Cnn mnist fashion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TP2.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "nOqn-gJR93VC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ]
    },
    {
      "metadata": {
        "id": "T1L6UQ5I93VD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import codecs, cv2, datetime, glob, itertools, keras, os, pickle\n",
        "import re, sklearn, string, sys, tensorflow, time\n",
        "from random import randint\n",
        "from keras import backend as K, regularizers, optimizers\n",
        "from keras.models import load_model, Sequential\n",
        "from keras.layers import MaxPooling2D, Convolution2D, Activation, Dropout, Flatten, Dense, InputLayer,MaxPool2D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.constraints import maxnorm\n",
        "from keras.optimizers import SGD,RMSprop\n",
        "from keras import backend as K"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RajnNf_N--vg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# parametres"
      ]
    },
    {
      "metadata": {
        "id": "QXKFZzeg93VK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "K.set_image_dim_ordering('tf')\n",
        "\n",
        "# dimension de l'image\n",
        "img_width  = 28\n",
        "img_height = 28\n",
        "channels   = 1\n",
        "\n",
        "#parametres d'apprentissage\n",
        "batch_size = 250\n",
        "num_epochs = 80 #80\n",
        "iterations = 7            # nombre modele entrainé\n",
        "number_of_augmentation = 2 # defines the amount of additional augmentation images of one image\n",
        "\n",
        "#Data\n",
        "train_data_dir      = 'data/train/fashion_mnist'\n",
        "test_data_dir       = 'data/test/fashion_mnist'\n",
        "classes             = {0: 'T-shirt/top', \n",
        "                       1: 'Trouser', \n",
        "                       2: 'Pullover', \n",
        "                       3: 'Dress', \n",
        "                       4: 'Coat',\n",
        "                       5: 'Sandal', \n",
        "                       6: 'Shirt', \n",
        "                       7: 'Sneaker', \n",
        "                       8: 'Bag', \n",
        "                       9: 'Ankle boot'\n",
        "                      }\n",
        "num_classes         = len(classes)\n",
        "classes_fashion     = ['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
        "                       'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZiicDo0_93VM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Création Model Réseau convolutif"
      ]
    },
    {
      "metadata": {
        "id": "AGmmXnLE9gsM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### INPUT -> [CONV -> RELU -> POOL] * 3"
      ]
    },
    {
      "metadata": {
        "id": "JU5GlXFJ9Vma",
        "colab_type": "code",
        "outputId": "25b63ee5-3859-43b2-eeb3-67fe3741ed22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_0():\n",
        "    cnn_0 = Sequential()\n",
        "    \n",
        "    #dimension de la couche d'entrée =  la dimension de l'image\n",
        "    cnn_0.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    \n",
        "    # Normalisation\n",
        "    cnn_0.add(BatchNormalization())\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_0.add(Convolution2D(64, (4, 4), activation='relu'))\n",
        "    cnn_0.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_0.add(Dropout(0.1))\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_0.add(Convolution2D(64, (4, 4), activation='relu'))\n",
        "    cnn_0.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "\n",
        "    # Dropout\n",
        "    cnn_0.add(Dropout(0.3))\n",
        "\n",
        "    # Converting 3D feature to 1D feature Vektor\n",
        "    cnn_0.add(Flatten())\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_0.add(BatchNormalization())\n",
        "\n",
        "    cnn_0.add(Dense(num_classes, activation='softmax'))\n",
        "    cnn_0.compile(loss='categorical_crossentropy',\n",
        "                optimizer=optimizers.Adam(),    # mettre optimizer='sgd'\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "    return cnn_0\n",
        "\n",
        "create_model_0().summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization_12 (Batc (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 25, 25, 64)        1088      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 9, 9, 64)          65600     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 1024)              4096      \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                10250     \n",
            "=================================================================\n",
            "Total params: 81,038\n",
            "Trainable params: 78,988\n",
            "Non-trainable params: 2,050\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "q8qDz_xm53fQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###  INPUT -> FC (Implémente un classifieur linéaire)"
      ]
    },
    {
      "metadata": {
        "id": "tCykI2qs93VN",
        "colab_type": "code",
        "outputId": "19d7d864-4800-44dc-d502-e4d1a80586a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_1():\n",
        "    cnn_1 = Sequential()\n",
        "    \n",
        "    #dimension de la couche d'entrée =  la dimension de l'image\n",
        "    cnn_1.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_1.add(BatchNormalization())\n",
        "\n",
        "    # Converting 3D feature to 1D feature Vektor\n",
        "    cnn_1.add(Flatten())\n",
        "    \n",
        "    # Fully Connected Layer\n",
        "    cnn_1.add(Dense(64, activation='relu'))\n",
        "    \n",
        "    # Normalisation\n",
        "    cnn_1.add(BatchNormalization())\n",
        "\n",
        "    cnn_1.add(Dense(num_classes, activation='softmax'))\n",
        "    cnn_1.compile(loss='categorical_crossentropy',\n",
        "                optimizer=optimizers.Adam(),    # mettre optimizer='sgd'\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "    return cnn_1\n",
        "\n",
        "create_model_1().summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization_14 (Batc (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 64)                50240     \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 51,150\n",
            "Trainable params: 51,020\n",
            "Non-trainable params: 130\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FODWhZAHlp7z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### INPUT -> CONV -> RELU -> FC"
      ]
    },
    {
      "metadata": {
        "id": "lIJeapAHlfNp",
        "colab_type": "code",
        "outputId": "5f6fe611-4e3c-41ce-ed32-41be75c399c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_2():\n",
        "    cnn_2 = Sequential()\n",
        "    \n",
        "    #dimension de la couche d'entrée = a la dimension de l'image\n",
        "    cnn_2.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_2.add(BatchNormalization())\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_2.add(Convolution2D(64, (4, 4), padding='same', activation='relu'))\n",
        "    cnn_2.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_2.add(Dropout(0.1))\n",
        "\n",
        "    # Converting 3D feature to 1D feature Vektor\n",
        "    cnn_2.add(Flatten())\n",
        "\n",
        "    # Fully Connected Layer\n",
        "    cnn_2.add(Dense(64, activation='relu'))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_2.add(BatchNormalization())\n",
        "\n",
        "    cnn_2.add(Dense(num_classes, activation='softmax'))\n",
        "    cnn_2.compile(loss='categorical_crossentropy',\n",
        "                optimizer=optimizers.Adam(),    # mettre optimizer='sgd'\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "    return cnn_2\n",
        "\n",
        "create_model_2().summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization_16 (Batc (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 28, 28, 64)        1088      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 12544)             0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 64)                802880    \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 804,878\n",
            "Trainable params: 804,748\n",
            "Non-trainable params: 130\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NrP7-1EszOr5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### CNN : INPUT -> [CONV -> RELU -> POOL] * 2 -> FC -> RELU -> FC"
      ]
    },
    {
      "metadata": {
        "id": "ySiCPwE7zJgj",
        "colab_type": "code",
        "outputId": "8081ceb3-1e43-4c7f-b893-70a0adc822a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_3():\n",
        "    cnn_3 = Sequential()\n",
        "    \n",
        "    #dimension de la couche d'entrée = a la dimension de l'image\n",
        "    cnn_3.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_3.add(BatchNormalization())\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_3.add(Convolution2D(64, (4, 4), padding='same', activation='relu'))\n",
        "    cnn_3.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_3.add(Dropout(0.1))\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_3.add(Convolution2D(64, (4, 4), activation='relu'))\n",
        "    cnn_3.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_3.add(Dropout(0.3))\n",
        "\n",
        "    # Converting 3D feature to 1D feature Vektor\n",
        "    cnn_3.add(Flatten())\n",
        "\n",
        "    # Fully Connected Layer\n",
        "    cnn_3.add(Dense(256, activation='relu'))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_3.add(Dropout(0.5))\n",
        "    \n",
        "    # Fully Connected Layer\n",
        "    cnn_3.add(Dense(64, activation='relu'))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_3.add(BatchNormalization())\n",
        "\n",
        "    cnn_3.add(Dense(num_classes, activation='softmax'))\n",
        "    cnn_3.compile(loss='categorical_crossentropy',\n",
        "                optimizer=optimizers.Adam(),    # mettre optimizer='sgd'\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "    return cnn_3\n",
        "\n",
        "create_model_3().summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization_18 (Batc (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 28, 28, 64)        1088      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 11, 11, 64)        65600     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_11 (Flatten)         (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 256)               409856    \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "batch_normalization_19 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 493,902\n",
            "Trainable params: 493,772\n",
            "Non-trainable params: 130\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zhe2DXan9IpU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### INPUT -> [CONV -> RELU -> CONV -> RELU -> POOL] * 3 -> [FC -> RELU] "
      ]
    },
    {
      "metadata": {
        "id": "snM6NqQG5yqG",
        "colab_type": "code",
        "outputId": "5d4fec7c-97d6-4286-f29d-a79e5da68470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_4():\n",
        "    cnn_4 = Sequential()\n",
        "    \n",
        "    #dimension de la couche d'entrée = a la dimension de l'image\n",
        "    cnn_4.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_4.add(BatchNormalization())\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_4.add(Convolution2D(64, (4, 4), padding='same', activation='relu'))\n",
        "    cnn_4.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_4.add(Dropout(0.1))\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_4.add(Convolution2D(64, (4, 4),padding='same', activation='relu'))\n",
        "    cnn_4.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_4.add(Dropout(0.3))\n",
        "    \n",
        "    # Conv + Maxpooling\n",
        "    cnn_4.add(Convolution2D(64, (4, 4), activation='relu'))\n",
        "    cnn_4.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_4.add(Dropout(0.5))\n",
        "\n",
        "    # Converting 3D feature to 1D feature Vektor\n",
        "    cnn_4.add(Flatten())\n",
        "\n",
        "    # Fully Connected Layer\n",
        "    cnn_4.add(Dense(64, activation='relu'))\n",
        "\n",
        "    # Dropout\n",
        "    cnn_4.add(Dropout(0.5))\n",
        "    \n",
        "    # Fully Connected Layer\n",
        "    cnn_4.add(Dense(32, activation='relu'))\n",
        "    \n",
        "    # Normalization\n",
        "    cnn_4.add(BatchNormalization())\n",
        "\n",
        "    cnn_4.add(Dense(num_classes, activation='softmax'))\n",
        "    cnn_4.compile(loss='categorical_crossentropy',\n",
        "                optimizer=optimizers.Adam(),    # mettre optimizer='sgd'\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "    return cnn_4\n",
        "\n",
        "create_model_4().summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization_20 (Batc (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 28, 28, 64)        1088      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 14, 14, 64)        65600     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_20 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 4, 4, 64)          65600     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_24 (Dropout)         (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_12 (Flatten)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dropout_25 (Dropout)         (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 32)                128       \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 151,278\n",
            "Trainable params: 151,212\n",
            "Non-trainable params: 66\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tE3KuQmo9E3y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###  INPUT -> [CONV -> RELU -> POOL] * 2->BN "
      ]
    },
    {
      "metadata": {
        "id": "dX1dCJOC9EgZ",
        "colab_type": "code",
        "outputId": "cded45a7-7257-4675-a653-bc149786630c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        }
      },
      "cell_type": "code",
      "source": [
        "def create_model_5():\n",
        "  cnn_5 = Sequential()\n",
        "  cnn_5.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "  cnn_5.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))\n",
        "  cnn_5.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))\n",
        "  cnn_5.add(MaxPool2D(pool_size=(2,2)))\n",
        "  cnn_5.add(Dropout(0.25))\n",
        "  cnn_5.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "  cnn_5.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "  cnn_5.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
        "  cnn_5.add(Dropout(0.25))\n",
        "  cnn_5.add(Flatten())\n",
        "  cnn_5.add(Dense(512, activation = \"relu\"))\n",
        "  cnn_5.add(BatchNormalization())\n",
        "  cnn_5.add(Dropout(0.5))\n",
        "  cnn_5.add(Dense(10, activation = \"softmax\"))\n",
        "  optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
        "  cnn_5.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "  return cnn_5\n",
        "\n",
        "create_model_5().summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_24 (Conv2D)           (None, 28, 28, 32)        832       \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_26 (Dropout)         (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_27 (Dropout)         (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_13 (Flatten)         (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 512)               1606144   \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "dropout_28 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,695,210\n",
            "Trainable params: 1,694,186\n",
            "Non-trainable params: 1,024\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sHhlKOfcIwcT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### INPUT -> [CONV -> RELU -> POOL] * 3 -> [FC -> RELU] *2 avec sgd et contraint maxnorm"
      ]
    },
    {
      "metadata": {
        "id": "S07rTXdYInDJ",
        "colab_type": "code",
        "outputId": "6c0d6e06-ff3f-45bd-94c4-d5dc32288046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def create_model_6():\n",
        "    # create model\n",
        "    model = Sequential()\n",
        "    model.add(InputLayer(input_shape=(img_height,img_width,channels)))\n",
        "    model.add(Conv2D(32, (5, 5), input_shape=(img_height,img_width,channels), padding='same', activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "\n",
        "    model.add(Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "\n",
        "    model.add(Conv2D(128, (1, 1), padding='same', activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
        "\n",
        "    model.add(Flatten())\n",
        "\n",
        "    model.add(Dense(1024, activation='relu', kernel_constraint=maxnorm(3)))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))\n",
        "    model.add(Dropout(0.5))\n",
        "\n",
        "    model.add(Dense(num_classes, activation='softmax'))\n",
        "    # Compile model\n",
        "    lrate = 0.01\n",
        "    decay = lrate / num_epochs\n",
        "    sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=True)\n",
        "    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
        "    return model\n",
        "create_model_6().summary()\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_28 (Conv2D)           (None, 28, 28, 32)        832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_24 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 14, 14, 64)        51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_25 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 7, 7, 128)         8320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_26 (MaxPooling (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_14 (Flatten)         (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 1024)              2098176   \n",
            "_________________________________________________________________\n",
            "dropout_29 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_31 (Dense)             (None, 512)               524800    \n",
            "_________________________________________________________________\n",
            "dropout_30 (Dropout)         (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_32 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 2,688,522\n",
            "Trainable params: 2,688,522\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WS50SDJHPd5r",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Import Data"
      ]
    },
    {
      "metadata": {
        "id": "fQjIo1TlQWfJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def shaping(data, target):\n",
        "    data = np.array(data, dtype=np.uint8)\n",
        "    target = np.array(target, dtype=np.uint8)\n",
        "    data = data.reshape(data.shape[0], img_height, img_width, channels)\n",
        "    target = np_utils.to_categorical(target, num_classes)\n",
        "    data = data.astype('float32')\n",
        "    data /= 255\n",
        "    return data, target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oL0mZKmePxWU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('fashion-mnist_test.csv')\n",
        "df_train = pd.read_csv('fashion-mnist_train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DNqbC2bjPojm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_data = df_train.drop(labels='label',axis=1).values\n",
        "train_target = df_train[\"label\"].values\n",
        "test_data = df_test.drop(labels='label',axis=1).values\n",
        "test_target = df_test[\"label\"].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S4BH5cwwUcyD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_data_shaped, train_target_shaped  = shaping(train_data, train_target)\n",
        "test_data_shaped, test_target_shaped    = shaping(test_data, test_target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3d_L6HYbENMG",
        "colab_type": "code",
        "outputId": "d2318aa6-ec73-4a5d-ab21-cf8faa1f674c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "cell_type": "code",
      "source": [
        "train_data_shaped.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "AaU9xCtaPzq7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data visualisation"
      ]
    },
    {
      "metadata": {
        "id": "Qpf8h4YnPvU3",
        "colab_type": "code",
        "outputId": "5194b825-537f-4a52-8a4e-b1bd5a133e21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "image=test_data_shaped\n",
        "image=image.reshape(-1,28,28)\n",
        "plt.imshow(image[1])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5780d0e2d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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96NGjvcb6+0Hd2wXzafiePXusuvnz59tz1tXV9bk9KyvrjrHvf//71pzp6en2/nNzc+3a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\nwEBYAoCBsAQAw/8B+4PmSjhvgE4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XIdHiXmqR3eZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Entrainement du des CNN"
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "id": "SyTo78OD93Vi",
        "colab_type": "code",
        "outputId": "c37c6b39-1d3b-4e6b-fd80-1b39c1c1ec06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 43293
        }
      },
      "cell_type": "code",
      "source": [
        "histories = []\n",
        "\n",
        "for i in range(0,iterations):\n",
        "    print('Running iteration: %i' % i)\n",
        "    \n",
        "    # Saving the best checkpoint for each iteration\n",
        "    filepath=\"fashion_mnist-%i.hdf5\" % i\n",
        "    checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1,\n",
        "                                 save_best_only=True, mode='min')\n",
        "    \n",
        "    X_train, X_val, y_train, y_val = train_test_split(train_data_shaped, train_target_shaped,\n",
        "                                                      test_size=0.2, random_state=42)\n",
        "\n",
        "    if i == 0:\n",
        "      print('create_model_0')\n",
        "      cnn = create_model_0()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 1:\n",
        "      print('create_model_1')\n",
        "      cnn = create_model_1()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "     \n",
        "    if i == 2:\n",
        "      print('create_model_2')\n",
        "      cnn = create_model_2()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 3:\n",
        "      print('create_model_3')\n",
        "      cnn = create_model_3()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 4:\n",
        "      print('create_model_4')\n",
        "      cnn = create_model_4()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 5:\n",
        "      print('create_model_5')\n",
        "      cnn = create_model_5()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 5:\n",
        "      print('create_model_5')\n",
        "      cnn = create_model_5()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 6:\n",
        "      print('create_model_6')\n",
        "      cnn = create_model_6()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "    if i == 7:\n",
        "      print('create_model_7')\n",
        "      cnn = create_model_7()\n",
        "      history = cnn.fit(\n",
        "          X_train,\n",
        "          y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=num_epochs,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val),\n",
        "          callbacks=[\n",
        "              #early_stopping,\n",
        "              checkpoint\n",
        "          ]\n",
        "      )\n",
        "      \n",
        "      \n",
        "      \n",
        "    \n",
        "    histories.append(history.history)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running iteration: 0\n",
            "create_model_0\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 10s 203us/step - loss: 0.6074 - acc: 0.7817 - val_loss: 0.4367 - val_acc: 0.8382\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.43668, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.3976 - acc: 0.8570 - val_loss: 0.3672 - val_acc: 0.8592\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.43668 to 0.36722, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.3530 - acc: 0.8732 - val_loss: 0.3200 - val_acc: 0.8806\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.36722 to 0.31999, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.3235 - acc: 0.8831 - val_loss: 0.3085 - val_acc: 0.8889\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.31999 to 0.30853, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.3088 - acc: 0.8863 - val_loss: 0.2950 - val_acc: 0.8915\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.30853 to 0.29499, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2888 - acc: 0.8953 - val_loss: 0.2855 - val_acc: 0.8968\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.29499 to 0.28548, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.2825 - acc: 0.8967 - val_loss: 0.3109 - val_acc: 0.8837\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.28548\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2752 - acc: 0.8977 - val_loss: 0.2833 - val_acc: 0.8953\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.28548 to 0.28335, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.2622 - acc: 0.9024 - val_loss: 0.2853 - val_acc: 0.8948\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.28335\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2571 - acc: 0.9057 - val_loss: 0.2718 - val_acc: 0.8979\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.28335 to 0.27179, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2482 - acc: 0.9088 - val_loss: 0.2756 - val_acc: 0.8992\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.27179\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.2418 - acc: 0.9101 - val_loss: 0.2650 - val_acc: 0.8999\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.27179 to 0.26496, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2345 - acc: 0.9125 - val_loss: 0.2669 - val_acc: 0.9031\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.26496\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2306 - acc: 0.9160 - val_loss: 0.2585 - val_acc: 0.9085\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.26496 to 0.25849, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.2251 - acc: 0.9178 - val_loss: 0.2595 - val_acc: 0.9051\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.25849\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.2208 - acc: 0.9176 - val_loss: 0.2619 - val_acc: 0.9031\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.25849\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2117 - acc: 0.9210 - val_loss: 0.2642 - val_acc: 0.9038\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.25849\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.2130 - acc: 0.9203 - val_loss: 0.2547 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.25849 to 0.25466, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.2083 - acc: 0.9240 - val_loss: 0.2590 - val_acc: 0.9065\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.25466\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.2024 - acc: 0.9255 - val_loss: 0.2727 - val_acc: 0.8968\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.25466\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.2023 - acc: 0.9249 - val_loss: 0.2635 - val_acc: 0.9062\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.25466\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1974 - acc: 0.9277 - val_loss: 0.2639 - val_acc: 0.9037\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.25466\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1965 - acc: 0.9258 - val_loss: 0.2614 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.25466\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1893 - acc: 0.9295 - val_loss: 0.2567 - val_acc: 0.9083\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.25466\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1868 - acc: 0.9309 - val_loss: 0.2560 - val_acc: 0.9070\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.25466\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1824 - acc: 0.9321 - val_loss: 0.2547 - val_acc: 0.9073\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.25466\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1857 - acc: 0.9310 - val_loss: 0.2563 - val_acc: 0.9101\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.25466\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1813 - acc: 0.9331 - val_loss: 0.2664 - val_acc: 0.9054\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.25466\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1782 - acc: 0.9328 - val_loss: 0.2543 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00029: val_loss improved from 0.25466 to 0.25431, saving model to fashion_mnist-0.hdf5\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.1755 - acc: 0.9353 - val_loss: 0.2603 - val_acc: 0.9063\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.25431\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1773 - acc: 0.9330 - val_loss: 0.2707 - val_acc: 0.9059\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.25431\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1734 - acc: 0.9360 - val_loss: 0.2929 - val_acc: 0.8997\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.25431\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1690 - acc: 0.9362 - val_loss: 0.2559 - val_acc: 0.9126\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.25431\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1652 - acc: 0.9383 - val_loss: 0.2620 - val_acc: 0.9079\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.25431\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1669 - acc: 0.9375 - val_loss: 0.2655 - val_acc: 0.9081\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.25431\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1673 - acc: 0.9378 - val_loss: 0.2668 - val_acc: 0.9048\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.25431\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1610 - acc: 0.9395 - val_loss: 0.2663 - val_acc: 0.9057\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.25431\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1591 - acc: 0.9405 - val_loss: 0.2693 - val_acc: 0.9073\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.25431\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1585 - acc: 0.9405 - val_loss: 0.2778 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.25431\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1588 - acc: 0.9407 - val_loss: 0.2717 - val_acc: 0.9080\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.25431\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1582 - acc: 0.9409 - val_loss: 0.2680 - val_acc: 0.9097\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.25431\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1508 - acc: 0.9422 - val_loss: 0.2871 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.25431\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1520 - acc: 0.9434 - val_loss: 0.2720 - val_acc: 0.9075\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.25431\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1453 - acc: 0.9462 - val_loss: 0.2848 - val_acc: 0.9039\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.25431\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1530 - acc: 0.9418 - val_loss: 0.2714 - val_acc: 0.9124\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.25431\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1503 - acc: 0.9438 - val_loss: 0.2872 - val_acc: 0.9008\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.25431\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1480 - acc: 0.9444 - val_loss: 0.2793 - val_acc: 0.9064\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.25431\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1468 - acc: 0.9452 - val_loss: 0.2747 - val_acc: 0.9070\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.25431\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 5s 104us/step - loss: 0.1438 - acc: 0.9457 - val_loss: 0.2660 - val_acc: 0.9106\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.25431\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1469 - acc: 0.9451 - val_loss: 0.2760 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.25431\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1416 - acc: 0.9461 - val_loss: 0.2785 - val_acc: 0.9081\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.25431\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1421 - acc: 0.9466 - val_loss: 0.2809 - val_acc: 0.9117\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.25431\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1388 - acc: 0.9478 - val_loss: 0.2729 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.25431\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1398 - acc: 0.9480 - val_loss: 0.2738 - val_acc: 0.9098\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.25431\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1435 - acc: 0.9462 - val_loss: 0.2985 - val_acc: 0.9045\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.25431\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1398 - acc: 0.9474 - val_loss: 0.2753 - val_acc: 0.9062\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.25431\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1356 - acc: 0.9484 - val_loss: 0.2854 - val_acc: 0.9035\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.25431\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1353 - acc: 0.9491 - val_loss: 0.2874 - val_acc: 0.9047\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.25431\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.1320 - acc: 0.9514 - val_loss: 0.2717 - val_acc: 0.9117\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.25431\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1331 - acc: 0.9500 - val_loss: 0.2772 - val_acc: 0.9105\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.25431\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1325 - acc: 0.9507 - val_loss: 0.2856 - val_acc: 0.9092\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.25431\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1353 - acc: 0.9501 - val_loss: 0.2891 - val_acc: 0.9086\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.25431\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1316 - acc: 0.9508 - val_loss: 0.2839 - val_acc: 0.9037\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.25431\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1323 - acc: 0.9502 - val_loss: 0.2733 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.25431\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.1304 - acc: 0.9511 - val_loss: 0.2818 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.25431\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1282 - acc: 0.9522 - val_loss: 0.2818 - val_acc: 0.9113\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.25431\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1254 - acc: 0.9528 - val_loss: 0.2777 - val_acc: 0.9107\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.25431\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1284 - acc: 0.9516 - val_loss: 0.2788 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.25431\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1280 - acc: 0.9525 - val_loss: 0.2958 - val_acc: 0.9024\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.25431\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1258 - acc: 0.9529 - val_loss: 0.2829 - val_acc: 0.9127\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.25431\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1222 - acc: 0.9540 - val_loss: 0.2844 - val_acc: 0.9052\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.25431\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1235 - acc: 0.9535 - val_loss: 0.3003 - val_acc: 0.9035\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.25431\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1220 - acc: 0.9547 - val_loss: 0.2933 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.25431\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1236 - acc: 0.9539 - val_loss: 0.2798 - val_acc: 0.9071\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.25431\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1213 - acc: 0.9549 - val_loss: 0.2825 - val_acc: 0.9078\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.25431\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1253 - acc: 0.9529 - val_loss: 0.2855 - val_acc: 0.9091\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.25431\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.1192 - acc: 0.9559 - val_loss: 0.2967 - val_acc: 0.9078\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.25431\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 5s 106us/step - loss: 0.1223 - acc: 0.9543 - val_loss: 0.2955 - val_acc: 0.9039\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.25431\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 5s 105us/step - loss: 0.1162 - acc: 0.9565 - val_loss: 0.2956 - val_acc: 0.9055\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.25431\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 5s 107us/step - loss: 0.1177 - acc: 0.9557 - val_loss: 0.2982 - val_acc: 0.9087\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.25431\n",
            "Running iteration: 1\n",
            "create_model_1\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 3s 62us/step - loss: 0.5814 - acc: 0.8003 - val_loss: 0.4378 - val_acc: 0.8499\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.43777, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.4063 - acc: 0.8577 - val_loss: 0.3985 - val_acc: 0.8605\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.43777 to 0.39846, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.3655 - acc: 0.8705 - val_loss: 0.3773 - val_acc: 0.8692\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.39846 to 0.37733, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.3404 - acc: 0.8792 - val_loss: 0.3688 - val_acc: 0.8670\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.37733 to 0.36883, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.3223 - acc: 0.8839 - val_loss: 0.3734 - val_acc: 0.8690\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.36883\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 2s 40us/step - loss: 0.3043 - acc: 0.8900 - val_loss: 0.3457 - val_acc: 0.8759\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.36883 to 0.34574, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2902 - acc: 0.8961 - val_loss: 0.3778 - val_acc: 0.8634\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.34574\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2814 - acc: 0.8985 - val_loss: 0.3730 - val_acc: 0.8635\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.34574\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 2s 41us/step - loss: 0.2713 - acc: 0.9006 - val_loss: 0.3453 - val_acc: 0.8760\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.34574 to 0.34530, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.2589 - acc: 0.9059 - val_loss: 0.3509 - val_acc: 0.8782\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.34530\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.2486 - acc: 0.9095 - val_loss: 0.3509 - val_acc: 0.8782\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.34530\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2423 - acc: 0.9126 - val_loss: 0.3661 - val_acc: 0.8739\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.34530\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2371 - acc: 0.9142 - val_loss: 0.3394 - val_acc: 0.8802\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.34530 to 0.33945, saving model to fashion_mnist-1.hdf5\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2321 - acc: 0.9156 - val_loss: 0.3458 - val_acc: 0.8821\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.33945\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2228 - acc: 0.9180 - val_loss: 0.3511 - val_acc: 0.8814\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.33945\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.2179 - acc: 0.9209 - val_loss: 0.3928 - val_acc: 0.8645\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.33945\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 2s 40us/step - loss: 0.2134 - acc: 0.9225 - val_loss: 0.3560 - val_acc: 0.8749\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.33945\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.2043 - acc: 0.9248 - val_loss: 0.3498 - val_acc: 0.8817\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.33945\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1991 - acc: 0.9276 - val_loss: 0.3594 - val_acc: 0.8827\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.33945\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1957 - acc: 0.9280 - val_loss: 0.3568 - val_acc: 0.8806\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.33945\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1913 - acc: 0.9302 - val_loss: 0.3423 - val_acc: 0.8869\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.33945\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1915 - acc: 0.9291 - val_loss: 0.3596 - val_acc: 0.8761\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.33945\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 2s 40us/step - loss: 0.1844 - acc: 0.9325 - val_loss: 0.3576 - val_acc: 0.8817\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.33945\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1787 - acc: 0.9341 - val_loss: 0.3743 - val_acc: 0.8722\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.33945\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1753 - acc: 0.9361 - val_loss: 0.3948 - val_acc: 0.8756\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.33945\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1702 - acc: 0.9393 - val_loss: 0.3884 - val_acc: 0.8767\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.33945\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1674 - acc: 0.9394 - val_loss: 0.3697 - val_acc: 0.8832\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.33945\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1643 - acc: 0.9400 - val_loss: 0.3977 - val_acc: 0.8739\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.33945\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1638 - acc: 0.9396 - val_loss: 0.4079 - val_acc: 0.8727\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.33945\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1581 - acc: 0.9431 - val_loss: 0.3947 - val_acc: 0.8752\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.33945\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1543 - acc: 0.9448 - val_loss: 0.3925 - val_acc: 0.8780\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.33945\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1488 - acc: 0.9457 - val_loss: 0.3806 - val_acc: 0.8808\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.33945\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1486 - acc: 0.9454 - val_loss: 0.4011 - val_acc: 0.8802\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.33945\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1485 - acc: 0.9459 - val_loss: 0.4418 - val_acc: 0.8721\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.33945\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1427 - acc: 0.9483 - val_loss: 0.4023 - val_acc: 0.8776\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.33945\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1403 - acc: 0.9498 - val_loss: 0.4119 - val_acc: 0.8757\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.33945\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1380 - acc: 0.9502 - val_loss: 0.4290 - val_acc: 0.8719\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.33945\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1364 - acc: 0.9502 - val_loss: 0.4217 - val_acc: 0.8750\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.33945\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1333 - acc: 0.9519 - val_loss: 0.4202 - val_acc: 0.8748\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.33945\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1313 - acc: 0.9530 - val_loss: 0.4475 - val_acc: 0.8728\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.33945\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1318 - acc: 0.9525 - val_loss: 0.4333 - val_acc: 0.8802\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.33945\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1271 - acc: 0.9545 - val_loss: 0.4250 - val_acc: 0.8836\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.33945\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1236 - acc: 0.9562 - val_loss: 0.4274 - val_acc: 0.8812\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.33945\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1208 - acc: 0.9560 - val_loss: 0.4487 - val_acc: 0.8751\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.33945\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1195 - acc: 0.9566 - val_loss: 0.4516 - val_acc: 0.8737\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.33945\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1178 - acc: 0.9567 - val_loss: 0.4752 - val_acc: 0.8734\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.33945\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1147 - acc: 0.9585 - val_loss: 0.4433 - val_acc: 0.8760\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.33945\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1154 - acc: 0.9600 - val_loss: 0.4901 - val_acc: 0.8682\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.33945\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1149 - acc: 0.9584 - val_loss: 0.4410 - val_acc: 0.8755\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.33945\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1083 - acc: 0.9610 - val_loss: 0.4803 - val_acc: 0.8674\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.33945\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1091 - acc: 0.9608 - val_loss: 0.4507 - val_acc: 0.8816\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.33945\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1081 - acc: 0.9617 - val_loss: 0.4775 - val_acc: 0.8767\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.33945\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1091 - acc: 0.9607 - val_loss: 0.4507 - val_acc: 0.8807\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.33945\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1061 - acc: 0.9619 - val_loss: 0.5126 - val_acc: 0.8663\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.33945\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 2s 40us/step - loss: 0.1042 - acc: 0.9637 - val_loss: 0.4815 - val_acc: 0.8764\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.33945\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.1017 - acc: 0.9628 - val_loss: 0.5543 - val_acc: 0.8627\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.33945\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.1049 - acc: 0.9626 - val_loss: 0.5141 - val_acc: 0.8698\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.33945\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0990 - acc: 0.9646 - val_loss: 0.4760 - val_acc: 0.8752\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.33945\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0992 - acc: 0.9642 - val_loss: 0.4757 - val_acc: 0.8763\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.33945\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0998 - acc: 0.9646 - val_loss: 0.5052 - val_acc: 0.8708\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.33945\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0962 - acc: 0.9657 - val_loss: 0.5057 - val_acc: 0.8736\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.33945\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0969 - acc: 0.9649 - val_loss: 0.4880 - val_acc: 0.8797\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.33945\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0943 - acc: 0.9660 - val_loss: 0.4991 - val_acc: 0.8772\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.33945\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0924 - acc: 0.9673 - val_loss: 0.5191 - val_acc: 0.8712\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.33945\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0930 - acc: 0.9670 - val_loss: 0.4994 - val_acc: 0.8714\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.33945\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0874 - acc: 0.9691 - val_loss: 0.5570 - val_acc: 0.8647\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.33945\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0878 - acc: 0.9679 - val_loss: 0.5096 - val_acc: 0.8758\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.33945\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0876 - acc: 0.9686 - val_loss: 0.5444 - val_acc: 0.8721\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.33945\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0834 - acc: 0.9709 - val_loss: 0.5262 - val_acc: 0.8790\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.33945\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0899 - acc: 0.9671 - val_loss: 0.5327 - val_acc: 0.8749\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.33945\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0799 - acc: 0.9716 - val_loss: 0.5120 - val_acc: 0.8808\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.33945\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 2s 40us/step - loss: 0.0820 - acc: 0.9713 - val_loss: 0.5315 - val_acc: 0.8751\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.33945\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0773 - acc: 0.9729 - val_loss: 0.5780 - val_acc: 0.8697\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.33945\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0794 - acc: 0.9717 - val_loss: 0.5349 - val_acc: 0.8791\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.33945\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0831 - acc: 0.9700 - val_loss: 0.5508 - val_acc: 0.8743\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.33945\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0786 - acc: 0.9712 - val_loss: 0.5406 - val_acc: 0.8782\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.33945\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 2s 39us/step - loss: 0.0771 - acc: 0.9725 - val_loss: 0.5439 - val_acc: 0.8753\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.33945\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0749 - acc: 0.9735 - val_loss: 0.5769 - val_acc: 0.8655\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.33945\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0741 - acc: 0.9734 - val_loss: 0.5618 - val_acc: 0.8750\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.33945\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 2s 38us/step - loss: 0.0730 - acc: 0.9736 - val_loss: 0.5418 - val_acc: 0.8789\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.33945\n",
            "Running iteration: 2\n",
            "create_model_2\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 6s 124us/step - loss: 0.4451 - acc: 0.8466 - val_loss: 0.3742 - val_acc: 0.8737\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.37424, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.2865 - acc: 0.8986 - val_loss: 0.3081 - val_acc: 0.8943\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.37424 to 0.30805, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.2395 - acc: 0.9128 - val_loss: 0.2847 - val_acc: 0.8976\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.30805 to 0.28473, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.2025 - acc: 0.9279 - val_loss: 0.2656 - val_acc: 0.9043\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.28473 to 0.26564, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.1787 - acc: 0.9348 - val_loss: 0.2542 - val_acc: 0.9087\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.26564 to 0.25417, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.1550 - acc: 0.9439 - val_loss: 0.2812 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.25417\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.1377 - acc: 0.9516 - val_loss: 0.2855 - val_acc: 0.9039\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.25417\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.1222 - acc: 0.9564 - val_loss: 0.2536 - val_acc: 0.9137\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.25417 to 0.25356, saving model to fashion_mnist-2.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.1066 - acc: 0.9614 - val_loss: 0.2704 - val_acc: 0.9138\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.25356\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0933 - acc: 0.9671 - val_loss: 0.3203 - val_acc: 0.9016\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.25356\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0851 - acc: 0.9700 - val_loss: 0.2979 - val_acc: 0.9065\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.25356\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0802 - acc: 0.9715 - val_loss: 0.3244 - val_acc: 0.9028\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.25356\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0672 - acc: 0.9770 - val_loss: 0.3263 - val_acc: 0.9065\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.25356\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0602 - acc: 0.9796 - val_loss: 0.3692 - val_acc: 0.9007\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.25356\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0571 - acc: 0.9800 - val_loss: 0.3232 - val_acc: 0.9060\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.25356\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0496 - acc: 0.9830 - val_loss: 0.3522 - val_acc: 0.9043\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.25356\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0417 - acc: 0.9858 - val_loss: 0.3707 - val_acc: 0.9002\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.25356\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0437 - acc: 0.9850 - val_loss: 0.3539 - val_acc: 0.9088\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.25356\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0401 - acc: 0.9865 - val_loss: 0.3604 - val_acc: 0.9102\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.25356\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0371 - acc: 0.9877 - val_loss: 0.3644 - val_acc: 0.9073\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.25356\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0300 - acc: 0.9906 - val_loss: 0.3714 - val_acc: 0.9109\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.25356\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0313 - acc: 0.9897 - val_loss: 0.3605 - val_acc: 0.9095\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.25356\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0298 - acc: 0.9900 - val_loss: 0.4380 - val_acc: 0.8985\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.25356\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0311 - acc: 0.9890 - val_loss: 0.4091 - val_acc: 0.9064\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.25356\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0281 - acc: 0.9909 - val_loss: 0.4073 - val_acc: 0.9109\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.25356\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0258 - acc: 0.9915 - val_loss: 0.4458 - val_acc: 0.9046\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.25356\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0228 - acc: 0.9927 - val_loss: 0.4394 - val_acc: 0.9054\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.25356\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0230 - acc: 0.9925 - val_loss: 0.4366 - val_acc: 0.9059\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.25356\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0256 - acc: 0.9920 - val_loss: 0.4166 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.25356\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0177 - acc: 0.9943 - val_loss: 0.4192 - val_acc: 0.9114\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.25356\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0182 - acc: 0.9943 - val_loss: 0.4303 - val_acc: 0.9075\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.25356\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0188 - acc: 0.9935 - val_loss: 0.4397 - val_acc: 0.9072\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.25356\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0183 - acc: 0.9939 - val_loss: 0.4979 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.25356\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0209 - acc: 0.9932 - val_loss: 0.4616 - val_acc: 0.9026\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.25356\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0240 - acc: 0.9920 - val_loss: 0.5132 - val_acc: 0.9048\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.25356\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0193 - acc: 0.9935 - val_loss: 0.4456 - val_acc: 0.9078\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.25356\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0131 - acc: 0.9958 - val_loss: 0.4553 - val_acc: 0.9073\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.25356\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0142 - acc: 0.9952 - val_loss: 0.4737 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.25356\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0177 - acc: 0.9944 - val_loss: 0.4667 - val_acc: 0.9072\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.25356\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0151 - acc: 0.9956 - val_loss: 0.4753 - val_acc: 0.9080\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.25356\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0105 - acc: 0.9966 - val_loss: 0.4699 - val_acc: 0.9102\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.25356\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0107 - acc: 0.9965 - val_loss: 0.4761 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.25356\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0125 - acc: 0.9958 - val_loss: 0.4615 - val_acc: 0.9094\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.25356\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0147 - acc: 0.9956 - val_loss: 0.5043 - val_acc: 0.9014\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.25356\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0139 - acc: 0.9956 - val_loss: 0.5055 - val_acc: 0.9068\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.25356\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0125 - acc: 0.9960 - val_loss: 0.4854 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.25356\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0085 - acc: 0.9972 - val_loss: 0.5135 - val_acc: 0.9076\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.25356\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0140 - acc: 0.9951 - val_loss: 0.5712 - val_acc: 0.9011\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.25356\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0123 - acc: 0.9960 - val_loss: 0.5101 - val_acc: 0.9075\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.25356\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0123 - acc: 0.9960 - val_loss: 0.4875 - val_acc: 0.9055\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.25356\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0084 - acc: 0.9975 - val_loss: 0.5086 - val_acc: 0.9064\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.25356\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0118 - acc: 0.9960 - val_loss: 0.5379 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.25356\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0122 - acc: 0.9963 - val_loss: 0.5446 - val_acc: 0.9039\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.25356\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0115 - acc: 0.9960 - val_loss: 0.5292 - val_acc: 0.9017\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.25356\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0118 - acc: 0.9962 - val_loss: 0.5047 - val_acc: 0.9065\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.25356\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0124 - acc: 0.9960 - val_loss: 0.5134 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.25356\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0083 - acc: 0.9972 - val_loss: 0.5618 - val_acc: 0.9074\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.25356\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0072 - acc: 0.9978 - val_loss: 0.5124 - val_acc: 0.9077\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.25356\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0099 - acc: 0.9969 - val_loss: 0.5353 - val_acc: 0.9071\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.25356\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0076 - acc: 0.9975 - val_loss: 0.5179 - val_acc: 0.9110\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.25356\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0090 - acc: 0.9970 - val_loss: 0.5632 - val_acc: 0.9086\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.25356\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0107 - acc: 0.9962 - val_loss: 0.5556 - val_acc: 0.9047\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.25356\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0107 - acc: 0.9968 - val_loss: 0.5137 - val_acc: 0.9095\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.25356\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0088 - acc: 0.9971 - val_loss: 0.5053 - val_acc: 0.9078\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.25356\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0080 - acc: 0.9976 - val_loss: 0.5453 - val_acc: 0.9091\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.25356\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0075 - acc: 0.9976 - val_loss: 0.5398 - val_acc: 0.9065\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.25356\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0069 - acc: 0.9979 - val_loss: 0.5311 - val_acc: 0.9098\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.25356\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 4s 90us/step - loss: 0.0056 - acc: 0.9986 - val_loss: 0.5605 - val_acc: 0.9053\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.25356\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 4s 90us/step - loss: 0.0061 - acc: 0.9980 - val_loss: 0.5590 - val_acc: 0.9058\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.25356\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0060 - acc: 0.9980 - val_loss: 0.5703 - val_acc: 0.9057\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.25356\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0116 - acc: 0.9960 - val_loss: 0.5977 - val_acc: 0.8968\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.25356\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0130 - acc: 0.9955 - val_loss: 0.5616 - val_acc: 0.9064\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.25356\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0099 - acc: 0.9964 - val_loss: 0.5706 - val_acc: 0.9077\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.25356\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0067 - acc: 0.9979 - val_loss: 0.5905 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.25356\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0063 - acc: 0.9979 - val_loss: 0.5646 - val_acc: 0.9110\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.25356\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 4s 92us/step - loss: 0.0067 - acc: 0.9977 - val_loss: 0.5646 - val_acc: 0.9085\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.25356\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0079 - acc: 0.9974 - val_loss: 0.5720 - val_acc: 0.9082\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.25356\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 4s 93us/step - loss: 0.0074 - acc: 0.9975 - val_loss: 0.5471 - val_acc: 0.9073\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.25356\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0068 - acc: 0.9980 - val_loss: 0.5646 - val_acc: 0.9056\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.25356\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 4s 91us/step - loss: 0.0065 - acc: 0.9980 - val_loss: 0.5617 - val_acc: 0.9063\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.25356\n",
            "Running iteration: 3\n",
            "create_model_3\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.6823 - acc: 0.7568 - val_loss: 0.4156 - val_acc: 0.8612\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.41556, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.4078 - acc: 0.8531 - val_loss: 0.3202 - val_acc: 0.8867\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.41556 to 0.32023, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.3463 - acc: 0.8749 - val_loss: 0.2904 - val_acc: 0.8923\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.32023 to 0.29039, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.3158 - acc: 0.8861 - val_loss: 0.2687 - val_acc: 0.9014\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.29039 to 0.26873, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2902 - acc: 0.8945 - val_loss: 0.2594 - val_acc: 0.9023\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.26873 to 0.25944, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2743 - acc: 0.8990 - val_loss: 0.2561 - val_acc: 0.9057\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.25944 to 0.25611, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2634 - acc: 0.9030 - val_loss: 0.2422 - val_acc: 0.9135\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.25611 to 0.24224, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 6s 132us/step - loss: 0.2486 - acc: 0.9078 - val_loss: 0.2324 - val_acc: 0.9143\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.24224 to 0.23241, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 6s 132us/step - loss: 0.2388 - acc: 0.9118 - val_loss: 0.2481 - val_acc: 0.9100\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.23241\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2326 - acc: 0.9152 - val_loss: 0.2213 - val_acc: 0.9195\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.23241 to 0.22135, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 7s 136us/step - loss: 0.2246 - acc: 0.9166 - val_loss: 0.2271 - val_acc: 0.9137\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.22135\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 7s 136us/step - loss: 0.2167 - acc: 0.9189 - val_loss: 0.2236 - val_acc: 0.9172\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.22135\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2082 - acc: 0.9232 - val_loss: 0.2204 - val_acc: 0.9180\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.22135 to 0.22040, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.2083 - acc: 0.9222 - val_loss: 0.2206 - val_acc: 0.9198\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.22040\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1962 - acc: 0.9265 - val_loss: 0.2305 - val_acc: 0.9187\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.22040\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1924 - acc: 0.9275 - val_loss: 0.2128 - val_acc: 0.9246\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.22040 to 0.21285, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1877 - acc: 0.9299 - val_loss: 0.2122 - val_acc: 0.9240\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.21285 to 0.21217, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1811 - acc: 0.9322 - val_loss: 0.2218 - val_acc: 0.9242\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.21217\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1814 - acc: 0.9326 - val_loss: 0.2228 - val_acc: 0.9231\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.21217\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1768 - acc: 0.9327 - val_loss: 0.2214 - val_acc: 0.9254\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.21217\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 6s 132us/step - loss: 0.1694 - acc: 0.9372 - val_loss: 0.2148 - val_acc: 0.9256\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.21217\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1682 - acc: 0.9371 - val_loss: 0.2102 - val_acc: 0.9244\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.21217 to 0.21018, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1658 - acc: 0.9382 - val_loss: 0.2150 - val_acc: 0.9252\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.21018\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1615 - acc: 0.9395 - val_loss: 0.2121 - val_acc: 0.9287\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.21018\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1577 - acc: 0.9408 - val_loss: 0.2088 - val_acc: 0.9277\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.21018 to 0.20880, saving model to fashion_mnist-3.hdf5\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1565 - acc: 0.9409 - val_loss: 0.2145 - val_acc: 0.9281\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.20880\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1459 - acc: 0.9451 - val_loss: 0.2131 - val_acc: 0.9259\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.20880\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1509 - acc: 0.9441 - val_loss: 0.2256 - val_acc: 0.9227\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.20880\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1469 - acc: 0.9439 - val_loss: 0.2190 - val_acc: 0.9258\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.20880\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1441 - acc: 0.9460 - val_loss: 0.2172 - val_acc: 0.9252\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.20880\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1386 - acc: 0.9480 - val_loss: 0.2237 - val_acc: 0.9243\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.20880\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1427 - acc: 0.9459 - val_loss: 0.2231 - val_acc: 0.9268\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.20880\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1356 - acc: 0.9489 - val_loss: 0.2136 - val_acc: 0.9262\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.20880\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1319 - acc: 0.9507 - val_loss: 0.2526 - val_acc: 0.9178\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.20880\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1348 - acc: 0.9487 - val_loss: 0.2416 - val_acc: 0.9178\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.20880\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1322 - acc: 0.9498 - val_loss: 0.2181 - val_acc: 0.9253\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.20880\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1289 - acc: 0.9507 - val_loss: 0.2207 - val_acc: 0.9262\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.20880\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1261 - acc: 0.9525 - val_loss: 0.2140 - val_acc: 0.9307\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.20880\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1214 - acc: 0.9538 - val_loss: 0.2178 - val_acc: 0.9301\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.20880\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1249 - acc: 0.9534 - val_loss: 0.2326 - val_acc: 0.9268\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.20880\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.1248 - acc: 0.9548 - val_loss: 0.2231 - val_acc: 0.9247\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.20880\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1170 - acc: 0.9566 - val_loss: 0.2127 - val_acc: 0.9299\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.20880\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1162 - acc: 0.9558 - val_loss: 0.2195 - val_acc: 0.9292\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.20880\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1158 - acc: 0.9568 - val_loss: 0.2217 - val_acc: 0.9284\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.20880\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1135 - acc: 0.9566 - val_loss: 0.2252 - val_acc: 0.9288\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.20880\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1128 - acc: 0.9572 - val_loss: 0.2265 - val_acc: 0.9251\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.20880\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1147 - acc: 0.9564 - val_loss: 0.2221 - val_acc: 0.9285\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.20880\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1085 - acc: 0.9585 - val_loss: 0.2455 - val_acc: 0.9221\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.20880\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1106 - acc: 0.9583 - val_loss: 0.2213 - val_acc: 0.9271\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.20880\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1083 - acc: 0.9593 - val_loss: 0.2199 - val_acc: 0.9309\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.20880\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1047 - acc: 0.9608 - val_loss: 0.2342 - val_acc: 0.9238\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.20880\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1070 - acc: 0.9605 - val_loss: 0.2285 - val_acc: 0.9266\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.20880\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.1062 - acc: 0.9606 - val_loss: 0.2303 - val_acc: 0.9282\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.20880\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1032 - acc: 0.9609 - val_loss: 0.2199 - val_acc: 0.9280\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.20880\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1047 - acc: 0.9611 - val_loss: 0.2361 - val_acc: 0.9272\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.20880\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0987 - acc: 0.9628 - val_loss: 0.2311 - val_acc: 0.9289\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.20880\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.1025 - acc: 0.9616 - val_loss: 0.2288 - val_acc: 0.9275\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.20880\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.0983 - acc: 0.9628 - val_loss: 0.2435 - val_acc: 0.9263\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.20880\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0996 - acc: 0.9627 - val_loss: 0.2273 - val_acc: 0.9297\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.20880\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0954 - acc: 0.9645 - val_loss: 0.2314 - val_acc: 0.9251\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.20880\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0974 - acc: 0.9635 - val_loss: 0.2291 - val_acc: 0.9277\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.20880\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.0950 - acc: 0.9636 - val_loss: 0.2291 - val_acc: 0.9298\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.20880\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0927 - acc: 0.9660 - val_loss: 0.2273 - val_acc: 0.9306\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.20880\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0926 - acc: 0.9649 - val_loss: 0.2309 - val_acc: 0.9293\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.20880\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 6s 133us/step - loss: 0.0921 - acc: 0.9651 - val_loss: 0.2365 - val_acc: 0.9301\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.20880\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0920 - acc: 0.9652 - val_loss: 0.2388 - val_acc: 0.9284\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.20880\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 6s 135us/step - loss: 0.0897 - acc: 0.9666 - val_loss: 0.2299 - val_acc: 0.9307\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.20880\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0893 - acc: 0.9665 - val_loss: 0.2370 - val_acc: 0.9260\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.20880\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0885 - acc: 0.9672 - val_loss: 0.2400 - val_acc: 0.9287\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.20880\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0880 - acc: 0.9670 - val_loss: 0.2340 - val_acc: 0.9313\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.20880\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0871 - acc: 0.9679 - val_loss: 0.2403 - val_acc: 0.9235\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.20880\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0885 - acc: 0.9661 - val_loss: 0.2482 - val_acc: 0.9306\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.20880\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0904 - acc: 0.9671 - val_loss: 0.2415 - val_acc: 0.9298\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.20880\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0860 - acc: 0.9681 - val_loss: 0.2369 - val_acc: 0.9309\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.20880\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0829 - acc: 0.9698 - val_loss: 0.2362 - val_acc: 0.9310\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.20880\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0819 - acc: 0.9696 - val_loss: 0.2328 - val_acc: 0.9279\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.20880\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0861 - acc: 0.9677 - val_loss: 0.2427 - val_acc: 0.9285\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.20880\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0824 - acc: 0.9691 - val_loss: 0.2407 - val_acc: 0.9303\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.20880\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0808 - acc: 0.9693 - val_loss: 0.2475 - val_acc: 0.9290\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.20880\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 6s 134us/step - loss: 0.0807 - acc: 0.9699 - val_loss: 0.2437 - val_acc: 0.9314\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.20880\n",
            "Running iteration: 4\n",
            "create_model_4\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 11s 226us/step - loss: 1.3287 - acc: 0.5411 - val_loss: 0.6888 - val_acc: 0.7918\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.68877, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 8s 176us/step - loss: 0.6568 - acc: 0.7708 - val_loss: 0.4341 - val_acc: 0.8406\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.68877 to 0.43409, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 8s 177us/step - loss: 0.5340 - acc: 0.8130 - val_loss: 0.3632 - val_acc: 0.8733\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.43409 to 0.36323, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 8s 176us/step - loss: 0.4706 - acc: 0.8367 - val_loss: 0.3237 - val_acc: 0.8844\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.36323 to 0.32368, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 8s 175us/step - loss: 0.4303 - acc: 0.8502 - val_loss: 0.3087 - val_acc: 0.8897\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.32368 to 0.30865, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 8s 175us/step - loss: 0.4031 - acc: 0.8600 - val_loss: 0.2930 - val_acc: 0.8960\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.30865 to 0.29300, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.3826 - acc: 0.8671 - val_loss: 0.2893 - val_acc: 0.8947\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.29300 to 0.28932, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 8s 176us/step - loss: 0.3729 - acc: 0.8700 - val_loss: 0.2757 - val_acc: 0.9031\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.28932 to 0.27565, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.3551 - acc: 0.8750 - val_loss: 0.2656 - val_acc: 0.9030\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.27565 to 0.26556, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.3463 - acc: 0.8796 - val_loss: 0.2609 - val_acc: 0.9061\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.26556 to 0.26091, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.3298 - acc: 0.8842 - val_loss: 0.2563 - val_acc: 0.9072\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.26091 to 0.25635, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.3230 - acc: 0.8856 - val_loss: 0.2736 - val_acc: 0.9006\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.25635\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.3193 - acc: 0.8885 - val_loss: 0.2445 - val_acc: 0.9110\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.25635 to 0.24451, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 9s 183us/step - loss: 0.3101 - acc: 0.8917 - val_loss: 0.2536 - val_acc: 0.9079\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.24451\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.3057 - acc: 0.8918 - val_loss: 0.2430 - val_acc: 0.9118\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.24451 to 0.24300, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.3013 - acc: 0.8952 - val_loss: 0.2426 - val_acc: 0.9104\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.24300 to 0.24265, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.3018 - acc: 0.8944 - val_loss: 0.2485 - val_acc: 0.9103\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.24265\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.2899 - acc: 0.8982 - val_loss: 0.2340 - val_acc: 0.9154\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.24265 to 0.23398, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2864 - acc: 0.8985 - val_loss: 0.2329 - val_acc: 0.9162\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.23398 to 0.23289, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2860 - acc: 0.8988 - val_loss: 0.2418 - val_acc: 0.9117\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.23289\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.2828 - acc: 0.9003 - val_loss: 0.2335 - val_acc: 0.9147\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.23289\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.2776 - acc: 0.9017 - val_loss: 0.2401 - val_acc: 0.9109\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.23289\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.2752 - acc: 0.9039 - val_loss: 0.2405 - val_acc: 0.9132\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.23289\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 8s 175us/step - loss: 0.2720 - acc: 0.9040 - val_loss: 0.2470 - val_acc: 0.9097\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.23289\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2683 - acc: 0.9047 - val_loss: 0.2408 - val_acc: 0.9125\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.23289\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2682 - acc: 0.9061 - val_loss: 0.2228 - val_acc: 0.9209\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.23289 to 0.22282, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2603 - acc: 0.9080 - val_loss: 0.2256 - val_acc: 0.9196\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.22282\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.2588 - acc: 0.9098 - val_loss: 0.2226 - val_acc: 0.9200\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.22282 to 0.22264, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2563 - acc: 0.9106 - val_loss: 0.2287 - val_acc: 0.9192\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.22264\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2566 - acc: 0.9104 - val_loss: 0.2268 - val_acc: 0.9191\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.22264\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2558 - acc: 0.9108 - val_loss: 0.2187 - val_acc: 0.9219\n",
            "\n",
            "Epoch 00031: val_loss improved from 0.22264 to 0.21867, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 9s 177us/step - loss: 0.2520 - acc: 0.9102 - val_loss: 0.2226 - val_acc: 0.9219\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.21867\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 8s 177us/step - loss: 0.2478 - acc: 0.9109 - val_loss: 0.2192 - val_acc: 0.9229\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.21867\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 8s 177us/step - loss: 0.2456 - acc: 0.9136 - val_loss: 0.2297 - val_acc: 0.9171\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.21867\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 9s 184us/step - loss: 0.2460 - acc: 0.9133 - val_loss: 0.2194 - val_acc: 0.9224\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.21867\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2464 - acc: 0.9136 - val_loss: 0.2379 - val_acc: 0.9118\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.21867\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2429 - acc: 0.9133 - val_loss: 0.2200 - val_acc: 0.9217\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.21867\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2411 - acc: 0.9159 - val_loss: 0.2184 - val_acc: 0.9230\n",
            "\n",
            "Epoch 00038: val_loss improved from 0.21867 to 0.21842, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2413 - acc: 0.9152 - val_loss: 0.2201 - val_acc: 0.9227\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.21842\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2313 - acc: 0.9170 - val_loss: 0.2176 - val_acc: 0.9219\n",
            "\n",
            "Epoch 00040: val_loss improved from 0.21842 to 0.21760, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 9s 186us/step - loss: 0.2377 - acc: 0.9163 - val_loss: 0.2196 - val_acc: 0.9230\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.21760\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 9s 186us/step - loss: 0.2303 - acc: 0.9191 - val_loss: 0.2148 - val_acc: 0.9233\n",
            "\n",
            "Epoch 00042: val_loss improved from 0.21760 to 0.21480, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2300 - acc: 0.9177 - val_loss: 0.2173 - val_acc: 0.9196\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.21480\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.2327 - acc: 0.9176 - val_loss: 0.2139 - val_acc: 0.9228\n",
            "\n",
            "Epoch 00044: val_loss improved from 0.21480 to 0.21393, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2283 - acc: 0.9193 - val_loss: 0.2167 - val_acc: 0.9243\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.21393\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 9s 186us/step - loss: 0.2249 - acc: 0.9201 - val_loss: 0.2198 - val_acc: 0.9229\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.21393\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 9s 191us/step - loss: 0.2241 - acc: 0.9214 - val_loss: 0.2119 - val_acc: 0.9236\n",
            "\n",
            "Epoch 00047: val_loss improved from 0.21393 to 0.21191, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2237 - acc: 0.9205 - val_loss: 0.2130 - val_acc: 0.9256\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.21191\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 9s 192us/step - loss: 0.2205 - acc: 0.9220 - val_loss: 0.2162 - val_acc: 0.9238\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.21191\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2200 - acc: 0.9227 - val_loss: 0.2361 - val_acc: 0.9119\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.21191\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2200 - acc: 0.9229 - val_loss: 0.2226 - val_acc: 0.9227\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.21191\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 9s 191us/step - loss: 0.2257 - acc: 0.9214 - val_loss: 0.2066 - val_acc: 0.9264\n",
            "\n",
            "Epoch 00052: val_loss improved from 0.21191 to 0.20656, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2206 - acc: 0.9230 - val_loss: 0.2072 - val_acc: 0.9282\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.20656\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 9s 191us/step - loss: 0.2180 - acc: 0.9236 - val_loss: 0.2104 - val_acc: 0.9252\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.20656\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 9s 187us/step - loss: 0.2143 - acc: 0.9241 - val_loss: 0.2087 - val_acc: 0.9263\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.20656\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2158 - acc: 0.9234 - val_loss: 0.2085 - val_acc: 0.9263\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.20656\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2119 - acc: 0.9259 - val_loss: 0.2096 - val_acc: 0.9262\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.20656\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2160 - acc: 0.9231 - val_loss: 0.2100 - val_acc: 0.9248\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.20656\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2125 - acc: 0.9253 - val_loss: 0.2187 - val_acc: 0.9229\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.20656\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 9s 188us/step - loss: 0.2166 - acc: 0.9241 - val_loss: 0.2081 - val_acc: 0.9284\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.20656\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2127 - acc: 0.9263 - val_loss: 0.2123 - val_acc: 0.9263\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.20656\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 9s 189us/step - loss: 0.2116 - acc: 0.9246 - val_loss: 0.2173 - val_acc: 0.9225\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.20656\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 9s 191us/step - loss: 0.2095 - acc: 0.9261 - val_loss: 0.2089 - val_acc: 0.9242\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.20656\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 9s 190us/step - loss: 0.2067 - acc: 0.9275 - val_loss: 0.2086 - val_acc: 0.9271\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.20656\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 9s 183us/step - loss: 0.2066 - acc: 0.9276 - val_loss: 0.2142 - val_acc: 0.9239\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.20656\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2051 - acc: 0.9275 - val_loss: 0.2073 - val_acc: 0.9269\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.20656\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 9s 180us/step - loss: 0.2062 - acc: 0.9265 - val_loss: 0.2100 - val_acc: 0.9273\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.20656\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2040 - acc: 0.9272 - val_loss: 0.2111 - val_acc: 0.9258\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.20656\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2058 - acc: 0.9282 - val_loss: 0.2206 - val_acc: 0.9219\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.20656\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.2019 - acc: 0.9294 - val_loss: 0.2144 - val_acc: 0.9253\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.20656\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2010 - acc: 0.9287 - val_loss: 0.2058 - val_acc: 0.9272\n",
            "\n",
            "Epoch 00071: val_loss improved from 0.20656 to 0.20585, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.2027 - acc: 0.9290 - val_loss: 0.2034 - val_acc: 0.9267\n",
            "\n",
            "Epoch 00072: val_loss improved from 0.20585 to 0.20339, saving model to fashion_mnist-4.hdf5\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.2022 - acc: 0.9290 - val_loss: 0.2082 - val_acc: 0.9273\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.20339\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.2039 - acc: 0.9278 - val_loss: 0.2120 - val_acc: 0.9268\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.20339\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 9s 179us/step - loss: 0.1980 - acc: 0.9302 - val_loss: 0.2088 - val_acc: 0.9263\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.20339\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 8s 175us/step - loss: 0.2003 - acc: 0.9297 - val_loss: 0.2135 - val_acc: 0.9223\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.20339\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.2005 - acc: 0.9286 - val_loss: 0.2091 - val_acc: 0.9262\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.20339\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 9s 178us/step - loss: 0.1954 - acc: 0.9315 - val_loss: 0.2082 - val_acc: 0.9264\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.20339\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 9s 181us/step - loss: 0.1942 - acc: 0.9315 - val_loss: 0.2079 - val_acc: 0.9268\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.20339\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 9s 182us/step - loss: 0.1934 - acc: 0.9326 - val_loss: 0.2126 - val_acc: 0.9225\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.20339\n",
            "Running iteration: 5\n",
            "create_model_5\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 10s 217us/step - loss: 0.5408 - acc: 0.8082 - val_loss: 0.3389 - val_acc: 0.8778\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.33890, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.3479 - acc: 0.8738 - val_loss: 0.3042 - val_acc: 0.8887\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.33890 to 0.30416, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.2961 - acc: 0.8913 - val_loss: 0.2623 - val_acc: 0.9102\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.30416 to 0.26226, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.2672 - acc: 0.9020 - val_loss: 0.2433 - val_acc: 0.9143\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.26226 to 0.24335, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.2464 - acc: 0.9102 - val_loss: 0.2487 - val_acc: 0.9100\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.24335\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.2329 - acc: 0.9151 - val_loss: 0.2289 - val_acc: 0.9171\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.24335 to 0.22892, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.2174 - acc: 0.9213 - val_loss: 0.2250 - val_acc: 0.9189\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.22892 to 0.22500, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.2066 - acc: 0.9253 - val_loss: 0.2139 - val_acc: 0.9256\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.22500 to 0.21394, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1947 - acc: 0.9281 - val_loss: 0.2271 - val_acc: 0.9233\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.21394\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1919 - acc: 0.9290 - val_loss: 0.2209 - val_acc: 0.9249\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.21394\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1818 - acc: 0.9336 - val_loss: 0.2400 - val_acc: 0.9138\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.21394\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1717 - acc: 0.9355 - val_loss: 0.2092 - val_acc: 0.9273\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.21394 to 0.20916, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1692 - acc: 0.9378 - val_loss: 0.2111 - val_acc: 0.9235\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.20916\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1614 - acc: 0.9406 - val_loss: 0.1928 - val_acc: 0.9339\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.20916 to 0.19283, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1582 - acc: 0.9414 - val_loss: 0.2121 - val_acc: 0.9279\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.19283\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1522 - acc: 0.9441 - val_loss: 0.2057 - val_acc: 0.9303\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.19283\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 8s 167us/step - loss: 0.1468 - acc: 0.9455 - val_loss: 0.2103 - val_acc: 0.9284\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.19283\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1424 - acc: 0.9487 - val_loss: 0.2000 - val_acc: 0.9313\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.19283\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1343 - acc: 0.9501 - val_loss: 0.2061 - val_acc: 0.9320\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.19283\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 8s 167us/step - loss: 0.1338 - acc: 0.9501 - val_loss: 0.1952 - val_acc: 0.9318\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.19283\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1303 - acc: 0.9518 - val_loss: 0.2064 - val_acc: 0.9287\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.19283\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1279 - acc: 0.9524 - val_loss: 0.1952 - val_acc: 0.9300\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.19283\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1242 - acc: 0.9545 - val_loss: 0.2055 - val_acc: 0.9287\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.19283\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1207 - acc: 0.9548 - val_loss: 0.2298 - val_acc: 0.9312\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.19283\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1189 - acc: 0.9559 - val_loss: 0.2058 - val_acc: 0.9355\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.19283\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1178 - acc: 0.9566 - val_loss: 0.1894 - val_acc: 0.9341\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.19283 to 0.18936, saving model to fashion_mnist-5.hdf5\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1145 - acc: 0.9581 - val_loss: 0.2003 - val_acc: 0.9317\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.18936\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1112 - acc: 0.9595 - val_loss: 0.2174 - val_acc: 0.9339\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.18936\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1101 - acc: 0.9597 - val_loss: 0.2133 - val_acc: 0.9330\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.18936\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1048 - acc: 0.9612 - val_loss: 0.2227 - val_acc: 0.9314\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.18936\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1052 - acc: 0.9614 - val_loss: 0.2181 - val_acc: 0.9347\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.18936\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.1039 - acc: 0.9621 - val_loss: 0.2301 - val_acc: 0.9324\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.18936\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0984 - acc: 0.9629 - val_loss: 0.2053 - val_acc: 0.9321\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.18936\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0996 - acc: 0.9635 - val_loss: 0.2330 - val_acc: 0.9321\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.18936\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 8s 167us/step - loss: 0.0965 - acc: 0.9644 - val_loss: 0.2207 - val_acc: 0.9345\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.18936\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0969 - acc: 0.9640 - val_loss: 0.2487 - val_acc: 0.9337\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.18936\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0949 - acc: 0.9659 - val_loss: 0.2458 - val_acc: 0.9322\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.18936\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0937 - acc: 0.9650 - val_loss: 0.2049 - val_acc: 0.9367\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.18936\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 8s 167us/step - loss: 0.0949 - acc: 0.9655 - val_loss: 0.2231 - val_acc: 0.9348\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.18936\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0907 - acc: 0.9665 - val_loss: 0.2442 - val_acc: 0.9352\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.18936\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0873 - acc: 0.9672 - val_loss: 0.2217 - val_acc: 0.9373\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.18936\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0889 - acc: 0.9681 - val_loss: 0.2461 - val_acc: 0.9347\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.18936\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0866 - acc: 0.9689 - val_loss: 0.2181 - val_acc: 0.9314\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.18936\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0834 - acc: 0.9693 - val_loss: 0.2272 - val_acc: 0.9344\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.18936\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 8s 167us/step - loss: 0.0860 - acc: 0.9687 - val_loss: 0.2104 - val_acc: 0.9325\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.18936\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 8s 169us/step - loss: 0.0844 - acc: 0.9695 - val_loss: 0.2489 - val_acc: 0.9343\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.18936\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 8s 166us/step - loss: 0.0853 - acc: 0.9688 - val_loss: 0.2667 - val_acc: 0.9342\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.18936\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0842 - acc: 0.9699 - val_loss: 0.2274 - val_acc: 0.9362\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.18936\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 8s 161us/step - loss: 0.0815 - acc: 0.9704 - val_loss: 0.2201 - val_acc: 0.9330\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.18936\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0790 - acc: 0.9710 - val_loss: 0.2107 - val_acc: 0.9334\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.18936\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0796 - acc: 0.9720 - val_loss: 0.2452 - val_acc: 0.9365\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.18936\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 8s 162us/step - loss: 0.0787 - acc: 0.9714 - val_loss: 0.2367 - val_acc: 0.9375\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.18936\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0756 - acc: 0.9728 - val_loss: 0.2280 - val_acc: 0.9354\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.18936\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0768 - acc: 0.9716 - val_loss: 0.2235 - val_acc: 0.9374\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.18936\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0785 - acc: 0.9725 - val_loss: 0.2703 - val_acc: 0.9327\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.18936\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0767 - acc: 0.9724 - val_loss: 0.2203 - val_acc: 0.9327\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.18936\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0759 - acc: 0.9735 - val_loss: 0.2762 - val_acc: 0.9281\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.18936\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0747 - acc: 0.9739 - val_loss: 0.1971 - val_acc: 0.9332\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.18936\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0753 - acc: 0.9726 - val_loss: 0.2708 - val_acc: 0.9351\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.18936\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 8s 162us/step - loss: 0.0722 - acc: 0.9742 - val_loss: 0.2749 - val_acc: 0.9385\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.18936\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0703 - acc: 0.9746 - val_loss: 0.2266 - val_acc: 0.9337\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.18936\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0723 - acc: 0.9739 - val_loss: 0.2040 - val_acc: 0.9324\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.18936\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0723 - acc: 0.9747 - val_loss: 0.2341 - val_acc: 0.9355\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.18936\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0714 - acc: 0.9746 - val_loss: 0.2149 - val_acc: 0.9373\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.18936\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0688 - acc: 0.9749 - val_loss: 0.2316 - val_acc: 0.9382\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.18936\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0664 - acc: 0.9765 - val_loss: 0.2480 - val_acc: 0.9350\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.18936\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0700 - acc: 0.9752 - val_loss: 0.2438 - val_acc: 0.9377\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.18936\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0688 - acc: 0.9754 - val_loss: 0.2859 - val_acc: 0.9385\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.18936\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0674 - acc: 0.9755 - val_loss: 0.2743 - val_acc: 0.9342\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.18936\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0653 - acc: 0.9768 - val_loss: 0.2381 - val_acc: 0.9350\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.18936\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0679 - acc: 0.9754 - val_loss: 0.2411 - val_acc: 0.9353\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.18936\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0658 - acc: 0.9764 - val_loss: 0.2470 - val_acc: 0.9349\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.18936\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 8s 161us/step - loss: 0.0657 - acc: 0.9764 - val_loss: 0.2350 - val_acc: 0.9365\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.18936\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0656 - acc: 0.9767 - val_loss: 0.2399 - val_acc: 0.9369\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.18936\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0648 - acc: 0.9772 - val_loss: 0.2280 - val_acc: 0.9392\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.18936\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0664 - acc: 0.9769 - val_loss: 0.2288 - val_acc: 0.9336\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.18936\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0625 - acc: 0.9778 - val_loss: 0.2080 - val_acc: 0.9370\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.18936\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0632 - acc: 0.9788 - val_loss: 0.2301 - val_acc: 0.9387\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.18936\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0625 - acc: 0.9782 - val_loss: 0.2278 - val_acc: 0.9363\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.18936\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0609 - acc: 0.9776 - val_loss: 0.2068 - val_acc: 0.9356\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.18936\n",
            "create_model_5\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 10s 211us/step - loss: 0.5308 - acc: 0.8099 - val_loss: 0.3481 - val_acc: 0.8737\n",
            "\n",
            "Epoch 00001: val_loss did not improve from 0.18936\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.3416 - acc: 0.8756 - val_loss: 0.2808 - val_acc: 0.8989\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.18936\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.2954 - acc: 0.8922 - val_loss: 0.2865 - val_acc: 0.8949\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.18936\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.2670 - acc: 0.9025 - val_loss: 0.2702 - val_acc: 0.9096\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 0.18936\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.2464 - acc: 0.9099 - val_loss: 0.2428 - val_acc: 0.9142\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.18936\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.2328 - acc: 0.9143 - val_loss: 0.2381 - val_acc: 0.9148\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.18936\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.2170 - acc: 0.9210 - val_loss: 0.2551 - val_acc: 0.9059\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.18936\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.2068 - acc: 0.9249 - val_loss: 0.2328 - val_acc: 0.9163\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.18936\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1972 - acc: 0.9291 - val_loss: 0.2266 - val_acc: 0.9202\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.18936\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1901 - acc: 0.9309 - val_loss: 0.2393 - val_acc: 0.9203\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.18936\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1798 - acc: 0.9342 - val_loss: 0.2081 - val_acc: 0.9247\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.18936\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1739 - acc: 0.9361 - val_loss: 0.2041 - val_acc: 0.9285\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.18936\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1708 - acc: 0.9369 - val_loss: 0.1962 - val_acc: 0.9279\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.18936\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1633 - acc: 0.9396 - val_loss: 0.2021 - val_acc: 0.9308\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.18936\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1567 - acc: 0.9417 - val_loss: 0.1971 - val_acc: 0.9301\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.18936\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1537 - acc: 0.9437 - val_loss: 0.2144 - val_acc: 0.9305\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.18936\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1468 - acc: 0.9464 - val_loss: 0.1973 - val_acc: 0.9295\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.18936\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1436 - acc: 0.9469 - val_loss: 0.2086 - val_acc: 0.9271\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.18936\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1377 - acc: 0.9495 - val_loss: 0.2132 - val_acc: 0.9337\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.18936\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1349 - acc: 0.9509 - val_loss: 0.2069 - val_acc: 0.9332\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.18936\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1328 - acc: 0.9504 - val_loss: 0.2036 - val_acc: 0.9297\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.18936\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1283 - acc: 0.9529 - val_loss: 0.2003 - val_acc: 0.9347\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.18936\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1282 - acc: 0.9525 - val_loss: 0.2333 - val_acc: 0.9286\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.18936\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1192 - acc: 0.9560 - val_loss: 0.2006 - val_acc: 0.9334\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.18936\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1215 - acc: 0.9562 - val_loss: 0.2267 - val_acc: 0.9294\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.18936\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1170 - acc: 0.9563 - val_loss: 0.2150 - val_acc: 0.9343\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.18936\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1147 - acc: 0.9570 - val_loss: 0.2170 - val_acc: 0.9338\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.18936\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1140 - acc: 0.9587 - val_loss: 0.2175 - val_acc: 0.9332\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.18936\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1111 - acc: 0.9587 - val_loss: 0.2299 - val_acc: 0.9366\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.18936\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1101 - acc: 0.9589 - val_loss: 0.1966 - val_acc: 0.9327\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.18936\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.1071 - acc: 0.9601 - val_loss: 0.2088 - val_acc: 0.9327\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.18936\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 8s 162us/step - loss: 0.1043 - acc: 0.9619 - val_loss: 0.2222 - val_acc: 0.9338\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.18936\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.1030 - acc: 0.9624 - val_loss: 0.2146 - val_acc: 0.9283\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.18936\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.1014 - acc: 0.9619 - val_loss: 0.2358 - val_acc: 0.9248\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.18936\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0984 - acc: 0.9635 - val_loss: 0.2368 - val_acc: 0.9328\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.18936\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0985 - acc: 0.9646 - val_loss: 0.2162 - val_acc: 0.9356\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.18936\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0972 - acc: 0.9645 - val_loss: 0.2199 - val_acc: 0.9319\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.18936\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0935 - acc: 0.9654 - val_loss: 0.2386 - val_acc: 0.9305\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.18936\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0921 - acc: 0.9654 - val_loss: 0.2064 - val_acc: 0.9315\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.18936\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0938 - acc: 0.9662 - val_loss: 0.2613 - val_acc: 0.9363\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.18936\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0904 - acc: 0.9671 - val_loss: 0.2424 - val_acc: 0.9365\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.18936\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0894 - acc: 0.9683 - val_loss: 0.2877 - val_acc: 0.9323\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.18936\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0878 - acc: 0.9678 - val_loss: 0.2668 - val_acc: 0.9341\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.18936\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0882 - acc: 0.9676 - val_loss: 0.2129 - val_acc: 0.9332\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.18936\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0874 - acc: 0.9685 - val_loss: 0.2166 - val_acc: 0.9328\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.18936\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0849 - acc: 0.9684 - val_loss: 0.2390 - val_acc: 0.9341\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.18936\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0834 - acc: 0.9702 - val_loss: 0.2317 - val_acc: 0.9325\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.18936\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0826 - acc: 0.9701 - val_loss: 0.2153 - val_acc: 0.9332\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.18936\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0802 - acc: 0.9698 - val_loss: 0.2406 - val_acc: 0.9372\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.18936\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0816 - acc: 0.9703 - val_loss: 0.2455 - val_acc: 0.9333\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.18936\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0806 - acc: 0.9698 - val_loss: 0.2852 - val_acc: 0.9333\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.18936\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0809 - acc: 0.9699 - val_loss: 0.2103 - val_acc: 0.9295\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.18936\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0796 - acc: 0.9714 - val_loss: 0.2298 - val_acc: 0.9322\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.18936\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0772 - acc: 0.9722 - val_loss: 0.2607 - val_acc: 0.9345\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.18936\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0785 - acc: 0.9714 - val_loss: 0.2375 - val_acc: 0.9365\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.18936\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0753 - acc: 0.9729 - val_loss: 0.2242 - val_acc: 0.9348\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.18936\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0759 - acc: 0.9720 - val_loss: 0.2603 - val_acc: 0.9290\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.18936\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0751 - acc: 0.9727 - val_loss: 0.2147 - val_acc: 0.9341\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.18936\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0748 - acc: 0.9725 - val_loss: 0.2608 - val_acc: 0.9337\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.18936\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0756 - acc: 0.9734 - val_loss: 0.2249 - val_acc: 0.9326\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.18936\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0731 - acc: 0.9736 - val_loss: 0.2203 - val_acc: 0.9373\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.18936\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0752 - acc: 0.9725 - val_loss: 0.2165 - val_acc: 0.9352\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.18936\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0721 - acc: 0.9736 - val_loss: 0.2190 - val_acc: 0.9373\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.18936\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0741 - acc: 0.9740 - val_loss: 0.2470 - val_acc: 0.9372\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.18936\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0698 - acc: 0.9745 - val_loss: 0.2279 - val_acc: 0.9307\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.18936\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0690 - acc: 0.9747 - val_loss: 0.2567 - val_acc: 0.9293\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.18936\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0717 - acc: 0.9742 - val_loss: 0.2220 - val_acc: 0.9364\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.18936\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0694 - acc: 0.9752 - val_loss: 0.2540 - val_acc: 0.9334\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.18936\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0685 - acc: 0.9748 - val_loss: 0.2385 - val_acc: 0.9379\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.18936\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0663 - acc: 0.9764 - val_loss: 0.2443 - val_acc: 0.9358\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.18936\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 8s 162us/step - loss: 0.0662 - acc: 0.9755 - val_loss: 0.2778 - val_acc: 0.9373\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.18936\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 8s 162us/step - loss: 0.0670 - acc: 0.9760 - val_loss: 0.2559 - val_acc: 0.9354\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.18936\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 0.0687 - acc: 0.9757 - val_loss: 0.2594 - val_acc: 0.9347\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.18936\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0640 - acc: 0.9768 - val_loss: 0.2431 - val_acc: 0.9324\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.18936\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0642 - acc: 0.9769 - val_loss: 0.2567 - val_acc: 0.9353\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.18936\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0653 - acc: 0.9774 - val_loss: 0.2495 - val_acc: 0.9356\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.18936\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 8s 165us/step - loss: 0.0651 - acc: 0.9772 - val_loss: 0.2433 - val_acc: 0.9377\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.18936\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0630 - acc: 0.9770 - val_loss: 0.2463 - val_acc: 0.9382\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.18936\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0658 - acc: 0.9773 - val_loss: 0.3098 - val_acc: 0.9366\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.18936\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 8s 163us/step - loss: 0.0624 - acc: 0.9776 - val_loss: 0.2343 - val_acc: 0.9347\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.18936\n",
            "Running iteration: 6\n",
            "create_model_6\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/80\n",
            "48000/48000 [==============================] - 8s 164us/step - loss: 1.1580 - acc: 0.5700 - val_loss: 0.6526 - val_acc: 0.7566\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.65259, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 2/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.6531 - acc: 0.7549 - val_loss: 0.5227 - val_acc: 0.7993\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.65259 to 0.52268, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 3/80\n",
            "48000/48000 [==============================] - 5s 114us/step - loss: 0.5537 - acc: 0.7916 - val_loss: 0.4680 - val_acc: 0.8218\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.52268 to 0.46805, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 4/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.4947 - acc: 0.8170 - val_loss: 0.4246 - val_acc: 0.8416\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.46805 to 0.42461, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 5/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.4541 - acc: 0.8355 - val_loss: 0.3975 - val_acc: 0.8565\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.42461 to 0.39753, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 6/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.4249 - acc: 0.8453 - val_loss: 0.3716 - val_acc: 0.8623\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.39753 to 0.37155, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 7/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.4024 - acc: 0.8536 - val_loss: 0.3579 - val_acc: 0.8700\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.37155 to 0.35794, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 8/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.3764 - acc: 0.8642 - val_loss: 0.3396 - val_acc: 0.8764\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.35794 to 0.33959, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 9/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.3660 - acc: 0.8666 - val_loss: 0.3307 - val_acc: 0.8795\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.33959 to 0.33072, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 10/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.3537 - acc: 0.8715 - val_loss: 0.3178 - val_acc: 0.8830\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.33072 to 0.31781, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 11/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.3400 - acc: 0.8759 - val_loss: 0.3087 - val_acc: 0.8882\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.31781 to 0.30867, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 12/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.3259 - acc: 0.8819 - val_loss: 0.3006 - val_acc: 0.8870\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.30867 to 0.30057, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 13/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.3178 - acc: 0.8855 - val_loss: 0.2924 - val_acc: 0.8915\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.30057 to 0.29243, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 14/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.3089 - acc: 0.8880 - val_loss: 0.2957 - val_acc: 0.8927\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.29243\n",
            "Epoch 15/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2999 - acc: 0.8921 - val_loss: 0.2849 - val_acc: 0.8932\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.29243 to 0.28487, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 16/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2930 - acc: 0.8943 - val_loss: 0.2775 - val_acc: 0.8960\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.28487 to 0.27753, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 17/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2869 - acc: 0.8950 - val_loss: 0.2696 - val_acc: 0.9002\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.27753 to 0.26965, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 18/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2800 - acc: 0.8984 - val_loss: 0.2686 - val_acc: 0.9004\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.26965 to 0.26862, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 19/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2759 - acc: 0.8983 - val_loss: 0.2657 - val_acc: 0.9018\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.26862 to 0.26569, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 20/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2701 - acc: 0.9020 - val_loss: 0.2647 - val_acc: 0.9037\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.26569 to 0.26465, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 21/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2631 - acc: 0.9037 - val_loss: 0.2607 - val_acc: 0.9045\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.26465 to 0.26073, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 22/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2585 - acc: 0.9075 - val_loss: 0.2576 - val_acc: 0.9034\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.26073 to 0.25758, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 23/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2526 - acc: 0.9067 - val_loss: 0.2551 - val_acc: 0.9062\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.25758 to 0.25505, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 24/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2510 - acc: 0.9092 - val_loss: 0.2537 - val_acc: 0.9048\n",
            "\n",
            "Epoch 00024: val_loss improved from 0.25505 to 0.25372, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 25/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2450 - acc: 0.9095 - val_loss: 0.2521 - val_acc: 0.9087\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.25372 to 0.25212, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 26/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2404 - acc: 0.9119 - val_loss: 0.2467 - val_acc: 0.9095\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.25212 to 0.24673, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 27/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2344 - acc: 0.9130 - val_loss: 0.2438 - val_acc: 0.9099\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.24673 to 0.24376, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 28/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2298 - acc: 0.9156 - val_loss: 0.2421 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.24376 to 0.24211, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 29/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2260 - acc: 0.9175 - val_loss: 0.2440 - val_acc: 0.9104\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.24211\n",
            "Epoch 30/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2241 - acc: 0.9187 - val_loss: 0.2363 - val_acc: 0.9133\n",
            "\n",
            "Epoch 00030: val_loss improved from 0.24211 to 0.23628, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 31/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2190 - acc: 0.9198 - val_loss: 0.2418 - val_acc: 0.9112\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.23628\n",
            "Epoch 32/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2168 - acc: 0.9218 - val_loss: 0.2412 - val_acc: 0.9103\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.23628\n",
            "Epoch 33/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2117 - acc: 0.9218 - val_loss: 0.2340 - val_acc: 0.9144\n",
            "\n",
            "Epoch 00033: val_loss improved from 0.23628 to 0.23400, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 34/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2096 - acc: 0.9232 - val_loss: 0.2495 - val_acc: 0.9100\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.23400\n",
            "Epoch 35/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2070 - acc: 0.9242 - val_loss: 0.2328 - val_acc: 0.9152\n",
            "\n",
            "Epoch 00035: val_loss improved from 0.23400 to 0.23280, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 36/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.2017 - acc: 0.9262 - val_loss: 0.2343 - val_acc: 0.9133\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.23280\n",
            "Epoch 37/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.2003 - acc: 0.9267 - val_loss: 0.2369 - val_acc: 0.9148\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.23280\n",
            "Epoch 38/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1965 - acc: 0.9279 - val_loss: 0.2317 - val_acc: 0.9154\n",
            "\n",
            "Epoch 00038: val_loss improved from 0.23280 to 0.23173, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 39/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1942 - acc: 0.9290 - val_loss: 0.2279 - val_acc: 0.9159\n",
            "\n",
            "Epoch 00039: val_loss improved from 0.23173 to 0.22786, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 40/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1899 - acc: 0.9296 - val_loss: 0.2366 - val_acc: 0.9132\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.22786\n",
            "Epoch 41/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1869 - acc: 0.9315 - val_loss: 0.2280 - val_acc: 0.9164\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.22786\n",
            "Epoch 42/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1855 - acc: 0.9322 - val_loss: 0.2302 - val_acc: 0.9177\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.22786\n",
            "Epoch 43/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1817 - acc: 0.9332 - val_loss: 0.2310 - val_acc: 0.9173\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.22786\n",
            "Epoch 44/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1787 - acc: 0.9345 - val_loss: 0.2280 - val_acc: 0.9178\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.22786\n",
            "Epoch 45/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1773 - acc: 0.9353 - val_loss: 0.2234 - val_acc: 0.9196\n",
            "\n",
            "Epoch 00045: val_loss improved from 0.22786 to 0.22337, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 46/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1739 - acc: 0.9369 - val_loss: 0.2247 - val_acc: 0.9204\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.22337\n",
            "Epoch 47/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1735 - acc: 0.9362 - val_loss: 0.2229 - val_acc: 0.9200\n",
            "\n",
            "Epoch 00047: val_loss improved from 0.22337 to 0.22285, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 48/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1673 - acc: 0.9383 - val_loss: 0.2309 - val_acc: 0.9203\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.22285\n",
            "Epoch 49/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1651 - acc: 0.9392 - val_loss: 0.2273 - val_acc: 0.9194\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.22285\n",
            "Epoch 50/80\n",
            "48000/48000 [==============================] - 5s 114us/step - loss: 0.1615 - acc: 0.9415 - val_loss: 0.2286 - val_acc: 0.9204\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.22285\n",
            "Epoch 51/80\n",
            "48000/48000 [==============================] - 5s 115us/step - loss: 0.1594 - acc: 0.9416 - val_loss: 0.2234 - val_acc: 0.9202\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.22285\n",
            "Epoch 52/80\n",
            "48000/48000 [==============================] - 5s 115us/step - loss: 0.1575 - acc: 0.9421 - val_loss: 0.2286 - val_acc: 0.9201\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.22285\n",
            "Epoch 53/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1548 - acc: 0.9430 - val_loss: 0.2316 - val_acc: 0.9180\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.22285\n",
            "Epoch 54/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1525 - acc: 0.9442 - val_loss: 0.2246 - val_acc: 0.9223\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.22285\n",
            "Epoch 55/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1488 - acc: 0.9457 - val_loss: 0.2217 - val_acc: 0.9217\n",
            "\n",
            "Epoch 00055: val_loss improved from 0.22285 to 0.22165, saving model to fashion_mnist-6.hdf5\n",
            "Epoch 56/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1489 - acc: 0.9445 - val_loss: 0.2266 - val_acc: 0.9199\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.22165\n",
            "Epoch 57/80\n",
            "48000/48000 [==============================] - 5s 114us/step - loss: 0.1483 - acc: 0.9451 - val_loss: 0.2259 - val_acc: 0.9205\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.22165\n",
            "Epoch 58/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1430 - acc: 0.9476 - val_loss: 0.2322 - val_acc: 0.9197\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.22165\n",
            "Epoch 59/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1418 - acc: 0.9476 - val_loss: 0.2265 - val_acc: 0.9218\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.22165\n",
            "Epoch 60/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1380 - acc: 0.9487 - val_loss: 0.2250 - val_acc: 0.9215\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.22165\n",
            "Epoch 61/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1353 - acc: 0.9499 - val_loss: 0.2346 - val_acc: 0.9199\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.22165\n",
            "Epoch 62/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1362 - acc: 0.9499 - val_loss: 0.2294 - val_acc: 0.9230\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.22165\n",
            "Epoch 63/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1329 - acc: 0.9504 - val_loss: 0.2309 - val_acc: 0.9207\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.22165\n",
            "Epoch 64/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1291 - acc: 0.9521 - val_loss: 0.2302 - val_acc: 0.9221\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.22165\n",
            "Epoch 65/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1299 - acc: 0.9524 - val_loss: 0.2337 - val_acc: 0.9207\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.22165\n",
            "Epoch 66/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1248 - acc: 0.9539 - val_loss: 0.2314 - val_acc: 0.9224\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.22165\n",
            "Epoch 67/80\n",
            "48000/48000 [==============================] - 5s 114us/step - loss: 0.1259 - acc: 0.9535 - val_loss: 0.2358 - val_acc: 0.9199\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.22165\n",
            "Epoch 68/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1226 - acc: 0.9541 - val_loss: 0.2431 - val_acc: 0.9210\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.22165\n",
            "Epoch 69/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1208 - acc: 0.9550 - val_loss: 0.2400 - val_acc: 0.9221\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.22165\n",
            "Epoch 70/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1188 - acc: 0.9565 - val_loss: 0.2319 - val_acc: 0.9221\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.22165\n",
            "Epoch 71/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1180 - acc: 0.9566 - val_loss: 0.2296 - val_acc: 0.9227\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.22165\n",
            "Epoch 72/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1161 - acc: 0.9570 - val_loss: 0.2357 - val_acc: 0.9224\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.22165\n",
            "Epoch 73/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1143 - acc: 0.9582 - val_loss: 0.2364 - val_acc: 0.9232\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.22165\n",
            "Epoch 74/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1138 - acc: 0.9588 - val_loss: 0.2423 - val_acc: 0.9200\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.22165\n",
            "Epoch 75/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1105 - acc: 0.9591 - val_loss: 0.2380 - val_acc: 0.9222\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.22165\n",
            "Epoch 76/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1083 - acc: 0.9605 - val_loss: 0.2415 - val_acc: 0.9215\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.22165\n",
            "Epoch 77/80\n",
            "48000/48000 [==============================] - 5s 115us/step - loss: 0.1063 - acc: 0.9611 - val_loss: 0.2398 - val_acc: 0.9235\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.22165\n",
            "Epoch 78/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1037 - acc: 0.9623 - val_loss: 0.2375 - val_acc: 0.9241\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.22165\n",
            "Epoch 79/80\n",
            "48000/48000 [==============================] - 6s 115us/step - loss: 0.1021 - acc: 0.9622 - val_loss: 0.2320 - val_acc: 0.9244\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.22165\n",
            "Epoch 80/80\n",
            "48000/48000 [==============================] - 6s 116us/step - loss: 0.1012 - acc: 0.9626 - val_loss: 0.2456 - val_acc: 0.9225\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.22165\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w3vBjIt0nrnO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Résultats"
      ]
    },
    {
      "metadata": {
        "id": "RpZjNvOc93Vl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### On sauve l'historique\n",
        "Ca prend du temps d'entrainer le réseau de neuronnes, on enregistre tout les résultats "
      ]
    },
    {
      "metadata": {
        "id": "KbRvf_NE93Vm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "with open('fashion_mnist-history.pkl', 'wb') as f:\n",
        "    pickle.dump(histories, f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sxnOboP793Vp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Evaluation"
      ]
    },
    {
      "metadata": {
        "id": "Mmvak5fD93Vq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "histories = pickle.load(open('fashion_mnist-history.pkl', 'rb'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_wERWm3V93Vu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Résultats des entrainement pour loss et accuracy "
      ]
    },
    {
      "metadata": {
        "id": "z3z6bya793Vw",
        "colab_type": "code",
        "outputId": "c4811a69-5453-4828-b5e5-606b8aff1f7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "def get_avg(histories, his_key):\n",
        "    tmp = []\n",
        "    for history in histories:\n",
        "        tmp.append(history[his_key][np.argmin(history['val_loss'])])\n",
        "    return np.mean(tmp)\n",
        "    \n",
        "print('Training: \\t%0.8f loss / %0.8f acc' % (get_avg(histories, 'loss'),\n",
        "                                              get_avg(histories, 'acc')))\n",
        "print('Validation: \\t%0.8f loss / %0.8f acc' % (get_avg(histories, 'val_loss'),\n",
        "                                                get_avg(histories, 'val_acc')))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training: \t0.17393089 loss / 0.93653274 acc\n",
            "Validation: \t0.23962014 loss / 0.91529762 acc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "my4cbws693V1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### loss / accuarcy\n",
        "Déterminer la perte et la précision de tous les modèles."
      ]
    },
    {
      "metadata": {
        "id": "5DyV7Uxk93V3",
        "colab_type": "code",
        "outputId": "5d4084c1-76ee-442f-b920-1a1d09c98edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        }
      },
      "cell_type": "code",
      "source": [
        "test_loss = []\n",
        "test_accs = []\n",
        "\n",
        "for i in range(0,iterations):\n",
        "    if i == 0:\n",
        "      cnn_ = create_model_0()\n",
        "    if i == 1:\n",
        "      cnn_ = create_model_1()\n",
        "    if i == 2:\n",
        "      cnn_ = create_model_2()\n",
        "    if i == 3:\n",
        "      cnn_ = create_model_3()\n",
        "    if i == 4:\n",
        "      cnn_ = create_model_4()\n",
        "    if i == 5:\n",
        "      cnn_ = create_model_5()\n",
        "    if i == 6:\n",
        "      cnn_ = create_model_6()\n",
        "    \n",
        "    cnn_.load_weights(\"fashion_mnist-%i.hdf5\" % i)\n",
        "    \n",
        "    score = cnn_.evaluate(test_data_shaped, test_target_shaped, verbose=0)\n",
        "    test_loss.append(score[0])\n",
        "    test_accs.append(score[1])\n",
        "    \n",
        "    print('Running final test with model %i: %0.4f loss / %0.4f acc' % (i, score[0], score[1]))\n",
        "    \n",
        "print('\\nAverage loss / accuracy on testset: %0.4f loss / %0.5f acc' % (np.mean(test_loss),\n",
        "                                                                        np.mean(test_accs)))\n",
        "print('Standard deviation: (+-%0.4f) loss / (+-%0.4f) acc' % (np.std(test_loss), np.std(test_accs)))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running final test with model 0: 0.2433 loss / 0.9155 acc\n",
            "Running final test with model 1: 0.3329 loss / 0.8805 acc\n",
            "Running final test with model 2: 0.2467 loss / 0.9175 acc\n",
            "Running final test with model 3: 0.1956 loss / 0.9297 acc\n",
            "Running final test with model 4: 0.1923 loss / 0.9307 acc\n",
            "Running final test with model 5: 0.1792 loss / 0.9382 acc\n",
            "Running final test with model 6: 0.2128 loss / 0.9252 acc\n",
            "\n",
            "Average loss / accuracy on testset: 0.2290 loss / 0.91961 acc\n",
            "Standard deviation: (+-0.0486) loss / (+-0.0175) acc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WbOyeZau93V7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Afichage des courbes Loss et accuarcy"
      ]
    },
    {
      "metadata": {
        "id": "JZuT8GwG93V7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_acc_loss(title, histories, key_acc, key_loss):\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "    # Accuracy\n",
        "    ax1.set_title('Model accuracy (%s)' % title)\n",
        "    names = []\n",
        "    for i, model in enumerate(histories):\n",
        "        ax1.plot(model[key_acc])\n",
        "        ax1.set_xlabel('epoch')\n",
        "        names.append('Model %i' % i)\n",
        "        ax1.set_ylabel('accuracy')\n",
        "    ax1.legend(names, loc='upper left')\n",
        "    # Loss\n",
        "    ax2.set_title('Model loss (%s)' % title)\n",
        "    for model in histories:\n",
        "        ax2.plot(model[key_loss])\n",
        "        ax2.set_xlabel('epoch')\n",
        "        ax2.set_ylabel('loss')\n",
        "    ax2.legend(names, loc='upper right')\n",
        "    fig.set_size_inches(20, 5)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CtYVO-bP93V9",
        "colab_type": "code",
        "outputId": "ac9b7bca-0ce0-46be-de01-bce6d0804036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        }
      },
      "cell_type": "code",
      "source": [
        "plot_acc_loss('training', histories, 'acc', 'loss')\n",
        "plot_acc_loss('validation', histories, 'val_acc', 'val_loss')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rmqUqn8fMd6N3db3pQ76EEEIIMfMUNUovCiRoJIQQQsxaEjQSuy0Mw9b064Fj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\n45y8mdWVmZW9VFd11flJn75z6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3XFqbuanLorD3MeDQKuXOgwHOw9tO3qA2mScemFHS4X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\nIiIiIuMoNBIRERERERERkXH+PzsrT0iKHqI3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UksQNQ3Ow69RUtbh4dk8zt5XnM+okakYd9Ad5/WAHaoXEXSMKjhncGGsx8KVh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WKw18fLv21ky02VrFxfMud2wWSao+7AlKOm1jxP1y6Nio5glHgmg0Y+//rTn0jRHYqxxKgl\n7yOi9GJdTL5EiuFogmqTDq1CjlouQ6eQ0RWKTZUaZtJZEEAul5HOZukLxcnXqqYyjarMOlonIniu\ncDv7kWhiSlSrMevnfI6Pe4JkRNjgMCMIAsttBgYicc76I2xwzhRJQqk0HYEoRTo1Tq0Kh0ZJTzBG\nayDCvhE/NxbaZtwnlc1OPacr82Y/xqlUhsPvddN0bAhBgOXrihgZCNDRPE5Pu4c1m8tYuaF4mtaw\n2WmmPZBzI/3kbD+jsSS1Zh13ljrmfC8RBIE1dhPLbQYOjQXYN+Jj76CXA2MBbiqyYVMr6Q7G6ApF\nGYzEmeypQJlBQzKR5viBPhqPDpLNithdBsLBOM0nh2k+OUys1EAwneEf3hnk7/54E/m2q9uB8qp9\nSoiiOONvJ0+epKKiYobo9Nxzz/E3f/M3U7//6Z/+Kdu3b8dsNvOtb32L119/ndtuu23Ox/L7o5dv\n4LNgtxvwBeKUOPVzBjB+GnA4jJ/q+Z3jepknXD9zleb56eN6mev1Mk+4PHOVBCqJS0WnkLMqz8gx\nT5A9Ax62uiwz8jBe6nMTTGW4pciGrFrk7Mlh3nyphd331VOkU9PoC7OzIEHBR0pNRFHknVdaiEaS\nLF9bxJkTQzQdG6J2ef5lK2sSZApMzs0ERj/EVnI7etuKWbdTqMy4qr+Mu+tJov4m4sFOBLkaQZAj\nyBQIggIQSUaHAVAbyjAX7EJjKEMURTLpKFF/E77BPdhK7lr0+DPpKNl0FKXGfqlTJhbsYmL4LVKx\nMQRBgcm1DYNjPRHvSYJjB5kYeoPQ+CHMBTvR21YiCDnhRRAEllXksawij2A0SVv/BK19flr7/Zzp\n9nGm2zf1GCqlDKdFh8umxWnREktmGPZEGPZECMfO55poVHI+e2MVO1YWzntMYok0J1rHiYTjqFVy\nNCo5amXuf41q/hynY63j7Ds1jAa4e20xphIzP/ztKTqzWdYWmehqdZNOZdh9XwOKC4QhXw4GI3E0\nchl5H3uNrMkzctwTpDcUo9CgmbdE6eMU6tXIhNy+rySiKHL4/R4AThzsp35VIcqPHDNRFOkNxzk0\nPkGzP0xWBLVcxq4CK+XzlFLZJwUmTyxFsWH+5+DEpNCydhaRZTEuprOT5XHLbLk1tEwQKDfqaPaH\n8SVSaDPw/OMnUKnk3P/oGgYTSdKiSPlHnGXLrAZaJyI0+8PsLJgpxlwuDo1PTP382oCbWrMOzceM\nIFlR5Kg7iFImsHry2CyzGnhtwEOTPzSrwNToDZFOZ4l1TPAX7w9S6jJSWWKmN5nlrUEPSwyaqfyk\nVDbLMXeQ90Z8BFMZXFrVlLD5UQb7/Dz3n8cJ+GJY8nTceOdSXIUmslmRtqZRDr/XzZH3e2g5PcKW\nGyspr8l1jhQEgQfLXfzvD9rp7A1SVGDgsxX5yC/wXhmOpYgn02x1WVjnMPH+iJ/9YxO80Ds+tY0A\n5KuUVFn1VJp0pPsCPPnCEaKRJAaTmi03VlJR60AURUYGAnS3eXizY4xYKIlaLUeYdLddTa6YwOR0\nOvF4PFO/j4+P43A4pm2zb98+Nm/ePO1v0WiU0dFRiouLp/523333Tf28Y8cO2tvb5xWYrjThWIp0\nJovFcO3UrEpISEhISEhISCye7flWzk6E2T82wYGxCWrNejY4TdSY9TT7w5z2hSjRa9hRYAOXlaA/\nRl+nl1efbmLnzeX8pm+ct4a8PFp9Pqfo1OEBBnr8lFbY2HpzFZFwgu42DyMDAQpLcyUTmayITOCS\nBCeTa+uCspXkCh3OqkfxD+4hERlEzKbJZuOI6TRiNg1iBrWhFHP+TjTG8qn7CYKArfQuUnEPEe9J\nVLoCjPZ1Cx5fLNiJt/cFspkYJtdWzPm7pjmsPo4oZolOtJBO+BCzqcl/abLZFJmkn0RkAAC9bSXm\ngl0oVLmFpzl/Bwb7OoJj+wm7j+Lrf5nQ+CGcVY8iV06/kG3SqVi/1Mn6pbmuTxPhBK39fgbHI4z5\no4z7c2Hhg+7zLeAFwGHVUlVkpsCuQ6NSsPdwH7/a28ax1nG+dPtS7ObpIkQwkuTNY7kMqFhi9kWe\nSafk4Ruq2LJspvA47o/yyz0tyAWBKhGqax3kOQ3csq6EN48NEC2zUqKS09fl48A7Xey4tWZhT8pF\nEk1n8CZSVJt0M8YqCAL3ljn46dkB1uTPLAmaD6VMRoFWzXA0QTqbnber16Uw0ONjfDiEQikjHk3R\ncnqEFety601PPMmTnSOMxHKlbi6tik1OM6vyTKgv4A5zaM/nMBUb5na0ZUWRE96ciLLMNlNgWoyL\n6Yw/jADUfaR0r8KopdkfpjMQZeStHkKBnGC3/+1OMity4m7FR5xlORELzviunMAUS2c45c0Fmq+1\nm3h72MebQz7uLpuuCXQFo/gSKdbaTVM5RBa1khK9hu5ZyuREUeTdphE8TeOMJ7OolDJOtLs50e4G\nQKaS8aOzPnaU2Ciot/PBWIBgKo1KJrAj38r2fOu0cziZSHPiYB+nDg8girBifTEbd5RPibYymUDd\nygIqah0c399L0/EhXn+hGYtNy9KVBSjzdLx+bIDhXj8AbV0B/keLny3L8tnckD/ldBRFkVFflFOd\nHk53eOgYCiCKuVw5l1VLnlGNbDxMVi9HpZJjimbQBpLIMiL9MoFxrZJoJIlCIWP9tiWs2lgyNUZB\nECgqszIUTTJ4YgCZWs7SjYU4XVf/QtwVE5i2bt3KP//zP/PII4/Q3NyM0+mc4VRqamrijjvumPa3\n1tZWKioqpn4PhUJ85zvf4ac//SkqlYqjR49y6623XqlhLwjf5AtWEpgkJCQkJCQkJH6/cWhV/MWK\nchp9IY64A7QGIrQGIphVCpKZLApB4KFyV+5qtFzgtgeW8fbLLfR0eAj/rpXizfm0TEQYCMcpMWgY\nHQxw+L1u9AYVN961NFfusa6Y7jYPjccGKSy1MBiO88v2IdY5TNxe4rjwIOdhoQKVTK4ir+zeWW+b\nr4OXTKbEUfEwo22/wD+4F5XGidowfxc5UcwSGH2f4Oj7IMiRK00Ex/YTD/WQV3Y/Ss3M3Jd4qBv/\n4Buk4uOz7DGH2lCOtegWVLr8GbfJFTqsRbdgdGxkYvgdov5G/IN7sZc/NO9YLQY1m+rzof6j4xcJ\nRJKM+2NoVHLybTpUH3MIbV2Wz6/2ttHU7eWv/uMIn7mxip0rC/EG4uw90s8HjSOk0llMOiV33FBF\nKpkmkcqQSGaIpzLEExla+vz8x6stfNg4wh/cVktBXk4wSKWz/PR3zcQSGaoVchw6FTZH7raHdlVw\nttfHvlPDfPvB5QQn4rScHmH1plKM5vML2URkALW+eMrFNR8t/jAdwSh3ljrmdF2ccxgVz+L8gFyu\nzPdXVVDoMuHxhGfdZi6KDRqGoglGoslp5XcXIiuKPN09CsBtxfY5u3GJosixD/sAuP3BZex57gyn\nDg/QsLoQuVzGngEPI7EkDVYDm51myj+Sc3MhHJPlZRfKYeoNxfAn0qzOM84pWi3ExRRMpukPxyk3\naqeJLufEo6OdbuQDAcpr7IQmz420S4kA05xYWoWcSmMuHNufSM0aYn6pnPSGSGVFNjrNbHVZOOUN\ncWh8grV2I4UfOY8Oj+XK1uz+BPta2wj4olQ3uFjuypXJNfvDbHTmhEvPRIzH9rbS3etHkAncv72c\nW9aV0Nnl5XjzGJ3DAUYTacKjUV4bjaLu9mCrs7G9LI/t+dZpxyyTyXL21DDH9vcRj6aw2HTsvK1m\n6iLAx1FrFGy5qYq6VYUcP9BLY8s4R97tJDB5e4XTwM51xZzp9nGyw8Nz73Xz/HvdLC2zUmjX09Tt\nZfz/Z+89g+Q4z3vfX3dPT85hZ3PexUYARCBAAgRBEiTBJEqkaMlyoH0knePrex0++MMt3brWrbpl\nl6tu2dfXPj4OR7IlWbZEiQkSRVIESYAJOacN2Jx3cs7TfT/McoElMgjm/lVNzc5Mh/d9u3t23n8/\nz/+JVszWBaCtzoHHYWQxkmE+nGY2VKlYyNKTKIDHYsChlzApKvlsiVVdPu68p235Wr+Y8zMxvvfS\nAEa9hGddFWXb9XmN3Wo+MoFp3bp19Pb28vWvfx1BEPjud7/L888/j81m4/777wcgGAzi8az8BxMM\nBnG7L6ioNpuNbdu28bWvfQ2DwUBPT88nGr0EEE5UvmAd1o8uX1VDQ0NDQ0NDQ+PjQS+JbPA52OBz\nMJfOcSgY50Q4SUFReaTBu1yOHEDWSzz4RC8H9o5V7ngfXoB+N7tnQ3yj0c/uX5wDYMeXejCZK+vV\n1Dvw+q1MnA8xHkjwH7MhsmWFQ8EEO+o8l5i6ftxcaxKi0zvxNj9JYOTHBMd/flXT73IxTXjyeXLJ\ncSS9E2/LV5ENHqIzr5KOnGRh6F9w1e/E4l4LQDEXJja7m2xiGACLey1mVw+CqEcUZYTlhx5Jd21v\nH53ejqfpccqFKJnYOTKxQczOrhseD6fVcNWbyW67kT99ajXvnV7gJ2+c50evDvHGkRnmwxkUVcVj\nN7JzUyN3ra6hrtZ52ZTgcDzHf74+zPHzIf78+4d4aHMTj97RxM/2jDC5mGRdqwdpLEpTu4dIvshk\nKke6WKb39lrmXh3hH391jvbbaxHPhjl2YIq7l6KYEovvEJ/fi9W7HlvNTiYWkozOJhibixNN5bm9\n28/W/hpMBh2JQomfjS+SLyt0OS10Oi5vaP2+wXeVQcfP94zgtBq4b0P9ChFEL4k3NaFttBg5SJzp\ndO6GBKYz0RSnlsrUD8bS7KjzcIffeYlINjMRZXEuQUuHl/pmNz1razl1eIahMwt4O70MxNI0WIx8\no21lJFm5mCSfnsHk6Lpiv65XYDq2ZEi9/gom3FCJYupzWjgdS/PWVIh7mi4Vn8/FLlSPW7GuUY8R\ngQW1RK+3kt6VTuX52Q+OslAs4TPpL6ko1uuycj6R4Ww0dcUiBzeLqqocCMSQBIH1Xjs6UeRLTT7+\nbXiOXZNBvlHj4czROSbn45zrtCGni5w9NMv7ozw3Haehrwr8MqcjKda6bLx5bIZd745TKCroXQZ+\n4542TLNp/v2/76NUVACoE2C138ZYnYmJsRj5cI7Au3OMnI7QvaMTS3tFexgbCnLwrXHi0SyyXmLj\nXc3c91A38UR2RR/G5hJEk3ky+RK5fKnyXCgzmylwVqns0y1LVBUVbIEMI3vG8Vj13O+3ESiWmUoX\nGJiMMjAZRa8TWd/pY22Hl/42D/al/w/nzy3y5ksDFBDo2FCPyWtmOpBiYiHJdCBFcKl6PcDcYgLD\nWJgt/dXIFx3PxWiGv3/uNIqi8odf7eedbIbZTI6yoiJdJQ33o+Aj9WD6sz/7sxWvu7pWfrn/8pe/\nvGSdBx988JIIpaeffpqnn3761jfwJokmtAgmDQ0NDQ0NDY3PI7UWI1+2GHmowUcgW1guyX4xgiBw\nxz1tON1m3v71MMZIjhHgxTeGSSXybNjavOIuuCAIrN5Qz2uvD/Pj8UWyItSaK2lB56Lpj9zc+FZg\ntLXgrLuf2OxrhMZ/hr36LiSdFUm2IumsCKJELjVFeOI5ysUkJnsnnqbHEZdEIU/T4xjt7USmf0Vk\n6pdk4+fJR7wEpvYBCgZrE666B9CbP7wReiW17zHmB/+Z6PTLGK3NiLpbb8gtCAJbV9fQ2+Lmh68O\ncmo0TK3XwsObG7m923/NCnUeh5E/enI1x4aD/MfuYV7aN8F7p+eJJvPU+Sysdpk5SxRXs5P/78wU\npYs8bS2tdlIjcUYHQnh7XJzeP09Np4dEJs7Y+fOE063Mxksspt6irFzUZmB0NsGL74yxbU0taa+B\n/NICJ0LJKwpMM+kcxVSRZ14cYCFc8bs9NRbmW4/24LB8uJvu74tK06kc+K9vnbKq8vpsGFGAB+o8\nvL0Q5eXpEMdDCb7c7F/epqqqHH53AoD1W5oAWHt7A2eOzXJ8/xRGW+UYbatZmTalqirB0Z9SyM5j\nr96Gs2b78meB+QSL0wm8tVZssoRBFAle5M/1QfJlhTORFC69juar+DmpqorxbBjRK7N7Mcr8YJB7\n++rw19qX23Y2+r7AtPI4hRZTSIEMSpWJ2x9dhd6gQ2/Q0XFPC5PlHMJ8GqVPXeH51e2ysGuSKwpM\n04EUP3lzhM46B7d1eK/qF/ZBRpNZQrkiaz225aihDoeFLouJwXSWf/nFaaT5DJkGK6oAHaLM+odW\n4au2oTdIvP7LAabOLCIqbo4PRDj68ij5QhmrScbT7UHyGph6fZxsIo/FZqCr30t9s5PaRicGo4yi\nqpQUhV/vm+QX+yY4Hs8x/dwp1lXbkQWBwHwSURToW1fH+i1NmC169IYL0sjQVJTn3hpjZDZ+pS7S\n2eDky1tbWNXoJLiQZODkPNPjUZKJHIV8GRFoBqoRKAKWkoptLknRpCds0GFscnHq8AyH351Ab5B4\n6PFeGltXpiuWygrz4QwT8wmGp2McHFjkR78e4sV3x7l/Qz333FaPoqr87c9PkcoWeXrnKvpaPAyO\nF5lO54jkiytukHwcfPKlID6DRDSBSUNDQ0NDQ0Pjc41BEq8ZTdG9pga708iu3UPMuI0MmAW62l2s\nv7PpkmVrOzyEIz4KItxb7WK1187fnpnkeDjxmRCYAGy+TRQy82SipwmNPbPiM1EyoZSXfiPX3ndZ\nbyiLqxeDpZ7w5Itk44Nk46DTu3DW3Y/JseqKUSKRfJH/cW6anfUeNviur+y2bPTiqN5GfH4P0bnd\neBofu4keXx8um4E/+epqArEsPqfpusrMX8y6Th/dTS52vTvO7iPTGGSJP/xyH3ufP4tOFjkrlimp\nKtuqXTRZjVhkCWOvyPeeP8PITJy5aJ7ZksKxZ04ubbHioyUKCrWOLF1tbbTVuWirs2OQJfYen+XN\nY7P8+lDF08pVY8XeZOOMkOTxctUlKVyqqnJuOETkXARVUemvtZMqljk7HuH/+tdDfPuxHnqab97H\nx2OQMUki0zdg9H0ilCCUK7LRZ2dbjZv1XgevzoQ4GkrwTwPT3O5z8GC9h+B0nMXZSvSSr7pynVls\nBrpW13D67AIL4SReo7zCzwggHTlBITsPQGLhbXR6F1bPGsLBFL/4yUmKhTJWu4HbNjfiNcosZAuU\nVfWS6ClVVdk3FSZfVNha7bq6efeJOdo0lLoAACAASURBVBYGw3Q0OzjfYuGsQyKw6wwNFiP9G+qp\naXcznsjSYDHi0F9IactmCvz6+TPorRLpKhOLKEQHAwgClLxGWMxRnk5yfP8k67c0L69nk3U02UxM\nJrNEMnlcpgsVFI8OBfjeSwPki2V2Az6nkR3rG9i6uhL5di0OLFbMvTcvGXSHFlMcfm+C6bEQiSoT\nwUiOMipMJxHmUzhrHehVkSZZrKSktrk4H04RPxcGwGTU8eidTdTV29kVjmGYS5NPF1h/ZxPr7mi8\nxOReFAT0ksRjd7WyrtvPP71wmtlwhr0LCRoQ6Gv3sP2+dhyulRXWJhYSPPfWGGfHK8b/t3V46Wl2\nY9RLmAy6pYeE1SjjcRiXx6uqxk5VzYXoNFVVKeTLFPIlCvkSkVCaqdEIU2NhBk7OM3ByHkEAVQWb\nw8jDX+1fToO9GJ0k0lBlpaHKyl1ranlyexu7j0yz9/gsz701xq/2T+KyGViMZHhocyN3r60DoNFi\n4ngoicKlhdY+ajSB6Sa44MGkpchpaGhoaGhoaHyRqWty8Y0nVvM/D40R9Rg4bZPRTSyyo86z7GtS\nKCv8x9gCBbMO61QKv2iiqsFLg8XI+XiGRKGEXf/R/ixXFJXTR2YwWfR09l5nmMgHEAQBT9OXMDtW\nUSrEKBdTlEspysUUSimNpHfgqn8Qo/VSge19dHoHVe2/Qyp0BKvVAIZeBPHqfT8aSpAplXlvMXbd\nAhOA3X8nmeg50uHjWFx9KwzMbxZVVZlO5/CbDCuEGEEQ8Ltuvhy4yaDj6/d1cPfailm8WRSIhTN4\ner2ciKaoNenYUWNDp7twg/u/PtbDX//0BJF8EcEmU11M0FkzR5XXTVvHvViKB8hFjmKvMuCsu5BJ\n8tiWFu7b2MBfvj5AYCxGdD5FdD6FaJD454UCD99WT2utHVEQyBfKfP/VAYLnwuhkkQe6/QTPBjCg\n4qq2cTKQ4q9/eoJH7mzi8a0XxldVVULxHFOLSWZDaZKZIulckUyuRDpbJJUrUS4rtNba6Wp04UVk\nKle4xND5cpQUhTfmIugEYbkcvUWWeLLFzzqvnRcnAhwMxgnkCngOVzy91m9p4tRoiFcOTFHlMtFV\n5yARt6AAW/3OFcKPUsoRm3sDQZTxtXyN4MSzRKZ/iaJaePW5EMVCmZ41tQyfXeCd186TXO2h7DMS\nSufxW42E4zlOj4Y4dT7E+bkE6XwJ0SAhW22otZf3O4uE0rz3xigGo44nH+lhulTkxyPzRNb5EA8u\nEvjlAPkmK0q7A2s0z+hgAK/fitVuZPeucyQTeTasbuSlUIqfHD5HOlFJqzJaZAxNNhzFSiRXXbOL\n6rrKNZTLFvGmy0wA33vuFJ5QDk+VlRlF4dhsHL1O5H95YjXnxkLsO1NJBX3hnTHuWl3L5l4/mVyJ\ncCJHKJ4jHM8RTuTIFUrIssRMLo9Zr+OFoQTZRJ5AIkcCUADm0gg6EWetlVxZQcqUGJqKMTQVWzEm\nsk5kVZ2DgE+PTQD9fIo3YkmosdCMxCPf3IjTfe1rrs5r4bv/5XZ2vTvOywcmGVVVRkdC7F1I0OS3\n0Vxto9Zr4dTLA+w7VREVe5pdPLGtjdbaK6c0Xg1BEDAYdRiMlXPZU2Wlo8ePoqgE5hNLYlMEi1XP\n3Q+twnydUYBOq4GntrfzyOZm9p6YZffhaebDGTas8vHk3W3Ly2302elxWa55LX0UCKqqfvyy1kfM\nR13i+XtLJ9/f/tFW7B8yJPTTzBelXPYXpZ/wxemr1s/PH1+Uvn5R+gm3pq8+32cj6uOLxEd5/n7a\nr49yWWEgmuLNhSgL2QKSABt9DrZVu9g1GWAonqHPbibxixEcDiNf//btHAzG+cVkkJ31nkqVuiVu\ndV+LhfKyMbkgwFd+Zx3+m5w43Uqup5+qqvLXpyeJ5CspSH/U20iN+fqzCPKZORaHvo9O76S6+w8Q\nxQ9nZvzaxBh7g2XWmiLs9JXQGb3IRh86vfOKhto3czxPHZ7h3TdGKO5oYFFV+JK0lwZDlqq2byAb\nvSuWPRZK8Oz4Iq3qJDukQ9T3/6/o9A6UcoH5wX+iXIhTveqb6M0XKh2+NhNi73yULVUO2pB548Qs\nx84HUUuVqaHLZmD9Kh9nxyPMhzPIdj33bKwj9uYkZqsei9VAYD6J3mNisFginMjTXu+gp9XD0HiE\nqUDqitXzJFHAYpJRVZVk5kJqmWiQ6G5ycWdnFetW+TDIl684uH8xxi+ngmzxO3mk8VKfopKi8pPR\neQZiaRwjcXp0eqI2PXtPzK1YTpAEDE4DO5q93HtHE7lCmUgix8zkEQLBGbJiKwZzNRvbwJJ+llJJ\n4t39a+ha080jT65mciLMyUPTvBuMEfSbEU+FSOfLpEsXchJ1AkgeI/lwDlToa3XzW/d3rhAjS6Uy\nz//wGOFgmp1P9NLSWenTwUCcXZMBXLKO9eESB5QCKaee6n0LyNlyZSwlgXJZxVFvZ0aEwSWRZkt/\nNZIo8PapeVDBZdHjSBdpthvZtK2F0cEg02MRCrLA/NYabKkS9cNxTkQzRAE90IGATZIQJYGiqrKo\nKMyXFa6UDChQEYUKF/X/YjxWA+u7q1jd7mF3PMHi0jX9v/U04NLpmFpMMrmQZC6cprnazu3dVZiN\nMv9wepLZbJ6afYssbqrCJEv87+takW7Cu25yIcmJkRCTC0kmF5NEk/kVn7fW2nlyWyvdHyIi7+Ok\nWCozMhOno8F5zZTc9/mof39pEUw3QSSeQxIFrOZb77avoaGhoaGhoaHx2UOSRPq8dno8Nk5Fkrw+\nG+FAIM7BQBwV6HSY+Vp7LXu7EwyfWWRqLMKaJie/mgpxNJTkdqeVM0fnGDqzQFW1nZpGB83tHqz2\nD+cdlE7meeW50wQXUnj9VkKLKfa8PMhTv7cBSffJmotfDzPpPJF8EbsskShWyp5fS2CaSec4GIhz\nf50Hu7kWW9VmkoH9xOf34qq7/5Lly8U0CAKSbmU0RCZdQJYlZL1EPjPH3rFzvJWtRGcNZC1snH0R\nSViaTAsSstGHo3rbDZuKX47J0TA5j4GQqtAozFErBigXyiwO/yve1q+tiBJb67HxzvQ4Y6UmDk2l\n8HYaseqXKgc2PEpg9MeEp35J9apvIQgSgWyBdxaiOPQ6dtR7MUgiXU0u/uH0JGMzcdqLEmdGw7x+\nZAaA9lUekrUmIqcDSKLAg1/pw1Nl4a1Xhxk+s0iXRSbR5OL0ZJSRmTgC4Heb6W910+i3Ue+z4rTq\nsZpkzEYdBllCEITlsu2DUzEOnQ8yPB3j7HCIs8MhTLt13NHrZ/vaOuqrrEspRyWi8SyvT4fQAfbJ\nJHvOhLHZjXT2+bE7K/5GOlHgK01VjIRGCXoMvDkcJz5aoN5n4ZuP9FAsK/zi+DQDoxFy4RwvhWd4\n6ejMRaOvo+KeowBz7D0B1bbN3NU6xOaNA7Su3waAySzjbnOTn48R2l+JfBEBjyRS5zDSWm1nuFpP\nRFBxHQsxG81yZizC//m9Qzy8uZGHNzehlyUO7B0jHEzTc1vtsrgEsKnKQaxQZM9UmL2ZPKFAGtOi\nhKnBiVIoU8oVyaYKRMsKh2cqwpKv2orSZOGRTW2kS2UGrGAP5JkcixFFZS6R5dxL5zAg4HUY6Orw\noeh0zEslMjqBKNBcZeXBVX5S0QypeJ5CoSIUeoFuVWU+VyKcL0KhjF6tiFHvP4QyzG6pQVWhfzpL\ndb0Dt99GdZUV/0XRRpakiX8enKHeYliuKLeq0cWqxku9oG6rcjA7FYRt9SjlEhv8zpsSlwCaqm00\nVV8QR+LpAlOLFTPt7jYvzV7zJ1J57WaRddKnTgzTBKabIJLIYbfobzi/WkNDQ0NDQ0ND4/ONKAis\n9djpd9k4EkqwZy6CxyjzjbYaJLFi9j18ZpHTR2Z4tM3DKpuJc4kM3//xUcRwHp1OZGQwwMhggHde\nO4/Xb6Wp3UNzuwdfte2GJj+hxRQvP3uadDJP1+pqtj3YyXuvj3D2+BxH901y+7YPnzL2UXMiXLnT\n/mijj+cnApwKJ3mw3nPF3+GqqrJrIsBsJs9kKsu3VtXjqNlONjZIMnAAs7MHUWcin5oin54mn5qi\nlK/4vMhGHwZrM0ZbM7G4k189ex6nI8v69QuMKCJvKZsxCwWarEYGkgYS/q/SLIUo5kKU8iEK2UVC\n4z/D4l6Lq/5BRMlAqVRmejxCMpVbMl6W0Bt0SFeJNijkS8xOR8lvdSGgcIc8iL/j9ylmA0SmXiIw\n8mM8TV/G4uoFIJ8cY7O6j13s4JytnZoDU9x1fwcARnsrFvda0pETJBb3Yfdv5cXJAGUVvtToW5Hm\nt97vYDZXoL/By7cf6WZwMorJoGN3PE4qlUMN5bjrgY7l6Ld7H+nC67ey/81RTNNxfnNzE+s2NmKR\nBYzXkfIpCAI1Hgs1Hgub+qv5v4+NUouO+hy8c2qON49VvKJ8Fj3esoouVyLmN5HwGNHNp3kpmidP\nRdQxvTtGjcfC6u4q1t9WR2ghSe7gIpFcRRy5f2MDX727DVknUlJUCkEztdUGVo1mGBiPYK21UeW1\noC8OYxYXaGjaTE1NG7FUgRfeHGEsmOLnJ7txGHPcGXqVVb338MLeMSYXK+enzibT11PFtza1YF6q\nDnYkGOfARIB+t5Udj1fz0s9OMpcpsiAJ/OK9CfadWaCv3snC2UXq3WbuvLdtxfjMhtIsngkRPj1P\nuVyJLMsB+0ldMpZNfhtP3dNG1iLx3ESAsWSWdLGMZNTx5H0N+B+QefXgJG8emyWgqIDKdDzH8SPT\nSwcDUGFTfzXf3Nm1HA1ztWiXclkhGc8RDWeIRTLEwhnm9QIzRpHNXjtf2nblVNwmm4lvrapbTiO+\nGr0uK7+aCjJfrhzLtd5bF73ssOjpb/XQ3+r51EfJflbQBKYbRFVVIok8DVXWay+soaGhoaGhoaHx\nhUQSBTZVObjdV5mMvy8M+apt1NQ7mB6P8vZrw0RmY9DnIlVt5oH+OvrW1WI2GTh2aJLJkTCzkzFC\niymOvjeJ0SxT3+yiodlFfYsbq+3KkTyTo2F27zpHsVBm8/ZW1m5qQBAENm9vZXI0zLH9k7R0XjA+\nvl7KZYWZiSjpZJ5ctkg2UySXKZLNFCiVFCw2Aza7AavdiNVWeXa4TMj6y6c7XXVfqsrpSBKzTqTb\naaXPleFIKMFEMkur/fLeKxOpHLOZPBadRChX5PtDM3xzVT3uxkcJjPw7i8P/ChcZ3wqiHqOtDVDJ\np6cphg6TCh0GYMsmExZzljGlgbfKd2AQVb7V005RURkYmGYw52RN24VopWI2SGjyBdKRE+RSk2TV\nbex/O0MqsTINx2LJ4GpMUXKa2dxci7+ue7naHsD0WAjHbQmmdHX0SNP0dD2FbHBjMNciyTZC4z+v\nVOsrxLH6NhKdfpkaIUa3TWIAA4fPRVmXrFTXAnDV3U82MUJ84W2GlFYmkll6nBa6P1Dqvt9l46Wp\nICfCSbZWu+hr9VAqK8zMLiKnSvT0+elec6HKnyAIrNnYgNtrYfeuc4wcmMZn1LNmU/0NH2uzTsJr\n1BMplnjAZcFU5+TkaJiFUplgukDw/QUXM5XHB4gB8+E0x94d5wfvjqMDSlSijExdTkxtduSliL2T\nkSSJYpmtficb66uJj0epESUe2CISGjuJ0daKr201giAg5uP4wllcehlju5tDQwu8csbIK2dOIgiw\nfpWP+9bX86OFIAabaVlcypbKvDoTRi8KPNzgxaGXefJ31/PSz07hiGTIeiwMRTPsPbsAwGg8y9xz\np1jV6MLrMPLuqXkGJqMAuO0GrPU2ci6ZpzvqMAGZXKniZ5UrYrfoWdPuRRQEoktpZ2OJDOlSGQFo\ntpow6iR+c0cnX76rlUA0SzCWJRjPEorlmI9mGI+kMdRYmKyS+f7wLBt9DvpcV5/vSpKI021e4YP0\nLwPTkMpxZ82lkUgf5ErX8Adx6HU0WY1MpHLUmg34TVqhrU8zmsB0g6RzJUplRTP41tDQ0NDQ0NDQ\nuCaXizjq31DP/Eycs8fmsFlkYioU6qz0r61HJ1Ymbf3r6+lfX08hX2J6PMrkaJiZiQgj5wKMnKsY\nF7u85uUS5qqioigqiqpSLipMjIQQJZEHvtxDW1fV8r71Bh3bH1rFS8+cYu/LQzzx9LqrRtNcTC5b\n5NcvnGXuA2a810LWS6y7o5HVG+vR6a5faBpLZEiVymzyOZBEgTWeSlTYiXDyipPT9xYqk/Lfaq9h\nIJbinYUY3x+a4VtdDdiq7iQTPYPBUo/B0oDB2ohs8i97J6lKmfDCGKcPHsRhi+DxpJiVung9txbK\nKo7DIU5MFth8Twtug8xALEWhrKBfGj/Z5KO685vMDv+aUuYIMruo8zdi6dmKqsQxSJNYjFPkZYUX\nyveTw8hUIMTd4X+hxmrHaG/HaGslHXuVUccGdJR5ZNUGZMMFvyyTvQ1/x+8RHPsJsbnXSUdOUipE\nsVVt5hFPA0OnJ4i22jhyYIq7l6KYMhmBgriFbPEdXpmJIYsyD9eaUdWVhtMWWaLTYWEwlmYqPIGb\nKKcn0yiCH1dJ5a4HOi57Pje0uHnid9ey56XDHHz7PKFAknse6brqeaWqKpFgmuBiivBiilAgRcEh\nkq8y8es9o8iZEnUOI1s6PDiqbZwPpzmzmCColOmvdnBnoxuXTY9NTlLCxGJMZWw6zuBoiNlgmkxZ\nocVu5E9+fwM/GJ3nUDBBp8NCl9PCO/NRRAHu9DtxGmSa2txMj4eYOvs6RqPAmYFW9BMjWG0GThyc\nRlVVHn+ij/pmF1/LtPLqnl+RSqfYvqGH5tZ+ADyxOMHcBXei3bNhMqUyO+s9yxXf7E4TX/nt23j5\n2dME5pKslyUiShlbvYNgvsi5iSjnJqLL2+hqdHLf+gbWdnhQEYgViniNV5+DugwyLoOOsWSWoqJQ\nazZgvOiaMxl0l6SJQcW3aiCW4nAwwUgiw2Qqx0tTQe4Ie+g0GWiwGq+ZvbOQyTORytFhN1+znTfK\nao+NiVSOdd7r845LR06Tz8xi921GZ3De0rZoXB1NYLpBYktGYE6rppxqaGhoaGhoaGjcOC2dHm7b\n3IjZqqdnTQ2vzUd4dzHGYCxNn3vlxE9v0NHW5aOty4eqqkTDGabHI8yMR5mbihENXRrNAWC26tn5\nRN9lzbwbWtx0ra5m8NQCJw5MrShdfiWioTQvP3uaRCxHc7uH1i5fJTrErMdokjGaZSRJIJ0skErm\nSSVypBJ5kokcowNBDr41zrkT82ze3kpbl++6Uv1ORirpKms8lTFpsZmwyzrORFM81uRD/oAPSyhX\nYCCWpt5ioMlqpMla8XZ5ZyHG9wZn+FbXdurqdlxxf+lUkZdfDJFMNbDpvu3kWpz8enQBSYSvVLmY\ncGQYPx9icjSMod9DxGvglSOTrHbZcHpMgMCht8cZGzLjdq1mw7oR2lunkOQXKRcrxymHiVeVneQw\n4hcVFhUvz5YeZF3iHLel9iIJexjV95JRzNztt+OyXHr89OZq/J3/heDoTyjmAkh6B47q7YiSzJ1+\nJ+8uxjg4nyT7/BlmwmnCFolslZWC43EQ4A7hGKnhIVKCDtngQqd3IUh6SvkIjRkjg2zi4PhJNkkn\niattgJ8+7xkiE8fRW+oxmOsAlUI2QDEXoJgNUMyHWN+vUOrWMzldxesvxrjn0dvRX6akfWgxxbu7\nzzM/E1/xvrXTSRJo2FTHtlYfbq9l+TxpKZY4dWqCahGerI2hpI+Tm58gVs4hiHoa6h6ga+ttPHJX\nC6qqElxI4nSb0Rt0fK2tmn84O83zEwEerPcQyBW4zWPDuZSetWVHB0NHhzGbskxM1TM0UABml9u1\n9f526psrETlWs4kv3/8Ai8P/gpJ4nUK2Gb2pCq9RZiieIVMqEyuUOBiI4zXK3OlfGcljMuv50m+u\nZfeL55gcDbO21cPDT/UjCAKJTIHhqRjzkQy3tXup/0DGzPWKNq02M0dDicrfFwmx5VKG+PxeDJYG\nLO7+FevoRIF+t41+t41IvsiRYJyjoQR7JoPsAWyyRK/LSq/LSrPNxPtXXrxQYjqdYzqVYyieBire\nUbea230OXHqZDsfVo55UVSE2u5tk8CBApVqldwMO/1YkWctA+jjQBKYbJJZ6X2DSIpg0NDQ0NDQ0\nNDRuHFEU2by9dfn1Oq+ddxdjHAsnLxGYLkYQBNxeC26vhTUbGyiXFBKxLIIoIIpCJaVHFBDESons\nq0WQ3HlvG9NjEY68N0lzpxeP78qTr6mxSrpdIV9m/Z1NbLyr+YoCkc1hxOYwAhcmmZu2tXB03xSn\nj8ywe9c5Th+1s+W+9qtWIioqCmcjaZx6HY1LQpEoVKKY3lmIMhTL0Ode2eZ9izFUYKvftdy+nfVe\nVBXeXYzxvcFZvrmqjpKispjLE8gWWMwUWMwVSBZKZPIllA0eEASeV7JwPosowO+017LKaWFtk5ux\noSBH3pskMBIDr5+T4SRzeyZXtMNfa+fOe2+jqmYn0Zlfk00MYbR3YHD08krAQTRdYFu1i50NXg6M\nB3l5LswRfT/D+U62WmY4VmhGRuXuupXV4i5Gp3fg7/g9EoH3Kr5SUmVucm+dmyOLMeINFvblypQa\nlgyAVaiVdbRLERyzReZVLxZzDqstRjG3lIAmSDRJbuRSiYFSG8aBDGM1TeCAGn2WXHKKXHLskrYI\noozeVINO7ySfnqCtZQZVnWFg/0maurfjqFoFqko6PsPI6dNkkzN0NidZ25NHEfwYrG24/N2ERSf/\nODCD4Lfg8VWMvfO5MGPhWfaFShQUGxs5SnpuGABJ78Bg7yCbGCYy/RKZ+CDuhkfR6e1U1VwQ5vwm\nAzsbvLw0FeSFiUr0313VFeFHVcoYdPPU+M4jiGa2PPybbLxfIhmvCKR6g466ppURMDq9nebepxg9\n8UPCE8/hX/UtfEY9Q/EMwWyBV2dCqMBjjT504qXXiSxL7Hyyl6nRCHVNzuVz1W7Ws+GiaMObpdVm\nuiAw2Sqpl/nUFKGJ5ykXE6RCRygVYtj9Wy97HbsNMg/Ue7mvzkMQlX0TQc7FUhwIxDkQiGMSingJ\nExWrSJUvfMcIQI+zEiV2qxEFgVXX2G65lCU88Sy55Diy0YfVu4FEYD+p4CHS4ePYfJux++9AlD5c\n4QSNq6MJTDdILFUAtAgmDQ0NDQ0NDQ2NW0O12UCt2cBwLE2yWOLSwuuXR9KJuLw3N5kzGGW27ezk\n5WfP8Ox7Yzx2fwe1FtOKZVRV5dSRGfa/OYooCtz3WDedvVc27r3avu68t43e22rYv2eM8eEQz/3w\nGOdWz9PeU7Vikv0+g7E0eUVhvd3KyYPTjA4GsNgMmBorwsHJcGKFwJQplTkaSuDU6+i96H1BEHio\nwYuiqOwLxvmrk+OXtE8nCEi5ElK+jNOsx+M2o5dEDEtpeR0Oy/K22rqqaOuqolxS+Luzk4R9JtZt\nbyETyZJJF1jVV70iQsvT9Dg+n43FQIJnxhaYTKfod1t5oN4DwOYWH50eGz88OEbQaeDlfBuIsMlk\nwihdPZ1Q1Blx1t634j2jJLGzyceLk0GwSXQ7zPS4rHQ5LFhkCWhB7VvH0OkFDr49TiaVx+ESaGl3\nMj6SIx7Noe/Kk66zkHKupug2IasKvb1PQzlLIT1LPjOLIIjIRj96UxWS/sLx87iNTAwfYH70PVyO\nIIm5n5NcMKMoOQQU3DbABqBHNnoo5uYpp+YJpd4F2YnEg4zHwhwZOs5gWmS87CdL5bx0CQnWuXRY\n7Q9jtLVWIq8EgVIhQWTqF+QSI8wP/hPu+ocwu/pWnFN3VDkYjqcZjmfosMpY0icJzo+RS02gKpX5\nnat+J5JswiRXIo2qargizqo+rN6NpEKHic3uxmfaAlRS4yZTOXpdluXz5rLHThRp7riygPhheD9q\nSQSarEbiC+8Sn98DgL3qTtKxs8Tn91AupXHVPXhFsVgSBPp9dnzlHPcYzzMUGGek6GFcrWeaaszl\nLO36Aq3eRhptVuosxhWm8VdDVUpkEyPo9A705qsM9HVSzAYJjv2UUiGKyd6Jp/kriJIBq2cdqfAx\n4gtvk1h8h1ToCI6au7F6N37i1eJUpYSKiih+virTawLTDfJ+BJNDE5g0NDQ0NDQ0NDRuEeu8dl6a\nCnIynKS19toGubeC5nYvtg3VDDgk/unkFOvnC1j00lK1Mx3JWJaRgSBmi56dT14+3a6kqJyLpTgc\njBPOFdlc5eAOv/OS9DUAh8vMzif6mJ2Msn/PKAOn5hk4NY/DZaJnbQ2r+qsxmfWoqsqh2YoXzfgr\nI0wniggCBBdSqOfD6DZVcVZJ8cvnTtPY4KSh1c2pQo6ionKn34l00cSxVCxz7uQ84QNT2L0Gsj4j\ncqaEnCoip0vI6SJStmKG3LW6mu33tl/XxFPSiayvcvDqTBixzck9m5uuuvzumTCnIymarEa+2uJf\n4Wfjthv543u72LXnPCdkBbGssGP1zVf42+hzUG814TXIy/5QFyMIAl2ra2jr8nH8wDQnDk1z4nAM\nnSzS1uWjr83FK7k0dLuJRittlgQBdGZMjg5Mjo4r7luUZGzetVg9azix7yjpyDGqq0Jks2biSTtu\nfyutvX0YzBURrlxMkU2MkkuMkE2O4iXMYtHH88VKhJ9JKLHGkqfX7aTT24JeWn/JPnV6O7623yId\nPkZ09jXCky+QiQ1gsrdTLqUoF9OUSym2KXkkqYb+7GmiM5UIH53Bg9Hehsnegcnedsm2r4azbgf5\n1CSp0BFs1a2AxFgyiywKPNJwvTLxrceh19HpMGMQVBITPyWXHEWSbXian8BobcLqu53g6H+SCh5C\nKabxNH0ZQVwpZqqqSiEzw0TgLJG5Y6hqiTpRT6ffi8VbQ1aRyM3sopCZRoo58DqewCA1XLNtSrlA\nKnyMZGA/5WIlBdbiXouz9t6bTmHLxocJTTyPqhSw+7fiqLln+RoWRAmbbyMW9xqSwUMkAu8RnXmV\nbGIET9OXkXTXZzR+PZTyUQq5xmZOjwAAHU1JREFUAAZzPZJ8ZXGxkA2QCh0lHTkFahmTswurZy0G\na8stEb2UcoFM9AyF7CLO2nsRpY9Xt9AEphtES5HT0NDQ0NDQ0NC41axx23hlOsjxUIKvfEz7jOWL\njLp0UFYoySKnDAqeM2EunuJ4/VYeerIPq31lWkkoV+BwMM6xUJJ0qQyALAq8OhNmfyDOjjoPt3ls\nlzUGrmty8eTT68mlS+zbM8LoYID9e8Y4+PY4ze1eIvEso9125EwJr05H744mVvX5KeTLzE3FeC8U\nZ0QUGExnmXkzjLpnlIWt1Ug6EW+sSMFVQhAFzh2f48TBaTLpAjpZ5G6/n46eKjLpIqlEjmQ8RzKR\nIxnP46+xsfmethua4K1223h1JsyJcJKNviv7zrw9FeKthSgeg8zvdNReVnyTJJEndqxi9UAAURIw\nXsa76HoRBIFa87UnlbJex+3bWuhZW0MskqW6zo5OllBUlX2nJjgdTQHQYL3xlCJBELhtywbOHq9l\nzxsjNHd6ueOetksqH0qyFatnDVbPGlRVYdPsJPtDBdocZvo8HppsphWC4dX2Z/Wux2hrJTy1i2x8\nkGx8cMUyOgTukQMYLPUY7XdhsrV9KANoUZTxNj/JwtD/RAq8CjwCwPYa97K/0+VQygXyqQny6WlE\nyYTO6EE2eCoRWeL1m+CrSolyMYVSzqKqpUpEjFJCVUt81Z0lPr+XXCmF0d6+QkzR6e34O54mOPZT\nMrGzKOUs3pbfQJT0FPMRMpHTpKOnKeUjS8u7sPo2YvWsXU4vMwBq59OVyKCFd1g8/wMcNdux+7cs\nm+av6HMpRzJ0iGTgIEo5iyDKWL0byaemSEdOkIkN4Ki5G5tvI4JwqdhVyocpZOYpF5OUi6ll4VAp\npSjmggiCDk/zE1hcfZc/VpIeR/VWrJ61hCdfJJcYYWHwn/E0fQWjrfm6x/ySduVCZOIDZGKDFLML\ny5/JphpM9laMtjYMlgZAJRMbJBU6Qj49BYAk2xBEmUz0DJnoGSS9A4t7DVb32ps6LwuZBVLhY6Qj\np1CVAoKgw161WROYPu3E30+Ru0pZWA0NDQ0NDQ0NDY0bwSJLrHJYOBdLMxBK4OWjTd9QVZXnJxYp\nKCpfafFzNJhgyg/r1zTQbtBTyJcol1Wq6+0rKr8FsgV2TQYYT2YBMOtEtvqdbPA5sMkSb81H2bcY\n47nxRd5biPJgvZdOh/kS4UYQBBpb3Jis3WzZ0c7wmUXOnZhjbChIutYMosA6v4Mvbe9eXtdglFnV\nX01V3sP/c2oC27pqtq+VObwQY0YvYZ1KsWfPHG+JAjpZopAvIeslbrujkTUb6zGZb+0NYqdBrpRP\nT2aJF0o49JdOrQZjaf5jZA6zTuL3OmsxX6OKX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            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8LMSqcD0pZDC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Grace à ces courbes, on remarque que dans le modèle 1 et le modèle 2 apparait du sur apprentissage très rapidement (à partir de la 10 eme epoch). On va donc s'intéressé au modèles 0,3,4.\n",
        "Parmi ces 3 modèle, le model 0 présente la moins bonne accuracy, et le plus de pertes. Enfin on trouver des résultats très similaires entre le modèle 3 et 4. Le modèle 3 comporte moins de neurones que le modèle 4 donc il est plus rapide a entrainé comme le montre les temps d'exécution entre chaque epoches.\n",
        "\n",
        "Dans la suite de code on va donc faire des mesures approfondi pour départager le model 3 et 4\n"
      ]
    },
    {
      "metadata": {
        "id": "SxHipnKIWmr1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "Evaluation du model 3"
      ]
    },
    {
      "metadata": {
        "id": "fqMTNbt5Wb8Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "RUN = 5 # you can choose one of the different models trained above\n",
        "model = create_model_5()\n",
        "model.load_weights(\"fashion_mnist-%i.hdf5\" % RUN)\n",
        "predictions = model.predict_classes(test_data_shaped, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bfUG9hs2WkH0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "Plotting accuracy and loss for one model"
      ]
    },
    {
      "metadata": {
        "id": "af_iPJfaWbeg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_train_val(title, history):\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "\n",
        "    # Accuracy\n",
        "    ax1.set_title('Model accuracy - %s' % title)\n",
        "    ax1.plot(history['acc'])\n",
        "    ax1.plot(history['val_acc'])\n",
        "    ax1.set_xlabel('epoch')\n",
        "    ax1.set_ylabel('accuracy')\n",
        "    ax1.legend(['train', 'validation'], loc='upper left')\n",
        "\n",
        "    # Loss\n",
        "    ax2.set_title('Model loss - %s' % title)\n",
        "    ax2.plot(history['loss'])\n",
        "    ax2.plot(history['val_loss'])\n",
        "    ax2.set_xlabel('epoch')\n",
        "    ax2.set_ylabel('loss')\n",
        "    ax2.legend(['train', 'validation'], loc='upper left')\n",
        "\n",
        "    fig.set_size_inches(20, 5)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x0Xo1yCPWbO7",
        "colab_type": "code",
        "outputId": "ed54f1b6-90ee-4662-ad5a-e8bbb783c650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "cell_type": "code",
      "source": [
        "plot_train_val('Model %i' % RUN, histories[RUN])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k5R2iurqqye13797JgAGDADCbzXTq1IUjR44A0L//QAASEhKorq4+k9SFaDGq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\nDpgkSZIkSZIUl/8ApzcM49twvqQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wBei3fxUq-CA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### affichage des prédiction"
      ]
    },
    {
      "metadata": {
        "id": "CtLlO3zCaWkM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_sample_predictions(classes, model, X_test, y_test):\n",
        "    \"\"\"\n",
        "    This function displays images randomly and tries to predict the correct class\n",
        "    \"\"\"\n",
        "\n",
        "    class_ = 0\n",
        "    images_per_row = 5\n",
        "    rows = len(classes) / images_per_row\n",
        "\n",
        "    for i in range(rows):\n",
        "        fig, axis = plt.subplots(1, images_per_row)\n",
        "\n",
        "        for i, axis in enumerate(axis):\n",
        "            start = 0\n",
        "            end   = len(y_test)\n",
        "            random = randint(start, end)\n",
        "            X = X_test[random]\n",
        "            y = y_test[random]\n",
        "            X_shaped, _ = shaping([X_test[random]], [y_test[random]])\n",
        "\n",
        "            fig.set_size_inches(10, 20)\n",
        "            axis.text(0,32,'Predicted: {}'.format(classes[np.argmax(model.predict(X_shaped))]))\n",
        "            axis.text(0,36,'Correct: {}'.format((classes)[y]))\n",
        "            X=X.reshape(28,28)\n",
        "            axis.imshow(X, cmap='gray')\n",
        "            axis.axis('off')\n",
        "            class_ +=1\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hm35FJL9aY9Q",
        "colab_type": "code",
        "outputId": "5cb421f8-94e5-445c-c7f7-bc5b8641712c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "plot_sample_predictions(classes_fashion, model, test_data, test_target)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sWVqmq84A0/WSkpJi6I4ePaplunKVtz/qauHtiGdpp9BC3ryQ7ZkzZ7TM7VylShUt//Of\n/zR0zz77rJb/+OMPQ+fr6+v0XvID7nrhbhUK7ce4S5q+D24z6kbl7k/qpqXfGW8r1IbcrU3bMV+1\nx4sKU+gz8GzktP3xfr8kQ78X7kpztiKO9/+0f+HnoL8rbAWwc6Lw36EgCIIgCEIhQQZOgiAIgiAI\nNpGBkyAIgiAIgk2KfYyTFT179jS2R44cqWWa2Zdnl6U+WBpTAZg+f+7HtaoATc/Jfbw0AzmPiXn+\n+eednrMgsJsKwup3HKs4tSFDhmj55ZdfNnQ0JoLGopw8edI4jtqlbdu2ho7Gxezfv9/Q0fQEPPaM\nZivm8SN0STdfpk1jLHgMUF7ExdD3fr8xTZzw8HAt87gzms2bPisA/Pjjj1rmMUE0hQRNVRAXF2cc\nR981f38Unu7gscce0zJfhk5jrzg0jql+/fqGjsb8bN261dB17drV6TnzA5qtGzD7HB6jRp+fv29n\nS9EBs6+icWkAsHnzZi23atVKy7wfoDFs3C70++H9sFU7ovdM4xT5NXhWeuHe0HfL01pYxTjR30nm\ncEEQBEEQhGKEDJwEQRAEQRBsUqhcdVaZvB9WMdJ33nlHy9yNMHPmTC13795dy3zJKp365TrqsrEq\n1mu1VJov+aXT0Hwak2a2LmzcbzoC/m6spm579+6tZZ6dmbrnqJuN24wuP9+yZYuhoy43nh6Apkbg\n09IU7sajbiLuqqNT2HypPX2ehwV1bVH3CWC6OnlRWtoGuPuYujt/+eUXQ0ddIdxN0qhRIy2HhoYa\nOpoKgraPJ554wjiOurW564ja0irTNE1dAZhL3bmLjz4fdyXxYsGUoUOHOtXlBzwLPk35wdMK0Czf\nM2bMMHQvvviilnl/St8xbzv03dDM/fxboqEPvD3QrO28GgD9rrk9t23bluP9A6atrVI0lDRon8z/\nFjtLH2DV//NUD/T7k8zhgiAIgiAIxQgZOAmCIAiCINhEBk6CIAiCIAg2yfcYp/uNVcpNvIyfn5+W\nJ0yYYOioD33JkiWGjsZOUD8rXaoMWJfhoH5xXiLDKuUA9fFyXz6FlxyhsSd8OTStTp5fWD2jlQ3p\n+7aKaerYsaOxTWMleDwNtQVdfs3fIbV7zZo1Dd358+e1zJfP0xgOb29vQ0eX3vLr8bIdFBoHx8tG\n5EXJFRojtHLlSkNH2wpf0n3hwgUt83Qd69ev1zKPJ+Pvl0LLqvz222+GjpY9oXEo3CY0PofGzgBm\nDFL58uWd3gePvaLtkcd6UR1fEk/7CZ7moqDh9qTPYRWv99lnnxnbQUFBWuaxmfT5+TdPUxDQWDqr\nsjgNGjQwtmlaER5fZpUqZNWqVVoeNGiQofvhhx+0zFMo5EU5qaIIf3Zn74X341ZlVe43jU1BITNO\ngiAIgiAINpGBkyAIgiAIgk3y3VWXmylOOmXOXSHULUWXpAPm8lk6/Q+Yy82Dg4MNHXVb0GlnuowZ\nMF0Y3H1CpyOt0ivwKuDUtUMzS3OoS49fgy8Hzg9XHX9G+vz3mwGWT+NSd2uHDh0M3fbt27XM3xt1\n1Vy6dEnLYWFhxnHU7cQrsNMUABcvXjR01P3H3RQU7sKhx3I3An12vjT7YWX2ptAK9mfPnjV01BVC\nXZaA6c7h3wBtt9xdZtfdYbXkmbph+JJxquNuQdpW+e+sKrLTe+FZtamLmduLpneg3ykAvPnmm1qm\n7q78wsodyZeKU3j6ExrGwF3SNPs7dzvTNkDfL782db3ytAK0z+TpFejzcLvw81DosdQ9DJhpS6i7\nuCRA2ypvt7Tt0PbA/1ZZ/T2g34C46gRBEARBEIoRMnASBEEQBEGwiQycBEEQBEEQbFLgJVf69++v\n5RYtWhg6GqPAl+hTPyiPT6C+dR6TQJcF85iYHTt2aDkwMFDLtWrVMo6j5+T3xZcrU+z6f7kvmB7L\nn4c+e0GUX+H+bnqvNNYMMONdeAmGli1bapkuVQZMO23cuNHQ0bgDHrdClzbTWAlacgEA2rdvr2Ue\nx0RjHvgSdvrs3O5UZ5VGgMct0W++du3aho4vIX8YnDhxwun1fH19texwOAwdXf7Nn4HGP/FYPorV\nt8PbAI19oe+Txrlw+DnovfDYK9o2+XJ8qrMqBxEfH29s03vjaVHmzJmj5ffee8/pOfMK/m5of2qV\nDoWX/aFxfrzkEW07PG0EjSukVKxY0dim6UZ4egAaJ8Wfh7ZHnk7G6puhsV881oa2j5IW42QVg+Qs\nbpF/R7Sf4O3IKsawMFK07lYQBEEQBKEAkYGTIAiCIAiCTfLdVTdw4EBje8CAAVo+fvy4oduzZ4+W\n+XJT6obhbh86Rcind6mLgbtQGjZsqGU6tcynmenvuMuNZkrm08f0d3yJLJ0+5lOh9FiepZlOLVO3\nVX7RtGlTY3vKlCla5tmCaVoHXoGdPj+fxqepBKhrAACqVq2q5bi4OENHXWv03XM3ze7du7XMvxfq\n3uHfAf0muc3o0my+9Jtu8++AuuN49mwfHx88bOiSa778msLdoE899ZSW+VJz+s6ofQBzup67+Oj0\nPU/TQF3U1JXGbUL7BX5+K3eAVeZ++m1auR+4C4imoaCuucKA1ZJv3m/RPpO7WOh5uC1ov8xTFdBv\nhro4efgEbbc8qzh937zd0r6Gf4Pc3ehMx78XqzQxxR36LqxShVjtv99UJIURmXESBEEQBEGwiQyc\nBEEQBEEQbCIDJ0EQBEEQBJvkS4zT008/reVGjRoZuujoaC3zGB1aAoWn4qfxK1bVvHlsFD0Pj9ug\nOurn575tGtfAS21QXz6/5+TkZC3zOBvq8+XxHfT56DkAM+aAVwjPD8aOHWts0/fGl+xS+3K70KXM\nPI7CKj6Jpp7gsQw0BoLahduMvnsep0LfN/fLJyQkaJnHKtEYIB5TYRUXQr81vrybx3DlJ/zbou1W\nKHrwNBE0HoXHptC2w9MFUHi8J23H/Jy0/6bn5MfRtspj1mgcE48jpDFxvE3T3/H2R/tenvqlINtf\nQWMVd+QsdskqdY5VeqHclGUrKGTGSRAEQRAEwSYycBIEQRAEQbBJvrjqqPvm0KFDho66y3799Ven\nOu4KoctZeToC6hq0cnvxiu902pa6Sbh7iE5d06Xy/PxWS3etpsP59ehUM58Op9SvX9/Ybt68udNj\nHwQ6fc6numnm7U8++cTQ0VQNo0ePNnTUnUWPA8zUBTyFBJ2i525M6vai75enSaDfJ1/yTKeUeQoF\n6kqmrjnA/HapawAwsyFbZdClWcQBwN/fH4LwMOBhClaVC2ib9vb2tn0N6nLh56RtlbZHq6XuPAM4\nzQ7Os7Zzt72z3508edLQ0b8z/G8H/Ttz9uxZp+cvjlhlk3f2d83Kdct1Vt9KYURmnARBEARBEGwi\nAydBEARBEASbyMBJEARBEATBJvkS40RLOXDfMC0xQWOTANOnTcsXAEBsbKyWo6KiDB2Ng+HLVKkP\nlsfE0OWu9Hp8GSxdms1jlRo0aGDr2jxVgVUFduoP5qkRaNwNv0++HPRhMWvWLC3zWAIaD/Haa68Z\nOhrDtmHDBkNH47Mef/xxQ0d93vw7oMua+buh75jGUfCYI/p++Tuj57cq98Lj8+h3wH32NE6Elyuh\n1+fPGhERoeWuXbtCEO4XHuNE4X3a4cOHtczTUtzv0nGagoDGhfJr0+2YmBhDR+MKeZoEGnvJ2zv9\n+8BTDNC4Qt6f0r9VJQ2rckUU2pfyv2NW0D6yVq1a9m+sgJAZJ0EQBEEQBJvIwEkQBEEQBMEm+eKq\no/CpXbqcny/tt4JOt/Ll6zwztDP4tDNdVkmXrOYmI/fGjRu1TJerA2amae4SolOVVtfjbh96z3xq\nNK8ysNKpb35N6p7k0+cdOnTQslW2dz4tTF1uPF0AtRN/XjpdT++Lfy/0d/zboa46voT24MGDWh4+\nfDic8dxzzxnbL774opYvXLhg6FavXq3lorAsVyia8LQe9NvmS8+3b9+u5QEDBhg62nasQgx4f0fT\nvdC2ya9N+wnaFgGzMgHXWWUtf+ONN7TM3X9//vOftcxd/zwDeUmCvkOrvytWrjqrv8v0nCdOnLif\nW8xXZMZJEARBEATBJjJwEgRBEARBsIkMnARBEARBEGxSZJ22NNaFx70UJDSu5j//+U8B3kneMXv2\nbC37+fkZut69e2uZllEBzDgHXkLHqjo2jfXhsQxUx2MQqL+dllzhy4xpXAWPY6LbPC7LKq6JQpdz\n57QtCPkN/85p++NlT2j6jK1btxo6WtaJt3erc9IYKxozaXVfPj4+ho7GRlnFTPLSKZ999pmWfX19\nDR19Vl5iiaeQKUlYpSOg/SntS3k/Tvt/Hu9k9a0URmTGSRAEQRAEwSYycBIEQRAEQbBJkXXVCYWD\nPXv2ON2uXr26oQsNDdVy48aNDd0zzzyjZV6BnU7r82zadMkwd9lSNx6dGqbLmPk5eSb0LVu2aJln\nQreLlQuRT2fTaW9JRyDkFTwlB3Wx8Ezb1A0WGRlp6ObMmaPlsLAwQ/fss89quXLlypbXv8vRo0eN\nbZrShbtwaPoanlbg3//+t5Z3796d47UAM0UKYKZQ4fdIn6ekQftFXhHjxo0bWqbfET+O9oO8b6PH\n8lQZhRGZcRIEQRAEQbCJDJwEQRAEQRBsIgMnQRAEQRAEm0iMk/BA8LgDGrNz/vx5Q7d06VJb5+Rl\nF2jFcr7kmaYn4NXLPT09czw/jzmiKf5pyZx7YZVCgep4+gO+LQj5DS0XBJixfJcvXzZ0Bw4ccHoe\nGt/y0UcfPaS7yz9oiSPAjOWh5ZwAYPPmzflyT4WRb7/9VsshISGGjvZ1lSpV0rJVCSwqA2afGBcX\n92A3mw/IjJMgCIIgCIJNZOAkCIIgCIJgExdlVepYEARBEARB0MiMkyAIgiAIgk1k4CQIgiAIgmAT\nGTgJgiAIgiDYRAZOgiAIgiAINpGBkyAIgiAIgk3yfeBUp04dBAYGIiQkBMHBwejWrRt+/PHHBz7v\nokWLMHHiRABZxSajo6Mtj//qq69yfY2oqCj4+/vbPv7zzz/HCy+8kC3ZnDPi4uJyLCTpbL8VH374\nId56661c/caKhIQEfP/999n2iz2dU9TsWRJseePGDUyfPh3BwcH6ORcvXpzr69nF398fUVFReXZ+\nK0qCPQHg8OHDGDhwIIKCghAQEICwsDAcOnQo19fk5/z9998f6BwPk5Jgy4kTJ6J58+YICQmBw+FA\np06dsHz58kJZ7LxAMod/9tlnOkPrwYMHMWLECGzatClbBe37Zfny5Zb6zMxMvPfee+jRo8dDuZ4z\n1q9fjzFjxmD9+vXw9fXN02vlNfv378e+ffvQrl27bDqxZ9HDmT2Luy0XLFiA1NRUfPPNN/Dw8EBC\nQgL69u2LJ554AqGhoXlyzYKkuNvzt99+w7Bhw/DOO+8gICAAAPD9999j6NChWLlyJWrVqnVf5129\nejV8fX3xzDPPPMzbfSCKuy0BYMCAARg5ciQA4OzZs5g8eTJOnTqFGTNm5Nk174cCd9X5+vriiSee\nwM8//4y4uDj4+flh1qxZ6NevH4CsD6Rbt24IDAxEjx49EBsbCyDrP8cxY8bA4XCgX79+uHDhgj4n\n/S9v3bp1CA4ORnBwMMaPH49bt25h0KBBSEtLQ0hICGJjY3HhwgUMHz5cH7dr1y59rkWLFqFNmzZ4\n+eWXsW/fPr3/yJEjGDJkiNPnOnHiBEqXLo3u3btjz549uHXrltb1798fy5YtQ+/evdG6dWuMGzcu\nW3p6AHjjjTcQERFh7FNKYcGCBQgODobD4cDMmTORmZmZ4z2kp6fjtddeg7+/P/r374+EhAQAWaVQ\nhgwZguDgYHTs2BHr1q3Tv/nXv/6Fjh07IiQkBAMGDEBMTAyio6MxY8YMbN68GWPHjnX6zIDYszjZ\nszja8vjx46hXrx48PDwAAN7e3lixYgUCAwP1/a1cuRKvvPIK/Pz8EBkZqX+7bds2vPTSS2jXrh0G\nDx6MpKQkAMD169cxZswYBAcHw9/fH++++26O1161ahW6d++OGzduOH2unN7zw6I42nPx4sXo2bOn\nHjQBQLt27bBgwQJ4eXkByLkNAM7t9sUXX2D9+vWYM2cOli1b9sDvPS8ojrbk1KxZE4sWLcK3336L\n06dP5+o5L168iLCwMHTo0AEBAQF4//33LffnGpXP1K5dW8XHxxv7OnfurHbv3q1iY2NVvXr11Jo1\na5RSSqWlpakmTZqoPXv2KKXV3RRlAAAX6UlEQVSU+uabb1SXLl2UUkp9/vnnqm/fviojI0MlJSUp\nh8OhJkyYoJRSyuFwqAMHDqjY2FjVvHlzdeHCBXXnzh01atQotXTpUhUbG6vq1q2rrz9gwAD1/vvv\nK6WUOnPmjGratKlKSkpSJ06cUE2aNFGXL19Wt2/fViNHjlQOh8PWc0ZGRqqvvvpKKaXUpEmT1Hff\nfad1/fr1U/369VPXr19X6enpqkWLFioqKsq4r48++kgNHTpU3b5929i/du1aFRoaqlJTU1VGRoYa\nNmyY+uyzz7Jdf/78+apx48YqJiZGKaXU66+/rt555x2llFKDBw9WS5YsUUopFRcXp3x9fVVsbKw6\nd+6c8vX1VWfOnFFKKfXxxx+rsLAwfb7Jkydnu47Ys/jYsyTY8vPPP1eNGjVS8+bNU4cOHVIZGRmG\n3uFwqHHjxqnbt2+rCxcuqHr16qn4+HgVExOjGjdurI4dO6aUUmrJkiVq9OjR+r2++uqr6s6dOyo5\nOVk1bdpUHThwwHjeAwcOqMDAQHX58mXL5+Lv+UEoCfZs3ry5ioqKcqq3agNWduvXr59at27dPa+f\nX5QEW06YMEEtXLgw2/5XX31VffHFF7l6zsjISPXhhx8qpZS6du2aGjt2rLp48aLT/bmlwGecdu3a\nhYSEBDz//PMAgIyMDP3f38GDB1GlShW0atUKANCxY0fExMTg/PnziIqKQmBgINzd3eHp6QmHw5Ht\n3Hv37kXjxo1RpUoVuLi4YO7cuRg4cKBxzLVr17B//369v2bNmvD19cWuXbtw4MABNGnSBN7e3nBz\nc0OnTp1sPVNmZiY2b96siyF26tQJ69evN44JCQlB6dKlUbZsWTz55JOIj4/Xup07d+K7777DvHnz\n4ObmZvxux44d6NatGypUqAB3d3d0797dKNBJ8fX1xeOPP66v98svvyAjIwP79u1Dnz59AAA1atRA\ns2bN8NNPP2Hv3r1o1qyZLqrbvXt37N+/P1dFacWexceexdGWffv2xaxZsxAdHY2BAweiefPmmDVr\nFm7evKmPeemll+Dm5oYqVarAy8sL8fHx2L17N5o2bYratWsDAHr16oXt27cjMzMTgwcPxqJFi+Di\n4oJKlSqhVq1aRqHS+Ph4TJo0CR9++CG8vb0tn4u/54dJcbRnSkoKvL29neqt2sC97FaYKY62dEb5\n8uWRlpaWq+f08vLCnj17EBUVBQ8PD8ybNw8+Pj5O9+eWAolx6t+/P9zc3KCUQo0aNbB06VKUK1cO\nV65cgZubm654n5qaitjYWKMas4eHB5KSkpCSkoIKFSro/RUrVkR6erpxnStXrqBixYp6+5FHHsl2\nL2lpaVBKoVevXnrftWvX0Lx5c1y7di3bNeywZ88eXLp0yfgob9y4gcTERD19fPcZAcDNzU27Z+7c\nuYO33noLTz31FMqVK5fj/X788cf48ssvAWT9UXfm46b7K1SogJSUFCQnJ0Mple257rod6DNWqFAB\nSqlsVcI5Ys/iY8/ibksAaN++Pdq3b49bt27hxx9/xMyZM/HII4/g9ddfB5CzLdPS0hAVFWU8b/ny\n5ZGcnIy0tDRERkbi9OnTcHV1xYULF9C1a1d93KxZs+Dq6qq/FavnuntNeg8PQnG3p6enJy5evKgH\nRhx+X7QNpKenW9qtsFHcbemMc+fOoXXr1gBg+zkHDhyIO3fuYPr06bh06RL69u2L0aNHO93v4uKS\nq3sq8OBwK3x8fPD0009jzZo12XQVK1bUo1AA+g8FxdPTEz///LPevnr1Km7cuGEc4+XlBTc3N6xe\nvTrbH7YVK1YY17jXAOIua9euxbvvvmsEm86cORPffPNNtpF7TqxYsQITJ07E8uXLsx3v4+MDf39/\nW/EPKSkpWk5NTcWjjz4KT09PuLq6IiUlBZUqVQIAJCcn606dvq+UlBS4urrC09PT8jpiT2uKkj2L\nsy0zMjKwa9cuOBwOuLm5wcPDA23atEFMTAz27Nlj+VsfHx+0bNkS8+fPz6YbP3486tWrh4ULF8LN\nzc34Y3JX/8cffyA8PByLFi2yfK6HPeNRnO0JAM2aNcOWLVvQtGlTY//q1atRu3ZteHl5OW0DEyZM\nsLRbYaO42zInYmNjcezYMTRv3jzb6jqr5wSAYcOGYdiwYfjjjz8wdOhQ+Pr6olWrVk7354YCd9VZ\n8dxzz+Hy5cs4fPgwgKyXOH78eCil0KhRIz1VnpSUhN27d2f7fZs2bXDo0CHExcVBKYW3334bX3/9\nNUqVKoU7d+7g6tWrcHd3R5s2bbBy5UoAWQGDkyZNQnx8PBo3boyDBw8iKSkJmZmZ2LBhwz3vOTU1\nFT/88APatGlj7A8ICMjm3skJV1dX1KxZE7Nnz8bixYtx+vRpQ9+uXTusX78e169fBwCsXLkSa9eu\nzfFcBw8exPnz5wEAmzZtgq+vL9zd3eHn56dnOGJiYhAVFYWWLVuiVatWiIqK0gF2K1euRKtWreDu\n7g53d3ejMdwPYs/iY8+iaEt3d3e8//77WLJkiZ4RvHr1KrZv344mTZpY/tbPz894l0eOHMHMmTMB\nAImJiahbty7c3Nywd+9enD17FteuXdO/feKJJzB69GjExMRg7dq1ls9VUBRFewLAiBEjsGHDBqPN\nbN26FXPnzkX58uUt24CV3R5Gf1dQFFVbcuLi4jBu3Dj06dMH1atXz9VzhoeHY+/evQCy2p+3tzdc\nXFyc7s8tBTLjZJfSpUtj/vz5iIiIQHp6OkqVKoW//vWvcHFxQY8ePRAVFYWAgABUr14dAQEB2T70\nqlWrYsaMGQgLC4ObmxsaNGiAQYMGoVSpUvD19YXD4cBHH32EadOm4e2338aqVasAZMWwVKtWDdWq\nVUOvXr3QpUsXPProowgNDcXx48cBZHWcH3zwAT7++GPjmt9++y0aNWqUbaq9SZMmOH/+vP79vXjy\nyScxatQoTJgwAXPnztX7AwICcOLECXTp0gVAlvHfeeedHM/h7++PiIgIHD9+HI899pjOAzR9+nRM\nmTIFa9asQalSpTBz5kxUq1YNQNZMysiRI5GRkYHHHntMrwJr1aoVli1bhm7dumH16tW2noEj9iw+\n9iyKtnRxccHSpUvx3nvvoX379rrD7NSpEwYNGmT5vD4+PoiIiMCoUaOQkZGBcuXKYfLkyQCy/njP\nnj0bixYtQrt27fDf//3fmD9/PurWrat/7+HhgcjISLz66qto0aKF0+cqqBibomhPAKhVqxY++eQT\nzJ07FwsWLICHhwdq1qyJTz/9FE899RQA523Aym4BAQGYM2cOYmNjMWnSpLx89Q+dompLAPjf//1f\nbNiwAbdu3UKpUqXQq1cvp7P6Vs/Zq1cvhIeHIyIiAkop+Pv7o0WLFnj00Udz3J9bXJTKYd20IAiC\nIAiCkI1C7aoTBEEQBEEoTMjASRAEQRAEwSYycBIEQRAEQbCJDJwEQRAEQRBsku8DJ6UUli1bho4d\nOyI4OBgBAQGYNm1agS39/O6773D16lXbx7/77rto2rSp7aXD+/fvzzELsLP9VkycOBGLFi3K1W+s\nOH36NA4cOPBA5xB7Wu+3orDZs6jaMikpCa+//rqumRUaGnpfVdztUqdOHaPGV2GlqNoTyMqM3bNn\nTwQHB6Ndu3YYMWIETp069UDX/+GHH3Q6j6JGUbVl//794efnh5CQELRp0wY9evSwlcalsJPvA6e/\n/e1v+O677/Dxxx9j8+bN2LBhAzIyMvDaa6/lWBg1r5k/f77txnz79m3s2LEDQ4YMue/cFIWJbdu2\nPfDASexZeHhQexZVW0ZERKBq1ar417/+hc2bN2PhwoWYO3eukcSvJFJU7blz50689dZbePPNN7F5\n82Zs27YNbdu2RZ8+fZCYmHjf1//000+L7MCpqNoSyEoAu2nTJuzatQvh4eH4xz/+gY8++iiP7zCP\nyXV1uwfgypUrqkGDBurkyZPG/hs3bqjvv/9eZWZmqhs3bqipU6eqoKAgFRISombPnq1u376tlMoq\nQvjhhx+qoKAgde7cOdWvXz81b948FRISog4ePKhSUlLUG2+8oYKCgpS/v7/6+uuv9TV27dqlOnTo\noIKCgtSwYcPUlStX1MSJE1Xt2rVVQECAOnDggNqyZYuaOHGi0/vfsWOHevPNN9WFCxdUaGiooXM4\nHOqLL75Q3bp1U61atVKzZ89WSin1008/qYCAAKWUUrdu3VL9+vVTH3/8sbH/5s2bKiIiQgUFBSmH\nw6EWL16c4/UnTJigwsPDVd++fVXbtm3VyJEjVXp6ulJKqd9++0317NlTBQcHq06dOqndu3fr3y1f\nvly1b99eBQcHq+HDh6vExET1/fffq+eff141a9ZM32tuEXsWH3sWZVt26NBBbdq0ydh38eJFfW+1\na9dWa9euVZ07d1atWrVSy5Yt08etXLlSBQcHK4fDocaOHauuX7+ulFLq8uXLavDgwVr3ySef6N/Q\ngqvz5s1TI0aMUJmZmerEiROqb9++KigoSHXs2FEdOXJEKZX1zfTs2VP95S9/UePGjcu1be6HomzP\nrl27qpUrV2bbv2PHDpWWlqaUyrkNKOXcbu+//7569tlnVdu2bdW333774C84HynKtsypWPIff/yh\nGjVqpFJTU3NsG1u3blUdO3ZU/v7+atCgQdq2x44dUz169FAdOnRQgYGBuhi6s/15Sb4OnHbu3KkC\nAwMtj7lbRT4jI0Ndv35ddevWTb94h8OhpkyZoo/t16+fGjx4sMrMzFRKZVWtf/PNN1VmZqZKTExU\nbdq0UceOHVPp6emqadOmurL5zJkz1bRp05RSOVeddsbo0aPV3r17lVJZlaEPHz6sdc6qqtM/qOHh\n4Wrq1KlKKfMP8IIFC1RYWJi6efOmSk9PVy+//LLavn17tutPmDBBORwOlZiYqG7fvq369u2rPv30\nU5WZmanat2+vvvnmG6WUUkeOHFFNmjRRaWlp6ueff1YvvviiSkhIUEopNWPGDDV58mR9vpyqUdtF\n7Fl87FmUbRkZGamaN2+ulixZoqKjo/U171K7dm01Z84cpZRShw8fVg0aNFC3b99WBw4cUC1atFAX\nLlxQSik1depUFRkZqZTKeq/h4eFKKaViYmJUvXr11Pnz5437+vbbb1WXLl1Uenq6yszMVEFBQeqr\nr75SSikVFRWl/Pz8VEZGhvrpp59UgwYN1L59++75LA+LomrP9PR0VadOHW2TnLBqA1Z2czgc6sCB\nA5bXL4wUVVvevRYfOCmlVPv27dXu3buztY2YmBjVuHFjfc0lS5ao0aNHK6Wy+us1a9YopZRKTExU\nI0aMUDdv3nS6Py/JV1cdraHljJ07d6JHjx5wd3dH6dKl8dJLL+kU6QDQtm1b4/g2bdrA1TXrMXbs\n2IEBAwbA1dUVlStXRmBgILZs2YJDhw6hatWqurL5+PHjc50NNiUlBdHR0boQZ6dOnbL5anOqqn6X\nFStWICYmBuHh4dnOvWPHDvTp0wceHh4oW7YsOnfujC1btuR4Hy+++CIqV64MNzc3BAYG4pdffkFc\nXBwSEhJ0LbUGDRqgevXqOHr0KHbu3Ing4GD93rt37268zwdB7Fl87FmUbTl+/HiMHTsWe/bsQY8e\nPeDn54eFCxcata06d+4MAKhXrx5u3ryJxMREbN++HR06dECVKlUAAL1799Z2mjJlCqZOnQoAePzx\nx/GnP/3JyOodHR2NDz74AIsXL0bZsmVx+vRpJCYm4pVXXgEA+Pr6onLlytpdWLp06fvKUHy/FFV7\npqamQillee9WbeBediuKFFVbWlG+fHkdn0Xbxu7du9G0aVN9zV69eukSMV5eXti8eTOio6Ph6emJ\nRYsWwcPDw+n+vCRfS67crWRtRVJSki5WCgCVKlUy/NpUx7fT0tIwZswYuLm5AQBu3ryJkJCQbNWe\n7+elbty4EZcuXdLFJJVS8PDwwMSJE1GqVCkAOVdVB4CEhATMnTsX/v7+cHfP/srT0tIwe/ZszJs3\nDwBw69YtNGzYMMf7qFy5spYrVKiA1NRUJCUloUKFCkbNnYoVKyIpKQlJSUnw8fEx9j9InABF7Fl8\n7FmUbenq6ooePXqgR48euHbtGnbu3ImIiAh4eXnpwq13q7Xfvf6dO3eQlpaGrVu36gK/SilkZGQA\nAI4ePYq5c+ciPj4erq6uuHz5sjEQe/vtt1GuXDn9jKmpqbhx4wbat2+vj7l69SqSk5NRsWLFbO8m\nrymq9qxUqRJcXV1x8eJF1KhRw+l9O2sD97JbUaSo2tKKc+fO6cEgv5eoqCiEhITofeXLl0dycjLe\neOMNfPTRRxgzZgxu3ryJ1157DX379nW6Py/J14FTo0aNkJiYiOjoaNSrV0/vz8jIwIIFCzB8+HB4\ne3sjOTlZ65KTk+Ht7W3r/D4+Pli4cKEerd5lz549RoXm69evIyUlxVal6busW7cOn332GRo1aqT3\njRgxArt27UJAQIDlbz08PLB27VqEhYVh69at2VZf+fj4YPDgwXA4HPe8j5SUFC2npqaiUqVK8PLy\nQkpKCpRS+o/t3f9SHuR93guxZ/GxZ1G1ZXp6Ov7973/rd122bFl06NABR44cuWcdQR8fH3Tp0gUT\nJkzIphs/fjzCwsLQu3dvuLi4oHXr1oZ+7ty5+PLLL/G3v/0NU6ZMgY+PD8qVK4dNmzZlO9f+/ftt\nPcvDpKjas0yZMmjYsCG2bNmSrX7gp59+Cn9/f8v7vpfdiiJF1ZbOiIqKws2bN9GwYUMcOXIk2720\nbNkS8+fPz/G348aNw7hx43DkyBEMHToULVu2xFNPPeV0f16Rr666ihUr4tVXX8WECRNw9uxZAFnG\nCA8Px3/+8x+UKVMGbdu2xddff43MzExcu3YN69evz1aZ3hn+/v66WvPt27cxa9YsREdHw9fXF5cv\nX9ZGWrRoERYuXAggqwp2amqq5XlPnTqF+Ph4PPfcc8b+gIAArFu3ztZzV69eHbNnz8b06dORlJRk\n6Nu1a4dVq1YhMzMTSiksWrQox6rVQNZUZkpKCjIzM7F161b4+vriscceQ9WqVfHdd98BAA4dOoSE\nhAQ0bNgQbdu2xdatW3UDWLlypX6fD1oBXOxZfOxZVG3p4uKCSZMmYc2aNXpfQkIC9u7diyZNmtzz\nnrZs2aLtt23bNvzP//wPACAxMRH169eHi4sL1q5di+vXr+PatWv6tzVr1sTUqVOxadMm7N+/HzVq\n1EDVqlX1wCkpKQnjxo0zfpOfFFV7AsBf//pXLFmyRLcZpRRWrFiB5cuXo0KFCpZtwMpuD9rfFRRF\n2Zac33//HW+99RbGjBmDMmXKZNP7+fkhKioKsbGxALKKAs+cORMAMHz4cJw4cQIAULt2bZQvXx4u\nLi5O9+cl+TrjBACjR49GpUqVMGLECGRmZsLV1RXt2rXDtGnTAGTlfYiNjUVoaChcXFwQEhJiTH9b\nMWbMGEyfPh3BwcEAgNatW6NOnTpwd3fHhx9+iPHjxwPI6vQiIyMBACEhIejVqxdmzpyJUqVKYfv2\n7Zg9e7Zx3rVr18Lf3z+bMRwOByIiIoyRvhUvvPACQkNDMW3aNGMqsU+fPoiLi0NoaCiUUqhfvz7C\nwsJyPIfD4cDo0aMRFxeH+vXro1u3bnBxccG8efPw9ttvY8GCBShTpgw++OADlC1bFg0bNsSwYcPQ\nt29f3LlzB3Xr1tXv2uFw4I033sC5c+ecjvDvhdiz+NizKNqybNmy+PTTTzF37lwsWbIEAFCqVCn0\n7dv3nvdWr149DB8+HP3798edO3fg5eWF6dOnA8j64z1q1Cg8+uij6NWrF3r27ImpU6dixYoV+vee\nnp6YPn06Jk2ahA0bNmDevHmYNm0a/v73v8PV1RWDBg1C2bJlbb2fvKAo2hMAWrZsiXnz5mH+/KzK\n925ubqhXrx7++c9/wtPTE56enk7bgJXdgoODMW7cOPzlL3/JNptV2CmqtgSAOXPmYPHixbhx4wYq\nVKiAESNG4OWXX87xWB8fH0RERGDUqFHIyMhAuXLlMHnyZABAv3798Prrr2t3ep8+ffDkk0863Z+X\nuChVAEkgBEEQBEEQiiBSckUQBEEQBMEmMnASBEEQBEGwiQycBEEQBEEQbCIDJ0EQBEEQBJvIwEkQ\nBEEQBMEmMnASBEEQBEGwiQycBEEQBEEQbCIDJ0EQBEEQBJvIwEkQBEEQBMEm/wfKRJVZJWnIRgAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YuHChcTxz5kwt81Ah1ARu5V5D+13uckL7CeqGAwBbt27V8urVqx3eMx8bqUmR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\nE5k4CYIgCIIg2OT/AdztYzsZ36OMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x1440 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zsm_k2hk93WT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### affichege matrice de confusion"
      ]
    },
    {
      "metadata": {
        "id": "4-AgrGkA93WT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm,class_,title='Confusion matrix',cmap=plt.cm.Reds):\n",
        "    \"\"\"\n",
        "    This function plots a confusion matrix\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    tick_marks = np.arange(len(class_))\n",
        "    plt.xticks(tick_marks, class_, rotation=90)\n",
        "    plt.yticks(tick_marks, class_)\n",
        "\n",
        "    fmt = 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True labels')\n",
        "    plt.xlabel('Predicted labels')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vy-WHlw393WV",
        "colab_type": "code",
        "outputId": "164b492f-ae45-4f19-8048-5a2964282d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        }
      },
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(confusion_matrix(test_target, predictions), classes_fashion)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MWtpTpv80Vydt0PXr+6JyZUoTdvqw+mflLz9z6uw5fv75Z07s20bY6cMkJj5m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\nfUEQBC3y/2r10kVAFpDLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gg_0z2gzrNW_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "On remarque que le modèle a des difficultés a discerné les shurtes qui peut se comprendre car ils ont une forme très similaire avec les t-shirt, et les pullovers.\n",
        "\n",
        "En visualisant les image a l'oeil nue avec la qualité de 28*28 on a aussi du mal à discerné certain vêtement donc les résultats sin tout à fait acceptable.\n"
      ]
    },
    {
      "metadata": {
        "id": "3BV-r4T793WX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Report with f1, prec, rec"
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "FHI-WWzN93WY",
        "colab_type": "code",
        "outputId": "16df3741-a0a2-4c92-b705-c4850f759899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "cell_type": "code",
      "source": [
        "print classification_report(test_target, predictions)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.88      0.89      1000\n",
            "           1       0.99      1.00      0.99      1000\n",
            "           2       0.92      0.89      0.90      1000\n",
            "           3       0.94      0.96      0.95      1000\n",
            "           4       0.91      0.90      0.91      1000\n",
            "           5       0.99      0.98      0.99      1000\n",
            "           6       0.80      0.84      0.82      1000\n",
            "           7       0.96      0.98      0.97      1000\n",
            "           8       0.99      0.99      0.99      1000\n",
            "           9       0.98      0.97      0.97      1000\n",
            "\n",
            "   micro avg       0.94      0.94      0.94     10000\n",
            "   macro avg       0.94      0.94      0.94     10000\n",
            "weighted avg       0.94      0.94      0.94     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ck0CyzRw5zWp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}