train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwvAinSsYU67",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "665deabd-61b4-4091-b11f-bccd70697fd4"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a9053be0-317d-47b8-ac3f-a3a809aeba6d\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-a9053be0-317d-47b8-ac3f-a3a809aeba6d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.zip to data.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mI1IwqDWZEHt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5cb78d6f-34aa-41aa-b3db-856cf907be18"
      },
      "source": [
        "%ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data.zip  \u001b[0m\u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ojeAoGOZqBI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "abbae1b7-af82-4c83-e65e-fb71fe9b0544"
      },
      "source": [
        "!unzip data.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: data/test/happy/im154.png  \n",
            "  inflating: data/test/happy/im156.png  \n",
            "  inflating: data/test/happy/im157.png  \n",
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            "  inflating: data/test/happy/im161.png  \n",
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            "  inflating: data/test/happy/im166.png  \n",
            "  inflating: data/test/happy/im167.png  \n",
            "  inflating: data/test/happy/im168.png  \n",
            "  inflating: data/test/happy/im170.png  \n",
            "  inflating: data/test/happy/im173.png  \n",
            "  inflating: data/test/happy/im176.png  \n",
            "  inflating: data/test/happy/im181.png  \n",
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            "  inflating: data/test/happy/im228.png  \n",
            "  inflating: data/test/happy/im229.png  \n",
            "  inflating: data/test/happy/im231.png  \n",
            "  inflating: data/test/happy/im233.png  \n",
            "  inflating: data/test/happy/im238.png  \n",
            "  inflating: data/test/happy/im239.png  \n",
            "  inflating: data/test/happy/im240.png  \n",
            "  inflating: data/test/happy/im241.png  \n",
            "  inflating: data/test/happy/im243.png  \n",
            "  inflating: data/test/happy/im244.png  \n",
            "  inflating: data/test/happy/im249.png  \n",
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            "  inflating: data/test/happy/im253.png  \n",
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            "  inflating: data/test/happy/im261.png  \n",
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            "  inflating: data/test/happy/im297.png  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGJDJjaxZsxG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34de630d-8df7-4cf6-99c3-dfebdbbf7626"
      },
      "source": [
        "%ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mdata\u001b[0m/  data.zip  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9LwJJtgfSlj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        },
        "outputId": "e9076b0b-d84c-408a-c0e5-f8e316b7753f"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout,Flatten\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBVcMP_kgK8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dir = 'data/train'\n",
        "val_dir = 'data/test'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEoo3eFFgS27",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_train=28709\n",
        "num_val=7178\n",
        "batch_size=64\n",
        "num_epochs=50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xsss9IX2gdnb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poRzJuG5gpZG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c01c09cc-9019-40af-9d99-91c1604705ff"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(48,48),\n",
        "    batch_size=batch_size,\n",
        "    color_mode=\"grayscale\",\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "validation_generator = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=(48,48),\n",
        "    batch_size=batch_size,\n",
        "    color_mode=\"grayscale\",\n",
        "    class_mode='categorical'\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 28709 images belonging to 7 classes.\n",
            "Found 7178 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XynfI_oPhWBD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "70e71e26-9986-4a8d-bd1d-01b7d9ee032d"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(48,48,1)))\n",
        "model.add(Conv2D(64,kernel_size=(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128,kernel_size=(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(128,kernel_size=(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1024,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(7,activation='softmax'))\n",
        "\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3frBvz0vigaq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001,decay=1e-6),metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRix0Fxfi2tB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8dff3a4f-d9d8-4777-d0fd-c6fc32f1a6e0"
      },
      "source": [
        "model_info = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=num_train//batch_size,\n",
        "    epochs=num_epochs,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=num_val//batch_size\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.8027 - acc: 0.2603Epoch 1/50\n",
            "448/448 [==============================] - 19s 43ms/step - loss: 1.8026 - acc: 0.2606 - val_loss: 1.7078 - val_acc: 0.3382\n",
            "Epoch 2/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 1.6233 - acc: 0.3689Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.6225 - acc: 0.3692 - val_loss: 1.5541 - val_acc: 0.4011\n",
            "Epoch 3/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.5160 - acc: 0.4179Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.5159 - acc: 0.4178 - val_loss: 1.4666 - val_acc: 0.4368\n",
            "Epoch 4/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.4429 - acc: 0.4486Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.4431 - acc: 0.4486 - val_loss: 1.3942 - val_acc: 0.4729\n",
            "Epoch 5/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.3792 - acc: 0.4775Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.3791 - acc: 0.4777 - val_loss: 1.3332 - val_acc: 0.4941\n",
            "Epoch 6/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.3250 - acc: 0.4957Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.3250 - acc: 0.4958 - val_loss: 1.2941 - val_acc: 0.5123\n",
            "Epoch 7/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.2898 - acc: 0.5117Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.2894 - acc: 0.5119 - val_loss: 1.2563 - val_acc: 0.5254\n",
            "Epoch 8/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 1.2484 - acc: 0.5298Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.2481 - acc: 0.5296 - val_loss: 1.2407 - val_acc: 0.5301\n",
            "Epoch 9/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 1.2148 - acc: 0.5416Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.2145 - acc: 0.5418 - val_loss: 1.2219 - val_acc: 0.5392\n",
            "Epoch 10/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.1853 - acc: 0.5558Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.1856 - acc: 0.5558 - val_loss: 1.1910 - val_acc: 0.5530\n",
            "Epoch 11/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 1.1555 - acc: 0.5652Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.1565 - acc: 0.5647 - val_loss: 1.1707 - val_acc: 0.5554\n",
            "Epoch 12/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.1273 - acc: 0.5784Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.1283 - acc: 0.5780 - val_loss: 1.1604 - val_acc: 0.5608\n",
            "Epoch 13/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.1042 - acc: 0.5867Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.1043 - acc: 0.5865 - val_loss: 1.1407 - val_acc: 0.5622\n",
            "Epoch 14/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.0805 - acc: 0.5978Epoch 1/50\n",
            "448/448 [==============================] - 13s 28ms/step - loss: 1.0809 - acc: 0.5975 - val_loss: 1.1251 - val_acc: 0.5762\n",
            "Epoch 15/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.0525 - acc: 0.6060Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.0526 - acc: 0.6058 - val_loss: 1.1186 - val_acc: 0.5794\n",
            "Epoch 16/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 1.0297 - acc: 0.6183Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 1.0296 - acc: 0.6183 - val_loss: 1.1052 - val_acc: 0.5833\n",
            "Epoch 17/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 1.0050 - acc: 0.6275Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 1.0050 - acc: 0.6274 - val_loss: 1.1052 - val_acc: 0.5844\n",
            "Epoch 18/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.9836 - acc: 0.6324Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.9831 - acc: 0.6326 - val_loss: 1.0990 - val_acc: 0.5859\n",
            "Epoch 19/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.9584 - acc: 0.6463Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.9584 - acc: 0.6463 - val_loss: 1.0926 - val_acc: 0.5898\n",
            "Epoch 20/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.9359 - acc: 0.6566Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.9361 - acc: 0.6567 - val_loss: 1.0853 - val_acc: 0.5963\n",
            "Epoch 21/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.9172 - acc: 0.6642Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.9169 - acc: 0.6643 - val_loss: 1.0789 - val_acc: 0.6002\n",
            "Epoch 22/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.8926 - acc: 0.6713Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.8926 - acc: 0.6712 - val_loss: 1.0852 - val_acc: 0.6010\n",
            "Epoch 23/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.8647 - acc: 0.6834Epoch 1/50\n",
            "448/448 [==============================] - 13s 28ms/step - loss: 0.8649 - acc: 0.6833 - val_loss: 1.0734 - val_acc: 0.6018\n",
            "Epoch 24/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.8467 - acc: 0.6893Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.8465 - acc: 0.6894 - val_loss: 1.0794 - val_acc: 0.5996\n",
            "Epoch 25/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.8204 - acc: 0.6986Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.8201 - acc: 0.6987 - val_loss: 1.0729 - val_acc: 0.6028\n",
            "Epoch 26/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.8004 - acc: 0.7072Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.8004 - acc: 0.7072 - val_loss: 1.0706 - val_acc: 0.6060\n",
            "Epoch 27/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.7754 - acc: 0.7165Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.7756 - acc: 0.7164 - val_loss: 1.0728 - val_acc: 0.6084\n",
            "Epoch 28/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.7545 - acc: 0.7256Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.7549 - acc: 0.7253 - val_loss: 1.0841 - val_acc: 0.6041\n",
            "Epoch 29/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.7274 - acc: 0.7365Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.7274 - acc: 0.7366 - val_loss: 1.0718 - val_acc: 0.6109\n",
            "Epoch 30/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.7085 - acc: 0.7442Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.7084 - acc: 0.7441 - val_loss: 1.0810 - val_acc: 0.6095\n",
            "Epoch 31/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.6844 - acc: 0.7498Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.6845 - acc: 0.7498 - val_loss: 1.0784 - val_acc: 0.6088\n",
            "Epoch 32/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.6639 - acc: 0.7576Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.6640 - acc: 0.7576 - val_loss: 1.0769 - val_acc: 0.6184\n",
            "Epoch 33/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.6400 - acc: 0.7676Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.6402 - acc: 0.7675 - val_loss: 1.0983 - val_acc: 0.6177\n",
            "Epoch 34/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.6187 - acc: 0.7745Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.6192 - acc: 0.7742 - val_loss: 1.0968 - val_acc: 0.6148\n",
            "Epoch 35/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.5984 - acc: 0.7821Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.5989 - acc: 0.7821 - val_loss: 1.1113 - val_acc: 0.6109\n",
            "Epoch 36/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.5807 - acc: 0.7896Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.5809 - acc: 0.7896 - val_loss: 1.0968 - val_acc: 0.6208\n",
            "Epoch 37/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.5609 - acc: 0.7961Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.5606 - acc: 0.7962 - val_loss: 1.1129 - val_acc: 0.6187\n",
            "Epoch 38/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.5430 - acc: 0.8047Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.5432 - acc: 0.8047 - val_loss: 1.1136 - val_acc: 0.6215\n",
            "Epoch 39/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.5266 - acc: 0.8099Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.5268 - acc: 0.8097 - val_loss: 1.1245 - val_acc: 0.6165\n",
            "Epoch 40/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.5160 - acc: 0.8150Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.5161 - acc: 0.8149 - val_loss: 1.1215 - val_acc: 0.6134\n",
            "Epoch 41/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.4945 - acc: 0.8239Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.4947 - acc: 0.8237 - val_loss: 1.1535 - val_acc: 0.6207\n",
            "Epoch 42/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.4716 - acc: 0.8308Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.4715 - acc: 0.8309 - val_loss: 1.1678 - val_acc: 0.6141\n",
            "Epoch 43/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.4626 - acc: 0.8342Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.4626 - acc: 0.8341 - val_loss: 1.1564 - val_acc: 0.6154\n",
            "Epoch 44/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.4452 - acc: 0.8414Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.4453 - acc: 0.8412 - val_loss: 1.1622 - val_acc: 0.6247\n",
            "Epoch 45/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.4336 - acc: 0.8437Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.4337 - acc: 0.8436 - val_loss: 1.1664 - val_acc: 0.6187\n",
            "Epoch 46/50\n",
            "446/448 [============================>.] - ETA: 0s - loss: 0.4223 - acc: 0.8482Epoch 1/50\n",
            "448/448 [==============================] - 13s 28ms/step - loss: 0.4222 - acc: 0.8484 - val_loss: 1.1657 - val_acc: 0.6210\n",
            "Epoch 47/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.4100 - acc: 0.8526Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.4106 - acc: 0.8526 - val_loss: 1.1913 - val_acc: 0.6205\n",
            "Epoch 48/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.3980 - acc: 0.8563Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.3974 - acc: 0.8563 - val_loss: 1.2028 - val_acc: 0.6205\n",
            "Epoch 49/50\n",
            "445/448 [============================>.] - ETA: 0s - loss: 0.3787 - acc: 0.8640Epoch 1/50\n",
            "448/448 [==============================] - 12s 28ms/step - loss: 0.3789 - acc: 0.8639 - val_loss: 1.1996 - val_acc: 0.6175\n",
            "Epoch 50/50\n",
            "447/448 [============================>.] - ETA: 0s - loss: 0.3711 - acc: 0.8678Epoch 1/50\n",
            "448/448 [==============================] - 12s 27ms/step - loss: 0.3709 - acc: 0.8678 - val_loss: 1.2270 - val_acc: 0.6223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zwvnwegjQ8c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "11fe98f0-ebbd-4f9d-b008-027f47bd6a0e"
      },
      "source": [
        "print(model_info)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.callbacks.History object at 0x7fc7cf49c9b0>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MU4gqdQKtakr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_model_history(model_history):\n",
        "  fig,ax = plt.subplots(1,2,figsize=(15,5))\n",
        "  ax[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])\n",
        "  ax[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])\n",
        "  ax[0].set_title('Model Accuracy')\n",
        "  ax[0].set_ylabel('Accuracy')\n",
        "  ax[0].set_xlabel('Epochs')\n",
        "  ax[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)\n",
        "  ax[0].legend(['train','validation'],loc='best')\n",
        "  # summarize history for loss\n",
        "  ax[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])\n",
        "  ax[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])\n",
        "  ax[1].set_title('Model Loss')\n",
        "  ax[1].set_ylabel('Loss')\n",
        "  ax[1].set_xlabel('Epochs')\n",
        "  ax[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)\n",
        "  ax[1].legend(['train','validation'],loc='best')\n",
        "  fig.savefig('plot.png')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_UVlon-wCUM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "e60c9d2a-0d60-4432-c2db-ce0ebc61fe18"
      },
      "source": [
        "plot_model_history(model_info)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gUte1SYNWPdxcvJQrMT6+XJwayz++XsaK3J2qfCsiUkuoB09EjtvWoj088+Mq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\nHuhTVgngSfYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIFRYrLNwGWy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights('model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2htSPJCAwPN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpofFUv5wbAw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}