training.ipynb

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  "metadata": {
    "colab": {
      "name": "training.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "56zu04MAchOf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "344e155d-8ea6-443e-fd56-04eda47fe302"
      },
      "cell_type": "code",
      "source": [
        "# for mounting drive with google colabs\n",
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\r\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VIcp0a35KSHS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pnHOW57MKWV0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_jsMmkQq0TD7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "9124a71c-26e3-404c-ce43-bf6efff377f4"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab  drive\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AwcI6o1NkCAE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import time \n",
        "import os\n",
        "import cv2\n",
        "import datetime\n",
        "from PIL import Image\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "% matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ep4jky9PJHZo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Reading data"
      ]
    },
    {
      "metadata": {
        "id": "Qz-FqetJmBTb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('drive/fer2013.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wvcWR4P4VMqr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "df48286f-051f-4af0-ba02-2080b805e1f3"
      },
      "cell_type": "code",
      "source": [
        "data['Usage'].unique()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Training', 'PublicTest', 'PrivateTest'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "KLS-6PRJUbNQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data_train = data.loc[data['Usage']=='Training']\n",
        "public_test = data.loc[data['Usage']=='PublicTest']\n",
        "private_test = data.loc[data['Usage']=='PrivateTest']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "W94GR_DZmHXJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "3c4aa808-6bde-4b65-f608-92caa255601c"
      },
      "cell_type": "code",
      "source": [
        "data_train.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>emotion</th>\n",
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            "text/plain": [
              "   emotion                                             pixels     Usage\n",
              "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
              "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
              "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
              "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
              "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
            ]
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        "outputId": "0571845c-3f66-41e1-a461-d9634a40c848"
      },
      "cell_type": "code",
      "source": [
        "public_test.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>28709</th>\n",
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              "      <th>28711</th>\n",
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              "      <th>28712</th>\n",
              "      <td>6</td>\n",
              "      <td>205 203 236 157 83 158 120 116 94 86 155 180 2...</td>\n",
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              "      <th>28713</th>\n",
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              "      <td>87 79 74 66 74 96 77 80 80 84 83 89 102 91 84 ...</td>\n",
              "      <td>PublicTest</td>\n",
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            "text/plain": [
              "       emotion                                             pixels       Usage\n",
              "28709        0  254 254 254 254 254 249 255 160 2 58 53 70 77 ...  PublicTest\n",
              "28710        1  156 184 198 202 204 207 210 212 213 214 215 21...  PublicTest\n",
              "28711        4  69 118 61 60 96 121 103 87 103 88 70 90 115 12...  PublicTest\n",
              "28712        6  205 203 236 157 83 158 120 116 94 86 155 180 2...  PublicTest\n",
              "28713        3  87 79 74 66 74 96 77 80 80 84 83 89 102 91 84 ...  PublicTest"
            ]
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        }
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      "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 196
        },
        "outputId": "dadf362c-9866-4f5c-ff58-9bdb646cf809"
      },
      "cell_type": "code",
      "source": [
        "private_test.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>emotion</th>\n",
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              "      <th>32298</th>\n",
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              "      <td>PrivateTest</td>\n",
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              "      <th>32299</th>\n",
              "      <td>5</td>\n",
              "      <td>7 5 8 6 7 3 2 6 5 4 4 5 7 5 5 5 6 7 7 7 10 10 ...</td>\n",
              "      <td>PrivateTest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32300</th>\n",
              "      <td>6</td>\n",
              "      <td>232 240 241 239 237 235 246 117 24 24 22 13 12...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>32301</th>\n",
              "      <td>4</td>\n",
              "      <td>200 197 149 139 156 89 111 58 62 95 113 117 11...</td>\n",
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              "      <th>32302</th>\n",
              "      <td>2</td>\n",
              "      <td>40 28 33 56 45 33 31 78 152 194 200 186 196 20...</td>\n",
              "      <td>PrivateTest</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       emotion                                             pixels        Usage\n",
              "32298        0  170 118 101 88 88 75 78 82 66 74 68 59 63 64 6...  PrivateTest\n",
              "32299        5  7 5 8 6 7 3 2 6 5 4 4 5 7 5 5 5 6 7 7 7 10 10 ...  PrivateTest\n",
              "32300        6  232 240 241 239 237 235 246 117 24 24 22 13 12...  PrivateTest\n",
              "32301        4  200 197 149 139 156 89 111 58 62 95 113 117 11...  PrivateTest\n",
              "32302        2  40 28 33 56 45 33 31 78 152 194 200 186 196 20...  PrivateTest"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "2gyerwiGNZ8F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "cdaf0b29-5baa-4562-ddd7-3954202b461e"
      },
      "cell_type": "code",
      "source": [
        "data_train.describe()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>28709.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.317427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.876632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            emotion\n",
              "count  28709.000000\n",
              "mean       3.317427\n",
              "std        1.876632\n",
              "min        0.000000\n",
              "25%        2.000000\n",
              "50%        3.000000\n",
              "75%        5.000000\n",
              "max        6.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "dXhgd_VRK9mH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "k = data_train.iloc[1,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7musTnWRMMhi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "1b06636e-35e9-424f-a2ba-80b4d476ddc7"
      },
      "cell_type": "code",
      "source": [
        "len(k.split())"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2304"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "gXUbIpYURgAN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing\n",
        "## Transforming csv to understandable image data"
      ]
    },
    {
      "metadata": {
        "id": "lhU2G8EKOeHP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "data_train = shuffle(data_train)\n",
        "public_test = shuffle(public_test)\n",
        "private_test = shuffle(private_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hVU6a7pnMT1v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "27a8269c-36e4-4206-cf0c-99148cabccdd"
      },
      "cell_type": "code",
      "source": [
        "train = np.array(data_train.iloc[:,1])\n",
        "validation = np.array(private_test.iloc[:,1])\n",
        "test = np.array(public_test.iloc[:,1])\n",
        "print(train.shape, test.shape, validation.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28709,) (3589,) (3589,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-KKN2c-lPv5f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = train.reshape(28709,1)\n",
        "test = test.reshape(3589,1)\n",
        "validation = validation.reshape(3589,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uS8iZp6SLcBq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def data_augmentation(img):\n",
        "  img = cv2.flip(img.reshape(48,48), 1)\n",
        "  return np.array(img.reshape(48,48,1)).astype(np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7j56gh-nSIwd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Trying 48,48,3 because of vgg\n",
        "import cv2\n",
        "X_train_flip = list()\n",
        "\n",
        "def reform_data(img_str):\n",
        "  t1 = img_str[0].split()\n",
        "  t2 = np.array(t1).astype(np.float32)\n",
        "  t3 = t2.reshape(48, 48)\n",
        "  t4 = cv2.cvtColor(t3, cv2.COLOR_GRAY2BGR)\n",
        "  return t4\n",
        "\n",
        "\n",
        "def reform_data_aug(img_str):\n",
        "  t1 = img_str[0].split()\n",
        "  t2 = np.array(t1).astype(np.float32)\n",
        "  t3 = t2.reshape(48,48)\n",
        "  t_1 = data_augmentation(t3)\n",
        "  t4 = cv2.cvtColor(t3, cv2.COLOR_GRAY2BGR)\n",
        "  X_train_flip.append(cv2.cvtColor(t_1, cv2.COLOR_GRAY2BGR))\n",
        "  return t4\n",
        "\n",
        "X_train_1 = np.apply_along_axis(reform_data_aug, 1, train)\n",
        "X_train = np.array(list(X_train_1) + X_train_flip)\n",
        "X_test = np.apply_along_axis(reform_data, 1, test)\n",
        "X_valid = np.apply_along_axis(reform_data, 1, validation)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-t7nnjYhRfmE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "38edc4f6-b271-4c2b-dc32-e0652b8412d7"
      },
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(X_valid.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(57418, 48, 48, 3)\n",
            "(3589, 48, 48, 3)\n",
            "(3589, 48, 48, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VbThqV1cRZaw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "16b69109-e704-4e37-ec0f-d771bbd1be9e"
      },
      "cell_type": "code",
      "source": [
        "# Sample image\n",
        "plt.imshow(X_train[0])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff0fe3fbe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FlW3btiWiydVXX13zXEAqtC1atCjpwyLNqlWrkj7z5s0rtZXw+Lvf/e6Dvzs6OqSItWLF\nilJbVYE5/fTTk20sNKrsORYan3nmmaQPBzDxPRw3bhzmz59f2qYyzFigUqWc+b7mZqqxYKiCk4rX\n0dLSkrX2mjo/76cEXT5/TgWiIv5GN6YC2NCNqQA2dGMqQF18dOXvsA/84Q9/ONmPK5oov/Wiiy4q\ntVVgA/tFKrCBkxg4sGLy5Mm48sorax7n+eefT7Zx4I9KNGE/TR2bfWmV+FL0d6dNm4bnnnsu6cNJ\nPap6i/IB2QdW/iYnkdx8881JH67oev7555fat9xyS5Kc9PDDDyfHYV3jiiuuSPrws6eq26pEF/aT\nVYBKMRhmwoQJWfc1J/BH+eiMq8AaYxJs6MZUABu6MRUgy0cPIdwBYOFA/38AsBLAAwDGA9gK4JoY\nY1pNwRjTENQ09BDCpQDOiTF+IoTwIQC/A/AUgLtjjA+FEL4DYAmAHwx1DFUdg8UNJUBwgEyMsdZw\ns0ruqoAZLjnMZYs7Ojrw6KOPlrapzCwVtMHCyS9+8YukDwefsEAFpMFAas42bNhQ+ltl2PHcq0w1\nJfbwfipAhNdeX7BgQdJn06ZNpTaLc++9914SLKWqx7CAe9lllyV9WFTcuHFj0kcFHuUEsRTFyN7e\nXpkpyIKlqubDqMpFSsQbCTmv7s8A+IuBv98DMAnAYgCPDGx7FMCn/qhRGGOOKE3qU3koQgh/hUOv\n8J+JMc4Y2HYqgAdijBcPtV9fX19/TlF6Y8wfxZC/uWVbXwjhKgBfAXA5gOI7ZM0f9IqvZjNmzEBX\nV1fyO+ljjz2W7Me/7957771JH37FVb9j86uienXn4gPFWOsHH3wQX/rSl5JXvNG+uqvXwJxXd34N\nVcd55ZVXAADLly/HddddhxdffDHpwx+6ajVRVVSC489VrDvP41lnnZX0Ge7VfenSpbj33nvx6quv\nlvr89Kc/TY7Dr+7f+ta3kj5H8tV98FX9+uuvxz333CNf3VUh0Fqo49Ra3QYAbrjhhiGPmSvGfQbA\nLQCuiDHuDCH0hBDaY4x7AMwGMGw5FB74gQMHkiCFt956K9lPXQzDxqb2YePL8T+5Usrq1auTgAiu\nQgIA3d3dNY+t3m74WGqZ4JwloYoPcjHBpQhfvwoYyZl79fBzwJIyLK6KWwyEWrp0KVasWJFUqv34\nxz+eHIcTb9ivB/TSWoz6UFuzZk2prYJhilWJWltbpW+tnn2Gg6PUWzZvU8cZjpo+eghhKoB/BHBl\njHHw6XsSwGDq1tUAHh/RWY0xdSXnG/2LAKYD+GkhvPE6APeGEK4HsBHA8iMzPGPM4aCmoccYfwjg\nh+K/Pn34h2OMORI4Ms6YCjBm5Z65vLISyDjLSmUQsXqvAhJYgFGZYSyicYnm2bNnJ3044w3Qij4r\nryozjEsuq2WjeBsHpwBlEW3cuHFSfGIFV61pftJJJyXbeNw5QRxKMOTjcOWcefPmJdeqfuHgZ0bd\njxNPPLHUVr8UKBGNqwKpykXFZ22o5a+4eo3K+OPnMSeoRj3Dw+FvdGMqgA3dmApgQzemAoxZXCr7\nd+wTAcAbb7xRaqvldDdv3lxq51TnUH4SByCwT7h9+/bE31O6gvL3cvQI3k9VymEfUPmbxWCc9vZ2\n6aOzjqCWqMpZXkjpERwMpK6D4eCctra2JCpS6QGsfai5Zx9ZzYdK6mHth+8hUJ633bt3S12FnzX1\nfPI8qrnneVXjGQ5/oxtTAWzoxlQAG7oxFcCGbkwFqIsYx2LDuHHjEiHlwgsvTPbjIAUVfMEihRK6\nWERS4hNXwWERZ9u2bYkYps6lsr64TLMK7OD9VClnRmXKFbOc+vv7pfiTky2lBEu+fiWQ8bGU+MXX\nqioAccbh8ccfnxyH51UFrHD2nBIw1TaeN7VsU/H69+7dKzPK+DhKaMuptsQ2NNKKM/5GN6YC2NCN\nqQA2dGMqgA3dmApQFzGORaumpqZE7FGRcbNmzSq1V65cmfThLDMldrBwoSK6lNhSpK+vLxHR1LlU\nWSYWDHNqxilRj0VEFdFVXDNuypQpUiBigUyJUccee2yyTc0bw2Ici5xAmr3GY5w8ebIUXmuNsdY9\nBLRgp9aH5zlSc13so4RRIJ0zVVOQ518JbTxHIynqCvgb3ZhKYEM3pgLY0I2pAGOWvcb+rfJJeX10\nVV6Zg2FUBhP7M6rCC8PH6e/vT3wndRy1ja9VVcphv51rnwOp33j22WcnfYq+5KRJk5IsLHUchZpH\nnmulNfC1qew19mf53re2tibHUcFJtTIOgVTXUFlfKjiIfWt1z4rPVW9vr9Q6eNw5C5koLSQnK3PY\nY/5Rextjjgps6MZUABu6MRXAhm5MBaiLGMdiWH9/f1aW09SpU0ttJXbwfsMthjeIEl9YaONzT506\nNSllxeWOAJ2ZxmWZ1Rh5rbUzzjgj6cMBIqp0UVF827FjR5bQpISunOw1FXzC21QpaRbE1q9fX2of\nOHAgCSzJuQ6V4cbPmTqOEoJZNKu1gOK+ffvkfeXrUM85b1OCHT+fKhBpOPyNbkwFsKEbUwFs6MZU\ngDGrMJPjY3CQglqqhn0udVzuk5MQkFMVRyVeqAQJ9rmUn7hw4cJSW/mbnNSjkmq2bt1a+lv1OfXU\nU0ttFaCh5lEldjA55Z7POuusUpuTUWbOnJmsoa7Gw/dDaTistagAIj4OkOooao6Kvn1bW5t8rvgZ\nVoE3OeWe1fM4EvyNbkwFsKEbUwFs6MZUABu6MRWgLmJcUaDq6OhAT09PIpyogIRitRRABxvwcd58\n882kDwdEqHNxYAMLRE1NTUkwiBKalEjCGW0q0IYr7Dz//PNJn7lz55ban/vc55I+99xzzwd/n3vu\nubLqCmcBqmoyp5xySrKNq/moTDAO/FFzxGLkaaedlrSfeOKJ0jYVnMOouecMSJ5DQGe9saiqnr2i\nYNfa2irFYr73aowcsKT65IjOw+FvdGMqgA3dmApQ89U9hDARwH0AOgBMAPBtAKsBPABgPICtAK6J\nMaY/RhpjGoIcH/3PAKyKMd4RQpgL4D8ArABwd4zxoRDCdwAsAfCDoQ6gkiE40YN9KQA46aSTag4u\nZ5kk9mdy/CQF+/YqsEH5cuzLnnnmmUkfXgte+aSsNahqNp/85CdLf/OyVmo8avknlVTz0Y9+tNRW\n9+y1114rtX/7298mfTioRj0fW7ZsKW1Tc83aggpE4vvBug+gg5M4iUQ9H8X5b29vl0ktHESj9CF+\nHlUfvraRBszUNPQY478WmnMAbAKwGMBfD2x7FMDfYRhDN8aMLdmqewjh1wBOBHAlgCcLr+pdAGYN\nuaMxZsxpGkkh+BDChwHcD2BWjPH4gW2nAbg/xnjxUPvt27evX+X8GmMOK0P6nzli3EcAdMUY34ox\nvhRCaAbQHUJojzHuATAbwJbhjlEsLDB//vzEjwO0v8e+9c0335z04Q+Qt99+O+nD/l7O6iHFKqiv\nvPIKzjnnnGSfXB+dtQb2dQHgrbfeKrXVdcybN6/U/vznP5/0WbVqFQDgG9/4Bu68885R++jnnntu\nsu1w+eh8nOL9OO+88/D73/8et912W6lPjo+u/Ojp06eX2spHV88DxwMM56PfeuutuP3226WPznOt\nVmE5XD76jTfemGwbJOfV/RIAcwH8bQihA8BkAI8DuBrAgwP/Pj7cAXiyp0+fnlRiKWZdDcJBJBdd\ndFHS57HHHiu11cPHWWZK6OLMLJ7Y3t7erMosKmiCr58fIgB49dVXS231QPLD/tBDDyV9iplYr732\nmsyW4jHy+AAdRPPyyy+X2uvWrUv6cClnDoYB0oo7xWs/77zz8MILLyRBPSoQiudDXQffVzX3qox4\nzjJJRcNW86zGqJ4Zzp7LWVYsRzwukmPo/xfAv4QQngXQDuBrAFYBuD+EcD2AjQCWj+isxpi6kqO6\n7wHw38V/ffrwD8cYcyRwZJwxFaAuSS0sdvT19SUihRIXeFmiBQsWJH3Yb1Q+GCes5FShyaFW1ZFB\n2Cdeu3ZtzWOrSjU8H8qXK2ofr7/+uvQdWcdQfZQgxGNiXQFIfVs111y9ln/56e7uzkpEGk0Qiaom\noyrT8Jyo8xcDf5qbm6XfzL61Og5fvzoOBxmN1Ef3N7oxFcCGbkwFsKEbUwFs6MZUgDFbH51FCRbM\ngFTE4gg3Rc462kogqrVmuFomR+2jxDguXcxBJUAqCKnKMBxoogJ/ioJUT0+PXOecj60qrKjMOBbx\ncq4/Z5ki7rNv376kmo0KzuG1z9W8cuCPGo+aR372lNDHSzKpe8/zn7P8U+4SWSPB3+jGVAAbujEV\nwIZuTAUYMx+dgwRyMsFUwgr7l8rXz6nywb4T+/FNTU1Zy+KogAweo9qPk3yUJpAzZ3xs5ZMy27Zt\nS7apCq88tznVUpS/yX4r+589PT3JUkqq4s2LL75Yaqv54KQWPi6gtQa+NnWtxf1aWlrkklX8XKn7\nyhrBaCvFDoe/0Y2pADZ0YyqADd2YCmBDN6YCjJkYx+KCEm1YpFBVT77whS+U2j/72c+SPhz8oAQq\nPj9nHakKI2qbCojgQA4VoJGzxA6LVkrYKQpSvb29cs64Eos6znHHHZds44wuVW+QRSt1rbyNK7z0\n9/cnczZnzpzkOMUSZYB+hvgeqTErMY77KaGteB3vv/++zILjZ02dP0doG2m2GuNvdGMqgA3dmApg\nQzemAoyZj54D+yrKT7r88stL7cFyx0VUQAjDfiP7bS0tLYkfrSq+Kti3zvEllZ/GvqzyrYv7TZ8+\nXVY45evgBBJAV53hRBeVeJMTVJSzlgDPv9pn1qzyuiGc5AKkQTQq8EXdDx53rXvd0tKSlbCizs/3\nXgX+MCNZjwHwN7oxlcCGbkwFsKEbUwFs6MZUgIYW45haJXcB4Pzzz0/6rFy5cth91LFVwAyLLUro\nUuITB62oYBQWlk499dSkD6/jrUSb4rUtXbo0Ka0MpCWxQwhJHyU+sRiqMvU4M01Vr+FtLCz19/fL\nyjgMz0fOPirIKScLUAXDFOf62GOPlaIeB8yoAKKcTDkej3qGh8Pf6MZUABu6MRXAhm5MBbChG1MB\n6iLGqUy1kUb2AFro4qgzJcZxCSpVOpejxZRAxKKNygxTIsmJJ55Yas+fPz/pw9fGa8oB6bhPOOGE\npM+MGTM++Luzs1OOhyPhVESX2sZlmJQYqeaN4fXqX3jhhVK7tbU1iWZUJcJ4PDmRaSpLUD1XLNop\nEa0YvdfU1JS1Xp0qo52z9lpOtudw+BvdmApgQzemAtjQjakAR1XAjPJLOGjjpJNOSvqcdtpppbZa\n3qdWdlJzc3MSMKICNDiIAwA6OjpK7WeeeSbp84c//KHUVr4kB1/wPsB/+rJf//rX8fOf/1wGiHCF\nGTWvqrQ2awIqw439ZDWvHHxy4YUXJu1f/vKXpW0qU44Dj1SJava/1Xwo35rHXWvJrqamJhl4w8dW\nQUY52WrcJ6eMdxF/oxtTAWzoxlSArFf3EEI7gFcAfBvAUwAeADAewFYA18QY0/cRY0zDkPuN/vcA\nBjMhbgdwd4xxIYA3ACw5EgMzxhw+an6jhxDmAzgLwGMDmxYD+OuBvx8F8HcAfnAkBsfkBBIoLrnk\nklL7ySefTPpw0IIq7cQBKzlruAGpaLZp06akDwtUqiwSC0vq/Dnj4etQGW5KNOI5Ull4OVlV3IdF\nvQ0bNmDRokWlbZyBCAC7du0qtUc7HznlrlTATLFPb2+vFFBZ1FPPa07ADPc5EtlrdwK4qdCeVHhV\n7wIwK93FGNNINA0XihpCuBbASTHG/xVCWAZgA4A7YowzBv7/NAD3xxgvHu4kfX19/SP9BDLGjJgh\nX29rWd+fApgXQrgSwIkA9gHoCSG0xxj3AJgNYEuts+/YseODvzs6OtDZ2TmqWHf1SsO/L6qY6Oee\ne67UvvXWW5M+/Dtp8dV9xYoVWLBgQfKKO2/evOQ4c+fOTbbxfmvXrq15fvXqzu6E+i11MP78iSee\nwOWXXy7ng19f1au7iuPn4hhH4tX9xhtvxF133ZWszKJe3flVmVd3AdLYchXnoH4jH8kr9w033IDv\nf//7o351V0t0D3WuQVQMw7XXXptsG2TYuxJj/OLg34Vv9IsBXA3gwYF/Hx/uGGqQyshHWxY4pyT0\nBRdcUGqfeeaZSZ8YY6nND0hra2vygKoH5M0330y21aqoAqTJD2oJIr5W3gcof9BNnDhR+q38YCmD\nVYEl/ACqDyMOWlH3lT+cORgyEeu9AAAGr0lEQVRmz549SVILBz0BwG9+85tSW/nRrH3kLIkEpPdW\nGXHxOoYKesnRkHiOcsc4EkbzO/r/BHBdCOFZANMALP+jRmCMOeJkO84xxmWF5qcP/1CMMUcKR8YZ\nUwFs6MZUgLr85qVKJ+cIdCxA5IhzSnziSii8pjoA3HHHHaU2K9NtbW3JNnUuJcrkrK3F6rgSw1j8\nUsJjkV27dsmMLhaalDKvzl8rqAhI5yRHeGWRs7m5OQnqUVWBOGBG/QrBYpzKpstZQ08JfcXrb2pq\nkufnZ1j9KsFipDoOz6srzBhjEmzoxlQAG7oxFaAuPvqaNWs++Hvx4sVYs2ZNVpQV+7I5fkmOb7tw\n4cKkD1ci5SqsbW1tSYUV5W+pwArlJ9dCBaPkJG0Ux9Tc3CyXEmKtQQX+qGtjf1sliHCfHM1CwX1U\nhRkOalK+Nvv2KlknJ5pPzVFRa2hubpbXxfspX5/HlDMepasMh7/RjakANnRjKoAN3ZgKYEM3pgLU\nRYwrlldevHgx1q1bh66urlIfzjAD0iV3csri5gTeqDLNnNH29NNPl9rbt29PAj2UQKQyymbOnFlq\nq1TWjRs3lto5Swcp0aYokLW1tWWVIOZ5BvLWR1eZaTzXOcsd5Sw3pISunLXY+R6p61Ln436qjHgx\nEGsoAS0nyEst08SwqFhceisHf6MbUwFs6MZUABu6MRWgLj46B+m3trYmviz77AAwbdq0UjvHb1U+\nIfukakli9qXOPvvspM1VT5RvVyybNdSYdu7cmfRRSRsMB6gov5UDZtQYc6rZKp+Tk4Ny/G8FB5Eo\nH52DSFTlXL4OXp4aAE4//fSa41PBSVxeS/nRxetoaWnBhg0bao5RlR/LSXpiG8pJxCnib3RjKoAN\n3ZgKYEM3pgLY0I2pAHUR45TYwgKECixgUUKJP4zqw8KKEsxY7FiyZEnSvu+++0rbtm7dmhyH1x4H\nUhFRnZ+FrhxBRlUiKWYFzpo1KytbSgk7OWWaVUYXb1P3tVaASGtraxKco2rPc432+fPnJ314bXo1\nHiXycnalEiyL2w4cOCCDpZicDExVOYgDZEa6LoK/0Y2pADZ0YyqADd2YClAXH11VfM3xMXKSHXJg\nP1X5ZFyx44wzzkjaHJCxZUu67Jzyidm/VJVZOEBF+fHsJ6rEhmJl1F27dkn/m69V+b9Kf+BlolQf\n9v+VH8/+L19Ha2trkoySs2SXOhdrQbWquQ7Cz4hKhCoGea1bt0761jzuYoLXIPw8zJ49O+nDesz6\n9euTPh/72MeSbR+MY8j/Mcb8l8GGbkwFsKEbUwFs6MZUgKaR/vBujDn68De6MRXAhm5MBbChG1MB\nbOjGVAAbujEVwIZuTAWoS6w7AIQQvgfgIgD9AP4mxriyXuceKSGEcwA8DOB7Mca7QghzADwAYDyA\nrQCuiTGmy3KOISGEOwAsxKF7+g8AVqKBxxxCmAjgPgAdACYA+DaA1WjgMRcJIbQDeAWHxv0UGnzc\ndflGDyEsAnB6jPETAL4C4J/qcd7REEKYBOCfcejmDXI7gLtjjAsBvAFgidp3rAghXArgnIH5vQLA\n/0GDjxnAnwFYFWNcBOALAL6Lxh9zkb8H8M7A3w0/7nq9ul8G4N8AIMb4KoDjQggjXzS8PuwD8CcA\niqlpiwE8MvD3owA+Vecx1eIZAH8x8Pd7ACahwcccY/zXGOMdA805ADahwcc8SAhhPoCzADw2sGkx\nGnzc9TL0mQCKRdG3DWxrOGKMfTFGXlRtUuFVrAvALDQQMcYDMcbB4uRfAfDvaPAxDxJC+DWA/wfg\nb3GUjBnAnQBuKrQbftxjJcaNLrG8MWjYsYcQrsIhQ7+R/qthxxxjvBjAZwE8iPI4G3LMIYRrAfwm\nxpgmhB+iIcddL0PfgvI3+Ak4JFocLfQMiC8AMBvl1/qGIITwGQC3APhvMcadaPAxhxA+MiByIsb4\nEg6JiN2NPOYB/hTAVSGE3wJYCuB/oMHnGqifoT8B4M8BIIRwAYAtMcbuOp37cPAkgKsH/r4awONj\nOJaEEMJUAP8I4MoY46BA1NBjBnAJgG8AQAihA8BkNP6YEWP8YozxozHGiwDci0Oqe8OPu27ZayGE\n/41DN/cggK/FGFfX5cQjJITwERzywU4GsB/AZgB/iUM/BU0AsBHAl2OMaU2iMSKE8FcAlgFYW9h8\nHQ49iI065nYA/4JDQlw7gNsArAJwPxp0zEwIYRmADQB+gQYft9NUjakAjowzpgLY0I2pADZ0YyqA\nDd2YCmBDN6YC2NCNqQA2dGMqgA3dmArw/wFlHkKAmVItEAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff0fe4ed5c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WYB7ls1PTmSe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Normalization "
      ]
    },
    {
      "metadata": {
        "id": "SlrUS2YXSi3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train/255\n",
        "X_test = X_test/255\n",
        "X_valid = X_valid/255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VwsAGZnLT6LD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### One Hot Encoding"
      ]
    },
    {
      "metadata": {
        "id": "DddjY8kpTpRH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = data_train['emotion']\n",
        "Y_test = public_test['emotion']\n",
        "Y_validation = private_test['emotion']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pqH6KdN9ZmaP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "69c43a47-30c4-462c-dfdf-94742883bdba"
      },
      "cell_type": "code",
      "source": [
        "print(Y_train.shape, Y_test.shape, Y_validation.shape)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28709,) (3589,) (3589,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zvweNZgLZo4R",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def onehot(Y, C):\n",
        "  Y = np.eye(C)[Y].T\n",
        "  return Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VsNoXc5HbDHR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e46a925f-9259-4bd5-8df5-51342fa93a71"
      },
      "cell_type": "code",
      "source": [
        "data['emotion'].unique()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 4, 6, 3, 5, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "metadata": {
        "id": "yNWyfJYNbMFB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_test = onehot(Y_test, 7).T\n",
        "Y_train = onehot(Y_train, 7).T\n",
        "Y_valid = onehot(Y_validation, 7).T"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_HLB4j6WbcWR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "540d3f41-c8dc-4d9c-a822-06693a4ba74f"
      },
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "0iMkGB1wtYNj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = np.array(list(Y_train) + list(Y_train))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pw_OvfvF4rBT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5ea68714-3070-4297-d8cc-677785af3290"
      },
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(57418, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "h_86wLen4vJR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, Y_train = shuffle(X_train, Y_train, random_state=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GpkHLbumbdvX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Clear memory\n",
        "del data\n",
        "del data_train\n",
        "del public_test\n",
        "del private_test\n",
        "del Y_validation\n",
        "del X_train_1\n",
        "del X_train_flip\n",
        "del train\n",
        "del validation\n",
        "del test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "00cI1F-aSFhF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## CNN Models\n",
        "Taking 3 - 4 models for ensemble voting"
      ]
    },
    {
      "metadata": {
        "id": "3o79vhYEYKiI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "9ea116f5-dccb-4877-f373-03174bbca1f1"
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers import Conv2D, Conv3D, MaxPooling2D, BatchNormalization, AveragePooling2D\n",
        "from keras.losses import categorical_crossentropy\n",
        "from keras.optimizers import Adam, Adamax\n",
        "from keras.callbacks import ReduceLROnPlateau, TensorBoard, EarlyStopping, ModelCheckpoint\n",
        "from keras.models import load_model\n",
        "from keras.regularizers import l2"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "S_M_Ig2oSLY1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1-Basic 5 layer model"
      ]
    },
    {
      "metadata": {
        "id": "eKGfnDEiSNlh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_features = 64\n",
        "num_labels = 7\n",
        "batch_size = 64\n",
        "epochs = 100\n",
        "input_shape = (48, 48, 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_aM9YHvESPbQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Model1(input_shape=(48,48,3)):\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(1, kernel_size=(1, 1), activation='relu', input_shape=input_shape, data_format='channels_last', kernel_regularizer=l2(0.01)))\n",
        "  model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu'))\n",
        "  model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Conv2D(2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Conv2D(2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Conv2D(2*2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(2*2*2*num_features, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Flatten())\n",
        "\n",
        "  model.add(Dense(2*2*2*num_features, activation='relu'))\n",
        "  model.add(Dropout(0.4))\n",
        "  model.add(Dense(2*2*num_features, activation='relu'))\n",
        "  model.add(Dropout(0.4))\n",
        "  model.add(Dense(2*num_features, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Dense(num_labels, activation='softmax'))\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zI99IQASU6t-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1204
        },
        "outputId": "75629993-c68d-469e-b46d-8cb4779cb360"
      },
      "cell_type": "code",
      "source": [
        "model1 = Model1()\n",
        "model1.summary()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 48, 48, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 46, 46, 64)        640       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 46, 46, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 46, 46, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 23, 23, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 23, 23, 128)       73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 23, 23, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 23, 23, 128)       147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 23, 23, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 11, 11, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 11, 11, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 11, 11, 256)       295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 11, 11, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 11, 11, 256)       590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 11, 11, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 5, 5, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 5, 5, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 5, 5, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 5, 5, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 5, 5, 512)         2048      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1049088   \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 7)                 903       \n",
            "=================================================================\n",
            "Total params: 5,905,867\n",
            "Trainable params: 5,902,155\n",
            "Non-trainable params: 3,712\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8piOth2iZGjD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1.compile(loss=categorical_crossentropy,\n",
        "              optimizer=Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8U4Oe6R5d95Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3, verbose=1)\n",
        "tensorboard = TensorBoard(log_dir='./logs')\n",
        "early_stopper = EarlyStopping(monitor='val_loss', min_delta=0, patience=8, verbose=1, mode='auto')\n",
        "checkpointer = ModelCheckpoint(\"/drive\", monitor='val_loss', verbose=1, save_best_only=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QjWde_EayatC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1.load_weights(\"drive/face_expr_model1.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C3ganIoqepJ_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "c8a23a21-5f04-4823-c1f9-dda6d5af22bf"
      },
      "cell_type": "code",
      "source": [
        "model1.fit(X_train,Y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=5,\n",
        "          verbose=1,\n",
        "          validation_data=(X_valid, Y_valid),\n",
        "          shuffle=True,\n",
        "          callbacks=[lr_reducer, tensorboard, checkpointer])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 57418 samples, validate on 3589 samples\n",
            "Epoch 1/5\n",
            "44480/57418 [======================>.......] - ETA: 27s - loss: 0.4322 - acc: 0.8484"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 122s 2ms/step - loss: 0.4353 - acc: 0.8473 - val_loss: 1.1033 - val_acc: 0.6804\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.10326, saving model to /drive\n",
            "Epoch 2/5\n",
            "17728/57418 [========>.....................] - ETA: 1:20 - loss: 0.4295 - acc: 0.8489"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 119s 2ms/step - loss: 0.4293 - acc: 0.8500 - val_loss: 1.1096 - val_acc: 0.6748\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 1.10326\n",
            "Epoch 3/5\n",
            "11072/57418 [====>.........................] - ETA: 1:34 - loss: 0.4086 - acc: 0.8595"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 119s 2ms/step - loss: 0.4198 - acc: 0.8533 - val_loss: 1.1209 - val_acc: 0.6799\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 1.10326\n",
            "Epoch 4/5\n",
            " 9024/57418 [===>..........................] - ETA: 1:38 - loss: 0.4231 - acc: 0.8522"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 119s 2ms/step - loss: 0.4237 - acc: 0.8527 - val_loss: 1.1449 - val_acc: 0.6704\n",
            "\n",
            "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0004500000213738531.\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 1.10326\n",
            "Epoch 5/5\n",
            " 6400/57418 [==>...........................] - ETA: 1:43 - loss: 0.4059 - acc: 0.8594"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 119s 2ms/step - loss: 0.4087 - acc: 0.8575 - val_loss: 1.1278 - val_acc: 0.6760\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 1.10326\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa1347edf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "rXNlohhle1zS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1.save_weights(\"drive/face_expr_model1.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sLtAOTy6wMeA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3824846d-b72c-4ac3-8495-3f87c43b3599"
      },
      "cell_type": "code",
      "source": [
        "model1.evaluate(X_test, Y_test, batch_size=batch_size)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 3s 713us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1886951760034703, 0.667038172209419]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "mmSMPa3hfV9r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1_2 = Model1()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ncSw76CFD7QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "gen = ImageDataGenerator(horizontal_flip=True,\n",
        "                        vertical_flip=True)\n",
        "train_generator_1 = gen.flow(X_train, Y_train, batch_size=batch_size)\n",
        " \n",
        "model1_2.compile(loss=categorical_crossentropy\n",
        ", optimizer=Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-7)\n",
        ", metrics=['accuracy']\n",
        ")\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rotCS-ZtJcBO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1_2.load_weights(\"drive/face_expr_model1_2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M5hTjiarEnCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33508
        },
        "outputId": "4c2b004d-f77d-4b13-8eb9-fc1651bae942"
      },
      "cell_type": "code",
      "source": [
        "model1_2.fit_generator(train_generator_1, steps_per_epoch=50, epochs=1000)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 1.2164 - acc: 0.5447\n",
            "Epoch 2/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 1.2337 - acc: 0.5406\n",
            "Epoch 3/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2276 - acc: 0.5369\n",
            "Epoch 4/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2102 - acc: 0.5422\n",
            "Epoch 5/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 1.2173 - acc: 0.5466\n",
            "Epoch 6/1000\n",
            "47/50 [===========================>..] - ETA: 0s - loss: 1.2128 - acc: 0.5422"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2160 - acc: 0.5425\n",
            "Epoch 7/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2334 - acc: 0.5359\n",
            "Epoch 8/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1991 - acc: 0.5603\n",
            "Epoch 9/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2359 - acc: 0.5363\n",
            "Epoch 10/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1842 - acc: 0.5403\n",
            "Epoch 11/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2196 - acc: 0.5459\n",
            "Epoch 12/1000\n",
            " 8/50 [===>..........................] - ETA: 5s - loss: 1.2028 - acc: 0.5137"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2018 - acc: 0.5319\n",
            "Epoch 13/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2138 - acc: 0.5491\n",
            "Epoch 14/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1901 - acc: 0.5534\n",
            "Epoch 15/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1904 - acc: 0.5441\n",
            "Epoch 16/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 1.2434 - acc: 0.5338\n",
            "Epoch 17/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 1.2143 - acc: 0.5363\n",
            "Epoch 18/1000\n",
            " 2/50 [>.............................] - ETA: 6s - loss: 1.1376 - acc: 0.5703"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1911 - acc: 0.5494\n",
            "Epoch 19/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1735 - acc: 0.5572\n",
            "Epoch 20/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 1.2137 - acc: 0.5450\n",
            "Epoch 21/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1781 - acc: 0.5525\n",
            "Epoch 22/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.1658 - acc: 0.5537\n",
            "Epoch 23/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 1.2156 - acc: 0.5356\n",
            "Epoch 24/1000\n",
            " 2/50 [>.............................] - ETA: 6s - loss: 1.1860 - acc: 0.5391"
          ],
          "name": "stdout"
        },
        {
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            "\n",
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            "50/50 [==============================] - 7s 132ms/step - loss: 0.7246 - acc: 0.7391\n",
            "Epoch 937/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7495 - acc: 0.7303\n",
            "Epoch 938/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7626 - acc: 0.7122\n",
            "Epoch 939/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7457 - acc: 0.7306\n",
            "Epoch 940/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7591 - acc: 0.7119\n",
            "Epoch 941/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7342 - acc: 0.7225\n",
            "Epoch 942/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7230 - acc: 0.7400\n",
            "Epoch 943/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7607 - acc: 0.7116\n",
            "Epoch 944/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7434 - acc: 0.7291\n",
            "Epoch 945/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7579 - acc: 0.7219\n",
            "Epoch 946/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 130ms/step - loss: 0.7725 - acc: 0.7142\n",
            "Epoch 947/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7555 - acc: 0.7228\n",
            "Epoch 948/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7916 - acc: 0.7128\n",
            "Epoch 949/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7446 - acc: 0.7272\n",
            "Epoch 950/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7511 - acc: 0.7175\n",
            "Epoch 951/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7325 - acc: 0.7316\n",
            "Epoch 952/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7476 - acc: 0.7219\n",
            "Epoch 953/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7839 - acc: 0.7250\n",
            "Epoch 954/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7550 - acc: 0.7156\n",
            "Epoch 955/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7246 - acc: 0.7362\n",
            "Epoch 956/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7409 - acc: 0.7269\n",
            "Epoch 957/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7270 - acc: 0.7362\n",
            "Epoch 958/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7426 - acc: 0.7262\n",
            "Epoch 959/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7305 - acc: 0.7250\n",
            "Epoch 960/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7530 - acc: 0.7172\n",
            "Epoch 961/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7370 - acc: 0.7275\n",
            "Epoch 962/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7324 - acc: 0.7262\n",
            "Epoch 963/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7405 - acc: 0.7338\n",
            "Epoch 964/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7159 - acc: 0.7437\n",
            "Epoch 965/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7481 - acc: 0.7262\n",
            "Epoch 966/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7366 - acc: 0.7219\n",
            "Epoch 967/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7411 - acc: 0.7269\n",
            "Epoch 968/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7469 - acc: 0.7191\n",
            "Epoch 969/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7252 - acc: 0.7353\n",
            "Epoch 970/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7331 - acc: 0.7203\n",
            "Epoch 971/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7002 - acc: 0.7409\n",
            "Epoch 972/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7249 - acc: 0.7344\n",
            "Epoch 973/1000\n",
            "50/50 [==============================] - 6s 129ms/step - loss: 0.7164 - acc: 0.7353\n",
            "Epoch 974/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7451 - acc: 0.7206\n",
            "Epoch 975/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7133 - acc: 0.7338\n",
            "Epoch 976/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7460 - acc: 0.7334\n",
            "Epoch 977/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7609 - acc: 0.7209\n",
            "Epoch 978/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7429 - acc: 0.7341\n",
            "Epoch 979/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7243 - acc: 0.7419\n",
            "Epoch 980/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7161 - acc: 0.7397\n",
            "Epoch 981/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7438 - acc: 0.7316\n",
            "Epoch 982/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7768 - acc: 0.7100\n",
            "Epoch 983/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7266 - acc: 0.7272\n",
            "Epoch 984/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7535 - acc: 0.7284\n",
            "Epoch 985/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7224 - acc: 0.7406\n",
            "Epoch 986/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7282 - acc: 0.7272\n",
            "Epoch 987/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7401 - acc: 0.7325\n",
            "Epoch 988/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7251 - acc: 0.7262\n",
            "Epoch 989/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7155 - acc: 0.7338\n",
            "Epoch 990/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7299 - acc: 0.7265\n",
            "Epoch 991/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7342 - acc: 0.7362\n",
            "Epoch 992/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7139 - acc: 0.7391\n",
            "Epoch 993/1000\n",
            "50/50 [==============================] - 7s 133ms/step - loss: 0.7341 - acc: 0.7203\n",
            "Epoch 994/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7624 - acc: 0.7163\n",
            "Epoch 995/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7208 - acc: 0.7350\n",
            "Epoch 996/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7335 - acc: 0.7294\n",
            "Epoch 997/1000\n",
            "50/50 [==============================] - 7s 131ms/step - loss: 0.7188 - acc: 0.7341\n",
            "Epoch 998/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7257 - acc: 0.7350\n",
            "Epoch 999/1000\n",
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7106 - acc: 0.7353\n",
            "Epoch 1000/1000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 7s 132ms/step - loss: 0.7031 - acc: 0.7419\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa133695320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "metadata": {
        "id": "YjWGZALYrXWl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model1_2.save(\"drive/face_expr_model1_2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xEg5Pvgarfuq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "d0b3a590-220b-4f30-acb4-1669a4cf4958"
      },
      "cell_type": "code",
      "source": [
        "model1_2.evaluate(X_valid, Y_valid, batch_size=64)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 3s 788us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9549317865957619, 0.6726107551181971]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "aYA-f0zNlcsn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2- Model2 - https://arxiv.org/pdf/1706.01509"
      ]
    },
    {
      "metadata": {
        "id": "cWN5Pq1-lgi5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Model2(input_shape=(48,48,3)):\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(1, kernel_size=(1, 1), activation='relu', input_shape=input_shape, data_format='channels_last', kernel_regularizer=l2(0.01)))\n",
        "  model.add(Conv2D(10, (5,5), activation='relu'))\n",
        "  model.add(MaxPooling2D())\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(Conv2D(10, (5,5), activation='relu'))\n",
        "  model.add(MaxPooling2D())\n",
        "  model.add(Dropout(0.15))\n",
        "  model.add(Conv2D(10, (3,3), activation='relu'))\n",
        "  model.add(MaxPooling2D()) \n",
        "  model.add(Dropout(0.1))\n",
        "\n",
        "  model.add(Flatten())\n",
        "\n",
        "  model.add(Dense(256, activation='relu'))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Dense(128, activation='relu'))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Dense(num_labels, activation='softmax'))\n",
        "  return model\n",
        "\n",
        "# TODO - replace "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "39oaxtDKxcCq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "880d9c6e-5977-4227-8b1c-8dfafb8e3209"
      },
      "cell_type": "code",
      "source": [
        "model2 = Model2()\n",
        "model2.summary()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_19 (Conv2D)           (None, 48, 48, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 44, 44, 10)        260       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 22, 22, 10)        0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 22, 22, 10)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 18, 18, 10)        2510      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 9, 9, 10)          0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 9, 9, 10)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 7, 7, 10)          910       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 3, 3, 10)          0         \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 3, 3, 10)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 90)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 256)               23296     \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 7)                 903       \n",
            "=================================================================\n",
            "Total params: 60,779\n",
            "Trainable params: 60,779\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zpHBq76n6hJk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_reducer2 = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3, verbose=1)\n",
        "tensorboard2 = TensorBoard(log_dir='./logs')\n",
        "early_stopper2 = EarlyStopping(monitor='val_loss', min_delta=0, patience=8, verbose=1, mode='auto')\n",
        "checkpointer2 = ModelCheckpoint(\"/drive\", monitor='val_loss', verbose=1, save_best_only=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MRGCpXAsxfv8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model2.compile(loss=categorical_crossentropy,\n",
        "              optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "skdbB7IwzwzF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model2.load_weights(\"drive/face_expr_model2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gh4JqzcLxnLs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1592
        },
        "outputId": "df77f9d0-0ebd-45fe-d398-6e3db099940a"
      },
      "cell_type": "code",
      "source": [
        "model2.fit(X_train,Y_train,\n",
        "          batch_size=batch_size,\n",
        "          epochs=500,\n",
        "          verbose=1,\n",
        "          validation_data=(X_valid, Y_valid),\n",
        "          shuffle=True,\n",
        "          callbacks=[lr_reducer2, tensorboard2, checkpointer2, early_stopper2])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 57418 samples, validate on 3589 samples\n",
            "Epoch 1/500\n",
            "57418/57418 [==============================] - 16s 284us/step - loss: 1.1250 - acc: 0.5673 - val_loss: 1.1268 - val_acc: 0.5704\n",
            "\n",
            "Epoch 00001: val_loss did not improve from 1.12588\n",
            "Epoch 2/500\n",
            "33024/57418 [================>.............] - ETA: 6s - loss: 1.1167 - acc: 0.5743"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 280us/step - loss: 1.1234 - acc: 0.5704 - val_loss: 1.1273 - val_acc: 0.5720\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 1.12588\n",
            "Epoch 3/500\n",
            "57418/57418 [==============================] - 16s 284us/step - loss: 1.1276 - acc: 0.5683 - val_loss: 1.1267 - val_acc: 0.5704\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 1.12588\n",
            "Epoch 4/500\n",
            " 2560/57418 [>.............................] - ETA: 15s - loss: 1.0876 - acc: 0.5754"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 283us/step - loss: 1.1223 - acc: 0.5680 - val_loss: 1.1269 - val_acc: 0.5678\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 1.12588\n",
            "Epoch 5/500\n",
            "53312/57418 [==========================>...] - ETA: 1s - loss: 1.1230 - acc: 0.5676"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 282us/step - loss: 1.1240 - acc: 0.5672 - val_loss: 1.1273 - val_acc: 0.5681\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 1.12588\n",
            "Epoch 6/500\n",
            "57418/57418 [==============================] - 16s 281us/step - loss: 1.1254 - acc: 0.5670 - val_loss: 1.1273 - val_acc: 0.5690\n",
            "\n",
            "Epoch 00006: ReduceLROnPlateau reducing learning rate to 3.7571025586657926e-06.\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 1.12588\n",
            "Epoch 7/500\n",
            " 5824/57418 [==>...........................] - ETA: 14s - loss: 1.1126 - acc: 0.5682"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 284us/step - loss: 1.1241 - acc: 0.5666 - val_loss: 1.1271 - val_acc: 0.5678\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 1.12588\n",
            "Epoch 8/500\n",
            "54592/57418 [===========================>..] - ETA: 0s - loss: 1.1270 - acc: 0.5680"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 282us/step - loss: 1.1254 - acc: 0.5687 - val_loss: 1.1266 - val_acc: 0.5678\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 1.12588\n",
            "Epoch 9/500\n",
            "57418/57418 [==============================] - 16s 280us/step - loss: 1.1234 - acc: 0.5669 - val_loss: 1.1272 - val_acc: 0.5709\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 1.12588\n",
            "Epoch 10/500\n",
            " 7168/57418 [==>...........................] - ETA: 13s - loss: 1.1251 - acc: 0.5709"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 282us/step - loss: 1.1222 - acc: 0.5669 - val_loss: 1.1273 - val_acc: 0.5684\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 1.12588\n",
            "Epoch 11/500\n",
            "54144/57418 [===========================>..] - ETA: 0s - loss: 1.1265 - acc: 0.5683"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 287us/step - loss: 1.1247 - acc: 0.5690 - val_loss: 1.1271 - val_acc: 0.5684\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 3.381392343726475e-06.\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 1.12588\n",
            "Epoch 12/500\n",
            "52672/57418 [==========================>...] - ETA: 1s - loss: 1.1224 - acc: 0.5657"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 17s 288us/step - loss: 1.1234 - acc: 0.5653 - val_loss: 1.1265 - val_acc: 0.5698\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 1.12588\n",
            "Epoch 13/500\n",
            "57418/57418 [==============================] - 16s 283us/step - loss: 1.1232 - acc: 0.5675 - val_loss: 1.1267 - val_acc: 0.5701\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 1.12588\n",
            "Epoch 14/500\n",
            " 6208/57418 [==>...........................] - ETA: 14s - loss: 1.1378 - acc: 0.5583"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 286us/step - loss: 1.1265 - acc: 0.5676 - val_loss: 1.1270 - val_acc: 0.5692\n",
            "\n",
            "Epoch 00014: ReduceLROnPlateau reducing learning rate to 3.043253150281089e-06.\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 1.12588\n",
            "Epoch 15/500\n",
            "42112/57418 [=====================>........] - ETA: 4s - loss: 1.1188 - acc: 0.5696"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 280us/step - loss: 1.1230 - acc: 0.5683 - val_loss: 1.1271 - val_acc: 0.5690\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 1.12588\n",
            "Epoch 16/500\n",
            "57418/57418 [==============================] - 16s 282us/step - loss: 1.1265 - acc: 0.5685 - val_loss: 1.1271 - val_acc: 0.5695\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 1.12588\n",
            "Epoch 17/500\n",
            " 4288/57418 [=>............................] - ETA: 15s - loss: 1.1360 - acc: 0.5648"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 282us/step - loss: 1.1245 - acc: 0.5667 - val_loss: 1.1271 - val_acc: 0.5684\n",
            "\n",
            "Epoch 00017: ReduceLROnPlateau reducing learning rate to 2.7389278557166107e-06.\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 1.12588\n",
            "Epoch 18/500\n",
            "41344/57418 [====================>.........] - ETA: 4s - loss: 1.1274 - acc: 0.5668"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 284us/step - loss: 1.1274 - acc: 0.5657 - val_loss: 1.1271 - val_acc: 0.5701\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 1.12588\n",
            "Epoch 19/500\n",
            "57418/57418 [==============================] - 16s 280us/step - loss: 1.1263 - acc: 0.5685 - val_loss: 1.1272 - val_acc: 0.5687\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 1.12588\n",
            "Epoch 20/500\n",
            " 4864/57418 [=>............................] - ETA: 14s - loss: 1.1097 - acc: 0.5730"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 16s 279us/step - loss: 1.1206 - acc: 0.5681 - val_loss: 1.1272 - val_acc: 0.5687\n",
            "\n",
            "Epoch 00020: ReduceLROnPlateau reducing learning rate to 2.465035049681319e-06.\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 1.12588\n",
            "Epoch 00020: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa12fa54828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "2z5Xq5RgyJgn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model2.save_weights(\"drive/face_expr_model2.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YBUGjVtXyUDZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "811a09dd-7e00-420a-8c80-a56ce3365deb"
      },
      "cell_type": "code",
      "source": [
        "model2.evaluate(X_test, Y_test, batch_size=batch_size)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 0s 89us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1752877810367404, 0.5522429646307062]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "metadata": {
        "id": "RlI2rjGdI85C",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model3 - My own"
      ]
    },
    {
      "metadata": {
        "id": "JQhWxjYBAiaG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Model3(input_shape=(48,48,3)):\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(1, kernel_size=(1, 1), activation='relu', input_shape=input_shape, data_format='channels_last', kernel_regularizer=l2(0.01)))\n",
        "  model.add(Conv2D(64, (5, 5), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))\n",
        "  model.add(Dropout(0.2))\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(3,3), strides=(2, 2)))\n",
        "  model.add(Dropout(0.2))\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(3,3), strides=(2, 2)))\n",
        "  model.add(Dropout(0.2))\n",
        "  \n",
        "  model.add(Flatten())\n",
        "\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dropout(0.1))\n",
        "\n",
        "  model.add(Dense(7, activation='softmax'))\n",
        "  return model\n",
        "\n",
        "# TODO - replace MaxPooling with stochastic pooling"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QfA9kpRUNSit",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model3=Model3()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ApvAbwezztRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "bc57a8fe-3064-415e-9135-0ada1af14c41"
      },
      "cell_type": "code",
      "source": [
        "model3.summary()"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_23 (Conv2D)           (None, 48, 48, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 44, 44, 64)        1664      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 20, 20, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 20, 20, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 18, 18, 64)        36928     \n",
            "_________________________________________________________________\n",
            "conv2d_26 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_27 (Conv2D)           (None, 5, 5, 128)         73856     \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 3, 3, 128)         147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 1, 1, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1024)              132096    \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1024)              1049600   \n",
            "_________________________________________________________________\n",
            "dropout_24 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 7)                 7175      \n",
            "=================================================================\n",
            "Total params: 1,485,835\n",
            "Trainable params: 1,485,835\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R4NakEqqxw1J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "gen = ImageDataGenerator()\n",
        "train_generator = gen.flow(X_train, Y_train, batch_size=batch_size)\n",
        " \n",
        "model3.compile(loss='categorical_crossentropy'\n",
        ", optimizer=Adam()\n",
        ", metrics=['accuracy']\n",
        ")\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "F_tqVd2yUxtv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model3.load_weights(\"drive/face_expr_model3.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zuvOjzW8JRxg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33508
        },
        "outputId": "5c4a64d2-cad8-4699-89ce-6df3610d9ec8"
      },
      "cell_type": "code",
      "source": [
        "model3.fit_generator(train_generator, steps_per_epoch=50, epochs=1000)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "50/50 [==============================] - 3s 65ms/step - loss: 1.8597 - acc: 0.2484\n",
            "Epoch 2/1000\n",
            "50/50 [==============================] - 2s 38ms/step - loss: 1.8210 - acc: 0.2662\n",
            "Epoch 3/1000\n",
            "50/50 [==============================] - 2s 37ms/step - loss: 1.8314 - acc: 0.2406\n",
            "Epoch 4/1000\n",
            "50/50 [==============================] - 2s 37ms/step - loss: 1.8306 - acc: 0.2437\n",
            "Epoch 5/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.8329 - acc: 0.2459\n",
            "Epoch 6/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.8276 - acc: 0.2462\n",
            "Epoch 7/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8150 - acc: 0.2519\n",
            "Epoch 8/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8344 - acc: 0.2387\n",
            "Epoch 9/1000\n",
            "15/50 [========>.....................] - ETA: 1s - loss: 1.8351 - acc: 0.2437"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8220 - acc: 0.2481\n",
            "Epoch 10/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.8159 - acc: 0.2553\n",
            "Epoch 11/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8257 - acc: 0.2509\n",
            "Epoch 12/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8202 - acc: 0.2587\n",
            "Epoch 13/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.8136 - acc: 0.2469\n",
            "Epoch 14/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.8043 - acc: 0.2497\n",
            "Epoch 15/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.7894 - acc: 0.2553\n",
            "Epoch 16/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.7603 - acc: 0.2734\n",
            "Epoch 17/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 1.7453 - acc: 0.2853"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.7346 - acc: 0.2925\n",
            "Epoch 18/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.7367 - acc: 0.2972\n",
            "Epoch 19/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.7008 - acc: 0.3041\n",
            "Epoch 20/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.7196 - acc: 0.3063\n",
            "Epoch 21/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6925 - acc: 0.2988\n",
            "Epoch 22/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6961 - acc: 0.3041\n",
            "Epoch 23/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6684 - acc: 0.3200\n",
            "Epoch 24/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6780 - acc: 0.3150\n",
            "Epoch 25/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 1.6720 - acc: 0.3390"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6548 - acc: 0.3412\n",
            "Epoch 26/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.6544 - acc: 0.3344\n",
            "Epoch 27/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.6497 - acc: 0.3316\n",
            "Epoch 28/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.6197 - acc: 0.3603\n",
            "Epoch 29/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5966 - acc: 0.3616\n",
            "Epoch 30/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.6088 - acc: 0.3641\n",
            "Epoch 31/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5653 - acc: 0.3753\n",
            "Epoch 32/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5666 - acc: 0.3806\n",
            "Epoch 33/1000\n",
            "21/50 [===========>..................] - ETA: 1s - loss: 1.5493 - acc: 0.3943"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.5436 - acc: 0.4003\n",
            "Epoch 34/1000\n",
            "50/50 [==============================] - 2s 37ms/step - loss: 1.5308 - acc: 0.4037\n",
            "Epoch 35/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5265 - acc: 0.4075\n",
            "Epoch 36/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5223 - acc: 0.4009\n",
            "Epoch 37/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4969 - acc: 0.4216\n",
            "Epoch 38/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5053 - acc: 0.4163\n",
            "Epoch 39/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4901 - acc: 0.4191\n",
            "Epoch 40/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.5002 - acc: 0.4194\n",
            "Epoch 41/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 1.4828 - acc: 0.4158"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4668 - acc: 0.4303\n",
            "Epoch 42/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4757 - acc: 0.4199\n",
            "Epoch 43/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4773 - acc: 0.4237\n",
            "Epoch 44/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4538 - acc: 0.4328\n",
            "Epoch 45/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4627 - acc: 0.4387\n",
            "Epoch 46/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4297 - acc: 0.4409\n",
            "Epoch 47/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4470 - acc: 0.4425\n",
            "Epoch 48/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4490 - acc: 0.4403\n",
            "Epoch 49/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 1.4585 - acc: 0.4382"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4226 - acc: 0.4494\n",
            "Epoch 50/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.4250 - acc: 0.4584\n",
            "Epoch 51/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4307 - acc: 0.4556\n",
            "Epoch 52/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3886 - acc: 0.4581\n",
            "Epoch 53/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4193 - acc: 0.4453\n",
            "Epoch 54/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.4138 - acc: 0.4491\n",
            "Epoch 55/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3820 - acc: 0.4816\n",
            "Epoch 56/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3767 - acc: 0.4719\n",
            "Epoch 57/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 1.3626 - acc: 0.4721"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3638 - acc: 0.4725\n",
            "Epoch 58/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3760 - acc: 0.4753\n",
            "Epoch 59/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3706 - acc: 0.4791\n",
            "Epoch 60/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3588 - acc: 0.4734\n",
            "Epoch 61/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3979 - acc: 0.4675\n",
            "Epoch 62/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3708 - acc: 0.4716\n",
            "Epoch 63/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3688 - acc: 0.4690\n",
            "Epoch 64/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3832 - acc: 0.4722\n",
            "Epoch 65/1000\n",
            "25/50 [==============>...............] - ETA: 0s - loss: 1.3295 - acc: 0.4800"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3456 - acc: 0.4791\n",
            "Epoch 66/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3668 - acc: 0.4681\n",
            "Epoch 67/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3510 - acc: 0.4859\n",
            "Epoch 68/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3636 - acc: 0.4722\n",
            "Epoch 69/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.3807 - acc: 0.4688\n",
            "Epoch 70/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3417 - acc: 0.4850\n",
            "Epoch 71/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.3737 - acc: 0.4738\n",
            "Epoch 72/1000\n",
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            "50/50 [==============================] - 2s 36ms/step - loss: 0.9481 - acc: 0.6434\n",
            "Epoch 947/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9531 - acc: 0.6516\n",
            "Epoch 948/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9297 - acc: 0.6503\n",
            "Epoch 949/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 1.0061 - acc: 0.6212\n",
            "Epoch 950/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9798 - acc: 0.6225\n",
            "Epoch 951/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 1.0033 - acc: 0.6216\n",
            "Epoch 952/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9595 - acc: 0.6344\n",
            "Epoch 953/1000\n",
            "21/50 [===========>..................] - ETA: 1s - loss: 0.9747 - acc: 0.6287"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9563 - acc: 0.6328\n",
            "Epoch 954/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9242 - acc: 0.6466\n",
            "Epoch 955/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9574 - acc: 0.6437\n",
            "Epoch 956/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9370 - acc: 0.6540\n",
            "Epoch 957/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9394 - acc: 0.6397\n",
            "Epoch 958/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9552 - acc: 0.6437\n",
            "Epoch 959/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9694 - acc: 0.6331\n",
            "Epoch 960/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9487 - acc: 0.6425\n",
            "Epoch 961/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 0.9760 - acc: 0.6352"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9683 - acc: 0.6384\n",
            "Epoch 962/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9625 - acc: 0.6325\n",
            "Epoch 963/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9456 - acc: 0.6434\n",
            "Epoch 964/1000\n",
            "50/50 [==============================] - 2s 37ms/step - loss: 0.9793 - acc: 0.6281\n",
            "Epoch 965/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9313 - acc: 0.6434\n",
            "Epoch 966/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9762 - acc: 0.6403\n",
            "Epoch 967/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9803 - acc: 0.6397\n",
            "Epoch 968/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9725 - acc: 0.6262\n",
            "Epoch 969/1000\n",
            "21/50 [===========>..................] - ETA: 1s - loss: 0.9446 - acc: 0.6280"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9602 - acc: 0.6294\n",
            "Epoch 970/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9890 - acc: 0.6153\n",
            "Epoch 971/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9570 - acc: 0.6319\n",
            "Epoch 972/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9351 - acc: 0.6469\n",
            "Epoch 973/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9499 - acc: 0.6506\n",
            "Epoch 974/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9651 - acc: 0.6272\n",
            "Epoch 975/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9399 - acc: 0.6394\n",
            "Epoch 976/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9564 - acc: 0.6372\n",
            "Epoch 977/1000\n",
            "23/50 [============>.................] - ETA: 0s - loss: 0.9442 - acc: 0.6250"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9344 - acc: 0.6450\n",
            "Epoch 978/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9346 - acc: 0.6478\n",
            "Epoch 979/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9598 - acc: 0.6300\n",
            "Epoch 980/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9696 - acc: 0.6403\n",
            "Epoch 981/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9537 - acc: 0.6472\n",
            "Epoch 982/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9965 - acc: 0.6202\n",
            "Epoch 983/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9584 - acc: 0.6356\n",
            "Epoch 984/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9830 - acc: 0.6281\n",
            "Epoch 985/1000\n",
            "21/50 [===========>..................] - ETA: 1s - loss: 0.9533 - acc: 0.6332"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9692 - acc: 0.6238\n",
            "Epoch 986/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9934 - acc: 0.6259\n",
            "Epoch 987/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9390 - acc: 0.6459\n",
            "Epoch 988/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9766 - acc: 0.6149\n",
            "Epoch 989/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9739 - acc: 0.6359\n",
            "Epoch 990/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9433 - acc: 0.6428\n",
            "Epoch 991/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9685 - acc: 0.6222\n",
            "Epoch 992/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9495 - acc: 0.6425\n",
            "Epoch 993/1000\n",
            "21/50 [===========>..................] - ETA: 1s - loss: 0.9841 - acc: 0.6198"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9636 - acc: 0.6316\n",
            "Epoch 994/1000\n",
            "50/50 [==============================] - 2s 35ms/step - loss: 0.9484 - acc: 0.6466\n",
            "Epoch 995/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9723 - acc: 0.6391\n",
            "Epoch 996/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9343 - acc: 0.6597\n",
            "Epoch 997/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9514 - acc: 0.6319\n",
            "Epoch 998/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9302 - acc: 0.6562\n",
            "Epoch 999/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9441 - acc: 0.6388\n",
            "Epoch 1000/1000\n",
            "50/50 [==============================] - 2s 36ms/step - loss: 0.9590 - acc: 0.6391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa12fb78550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "id": "qSth_XTcMwv7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model3.save_weights(\"drive/face_expr_model3.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jQzCcQQWOBxO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "01d1575e-f803-436e-d29d-efdc3da66744"
      },
      "cell_type": "code",
      "source": [
        "model3.evaluate(X_test, Y_test, batch_size=64)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 1s 413us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0909943778589928, 0.5840066871077744]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "qNHEl5EC4YhG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model 4 "
      ]
    },
    {
      "metadata": {
        "id": "Lln36skXL0w9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras.backend as K"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hYkrLGOp4cxl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Model4():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(MaxPooling2D(pool_size=(7,7), input_shape=(48,48,1)))\n",
        "  model.add(Conv2D(32, (5, 5), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(3,3)))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "  model.add(MaxPooling2D(pool_size=(3,3)))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Flatten())\n",
        "\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "\n",
        "  model.add(Dense(7, activation='softmax'))\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VVQqRsWCNjPn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model4 = Model4()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uJdqFwcB5jnG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_reducer3 = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3, verbose=1)\n",
        "tensorboard3 = TensorBoard(log_dir='./logs')\n",
        "early_stopper3 = EarlyStopping(monitor='val_loss', min_delta=0, patience=8, verbose=1, mode='auto')\n",
        "checkpointer3 = ModelCheckpoint(\"/drive\", monitor='val_loss', verbose=1, save_best_only=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "v9M2ajRN529S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model4.compile(loss=categorical_crossentropy,\n",
        "              optimizer=Adam(lr=0.005, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x_WBAB825_z2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# overfitting\n",
        "model4.fit(X_train,Y_train,\n",
        "          batch_size=128,\n",
        "          epochs=100,\n",
        "          verbose=1,\n",
        "          validation_data=(X_validation, Y_valid),\n",
        "          shuffle=True,\n",
        "          callbacks=[lr_reducer3, tensorboard3, checkpointer3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_6KlM2yn6J3Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pL8axmhgL1Ew",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Model5 - VGG"
      ]
    },
    {
      "metadata": {
        "id": "6mhvywIhL3Gz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.applications import VGG16\n",
        "from keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y2zTBo6mS4gv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def Model5():\n",
        "  vgg = VGG16(include_top=False, input_shape=(48,48,3))\n",
        "  k = Flatten()(vgg.output)\n",
        "  k = Dense(256, activation='relu')(k)\n",
        "  k = Dropout(0.2)(k)\n",
        "  k = Dense(128, activation='relu')(k)\n",
        "  k = Dropout(0.2)(k)\n",
        "  k = Dense(64, activation='relu')(k)\n",
        "\n",
        "  pred = Dense(7, activation='softmax')(k)\n",
        "  model = Model(input=vgg.input, output=pred)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GWeoU1ixUF9w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "f29df75a-81ac-406f-8159-330c9a8f2128"
      },
      "cell_type": "code",
      "source": [
        "model5 = Model5()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 7s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "c0I2f0gmyZj3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1004
        },
        "outputId": "e0f77a8f-fd0b-4dfc-963f-1b96dfe544b7"
      },
      "cell_type": "code",
      "source": [
        "model5.summary()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 48, 48, 3)         0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 48, 48, 64)        1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 48, 48, 64)        36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 24, 24, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 24, 24, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 12, 12, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 12, 12, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 12, 12, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 6, 6, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 3, 3, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_25 (Dropout)         (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_26 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 7)                 455       \n",
            "=================================================================\n",
            "Total params: 14,887,623\n",
            "Trainable params: 14,887,623\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ltgJwnMnOtIg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr_reducer5 = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=3, verbose=1)\n",
        "tensorboard5 = TensorBoard(log_dir='./logs')\n",
        "early_stopper5 = EarlyStopping(monitor='val_loss', min_delta=0, patience=8, verbose=1, mode='auto')\n",
        "checkpointer5 = ModelCheckpoint(\"/drive\", monitor='val_loss', verbose=1, save_best_only=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VBtpeIeV8tzc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model5.compile(loss=categorical_crossentropy,\n",
        "              optimizer=Adamax(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XK5TEt5K6_F1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model5.load_weights('drive/face_expr_model5.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GS4MYbjb8WwN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "c43e2e19-8fef-4f46-f232-3e87b198bee6"
      },
      "cell_type": "code",
      "source": [
        "model5.fit(X_train,Y_train,\n",
        "          batch_size=32,\n",
        "          epochs=500,\n",
        "          verbose=1,\n",
        "          validation_data=(X_valid, Y_valid),\n",
        "          shuffle=True,\n",
        "          callbacks=[lr_reducer5, tensorboard5, early_stopper5, checkpointer5])"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 57418 samples, validate on 3589 samples\n",
            "Epoch 1/500\n",
            "21760/57418 [==========>...................] - ETA: 1:57 - loss: 0.4441 - acc: 0.8551"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 191s 3ms/step - loss: 0.4275 - acc: 0.8567 - val_loss: 1.2886 - val_acc: 0.6417\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.28858, saving model to /drive\n",
            "Epoch 2/500\n",
            "  544/57418 [..............................] - ETA: 3:03 - loss: 0.2946 - acc: 0.8915"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57344/57418 [============================>.] - ETA: 0s - loss: 0.3540 - acc: 0.8784"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 189s 3ms/step - loss: 0.3539 - acc: 0.8784 - val_loss: 1.4464 - val_acc: 0.6445\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 1.28858\n",
            "Epoch 3/500\n",
            "11488/57418 [=====>........................] - ETA: 2:28 - loss: 0.2996 - acc: 0.8993"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.3168 - acc: 0.8944"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 189s 3ms/step - loss: 0.3169 - acc: 0.8944 - val_loss: 1.4762 - val_acc: 0.6403\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 1.28858\n",
            "Epoch 4/500\n",
            "11520/57418 [=====>........................] - ETA: 2:28 - loss: 0.2329 - acc: 0.9260"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.2590 - acc: 0.9146"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 188s 3ms/step - loss: 0.2590 - acc: 0.9146 - val_loss: 1.4342 - val_acc: 0.6297\n",
            "\n",
            "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0018000000854954123.\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 1.28858\n",
            "Epoch 5/500\n",
            " 8896/57418 [===>..........................] - ETA: 2:36 - loss: 0.1904 - acc: 0.9405"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.1994 - acc: 0.9353"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 189s 3ms/step - loss: 0.1993 - acc: 0.9353 - val_loss: 1.7292 - val_acc: 0.6436\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 1.28858\n",
            "Epoch 6/500\n",
            "11520/57418 [=====>........................] - ETA: 2:28 - loss: 0.1342 - acc: 0.9588"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.1463 - acc: 0.9537"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 189s 3ms/step - loss: 0.1463 - acc: 0.9537 - val_loss: 1.9447 - val_acc: 0.6445\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 1.28858\n",
            "Epoch 7/500\n",
            "11520/57418 [=====>........................] - ETA: 2:28 - loss: 0.1197 - acc: 0.9635"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.1404 - acc: 0.9561"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 189s 3ms/step - loss: 0.1404 - acc: 0.9560 - val_loss: 1.7940 - val_acc: 0.6473\n",
            "\n",
            "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0016200000769458712.\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 1.28858\n",
            "Epoch 8/500\n",
            " 8896/57418 [===>..........................] - ETA: 2:36 - loss: 0.0885 - acc: 0.9721"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57408/57418 [============================>.] - ETA: 0s - loss: 0.1127 - acc: 0.9647"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 188s 3ms/step - loss: 0.1127 - acc: 0.9647 - val_loss: 1.8260 - val_acc: 0.6562\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 1.28858\n",
            "Epoch 9/500\n",
            "11552/57418 [=====>........................] - ETA: 2:28 - loss: 0.0849 - acc: 0.9738"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "51360/57418 [=========================>....] - ETA: 19s - loss: 0.0953 - acc: 0.9702"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "57418/57418 [==============================] - 188s 3ms/step - loss: 0.0990 - acc: 0.9691 - val_loss: 1.7228 - val_acc: 0.6567\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 1.28858\n",
            "Epoch 00009: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa12f4eda90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "id": "_4WULlkr7bwM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "21b8a115-3ea2-4fc3-e25d-cc6703e39e52"
      },
      "cell_type": "code",
      "source": [
        "model5.evaluate(X_test, Y_test)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 3s 879us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8532601447449517, 0.6336026748397883]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "id": "-judzES9TZoN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model5.save_weights('drive/face_expr_model5.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PL2kg2MbSX_7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Since the training error is much larger than the dev error .. this proves high variance thus overfitting . This is common when needed features are less and model is too big . So to solve that we will try data augmentation\""
      ]
    },
    {
      "metadata": {
        "id": "goDdCIZyTFpk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "af42ca18-9a07-4ad6-be6f-c52e9d16abdb"
      },
      "cell_type": "code",
      "source": [
        "model5_n = Model5()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ITR_xt8c0Qyh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "gen = ImageDataGenerator()\n",
        "train_generator = gen.flow(X_train, Y_train, batch_size=32)\n",
        " \n",
        "model5_n.compile(loss='categorical_crossentropy'\n",
        ", optimizer=Adamax(lr=0.0005)\n",
        ", metrics=['accuracy']\n",
        ")\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aeQz7Yqs6OOZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model5_n.load_weights('drive/face_expr_model5_n.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H-grGHqU7qCw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3399
        },
        "outputId": "2af9ea8b-c266-40f5-c78b-77704672942a"
      },
      "cell_type": "code",
      "source": [
        "model5_n.fit_generator(train_generator, steps_per_epoch=50, epochs=100)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "50/50 [==============================] - 7s 136ms/step - loss: 0.4272 - acc: 0.8569\n",
            "Epoch 2/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.3677 - acc: 0.8750\n",
            "Epoch 3/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.3300 - acc: 0.8869\n",
            "Epoch 4/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2821 - acc: 0.9087\n",
            "Epoch 5/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2797 - acc: 0.9081\n",
            "Epoch 6/100\n",
            "49/50 [============================>.] - ETA: 0s - loss: 0.2896 - acc: 0.9069"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r50/50 [==============================] - 5s 104ms/step - loss: 0.2866 - acc: 0.9075\n",
            "Epoch 7/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2839 - acc: 0.9106\n",
            "Epoch 8/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2827 - acc: 0.9062\n",
            "Epoch 9/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2860 - acc: 0.9044\n",
            "Epoch 10/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2374 - acc: 0.9231\n",
            "Epoch 11/100\n",
            "29/50 [================>.............] - ETA: 2s - loss: 0.2147 - acc: 0.9300"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2381 - acc: 0.9244\n",
            "Epoch 12/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2708 - acc: 0.9106\n",
            "Epoch 13/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2528 - acc: 0.9150\n",
            "Epoch 14/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2752 - acc: 0.9038\n",
            "Epoch 15/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2855 - acc: 0.9125\n",
            "Epoch 16/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2142 - acc: 0.9263\n",
            "Epoch 17/100\n",
            " 6/50 [==>...........................] - ETA: 4s - loss: 0.3226 - acc: 0.8698"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2548 - acc: 0.9213\n",
            "Epoch 18/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2046 - acc: 0.9299\n",
            "Epoch 19/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2202 - acc: 0.9313\n",
            "Epoch 20/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2582 - acc: 0.9194\n",
            "Epoch 21/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2834 - acc: 0.9113\n",
            "Epoch 22/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2376 - acc: 0.9213\n",
            "Epoch 23/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.3762 - acc: 0.9167"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2593 - acc: 0.9194\n",
            "Epoch 24/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2141 - acc: 0.9269\n",
            "Epoch 25/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2516 - acc: 0.9131\n",
            "Epoch 26/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2209 - acc: 0.9237\n",
            "Epoch 27/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1997 - acc: 0.9275\n",
            "Epoch 28/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2734 - acc: 0.9081\n",
            "Epoch 29/100\n",
            " 2/50 [>.............................] - ETA: 4s - loss: 0.2797 - acc: 0.9219"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2449 - acc: 0.9144\n",
            "Epoch 30/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2481 - acc: 0.9175\n",
            "Epoch 31/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2491 - acc: 0.9244\n",
            "Epoch 32/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2677 - acc: 0.9169\n",
            "Epoch 33/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2359 - acc: 0.9175\n",
            "Epoch 34/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2312 - acc: 0.9250\n",
            "Epoch 35/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.3005 - acc: 0.9062"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2391 - acc: 0.9194\n",
            "Epoch 36/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2359 - acc: 0.9300\n",
            "Epoch 37/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1451 - acc: 0.9562\n",
            "Epoch 38/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1733 - acc: 0.9387\n",
            "Epoch 39/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1374 - acc: 0.9562\n",
            "Epoch 40/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1805 - acc: 0.9506\n",
            "Epoch 41/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.1994 - acc: 0.9271"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1962 - acc: 0.9363\n",
            "Epoch 42/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2083 - acc: 0.9319\n",
            "Epoch 43/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1385 - acc: 0.9519\n",
            "Epoch 44/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1637 - acc: 0.9406\n",
            "Epoch 45/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2047 - acc: 0.9331\n",
            "Epoch 46/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1717 - acc: 0.9525\n",
            "Epoch 47/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.2190 - acc: 0.9583"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1452 - acc: 0.9556\n",
            "Epoch 48/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1780 - acc: 0.9481\n",
            "Epoch 49/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1768 - acc: 0.9444\n",
            "Epoch 50/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1510 - acc: 0.9519\n",
            "Epoch 51/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1504 - acc: 0.9425\n",
            "Epoch 52/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1921 - acc: 0.9375\n",
            "Epoch 53/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.2207 - acc: 0.9271"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2008 - acc: 0.9387\n",
            "Epoch 54/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1839 - acc: 0.9425\n",
            "Epoch 55/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1810 - acc: 0.9431\n",
            "Epoch 56/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1699 - acc: 0.9431\n",
            "Epoch 57/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2150 - acc: 0.9306\n",
            "Epoch 58/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1757 - acc: 0.9487\n",
            "Epoch 59/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.0997 - acc: 0.9583"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1722 - acc: 0.9425\n",
            "Epoch 60/100\n",
            "50/50 [==============================] - 5s 103ms/step - loss: 0.2202 - acc: 0.9287\n",
            "Epoch 61/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1681 - acc: 0.9461\n",
            "Epoch 62/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1810 - acc: 0.9456\n",
            "Epoch 63/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2304 - acc: 0.9263\n",
            "Epoch 64/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1942 - acc: 0.9375\n",
            "Epoch 65/100\n",
            " 2/50 [>.............................] - ETA: 4s - loss: 0.1196 - acc: 0.9688"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1731 - acc: 0.9437\n",
            "Epoch 66/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2592 - acc: 0.9213\n",
            "Epoch 67/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1634 - acc: 0.9456\n",
            "Epoch 68/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.2093 - acc: 0.9344\n",
            "Epoch 69/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1997 - acc: 0.9394\n",
            "Epoch 70/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1672 - acc: 0.9450\n",
            "Epoch 71/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.1190 - acc: 0.9583"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.2068 - acc: 0.9356\n",
            "Epoch 72/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1692 - acc: 0.9406\n",
            "Epoch 73/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1443 - acc: 0.9513\n",
            "Epoch 74/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1505 - acc: 0.9544\n",
            "Epoch 75/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1161 - acc: 0.9650\n",
            "Epoch 76/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1513 - acc: 0.9500\n",
            "Epoch 77/100\n",
            " 2/50 [>.............................] - ETA: 4s - loss: 0.1415 - acc: 0.9219"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1279 - acc: 0.9594\n",
            "Epoch 78/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1116 - acc: 0.9625\n",
            "Epoch 79/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1835 - acc: 0.9450\n",
            "Epoch 80/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1134 - acc: 0.9613\n",
            "Epoch 81/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1177 - acc: 0.9600\n",
            "Epoch 82/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1420 - acc: 0.9550\n",
            "Epoch 83/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.1428 - acc: 0.9479"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1610 - acc: 0.9475\n",
            "Epoch 84/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1456 - acc: 0.9544\n",
            "Epoch 85/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1317 - acc: 0.9556\n",
            "Epoch 86/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1378 - acc: 0.9544\n",
            "Epoch 87/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1497 - acc: 0.9519\n",
            "Epoch 88/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1600 - acc: 0.9500\n",
            "Epoch 89/100\n",
            " 3/50 [>.............................] - ETA: 4s - loss: 0.0531 - acc: 0.9792"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1418 - acc: 0.9481\n",
            "Epoch 90/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1759 - acc: 0.9469\n",
            "Epoch 91/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1508 - acc: 0.9487\n",
            "Epoch 92/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1725 - acc: 0.9450\n",
            "Epoch 93/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1472 - acc: 0.9487\n",
            "Epoch 94/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1254 - acc: 0.9556\n",
            "Epoch 95/100\n",
            " 2/50 [>.............................] - ETA: 4s - loss: 0.0492 - acc: 0.9844"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1586 - acc: 0.9481\n",
            "Epoch 96/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1348 - acc: 0.9531\n",
            "Epoch 97/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1414 - acc: 0.9506\n",
            "Epoch 98/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1389 - acc: 0.9525\n",
            "Epoch 99/100\n",
            "50/50 [==============================] - 5s 105ms/step - loss: 0.1335 - acc: 0.9513\n",
            "Epoch 100/100\n",
            "50/50 [==============================] - 5s 104ms/step - loss: 0.1587 - acc: 0.9475\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa12c1ec080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "ec-GKvX9U4Ui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "19c02d0d-35c2-43de-8f48-7f8db8aa52eb"
      },
      "cell_type": "code",
      "source": [
        "model5_n.evaluate(X_valid, Y_valid, batch_size=64)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 4s 1ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.6200687059887142, 0.6589579270157717]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "id": "zuWjRnPVXV66",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model5_n.save_weights('drive/face_expr_model5_n.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dd1ryUoX44FW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ensemble Average\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "VDIRBm6YqFIU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import layers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cBYB6FSz26br",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xuj7eF5OZduA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "models = [model1, model1_2, model2, model3, model5, model5_n]\n",
        "model_input = Input(shape=(48, 48, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "330oqKK77xKv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def ensemble(models, model_input):\n",
        "    \n",
        "    ens_models=[model(model_input) for model in models] \n",
        "    yAvg=layers.average(ens_models) \n",
        "    modelEns = Model(inputs=model_input, outputs=yAvg,name='ensemble')\n",
        "    return modelEns\n",
        "ensemble_model = ensemble(models, model_input)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pe1GiN6vpveN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "df5bb85e-6f6e-4f7c-cee5-90951f71fd21"
      },
      "cell_type": "code",
      "source": [
        "ensemble_model.summary()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_11 (InputLayer)           (None, 48, 48, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "sequential_8 (Sequential)       (None, 7)            5905867     input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "sequential_2 (Sequential)       (None, 7)            5905867     input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "sequential_9 (Sequential)       (None, 7)            60779       input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "sequential_10 (Sequential)      (None, 7)            1485835     input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "model_4 (Model)                 (None, 7)            14887623    input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "model_2 (Model)                 (None, 7)            14887623    input_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "average_12 (Average)            (None, 7)            0           sequential_8[5][0]               \n",
            "                                                                 sequential_2[3][0]               \n",
            "                                                                 sequential_9[5][0]               \n",
            "                                                                 sequential_10[5][0]              \n",
            "                                                                 model_4[5][0]                    \n",
            "                                                                 model_2[3][0]                    \n",
            "==================================================================================================\n",
            "Total params: 43,133,594\n",
            "Trainable params: 43,126,170\n",
            "Non-trainable params: 7,424\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jGwxaM5D33k6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ensemble_model.compile(loss=categorical_crossentropy,\n",
        "              optimizer=Adamax(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "izlV4AEP3lUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f6f6cbe9-0713-494c-cf7f-1eb4f517362d"
      },
      "cell_type": "code",
      "source": [
        "ensemble_model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3589/3589 [==============================] - 13s 4ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.875687417262575, 0.6910002786374484]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "metadata": {
        "id": "lxHiKlTW3t6P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ensemble_model.save('drive/face_expr_ensmodel.json')\n",
        "ensemble_model.save_weights('drive/face_expr_ensmodel.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ae578-0L4i7A",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}