kap7-cnn.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DJCordhose/buch-machine-learning-notebooks/blob/master/kap7-cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzCzd16RpoPh"
      },
      "source": [
        "# Kapitel 7: Neuronale Netzwerke - Bilderkennung"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pjxdBjpNpoPi"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xOCX6mopoPm",
        "outputId": "703aa232-8459-4c62-b439-feab6808ea05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "%pylab inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "hihiDGcLpoPp"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pylab as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "CBk_vCWwpoPu"
      },
      "outputs": [],
      "source": [
        "colors = 'bwr'#['b','y','r']\n",
        "CMAP = colors#plt.cm.rainbow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iE8z4UPpoPy",
        "outputId": "e5e89605-f04e-4204-9dd6-7d65ba22dabf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0.2\n"
          ]
        }
      ],
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDMwrHxipoP1",
        "outputId": "1c8ee3c1-027c-4fdc-fe09-f5f9a42dec14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1402Tv0poP3",
        "outputId": "a5218575-0567-4f62-da1f-b827bb599f58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "import keras\n",
        "print(keras.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJ1jquySpoP6",
        "outputId": "f7397d1e-bb8f-4b4a-c77f-559ae129314f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.3.5\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "print(pd.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1gku3A3poP9"
      },
      "source": [
        "## Laden und Vorbereiten der Bild-Daten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5uS-zXVpoP-",
        "outputId": "6b53d366-0d6d-49f6-8aed-bea0ebbb205a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 1810k  100 1810k    0     0  3701k      0 --:--:-- --:--:-- --:--:-- 3694k\n"
          ]
        }
      ],
      "source": [
        "!curl -O https://raw.githubusercontent.com/DJCordhose/buch-machine-learning-notebooks/master/data/speed-limit-signs.zip\n",
        "from zipfile import ZipFile\n",
        "zip = ZipFile(r'speed-limit-signs.zip')\n",
        "zip.extractall('.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6fwa6rzpoQA",
        "outputId": "9e80f82e-c6bf-4a56-b302-2a592d1d24d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 28\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 0\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 1\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 2\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 3\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 4\n",
            "drwxr-xr-x 2 root root 4096 May 25 08:47 5\n",
            "-rw-r--r-- 1 root root  380 May 25 08:47 README.md\n"
          ]
        }
      ],
      "source": [
        "!ls -l speed-limit-signs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wm9Q5NR3poQD",
        "outputId": "c1ac955d-a774-4c53-b66d-3341afa21989"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data extracted from http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset\n",
            "\n",
            "From http://benchmark.ini.rub.de/Dataset_GTSDB/FullIJCNN2013.zip just the samples for the speed limit signs \n",
            "\n",
            "## Format\n",
            "\n",
            "https://en.wikipedia.org/wiki/Netpbm_format\n",
            "\n",
            "Can be previewed on a Mac and be processed by http://scikit-image.org/\n",
            "\n",
            "## Labels\n",
            "- 0: 30\n",
            "- 1: 50\n",
            "- 2: 70\n",
            "- 3: 80\n",
            "- 4: 100\n",
            "- 5: 120"
          ]
        }
      ],
      "source": [
        "!cat speed-limit-signs/README.md"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9D-SaV3apoQF"
      },
      "source": [
        "# Big Kudos to [Waleed Abdulla](https://medium.com/@waleedka) for providing the initial idea and many of the functions used to prepare and display the images: https://medium.com/@waleedka/traffic-sign-recognition-with-tensorflow-629dffc391a6#.i728o84ib  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "dEyC7dgJsohW"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from skimage import data, io, filters\n",
        "\n",
        "ROOT_PATH = \"./\"\n",
        "data_dir = os.path.join(ROOT_PATH, \"speed-limit-signs\")\n",
        "type = \".ppm\"\n",
        "\n",
        "# Get all subdirectories of data_dir. Each represents a label.\n",
        "directories = [d for d in os.listdir(data_dir) \n",
        "                if os.path.isdir(os.path.join(data_dir, d))]\n",
        "# Loop through the label directories and collect the data in\n",
        "# two lists, labels and images.\n",
        "labels = []\n",
        "images = []\n",
        "for d in directories:\n",
        "    label_dir = os.path.join(data_dir, d)\n",
        "    file_names = [os.path.join(label_dir, f) for f in os.listdir(label_dir) if f.endswith(type)]\n",
        "    # For each label, load it's images and add them to the images list.\n",
        "    # And add the label number (i.e. directory name) to the labels list.\n",
        "    for f in file_names:\n",
        "        images.append(io.imread(f))\n",
        "        labels.append(int(d))\n",
        "\n",
        "images, labels = np.array(images), np.array(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "A6mqGPAEpoQJ",
        "outputId": "72d2c6fa-609b-4f0c-9147-e98793ab7dfb"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 2160x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "label_names = ['30', '50', '70', '80', '100', '120']\n",
        "\n",
        "plt.figure(figsize=(30, 15))\n",
        "\n",
        "unique_values, counts = np.unique(labels, return_counts=True)\n",
        "classes = len(unique_values)\n",
        "\n",
        "i = 1\n",
        "for label in unique_values:\n",
        "    # Pick the first image for each label.\n",
        "    image = images[np.where(labels == label)[0][0]]\n",
        "    plt.subplot(1, 8, i)\n",
        "    plt.title(\"{0} (#{1})\".format(label_names[label], counts[label]))\n",
        "    i += 1\n",
        "    plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "BFGPTn_atT-6"
      },
      "outputs": [],
      "source": [
        "import skimage.transform\n",
        "\n",
        "images_64 = np.array([skimage.transform.resize(image, (64, 64)) for image in images])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9_TAlqUpoQO"
      },
      "source": [
        "## Modell-Architektur"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ylVn6kfspoQP"
      },
      "outputs": [],
      "source": [
        "y = np.array(labels)\n",
        "X = np.array(images_64)\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "num_categories = 6\n",
        "\n",
        "y = to_categorical(y, num_categories)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "nEQCr3YgpoQR"
      },
      "outputs": [],
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Dense, Dropout, Flatten, Input\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "\n",
        "# input tensor for a 3-channel 64x64 image\n",
        "inputs = Input(shape=(64, 64, 3))\n",
        "\n",
        "# one block of convolutional layers\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(inputs)\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(x)\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# one more block\n",
        "x = Convolution2D(128, 3, activation='relu', padding='same')(x)\n",
        "x = Convolution2D(128, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "# one more block\n",
        "x = Convolution2D(256, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "\n",
        "# softmax activation, 6 categories\n",
        "predictions = Dense(6, activation='softmax')(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EJNZgpopoQT",
        "outputId": "7b50770d-151f-4912-8977-4437f5ee4d08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 32, 32, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 16, 16, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 8, 8, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 16384)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               4194560   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 1542      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,788,358\n",
            "Trainable params: 4,788,358\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "LP2w1xoypoQW"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbe-R5ilpoQZ"
      },
      "source": [
        "## Zuerst testen wir unser Modell, ob wir es überhaupt trainiert bekommen, indem wir auf einer kleinen Menge von Daten overfitten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "X_DsvSwtpoQZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k53co0k0poQb",
        "outputId": "3edce6bf-fdb2-4a90-8d6e-272705e8d457"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((37, 64, 64, 3), (37, 6))"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape, y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-AHBs2IapoQe",
        "outputId": "d264a546-4771-4505-c248-b072761ff78b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 13s 13s/step - loss: 1.7964 - accuracy: 0.0800 - val_loss: 1.8663 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.7193 - accuracy: 0.2800 - val_loss: 1.6843 - val_accuracy: 0.4167\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 1.6939 - accuracy: 0.2400 - val_loss: 2.0168 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.7047 - accuracy: 0.3200 - val_loss: 2.2384 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.6600 - accuracy: 0.3200 - val_loss: 2.1081 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.5433 - accuracy: 0.2800 - val_loss: 2.3743 - val_accuracy: 0.4167\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.4991 - accuracy: 0.3600 - val_loss: 3.1748 - val_accuracy: 0.0833\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.4215 - accuracy: 0.3600 - val_loss: 3.1404 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.3226 - accuracy: 0.4800 - val_loss: 3.6016 - val_accuracy: 0.3333\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.1681 - accuracy: 0.4800 - val_loss: 4.9233 - val_accuracy: 0.1667\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.0676 - accuracy: 0.5600 - val_loss: 6.5704 - val_accuracy: 0.0833\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.1680 - accuracy: 0.5600 - val_loss: 4.7376 - val_accuracy: 0.3333\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.2021 - accuracy: 0.5200 - val_loss: 3.8102 - val_accuracy: 0.4167\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.1306 - accuracy: 0.6000 - val_loss: 3.2758 - val_accuracy: 0.4167\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 1.0176 - accuracy: 0.6800 - val_loss: 3.5263 - val_accuracy: 0.1667\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.9591 - accuracy: 0.6400 - val_loss: 4.7862 - val_accuracy: 0.1667\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.9269 - accuracy: 0.6400 - val_loss: 5.7923 - val_accuracy: 0.1667\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.7278 - accuracy: 0.7200 - val_loss: 7.1891 - val_accuracy: 0.3333\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5559 - accuracy: 0.7600 - val_loss: 9.9795 - val_accuracy: 0.1667\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4294 - accuracy: 0.8000 - val_loss: 13.6621 - val_accuracy: 0.2500\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4340 - accuracy: 0.8000 - val_loss: 17.2054 - val_accuracy: 0.3333\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5293 - accuracy: 0.8000 - val_loss: 16.5948 - val_accuracy: 0.0833\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.8971 - accuracy: 0.7600 - val_loss: 15.6376 - val_accuracy: 0.3333\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4280 - accuracy: 0.7600 - val_loss: 15.6312 - val_accuracy: 0.3333\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.2681 - accuracy: 0.8800 - val_loss: 18.1379 - val_accuracy: 0.2500\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.5275 - accuracy: 0.8000 - val_loss: 14.3333 - val_accuracy: 0.2500\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.3034 - accuracy: 0.8800 - val_loss: 12.5620 - val_accuracy: 0.2500\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.3489 - accuracy: 0.8800 - val_loss: 11.1158 - val_accuracy: 0.2500\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2738 - accuracy: 0.9600 - val_loss: 10.4076 - val_accuracy: 0.3333\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1786 - accuracy: 0.9600 - val_loss: 10.5058 - val_accuracy: 0.0833\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1628 - accuracy: 1.0000 - val_loss: 11.0983 - val_accuracy: 0.0833\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1721 - accuracy: 1.0000 - val_loss: 11.4858 - val_accuracy: 0.0833\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1194 - accuracy: 1.0000 - val_loss: 12.0191 - val_accuracy: 0.0000e+00\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0761 - accuracy: 1.0000 - val_loss: 12.8356 - val_accuracy: 0.0000e+00\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0478 - accuracy: 1.0000 - val_loss: 14.0277 - val_accuracy: 0.0833\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.0252 - accuracy: 1.0000 - val_loss: 15.5742 - val_accuracy: 0.2500\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 17.3803 - val_accuracy: 0.2500\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0050 - accuracy: 1.0000 - val_loss: 19.4296 - val_accuracy: 0.2500\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 21.7787 - val_accuracy: 0.2500\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 24.3502 - val_accuracy: 0.2500\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 27.1797 - val_accuracy: 0.2500\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 3.9969e-04 - accuracy: 1.0000 - val_loss: 30.4000 - val_accuracy: 0.2500\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.4776e-04 - accuracy: 1.0000 - val_loss: 33.8465 - val_accuracy: 0.2500\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.1278e-04 - accuracy: 1.0000 - val_loss: 37.4508 - val_accuracy: 0.2500\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 1.3839e-04 - accuracy: 1.0000 - val_loss: 41.1012 - val_accuracy: 0.2500\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 2.0892e-04 - accuracy: 1.0000 - val_loss: 44.6865 - val_accuracy: 0.2500\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.9503e-04 - accuracy: 1.0000 - val_loss: 48.0486 - val_accuracy: 0.2500\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 3.1320e-04 - accuracy: 1.0000 - val_loss: 51.0824 - val_accuracy: 0.2500\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 2.5388e-04 - accuracy: 1.0000 - val_loss: 53.7812 - val_accuracy: 0.2500\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.7813e-04 - accuracy: 1.0000 - val_loss: 56.1556 - val_accuracy: 0.2500\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 9.9099e-05 - accuracy: 1.0000 - val_loss: 58.2768 - val_accuracy: 0.2500\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 4.8551e-05 - accuracy: 1.0000 - val_loss: 60.2036 - val_accuracy: 0.2500\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 2.4513e-05 - accuracy: 1.0000 - val_loss: 61.9789 - val_accuracy: 0.2500\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.4047e-05 - accuracy: 1.0000 - val_loss: 63.6226 - val_accuracy: 0.2500\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 9.2981e-06 - accuracy: 1.0000 - val_loss: 65.1533 - val_accuracy: 0.2500\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 6.9188e-06 - accuracy: 1.0000 - val_loss: 66.5805 - val_accuracy: 0.2500\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 5.7458e-06 - accuracy: 1.0000 - val_loss: 67.9100 - val_accuracy: 0.2500\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 5.2308e-06 - accuracy: 1.0000 - val_loss: 69.1494 - val_accuracy: 0.2500\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 5.1211e-06 - accuracy: 1.0000 - val_loss: 70.3033 - val_accuracy: 0.2500\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 5.3309e-06 - accuracy: 1.0000 - val_loss: 71.3766 - val_accuracy: 0.2500\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 5.7600e-06 - accuracy: 1.0000 - val_loss: 72.3696 - val_accuracy: 0.2500\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 6.2750e-06 - accuracy: 1.0000 - val_loss: 73.2902 - val_accuracy: 0.2500\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 6.6898e-06 - accuracy: 1.0000 - val_loss: 74.1457 - val_accuracy: 0.2500\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 6.9186e-06 - accuracy: 1.0000 - val_loss: 74.9422 - val_accuracy: 0.2500\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 6.9139e-06 - accuracy: 1.0000 - val_loss: 75.6827 - val_accuracy: 0.2500\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 6.6564e-06 - accuracy: 1.0000 - val_loss: 76.3707 - val_accuracy: 0.2500\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 6.2177e-06 - accuracy: 1.0000 - val_loss: 77.0115 - val_accuracy: 0.2500\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 5.6837e-06 - accuracy: 1.0000 - val_loss: 77.6068 - val_accuracy: 0.2500\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 5.1306e-06 - accuracy: 1.0000 - val_loss: 78.1588 - val_accuracy: 0.2500\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 4.6013e-06 - accuracy: 1.0000 - val_loss: 78.6694 - val_accuracy: 0.2500\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 4.1436e-06 - accuracy: 1.0000 - val_loss: 79.1405 - val_accuracy: 0.2500\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 3.7717e-06 - accuracy: 1.0000 - val_loss: 79.5735 - val_accuracy: 0.2500\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 3.4522e-06 - accuracy: 1.0000 - val_loss: 79.9698 - val_accuracy: 0.2500\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 3.1995e-06 - accuracy: 1.0000 - val_loss: 80.3311 - val_accuracy: 0.2500\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 3.0040e-06 - accuracy: 1.0000 - val_loss: 80.6587 - val_accuracy: 0.2500\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.8610e-06 - accuracy: 1.0000 - val_loss: 80.9538 - val_accuracy: 0.2500\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 2.7418e-06 - accuracy: 1.0000 - val_loss: 81.2182 - val_accuracy: 0.2500\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 2.6607e-06 - accuracy: 1.0000 - val_loss: 81.4532 - val_accuracy: 0.2500\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 2.5844e-06 - accuracy: 1.0000 - val_loss: 81.6601 - val_accuracy: 0.2500\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 2.4938e-06 - accuracy: 1.0000 - val_loss: 81.8408 - val_accuracy: 0.2500\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 2.4271e-06 - accuracy: 1.0000 - val_loss: 81.9977 - val_accuracy: 0.2500\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 2.3555e-06 - accuracy: 1.0000 - val_loss: 82.1320 - val_accuracy: 0.2500\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 2.2745e-06 - accuracy: 1.0000 - val_loss: 82.2452 - val_accuracy: 0.2500\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.1887e-06 - accuracy: 1.0000 - val_loss: 82.3390 - val_accuracy: 0.2500\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 2.1076e-06 - accuracy: 1.0000 - val_loss: 82.4155 - val_accuracy: 0.2500\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 2.0122e-06 - accuracy: 1.0000 - val_loss: 82.4766 - val_accuracy: 0.2500\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 1.9121e-06 - accuracy: 1.0000 - val_loss: 82.5242 - val_accuracy: 0.2500\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.8072e-06 - accuracy: 1.0000 - val_loss: 82.5598 - val_accuracy: 0.2500\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.7166e-06 - accuracy: 1.0000 - val_loss: 82.5852 - val_accuracy: 0.2500\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.6260e-06 - accuracy: 1.0000 - val_loss: 82.6019 - val_accuracy: 0.2500\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.5354e-06 - accuracy: 1.0000 - val_loss: 82.6112 - val_accuracy: 0.2500\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.4496e-06 - accuracy: 1.0000 - val_loss: 82.6142 - val_accuracy: 0.2500\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.3733e-06 - accuracy: 1.0000 - val_loss: 82.6122 - val_accuracy: 0.2500\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.2970e-06 - accuracy: 1.0000 - val_loss: 82.6059 - val_accuracy: 0.2500\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.2255e-06 - accuracy: 1.0000 - val_loss: 82.5962 - val_accuracy: 0.2500\n",
            "Epoch 96/100\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.1682e-06 - accuracy: 1.0000 - val_loss: 82.5839 - val_accuracy: 0.2500\n",
            "Epoch 97/100\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 1.1158e-06 - accuracy: 1.0000 - val_loss: 82.5694 - val_accuracy: 0.2500\n",
            "Epoch 98/100\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.0586e-06 - accuracy: 1.0000 - val_loss: 82.5533 - val_accuracy: 0.2500\n",
            "Epoch 99/100\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 1.0109e-06 - accuracy: 1.0000 - val_loss: 82.5360 - val_accuracy: 0.2500\n",
            "Epoch 100/100\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 9.6797e-07 - accuracy: 1.0000 - val_loss: 82.5179 - val_accuracy: 0.2500\n",
            "CPU times: user 6.95 s, sys: 1.01 s, total: 7.97 s\n",
            "Wall time: 17.8 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3f61049510>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%time model.fit(X_train, y_train, epochs=100, validation_split=0.3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHX352EVpoQh"
      },
      "source": [
        "## Das sieht gut aus, nun versuchen wir es mit dem kompletten Datensatz ohne Overfitting und mit Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "HsqrLu_ppoQi"
      },
      "outputs": [],
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, Input\n",
        "from keras.layers import Convolution2D, MaxPooling2D\n",
        "\n",
        "drop_out = 0.6\n",
        "\n",
        "# input tensor for a 3-channel 64x64 image\n",
        "inputs = Input(shape=(64, 64, 3))\n",
        "\n",
        "# one block of convolutional layers\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(inputs)\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(x)\n",
        "x = Convolution2D(64, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Dropout(drop_out)(x)\n",
        "\n",
        "# one more block\n",
        "x = Convolution2D(128, 3, activation='relu', padding='same')(x)\n",
        "x = Convolution2D(128, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Dropout(drop_out)(x)\n",
        "\n",
        "# one more block\n",
        "x = Convolution2D(256, 3, activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "x = Dropout(drop_out)(x)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "x = Dropout(drop_out)(x)\n",
        "\n",
        "# softmax activation, 6 categories\n",
        "predictions = Dense(6, activation='softmax')(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRousp1QpoQk",
        "outputId": "cea46eaa-3f65-4036-876e-33e23a1faaae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 32, 32, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 16, 16, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 16, 16, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 8, 8, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 16384)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 256)               4194560   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6)                 1542      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,788,358\n",
            "Trainable params: 4,788,358\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Model(inputs=inputs, outputs=predictions)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "KA0BHt8ZpoQm"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "M4zA_j07poQo"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "POU41fDPpoQq"
      },
      "outputs": [],
      "source": [
        "# wenn die Ergebnisse lange Zeit schlecht bleiben, brechen wir lieber ab\n",
        "early_stopping_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=25, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfN07IOLpoQt",
        "outputId": "99ceba50-b41a-4565-c1e3-a18e4ceb476c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "8/8 [==============================] - 1s 77ms/step - loss: 1.8165 - accuracy: 0.2149 - val_loss: 1.7891 - val_accuracy: 0.1148\n",
            "Epoch 2/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.7267 - accuracy: 0.2438 - val_loss: 1.7741 - val_accuracy: 0.1148\n",
            "Epoch 3/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.7313 - accuracy: 0.2107 - val_loss: 1.7732 - val_accuracy: 0.1148\n",
            "Epoch 4/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.7044 - accuracy: 0.2025 - val_loss: 1.7882 - val_accuracy: 0.0984\n",
            "Epoch 5/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.7053 - accuracy: 0.2686 - val_loss: 1.7655 - val_accuracy: 0.1803\n",
            "Epoch 6/500\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 1.6831 - accuracy: 0.2645 - val_loss: 1.7631 - val_accuracy: 0.1967\n",
            "Epoch 7/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6988 - accuracy: 0.2934 - val_loss: 1.7871 - val_accuracy: 0.1639\n",
            "Epoch 8/500\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 1.7127 - accuracy: 0.2893 - val_loss: 1.7629 - val_accuracy: 0.2131\n",
            "Epoch 9/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6815 - accuracy: 0.2893 - val_loss: 1.7480 - val_accuracy: 0.3115\n",
            "Epoch 10/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6720 - accuracy: 0.2975 - val_loss: 1.7623 - val_accuracy: 0.2295\n",
            "Epoch 11/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6591 - accuracy: 0.3140 - val_loss: 1.7451 - val_accuracy: 0.2131\n",
            "Epoch 12/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6904 - accuracy: 0.3017 - val_loss: 1.7577 - val_accuracy: 0.2131\n",
            "Epoch 13/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6850 - accuracy: 0.3017 - val_loss: 1.7632 - val_accuracy: 0.1639\n",
            "Epoch 14/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6729 - accuracy: 0.2645 - val_loss: 1.7580 - val_accuracy: 0.1803\n",
            "Epoch 15/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6856 - accuracy: 0.3058 - val_loss: 1.7174 - val_accuracy: 0.2295\n",
            "Epoch 16/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6825 - accuracy: 0.2727 - val_loss: 1.7421 - val_accuracy: 0.1967\n",
            "Epoch 17/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6444 - accuracy: 0.3223 - val_loss: 1.7136 - val_accuracy: 0.3607\n",
            "Epoch 18/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6476 - accuracy: 0.2810 - val_loss: 1.7126 - val_accuracy: 0.1967\n",
            "Epoch 19/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6272 - accuracy: 0.3388 - val_loss: 1.7041 - val_accuracy: 0.2131\n",
            "Epoch 20/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6657 - accuracy: 0.3223 - val_loss: 1.7539 - val_accuracy: 0.1803\n",
            "Epoch 21/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6485 - accuracy: 0.3140 - val_loss: 1.6697 - val_accuracy: 0.3115\n",
            "Epoch 22/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6106 - accuracy: 0.2893 - val_loss: 1.6426 - val_accuracy: 0.3279\n",
            "Epoch 23/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 1.5616 - accuracy: 0.3388 - val_loss: 1.6376 - val_accuracy: 0.2623\n",
            "Epoch 24/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.5576 - accuracy: 0.3223 - val_loss: 1.5894 - val_accuracy: 0.2951\n",
            "Epoch 25/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 1.5680 - accuracy: 0.3471 - val_loss: 1.6423 - val_accuracy: 0.2131\n",
            "Epoch 26/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.5892 - accuracy: 0.3058 - val_loss: 1.6643 - val_accuracy: 0.2623\n",
            "Epoch 27/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6415 - accuracy: 0.3099 - val_loss: 1.6936 - val_accuracy: 0.1967\n",
            "Epoch 28/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.6204 - accuracy: 0.2769 - val_loss: 1.6143 - val_accuracy: 0.2459\n",
            "Epoch 29/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.5869 - accuracy: 0.3347 - val_loss: 1.6053 - val_accuracy: 0.2295\n",
            "Epoch 30/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.5441 - accuracy: 0.3884 - val_loss: 1.5865 - val_accuracy: 0.3279\n",
            "Epoch 31/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.4891 - accuracy: 0.3678 - val_loss: 1.5539 - val_accuracy: 0.3443\n",
            "Epoch 32/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.4624 - accuracy: 0.3884 - val_loss: 1.5603 - val_accuracy: 0.3443\n",
            "Epoch 33/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 1.3849 - accuracy: 0.4959 - val_loss: 1.4319 - val_accuracy: 0.4426\n",
            "Epoch 34/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.3145 - accuracy: 0.5041 - val_loss: 1.3510 - val_accuracy: 0.5246\n",
            "Epoch 35/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 1.0949 - accuracy: 0.6322 - val_loss: 1.2229 - val_accuracy: 0.5410\n",
            "Epoch 36/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.0365 - accuracy: 0.6116 - val_loss: 1.1266 - val_accuracy: 0.5902\n",
            "Epoch 37/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 1.0022 - accuracy: 0.6405 - val_loss: 0.9908 - val_accuracy: 0.6721\n",
            "Epoch 38/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.7950 - accuracy: 0.7273 - val_loss: 0.9086 - val_accuracy: 0.7377\n",
            "Epoch 39/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.7204 - accuracy: 0.7355 - val_loss: 0.7806 - val_accuracy: 0.8197\n",
            "Epoch 40/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.7212 - accuracy: 0.7107 - val_loss: 0.8145 - val_accuracy: 0.7541\n",
            "Epoch 41/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.6294 - accuracy: 0.8099 - val_loss: 0.5542 - val_accuracy: 0.8033\n",
            "Epoch 42/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.4899 - accuracy: 0.7893 - val_loss: 0.6824 - val_accuracy: 0.7869\n",
            "Epoch 43/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.4665 - accuracy: 0.8678 - val_loss: 0.4566 - val_accuracy: 0.8689\n",
            "Epoch 44/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3749 - accuracy: 0.8760 - val_loss: 0.6201 - val_accuracy: 0.7869\n",
            "Epoch 45/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3464 - accuracy: 0.9132 - val_loss: 0.5395 - val_accuracy: 0.8525\n",
            "Epoch 46/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3839 - accuracy: 0.8636 - val_loss: 0.5330 - val_accuracy: 0.8525\n",
            "Epoch 47/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3595 - accuracy: 0.8719 - val_loss: 0.5075 - val_accuracy: 0.8852\n",
            "Epoch 48/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2948 - accuracy: 0.9008 - val_loss: 0.5252 - val_accuracy: 0.8525\n",
            "Epoch 49/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1978 - accuracy: 0.9256 - val_loss: 0.5640 - val_accuracy: 0.8361\n",
            "Epoch 50/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1919 - accuracy: 0.9339 - val_loss: 0.9125 - val_accuracy: 0.8525\n",
            "Epoch 51/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2577 - accuracy: 0.9215 - val_loss: 0.5247 - val_accuracy: 0.8689\n",
            "Epoch 52/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.3354 - accuracy: 0.8884 - val_loss: 0.3378 - val_accuracy: 0.9016\n",
            "Epoch 53/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2557 - accuracy: 0.9132 - val_loss: 0.3700 - val_accuracy: 0.9016\n",
            "Epoch 54/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2558 - accuracy: 0.9215 - val_loss: 0.4699 - val_accuracy: 0.9016\n",
            "Epoch 55/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1648 - accuracy: 0.9545 - val_loss: 0.3853 - val_accuracy: 0.8689\n",
            "Epoch 56/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.2259 - accuracy: 0.9215 - val_loss: 0.7838 - val_accuracy: 0.9016\n",
            "Epoch 57/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2538 - accuracy: 0.9215 - val_loss: 0.3466 - val_accuracy: 0.9180\n",
            "Epoch 58/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.3814 - accuracy: 0.8719 - val_loss: 0.4056 - val_accuracy: 0.9180\n",
            "Epoch 59/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.2355 - accuracy: 0.9587 - val_loss: 0.4194 - val_accuracy: 0.9016\n",
            "Epoch 60/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1612 - accuracy: 0.9587 - val_loss: 0.3513 - val_accuracy: 0.8852\n",
            "Epoch 61/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1366 - accuracy: 0.9587 - val_loss: 0.3967 - val_accuracy: 0.9016\n",
            "Epoch 62/500\n",
            "8/8 [==============================] - 0s 21ms/step - loss: 0.1490 - accuracy: 0.9545 - val_loss: 0.4551 - val_accuracy: 0.8689\n",
            "Epoch 63/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1943 - accuracy: 0.9463 - val_loss: 0.4351 - val_accuracy: 0.9016\n",
            "Epoch 64/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.1559 - accuracy: 0.9463 - val_loss: 0.6816 - val_accuracy: 0.8689\n",
            "Epoch 65/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1663 - accuracy: 0.9463 - val_loss: 0.5066 - val_accuracy: 0.9344\n",
            "Epoch 66/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2121 - accuracy: 0.9504 - val_loss: 0.3931 - val_accuracy: 0.8852\n",
            "Epoch 67/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1783 - accuracy: 0.9587 - val_loss: 0.3372 - val_accuracy: 0.9016\n",
            "Epoch 68/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1534 - accuracy: 0.9587 - val_loss: 0.2615 - val_accuracy: 0.9344\n",
            "Epoch 69/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.1121 - accuracy: 0.9752 - val_loss: 0.2517 - val_accuracy: 0.9180\n",
            "Epoch 70/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1244 - accuracy: 0.9504 - val_loss: 0.4641 - val_accuracy: 0.9180\n",
            "Epoch 71/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0347 - accuracy: 0.9917 - val_loss: 0.5695 - val_accuracy: 0.9180\n",
            "Epoch 72/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1565 - accuracy: 0.9463 - val_loss: 0.5813 - val_accuracy: 0.9344\n",
            "Epoch 73/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0961 - accuracy: 0.9587 - val_loss: 0.3946 - val_accuracy: 0.9180\n",
            "Epoch 74/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1102 - accuracy: 0.9587 - val_loss: 0.2344 - val_accuracy: 0.9180\n",
            "Epoch 75/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0773 - accuracy: 0.9669 - val_loss: 0.3032 - val_accuracy: 0.9016\n",
            "Epoch 76/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0986 - accuracy: 0.9545 - val_loss: 0.3449 - val_accuracy: 0.9016\n",
            "Epoch 77/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0690 - accuracy: 0.9793 - val_loss: 0.4224 - val_accuracy: 0.9016\n",
            "Epoch 78/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1006 - accuracy: 0.9669 - val_loss: 0.3212 - val_accuracy: 0.9344\n",
            "Epoch 79/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1132 - accuracy: 0.9793 - val_loss: 0.4474 - val_accuracy: 0.8852\n",
            "Epoch 80/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0960 - accuracy: 0.9628 - val_loss: 0.4469 - val_accuracy: 0.8689\n",
            "Epoch 81/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0798 - accuracy: 0.9711 - val_loss: 0.5560 - val_accuracy: 0.9016\n",
            "Epoch 82/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0518 - accuracy: 0.9793 - val_loss: 0.3609 - val_accuracy: 0.9016\n",
            "Epoch 83/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0528 - accuracy: 0.9752 - val_loss: 0.4213 - val_accuracy: 0.9180\n",
            "Epoch 84/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0405 - accuracy: 0.9876 - val_loss: 0.5574 - val_accuracy: 0.9016\n",
            "Epoch 85/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0921 - accuracy: 0.9835 - val_loss: 0.2437 - val_accuracy: 0.9016\n",
            "Epoch 86/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0891 - accuracy: 0.9669 - val_loss: 0.6108 - val_accuracy: 0.8852\n",
            "Epoch 87/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1194 - accuracy: 0.9587 - val_loss: 0.6123 - val_accuracy: 0.9016\n",
            "Epoch 88/500\n",
            "8/8 [==============================] - 0s 25ms/step - loss: 0.1806 - accuracy: 0.9628 - val_loss: 0.2769 - val_accuracy: 0.9180\n",
            "Epoch 89/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.2054 - accuracy: 0.9504 - val_loss: 0.2766 - val_accuracy: 0.8852\n",
            "Epoch 90/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0854 - accuracy: 0.9793 - val_loss: 0.2514 - val_accuracy: 0.9344\n",
            "Epoch 91/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0463 - accuracy: 0.9793 - val_loss: 0.2323 - val_accuracy: 0.9180\n",
            "Epoch 92/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0527 - accuracy: 0.9835 - val_loss: 0.3593 - val_accuracy: 0.8689\n",
            "Epoch 93/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1235 - accuracy: 0.9669 - val_loss: 0.3832 - val_accuracy: 0.9344\n",
            "Epoch 94/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0402 - accuracy: 0.9835 - val_loss: 0.4819 - val_accuracy: 0.9180\n",
            "Epoch 95/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1262 - accuracy: 0.9711 - val_loss: 0.3483 - val_accuracy: 0.9180\n",
            "Epoch 96/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0974 - accuracy: 0.9711 - val_loss: 0.6718 - val_accuracy: 0.9016\n",
            "Epoch 97/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1204 - accuracy: 0.9628 - val_loss: 0.5946 - val_accuracy: 0.9180\n",
            "Epoch 98/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0603 - accuracy: 0.9793 - val_loss: 0.3877 - val_accuracy: 0.9344\n",
            "Epoch 99/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0637 - accuracy: 0.9876 - val_loss: 0.3210 - val_accuracy: 0.9180\n",
            "Epoch 100/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0774 - accuracy: 0.9835 - val_loss: 0.3071 - val_accuracy: 0.9180\n",
            "Epoch 101/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1078 - accuracy: 0.9587 - val_loss: 0.4964 - val_accuracy: 0.9016\n",
            "Epoch 102/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.1450 - accuracy: 0.9669 - val_loss: 0.4284 - val_accuracy: 0.9016\n",
            "Epoch 103/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0875 - accuracy: 0.9793 - val_loss: 0.2429 - val_accuracy: 0.9180\n",
            "Epoch 104/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0918 - accuracy: 0.9711 - val_loss: 0.3879 - val_accuracy: 0.9016\n",
            "Epoch 105/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0792 - accuracy: 0.9752 - val_loss: 0.3911 - val_accuracy: 0.8852\n",
            "Epoch 106/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0557 - accuracy: 0.9752 - val_loss: 0.2858 - val_accuracy: 0.9180\n",
            "Epoch 107/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0647 - accuracy: 0.9793 - val_loss: 0.2137 - val_accuracy: 0.9344\n",
            "Epoch 108/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0331 - accuracy: 0.9917 - val_loss: 0.2201 - val_accuracy: 0.9344\n",
            "Epoch 109/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0569 - accuracy: 0.9793 - val_loss: 0.3381 - val_accuracy: 0.9016\n",
            "Epoch 110/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.3656 - val_accuracy: 0.9016\n",
            "Epoch 111/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0404 - accuracy: 0.9876 - val_loss: 0.5043 - val_accuracy: 0.9180\n",
            "Epoch 112/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0718 - accuracy: 0.9793 - val_loss: 0.5917 - val_accuracy: 0.8852\n",
            "Epoch 113/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0470 - accuracy: 0.9876 - val_loss: 0.6440 - val_accuracy: 0.9016\n",
            "Epoch 114/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0310 - accuracy: 0.9917 - val_loss: 0.4108 - val_accuracy: 0.9016\n",
            "Epoch 115/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0196 - accuracy: 0.9959 - val_loss: 0.4280 - val_accuracy: 0.8852\n",
            "Epoch 116/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0305 - accuracy: 0.9917 - val_loss: 0.3293 - val_accuracy: 0.9180\n",
            "Epoch 117/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0246 - accuracy: 0.9835 - val_loss: 0.3164 - val_accuracy: 0.9180\n",
            "Epoch 118/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0312 - accuracy: 0.9917 - val_loss: 0.1999 - val_accuracy: 0.9344\n",
            "Epoch 119/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0375 - accuracy: 0.9835 - val_loss: 0.2343 - val_accuracy: 0.9344\n",
            "Epoch 120/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0591 - accuracy: 0.9835 - val_loss: 0.2866 - val_accuracy: 0.9180\n",
            "Epoch 121/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0335 - accuracy: 0.9876 - val_loss: 0.3478 - val_accuracy: 0.8852\n",
            "Epoch 122/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0371 - accuracy: 0.9876 - val_loss: 0.5406 - val_accuracy: 0.8689\n",
            "Epoch 123/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0266 - accuracy: 0.9917 - val_loss: 0.5995 - val_accuracy: 0.8689\n",
            "Epoch 124/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1308 - accuracy: 0.9793 - val_loss: 0.3134 - val_accuracy: 0.9508\n",
            "Epoch 125/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0690 - accuracy: 0.9793 - val_loss: 0.4161 - val_accuracy: 0.9016\n",
            "Epoch 126/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0296 - accuracy: 0.9959 - val_loss: 0.6384 - val_accuracy: 0.9016\n",
            "Epoch 127/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0469 - accuracy: 0.9876 - val_loss: 0.5873 - val_accuracy: 0.9016\n",
            "Epoch 128/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0288 - accuracy: 0.9959 - val_loss: 0.4679 - val_accuracy: 0.9180\n",
            "Epoch 129/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0187 - accuracy: 0.9959 - val_loss: 0.3990 - val_accuracy: 0.9344\n",
            "Epoch 130/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0106 - accuracy: 0.9959 - val_loss: 0.3831 - val_accuracy: 0.9344\n",
            "Epoch 131/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0115 - accuracy: 0.9959 - val_loss: 0.3935 - val_accuracy: 0.9344\n",
            "Epoch 132/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.4008 - val_accuracy: 0.9344\n",
            "Epoch 133/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0229 - accuracy: 0.9959 - val_loss: 0.3811 - val_accuracy: 0.9344\n",
            "Epoch 134/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0885 - accuracy: 0.9752 - val_loss: 0.5269 - val_accuracy: 0.8852\n",
            "Epoch 135/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0191 - accuracy: 1.0000 - val_loss: 0.4660 - val_accuracy: 0.9016\n",
            "Epoch 136/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0426 - accuracy: 0.9835 - val_loss: 0.3459 - val_accuracy: 0.9180\n",
            "Epoch 137/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0237 - accuracy: 0.9876 - val_loss: 0.3599 - val_accuracy: 0.9180\n",
            "Epoch 138/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0109 - accuracy: 0.9959 - val_loss: 0.3431 - val_accuracy: 0.9344\n",
            "Epoch 139/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.3558 - val_accuracy: 0.9016\n",
            "Epoch 140/500\n",
            "8/8 [==============================] - 0s 24ms/step - loss: 0.0465 - accuracy: 0.9793 - val_loss: 0.7768 - val_accuracy: 0.9180\n",
            "Epoch 141/500\n",
            "8/8 [==============================] - 0s 23ms/step - loss: 0.1040 - accuracy: 0.9711 - val_loss: 0.4363 - val_accuracy: 0.9180\n",
            "Epoch 142/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0686 - accuracy: 0.9793 - val_loss: 0.2946 - val_accuracy: 0.8852\n",
            "Epoch 143/500\n",
            "8/8 [==============================] - 0s 22ms/step - loss: 0.0748 - accuracy: 0.9628 - val_loss: 0.3876 - val_accuracy: 0.9180\n",
            "Epoch 143: early stopping\n",
            "CPU times: user 22.7 s, sys: 1.15 s, total: 23.8 s\n",
            "Wall time: 27.6 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f3f50090810>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# auf Colab haben wir GPU support angeschaltet, d.h. auch dieses Training kann sehr schnell gehen\n",
        "# auf einem lokalen Recher ohne GPU kann sowas aber locker 2 Stunden oder mehr laufen\n",
        "# BATCH_SIZE = 500\n",
        "BATCH_SIZE = 32\n",
        "%time model.fit(X_train, y_train, epochs=500, batch_size=BATCH_SIZE, validation_split=0.2, callbacks=[early_stopping_callback])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o7UDsN7npoQw"
      },
      "source": [
        "# Bewertung"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7KhwcBOpoQw",
        "outputId": "75abcd80-539a-472f-c5fa-596326f9be32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10/10 [==============================] - 0s 14ms/step - loss: 0.0813 - accuracy: 0.9835\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(0.08125131577253342, 0.9834983348846436)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_loss, train_accuracy = model.evaluate(X_train, y_train, batch_size=BATCH_SIZE)\n",
        "train_loss, train_accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5DtJ2jcpoQy",
        "outputId": "673862bf-f8d6-49fb-8229-7e02bb500af5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3/3 [==============================] - 0s 8ms/step - loss: 0.2077 - accuracy: 0.9342\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(0.2076958864927292, 0.9342105388641357)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_loss, test_accuracy = model.evaluate(X_test, y_test, batch_size=BATCH_SIZE)\n",
        "test_loss, test_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTBpM_1vpoQ1"
      },
      "source": [
        "# Sichern und Laden des Modells\n",
        "\n",
        "https://www.tensorflow.org/tutorials/keras/save_and_load"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCNMOFkepoQ1",
        "outputId": "5eccb03d-30c1-4772-c8db-9f054da35d2f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'models': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!rm -r models\n",
        "!mkdir models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "bIkgD-XhpoQ3"
      },
      "outputs": [],
      "source": [
        "model.save('models/conv-vgg.hdf5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruxkxejrpoQ4",
        "outputId": "ab205881-c04b-416c-cd33-de7679ee0074"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 55M\n",
            "-rw-r--r-- 1 root root 55M May 25 08:48 conv-vgg.hdf5\n"
          ]
        }
      ],
      "source": [
        "!ls -lh models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "eT25_NmlXUO2"
      },
      "outputs": [],
      "source": [
        "# so lädt man das komplette Modell wieder in den Speicher\n",
        "new_model = tf.keras.models.load_model('models/conv-vgg.hdf5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FdOpaYeJpoQ7"
      },
      "source": [
        "# Ausprobieren auf ein paar Test-Daten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ocg1wPvOpoQ8",
        "outputId": "2a56d178-6f2f-4272-a0cf-92e7e706c336"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5, 2, 0, 5, 0, 5, 2, 1, 0, 5])"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import random\n",
        "\n",
        "# Pick 10 random images for test data set\n",
        "random.seed(3) # to make this deterministic\n",
        "sample_indexes = random.sample(range(len(X_test)), 10)\n",
        "sample_images = [X_test[i] for i in sample_indexes]\n",
        "sample_labels = [y_test[i] for i in sample_indexes]\n",
        "\n",
        "ground_truth = np.argmax(sample_labels, axis=1)\n",
        "\n",
        "X_sample = np.array(sample_images)\n",
        "prediction = model.predict(X_sample)\n",
        "predicted_categories = np.argmax(prediction, axis=1)\n",
        "predicted_categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "pWp-26pBpoQ9"
      },
      "outputs": [],
      "source": [
        "# Display the predictions and the ground truth visually.\n",
        "def display_prediction (images, true_labels, predicted_labels):\n",
        "    fig = plt.figure(figsize=(10, 10))\n",
        "    for i in range(len(true_labels)):\n",
        "        truth = true_labels[i]\n",
        "        prediction = predicted_labels[i]\n",
        "        plt.subplot(5, 2,1+i)\n",
        "        plt.axis('off')\n",
        "        color='green' if truth == prediction else 'red'\n",
        "        plt.text(80, 10, \"Truth:        {0}\\nPrediction: {1}\".format(truth, prediction), \n",
        "                 fontsize=12, color=color)\n",
        "        plt.imshow(images[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "nv5nUf4IpoQ_",
        "outputId": "bb81c201-cbf7-4c00-8b05-5d5a77fb153c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "display_prediction(sample_images, ground_truth, predicted_categories)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Yl9GnjM_poRB"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "include_colab_link": true,
      "name": "kap7-cnn.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}