM1+kdF0pyqlLmEd2kQ5o+E5czXo/s1P2yZGrWlay6DW2xSIMaNHGaX+rwvan\nPcv2JZF7t2FpAmEAK5/rUJDpWWzFYrR3FDSeq83tvgVx7Fx2RiSZGp2q1VUIrwfp96L2DiAnEsRf\nexfukRTq2SsFjYdaXraqCR3TcT92imdCRxj6uCBVZrL1N55FTOoveEYtxusVwu0daM2NKI0bKP2n\n42hNGzHaOwj+m708UfaNF0gVSOIoliXxths9vbgjFu6INeNbXSkJYw4MoTXUo/j9tuEEZE8/3mEL\n10gK10gKxZQYHgVXVKKkLGRi7ti71cRo74BE0l4vlBb6xZt4O6O24Tx0N2pJGK22BnHgLrT6WrT6\n2qzh1OrrVrXtr/7yR7nx4Od56TP3oNyzfVnuobxwlWQ5GL71nVmUwxp/JmfD7OvHGhiCwRHbIFoW\ngXPdtmPJNFF3brVTNZsbs6WHzWF7EKCWlq76Mwt2zObmfx5ACaQx7tuPefmqPYi7HmeixrIHZmUB\n5HjMziTKLD/E4/R/4ChWLEY6tPg3+ZLNif0/tleorQLKO6kHDqBH0ug+L5bfC5PGI+PZU5MWqbA9\n7YhXa6QDAl+fieFTceurP22fDXVLM8Z1e3FdGob9QGbe0s++iLV/F6KtA+VaEmNyJJpxiq32VF6Y\n0PLl38ATE1z7PRfN78zdX8z6z4xYFsF2SaRZIDVpK/qsceYcaYmpTqgtmzCvXF+BVi0eoWmoFeXI\ncDAbz2klEliTwe9WIoHaM4DWtCGnqmQGc2QEViEbrhDm5ats/8NGzPIgSrkdO5z06wSvKYiyUnjh\nCmbaXq5I3b8PNWkHzEc2W1QxFQO6GJbMMs2UYqiWlxGr1fGpAm3Mjejqw8zkuCeTqPW1mC4FNWkv\ntbsjFsmwipBgugXorsl0rKVq6dKghkKYkUjWIz8T8syFvKbPJ5NluRGWQB8H9xC4IhKl3c/Ye45Q\ndsIO4Dev3liSdlqJBOkA6yqieM4p6rR1+LVkONWSMMLrxRwYzHMiqVuaEYaJ1T+I1Vs4BVEtCa8p\nAzkXxo2bcAMmJkOSvCfa8FbsILa9En8qjXGrC2maeE+0kdq7GfXsFYL7dqNtaEAGvJiXZghJmCfL\nPqwT4RC+fgPfqXbMgYG8/VLXSJQqlFyxR6yWrhPosQheiRDfGITSEAwOLnczc1FUtI31mF29+XGq\nGaorUEwzOzKzXrWX3kNeFMPWHy25kswJA7qdxE8fQouZuHujdr7uSiLB2w/BLoNonUbJFTsOTk2a\nWJ32aFjd0bLohytD+IZBtFlDXVu+hxmZLcddrShHjq+s1sJcKH4/bNmINCX0DWVDlzKxxMLtRhgm\n6foyxGQIktA0Ij9zgEiTghYHPSapeuw6MFbwHlp9HbHd9XgGEig3uld9zX46rh/Y2UZtf3GEzb97\ngok3H2JiZy36rS5kMomZTKI+9RwWUPflC0wc2IIRUPFesn0WcnIJMbOUOF+W3XhaA0N4fbZnT62u\nykt7EuNxEmUCJW27Y0yXC1OH8c0hUn4FT9kKidlm2qPrqJubMdquzR4LODCUM6VVn3mRhpMaSmXF\nvHKAPd+zpw2r4Q5TUxDoMXGNGlT220ZCjaUQ3YOMv8EOkA9cKe5Bmg33QBz3UJh0YI1NH2ZgthG3\nOTg0r7znlUJ43Jj3bEEbm0C230JaVp56mTQMlFAQ5fSlKXk5wyD4tZOEXC6EEFiJxKyyiEZXN+6u\nbiSr88zOh9Lt9ozh1v2CuidVvBsacr6L8hV7kC9cI1mqYekCL4CURRvNDAueTN0e6iB0V064REZo\nwIpGEbd6EC4dJhJ551sjo+jjEikEUggsXWC6BZYKnhETdWxi6QOaZ0Na9nrQHPe8/Q8uDcN+AGcw\nnNqmpqVq4aJRDIlnKIXr7DWEYaEkDaSiQEUJwbO9BM/20n908TnoWnMjw7/cSqLGh7CYXUx5HSFr\nyle7CVMIgd45iHmxDSuRLBg1oZaWYvQN5L8UpEQmC58Dk1lTk/qu64GxqB2WJD0mkSYVmUgiDt6N\nWl2FVlONfs22Q8H2OCPb7YfRuNm54PsteOSplJbAyOjUW+726W1qKm3KHB1DSaWxJiZl8IUAXYdE\nAplK4RuwmKixjanhFvbUN27huz6Kdb2wgs1yIQ1zxi95Nr2rwFReq61BlobsX1JprM7unId1+gL8\naod+KEkTbTSBORZBjSaw/G6URNL+W5uZGUDDou9jBb14Ri36DuhZ1fo7AdE1NXvKrH2vGoY5JVQi\nc0P0Mw5Z2VgLZ/PXMZU9OxETKVAE9A7m+S0Wq+260vh/ZM9SN3xXYeA9UQZGtlD2D8ehtBRjZATF\nY4fkabqOr6dQikBxFGU8haaBZae2ybIwjIzOeKycuK1eiGWBlCg+H0pNFZgmVjSKNAz0mO0kAojX\nCpQ0hG4YiInkmnCuZMgYTW1DA0ZXT04MqtHTC/NMX1v1mLm0gRHyoEiJiMSwSnyY5X70gQByss5L\n9RfPLMrWCd2F6BvG2hZGGGB67hDLyeTUvcTO01/1ULrpBvO22VL2u6MpthD36Ys5z+xqBvUvhJkG\nHZlZbs0/2Ouaab8CLwaJNkJp626slIE6UYl5sc1es45PEOi2/w6LefkVN/LUVBRXACsaxfJos05t\nb5/WKiVhrN4EViKJ0FSENfWh+9oGEFts1erEkSieR0pxdQ4hZykmthpozY0AJDZVEPuJBpKlCpYO\ndX93dlGB1yuNNE2ENfnZmSbCtOwyKqn0kvUjed9uBu/ScUUk4s6xm1lE2J5lWC0lcHv9rRVkxpmS\nEKibmwCIV3mZqNBIHj5MrF4SvgblXygsCD2d7NLaGskymmnQYVbaL7JIaz3Br5wg9MgJQsDQ+1pB\nQKLaR7JEpfRWKOvoSoZUvMDwgzsJP7QwIZfi1jwty167BJSEUdSoMKNhKVQV6XUh3VPro+nqMO6+\nGO6+GD5XGmFKjJudBb3zq4lx4ybGjZtoPzxD+fFeyi8kcI9Ien9pD8o925Gv2JPNJ56OVl9nCxBM\nIl+xZyWbXRAp7JmEVVOOSFuIiRQylUJr3FBQGLdYXD84RbzBYnwDGN47z3rKsQhyLELH/WtzTVBx\nu7E6u7E6u3E/doqyR89R+/QwtSfsjL7Y2w8jX7EH4779eepfakkYdduWqWt5PFmlprWGWlrKwL4g\nA/uC9LzOdnnd+F92+mX5F48z2uLD/dgpgjcTmJGIncQjBP2vsEeeibKFx9Cto+g7BwcHh7VDccbT\nMKeUhQZnD6TNeOkUvx+tpjq7XaZTWG4N1KlbS1UQbQkRbQkxdLyGwM04Ew8eLKppy4UaChXcblxv\nR33qOSp/cJ2q58YZ21mC3jNKYmsNsbcfzmqBqi2bsIaGEfGpqY928SZX/nnfquojutoH7FjAlIFy\n4xZWyIsIhzA6uzE6u5ckHGfLh05Qe7jnjvGy51BZDpXlbPjjY6vdkhzU6ipbGCORQCanfAZWPI71\nwkv4f/ACtT8awz1sYOkK7o4RRl6/DfM1+9A2NaHs3gEVZbZPYlIYxEok6H5DTXZEt6Zw6ahJUJPw\n/df9NQDbj04lrpR+6Tjmq/ehd9lTfqW8DKQkWGM7w2q/OCUhWKxgTFHGU0oLGYvZmQiT8ZqZhfPb\nyXjqhMuF0T+IWlk5ddNEOicwXOoK8Qr7x39LwrMv0vOOVE4a3Gox22Kyde9e0HWUs22Ev30Wq7cf\nz5U+QpdGGT1QTffr6+i9r9oOYZpeykARND2kIK3VEDCzMW512Z/J0KjtdR+MgCVRPG4UjxvCSzMd\nvXWu9o7xsmcQmoYV9mGF7S/b9Gd7LVBIizND5ME9yDMXcJ+7gX7iIiIao/TMANp4mr77aml/a6md\nfXNb5lz1iSgta6zkMACpNOXnxig/N8abHv8t2j5/EON9k5/LZNqm+p/P0f8aezlNeu0IhA2/bhvT\n6Wv8Nz9c3HJacSNPOXmzaWrTwuud9RRzZAQsE2vSgaSWl2G98JL9/5ZNqLu24Trfib/fwt9v4Rsy\nGf/Zw5SXjBfVtNVA+dHzGJ23sBIJlNpq0oe2Y/YPYF64TOkzt6h4cYKqM1P90Job0ZobSd3dhP74\n6XnVSVku1OoqWy6v0n7AjJudCL8XaZpI0+TGO6vnuMLsjL3HLqS1+RtrKxtnKZCGgTIaQxmdrI46\nMEDyjas/UxK6a85nKvA12zliDg1j7t+OWVuB2XYNeepFKp6L0PBEPM8LP/buI/Dsi7Ma5dXCHBlB\n3OpD3OrDf03H06Xz0ocq6f32DqzmKRGTsn88zs0/Ooq81QPYPpj4Ww9nqxwARc8iio7zFLorawiF\n7sKaJVxpOpkwH9lQDUPDyFfsYaTRg3vUxH3hMt4eu6M3f8qHEZSUf7USZH4xqhWjSLVx48ZN1Bs3\ns4Mso/MWSuetqUGXEFhhOw5ttrTNlUDoWvZLJtIGQp1SmMlM8zb+v8fo/PhRPAOSys/N7Zm9nWBn\nErWykpFNC9fOXGuou7ZlxXNTG2wni9VUhv7EGdz/emo1m4Zwu1G8HszR+StOi2fO5kwK5JkLOaOp\nzKzy9kqxa42MB73xoXZ639iI4VOI3gxj/l4U7/db8b+zB/f97SSrDax4nIm3HMJ/PUKiRCHQnV8Q\ncL4UbTwzRlAtL8Maj2EVGYdp+lwIYLTFi78njffZa5iKSjpke/HTJRbumjgVf9y7umlgS53VJOWa\niauTaTtdz4pGsa61o1ZXIROJrHRXhg2fsN/EQ7/SirDst/d8UZ5+nq4PHyUVgjtl3j5ddVx9yn4B\n3vzTVrZe2bCoTJWlIJPDncNtlQ+KZaFpi8tFJmd/Joyubio+3011RTmDb9rKUJkL3SN4Y92LPEGQ\nwFUN5a7tqBPUzLsEAAAa4UlEQVQW0ZYwwa40+uOnufbwXgA2v/v5Ga9diKK97UowiNBdyEQSmUoV\nNDJZQQI9v8gYwlZ48YyYeH58CXNkhImf3k+iXCNRrrH1w2dw6QbmSouBvIwQQiB83uzDaA0NQ9pA\nayycVVT+98ezhrPrY0fnfZ9EhUSqd4bhnImt//s6Mr424iDzsEywTISmrWrBwaViPnW/lD07MQeH\nKP2n47T8f2kqz8X53NmfQKupJt5gIXr68d4Ywdufwv1f5wHYWtfH1rrCSlOz3qvoE/w+22Mei804\nOlMmS1UoZSV5+/QbfZhXbxBoG8kKa4xs1Sj9t8uU/ttlpGHwug2X885zWDqklAghUIJBW8bM5bLl\n9Trndghs/Nvzs+4XT9YjnrQX57WYWBf6nUVz5J7sf43ePoZev2WWg1cHobtQPB470F0otuFZAw7Y\n5UDx+bKectNnz2DVinKU9h7EM2cJnPFi9PZR97S013rbriEsmQ3+f2zbYzy27bGi7ytkEdNTIcQA\ncHPOA5eHRinlsrs1nT6uCC+Hfjp9XELWYj+LMp4ODg4ODjZOhpGDg4PDAnCMp4ODg8MCcIyng4OD\nwwJwjKeDg4PDAigq+Mul+aRXz81lTwc01LI09NmXUsaTSI8LqdlhEaZLoA1MprGV+xEmqNGErSmZ\nCXoVAibrHDGDhmdCTZAy4ssea+ESbulhZesmZUgQIyWTd1Qfk40+tHGBOjSVphllZHAlvLTOZ7m8\nrFQfYX79FB53nji1UFVbvN2y7FpNAS+KYWG5FJRY0tZDvQ2jyg8hAykFrptpJoxIwX4WZTy9epjW\npl+y4ztVhVtvqOQXfvnfeeSzD1D12cm8UEVl5GcPkQoLys8nURMGYvAcakmYkdfvoOzJGxhjfbbS\njklWccc6aEf5K08XjvJ/Vv9xMU1dMB78HBb3rci9Moj9u+g7HObG33xiRe63FH00X7OP7ld4MHbF\nCAYmUB8tp/RLBTKQOmDg11up+crFbMbKE/LRFQk5WY3PMsNJ+cMVuc/LoY8wez/VCrumlDk4lLUn\nGYV4xeOz9Tgmt8t7dmP4NCaqdHpfabHzT7qmyphMonkauPrLG3jwDSd46rNHuPqFTxa874Km7dFd\nFVz+vQB/9YHP8cXv3D9lOIGujx4m2ixwj0i0J8+gnrXL15qjY5SeHsiKIuc15OnnUZ5+3g5uva24\n3J3O2NYg/r7VS0ZV9uyccZ88urvgdvWp59jwyWM0//w5DFMl/baZJQoXkhu/XikmA2stMpME41pl\n7D1HMAeHckohC7cbubEOtWUTSmVusT5x7Bz6E2cIdCURfoNrv7Yx/6KaSslLUOWKYPhmHlgXbTxj\nW8vwf/AW3733s/zqo/8XTb9vfzHGf+4I4z93hNTecTY+FqXky/b26ZJPZtu1Oa8fP7gJ48D2Ypu1\nrCx35UstIQl0rE4Zj87fP4pU8x+QibccQugutLa5yyjXvOUS5n/NUW2zqmKhTVxTWK+cXbYs2LF6\nMoPAjNUu1ZLwnHqVE285xPhrC3/3Iu86sui2LTW3/sdRPCP5gw6ZTGKG3CSay7BKAgXTxLXTbXhf\n8pCsMkm+8WCOKpaMjmN6YJNrADFLRmiRknSSztcLfrD9X3nTU7/Jpo9NjSjG3zXG+LvG0F4MwLML\nr+niiixc5WS5mF75cjkY3awysmPx1fwWwoY/PoY8cyFv+9AODWVrc84bfTZqPz27nNdg69qpdb4Y\n9Euzy7KNbls9H+zIf2stWPHyI1cvMPrAjjnrU3m//Szebz9bcN9Y89ryLas7txJvSeE/k/t5yNbd\naM2NqPE03isDWC+8hNpQm38BKQlftwhc1TC8CoZ3Wv9UFWHAZn0AcxbFzaL+IpZb4/df+x0+1HOA\nHR9tz9lXG4pQG4rQ+JlzxVwyD/HMWZQfn537wNVmCfOElTQkS9ZW3vHGvzqXoyKUQd22Ba2+rsAZ\nszO4b+1mssV+5vDcB00yV+XTRPXc4hXLRcE1Z+B+X5rgVxcnK7fWFPMTdUH0Pj1vGXBkhw8si1SZ\nFzkeByntumO31eWy4nH83UlKrpt4e5N4e6c5miYShG6m2ON2k55lTFOckrwiOBHZzOM3tueuMegu\nYn/dQOyvG7JiH3c600uLLIbEmw7x9d/5c/a+a/UqMBZips/RvHwV6XXPWEFgJlp+e+1qQob+6/qS\nXCfyriPcePPnl+RaS8kDdatfcHCpETOsjugxifR5cF/IlQg0u3pQd7TYz+2kVJ9+vh1vbwItYv9k\nj41E6N/nZt/pd+CZRdxtbY3FHRwcHNYJRRlPJW1xdqCeVCo3wkmmU/i+dRLft04uSaMy9dHXMkZP\n75JcZ+vHL/B/Bl5N95H8taq1hrbB1vuMb6uw6x0VWTBrrbJUJa5/4w8fXXejvNQDB1a7CQvC3TeO\nkhJ5pb6DXz0BPf2Yff2YAwPZMCZpGIh4ArmxFnVLE+q2LQiPG71nFJFIIxJTvpb4Ww8jLEgeL6fq\nb2e2aUUZT2FYDLWXYkZceWsIQnchdBfq1s3FXLIgOcXSYEmuuRYZ+dcWvrDhGS4fWHtOskIM39uA\nEgziuzqCGYnM6YB4OVF3Isgju9fXc6pWlDOyrYBg+XqgdwA1AeN76vN2TVfAzywvKn4/1uAwwrAQ\nkXGsoIfUllqskA+RTCGSU+VLkmGF2AaLDX9xelYV/uKm7QIav2/hLptg7GCu0yBTOIyRSN7bYDGo\n1VXrSsRV3TZ/YdySP/Gvq5FK6JETWNEo5uXc2lJqKHRHKJXfTjFF3bqPRLP1n9YL5uAQ1X+1thxB\n88UcGkZIkOrcxyIEwuXCisUwL7ZhxeKotwaI1bsZ2x4mXVdGum4q1C5WK9j07WS25NBMFL3m6b05\nSnLEw3itmvuFmZT8NwcGQF/CL5K1dr20hbjdsGQoFGsmnlkHUQXzwIxEUCvvjDjO6ax2UTeH2al4\nMU2sRrWr8LZsmvE4rbkRs2WqxIwVjWL09qGPW1g6pMpcpMpcaA31pF+3n+ozKVwds0dVwEIcRpak\n8TsgzJlrish44fz0BTGPuiXrgbneYusFtaIcrbYmb/tSrQE7OMwX97+eInwjTaKxlERjaeGDhMDq\nG0Dqql2uIxjMzmT1cQNtQiIVgVQEyZZqpKaAJbEGl8N4Ar5rI2gTEnXXtsIHpNKFi78tABFYHdED\nh8KYg0MFDaWye8cqtMbh5Y7++Gk8PeN4esYLHyAUhKqivnAN0dSAFY2ibbRHoVIVSAWUtERJS0y3\nSjqgEK/WC9Zfu53ijaciQFUI9BgM7Stj4Ddasx6tDIn9mxDbZh5GF4VhLH0Z4GVAuWc7sbcfpv83\nj2Lct3/e5yzl+vBqYp27tNpNWDLkK+x16KWK5XVYXswLlwsmdCgeD1p9re3cjEYxL7YBYNzsRA2F\ncPeOZ9dNpQqRRg1fT5JAdwrpcc9536IXJy2PC6PETbJERUhJoMcieU8T7o5JQQGXTrRCR5vwsBRu\nHplIQnDtjz6tF17C/wJFiYNZL7zE6mZCOxQisxY9k4iNw/rAOLAdy6XiHo9hjuQK15iRCDLcjBaz\n0OP20qCwNJKlLrQJEzGPctLFZRhpCmPbg4zXudBjFnpc4h5JY7oVIruriOyuYmh/OdGNCtpA4bhF\ntboKtXwOEYlpmKOjxTTRYZlR/Gv/RebgAIAQmG6FicNbCqpFpYM62oSJq30QV/sg7jEJAlIhjfHd\nc6cgFzXyNN0KybCg7HISdcJASRhIXUWkLXwdtqXu/Kly1ImZFZTMvv5ibrkupuwLRgjU8rJ5i2+s\nBV4u6bcO6x/12Yu4SktAVcHnhUgkZ7/hV7FcCi637Z8JXh9H6exHVpZhBt0owWBBoZUMRRlPYULw\nlomrO4KIjIPXg1EVQkkaiFH7JrGGUjb8hzMZnRdSrivDORtqRfkd0xeHOwOZTGL09qH4fMh0ftSO\na9Qg0uTCNWLL+KXDOm6tBuVsG6qqzjlQKGrariYMfB0RpK5h9A2AaSJVBSVhYNaVY9aV4xlQ8L+0\nNOluAFrTxnUVJP9yxTGcDiuGMp/IeBu1ugrhdiM8boSmoZaWZs93HbtAoCtNqsRFqsRFokQlWeZG\n3t0yrxlWcWueiSTClAgpwTKRugZSYnl1UmEXqbAL1xiISSuv1dctao1M8XiQ7nWaPjYLWtNGOHLP\najfDwWHdIKYl3qih+Wvfmn39WOMxrGgUpbwMXDpq2F7/lIaBtzOCkLZN8w6ZeG9FUa53zXFVm+JT\ngVJpxISdhiYsienViNV7iFfZ1lwqYFaE4CZYpSGs7p6ib5FF1xd+7hrG6u2H9tlFddcCiseDlZjb\n6+jgsOxMm33K1MxaEFp9HTIcyIYlwVSCihyPoVRVZK+l1tZgtl2n+xftNFx/p6Dq8Suz5rNPp/g4\nz8ERZMwWhDBL/aTCGsICNSFRExLvoJVVJrfOv7Rgh4/asglRe2eoj99O/y/uXe0mzIuM4Uy86dC8\nzxl79xHG3r32SjY4rHOmmZHZBGmMrm7Mi232OmcmXncyI054J2ujTRrP9IZypGGgjwn0MUH6vjG0\n+gKq8zPg6Hk6ODg4LIDijaemgUtH29BAvMGPZyCFZyhNoNsg0G0gFYFwzx2dP2ujgkHMK0uj7r0W\nqfj8+qom6fn+szmxuYrPh3Vv7ujZfM0+AMIPnyD8sK0aL/bvWrlGOtzRyLQ9VZ9v2rcVj6PGUqi7\ntmH09CJ0F/GDmzArQiR22ll9+s0BlHu24xmWeIYlpQ8FuPh7dfMuM1OcnqemIQI+W2i0phT3aBr9\nxevoownUlIWaslVKZNpYVPngbGzVHRbjqTVttJ1FGX7YMPPBaww5MbX2acXjDN3lQS0vo/e37VK7\n6lPP5Z3T9kurU9TOYWZ6PrK+SyMXI7AjOnsR8QRqdRUyncI1lgLDIhW2XT1GTx9GqZdEuSBRLvB9\n8ySePo3OdzbN6/rFjTw1FVJp1IpyLF3BdbUPazwGhoUeSaFHUpRcSaBtqHMcDQUw2jswpjmKuh4v\nUDN6jWLF4zmi1JV/exxzaJhgV+7iulpdZWuwAi0fXJrKAg5Lx1xVTtc808OU5ghZMoeGbYW3oN8e\n+B07h3z+AsELQyR/6iBqZTnayUskSyXJUolaUU75eZPArfnFqRfnbZ/U1hRuN1p/BJlIIFwuUAWW\nbtthV9cIMrBE5RnugPhOxe/Pixm7/metmG5Jy2+vrwe5UNaY/9FcA2lsshfcRbGZZGsdRZ23F9Zh\nGZn+Gczj8zD7+qEP0q/bj/7EGXtj3wCeoAdrdAyZTBKafKw737sN74Ck/OsvzEtzojjjqQik1w2j\nEay+fltNSdOwNAU1NjmclhIxPIa6bQvS64K29gWVa1BDoXVrPNWWTZhXrhc0nAAt/zSUE0pxJyGO\nL6709JrFMZzrmqzhBEQ4hDx9Pvt75edsH8T1R/ZQ/pA+7xTk4qbtloShUWQyZYuKul0QtsVFpaIg\nFQXSBjKVQnp0uHITUVeN4vej3LO9qFtRVT73MWuUjLNrxvK9F9u4/qetK9mkFWfw1+7s/jmsQeY5\n2JoudBz7mcPZ//ue9RHZOP/xZJHG08Qaj6FUliN8XjvhXlMRCQPLp9s/I6OgqIiJFFYshjAtUBSU\nyOyjT7WiPFcXVLmzo6gCnetzVD1fPv7RL692ExxeZihe77yOmz6oGd4xtW4qLLBc8/9eClmER1sI\nMQDcnPPA5aFRSlm53Ddx+rgivBz66fRxCVmL/SzKeDo4ODg42NzZc2MHBweHZcIxng4ODg4LwDGe\nDg4ODgvAMZ4ODg4OC6CoIHmXcEtPUfUhl44EMVIyuezxPYX6KLweUmEVvTc3blPoGpgWRqkXdcje\nZ5b5UVMWjE/kHquqJOptwRQlKXANTiDN3DyGpepjRUWFbGpqWuxlVo0zZ84MrpQX18FhoRRlPD34\nOSzuW9IGqOVlmEPDcx53Uv5wSe87E7f3MflTB+l4g2LnaU8za9ar9mJpCtoPz6CKMkwxjFZfx0sf\n2Uj1SQh+9UTuhS2IvNLWuYw2Kijp/DzjpepjU1MTp0+fXpJrrQZCiNUMo3JwmBfFK8kvMebQMOnX\n7cc9YAfRW+cuAZC+/wDqhInyo+dXpV3qjhZu/Fwl6ZBFywdzDeHAr7diuQSV5+zRZcb4W6NjCAnR\njQpBgEN3w7MvZs8L/Yt9naFPtZKuSDN/2VUHB4e1xqLXPP/o+pmC2xVfvjiIumvb1P7dO7j+Z3YK\nn/7EGaxzl7KGE0D/jzO4OlenqJhWU83l3wuw/4GLNH87md3e9bGjdH3sKKVv76K0LY167ELOeVYs\nRvVJUJMw/N5WIpsLL3EIE0qrZi5p6uDgsPZZsPFUd7QQfccR/uem/Xn7Wk65ufaHu9E2NeVsNy9c\nzgqNmj4XnkFB5OcLl2yIv+VQjnzbSnLzv23m6Vf9NcfaNueMfH/3vY/yu+99lGpvFNcPTuVqC07K\nY4Wf6yPUYeAZs7A0kZtyOol7WBCJLpHylIODw6qw4Gm7eekKwUtX8ra3f7KVqz+SlF4Fsyu/+Fuy\npYZrn6jDe8NF8z93kmqqKHj94OURVkPHRqgKla/r4vvj2yj7Ua5q9VsD9lLcJ7//NjaTO5VPPrAP\nT28c8/kL+G52wT0tBNJmjohwhtLLBrF6N2ppKebIyPJ1xsHBYdlY8jXPxn+NZ2XJMomfWtPG7CjS\n1RNh+/tvcfX/2cuV32ig+b8XLklhXmyj8lgJA0dHl7qJs6MoeLU0dfoI3uFcb/ief/stAILd+QN2\nb2eUiY1B9NBelKefhzMXmCnxNXC+F8/dDRg7GxHPOMbTwWE94sR5Ojg4OCyAJRl5jr37SDaMJ/zQ\nibz9RnsH6fsPoD9+GunR6X3fPrZ+7hZWSYD0q/eh/md+/ZtfabvB2VgjAyts32XawKeleJV3iE+U\nKExfmdz2OTsiYHBPCKFpSMNACQaRWzYi4klcIynUeGpuFWrTYqLWRDxzdrm64eDgsMwszjJlnCQP\nnyD80ImChlMJBgGIbNR58OIQQ3tKqHo2ijU0gnX2IsLMn9wqwSB/ePZBzuxdnYHx+Se3Ela8RN8w\nnrNdvNSOeKkdV0wy9rMH4Mg9iI11SF1BJJIohpUTMTATcmKCloeKV9d3cHBYOyzOOs2jNMHEvdvp\n/uhRkPDpJ36Kiu9chGdfzFbIVJ55Ie+c6P07afy5F/O2rxSb//EWN9LjvHfHiazxBzsUyYrFKHlu\ngEiTwvhGH2JoFG0wSrqhHHnKbrPQXailpQit8MDeHBzKif90cHBYfyzr0E7xeHA/doq6T58kVSIo\ne1HBHB3LjQEtYID931jdqotGewevfex3qNCiXP5kgdrjY1EUE5ASq7oMGYlO1XBisjyqIpCmmVtq\n2MHB4Y5hWTOMMuWHh37lUDYVUd21jZHdpaSCgoq/O45s3b0mi4bt+PQAn3S9CS0hSL3+IK4fnMru\nk9FxkJAMK2BKZEM1ysBozlpnJutIxiZwcHC481iRRUXfgEnvh44CEN1aQuiRE1T83XFibz+8Jg0n\ngHn1Blv+2aTyOUn/Xh2tuTG7z4rHESZEGyHREMT0uzC6uvOuoVZXzStv38HBYf2x7Mbz2sN76Tmi\n0vDdbibefAjft6am5J6hNMLtXu4mLBjl6ecpPdFF2WWTVENZdrvQXcTrLVKVJmm/UtBrHn/bYcZe\ntWklm+vg4LCCLLvxVDo9WG6JjMbwfufZnH2u8510fDQ/vXMtYfb24//uGVwdg6hbN6Nu3UziJ3ej\nJgRYkPbn/wmVYJDxGpVUQKB41u7LwcHBYeEsu6pSJoNoultI3bWNaz9fRtMfHKfp62GYpyzdSqEE\ng9loAJm0hUGsvgGU2moAhrfrpBsShM948A6k886XWzYSvpHGM5hAKSvFijthSQ4OdxrLZjzVUAgz\nEim4z7xwmaY/yPxirinDCYCZHwFgJRJYN+zc9uiOKpjQqP9WB9H9dXnHKjdu4e/3IxMJ4gc347rV\ntexNdnBwWFmWbdo+k+HMO+7qjeVqwoKRqdSs+4XbJPSShtF5i1RAQautydlvTSQwuroxh4axtGUX\nv3dwcFgFVjW3Xa1cm5UWpGHkbpjMpMrgu+ih8qwdhqVNSPre1IxaXTV1fnJKA9TXMb+XiIODw/pi\nVZXkzYGB1bx9USg+H3JHMwBaHFznb2ICrqjJRIUC4SD09duGdlrgv+heP310cHCYP8s+8px486Hl\nvsWyo4YCiPoa4hv8xDf4maiSWBvsqbr3Ui/VT/bBgL1uq3g9OefKuBMk7+BwJ7LsxjN4rjf7/7U6\nTZ8TtxuRNpCKQCqCVJWBEXaDECS2VmNeuY45MoJaEsaK5VbYTB/evkqNdnBwWE4cPU8HBweHBbDs\nxtNo77CrSGKvcYr9+UIbQ7/autzNWDBC0zD7+pHRcfydcfydcUprIoy2uNFqqhnePhUEb46OoW7b\nknt+2kLd0rzSzXZwcFhmltV4jr3HLu4mVYXOj9u57fLMVMXJti8eAKD8C8dRd25F3bl1OZtTNEJ3\nZT3vVmQcZcT+GT9fRjIsuPLBZvx9uTGh0p3rg1N+fHZNhmM5ODgsjmU1nhlxZHH8HM0PdfHv3bk5\n4KHzLq5+xjaw5sU2zIttDL1v7YxCp1fHlOkUsqcf2dNPw5MpXFFJ+AoYbkH/B46ilpYCYL3wEpBb\nZtnBweHOY9lClYTuyjE+xo2bvPHwm+h//0aqj48x/MkUNW88lnde+RcLF4RbC2RGofoTZ6jq3Y70\n6kSbfOgxidxQTfrgZjxnrtsZU0buiFQcvDsrluzg4LD+WbaRZ05N80mMzlvUfuMqEw1+Kn57KhA9\n9vbDy9WMpUMIZDI5let+/iXkqRcJnxvEO2giEmlGN+mIYAAA8/LV7KmK3+8YTgeHO4wVD5I3+/rx\nfK8/RygkdG5gVWq0F4VQUMOThnF0zN6mqJht1/De6MRSBIpZhdRz/6TC7UZ43HBbCJODg8P6ZlUz\njDKYV66vdhPmxjLhdu3RyUyizCi78vgI9A3mHKJ4PciEPVqdeIudMOD9dq40n4ODw/pDSJlfvXLG\ng4UYAG4uX3NmpVFKuexR9ndCH1e5D0vBinzWDg6LoSjj6eDg4OBg42QYOTg4OCwAx3g6ODg4LADH\neDo4ODgsAMd4Ojg4OCwAx3g6ODg4LADHeDo4ODgsAMd4Ojg4OCwAx3g6ODg4LADHeDo4ODgsgP8f\nhtEkbg7TuYMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 32 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}