GetClassAccuracyCifar10BaselineVer02.ipynb

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  "metadata": {
    "colab": {
      "name": "Cifar10BaselineVer02.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/GetClassAccuracyCifar10BaselineVer02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "h54XT6Mhk5nW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from keras.callbacks import ModelCheckpoint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DcI3TOWRnQzo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(cm,\n",
        "                          target_names,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=None,\n",
        "                          normalize=True):\n",
        "    \"\"\"\n",
        "    given a sklearn confusion matrix (cm), make a nice plot\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
        "\n",
        "    target_names: given classification classes such as [0, 1, 2]\n",
        "                  the class names, for example: ['high', 'medium', 'low']\n",
        "\n",
        "    title:        the text to display at the top of the matrix\n",
        "\n",
        "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
        "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
        "                  plt.get_cmap('jet') or plt.cm.Blues\n",
        "\n",
        "    normalize:    If False, plot the raw numbers\n",
        "                  If True, plot the proportions\n",
        "\n",
        "    Usage\n",
        "    -----\n",
        "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
        "                                                              # sklearn.metrics.confusion_matrix\n",
        "                          normalize    = True,                # show proportions\n",
        "                          target_names = y_labels_vals,       # list of names of the classes\n",
        "                          title        = best_estimator_name) # title of graph\n",
        "\n",
        "    Citiation\n",
        "    ---------\n",
        "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "\n",
        "    \"\"\"\n",
        "    import matplotlib.pyplot as plt\n",
        "    import numpy as np\n",
        "    import itertools\n",
        "\n",
        "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
        "    misclass = 1 - accuracy\n",
        "\n",
        "    if cmap is None:\n",
        "        cmap = plt.get_cmap('Blues')\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "\n",
        "    if target_names is not None:\n",
        "        tick_marks = np.arange(len(target_names))\n",
        "        plt.xticks(tick_marks, target_names, rotation=45)\n",
        "        plt.yticks(tick_marks, target_names)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        if normalize:\n",
        "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        else:\n",
        "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-b6SqVo8k998",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    elif epoch > 100:\n",
        "        lrate = 0.0003        \n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A5VS6Ew3k_af",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yowZFp6PlA89",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#z-score\n",
        "mean = np.mean(x_train,axis=(0,1,2,3))\n",
        "std = np.std(x_train,axis=(0,1,2,3))\n",
        "x_train = (x_train-mean)/(std+1e-7)\n",
        "x_test = (x_test-mean)/(std+1e-7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "28UVoDUFlCzo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = 10\n",
        "y_train = np_utils.to_categorical(y_train,num_classes)\n",
        "y_test = np_utils.to_categorical(y_test,num_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9yp5f63slEzd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "weight_decay = 1e-4\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(num_classes, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gfR56NKwlHhU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1065
        },
        "outputId": "64c7d944-486f-4e49-bff8-1823b2a9baaa"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                20490     \n",
            "=================================================================\n",
            "Total params: 309,290\n",
            "Trainable params: 308,394\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Nm-sllTnlJr0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(x_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A0RtQeP3nqO8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "continue_training = False\n",
        "if continue_training:\n",
        "   model.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D05Eim8jmojT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_loss', mode='auto')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Szjij8W8lLZq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2241
        },
        "outputId": "5f51dfa5-b852-4d30-d3a2-388588fa46ac"
      },
      "cell_type": "code",
      "source": [
        "#training\n",
        "batch_size = 64\n",
        "\n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=x_train.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(x_test,y_test),callbacks=[mcp_save, LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/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",
            "Epoch 1/60\n",
            "781/781 [==============================] - 55s 70ms/step - loss: 1.8872 - acc: 0.4270 - val_loss: 1.5231 - val_acc: 0.5582\n",
            "Epoch 2/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 1.2531 - acc: 0.5967 - val_loss: 1.0816 - val_acc: 0.6604\n",
            "Epoch 3/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 1.0672 - acc: 0.6590 - val_loss: 0.9269 - val_acc: 0.7166\n",
            "Epoch 4/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.9741 - acc: 0.6917 - val_loss: 0.8749 - val_acc: 0.7379\n",
            "Epoch 5/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.9073 - acc: 0.7176 - val_loss: 0.9119 - val_acc: 0.7326\n",
            "Epoch 6/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.8562 - acc: 0.7392 - val_loss: 0.9353 - val_acc: 0.7318\n",
            "Epoch 7/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.8249 - acc: 0.7522 - val_loss: 0.7653 - val_acc: 0.7814\n",
            "Epoch 8/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.7887 - acc: 0.7651 - val_loss: 0.7517 - val_acc: 0.7841\n",
            "Epoch 9/60\n",
            "781/781 [==============================] - 50s 63ms/step - loss: 0.7704 - acc: 0.7728 - val_loss: 0.6762 - val_acc: 0.8095\n",
            "Epoch 10/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.7511 - acc: 0.7799 - val_loss: 0.7417 - val_acc: 0.7909\n",
            "Epoch 11/60\n",
            "781/781 [==============================] - 50s 65ms/step - loss: 0.7370 - acc: 0.7863 - val_loss: 0.7369 - val_acc: 0.7926\n",
            "Epoch 12/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.7217 - acc: 0.7947 - val_loss: 0.7436 - val_acc: 0.7956\n",
            "Epoch 13/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.7121 - acc: 0.7972 - val_loss: 0.6578 - val_acc: 0.8237\n",
            "Epoch 14/60\n",
            "781/781 [==============================] - 53s 68ms/step - loss: 0.7022 - acc: 0.8006 - val_loss: 0.7934 - val_acc: 0.7898\n",
            "Epoch 15/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6939 - acc: 0.8056 - val_loss: 0.6183 - val_acc: 0.8343\n",
            "Epoch 16/60\n",
            "781/781 [==============================] - 51s 65ms/step - loss: 0.6837 - acc: 0.8098 - val_loss: 0.7126 - val_acc: 0.8101\n",
            "Epoch 17/60\n",
            "781/781 [==============================] - 52s 66ms/step - loss: 0.6774 - acc: 0.8117 - val_loss: 0.7119 - val_acc: 0.8120\n",
            "Epoch 18/60\n",
            "781/781 [==============================] - 51s 65ms/step - loss: 0.6692 - acc: 0.8141 - val_loss: 0.6475 - val_acc: 0.8323\n",
            "Epoch 19/60\n",
            "781/781 [==============================] - 87s 111ms/step - loss: 0.6651 - acc: 0.8172 - val_loss: 0.6678 - val_acc: 0.8267\n",
            "Epoch 20/60\n",
            "781/781 [==============================] - 53s 67ms/step - loss: 0.6631 - acc: 0.8194 - val_loss: 0.6683 - val_acc: 0.8246\n",
            "Epoch 21/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6584 - acc: 0.8205 - val_loss: 0.6535 - val_acc: 0.8293\n",
            "Epoch 22/60\n",
            "781/781 [==============================] - 50s 65ms/step - loss: 0.6482 - acc: 0.8250 - val_loss: 0.6095 - val_acc: 0.8433\n",
            "Epoch 23/60\n",
            "781/781 [==============================] - 51s 65ms/step - loss: 0.6445 - acc: 0.8253 - val_loss: 0.5971 - val_acc: 0.8467\n",
            "Epoch 24/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6397 - acc: 0.8297 - val_loss: 0.6270 - val_acc: 0.8343\n",
            "Epoch 25/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6381 - acc: 0.8282 - val_loss: 0.6754 - val_acc: 0.8253\n",
            "Epoch 26/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6439 - acc: 0.8278 - val_loss: 0.6222 - val_acc: 0.8370\n",
            "Epoch 27/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6327 - acc: 0.8299 - val_loss: 0.6021 - val_acc: 0.8482\n",
            "Epoch 28/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6324 - acc: 0.8313 - val_loss: 0.5931 - val_acc: 0.8478\n",
            "Epoch 29/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6302 - acc: 0.8332 - val_loss: 0.6169 - val_acc: 0.8402\n",
            "Epoch 30/60\n",
            "781/781 [==============================] - 50s 63ms/step - loss: 0.6240 - acc: 0.8343 - val_loss: 0.5902 - val_acc: 0.8471\n",
            "Epoch 31/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6230 - acc: 0.8356 - val_loss: 0.6357 - val_acc: 0.8391\n",
            "Epoch 32/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6196 - acc: 0.8360 - val_loss: 0.5715 - val_acc: 0.8587\n",
            "Epoch 33/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.6203 - acc: 0.8393 - val_loss: 0.6406 - val_acc: 0.8383\n",
            "Epoch 34/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6148 - acc: 0.8396 - val_loss: 0.6648 - val_acc: 0.8279\n",
            "Epoch 35/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.6162 - acc: 0.8398 - val_loss: 0.5955 - val_acc: 0.8507\n",
            "Epoch 36/60\n",
            "781/781 [==============================] - 48s 61ms/step - loss: 0.6122 - acc: 0.8409 - val_loss: 0.5937 - val_acc: 0.8550\n",
            "Epoch 37/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6135 - acc: 0.8399 - val_loss: 0.6120 - val_acc: 0.8487\n",
            "Epoch 38/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6089 - acc: 0.8406 - val_loss: 0.5967 - val_acc: 0.8468\n",
            "Epoch 39/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.6085 - acc: 0.8408 - val_loss: 0.5983 - val_acc: 0.8473\n",
            "Epoch 40/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6091 - acc: 0.8431 - val_loss: 0.6034 - val_acc: 0.8520\n",
            "Epoch 41/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.6070 - acc: 0.8416 - val_loss: 0.6201 - val_acc: 0.8408\n",
            "Epoch 42/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.6071 - acc: 0.8430 - val_loss: 0.6162 - val_acc: 0.8482\n",
            "Epoch 43/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.6050 - acc: 0.8442 - val_loss: 0.6608 - val_acc: 0.8385\n",
            "Epoch 44/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.5989 - acc: 0.8450 - val_loss: 0.6122 - val_acc: 0.8490\n",
            "Epoch 45/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.6062 - acc: 0.8444 - val_loss: 0.5921 - val_acc: 0.8524\n",
            "Epoch 46/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.6005 - acc: 0.8461 - val_loss: 0.6228 - val_acc: 0.8444\n",
            "Epoch 47/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.5958 - acc: 0.8468 - val_loss: 0.6463 - val_acc: 0.8372\n",
            "Epoch 48/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.5963 - acc: 0.8472 - val_loss: 0.6076 - val_acc: 0.8480\n",
            "Epoch 49/60\n",
            "781/781 [==============================] - 51s 65ms/step - loss: 0.5943 - acc: 0.8472 - val_loss: 0.5993 - val_acc: 0.8506\n",
            "Epoch 50/60\n",
            "781/781 [==============================] - 50s 64ms/step - loss: 0.5934 - acc: 0.8469 - val_loss: 0.6304 - val_acc: 0.8459\n",
            "Epoch 51/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.5948 - acc: 0.8472 - val_loss: 0.6211 - val_acc: 0.8484\n",
            "Epoch 52/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.5934 - acc: 0.8490 - val_loss: 0.6299 - val_acc: 0.8397\n",
            "Epoch 53/60\n",
            "781/781 [==============================] - 49s 62ms/step - loss: 0.5941 - acc: 0.8480 - val_loss: 0.5514 - val_acc: 0.8677\n",
            "Epoch 54/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.5880 - acc: 0.8495 - val_loss: 0.5990 - val_acc: 0.8529\n",
            "Epoch 55/60\n",
            "781/781 [==============================] - 51s 65ms/step - loss: 0.5889 - acc: 0.8509 - val_loss: 0.5698 - val_acc: 0.8614\n",
            "Epoch 56/60\n",
            "781/781 [==============================] - 49s 63ms/step - loss: 0.5939 - acc: 0.8496 - val_loss: 0.6256 - val_acc: 0.8441\n",
            "Epoch 57/60\n",
            "781/781 [==============================] - 48s 61ms/step - loss: 0.5811 - acc: 0.8515 - val_loss: 0.6204 - val_acc: 0.8434\n",
            "Epoch 58/60\n",
            "781/781 [==============================] - 47s 61ms/step - loss: 0.5895 - acc: 0.8480 - val_loss: 0.6533 - val_acc: 0.8341\n",
            "Epoch 59/60\n",
            "781/781 [==============================] - 48s 62ms/step - loss: 0.5897 - acc: 0.8493 - val_loss: 0.5977 - val_acc: 0.8537\n",
            "Epoch 60/60\n",
            "781/781 [==============================] - 48s 61ms/step - loss: 0.5864 - acc: 0.8510 - val_loss: 0.5622 - val_acc: 0.8610\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f87cbf269b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "jhFxB_fGmxOL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "model.load_weights('weights.hdf5')\n",
        "model.save('BaselineCifar10Model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-1zgsu4c0A9n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "es5danT_lVtZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "\n",
        "model.save_weights('model.h5') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pMxoADr9nBdo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "3bc50a8e-11df-4fb7-b551-4defe14506f4"
      },
      "cell_type": "code",
      "source": [
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(60)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c/T13/waYC5xcqYwDe8Zetwc+jq6KmTd5MhxzTPkVqolj0CTeyUm9aESksUgn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\ni4jkIAV3EZEc9P8BLIfOrNUe6m8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mvi_qm0UnBay",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "afd9e44b-3917-42d9-f020-0424d3ecf290"
      },
      "cell_type": "code",
      "source": [
        "accuracy = model.history.history['acc']\n",
        "val_accuracy = model.history.history['val_acc']\n",
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VkTUislpE3ghvNYvLzYW9e63lbowxgZR6D1URqQlMAQYBWUC6iLynqmt8yrQD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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c/T13/waYC5xcqYwDe8Zetwc+jq6KmTd5MhxzTPkVqolj0CTeyUm9aESksUgn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\ni4jkIAV3EZEc9P8BLIfOrNUe6m8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ly8YcD43nBYc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predicted_classes = model.predict(x_test)\n",
        "predicted_classes = np.argmax(np.round(predicted_classes),axis=1)\n",
        "orig_classes = np.argmax(np.round(y_test),axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w_8JRITP0Qn5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GBouV4NenBVG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "f223e0d0-af90-4c64-8cfa-d97cc6029860"
      },
      "cell_type": "code",
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "plot_confusion_matrix(cm           = confusion_matrix(predicted_classes, orig_classes), \n",
        "                      normalize    = False,\n",
        "                      target_names = class_names,\n",
        "                      title        = \"Confusion Matrix\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TrVoQc+Znf03o+/f6U7hgHuLiE3h2zA9cunKd94d3ppqfL8YYjp06z5C3vk9a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\n+olS1T7RjsEYY0wQYmeq18ywTN0YY4whdjq7ZYZl6sYYY7K8WJoVLjO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Fe1wIl8FuxF0w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\nSZIkSTshlXqSJEmStBNSqSdJkiRJO+G/YzvqxgsWvEMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wIp7YsqInBSh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1c9d91a-b5de-472b-8980-8db52a536b9a"
      },
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(predicted_classes, orig_classes)\n",
        "per_class_accuracy  = np.diag(cm)\n",
        "per_class_accuracy"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([937, 951, 744, 703, 847, 632, 935, 888, 915, 915])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "ZzbqdZd0nauD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "fb28539d-ba05-4bc9-910f-86e9b61457cc"
      },
      "cell_type": "code",
      "source": [
        "np.diag(cm/cm.sum(axis=1)[:,None])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.56616314, 0.91442308, 0.91625616, 0.81460023, 0.88784067,\n",
              "       0.93491124, 0.85232452, 0.92020725, 0.95611285, 0.93272171])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "vBe7A8Isnari",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "01e76d0c-ba3a-4d52-a42d-13f4394cfd03"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "target_names = class_names\n",
        "print(classification_report(orig_classes, predicted_classes, target_names=target_names))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.57      0.94      0.71      1000\n",
            "  automobile       0.91      0.95      0.93      1000\n",
            "        bird       0.92      0.74      0.82      1000\n",
            "         cat       0.81      0.70      0.75      1000\n",
            "        deer       0.89      0.85      0.87      1000\n",
            "         dog       0.93      0.63      0.75      1000\n",
            "        frog       0.85      0.94      0.89      1000\n",
            "       horse       0.92      0.89      0.90      1000\n",
            "        ship       0.96      0.92      0.94      1000\n",
            "       truck       0.93      0.92      0.92      1000\n",
            "\n",
            "   micro avg       0.85      0.85      0.85     10000\n",
            "   macro avg       0.87      0.85      0.85     10000\n",
            "weighted avg       0.87      0.85      0.85     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "oB2l63k8naoL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7EvJxvyvnBPX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vRB8s88alNt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "aa8bfd05-7f65-41d5-fc9a-0e07d89e4e35"
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "scores = model.evaluate(x_test, y_test, batch_size=128, verbose=1)\n",
        "print('\\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 135us/step\n",
            "\n",
            "Test result: 86.770 loss: 0.551\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Z02U8LpnmcnB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}