violence_detection.ipynb

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  "metadata": {
    "colab": {
      "name": "ViolenceDetection22",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "metadata": {
        "id": "-x-x2hhlOKkl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Violence Detection using CNN + LSTM neural netowrk"
      ]
    },
    {
      "metadata": {
        "id": "9fG4ju6NOSIv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "by [Pedro.F Rodenas (Github)](https://github.com/pedrofrodenas)"
      ]
    },
    {
      "metadata": {
        "id": "nRQW1dLqOVO_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Introduction"
      ]
    },
    {
      "metadata": {
        "id": "a1iagEQ3OXEn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Today, the amount of public violence has increased dramatically. As much in high schools as in the street. This has resulted in the ubiquitous use of surveillance cameras. This has helped the authorities to identify these events and take the necessary measures. But almost all systems today require the human-inspection of these videos to identify such events, which is virtually inefficient. It is therefore necessary to have such a practical system that can automatically monitor and identify the surveillance videos.\n",
        "The development of various deep learning techniques, thanks to the availability of large data sets and computational resources, has resulted in a historic change in the community of computer vision. Various techniques have been developed to address problems such as object detection, recognition, tracking, action recognition, legend generation, etc. However, despite recent developments in deep learning, very few techniques based on deep learning have been proposed to address the problem of detecting violence from videos."
      ]
    },
    {
      "metadata": {
        "id": "L8w5vbqMObaw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Flowchart"
      ]
    },
    {
      "metadata": {
        "id": "6kz7yE3kOgMk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The method consists of extracting a set of frames belonging to the video, sending them to a pretrained network called VGG16, obtaining the output of one of its final layers and from these outputs train another network architecture with a type of special neurons called LSTM. These neurons have memory and are able to analyze the temporal information of the video, if at any time they detect violence, it will be classified as a violent video.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "UVXa7uMnOlkp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ]
    },
    {
      "metadata": {
        "id": "oDfDnlliPMd-",
        "colab_type": "code",
        "outputId": "7582c7ed-2ba2-4164-e043-d0c8d31ba19f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import cv2\n",
        "import os\n",
        "import numpy as np\n",
        "import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import download\n",
        "from random import shuffle\n",
        "from keras.applications import VGG16\n",
        "from keras import backend as K\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Input\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense, Activation\n",
        "import sys\n",
        "import h5py"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "kFU6Qg8mPXvv",
        "colab_type": "code",
        "outputId": "04a2d803-e425-4335-94e8-71e91d05d6b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "keras.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.4'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "v6pf1l28PzIO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Helper Functions"
      ]
    },
    {
      "metadata": {
        "id": "WKnyJkf8PzxE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We will use the function ```print_progress``` to print the amount of videos processed and ```download_data``` to download the datasets"
      ]
    },
    {
      "metadata": {
        "id": "qnafWmS7P3CG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def print_progress(count, max_count):\n",
        "    # Percentage completion.\n",
        "    pct_complete = count / max_count\n",
        "\n",
        "    # Status-message. Note the \\r which means the line should\n",
        "    # overwrite itself.\n",
        "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
        "\n",
        "    # Print it.\n",
        "    sys.stdout.write(msg)\n",
        "    sys.stdout.flush()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j1V5cxltP5U-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def download_data(in_dir, url):\n",
        "    \n",
        "    if not os.path.exists(in_dir):\n",
        "        os.makedirs(in_dir)\n",
        "    \n",
        "    download.maybe_download_and_extract(url,in_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tRf-KgkjP9Kt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load Data\n",
        "\n",
        "Firstly, we define the directory to place the video dataset"
      ]
    },
    {
      "metadata": {
        "id": "RiRKgwBgP-NY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "in_dir = \"data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "reLkhZglQBrD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We set the url to download the dataset"
      ]
    },
    {
      "metadata": {
        "id": "YX0Qk202QCvw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "url_hockey = \"http://visilab.etsii.uclm.es/personas/oscar/FightDetection/HockeyFights.zip\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K7zrKRupRFaz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "to download the dataset and decompress it:"
      ]
    },
    {
      "metadata": {
        "id": "urX5VyFzRF-o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec619e67-5d10-413f-d510-aaeadfbf3116"
      },
      "cell_type": "code",
      "source": [
        "download_data(in_dir,url_hockey)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data has apparently already been downloaded and unpacked.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UCZHrpJjRKky",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copy some of the data-dimensions for convenience."
      ]
    },
    {
      "metadata": {
        "id": "SXTNEj6SRLZZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Frame size  \n",
        "img_size = 224\n",
        "\n",
        "img_size_touple = (img_size, img_size)\n",
        "\n",
        "# Number of channels (RGB)\n",
        "num_channels = 3\n",
        "\n",
        "# Flat frame size\n",
        "img_size_flat = img_size * img_size * num_channels\n",
        "\n",
        "# Number of classes for classification (Violence-No Violence)\n",
        "num_classes = 2\n",
        "\n",
        "# Number of files to train\n",
        "_num_files_train = 1\n",
        "\n",
        "# Number of frames per video\n",
        "_images_per_file = 20\n",
        "\n",
        "# Number of frames per training set\n",
        "_num_images_train = _num_files_train * _images_per_file\n",
        "\n",
        "# Video extension\n",
        "video_exts = \".avi\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wodq7EaSRSS8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Helper-function for getting video frames\n",
        "Function used to get 20 frames from a video file and convert the frame to a suitable format for the neural net."
      ]
    },
    {
      "metadata": {
        "id": "eu9c4a-3RVkO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_frames(current_dir, file_name):\n",
        "    \n",
        "    in_file = os.path.join(current_dir, file_name)\n",
        "    \n",
        "    images = []\n",
        "    \n",
        "    vidcap = cv2.VideoCapture(in_file)\n",
        "    \n",
        "    success,image = vidcap.read()\n",
        "        \n",
        "    count = 0\n",
        "\n",
        "    while count<_images_per_file:\n",
        "                \n",
        "        RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "    \n",
        "        res = cv2.resize(RGB_img, dsize=(img_size, img_size),\n",
        "                                 interpolation=cv2.INTER_CUBIC)\n",
        "    \n",
        "        images.append(res)\n",
        "    \n",
        "        success,image = vidcap.read()\n",
        "    \n",
        "        count += 1\n",
        "        \n",
        "    resul = np.array(images)\n",
        "    \n",
        "    resul = (resul / 255.).astype(np.float16)\n",
        "        \n",
        "    return resul"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tLCjYFBtRZb-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Helper function to get the names of the data downloaded and label it"
      ]
    },
    {
      "metadata": {
        "id": "Qiv5NIJjRbIA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def label_video_names(in_dir):\n",
        "    \n",
        "    # list containing video names\n",
        "    names = []\n",
        "    # list containin video labels [1, 0] if it has violence and [0, 1] if not\n",
        "    labels = []\n",
        "    \n",
        "    \n",
        "    for current_dir, dir_names,file_names in os.walk(in_dir):\n",
        "        \n",
        "        for file_name in file_names:\n",
        "            \n",
        "            if file_name[0:2] == 'fi':\n",
        "                labels.append([1,0])\n",
        "                names.append(file_name)\n",
        "            elif file_name[0:2] == 'no':\n",
        "                labels.append([0,1])\n",
        "                names.append(file_name)\n",
        "                     \n",
        "            \n",
        "    c = list(zip(names,labels))\n",
        "    # Suffle the data (names and labels)\n",
        "    shuffle(c)\n",
        "    \n",
        "    names, labels = zip(*c)\n",
        "            \n",
        "    return names, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t3KW2kfgReKn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Plot a video frame to see if data is correct"
      ]
    },
    {
      "metadata": {
        "id": "dIsaAgcyRfIx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# First get the names and labels of the whole videos\n",
        "names, labels = label_video_names(in_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EqucOMsJRgqm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Then we are going to load 20 frames of one video, for example"
      ]
    },
    {
      "metadata": {
        "id": "xUfZO-0BRj0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e2b913dc-c39f-4d5f-a0d6-da1273692fbc"
      },
      "cell_type": "code",
      "source": [
        "names[12]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'fi191_xvid.avi'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "ORGJ2pS9RnWw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The video has violence, look at the name of the video, starts with 'fi'"
      ]
    },
    {
      "metadata": {
        "id": "EqBi8z6rRoMW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "frames = get_frames(in_dir, names[12])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vtgQEmI6RrmM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Convert back the frames to uint8 pixel format to plot the frame"
      ]
    },
    {
      "metadata": {
        "id": "9ihSA_ogRsNU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "visible_frame = (frames*255).astype('uint8')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PM1kNhaHRvSv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e2a6d324-cb62-42d2-dd8f-1cf1c464d168"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(visible_frame[3])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f37ef72fef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
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LIBUvv3iFU+tnscEiLxRLjNcfJHjwJ1n/8Ke55/FP03vnj3L+sY8j7/oQq2/9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\nC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D/t00ssLt7P0z/1JUwQkngiJwRNEMpSkQFhxefv/dwv8uU/+h08nNWvPO2Q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\nf0TEhuL/AS67yCoMRabAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_gVWtYPvR8n2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "f9858ffa-3981-4b85-9c4d-62057bb8972c"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(visible_frame[15])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f37ed6c6c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4U+KFwWNxJw/r/ckSRCKUJIgTvNAIFSB0cws4ofBSI3VAGEb0uqtYUXKYZ2R5xnJvCV+Y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LDXp6gbZvM5+4dHzBYLttc3We0odt+8zYsvvcLB3j5b3S5mNGZe1siypLexyebWGllr0DmajSEF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\n5xYuMLVg9gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "sF1QieG5SANp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pre-Trained Model: VGG16"
      ]
    },
    {
      "metadata": {
        "id": "0o6FA6slSA1l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The following creates an instance of the pre-trained VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already.\n",
        "\n",
        "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If include_top=True then the whole VGG16 model is downloaded which is about 528 MB. If include_top=False then only the convolutional part of the VGG16 model is downloaded which is just 57 MB."
      ]
    },
    {
      "metadata": {
        "id": "MjRN6oE4SC81",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "1a9fbd16-9741-406d-b4fd-e3a20cd34a0d"
      },
      "cell_type": "code",
      "source": [
        "image_model = VGG16(include_top=True, weights='imagenet')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "P_7z-y1mSPov",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's see the model summary"
      ]
    },
    {
      "metadata": {
        "id": "ud7OU0t7SQPi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "20718d3a-c1d3-4a06-d6f5-de3c465311df"
      },
      "cell_type": "code",
      "source": [
        "image_model.summary()\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-NxnGLlDSTwr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can observe the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3. Note that we have defined the frame size as 224x224x3. The video frame will be the input of the VGG16 net."
      ]
    },
    {
      "metadata": {
        "id": "9a0k89M5SUjP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "50971481-7dff-49de-b4ad-a3e69c2853ac"
      },
      "cell_type": "code",
      "source": [
        "input_shape = image_model.layers[0].output_shape[1:3]\n",
        "input_shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "kMR6iqg4SaOl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### VGG16 model flowchart"
      ]
    },
    {
      "metadata": {
        "id": "qFUDqTiBSa0H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The following chart shows how the data flows when using the VGG16 model for Transfer Learning. First we input and process 20 video frames in batch with the VGG16 model. Just prior to the final classification layer of the VGG16 model, we save the so-called Transfer Values to a cache-file.\n",
        "\n",
        "The reason for using a cache-file is that it takes a long time to process an image with the VGG16 model. If each image is processed more than once then we can save a lot of time by caching the transfer-values.\n",
        "\n",
        "When all the videos have been processed through the VGG16 model and the resulting transfer-values saved to a cache file, then we can use those transfer-values as the input to LSTM neural network. We will then train the second neural network using the classes from the violence dataset (Violence, No-Violence), so the network learns how to classify images based on the transfer-values from the VGG16 model."
      ]
    },
    {
      "metadata": {
        "id": "4YWFA-2tSdfB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "55dcb0c7-2b8a-4420-e25e-381152197a81"
      },
      "cell_type": "code",
      "source": [
        "# We will use the output of the layer prior to the final\n",
        "# classification-layer which is named fc2. This is a fully-connected (or dense) layer.\n",
        "transfer_layer = image_model.get_layer('fc2')\n",
        "\n",
        "image_model_transfer = Model(inputs=image_model.input,\n",
        "                             outputs=transfer_layer.output)\n",
        "\n",
        "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n",
        "\n",
        "\n",
        "print(\"The input of the VGG16 net have dimensions:\",K.int_shape(image_model.input)[1:3])\n",
        "\n",
        "print(\"The output of the selecter layer of VGG16 net have dimensions: \", transfer_values_size)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The input of the VGG16 net have dimensions: (224, 224)\n",
            "The output of the selecter layer of VGG16 net have dimensions:  4096\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ghum6s2Si1n",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Function to process 20 video frames through VGG16 and get transfer values"
      ]
    },
    {
      "metadata": {
        "id": "-AdxYAtiSlLF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_transfer_values(current_dir, file_name):\n",
        "    \n",
        "    # Pre-allocate input-batch-array for images.\n",
        "    shape = (_images_per_file,) + img_size_touple + (3,)\n",
        "    \n",
        "    image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
        "    \n",
        "    image_batch = get_frames(current_dir, file_name)\n",
        "      \n",
        "    # Pre-allocate output-array for transfer-values.\n",
        "    # Note that we use 16-bit floating-points to save memory.\n",
        "    shape = (_images_per_file, transfer_values_size)\n",
        "    transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
        "\n",
        "    transfer_values = \\\n",
        "            image_model_transfer.predict(image_batch)\n",
        "            \n",
        "    return transfer_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HGdRIG6oSooG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Generator that process one video through VGG16 each function call"
      ]
    },
    {
      "metadata": {
        "id": "D2BvaY3eSpSH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def proces_transfer(vid_names, in_dir, labels):\n",
        "    \n",
        "    count = 0\n",
        "    \n",
        "    tam = len(vid_names)\n",
        "    \n",
        "    # Pre-allocate input-batch-array for images.\n",
        "    shape = (_images_per_file,) + img_size_touple + (3,)\n",
        "    \n",
        "    while count<tam:\n",
        "        \n",
        "        video_name = vid_names[count]\n",
        "        \n",
        "        image_batch = np.zeros(shape=shape, dtype=np.float16)\n",
        "    \n",
        "        image_batch = get_frames(in_dir, video_name)\n",
        "        \n",
        "         # Note that we use 16-bit floating-points to save memory.\n",
        "        shape = (_images_per_file, transfer_values_size)\n",
        "        transfer_values = np.zeros(shape=shape, dtype=np.float16)\n",
        "        \n",
        "        transfer_values = \\\n",
        "            image_model_transfer.predict(image_batch)\n",
        "         \n",
        "        labels1 = labels[count]\n",
        "        \n",
        "        aux = np.ones([20,2])\n",
        "        \n",
        "        labelss = labels1*aux\n",
        "        \n",
        "        yield transfer_values, labelss\n",
        "        \n",
        "        count+=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "I-Cnv0fRSswF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Functions to save transfer values from VGG16 to later use\n",
        "We are going to define functions to get the transfer values from VGG16 with defined number of files. Then save the transfer values files used from training in one file and the ones uses for testing in another one. "
      ]
    },
    {
      "metadata": {
        "id": "tvU53ypSSvL0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_files(n_files):\n",
        "    \n",
        "    gen = proces_transfer(names_training, in_dir, labels_training)\n",
        "\n",
        "    numer = 1\n",
        "\n",
        "    # Read the first chunk to get the column dtypes\n",
        "    chunk = next(gen)\n",
        "\n",
        "    row_count = chunk[0].shape[0]\n",
        "    row_count2 = chunk[1].shape[0]\n",
        "    \n",
        "    with h5py.File('prueba.h5', 'w') as f:\n",
        "    \n",
        "        # Initialize a resizable dataset to hold the output\n",
        "        maxshape = (None,) + chunk[0].shape[1:]\n",
        "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
        "    \n",
        "    \n",
        "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
        "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
        "    \n",
        "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
        "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
        "    \n",
        "         # Write the first chunk of rows\n",
        "        dset[:] = chunk[0]\n",
        "        dset2[:] = chunk[1]\n",
        "\n",
        "        for chunk in gen:\n",
        "            \n",
        "            if numer == n_files:\n",
        "            \n",
        "                break\n",
        "\n",
        "            # Resize the dataset to accommodate the next chunk of rows\n",
        "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
        "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
        "\n",
        "            # Write the next chunk\n",
        "            dset[row_count:] = chunk[0]\n",
        "            dset2[row_count:] = chunk[1]\n",
        "\n",
        "            # Increment the row count\n",
        "            row_count += chunk[0].shape[0]\n",
        "            row_count2 += chunk[1].shape[0]\n",
        "            \n",
        "            print_progress(numer, n_files)\n",
        "        \n",
        "            numer += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8nK8uFExS0nX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_files_test(n_files):\n",
        "    \n",
        "    gen = proces_transfer(names_test, in_dir, labels_test)\n",
        "\n",
        "    numer = 1\n",
        "\n",
        "    # Read the first chunk to get the column dtypes\n",
        "    chunk = next(gen)\n",
        "\n",
        "    row_count = chunk[0].shape[0]\n",
        "    row_count2 = chunk[1].shape[0]\n",
        "    \n",
        "    with h5py.File('pruebavalidation.h5', 'w') as f:\n",
        "    \n",
        "        # Initialize a resizable dataset to hold the output\n",
        "        maxshape = (None,) + chunk[0].shape[1:]\n",
        "        maxshape2 = (None,) + chunk[1].shape[1:]\n",
        "    \n",
        "    \n",
        "        dset = f.create_dataset('data', shape=chunk[0].shape, maxshape=maxshape,\n",
        "                                chunks=chunk[0].shape, dtype=chunk[0].dtype)\n",
        "    \n",
        "        dset2 = f.create_dataset('labels', shape=chunk[1].shape, maxshape=maxshape2,\n",
        "                                 chunks=chunk[1].shape, dtype=chunk[1].dtype)\n",
        "    \n",
        "         # Write the first chunk of rows\n",
        "        dset[:] = chunk[0]\n",
        "        dset2[:] = chunk[1]\n",
        "\n",
        "        for chunk in gen:\n",
        "            \n",
        "            if numer == n_files:\n",
        "            \n",
        "                break\n",
        "\n",
        "            # Resize the dataset to accommodate the next chunk of rows\n",
        "            dset.resize(row_count + chunk[0].shape[0], axis=0)\n",
        "            dset2.resize(row_count2 + chunk[1].shape[0], axis=0)\n",
        "\n",
        "            # Write the next chunk\n",
        "            dset[row_count:] = chunk[0]\n",
        "            dset2[row_count:] = chunk[1]\n",
        "\n",
        "            # Increment the row count\n",
        "            row_count += chunk[0].shape[0]\n",
        "            row_count2 += chunk[1].shape[0]\n",
        "            \n",
        "            print_progress(numer, n_files)\n",
        "        \n",
        "            numer += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R9axnZ8dS64T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Split the dataset into training set and test set\n",
        "We are going to split the dataset into training set and testing. The training set is used to train the model and the test set to check the model accuracy."
      ]
    },
    {
      "metadata": {
        "id": "wjne3svdS9Y3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "training_set = int(len(names)*0.8)\n",
        "test_set = int(len(names)*0.2)\n",
        "\n",
        "names_training = names[0:training_set]\n",
        "names_test = names[training_set:]\n",
        "\n",
        "labels_training = labels[0:training_set]\n",
        "labels_test = labels[training_set:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xmiJi6G7TBnI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Then we are going to process all video frames through VGG16 and save the transfer values."
      ]
    },
    {
      "metadata": {
        "id": "pyO9WP-6TER4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94209006-bfb9-470f-d418-cd2acfbdb12d"
      },
      "cell_type": "code",
      "source": [
        "make_files(training_set)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "- Progress: 99.9%"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0ThoefXUXPrS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f4cdc1e-4bfb-4ab6-81c1-05abadc1239f"
      },
      "cell_type": "code",
      "source": [
        "make_files_test(test_set)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "- Progress: 99.5%"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0s2imcRixMAr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Load the cached transfer values into memory\n",
        "We have already saved all the videos transfer values into disk. But we have to load those transfer values into memory in order to train the LSTM net. One question would be: why not process transfer values and load them into RAM memory? Yes is a more eficient way to train the second net. But if you have to train the LSTM in different ways in order to see which way gets the best accuracy, if you didn't save the transfer values into disk you would have to process the whole videos each training. It's very time consuming processing the videos through VGG16 net. \n"
      ]
    },
    {
      "metadata": {
        "id": "RnK1JL-izzeS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In order to load the saved transfer values into RAM memory we are going to use this two functions:"
      ]
    },
    {
      "metadata": {
        "id": "Ez0blP2z0CsF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def process_alldata_training():\n",
        "    \n",
        "    joint_transfer=[]\n",
        "    frames_num=20\n",
        "    count = 0\n",
        "    \n",
        "    with h5py.File('prueba.h5', 'r') as f:\n",
        "            \n",
        "        X_batch = f['data'][:]\n",
        "        y_batch = f['labels'][:]\n",
        "\n",
        "    for i in range(int(len(X_batch)/frames_num)):\n",
        "        inc = count+frames_num\n",
        "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
        "        count =inc\n",
        "        \n",
        "    data =[]\n",
        "    target=[]\n",
        "    \n",
        "    for i in joint_transfer:\n",
        "        data.append(i[0])\n",
        "        target.append(np.array(i[1]))\n",
        "        \n",
        "    return data, target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Dne2XaQ90MGk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def process_alldata_test():\n",
        "    \n",
        "    joint_transfer=[]\n",
        "    frames_num=20\n",
        "    count = 0\n",
        "    \n",
        "    with h5py.File('pruebavalidation.h5', 'r') as f:\n",
        "            \n",
        "        X_batch = f['data'][:]\n",
        "        y_batch = f['labels'][:]\n",
        "\n",
        "    for i in range(int(len(X_batch)/frames_num)):\n",
        "        inc = count+frames_num\n",
        "        joint_transfer.append([X_batch[count:inc],y_batch[count]])\n",
        "        count =inc\n",
        "        \n",
        "    data =[]\n",
        "    target=[]\n",
        "    \n",
        "    for i in joint_transfer:\n",
        "        data.append(i[0])\n",
        "        target.append(np.array(i[1]))\n",
        "        \n",
        "    return data, target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "phyhoYc67VW8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data, target = process_alldata_training()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dJpXHvEg7Xhc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_test, target_test = process_alldata_test()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qjaawyqj0arq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Recurrent Neural Network"
      ]
    },
    {
      "metadata": {
        "id": "dXFhxfzE0hGb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The basic building block in a Recurrent Neural Network (RNN) is a Recurrent Unit (RU). There are many different variants of recurrent units such as the rather clunky LSTM (Long-Short-Term-Memory) and the somewhat simpler GRU (Gated Recurrent Unit) which we will use in this tutorial. Experiments in the literature suggest that the LSTM and GRU have roughly similar performance. Even simpler variants also exist and the literature suggests that they may perform even better than both LSTM and GRU, but they are not implemented in Keras which we will use in this tutorial.\n",
        "\n",
        "A recurrent neuron has an internal state that is being updated every time the unit receives a new input. This internal state serves as a kind of memory. However, it is not a traditional kind of computer memory which stores bits that are either on or off. Instead the recurrent unit stores floating-point values in its memory-state, which are read and written using matrix-operations so the operations are all differentiable. This means the memory-state can store arbitrary floating-point values (although typically limited between -1.0 and 1.0) and the network can be trained like a normal neural network using Gradient Descent.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "BsIrL3ix4Edp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Define LSTM architecture"
      ]
    },
    {
      "metadata": {
        "id": "qFiSjMaC4Q1c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "When defining the LSTM architecture we have to take into account the dimensions of the transfer values. From each frame the VGG16 network obtains as output a vector of 4096 transfer values. From each video we are processing 20 frames so we will have 20 x 4096 values per video. The classification must be done taking into account the 20 frames of the video. If any of them detects violence, the video will be classified as violent.\n"
      ]
    },
    {
      "metadata": {
        "id": "HAgSUeVu58N_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The first input dimension of LSTM neurons is the temporal dimension, in our case it is 20. The second is the size of the features vector (transfer values).\n"
      ]
    },
    {
      "metadata": {
        "id": "XWABZ91b6f7l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "chunk_size = 4096\n",
        "n_chunks = 20\n",
        "rnn_size = 512\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(rnn_size, input_shape=(n_chunks, chunk_size)))\n",
        "model.add(Dense(1024))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(50))\n",
        "model.add(Activation('sigmoid'))\n",
        "model.add(Dense(2))\n",
        "model.add(Activation('softmax'))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bVonOPYW7F_b",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model training\n"
      ]
    },
    {
      "metadata": {
        "id": "iRZlW4ZV_ygS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6834
        },
        "outputId": "c223a55f-8ccd-4e5a-f5d4-dc6a1daf2d0e"
      },
      "cell_type": "code",
      "source": [
        "epoch = 200\n",
        "batchS = 500\n",
        "\n",
        "history = model.fit(np.array(data[0:750]), np.array(target[0:750]), epochs=epoch,\n",
        "                    validation_data=(np.array(data[750:]), np.array(target[750:])), \n",
        "                    batch_size=batchS, verbose=2)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 750 samples, validate on 50 samples\n",
            "Epoch 1/200\n",
            " - 1s - loss: 0.0408 - acc: 0.9573 - val_loss: 0.0642 - val_acc: 0.9200\n",
            "Epoch 2/200\n",
            " - 1s - loss: 0.0383 - acc: 0.9613 - val_loss: 0.0598 - val_acc: 0.9400\n",
            "Epoch 3/200\n",
            " - 1s - loss: 0.0372 - acc: 0.9600 - val_loss: 0.0466 - val_acc: 0.9400\n",
            "Epoch 4/200\n",
            " - 1s - loss: 0.0375 - acc: 0.9587 - val_loss: 0.0822 - val_acc: 0.8800\n",
            "Epoch 5/200\n",
            " - 1s - loss: 0.0423 - acc: 0.9520 - val_loss: 0.0442 - val_acc: 0.9400\n",
            "Epoch 6/200\n",
            " - 1s - loss: 0.0385 - acc: 0.9600 - val_loss: 0.0441 - val_acc: 0.9400\n",
            "Epoch 7/200\n",
            " - 1s - loss: 0.0373 - acc: 0.9613 - val_loss: 0.0801 - val_acc: 0.8800\n",
            "Epoch 8/200\n",
            " - 1s - loss: 0.0387 - acc: 0.9587 - val_loss: 0.0470 - val_acc: 0.9400\n",
            "Epoch 9/200\n",
            " - 1s - loss: 0.0364 - acc: 0.9600 - val_loss: 0.0470 - val_acc: 0.9400\n",
            "Epoch 10/200\n",
            " - 1s - loss: 0.0358 - acc: 0.9613 - val_loss: 0.0659 - val_acc: 0.9200\n",
            "Epoch 11/200\n",
            " - 1s - loss: 0.0366 - acc: 0.9627 - val_loss: 0.0511 - val_acc: 0.9400\n",
            "Epoch 12/200\n",
            " - 1s - loss: 0.0370 - acc: 0.9613 - val_loss: 0.0610 - val_acc: 0.9400\n",
            "Epoch 13/200\n",
            " - 1s - loss: 0.0363 - acc: 0.9627 - val_loss: 0.0680 - val_acc: 0.9000\n",
            "Epoch 14/200\n",
            " - 1s - loss: 0.0356 - acc: 0.9627 - val_loss: 0.0414 - val_acc: 0.9600\n",
            "Epoch 15/200\n",
            " - 1s - loss: 0.0485 - acc: 0.9453 - val_loss: 0.1305 - val_acc: 0.8400\n",
            "Epoch 16/200\n",
            " - 1s - loss: 0.0583 - acc: 0.9333 - val_loss: 0.0531 - val_acc: 0.9200\n",
            "Epoch 17/200\n",
            " - 1s - loss: 0.0567 - acc: 0.9240 - val_loss: 0.1461 - val_acc: 0.8400\n",
            "Epoch 18/200\n",
            " - 1s - loss: 0.0732 - acc: 0.9067 - val_loss: 0.0593 - val_acc: 0.9000\n",
            "Epoch 19/200\n",
            " - 1s - loss: 0.0659 - acc: 0.9160 - val_loss: 0.1171 - val_acc: 0.8400\n",
            "Epoch 20/200\n",
            " - 1s - loss: 0.0566 - acc: 0.9267 - val_loss: 0.0551 - val_acc: 0.9400\n",
            "Epoch 21/200\n",
            " - 1s - loss: 0.0427 - acc: 0.9533 - val_loss: 0.0536 - val_acc: 0.9400\n",
            "Epoch 22/200\n",
            " - 1s - loss: 0.0421 - acc: 0.9520 - val_loss: 0.0746 - val_acc: 0.9000\n",
            "Epoch 23/200\n",
            " - 1s - loss: 0.0380 - acc: 0.9613 - val_loss: 0.0423 - val_acc: 0.9600\n",
            "Epoch 24/200\n",
            " - 1s - loss: 0.0443 - acc: 0.9493 - val_loss: 0.0877 - val_acc: 0.8800\n",
            "Epoch 25/200\n",
            " - 1s - loss: 0.0408 - acc: 0.9560 - val_loss: 0.0480 - val_acc: 0.9400\n",
            "Epoch 26/200\n",
            " - 1s - loss: 0.0409 - acc: 0.9507 - val_loss: 0.0564 - val_acc: 0.9400\n",
            "Epoch 27/200\n",
            " - 1s - loss: 0.0371 - acc: 0.9600 - val_loss: 0.0849 - val_acc: 0.8800\n",
            "Epoch 28/200\n",
            " - 1s - loss: 0.0401 - acc: 0.9560 - val_loss: 0.0462 - val_acc: 0.9400\n",
            "Epoch 29/200\n",
            " - 1s - loss: 0.0365 - acc: 0.9627 - val_loss: 0.0746 - val_acc: 0.9000\n",
            "Epoch 30/200\n",
            " - 1s - loss: 0.0386 - acc: 0.9613 - val_loss: 0.0621 - val_acc: 0.9400\n",
            "Epoch 31/200\n",
            " - 1s - loss: 0.0356 - acc: 0.9640 - val_loss: 0.0433 - val_acc: 0.9400\n",
            "Epoch 32/200\n",
            " - 1s - loss: 0.0376 - acc: 0.9600 - val_loss: 0.0828 - val_acc: 0.8800\n",
            "Epoch 33/200\n",
            " - 1s - loss: 0.0378 - acc: 0.9613 - val_loss: 0.0555 - val_acc: 0.9400\n",
            "Epoch 34/200\n",
            " - 1s - loss: 0.0355 - acc: 0.9640 - val_loss: 0.0464 - val_acc: 0.9400\n",
            "Epoch 35/200\n",
            " - 1s - loss: 0.0352 - acc: 0.9627 - val_loss: 0.0688 - val_acc: 0.9000\n",
            "Epoch 36/200\n",
            " - 1s - loss: 0.0360 - acc: 0.9627 - val_loss: 0.0572 - val_acc: 0.9400\n",
            "Epoch 37/200\n",
            " - 1s - loss: 0.0351 - acc: 0.9640 - val_loss: 0.0425 - val_acc: 0.9600\n",
            "Epoch 38/200\n",
            " - 1s - loss: 0.0369 - acc: 0.9600 - val_loss: 0.0785 - val_acc: 0.8800\n",
            "Epoch 39/200\n",
            " - 1s - loss: 0.0367 - acc: 0.9613 - val_loss: 0.0529 - val_acc: 0.9400\n",
            "Epoch 40/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9653 - val_loss: 0.0465 - val_acc: 0.9400\n",
            "Epoch 41/200\n",
            " - 1s - loss: 0.0342 - acc: 0.9653 - val_loss: 0.0734 - val_acc: 0.9000\n",
            "Epoch 42/200\n",
            " - 1s - loss: 0.0356 - acc: 0.9627 - val_loss: 0.0529 - val_acc: 0.9400\n",
            "Epoch 43/200\n",
            " - 1s - loss: 0.0352 - acc: 0.9627 - val_loss: 0.0481 - val_acc: 0.9400\n",
            "Epoch 44/200\n",
            " - 1s - loss: 0.0340 - acc: 0.9640 - val_loss: 0.0766 - val_acc: 0.8800\n",
            "Epoch 45/200\n",
            " - 1s - loss: 0.0357 - acc: 0.9627 - val_loss: 0.0511 - val_acc: 0.9400\n",
            "Epoch 46/200\n",
            " - 1s - loss: 0.0343 - acc: 0.9640 - val_loss: 0.0450 - val_acc: 0.9400\n",
            "Epoch 47/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9640 - val_loss: 0.0649 - val_acc: 0.9000\n",
            "Epoch 48/200\n",
            " - 1s - loss: 0.0340 - acc: 0.9640 - val_loss: 0.0419 - val_acc: 0.9600\n",
            "Epoch 49/200\n",
            " - 1s - loss: 0.0348 - acc: 0.9653 - val_loss: 0.0644 - val_acc: 0.9000\n",
            "Epoch 50/200\n",
            " - 1s - loss: 0.0349 - acc: 0.9627 - val_loss: 0.0758 - val_acc: 0.8800\n",
            "Epoch 51/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9640 - val_loss: 0.0454 - val_acc: 0.9400\n",
            "Epoch 52/200\n",
            " - 1s - loss: 0.0333 - acc: 0.9667 - val_loss: 0.0544 - val_acc: 0.9400\n",
            "Epoch 53/200\n",
            " - 1s - loss: 0.0332 - acc: 0.9640 - val_loss: 0.0504 - val_acc: 0.9400\n",
            "Epoch 54/200\n",
            " - 1s - loss: 0.0337 - acc: 0.9653 - val_loss: 0.0571 - val_acc: 0.9400\n",
            "Epoch 55/200\n",
            " - 1s - loss: 0.0330 - acc: 0.9640 - val_loss: 0.0716 - val_acc: 0.9000\n",
            "Epoch 56/200\n",
            " - 1s - loss: 0.0338 - acc: 0.9640 - val_loss: 0.0478 - val_acc: 0.9400\n",
            "Epoch 57/200\n",
            " - 1s - loss: 0.0324 - acc: 0.9680 - val_loss: 0.0540 - val_acc: 0.9400\n",
            "Epoch 58/200\n",
            " - 1s - loss: 0.0324 - acc: 0.9667 - val_loss: 0.0528 - val_acc: 0.9400\n",
            "Epoch 59/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9680 - val_loss: 0.0430 - val_acc: 0.9600\n",
            "Epoch 60/200\n",
            " - 1s - loss: 0.0330 - acc: 0.9680 - val_loss: 0.0693 - val_acc: 0.9000\n",
            "Epoch 61/200\n",
            " - 1s - loss: 0.0326 - acc: 0.9653 - val_loss: 0.0462 - val_acc: 0.9400\n",
            "Epoch 62/200\n",
            " - 1s - loss: 0.0320 - acc: 0.9693 - val_loss: 0.0603 - val_acc: 0.9200\n",
            "Epoch 63/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9667 - val_loss: 0.0556 - val_acc: 0.9200\n",
            "Epoch 64/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9680 - val_loss: 0.0481 - val_acc: 0.9400\n",
            "Epoch 65/200\n",
            " - 1s - loss: 0.0313 - acc: 0.9693 - val_loss: 0.0699 - val_acc: 0.9000\n",
            "Epoch 66/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9667 - val_loss: 0.0467 - val_acc: 0.9400\n",
            "Epoch 67/200\n",
            " - 1s - loss: 0.0317 - acc: 0.9693 - val_loss: 0.0675 - val_acc: 0.9000\n",
            "Epoch 68/200\n",
            " - 1s - loss: 0.0317 - acc: 0.9667 - val_loss: 0.0599 - val_acc: 0.9200\n",
            "Epoch 69/200\n",
            " - 1s - loss: 0.0312 - acc: 0.9693 - val_loss: 0.0496 - val_acc: 0.9400\n",
            "Epoch 70/200\n",
            " - 1s - loss: 0.0309 - acc: 0.9693 - val_loss: 0.0744 - val_acc: 0.9000\n",
            "Epoch 71/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9653 - val_loss: 0.0488 - val_acc: 0.9400\n",
            "Epoch 72/200\n",
            " - 1s - loss: 0.0311 - acc: 0.9693 - val_loss: 0.0540 - val_acc: 0.9400\n",
            "Epoch 73/200\n",
            " - 1s - loss: 0.0308 - acc: 0.9693 - val_loss: 0.0648 - val_acc: 0.9200\n",
            "Epoch 74/200\n",
            " - 1s - loss: 0.0312 - acc: 0.9693 - val_loss: 0.0458 - val_acc: 0.9600\n",
            "Epoch 75/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9693 - val_loss: 0.0694 - val_acc: 0.9000\n",
            "Epoch 76/200\n",
            " - 1s - loss: 0.0315 - acc: 0.9667 - val_loss: 0.0446 - val_acc: 0.9600\n",
            "Epoch 77/200\n",
            " - 1s - loss: 0.0319 - acc: 0.9693 - val_loss: 0.0803 - val_acc: 0.8800\n",
            "Epoch 78/200\n",
            " - 1s - loss: 0.0321 - acc: 0.9653 - val_loss: 0.0782 - val_acc: 0.8800\n",
            "Epoch 79/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9680 - val_loss: 0.0423 - val_acc: 0.9600\n",
            "Epoch 80/200\n",
            " - 1s - loss: 0.0319 - acc: 0.9693 - val_loss: 0.0745 - val_acc: 0.9000\n",
            "Epoch 81/200\n",
            " - 1s - loss: 0.0312 - acc: 0.9680 - val_loss: 0.0552 - val_acc: 0.9200\n",
            "Epoch 82/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9693 - val_loss: 0.0620 - val_acc: 0.9200\n",
            "Epoch 83/200\n",
            " - 1s - loss: 0.0306 - acc: 0.9693 - val_loss: 0.0804 - val_acc: 0.8800\n",
            "Epoch 84/200\n",
            " - 1s - loss: 0.0316 - acc: 0.9667 - val_loss: 0.0398 - val_acc: 0.9600\n",
            "Epoch 85/200\n",
            " - 1s - loss: 0.0369 - acc: 0.9587 - val_loss: 0.0979 - val_acc: 0.8800\n",
            "Epoch 86/200\n",
            " - 1s - loss: 0.0348 - acc: 0.9640 - val_loss: 0.0393 - val_acc: 0.9600\n",
            "Epoch 87/200\n",
            " - 1s - loss: 0.0386 - acc: 0.9560 - val_loss: 0.0925 - val_acc: 0.8800\n",
            "Epoch 88/200\n",
            " - 1s - loss: 0.0380 - acc: 0.9573 - val_loss: 0.1101 - val_acc: 0.8600\n",
            "Epoch 89/200\n",
            " - 1s - loss: 0.0421 - acc: 0.9533 - val_loss: 0.0431 - val_acc: 0.9600\n",
            "Epoch 90/200\n",
            " - 1s - loss: 0.0399 - acc: 0.9547 - val_loss: 0.1092 - val_acc: 0.8600\n",
            "Epoch 91/200\n",
            " - 1s - loss: 0.0427 - acc: 0.9507 - val_loss: 0.0547 - val_acc: 0.9400\n",
            "Epoch 92/200\n",
            " - 1s - loss: 0.0355 - acc: 0.9613 - val_loss: 0.1129 - val_acc: 0.8600\n",
            "Epoch 93/200\n",
            " - 1s - loss: 0.0446 - acc: 0.9467 - val_loss: 0.0466 - val_acc: 0.9400\n",
            "Epoch 94/200\n",
            " - 1s - loss: 0.0374 - acc: 0.9573 - val_loss: 0.1012 - val_acc: 0.8800\n",
            "Epoch 95/200\n",
            " - 1s - loss: 0.0484 - acc: 0.9453 - val_loss: 0.0436 - val_acc: 0.9400\n",
            "Epoch 96/200\n",
            " - 1s - loss: 0.0613 - acc: 0.9240 - val_loss: 0.1522 - val_acc: 0.8200\n",
            "Epoch 97/200\n",
            " - 1s - loss: 0.0937 - acc: 0.8867 - val_loss: 0.0569 - val_acc: 0.9400\n",
            "Epoch 98/200\n",
            " - 1s - loss: 0.0674 - acc: 0.9160 - val_loss: 0.0830 - val_acc: 0.9000\n",
            "Epoch 99/200\n",
            " - 1s - loss: 0.0531 - acc: 0.9373 - val_loss: 0.1202 - val_acc: 0.8400\n",
            "Epoch 100/200\n",
            " - 1s - loss: 0.0491 - acc: 0.9427 - val_loss: 0.0555 - val_acc: 0.9200\n",
            "Epoch 101/200\n",
            " - 1s - loss: 0.0655 - acc: 0.9173 - val_loss: 0.1289 - val_acc: 0.8600\n",
            "Epoch 102/200\n",
            " - 1s - loss: 0.0648 - acc: 0.9200 - val_loss: 0.0551 - val_acc: 0.9400\n",
            "Epoch 103/200\n",
            " - 1s - loss: 0.0503 - acc: 0.9360 - val_loss: 0.0650 - val_acc: 0.9400\n",
            "Epoch 104/200\n",
            " - 1s - loss: 0.0475 - acc: 0.9480 - val_loss: 0.1227 - val_acc: 0.8400\n",
            "Epoch 105/200\n",
            " - 1s - loss: 0.0551 - acc: 0.9360 - val_loss: 0.0518 - val_acc: 0.9200\n",
            "Epoch 106/200\n",
            " - 1s - loss: 0.0586 - acc: 0.9267 - val_loss: 0.0735 - val_acc: 0.9000\n",
            "Epoch 107/200\n",
            " - 1s - loss: 0.0435 - acc: 0.9533 - val_loss: 0.0876 - val_acc: 0.9000\n",
            "Epoch 108/200\n",
            " - 1s - loss: 0.0403 - acc: 0.9547 - val_loss: 0.0493 - val_acc: 0.9400\n",
            "Epoch 109/200\n",
            " - 1s - loss: 0.0430 - acc: 0.9520 - val_loss: 0.0669 - val_acc: 0.9000\n",
            "Epoch 110/200\n",
            " - 1s - loss: 0.0359 - acc: 0.9613 - val_loss: 0.0727 - val_acc: 0.9000\n",
            "Epoch 111/200\n",
            " - 1s - loss: 0.0369 - acc: 0.9587 - val_loss: 0.0561 - val_acc: 0.9400\n",
            "Epoch 112/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9613 - val_loss: 0.0592 - val_acc: 0.9400\n",
            "Epoch 113/200\n",
            " - 1s - loss: 0.0342 - acc: 0.9640 - val_loss: 0.0649 - val_acc: 0.9200\n",
            "Epoch 114/200\n",
            " - 1s - loss: 0.0342 - acc: 0.9653 - val_loss: 0.0567 - val_acc: 0.9400\n",
            "Epoch 115/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9627 - val_loss: 0.0650 - val_acc: 0.9200\n",
            "Epoch 116/200\n",
            " - 1s - loss: 0.0338 - acc: 0.9653 - val_loss: 0.0797 - val_acc: 0.9000\n",
            "Epoch 117/200\n",
            " - 1s - loss: 0.0355 - acc: 0.9627 - val_loss: 0.0493 - val_acc: 0.9400\n",
            "Epoch 118/200\n",
            " - 1s - loss: 0.0341 - acc: 0.9653 - val_loss: 0.0669 - val_acc: 0.9200\n",
            "Epoch 119/200\n",
            " - 1s - loss: 0.0349 - acc: 0.9613 - val_loss: 0.0616 - val_acc: 0.9400\n",
            "Epoch 120/200\n",
            " - 1s - loss: 0.0317 - acc: 0.9680 - val_loss: 0.0411 - val_acc: 0.9600\n",
            "Epoch 121/200\n",
            " - 1s - loss: 0.0383 - acc: 0.9600 - val_loss: 0.1143 - val_acc: 0.8800\n",
            "Epoch 122/200\n",
            " - 1s - loss: 0.0459 - acc: 0.9440 - val_loss: 0.0404 - val_acc: 0.9600\n",
            "Epoch 123/200\n",
            " - 1s - loss: 0.0424 - acc: 0.9493 - val_loss: 0.0834 - val_acc: 0.8800\n",
            "Epoch 124/200\n",
            " - 1s - loss: 0.0344 - acc: 0.9627 - val_loss: 0.0645 - val_acc: 0.9200\n",
            "Epoch 125/200\n",
            " - 1s - loss: 0.0323 - acc: 0.9653 - val_loss: 0.0497 - val_acc: 0.9400\n",
            "Epoch 126/200\n",
            " - 1s - loss: 0.0319 - acc: 0.9667 - val_loss: 0.0696 - val_acc: 0.9200\n",
            "Epoch 127/200\n",
            " - 1s - loss: 0.0328 - acc: 0.9653 - val_loss: 0.0532 - val_acc: 0.9400\n",
            "Epoch 128/200\n",
            " - 1s - loss: 0.0326 - acc: 0.9667 - val_loss: 0.0555 - val_acc: 0.9400\n",
            "Epoch 129/200\n",
            " - 1s - loss: 0.0318 - acc: 0.9680 - val_loss: 0.0885 - val_acc: 0.8800\n",
            "Epoch 130/200\n",
            " - 1s - loss: 0.0334 - acc: 0.9640 - val_loss: 0.0489 - val_acc: 0.9400\n",
            "Epoch 131/200\n",
            " - 1s - loss: 0.0314 - acc: 0.9680 - val_loss: 0.0492 - val_acc: 0.9400\n",
            "Epoch 132/200\n",
            " - 1s - loss: 0.0307 - acc: 0.9680 - val_loss: 0.0766 - val_acc: 0.8800\n",
            "Epoch 133/200\n",
            " - 1s - loss: 0.0317 - acc: 0.9653 - val_loss: 0.0496 - val_acc: 0.9400\n",
            "Epoch 134/200\n",
            " - 1s - loss: 0.0311 - acc: 0.9693 - val_loss: 0.0539 - val_acc: 0.9400\n",
            "Epoch 135/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9680 - val_loss: 0.0665 - val_acc: 0.9200\n",
            "Epoch 136/200\n",
            " - 1s - loss: 0.0306 - acc: 0.9693 - val_loss: 0.0444 - val_acc: 0.9400\n",
            "Epoch 137/200\n",
            " - 1s - loss: 0.0307 - acc: 0.9693 - val_loss: 0.0502 - val_acc: 0.9400\n",
            "Epoch 138/200\n",
            " - 1s - loss: 0.0307 - acc: 0.9693 - val_loss: 0.0581 - val_acc: 0.9200\n",
            "Epoch 139/200\n",
            " - 1s - loss: 0.0303 - acc: 0.9693 - val_loss: 0.0454 - val_acc: 0.9400\n",
            "Epoch 140/200\n",
            " - 1s - loss: 0.0305 - acc: 0.9693 - val_loss: 0.0601 - val_acc: 0.9200\n",
            "Epoch 141/200\n",
            " - 1s - loss: 0.0304 - acc: 0.9693 - val_loss: 0.0651 - val_acc: 0.9200\n",
            "Epoch 142/200\n",
            " - 1s - loss: 0.0301 - acc: 0.9693 - val_loss: 0.0463 - val_acc: 0.9400\n",
            "Epoch 143/200\n",
            " - 1s - loss: 0.0305 - acc: 0.9693 - val_loss: 0.0591 - val_acc: 0.9200\n",
            "Epoch 144/200\n",
            " - 1s - loss: 0.0300 - acc: 0.9680 - val_loss: 0.0803 - val_acc: 0.8800\n",
            "Epoch 145/200\n",
            " - 1s - loss: 0.0307 - acc: 0.9680 - val_loss: 0.0446 - val_acc: 0.9400\n",
            "Epoch 146/200\n",
            " - 1s - loss: 0.0306 - acc: 0.9693 - val_loss: 0.0440 - val_acc: 0.9400\n",
            "Epoch 147/200\n",
            " - 1s - loss: 0.0299 - acc: 0.9693 - val_loss: 0.0904 - val_acc: 0.8800\n",
            "Epoch 148/200\n",
            " - 1s - loss: 0.0327 - acc: 0.9627 - val_loss: 0.0462 - val_acc: 0.9400\n",
            "Epoch 149/200\n",
            " - 1s - loss: 0.0325 - acc: 0.9627 - val_loss: 0.0795 - val_acc: 0.8800\n",
            "Epoch 150/200\n",
            " - 1s - loss: 0.0415 - acc: 0.9507 - val_loss: 0.0417 - val_acc: 0.9600\n",
            "Epoch 151/200\n",
            " - 1s - loss: 0.0456 - acc: 0.9440 - val_loss: 0.1019 - val_acc: 0.8600\n",
            "Epoch 152/200\n",
            " - 1s - loss: 0.0368 - acc: 0.9600 - val_loss: 0.0627 - val_acc: 0.9200\n",
            "Epoch 153/200\n",
            " - 1s - loss: 0.0331 - acc: 0.9653 - val_loss: 0.0596 - val_acc: 0.9400\n",
            "Epoch 154/200\n",
            " - 1s - loss: 0.0303 - acc: 0.9693 - val_loss: 0.1114 - val_acc: 0.8800\n",
            "Epoch 155/200\n",
            " - 1s - loss: 0.0404 - acc: 0.9547 - val_loss: 0.0432 - val_acc: 0.9600\n",
            "Epoch 156/200\n",
            " - 1s - loss: 0.0600 - acc: 0.9187 - val_loss: 0.1331 - val_acc: 0.8400\n",
            "Epoch 157/200\n",
            " - 1s - loss: 0.0555 - acc: 0.9307 - val_loss: 0.0662 - val_acc: 0.8800\n",
            "Epoch 158/200\n",
            " - 1s - loss: 0.0783 - acc: 0.9053 - val_loss: 0.1384 - val_acc: 0.8400\n",
            "Epoch 159/200\n",
            " - 1s - loss: 0.0666 - acc: 0.9200 - val_loss: 0.0431 - val_acc: 0.9600\n",
            "Epoch 160/200\n",
            " - 1s - loss: 0.0635 - acc: 0.9200 - val_loss: 0.1341 - val_acc: 0.8400\n",
            "Epoch 161/200\n",
            " - 1s - loss: 0.0922 - acc: 0.8880 - val_loss: 0.1095 - val_acc: 0.8600\n",
            "Epoch 162/200\n",
            " - 1s - loss: 0.0446 - acc: 0.9467 - val_loss: 0.0658 - val_acc: 0.8800\n",
            "Epoch 163/200\n",
            " - 1s - loss: 0.0719 - acc: 0.9093 - val_loss: 0.1506 - val_acc: 0.8400\n",
            "Epoch 164/200\n",
            " - 1s - loss: 0.0898 - acc: 0.8933 - val_loss: 0.0614 - val_acc: 0.9400\n",
            "Epoch 165/200\n",
            " - 1s - loss: 0.0534 - acc: 0.9333 - val_loss: 0.0535 - val_acc: 0.9400\n",
            "Epoch 166/200\n",
            " - 1s - loss: 0.0448 - acc: 0.9440 - val_loss: 0.1149 - val_acc: 0.8600\n",
            "Epoch 167/200\n",
            " - 1s - loss: 0.0536 - acc: 0.9373 - val_loss: 0.0538 - val_acc: 0.9400\n",
            "Epoch 168/200\n",
            " - 1s - loss: 0.0467 - acc: 0.9413 - val_loss: 0.0544 - val_acc: 0.9400\n",
            "Epoch 169/200\n",
            " - 1s - loss: 0.0411 - acc: 0.9547 - val_loss: 0.1068 - val_acc: 0.8400\n",
            "Epoch 170/200\n",
            " - 1s - loss: 0.0520 - acc: 0.9387 - val_loss: 0.0574 - val_acc: 0.9400\n",
            "Epoch 171/200\n",
            " - 1s - loss: 0.0388 - acc: 0.9587 - val_loss: 0.0518 - val_acc: 0.9400\n",
            "Epoch 172/200\n",
            " - 1s - loss: 0.0385 - acc: 0.9573 - val_loss: 0.0787 - val_acc: 0.9000\n",
            "Epoch 173/200\n",
            " - 1s - loss: 0.0397 - acc: 0.9573 - val_loss: 0.0698 - val_acc: 0.9000\n",
            "Epoch 174/200\n",
            " - 1s - loss: 0.0365 - acc: 0.9587 - val_loss: 0.0526 - val_acc: 0.9400\n",
            "Epoch 175/200\n",
            " - 1s - loss: 0.0372 - acc: 0.9613 - val_loss: 0.0579 - val_acc: 0.9400\n",
            "Epoch 176/200\n",
            " - 1s - loss: 0.0343 - acc: 0.9640 - val_loss: 0.0679 - val_acc: 0.9200\n",
            "Epoch 177/200\n",
            " - 1s - loss: 0.0354 - acc: 0.9640 - val_loss: 0.0582 - val_acc: 0.9400\n",
            "Epoch 178/200\n",
            " - 1s - loss: 0.0342 - acc: 0.9640 - val_loss: 0.0523 - val_acc: 0.9400\n",
            "Epoch 179/200\n",
            " - 1s - loss: 0.0348 - acc: 0.9640 - val_loss: 0.0716 - val_acc: 0.9000\n",
            "Epoch 180/200\n",
            " - 1s - loss: 0.0352 - acc: 0.9613 - val_loss: 0.0599 - val_acc: 0.9200\n",
            "Epoch 181/200\n",
            " - 1s - loss: 0.0347 - acc: 0.9640 - val_loss: 0.0533 - val_acc: 0.9400\n",
            "Epoch 182/200\n",
            " - 1s - loss: 0.0343 - acc: 0.9627 - val_loss: 0.0793 - val_acc: 0.9000\n",
            "Epoch 183/200\n",
            " - 1s - loss: 0.0350 - acc: 0.9600 - val_loss: 0.0565 - val_acc: 0.9400\n",
            "Epoch 184/200\n",
            " - 1s - loss: 0.0329 - acc: 0.9667 - val_loss: 0.0567 - val_acc: 0.9400\n",
            "Epoch 185/200\n",
            " - 1s - loss: 0.0325 - acc: 0.9653 - val_loss: 0.0724 - val_acc: 0.9000\n",
            "Epoch 186/200\n",
            " - 1s - loss: 0.0327 - acc: 0.9640 - val_loss: 0.0551 - val_acc: 0.9400\n",
            "Epoch 187/200\n",
            " - 1s - loss: 0.0315 - acc: 0.9693 - val_loss: 0.0541 - val_acc: 0.9400\n",
            "Epoch 188/200\n",
            " - 1s - loss: 0.0312 - acc: 0.9693 - val_loss: 0.0658 - val_acc: 0.9200\n",
            "Epoch 189/200\n",
            " - 1s - loss: 0.0315 - acc: 0.9653 - val_loss: 0.0670 - val_acc: 0.9200\n",
            "Epoch 190/200\n",
            " - 1s - loss: 0.0311 - acc: 0.9653 - val_loss: 0.0521 - val_acc: 0.9400\n",
            "Epoch 191/200\n",
            " - 1s - loss: 0.0313 - acc: 0.9693 - val_loss: 0.0579 - val_acc: 0.9200\n",
            "Epoch 192/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9680 - val_loss: 0.0656 - val_acc: 0.9200\n",
            "Epoch 193/200\n",
            " - 1s - loss: 0.0308 - acc: 0.9680 - val_loss: 0.0500 - val_acc: 0.9400\n",
            "Epoch 194/200\n",
            " - 1s - loss: 0.0310 - acc: 0.9693 - val_loss: 0.0567 - val_acc: 0.9200\n",
            "Epoch 195/200\n",
            " - 1s - loss: 0.0303 - acc: 0.9680 - val_loss: 0.0630 - val_acc: 0.9200\n",
            "Epoch 196/200\n",
            " - 1s - loss: 0.0301 - acc: 0.9693 - val_loss: 0.0536 - val_acc: 0.9400\n",
            "Epoch 197/200\n",
            " - 1s - loss: 0.0301 - acc: 0.9693 - val_loss: 0.0539 - val_acc: 0.9200\n",
            "Epoch 198/200\n",
            " - 1s - loss: 0.0298 - acc: 0.9693 - val_loss: 0.0637 - val_acc: 0.9200\n",
            "Epoch 199/200\n",
            " - 1s - loss: 0.0299 - acc: 0.9693 - val_loss: 0.0575 - val_acc: 0.9200\n",
            "Epoch 200/200\n",
            " - 1s - loss: 0.0293 - acc: 0.9693 - val_loss: 0.0513 - val_acc: 0.9200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BVJCgz3bA4A3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Test the model"
      ]
    },
    {
      "metadata": {
        "id": "3MwCq0LpA59G",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We are going to test the model with 20 % of the total videos. This videos have not been used to train the network. "
      ]
    },
    {
      "metadata": {
        "id": "VDXFFy6zBG3X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6ac389f9-6400-401d-dfe7-c3e90ce8a0e4"
      },
      "cell_type": "code",
      "source": [
        "result = model.evaluate(np.array(data_test), np.array(target_test))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200/200 [==============================] - 0s 2ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8YG-bPW4BL6U",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Print the model accuracy"
      ]
    },
    {
      "metadata": {
        "id": "wBV2t2Q6BOt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1dadcb49-b517-4cdc-9624-cb76c529b457"
      },
      "cell_type": "code",
      "source": [
        "for name, value in zip(model.metrics_names, result):\n",
        "    print(name, value)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss 0.042483150847256186\n",
            "acc 0.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zO6YSbxgBZ3f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "f8a99f0c-2cf2-4228-f6ac-9083c39443fb"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.savefig('destination_path.eps', format='eps', dpi=1000)\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.savefig('destination_path1.eps', format='eps', dpi=1000)\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m8/jGVvf73z9/iEQySzKd57PvXEk4pDCUKbBqUROLZtezaHa9e+7V585k7ZY2Fs2uJx4N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\nyQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cZLtWM/rIGsYxc3ONZZyWY2pqHJVKtfeOfUKNllDhhVSC6/t9/d6g2zVIrNW2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gPeBiwB3mOa5tbpbIthGDHgX4BHHJvvwdNPhmH8F/ApYB2QAp4zDONHpmn2THPb/hJrsPgfwzA+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\nDx8+JHyj4MOHDx8+JP4/xdQExICx3OIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}