keras_tf.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "keras_tf.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "kc0GkwaNbRgR",
        "colab_type": "code",
        "outputId": "156f04f5-62e5-4ae2-92fb-8f207f59f8d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import time\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from google.colab import drive\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dense, Activation, Flatten\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\n",
        "\n",
        "from keras.layers import Dense, MaxPool2D, Dropout\n",
        "from keras.layers import Flatten, InputLayer\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.utils import np_utils\n",
        "from keras.initializers import Constant\n",
        "from keras.layers.convolutional import Conv2D\n",
        "import psutil\n",
        "import tensorflow as tf\n",
        "import scipy.misc"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "XGfN8eC1io4j",
        "colab_type": "code",
        "outputId": "7c6b1e13-044a-4217-dcc8-0faa44068d48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "cell_type": "code",
      "source": [
        "!nvidia-smi "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Dec  4 20:07:14 2018       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 396.44                 Driver Version: 396.44                    |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   69C    P8    32W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Okdae8bZ9FAF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist, fashion_mnist "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8ru0Haq4q4PU",
        "colab_type": "code",
        "outputId": "3e872e3b-4cff-4bfa-fc32-471765861a5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lInRXLU0_rwP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Build and compile neural network"
      ]
    },
    {
      "metadata": {
        "id": "DFHxxr3k9PE_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 1\n"
      ]
    },
    {
      "metadata": {
        "id": "LTVylHeb0Y_M",
        "colab_type": "code",
        "outputId": "28aff8cb-b632-4931-fd91-6726703d4b01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# Create model in Keras\n",
        "# This model is linear stack of layers\n",
        "clf1 = Sequential()\n",
        "clf1.add(InputLayer(input_shape=(28,28,1)))\n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "#clf1.add(BatchNormalization())\n",
        "clf1.add(Conv2D(16, (5, 5), activation='relu', padding='same')) \n",
        "# Add max pooling layer for 2D data.\n",
        "clf1.add(MaxPool2D(padding='same'))\n",
        "clf1.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "clf1.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "clf1.add(Flatten())\n",
        "clf1.add(Dense(10,  activation='softmax'))\n",
        "clf1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "              \n",
        "clf1.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 16)        416       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 14, 14, 32)        12832     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                15690     \n",
            "=================================================================\n",
            "Total params: 28,938\n",
            "Trainable params: 28,938\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4loJmGcdtahU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 2"
      ]
    },
    {
      "metadata": {
        "id": "hzHWx2p1tBQ1",
        "colab_type": "code",
        "outputId": "6e0d9895-3eb8-4216-85e7-e2b46b3f2f70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "cell_type": "code",
      "source": [
        "clf2 = Sequential()\n",
        "clf2.add(InputLayer(input_shape=(28,28,1)))\n",
        "clf2.add(Conv2D(16, (2, 2), activation='relu', padding='same'))\n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "clf2.add(BatchNormalization())\n",
        "# Add max pooling layer for 2D data.\n",
        "clf2.add(MaxPool2D(padding='same'))\n",
        "clf2.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "clf2.add(BatchNormalization())\n",
        "clf2.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "clf2.add(Flatten())\n",
        "clf2.add(Dense(10,  activation='softmax'))\n",
        "clf2.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])\n",
        "              \n",
        "clf2.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 28, 28, 16)        80        \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 28, 28, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 32)        12832     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 14, 14, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                15690     \n",
            "=================================================================\n",
            "Total params: 28,794\n",
            "Trainable params: 28,698\n",
            "Non-trainable params: 96\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xe4kD6tRuIcO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 3"
      ]
    },
    {
      "metadata": {
        "id": "TuA0LcZ1NSoy",
        "colab_type": "code",
        "outputId": "1bba7bc1-afba-49a5-b980-f73e36248a2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "cell_type": "code",
      "source": [
        "clf3 = Sequential()\n",
        "clf3.add(InputLayer(input_shape=(28,28,1)))\n",
        "clf3.add(Conv2D(128, (5, 5), padding='same', activation='relu'))\n",
        "clf3.add(Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
        "clf3.add(BatchNormalization())\n",
        "clf3.add(MaxPool2D(padding='same'))\n",
        "clf3.add(Conv2D(32, (5, 5), padding='same', activation='relu'))\n",
        "clf3.add(Conv2D(16, (5, 5), padding='same', activation='relu'))\n",
        "clf3.add(BatchNormalization())\n",
        "clf3.add(MaxPool2D(padding='same'))\n",
        "clf3.add(Flatten())\n",
        "clf3.add(Dense(10, activation='softmax')) \n",
        "\n",
        "clf3.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "clf3.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_5 (Conv2D)            (None, 28, 28, 128)       3328      \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 28, 28, 64)        204864    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 28, 28, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 14, 14, 32)        51232     \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 14, 14, 16)        12816     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 14, 14, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 7, 7, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                7850      \n",
            "=================================================================\n",
            "Total params: 280,410\n",
            "Trainable params: 280,250\n",
            "Non-trainable params: 160\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qkn80TfruQ6R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Architecture 4"
      ]
    },
    {
      "metadata": {
        "id": "BMGt66XRuPjq",
        "colab_type": "code",
        "outputId": "17cf5f83-d3f4-4078-9894-904ea8c78178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "cell_type": "code",
      "source": [
        "# Must define the input shape in the first layer of the neural network\n",
        "clf4 = Sequential()\n",
        "clf4.add(InputLayer(input_shape=(28,28,1)))\n",
        "clf4.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "clf4.add(BatchNormalization())\n",
        "clf4.add(MaxPooling2D(pool_size=2))\n",
        "clf4.add(Flatten())\n",
        "clf4.add(Dropout(0.3))\n",
        "clf4.add(Dense(10, activation='softmax'))\n",
        "clf4.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])\n",
        "              \n",
        "clf4.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 28, 28, 32)        832       \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 28, 28, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                62730     \n",
            "=================================================================\n",
            "Total params: 63,690\n",
            "Trainable params: 63,626\n",
            "Non-trainable params: 64\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "u-BDrpSgA0hv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 5"
      ]
    },
    {
      "metadata": {
        "id": "DPmh22bWAsgk",
        "colab_type": "code",
        "outputId": "e95feaa4-eeab-47c2-fe97-1c3d8da647ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "clf5 = Sequential()\n",
        "clf5.add(InputLayer(input_shape=(28,28,1)))\n",
        "\n",
        "#clf5.add(BatchNormalization())\n",
        "clf5.add(Conv2D(8, (5, 5), activation='relu', padding='same')) \n",
        "# Add max pooling layer for 2D data.\n",
        "clf5.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "clf5.add(Flatten())\n",
        "clf5.add(Dense(10, activation='softmax'))\n",
        "clf5.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "              \n",
        "clf5.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_10 (Conv2D)           (None, 28, 28, 8)         208       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 8)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 1568)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                15690     \n",
            "=================================================================\n",
            "Total params: 15,898\n",
            "Trainable params: 15,898\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fi0Fb9TfbGPT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Fashion MNIST"
      ]
    },
    {
      "metadata": {
        "id": "siY2kaz9kb6D",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Code heavily borrowed from  https://medium.com/@lukaszlipinski/fashion-mnist-with-keras-in-5-minuts-20ab9eb7b905\n",
        "\n",
        "https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a"
      ]
    },
    {
      "metadata": {
        "id": "lYx2Og9ecBSH",
        "colab_type": "code",
        "outputId": "f582be99-b0d9-49e2-cfd5-31d74a7602d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the fashion-mnist pre-shuffled train data and test data\n",
        "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
        "print(\"x_train shape:\", x_train.shape, \"y_train shape:\", y_train.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 1us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "x_train shape: (60000, 28, 28) y_train shape: (60000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fuVnu26dcvwk",
        "colab_type": "code",
        "outputId": "a978c412-27c8-43d6-d7da-4c9be6fe5469",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Show some images from the training dataset\n",
        "plt.figure()\n",
        "for i in range(10):\n",
        "    plt.subplot(5,6,i+1)\n",
        "    plt.imshow(x_train[i])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SUVBQ4Gyvz/vHf/zHDJ155ggXoI9X4OQ73/kOEFznoEGD4pZO1Ou+mzc3N9dN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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8a48b225f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BWSH6C89dGuu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Prepare datasets\n",
        "# This step contains normalization and reshaping of input.\n",
        "# For output, it is important to change number to one-hot vector. \n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "y_train = np_utils.to_categorical(y_train, 10)\n",
        "y_test = np_utils.to_categorical(y_test, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rz1G5Fhi8I0q",
        "colab_type": "code",
        "outputId": "b71fbfaf-862e-41a5-a854-7d667f98e5f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZMBLiGxhilEm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def go(model = clf1):\n",
        "    start=time.time()\n",
        "    mem_before = psutil.virtual_memory()\n",
        "    cpu_before = psutil.cpu_percent(interval=0.0)\n",
        "    hist = model.fit(x_train, y_train, batch_size=32, epochs=20, verbose=1, validation_data=(x_test, y_test))\n",
        "    end = time.time()\n",
        "    mem_after = psutil.virtual_memory()\n",
        "    memory = mem_after.percent - mem_before.percent\n",
        "    cpu_after = psutil.cpu_percent(interval=0.0)\n",
        "    cpu_usage = cpu_after - cpu_before\n",
        "    print('Total Training time: %s' % (end - start))\n",
        "    print('\\n', 'Test accuracy:', model.evaluate(x_test, y_test, verbose=1)[1])\n",
        "    return {'CPU_use':cpu_usage, 'memory_percent_use':memory}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kPDMUbSPhr6n",
        "colab_type": "code",
        "outputId": "11c80bd1-b11d-4a82-fec0-8a1f9c186e31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "cell_type": "code",
      "source": [
        "go()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 20s 326us/step - loss: 0.4581 - acc: 0.8364 - val_loss: 0.3679 - val_acc: 0.8683\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 16s 270us/step - loss: 0.3048 - acc: 0.8915 - val_loss: 0.3191 - val_acc: 0.8847\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 16s 270us/step - loss: 0.2626 - acc: 0.9049 - val_loss: 0.2968 - val_acc: 0.8938\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 16s 268us/step - loss: 0.2368 - acc: 0.9153 - val_loss: 0.2717 - val_acc: 0.9036\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 16s 267us/step - loss: 0.2176 - acc: 0.9211 - val_loss: 0.2713 - val_acc: 0.9026\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 16s 267us/step - loss: 0.2025 - acc: 0.9258 - val_loss: 0.2630 - val_acc: 0.9063\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 16s 267us/step - loss: 0.1877 - acc: 0.9320 - val_loss: 0.2531 - val_acc: 0.9108\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 16s 268us/step - loss: 0.1762 - acc: 0.9356 - val_loss: 0.2715 - val_acc: 0.9043\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 16s 270us/step - loss: 0.1681 - acc: 0.9386 - val_loss: 0.2728 - val_acc: 0.9110\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 16s 270us/step - loss: 0.1568 - acc: 0.9425 - val_loss: 0.2622 - val_acc: 0.9095\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 18s 300us/step - loss: 0.1490 - acc: 0.9459 - val_loss: 0.2800 - val_acc: 0.9114\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 18s 303us/step - loss: 0.1401 - acc: 0.9491 - val_loss: 0.2847 - val_acc: 0.9119\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 18s 301us/step - loss: 0.1359 - acc: 0.9495 - val_loss: 0.2727 - val_acc: 0.9143\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 19s 309us/step - loss: 0.1281 - acc: 0.9520 - val_loss: 0.2865 - val_acc: 0.9085\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 18s 307us/step - loss: 0.1222 - acc: 0.9545 - val_loss: 0.3109 - val_acc: 0.9029\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 17s 281us/step - loss: 0.1168 - acc: 0.9573 - val_loss: 0.2843 - val_acc: 0.9124\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 17s 281us/step - loss: 0.1127 - acc: 0.9582 - val_loss: 0.3026 - val_acc: 0.9103\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 17s 286us/step - loss: 0.1075 - acc: 0.9602 - val_loss: 0.3222 - val_acc: 0.9113\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 17s 281us/step - loss: 0.1044 - acc: 0.9618 - val_loss: 0.3190 - val_acc: 0.9065\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 17s 284us/step - loss: 0.0989 - acc: 0.9646 - val_loss: 0.3231 - val_acc: 0.9108\n",
            "Total Training time: 341.04558515548706\n",
            "10000/10000 [==============================] - 1s 94us/step\n",
            "\n",
            " Test accuracy: 0.9108\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 43.10000000000001, 'memory_percent_use': 2.5}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "l7JMn9bDOR_d",
        "colab_type": "code",
        "outputId": "1fef9971-6042-4f2d-ccc6-eaa709546c9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "cell_type": "code",
      "source": [
        "go(clf2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 19s 321us/step - loss: 0.4319 - acc: 0.8504 - val_loss: 0.3536 - val_acc: 0.8805\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 19s 312us/step - loss: 0.2986 - acc: 0.8943 - val_loss: 0.3171 - val_acc: 0.8873\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 21s 353us/step - loss: 0.2621 - acc: 0.9055 - val_loss: 0.3069 - val_acc: 0.8903\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 22s 361us/step - loss: 0.2387 - acc: 0.9141 - val_loss: 0.2824 - val_acc: 0.8958\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 21s 344us/step - loss: 0.2200 - acc: 0.9218 - val_loss: 0.2847 - val_acc: 0.8991\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 19s 318us/step - loss: 0.2084 - acc: 0.9245 - val_loss: 0.2857 - val_acc: 0.8991\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 19s 315us/step - loss: 0.1959 - acc: 0.9288 - val_loss: 0.2621 - val_acc: 0.9076\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 19s 309us/step - loss: 0.1855 - acc: 0.9324 - val_loss: 0.2892 - val_acc: 0.9020\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 18s 308us/step - loss: 0.1783 - acc: 0.9356 - val_loss: 0.2647 - val_acc: 0.9082\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 19s 312us/step - loss: 0.1682 - acc: 0.9395 - val_loss: 0.3179 - val_acc: 0.8930\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 19s 312us/step - loss: 0.1613 - acc: 0.9415 - val_loss: 0.2877 - val_acc: 0.9037\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 18s 306us/step - loss: 0.1550 - acc: 0.9436 - val_loss: 0.2837 - val_acc: 0.9027\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 19s 308us/step - loss: 0.1488 - acc: 0.9459 - val_loss: 0.3299 - val_acc: 0.8897\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 18s 308us/step - loss: 0.1413 - acc: 0.9489 - val_loss: 0.3276 - val_acc: 0.8975\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 18s 305us/step - loss: 0.1377 - acc: 0.9496 - val_loss: 0.3285 - val_acc: 0.8904\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 19s 310us/step - loss: 0.1319 - acc: 0.9509 - val_loss: 0.3118 - val_acc: 0.8987\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 18s 306us/step - loss: 0.1283 - acc: 0.9525 - val_loss: 0.3054 - val_acc: 0.9019\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 18s 306us/step - loss: 0.1218 - acc: 0.9560 - val_loss: 0.3299 - val_acc: 0.8897\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 19s 310us/step - loss: 0.1202 - acc: 0.9565 - val_loss: 0.3043 - val_acc: 0.9028\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 18s 306us/step - loss: 0.1148 - acc: 0.9587 - val_loss: 0.3514 - val_acc: 0.8956\n",
            "Total Training time: 380.21157598495483\n",
            "10000/10000 [==============================] - 1s 97us/step\n",
            "\n",
            " Test accuracy: 0.8956\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': -0.10000000000000853, 'memory_percent_use': 0.09999999999999964}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "fqp1hGG2OWqQ",
        "colab_type": "code",
        "outputId": "aab9bf03-7a75-4991-b479-674a86bc77c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        }
      },
      "cell_type": "code",
      "source": [
        "go(clf3)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 40s 663us/step - loss: 0.4024 - acc: 0.8551 - val_loss: 0.3458 - val_acc: 0.8796\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 36s 603us/step - loss: 0.2754 - acc: 0.8997 - val_loss: 0.2978 - val_acc: 0.8936\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 36s 602us/step - loss: 0.2366 - acc: 0.9148 - val_loss: 0.2953 - val_acc: 0.8910\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 36s 601us/step - loss: 0.2105 - acc: 0.9232 - val_loss: 0.2453 - val_acc: 0.9099\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 36s 601us/step - loss: 0.1883 - acc: 0.9322 - val_loss: 0.2394 - val_acc: 0.9167\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 36s 600us/step - loss: 0.1682 - acc: 0.9395 - val_loss: 0.2538 - val_acc: 0.9111\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 36s 604us/step - loss: 0.1496 - acc: 0.9457 - val_loss: 0.2401 - val_acc: 0.9166\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 36s 602us/step - loss: 0.1361 - acc: 0.9508 - val_loss: 0.2270 - val_acc: 0.9230\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 36s 604us/step - loss: 0.1212 - acc: 0.9562 - val_loss: 0.2564 - val_acc: 0.9165\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 36s 603us/step - loss: 0.1073 - acc: 0.9603 - val_loss: 0.2582 - val_acc: 0.9214\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 36s 602us/step - loss: 0.0940 - acc: 0.9651 - val_loss: 0.2699 - val_acc: 0.9180\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 39s 644us/step - loss: 0.0850 - acc: 0.9683 - val_loss: 0.2754 - val_acc: 0.9201\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 36s 608us/step - loss: 0.0741 - acc: 0.9728 - val_loss: 0.2947 - val_acc: 0.9192\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 46s 768us/step - loss: 0.0647 - acc: 0.9756 - val_loss: 0.3011 - val_acc: 0.9212\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 52s 868us/step - loss: 0.0607 - acc: 0.9778 - val_loss: 0.3148 - val_acc: 0.9231\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 54s 903us/step - loss: 0.0523 - acc: 0.9804 - val_loss: 0.3491 - val_acc: 0.9177\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 52s 874us/step - loss: 0.0474 - acc: 0.9828 - val_loss: 0.3405 - val_acc: 0.9205\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 53s 883us/step - loss: 0.0453 - acc: 0.9831 - val_loss: 0.3487 - val_acc: 0.9169\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 54s 900us/step - loss: 0.0398 - acc: 0.9851 - val_loss: 0.3666 - val_acc: 0.9197\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 53s 880us/step - loss: 0.0373 - acc: 0.9859 - val_loss: 0.3857 - val_acc: 0.9173\n",
            "Total Training time: 841.8113341331482\n",
            "10000/10000 [==============================] - 3s 283us/step\n",
            "\n",
            " Test accuracy: 0.9173\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 60.400000000000006, 'memory_percent_use': 4.299999999999999}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "jvUnjCeBOYw3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "d4f15bd5-64d1-4eb5-9c2d-d066085e03d1"
      },
      "cell_type": "code",
      "source": [
        "go(clf4)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 23s 390us/step - loss: 0.4740 - acc: 0.8364 - val_loss: 0.3474 - val_acc: 0.8770\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 23s 386us/step - loss: 0.3365 - acc: 0.8801 - val_loss: 0.3154 - val_acc: 0.8889\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 23s 377us/step - loss: 0.3003 - acc: 0.8933 - val_loss: 0.3099 - val_acc: 0.8881\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 22s 373us/step - loss: 0.2827 - acc: 0.8977 - val_loss: 0.2836 - val_acc: 0.8977\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 23s 381us/step - loss: 0.2618 - acc: 0.9057 - val_loss: 0.3859 - val_acc: 0.8718\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 23s 389us/step - loss: 0.2583 - acc: 0.9050 - val_loss: 0.2963 - val_acc: 0.8975\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 23s 383us/step - loss: 0.2429 - acc: 0.9111 - val_loss: 0.3157 - val_acc: 0.8874\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 22s 375us/step - loss: 0.2390 - acc: 0.9131 - val_loss: 0.2769 - val_acc: 0.9045\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 23s 378us/step - loss: 0.2307 - acc: 0.9169 - val_loss: 0.2676 - val_acc: 0.9053\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 23s 375us/step - loss: 0.2243 - acc: 0.9185 - val_loss: 0.2975 - val_acc: 0.8938\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 23s 386us/step - loss: 0.2191 - acc: 0.9207 - val_loss: 0.2632 - val_acc: 0.9054\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 23s 375us/step - loss: 0.2175 - acc: 0.9192 - val_loss: 0.2710 - val_acc: 0.9054\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 23s 377us/step - loss: 0.2109 - acc: 0.9224 - val_loss: 0.2696 - val_acc: 0.9075\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 23s 376us/step - loss: 0.2095 - acc: 0.9232 - val_loss: 0.2858 - val_acc: 0.8999\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 22s 372us/step - loss: 0.2076 - acc: 0.9225 - val_loss: 0.2689 - val_acc: 0.9047\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 23s 384us/step - loss: 0.2038 - acc: 0.9258 - val_loss: 0.2665 - val_acc: 0.9084\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 23s 376us/step - loss: 0.1986 - acc: 0.9261 - val_loss: 0.2608 - val_acc: 0.9072\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 22s 371us/step - loss: 0.1987 - acc: 0.9276 - val_loss: 0.2643 - val_acc: 0.9071\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 22s 374us/step - loss: 0.1965 - acc: 0.9277 - val_loss: 0.2758 - val_acc: 0.9071\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 23s 384us/step - loss: 0.1924 - acc: 0.9299 - val_loss: 0.2753 - val_acc: 0.9057\n",
            "Total Training time: 455.4223611354828\n",
            "10000/10000 [==============================] - 2s 153us/step\n",
            "\n",
            " Test accuracy: 0.9057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 9.200000000000003, 'memory_percent_use': 0.09999999999999964}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "qjX4IY_RAZZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "88ac1a40-02a5-4b69-8ae8-39d482b92267"
      },
      "cell_type": "code",
      "source": [
        "go(clf5)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 21s 355us/step - loss: 0.5035 - acc: 0.8255 - val_loss: 0.4211 - val_acc: 0.8501\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 20s 338us/step - loss: 0.3662 - acc: 0.8717 - val_loss: 0.3602 - val_acc: 0.8737\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 20s 341us/step - loss: 0.3231 - acc: 0.8868 - val_loss: 0.3407 - val_acc: 0.8811\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 21s 343us/step - loss: 0.2954 - acc: 0.8961 - val_loss: 0.3232 - val_acc: 0.8863\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 21s 351us/step - loss: 0.2769 - acc: 0.9018 - val_loss: 0.3080 - val_acc: 0.8886\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 21s 348us/step - loss: 0.2629 - acc: 0.9066 - val_loss: 0.3122 - val_acc: 0.8870\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 21s 346us/step - loss: 0.2529 - acc: 0.9106 - val_loss: 0.2912 - val_acc: 0.8967\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 21s 350us/step - loss: 0.2446 - acc: 0.9132 - val_loss: 0.3058 - val_acc: 0.8918\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 21s 348us/step - loss: 0.2359 - acc: 0.9165 - val_loss: 0.3095 - val_acc: 0.8909\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 21s 352us/step - loss: 0.2298 - acc: 0.9183 - val_loss: 0.2900 - val_acc: 0.8980\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 21s 347us/step - loss: 0.2241 - acc: 0.9203 - val_loss: 0.2875 - val_acc: 0.8996\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 20s 338us/step - loss: 0.2197 - acc: 0.9216 - val_loss: 0.3030 - val_acc: 0.8963\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 20s 341us/step - loss: 0.2152 - acc: 0.9230 - val_loss: 0.2940 - val_acc: 0.8965\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 21s 344us/step - loss: 0.2119 - acc: 0.9244 - val_loss: 0.2865 - val_acc: 0.9001\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 20s 341us/step - loss: 0.2077 - acc: 0.9256 - val_loss: 0.2952 - val_acc: 0.8995\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 21s 351us/step - loss: 0.2051 - acc: 0.9273 - val_loss: 0.2915 - val_acc: 0.9024\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 20s 335us/step - loss: 0.2036 - acc: 0.9266 - val_loss: 0.2916 - val_acc: 0.9008\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.2001 - acc: 0.9282 - val_loss: 0.2950 - val_acc: 0.9023\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 21s 343us/step - loss: 0.1989 - acc: 0.9278 - val_loss: 0.3111 - val_acc: 0.8928\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 21s 342us/step - loss: 0.1959 - acc: 0.9306 - val_loss: 0.3008 - val_acc: 0.8990\n",
            "Total Training time: 414.4294719696045\n",
            "10000/10000 [==============================] - 1s 123us/step\n",
            "\n",
            " Test accuracy: 0.899\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 0.9000000000000057, 'memory_percent_use': -0.09999999999999964}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "8WyGCdlGhq-W",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# MNIST"
      ]
    },
    {
      "metadata": {
        "id": "qY7TJSNuhsLR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a9b3a4ac-282d-40aa-8cd1-b3a3c6cfecb6"
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4Tzn7478hnCm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a23e49c6-7c86-4399-aba2-abf1d86af896"
      },
      "cell_type": "code",
      "source": [
        "print(\"x_train shape:\", x_train.shape, \"y_train shape:\", y_train.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28) y_train shape: (60000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZcpWyBC3h5lR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "2664e66e-2e3a-450d-c44f-5311e50372f5"
      },
      "cell_type": "code",
      "source": [
        "# Show some images from the training dataset\n",
        "plt.figure()\n",
        "for i in range(10):\n",
        "    plt.subplot(5,6,i+1)\n",
        "    plt.imshow(x_train[i])\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JGqtEtfkja54diznTaLDtq2ydo83hHYqt+bP5VIvVddddB3hN8qV2xx9/POBt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\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f8a003f6550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fscbZSAUiZNX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Prepare datasets\n",
        "# This step contains normalization and reshaping of input.\n",
        "# For output, it is important to change number to one-hot vector. \n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "y_train = np_utils.to_categorical(y_train, 10)\n",
        "y_test = np_utils.to_categorical(y_test, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TnHICwGqiaiM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "e6037abf-b08e-4844-ae27-2133a17c6ab5"
      },
      "cell_type": "code",
      "source": [
        "go()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 26s 426us/step - loss: 0.1577 - acc: 0.9522 - val_loss: 0.0654 - val_acc: 0.9781\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 24s 407us/step - loss: 0.0495 - acc: 0.9845 - val_loss: 0.0366 - val_acc: 0.9867\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 24s 408us/step - loss: 0.0360 - acc: 0.9890 - val_loss: 0.0349 - val_acc: 0.9881\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 25s 411us/step - loss: 0.0284 - acc: 0.9912 - val_loss: 0.0348 - val_acc: 0.9892\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 25s 424us/step - loss: 0.0219 - acc: 0.9929 - val_loss: 0.0319 - val_acc: 0.9904\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 25s 409us/step - loss: 0.0176 - acc: 0.9942 - val_loss: 0.0304 - val_acc: 0.9908\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 24s 405us/step - loss: 0.0146 - acc: 0.9951 - val_loss: 0.0273 - val_acc: 0.9920\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 25s 413us/step - loss: 0.0121 - acc: 0.9960 - val_loss: 0.0381 - val_acc: 0.9885\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 25s 425us/step - loss: 0.0110 - acc: 0.9962 - val_loss: 0.0394 - val_acc: 0.9894\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 24s 406us/step - loss: 0.0090 - acc: 0.9970 - val_loss: 0.0289 - val_acc: 0.9919\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 24s 407us/step - loss: 0.0071 - acc: 0.9976 - val_loss: 0.0399 - val_acc: 0.9910\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 24s 405us/step - loss: 0.0074 - acc: 0.9974 - val_loss: 0.0456 - val_acc: 0.9895\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 26s 425us/step - loss: 0.0061 - acc: 0.9979 - val_loss: 0.0428 - val_acc: 0.9891\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 24s 406us/step - loss: 0.0052 - acc: 0.9983 - val_loss: 0.0404 - val_acc: 0.9905\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 25s 409us/step - loss: 0.0056 - acc: 0.9982 - val_loss: 0.0405 - val_acc: 0.9914\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 25s 412us/step - loss: 0.0044 - acc: 0.9985 - val_loss: 0.0484 - val_acc: 0.9899\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 25s 415us/step - loss: 0.0045 - acc: 0.9984 - val_loss: 0.0481 - val_acc: 0.9899\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 25s 419us/step - loss: 0.0051 - acc: 0.9986 - val_loss: 0.0483 - val_acc: 0.9907\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 25s 412us/step - loss: 0.0034 - acc: 0.9990 - val_loss: 0.0539 - val_acc: 0.9898\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 25s 410us/step - loss: 0.0043 - acc: 0.9987 - val_loss: 0.0405 - val_acc: 0.9922\n",
            "Total Training time: 496.1997239589691\n",
            "10000/10000 [==============================] - 1s 140us/step\n",
            "\n",
            " Test accuracy: 0.9922\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 4.700000000000003, 'memory_percent_use': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "RpAfN5YrU1ZS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "0639cd7b-3232-481b-e24f-a24ad9197726"
      },
      "cell_type": "code",
      "source": [
        "go(clf2)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 28s 467us/step - loss: 0.1430 - acc: 0.9576 - val_loss: 0.0606 - val_acc: 0.9802\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 28s 460us/step - loss: 0.0536 - acc: 0.9827 - val_loss: 0.0571 - val_acc: 0.9805\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 27s 444us/step - loss: 0.0384 - acc: 0.9876 - val_loss: 0.0405 - val_acc: 0.9855\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 27s 444us/step - loss: 0.0288 - acc: 0.9913 - val_loss: 0.0426 - val_acc: 0.9848\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 27s 449us/step - loss: 0.0237 - acc: 0.9929 - val_loss: 0.0435 - val_acc: 0.9850\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 27s 456us/step - loss: 0.0187 - acc: 0.9943 - val_loss: 0.0404 - val_acc: 0.9874\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 27s 447us/step - loss: 0.0154 - acc: 0.9957 - val_loss: 0.0333 - val_acc: 0.9879\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 27s 448us/step - loss: 0.0129 - acc: 0.9968 - val_loss: 0.0351 - val_acc: 0.9869\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 28s 465us/step - loss: 0.0106 - acc: 0.9977 - val_loss: 0.0406 - val_acc: 0.9873\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 27s 451us/step - loss: 0.0089 - acc: 0.9983 - val_loss: 0.0338 - val_acc: 0.9882\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 27s 448us/step - loss: 0.0074 - acc: 0.9986 - val_loss: 0.0348 - val_acc: 0.9889\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 27s 448us/step - loss: 0.0062 - acc: 0.9991 - val_loss: 0.0342 - val_acc: 0.9886\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 28s 461us/step - loss: 0.0057 - acc: 0.9993 - val_loss: 0.0360 - val_acc: 0.9878\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 24s 400us/step - loss: 0.0045 - acc: 0.9995 - val_loss: 0.0384 - val_acc: 0.9876\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 24s 400us/step - loss: 0.0039 - acc: 0.9997 - val_loss: 0.0340 - val_acc: 0.9883\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 24s 403us/step - loss: 0.0033 - acc: 0.9998 - val_loss: 0.0347 - val_acc: 0.9887\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 25s 412us/step - loss: 0.0029 - acc: 0.9999 - val_loss: 0.0362 - val_acc: 0.9889\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 25s 411us/step - loss: 0.0026 - acc: 0.9999 - val_loss: 0.0366 - val_acc: 0.9881\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 25s 409us/step - loss: 0.0024 - acc: 1.0000 - val_loss: 0.0357 - val_acc: 0.9885\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 25s 411us/step - loss: 0.0023 - acc: 0.9999 - val_loss: 0.0365 - val_acc: 0.9883\n",
            "Total Training time: 524.7650208473206\n",
            "10000/10000 [==============================] - 1s 135us/step\n",
            "\n",
            " Test accuracy: 0.9883\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': -0.5999999999999943, 'memory_percent_use': 0.20000000000000107}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "yWS7nivrU1uW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "c6469c52-be40-4578-c0b9-452bfb784a92"
      },
      "cell_type": "code",
      "source": [
        "go(clf3)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 46s 767us/step - loss: 0.1549 - acc: 0.9616 - val_loss: 0.0450 - val_acc: 0.9858\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 46s 761us/step - loss: 0.0355 - acc: 0.9892 - val_loss: 0.0340 - val_acc: 0.9888\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 44s 740us/step - loss: 0.0227 - acc: 0.9927 - val_loss: 0.0394 - val_acc: 0.9876\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 45s 745us/step - loss: 0.0153 - acc: 0.9949 - val_loss: 0.0322 - val_acc: 0.9895\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 44s 739us/step - loss: 0.0110 - acc: 0.9963 - val_loss: 0.0295 - val_acc: 0.9916\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 45s 754us/step - loss: 0.0112 - acc: 0.9964 - val_loss: 0.0399 - val_acc: 0.9896\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 45s 747us/step - loss: 0.0077 - acc: 0.9975 - val_loss: 0.0313 - val_acc: 0.9920\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 45s 745us/step - loss: 0.0077 - acc: 0.9973 - val_loss: 0.0359 - val_acc: 0.9904\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 45s 746us/step - loss: 0.0056 - acc: 0.9981 - val_loss: 0.0422 - val_acc: 0.9893\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 46s 770us/step - loss: 0.0061 - acc: 0.9980 - val_loss: 0.0368 - val_acc: 0.9901\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 45s 755us/step - loss: 0.0049 - acc: 0.9984 - val_loss: 0.0414 - val_acc: 0.9896\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 44s 738us/step - loss: 0.0044 - acc: 0.9986 - val_loss: 0.0379 - val_acc: 0.9909\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 45s 744us/step - loss: 0.0050 - acc: 0.9983 - val_loss: 0.0446 - val_acc: 0.9919\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 44s 741us/step - loss: 0.0043 - acc: 0.9986 - val_loss: 0.0371 - val_acc: 0.9916\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 44s 740us/step - loss: 0.0039 - acc: 0.9988 - val_loss: 0.0420 - val_acc: 0.9910\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 45s 742us/step - loss: 0.0040 - acc: 0.9988 - val_loss: 0.0416 - val_acc: 0.9910\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 44s 735us/step - loss: 0.0033 - acc: 0.9990 - val_loss: 0.0467 - val_acc: 0.9915\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 44s 736us/step - loss: 0.0042 - acc: 0.9987 - val_loss: 0.0373 - val_acc: 0.9928\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 44s 740us/step - loss: 0.0022 - acc: 0.9994 - val_loss: 0.0332 - val_acc: 0.9928\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 44s 737us/step - loss: 0.0039 - acc: 0.9988 - val_loss: 0.0429 - val_acc: 0.9902\n",
            "Total Training time: 895.5416271686554\n",
            "10000/10000 [==============================] - 2s 227us/step\n",
            "\n",
            " Test accuracy: 0.9902\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': -2.0, 'memory_percent_use': 0.09999999999999964}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "R2xdV7rsU18p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "f3291609-33d3-4503-b221-f6f5edc5749a"
      },
      "cell_type": "code",
      "source": [
        "go(clf4)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.2196 - acc: 0.9348 - val_loss: 0.0748 - val_acc: 0.9775\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 20s 336us/step - loss: 0.0843 - acc: 0.9739 - val_loss: 0.0535 - val_acc: 0.9822\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 20s 337us/step - loss: 0.0646 - acc: 0.9798 - val_loss: 0.0509 - val_acc: 0.9827\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 20s 338us/step - loss: 0.0537 - acc: 0.9830 - val_loss: 0.0506 - val_acc: 0.9827\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0480 - acc: 0.9850 - val_loss: 0.0452 - val_acc: 0.9844\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 21s 344us/step - loss: 0.0437 - acc: 0.9856 - val_loss: 0.0418 - val_acc: 0.9852\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 20s 338us/step - loss: 0.0391 - acc: 0.9873 - val_loss: 0.0437 - val_acc: 0.9855\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 20s 337us/step - loss: 0.0359 - acc: 0.9885 - val_loss: 0.0427 - val_acc: 0.9856\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0334 - acc: 0.9890 - val_loss: 0.0422 - val_acc: 0.9856\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0312 - acc: 0.9898 - val_loss: 0.0412 - val_acc: 0.9859\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 20s 339us/step - loss: 0.0299 - acc: 0.9900 - val_loss: 0.0376 - val_acc: 0.9876\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 21s 343us/step - loss: 0.0293 - acc: 0.9905 - val_loss: 0.0430 - val_acc: 0.9860\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 21s 342us/step - loss: 0.0252 - acc: 0.9917 - val_loss: 0.0459 - val_acc: 0.9862\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0254 - acc: 0.9918 - val_loss: 0.0425 - val_acc: 0.9866\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 21s 342us/step - loss: 0.0232 - acc: 0.9922 - val_loss: 0.0382 - val_acc: 0.9878\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 20s 341us/step - loss: 0.0219 - acc: 0.9924 - val_loss: 0.0410 - val_acc: 0.9871\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0217 - acc: 0.9929 - val_loss: 0.0395 - val_acc: 0.9867\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 20s 336us/step - loss: 0.0212 - acc: 0.9926 - val_loss: 0.0406 - val_acc: 0.9877\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 20s 340us/step - loss: 0.0203 - acc: 0.9931 - val_loss: 0.0408 - val_acc: 0.9875\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 20s 341us/step - loss: 0.0188 - acc: 0.9939 - val_loss: 0.0399 - val_acc: 0.9876\n",
            "Total Training time: 407.7735364437103\n",
            "10000/10000 [==============================] - 1s 121us/step\n",
            "\n",
            " Test accuracy: 0.9876\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 9.299999999999997, 'memory_percent_use': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "X_fWo29nVOZk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "256b93a7-3fcd-46f3-d41e-9de594991595"
      },
      "cell_type": "code",
      "source": [
        "go(clf5)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 19s 316us/step - loss: 0.3670 - acc: 0.9053 - val_loss: 0.1340 - val_acc: 0.9603\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 18s 307us/step - loss: 0.1121 - acc: 0.9662 - val_loss: 0.0900 - val_acc: 0.9713\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 18s 304us/step - loss: 0.0786 - acc: 0.9759 - val_loss: 0.0736 - val_acc: 0.9757\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 19s 312us/step - loss: 0.0620 - acc: 0.9811 - val_loss: 0.0638 - val_acc: 0.9798\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.0516 - acc: 0.9838 - val_loss: 0.0624 - val_acc: 0.9795\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 19s 313us/step - loss: 0.0442 - acc: 0.9865 - val_loss: 0.0595 - val_acc: 0.9813\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 18s 304us/step - loss: 0.0391 - acc: 0.9880 - val_loss: 0.0528 - val_acc: 0.9837\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 18s 303us/step - loss: 0.0347 - acc: 0.9891 - val_loss: 0.0573 - val_acc: 0.9821\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 18s 305us/step - loss: 0.0313 - acc: 0.9903 - val_loss: 0.0538 - val_acc: 0.9829\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 19s 312us/step - loss: 0.0277 - acc: 0.9913 - val_loss: 0.0582 - val_acc: 0.9832\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 18s 307us/step - loss: 0.0256 - acc: 0.9920 - val_loss: 0.0593 - val_acc: 0.9827\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 19s 309us/step - loss: 0.0230 - acc: 0.9927 - val_loss: 0.0558 - val_acc: 0.9835\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 19s 316us/step - loss: 0.0207 - acc: 0.9936 - val_loss: 0.0575 - val_acc: 0.9839\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 19s 323us/step - loss: 0.0194 - acc: 0.9937 - val_loss: 0.0576 - val_acc: 0.9840\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 18s 303us/step - loss: 0.0182 - acc: 0.9943 - val_loss: 0.0568 - val_acc: 0.9834\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 18s 304us/step - loss: 0.0157 - acc: 0.9948 - val_loss: 0.0651 - val_acc: 0.9825\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.0151 - acc: 0.9954 - val_loss: 0.0645 - val_acc: 0.9827\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 19s 311us/step - loss: 0.0140 - acc: 0.9955 - val_loss: 0.0649 - val_acc: 0.9838\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 18s 301us/step - loss: 0.0130 - acc: 0.9959 - val_loss: 0.0634 - val_acc: 0.9834\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 18s 306us/step - loss: 0.0116 - acc: 0.9965 - val_loss: 0.0657 - val_acc: 0.9828\n",
            "Total Training time: 370.77911734580994\n",
            "10000/10000 [==============================] - 1s 119us/step\n",
            "\n",
            " Test accuracy: 0.9828\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': -1.7999999999999972, 'memory_percent_use': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "8ViG68mQqMC6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Breast Cancer Data"
      ]
    },
    {
      "metadata": {
        "id": "wi4vXG5bc7-I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "code from https://www.kaggle.com/raoulma/cancer-image-tensorflow-cnn-80-valid-acc/notebook"
      ]
    },
    {
      "metadata": {
        "id": "I0h3frNdqQCz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "37ec7755-a88c-4c4c-9c56-e1bcb1e3a54d"
      },
      "cell_type": "code",
      "source": [
        "## import data\n",
        "\n",
        "# load images of shape (5547, 50, 50, 3)\n",
        "x_images = np.load('/content/gdrive/My Drive/dl_project/X.npy')  \n",
        "\n",
        "# load labels of shape (5547,1); (0 = no cancer, 1 = cancer)\n",
        "y_images = np.load('/content/gdrive/My Drive/dl_project/Y.npy')\n",
        "\n",
        "# shuffle data\n",
        "perm_array = np.arange(len(x_images))\n",
        "np.random.shuffle(perm_array)\n",
        "x_images = x_images[perm_array]\n",
        "y_images = y_images[perm_array]\n",
        "\n",
        "print('x_images.shape =', x_images.shape)\n",
        "print('x_images.min/mean/std/max = %.2f/%.2f/%.2f/%.2f'%(x_images.min(),\n",
        "                        x_images.mean(), x_images.std(), x_images.max()))\n",
        "print('')\n",
        "print('y_images.shape =', y_images.shape)\n",
        "print('y_images.min/mean/std/max = %.2f/%.2f/%.2f/%.2f'%(y_images.min(),\n",
        "                        y_images.mean(), y_images.std(), y_images.max()))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_images.shape = (5547, 50, 50, 3)\n",
            "x_images.min/mean/std/max = 2.00/185.03/47.25/255.00\n",
            "\n",
            "y_images.shape = (5547,)\n",
            "y_images.min/mean/std/max = 0.00/0.50/0.50/1.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v2y5f6tTrWt_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1142
        },
        "outputId": "2f676a5b-9f7f-441f-af00-c9121af87fd9"
      },
      "cell_type": "code",
      "source": [
        "## plot some images  \n",
        "\n",
        "imgs_0 = x_images[y_images == 0] # 0 = no cancer\n",
        "imgs_1 = x_images[y_images == 1] # 1 = cancer\n",
        "\n",
        "plt.figure(figsize=(20,20))\n",
        "for i in range(30):\n",
        "    plt.subplot(5,6,i+1)\n",
        "    plt.title('IDC = %d'%y_images[i])\n",
        "    plt.imshow(x_images[i])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zyfpLlPp2AfWbL4ek6MCOw065itEAY3WqLP3EMMLrorUXDqXFLsXz27pOXiY5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+X31aW9pMv0YW2S9F/pLxp4Al9ZUYY8fUip4IG5NdD/sRIZeicxnbRxFSpl99iJZto1/hqq+i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2h9qFCdqhr6PPTUfcbaCvp/ZRlxVUhjTpbp+HqkPuDEIdTeS6WRn+NkH99/3+5eO1hTc8fY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//vOnu6kgvOcR+xfmK2L7wnxFbF+Yr4jtC/MZsX/hvc7cqJknCIIgCIIgCIIgCIIg2MgH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XheVkCsblAOGJfzXHXiuThPi4hI2qO2kR65aBBEPsPj0QOAxfl4VOxDneBH0rTwukL1wxw+kWva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oNJZjvT6ndLierJMe+aqMJ+zbBqv+mbrvTS44dAu031M8nnIMZ1Rl36qGjju7YQ5k+qt773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\n8Y9/fNHL/uxnP0v33HOP9/dil79cILa/NLZPJPa/1FgK2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1WfW7wct3n4GPuGLX/xPlos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\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kY5wlPQSVE4O5Bf+8+n/9PD3z0b+JP8+1q1JoE0L5yeF+9UD2lynweKkdPKIcn27nl3ke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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f89eda83e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "n2O2o8mGrPfz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "ba4ebdc2-f069-48b4-f55b-4bce2d87ffe8"
      },
      "cell_type": "code",
      "source": [
        "## normalize data\n",
        "\n",
        "# convert one-hot encodings into labels\n",
        "def one_hot_to_dense(labels_one_hot):\n",
        "    return np.argmax(labels_one_hot,1)\n",
        "\n",
        "# convert class labels from scalars to one-hot vectors e.g. 1 => [0 1], 0 => [1 0]\n",
        "def dense_to_one_hot(labels_dense, num_classes):\n",
        "    num_labels = labels_dense.shape[0]\n",
        "    index_offset = np.arange(num_labels) * num_classes\n",
        "    labels_one_hot = np.zeros((num_labels, num_classes))\n",
        "    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n",
        "    return labels_one_hot\n",
        "\n",
        "# function to normalize data\n",
        "def normalize_data(data): \n",
        "    # scale features using statistics that are robust to outliers\n",
        "    #rs = sklearn.preprocessing.RobustScaler()\n",
        "    #rs.fit(data)\n",
        "    #data = rs.transform(data)\n",
        "    #data = (data-data.mean())/(data.std()) # standardisation\n",
        "    data = data / data.max() # convert from [0:255] to [0.:1.]\n",
        "    #data = ((data / 255.)-0.5)*2. # convert from [0:255] to [-1.:+1.]\n",
        "    return data\n",
        "\n",
        "# training and validation data\n",
        "x_train_valid = normalize_data(x_images)\n",
        "\n",
        "# use one-hot encoding for labels 0,1\n",
        "y_train_valid = dense_to_one_hot(y_images, 2).astype(np.uint8)\n",
        "\n",
        "# dictionaries for saving results\n",
        "y_valid_pred = {}\n",
        "y_train_pred = {}\n",
        "y_test_pred = {}\n",
        "train_loss, valid_loss = {}, {}\n",
        "train_acc, valid_acc = {}, {}\n",
        "cnf_valid_matrix = {}\n",
        "\n",
        "print('x_train_valid.shape =', x_train_valid.shape)\n",
        "print('x_train_valid.min/mean/std/max = %.2f/%.2f/%.2f/%.2f'%(x_train_valid.min(),\n",
        "                        x_train_valid.mean(), x_train_valid.std(), x_train_valid.max()))\n",
        "print('')\n",
        "print('y_train_valid.shape =', y_train_valid.shape)\n",
        "print('y_train_valid.min/mean/std/max = %.2f/%.2f/%.2f/%.2f'%(y_train_valid.min(),\n",
        "                        y_train_valid.mean(), y_train_valid.std(), y_train_valid.max()))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train_valid.shape = (5547, 50, 50, 3)\n",
            "x_train_valid.min/mean/std/max = 0.01/0.73/0.19/1.00\n",
            "\n",
            "y_train_valid.shape = (5547, 2)\n",
            "y_train_valid.min/mean/std/max = 0.00/0.50/0.50/1.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WM1oAW7kFLvP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Split the dataset\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_train_valid, y_train_valid, test_size=0.2, random_state=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "JB3usMgmdOgF"
      },
      "cell_type": "markdown",
      "source": [
        "#Revise compile neural network"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "3iCuBubLZdRv"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 1\n"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "-ST_-n4XZdRw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "1a2ed2b7-9412-4c06-c3ff-8e4a16195a8c"
      },
      "cell_type": "code",
      "source": [
        "# Create model in Keras\n",
        "# This model is linear stack of layers\n",
        "model = Sequential()\n",
        "model.add(InputLayer(input_shape=(50,50,3)))\n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "#clf1.add(BatchNormalization())\n",
        "model.add(Conv2D(16, (5, 5), activation='relu', padding='same')) \n",
        "# Add max pooling layer for 2D data.\n",
        "model.add(MaxPool2D(padding='same'))\n",
        "model.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "model.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "model.add(Flatten())\n",
        "model.add(Dense(2,  activation='softmax'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "              \n",
        "model.summary()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_11 (Conv2D)           (None, 50, 50, 16)        1216      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 25, 25, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 25, 25, 32)        12832     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 5408)              0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 2)                 10818     \n",
            "=================================================================\n",
            "Total params: 24,866\n",
            "Trainable params: 24,866\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "y8YY58FqbRXT"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 2"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "e4BfCLR7bRXU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "cab09a15-80af-4f74-de1c-b9e0f4b7ac39"
      },
      "cell_type": "code",
      "source": [
        "model2 = Sequential()\n",
        "model2.add(InputLayer(input_shape=(50,50,3)))\n",
        "model2.add(Conv2D(16, (2, 2), activation='relu', padding='same'))\n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "model2.add(BatchNormalization())\n",
        "# Add max pooling layer for 2D data.\n",
        "model2.add(MaxPool2D(padding='same'))\n",
        "model2.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "model2.add(BatchNormalization())\n",
        "model2.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "model2.add(Flatten())\n",
        "model2.add(Dense(2,  activation='softmax'))\n",
        "model2.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])\n",
        "              \n",
        "model2.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_13 (Conv2D)           (None, 50, 50, 16)        208       \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 50, 50, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 25, 25, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 25, 25, 32)        12832     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 25, 25, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_7 (Flatten)          (None, 5408)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 2)                 10818     \n",
            "=================================================================\n",
            "Total params: 24,050\n",
            "Trainable params: 23,954\n",
            "Non-trainable params: 96\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "0HkxCV-IbRXX"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 3"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "B2b8JEWubRXX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "a2c34506-de29-4624-82a4-5e4a9242694f"
      },
      "cell_type": "code",
      "source": [
        "model3 = Sequential()\n",
        "model3.add(InputLayer(input_shape=(50,50,3)))\n",
        "model3.add(Conv2D(128, (5, 5), padding='same', activation='relu'))\n",
        "model3.add(Conv2D(64, (5, 5), padding='same', activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPool2D(padding='same'))\n",
        "model3.add(Conv2D(32, (5, 5), padding='same', activation='relu'))\n",
        "model3.add(Conv2D(16, (5, 5), padding='same', activation='relu'))\n",
        "model3.add(BatchNormalization())\n",
        "model3.add(MaxPool2D(padding='same'))\n",
        "model3.add(Flatten())\n",
        "model3.add(Dense(2, activation='softmax')) \n",
        "\n",
        "model3.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model3.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_15 (Conv2D)           (None, 50, 50, 128)       9728      \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 50, 50, 64)        204864    \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 50, 50, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 25, 25, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 25, 25, 32)        51232     \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 25, 25, 16)        12816     \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 25, 25, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 13, 13, 16)        0         \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 2704)              0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 2)                 5410      \n",
            "=================================================================\n",
            "Total params: 284,370\n",
            "Trainable params: 284,210\n",
            "Non-trainable params: 160\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "dmqeNLvGbRXe"
      },
      "cell_type": "markdown",
      "source": [
        "## Architecture 4"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lSMy8Nj-bRXe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "cb8baaa7-bc25-4360-a9ce-1f59d4c9b607"
      },
      "cell_type": "code",
      "source": [
        "# Must define the input shape in the first layer of the neural network\n",
        "model4 = Sequential()\n",
        "model4.add(InputLayer(input_shape=(50,50,3)))\n",
        "model4.add(Conv2D(32, (5, 5), activation='relu', padding='same')) \n",
        "# Normalize the activations of the previous layer at each batch.\n",
        "model4.add(BatchNormalization())\n",
        "model4.add(MaxPooling2D(pool_size=2))\n",
        "model4.add(Flatten())\n",
        "model4.add(Dropout(0.3))\n",
        "model4.add(Dense(2, activation='softmax'))\n",
        "model4.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])\n",
        "              \n",
        "model4.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 50, 50, 32)        2432      \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 50, 50, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 25, 25, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 20000)             0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 20000)             0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 2)                 40002     \n",
            "=================================================================\n",
            "Total params: 42,562\n",
            "Trainable params: 42,498\n",
            "Non-trainable params: 64\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "s3Sve9-WbRXh"
      },
      "cell_type": "markdown",
      "source": [
        "##Architecture 5"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "pkqcO-q6bRXi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "080c100f-6df4-49d5-c2d7-ab44988058cf"
      },
      "cell_type": "code",
      "source": [
        "model5 = Sequential()\n",
        "model5.add(InputLayer(input_shape=(50,50,3)))\n",
        "\n",
        "#clf5.add(BatchNormalization())\n",
        "model5.add(Conv2D(8, (5, 5), activation='relu', padding='same')) \n",
        "# Add max pooling layer for 2D data.\n",
        "model5.add(MaxPool2D(padding='same'))\n",
        "# It is necessary to flatten input data to a vector.\n",
        "model5.add(Flatten())\n",
        "model5.add(Dense(2, activation='softmax'))\n",
        "model5.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "              \n",
        "model5.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_20 (Conv2D)           (None, 50, 50, 8)         608       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 25, 25, 8)         0         \n",
            "_________________________________________________________________\n",
            "flatten_10 (Flatten)         (None, 5000)              0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 2)                 10002     \n",
            "=================================================================\n",
            "Total params: 10,610\n",
            "Trainable params: 10,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OJnUPeWvc0S_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "3231a87e-4c61-44ba-843d-e016dceb4479"
      },
      "cell_type": "code",
      "source": [
        "go(model)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4437 samples, validate on 1110 samples\n",
            "Epoch 1/20\n",
            "4437/4437 [==============================] - 4s 792us/step - loss: 0.6252 - acc: 0.6423 - val_loss: 0.5285 - val_acc: 0.7667\n",
            "Epoch 2/20\n",
            "4437/4437 [==============================] - 2s 491us/step - loss: 0.5514 - acc: 0.7255 - val_loss: 0.5223 - val_acc: 0.7622\n",
            "Epoch 3/20\n",
            "4437/4437 [==============================] - 2s 494us/step - loss: 0.5527 - acc: 0.7318 - val_loss: 0.5687 - val_acc: 0.7018\n",
            "Epoch 4/20\n",
            "4437/4437 [==============================] - 2s 475us/step - loss: 0.5491 - acc: 0.7368 - val_loss: 0.5046 - val_acc: 0.7676\n",
            "Epoch 5/20\n",
            "4437/4437 [==============================] - 2s 477us/step - loss: 0.5443 - acc: 0.7332 - val_loss: 0.5509 - val_acc: 0.7180\n",
            "Epoch 6/20\n",
            "4437/4437 [==============================] - 2s 480us/step - loss: 0.5555 - acc: 0.7262 - val_loss: 0.4971 - val_acc: 0.7766\n",
            "Epoch 7/20\n",
            "4437/4437 [==============================] - 2s 497us/step - loss: 0.5297 - acc: 0.7467 - val_loss: 0.5047 - val_acc: 0.7829\n",
            "Epoch 8/20\n",
            "4437/4437 [==============================] - 2s 483us/step - loss: 0.5291 - acc: 0.7483 - val_loss: 0.5234 - val_acc: 0.7523\n",
            "Epoch 9/20\n",
            "4437/4437 [==============================] - 2s 492us/step - loss: 0.5281 - acc: 0.7485 - val_loss: 0.5115 - val_acc: 0.7766\n",
            "Epoch 10/20\n",
            "4437/4437 [==============================] - 2s 550us/step - loss: 0.5208 - acc: 0.7492 - val_loss: 0.4870 - val_acc: 0.7766\n",
            "Epoch 11/20\n",
            "4437/4437 [==============================] - 2s 489us/step - loss: 0.5151 - acc: 0.7530 - val_loss: 0.5023 - val_acc: 0.7685\n",
            "Epoch 12/20\n",
            "4437/4437 [==============================] - 2s 487us/step - loss: 0.5131 - acc: 0.7616 - val_loss: 0.5099 - val_acc: 0.7622\n",
            "Epoch 13/20\n",
            "4437/4437 [==============================] - 2s 491us/step - loss: 0.5040 - acc: 0.7661 - val_loss: 0.4817 - val_acc: 0.7802\n",
            "Epoch 14/20\n",
            "4437/4437 [==============================] - 2s 492us/step - loss: 0.5025 - acc: 0.7672 - val_loss: 0.4965 - val_acc: 0.7739\n",
            "Epoch 15/20\n",
            "4437/4437 [==============================] - 2s 476us/step - loss: 0.4932 - acc: 0.7692 - val_loss: 0.5582 - val_acc: 0.7171\n",
            "Epoch 16/20\n",
            "4437/4437 [==============================] - 2s 480us/step - loss: 0.4940 - acc: 0.7706 - val_loss: 0.5017 - val_acc: 0.7712\n",
            "Epoch 17/20\n",
            "4437/4437 [==============================] - 2s 485us/step - loss: 0.4743 - acc: 0.7757 - val_loss: 0.5233 - val_acc: 0.7523\n",
            "Epoch 18/20\n",
            "4437/4437 [==============================] - 2s 488us/step - loss: 0.4880 - acc: 0.7652 - val_loss: 0.4965 - val_acc: 0.7757\n",
            "Epoch 19/20\n",
            "4437/4437 [==============================] - 2s 481us/step - loss: 0.4736 - acc: 0.7796 - val_loss: 0.4963 - val_acc: 0.7766\n",
            "Epoch 20/20\n",
            "4437/4437 [==============================] - 2s 491us/step - loss: 0.4586 - acc: 0.7908 - val_loss: 0.5035 - val_acc: 0.7685\n",
            "Total Training time: 45.78024363517761\n",
            "1110/1110 [==============================] - 0s 215us/step\n",
            "\n",
            " Test accuracy: 0.7684684688980515\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 2.8000000000000114, 'memory_percent_use': -0.09999999999999964}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "8oJogErVFrxW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "f4b9e461-ef63-4e9d-b5f3-743ff06fc284"
      },
      "cell_type": "code",
      "source": [
        "go(model2)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4437 samples, validate on 1110 samples\n",
            "Epoch 1/20\n",
            "4437/4437 [==============================] - 3s 774us/step - loss: 5.4213 - acc: 0.5740 - val_loss: 7.6836 - val_acc: 0.4982\n",
            "Epoch 2/20\n",
            "4437/4437 [==============================] - 2s 531us/step - loss: 1.4617 - acc: 0.6960 - val_loss: 2.1881 - val_acc: 0.5802\n",
            "Epoch 3/20\n",
            "4437/4437 [==============================] - 2s 522us/step - loss: 1.0936 - acc: 0.7138 - val_loss: 0.9592 - val_acc: 0.5423\n",
            "Epoch 4/20\n",
            "4437/4437 [==============================] - 2s 552us/step - loss: 0.7893 - acc: 0.7363 - val_loss: 3.1080 - val_acc: 0.5009\n",
            "Epoch 5/20\n",
            "4437/4437 [==============================] - 2s 533us/step - loss: 0.6974 - acc: 0.7446 - val_loss: 1.1178 - val_acc: 0.7162\n",
            "Epoch 6/20\n",
            "4437/4437 [==============================] - 2s 542us/step - loss: 0.6201 - acc: 0.7566 - val_loss: 1.1093 - val_acc: 0.5703\n",
            "Epoch 7/20\n",
            "4437/4437 [==============================] - 3s 569us/step - loss: 0.5369 - acc: 0.7816 - val_loss: 0.6276 - val_acc: 0.7090\n",
            "Epoch 8/20\n",
            "4437/4437 [==============================] - 2s 545us/step - loss: 0.5047 - acc: 0.7875 - val_loss: 0.7261 - val_acc: 0.7541\n",
            "Epoch 9/20\n",
            "4437/4437 [==============================] - 2s 528us/step - loss: 0.4848 - acc: 0.7974 - val_loss: 0.7450 - val_acc: 0.6847\n",
            "Epoch 10/20\n",
            "4437/4437 [==============================] - 2s 522us/step - loss: 0.4399 - acc: 0.8082 - val_loss: 0.9528 - val_acc: 0.7649\n",
            "Epoch 11/20\n",
            "4437/4437 [==============================] - 2s 529us/step - loss: 0.4156 - acc: 0.8174 - val_loss: 0.8048 - val_acc: 0.6811\n",
            "Epoch 12/20\n",
            "4437/4437 [==============================] - 2s 515us/step - loss: 0.3629 - acc: 0.8443 - val_loss: 0.7064 - val_acc: 0.6730\n",
            "Epoch 13/20\n",
            "4437/4437 [==============================] - 2s 530us/step - loss: 0.3188 - acc: 0.8630 - val_loss: 0.6711 - val_acc: 0.7703\n",
            "Epoch 14/20\n",
            "4437/4437 [==============================] - 2s 511us/step - loss: 0.2962 - acc: 0.8772 - val_loss: 0.7043 - val_acc: 0.7405\n",
            "Epoch 15/20\n",
            "4437/4437 [==============================] - 2s 512us/step - loss: 0.2826 - acc: 0.8787 - val_loss: 0.6359 - val_acc: 0.7523\n",
            "Epoch 16/20\n",
            "4437/4437 [==============================] - 2s 521us/step - loss: 0.2512 - acc: 0.8887 - val_loss: 0.8457 - val_acc: 0.7532\n",
            "Epoch 17/20\n",
            "4437/4437 [==============================] - 2s 549us/step - loss: 0.2260 - acc: 0.9056 - val_loss: 0.7356 - val_acc: 0.7414\n",
            "Epoch 18/20\n",
            "4437/4437 [==============================] - 2s 545us/step - loss: 0.2014 - acc: 0.9213 - val_loss: 0.8455 - val_acc: 0.6622\n",
            "Epoch 19/20\n",
            "4437/4437 [==============================] - 2s 523us/step - loss: 0.1875 - acc: 0.9295 - val_loss: 2.2695 - val_acc: 0.6514\n",
            "Epoch 20/20\n",
            "4437/4437 [==============================] - 2s 539us/step - loss: 0.1597 - acc: 0.9389 - val_loss: 0.8681 - val_acc: 0.7378\n",
            "Total Training time: 48.9860782623291\n",
            "1110/1110 [==============================] - 0s 211us/step\n",
            "\n",
            " Test accuracy: 0.7378378382674209\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 1.2999999999999972, 'memory_percent_use': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "s2o4ZQGyFuTO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "b519eca1-2057-47f7-85d4-a156839f2a47"
      },
      "cell_type": "code",
      "source": [
        "go(model3)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4437 samples, validate on 1110 samples\n",
            "Epoch 1/20\n",
            "4437/4437 [==============================] - 10s 2ms/step - loss: 0.6728 - acc: 0.7113 - val_loss: 0.5314 - val_acc: 0.7937\n",
            "Epoch 2/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5751 - acc: 0.7365 - val_loss: 0.9193 - val_acc: 0.6685\n",
            "Epoch 3/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5571 - acc: 0.7476 - val_loss: 4.1853 - val_acc: 0.5135\n",
            "Epoch 4/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5618 - acc: 0.7419 - val_loss: 1.7438 - val_acc: 0.5126\n",
            "Epoch 5/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5581 - acc: 0.7410 - val_loss: 0.6221 - val_acc: 0.7685\n",
            "Epoch 6/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5449 - acc: 0.7503 - val_loss: 0.6325 - val_acc: 0.7036\n",
            "Epoch 7/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5063 - acc: 0.7719 - val_loss: 0.5907 - val_acc: 0.7640\n",
            "Epoch 8/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.5066 - acc: 0.7769 - val_loss: 0.9628 - val_acc: 0.6613\n",
            "Epoch 9/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4974 - acc: 0.7685 - val_loss: 0.7463 - val_acc: 0.6432\n",
            "Epoch 10/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4942 - acc: 0.7782 - val_loss: 1.3664 - val_acc: 0.5324\n",
            "Epoch 11/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4937 - acc: 0.7791 - val_loss: 1.0427 - val_acc: 0.5405\n",
            "Epoch 12/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4724 - acc: 0.7834 - val_loss: 0.7359 - val_acc: 0.5874\n",
            "Epoch 13/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4664 - acc: 0.7942 - val_loss: 0.5658 - val_acc: 0.7568\n",
            "Epoch 14/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4433 - acc: 0.8030 - val_loss: 0.6415 - val_acc: 0.7180\n",
            "Epoch 15/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4258 - acc: 0.8078 - val_loss: 0.7521 - val_acc: 0.7396\n",
            "Epoch 16/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.4088 - acc: 0.8161 - val_loss: 0.6244 - val_acc: 0.7252\n",
            "Epoch 17/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.3903 - acc: 0.8269 - val_loss: 0.6735 - val_acc: 0.7009\n",
            "Epoch 18/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.3783 - acc: 0.8355 - val_loss: 1.1493 - val_acc: 0.6270\n",
            "Epoch 19/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.3409 - acc: 0.8472 - val_loss: 2.5349 - val_acc: 0.5523\n",
            "Epoch 20/20\n",
            "4437/4437 [==============================] - 7s 2ms/step - loss: 0.3413 - acc: 0.8526 - val_loss: 1.4058 - val_acc: 0.6450\n",
            "Total Training time: 149.28003358840942\n",
            "1110/1110 [==============================] - 1s 569us/step\n",
            "\n",
            " Test accuracy: 0.6450450448302535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': -2.1999999999999886, 'memory_percent_use': 0.1999999999999993}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "leiCGO0jHc9Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "82c3b5fd-9940-43ad-cd1e-5bd309ca5be6"
      },
      "cell_type": "code",
      "source": [
        "go(model4)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4437 samples, validate on 1110 samples\n",
            "Epoch 1/20\n",
            "4437/4437 [==============================] - 3s 779us/step - loss: 3.0021 - acc: 0.6482 - val_loss: 1.5307 - val_acc: 0.6468\n",
            "Epoch 2/20\n",
            "4437/4437 [==============================] - 2s 498us/step - loss: 1.0824 - acc: 0.6779 - val_loss: 0.7741 - val_acc: 0.6694\n",
            "Epoch 3/20\n",
            "4437/4437 [==============================] - 2s 519us/step - loss: 0.6395 - acc: 0.7129 - val_loss: 0.6174 - val_acc: 0.7468\n",
            "Epoch 4/20\n",
            "4437/4437 [==============================] - 2s 527us/step - loss: 0.5818 - acc: 0.7257 - val_loss: 0.6638 - val_acc: 0.7306\n",
            "Epoch 5/20\n",
            "4437/4437 [==============================] - 2s 519us/step - loss: 0.5203 - acc: 0.7604 - val_loss: 0.5849 - val_acc: 0.7153\n",
            "Epoch 6/20\n",
            "4437/4437 [==============================] - 2s 523us/step - loss: 0.4950 - acc: 0.7735 - val_loss: 0.8599 - val_acc: 0.6171\n",
            "Epoch 7/20\n",
            "4437/4437 [==============================] - 2s 513us/step - loss: 0.4772 - acc: 0.7803 - val_loss: 0.6001 - val_acc: 0.7180\n",
            "Epoch 8/20\n",
            "4437/4437 [==============================] - 2s 524us/step - loss: 0.4536 - acc: 0.7918 - val_loss: 0.7149 - val_acc: 0.6676\n",
            "Epoch 9/20\n",
            "4437/4437 [==============================] - 2s 522us/step - loss: 0.4448 - acc: 0.7960 - val_loss: 1.2850 - val_acc: 0.5685\n",
            "Epoch 10/20\n",
            "4437/4437 [==============================] - 2s 523us/step - loss: 0.4231 - acc: 0.8145 - val_loss: 0.5799 - val_acc: 0.7324\n",
            "Epoch 11/20\n",
            "4437/4437 [==============================] - 2s 519us/step - loss: 0.4256 - acc: 0.8120 - val_loss: 0.5788 - val_acc: 0.7514\n",
            "Epoch 12/20\n",
            "4437/4437 [==============================] - 2s 503us/step - loss: 0.4096 - acc: 0.8213 - val_loss: 0.6323 - val_acc: 0.6622\n",
            "Epoch 13/20\n",
            "4437/4437 [==============================] - 2s 530us/step - loss: 0.3904 - acc: 0.8256 - val_loss: 1.7173 - val_acc: 0.5333\n",
            "Epoch 14/20\n",
            "4437/4437 [==============================] - 2s 519us/step - loss: 0.3872 - acc: 0.8314 - val_loss: 0.9642 - val_acc: 0.6315\n",
            "Epoch 15/20\n",
            "4437/4437 [==============================] - 2s 515us/step - loss: 0.3925 - acc: 0.8298 - val_loss: 0.9995 - val_acc: 0.6396\n",
            "Epoch 16/20\n",
            "4437/4437 [==============================] - 2s 524us/step - loss: 0.3819 - acc: 0.8235 - val_loss: 0.6271 - val_acc: 0.7333\n",
            "Epoch 17/20\n",
            "4437/4437 [==============================] - 2s 515us/step - loss: 0.3742 - acc: 0.8305 - val_loss: 1.3689 - val_acc: 0.5739\n",
            "Epoch 18/20\n",
            "4437/4437 [==============================] - 2s 515us/step - loss: 0.3591 - acc: 0.8472 - val_loss: 0.6640 - val_acc: 0.7261\n",
            "Epoch 19/20\n",
            "4437/4437 [==============================] - 2s 530us/step - loss: 0.3431 - acc: 0.8483 - val_loss: 0.9294 - val_acc: 0.6270\n",
            "Epoch 20/20\n",
            "4437/4437 [==============================] - 2s 505us/step - loss: 0.3387 - acc: 0.8576 - val_loss: 0.7031 - val_acc: 0.6928\n",
            "Total Training time: 47.778027296066284\n",
            "1110/1110 [==============================] - 0s 222us/step\n",
            "\n",
            " Test accuracy: 0.6927927925780013\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 12.700000000000003, 'memory_percent_use': 0.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "Kc9c3obkHgFd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "2b690b21-f2f1-44ec-a936-72a4f59a5d50"
      },
      "cell_type": "code",
      "source": [
        "go(model5)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 4437 samples, validate on 1110 samples\n",
            "Epoch 1/20\n",
            "4437/4437 [==============================] - 3s 733us/step - loss: 0.6483 - acc: 0.6250 - val_loss: 0.5961 - val_acc: 0.6694\n",
            "Epoch 2/20\n",
            "4437/4437 [==============================] - 2s 425us/step - loss: 0.5974 - acc: 0.6863 - val_loss: 0.5294 - val_acc: 0.7685\n",
            "Epoch 3/20\n",
            "4437/4437 [==============================] - 2s 419us/step - loss: 0.5514 - acc: 0.7352 - val_loss: 0.5794 - val_acc: 0.7045\n",
            "Epoch 4/20\n",
            "4437/4437 [==============================] - 2s 434us/step - loss: 0.5356 - acc: 0.7410 - val_loss: 0.5575 - val_acc: 0.7081\n",
            "Epoch 5/20\n",
            "4437/4437 [==============================] - 2s 428us/step - loss: 0.5360 - acc: 0.7379 - val_loss: 0.5494 - val_acc: 0.7207\n",
            "Epoch 6/20\n",
            "4437/4437 [==============================] - 2s 422us/step - loss: 0.5222 - acc: 0.7532 - val_loss: 0.5627 - val_acc: 0.7072\n",
            "Epoch 7/20\n",
            "4437/4437 [==============================] - 2s 423us/step - loss: 0.5080 - acc: 0.7548 - val_loss: 0.4928 - val_acc: 0.7829\n",
            "Epoch 8/20\n",
            "4437/4437 [==============================] - 2s 417us/step - loss: 0.5011 - acc: 0.7622 - val_loss: 0.5109 - val_acc: 0.7649\n",
            "Epoch 9/20\n",
            "4437/4437 [==============================] - 2s 426us/step - loss: 0.4900 - acc: 0.7710 - val_loss: 0.5087 - val_acc: 0.7730\n",
            "Epoch 10/20\n",
            "4437/4437 [==============================] - 2s 416us/step - loss: 0.4861 - acc: 0.7674 - val_loss: 0.5147 - val_acc: 0.7658\n",
            "Epoch 11/20\n",
            "4437/4437 [==============================] - 2s 418us/step - loss: 0.4771 - acc: 0.7785 - val_loss: 0.4981 - val_acc: 0.7838\n",
            "Epoch 12/20\n",
            "4437/4437 [==============================] - 2s 451us/step - loss: 0.4759 - acc: 0.7818 - val_loss: 0.5194 - val_acc: 0.7649\n",
            "Epoch 13/20\n",
            "4437/4437 [==============================] - 2s 412us/step - loss: 0.4609 - acc: 0.7850 - val_loss: 0.4958 - val_acc: 0.7784\n",
            "Epoch 14/20\n",
            "4437/4437 [==============================] - 2s 406us/step - loss: 0.4568 - acc: 0.7899 - val_loss: 0.5094 - val_acc: 0.7712\n",
            "Epoch 15/20\n",
            "4437/4437 [==============================] - 2s 442us/step - loss: 0.4478 - acc: 0.7965 - val_loss: 0.5173 - val_acc: 0.7649\n",
            "Epoch 16/20\n",
            "4437/4437 [==============================] - 2s 440us/step - loss: 0.4329 - acc: 0.8023 - val_loss: 0.5254 - val_acc: 0.7685\n",
            "Epoch 17/20\n",
            "4437/4437 [==============================] - 2s 406us/step - loss: 0.4287 - acc: 0.8001 - val_loss: 0.5107 - val_acc: 0.7658\n",
            "Epoch 18/20\n",
            "4437/4437 [==============================] - 2s 434us/step - loss: 0.4187 - acc: 0.8114 - val_loss: 0.5255 - val_acc: 0.7712\n",
            "Epoch 19/20\n",
            "4437/4437 [==============================] - 2s 423us/step - loss: 0.4180 - acc: 0.8147 - val_loss: 0.5388 - val_acc: 0.7550\n",
            "Epoch 20/20\n",
            "4437/4437 [==============================] - 2s 408us/step - loss: 0.4087 - acc: 0.8197 - val_loss: 0.5276 - val_acc: 0.7595\n",
            "Total Training time: 39.82548499107361\n",
            "1110/1110 [==============================] - 0s 176us/step\n",
            "\n",
            " Test accuracy: 0.7594594590298764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CPU_use': 3.200000000000003, 'memory_percent_use': 0.10000000000000142}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    }
  ]
}