image_personality.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "image_personality.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V97Ws1277GHs",
        "outputId": "77f9707e-ef97-4204-d6eb-0386b74fc190"
      },
      "source": [
        "!pip install tensorflow==2.2.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3d/be/679ce5254a8c8d07470efb4a4c00345fae91f766e64f1c2aece8796d7218/tensorflow-2.2.0-cp36-cp36m-manylinux2010_x86_64.whl (516.2MB)\n",
            "\u001b[K     |████████████████████████████████| 516.2MB 25kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.2.0) (1.19.5)\n",
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            "Collecting tensorboard<2.3.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/74/0a6fcb206dcc72a6da9a62dd81784bfdbff5fedb099982861dc2219014fb/tensorboard-2.2.2-py3-none-any.whl (3.0MB)\n",
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            "Collecting tensorflow-estimator<2.3.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/f5/926ae53d6a226ec0fda5208e0e581cffed895ccc89e36ba76a8e60895b78/tensorflow_estimator-2.2.0-py2.py3-none-any.whl (454kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 52.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.2.0) (1.1.2)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.3.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (53.0.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.23.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.2)\n",
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            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.25.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.4.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.0.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (1.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.7)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (4.2.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.4.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard<2.3.0,>=2.2.0->tensorflow==2.2.0) (0.4.8)\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, tensorflow\n",
            "  Found existing installation: tensorboard 2.4.1\n",
            "    Uninstalling tensorboard-2.4.1:\n",
            "      Successfully uninstalled tensorboard-2.4.1\n",
            "  Found existing installation: tensorflow-estimator 2.4.0\n",
            "    Uninstalling tensorflow-estimator-2.4.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.4.0\n",
            "  Found existing installation: tensorflow 2.4.1\n",
            "    Uninstalling tensorflow-2.4.1:\n",
            "      Successfully uninstalled tensorflow-2.4.1\n",
            "Successfully installed tensorboard-2.2.2 tensorflow-2.2.0 tensorflow-estimator-2.2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzeFvAcwiqV7"
      },
      "source": [
        "import os\r\n",
        "import numpy as np\r\n",
        "import tensorflow as tf\r\n",
        "from tensorflow import keras\r\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, Dropout, Flatten, Input, Dense, MaxPooling2D, Reshape\r\n",
        "from tensorflow.keras.optimizers import Adam\r\n",
        "from tensorflow.keras.models import Model, model_from_json, load_model\r\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\r\n",
        "from tensorflow.keras.losses import MeanSquaredError\r\n",
        "from tensorflow.keras import backend as K\r\n",
        "from PIL import Image, ImageOps\r\n",
        "import pickle\r\n",
        "import time\r\n",
        "import tqdm\r\n",
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Xf6lbBTP7w49",
        "outputId": "31511b63-97a6-4a41-bf92-83bacb3c6e7f"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1Jm6lGWjtEH",
        "outputId": "98d2dda8-16b0-42b0-c8b0-d476ba3bec5e"
      },
      "source": [
        "from google.colab import drive \r\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHBGcpC8kWfc",
        "outputId": "c426278d-fcbe-48cb-b817-efe973c77226"
      },
      "source": [
        "X_train = []\r\n",
        "Y_train = []\r\n",
        "base_path = '/content/gdrive/MyDrive/data_for_training&test'\r\n",
        "for i in tqdm.tqdm(range(1,4)):\r\n",
        "  file_name = 'train_clselfie_v4_' + str(i) + \".pickle\"\r\n",
        "  pic = pickle.load(open(os.path.join(base_path, file_name), \"rb\"))\r\n",
        "  for i in pic['X']:\r\n",
        "    x = np.array(i)\r\n",
        "    X_train.append(x)\r\n",
        "  for i in pic['Y']:\r\n",
        "    y = np.array(i)\r\n",
        "    Y_train.append(y)\r\n",
        "\r\n",
        "\r\n",
        "file_name = 'test_clselfie_v4.pickle'\r\n",
        "X_val = []\r\n",
        "Y_val = []\r\n",
        "pic = pickle.load(open(os.path.join(base_path, file_name), \"rb\"))\r\n",
        "for i in pic['X']:\r\n",
        "  x = np.array(i)\r\n",
        "  X_val.append(x)\r\n",
        "for i in pic['Y']:\r\n",
        "  y = np.array(i)\r\n",
        "  Y_val.append(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3/3 [00:34<00:00, 11.59s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjIStP4njfq8",
        "outputId": "4ec9879f-d460-4274-8335-8410afd3b8aa"
      },
      "source": [
        "X_train = np.array(X_train)\r\n",
        "Y_train = np.array(Y_train)\r\n",
        "X_val = np.array(X_val)\r\n",
        "Y_val = np.array(Y_val)\r\n",
        "print(X_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28152, 208, 208, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2b-exaFnCB7",
        "outputId": "113f3b54-9b7a-425e-f58d-7cf2a1028c38"
      },
      "source": [
        "input_cla = Input(shape=(208,208,1,))\r\n",
        "\r\n",
        "fm_size = 16;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(input_cla)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "fm_size = fm_size*2;\r\n",
        "x = Conv2D(fm_size, (3,3), activation='relu', padding='same')(x)\r\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\r\n",
        "\r\n",
        "em_size = math.ceil(208/(2**8));\r\n",
        "\r\n",
        "x = Reshape((em_size*em_size*fm_size,),input_shape=(em_size,em_size,fm_size))(x)\r\n",
        "x=Dense(50,activation='relu', input_shape=(em_size*em_size*fm_size,))(x)\r\n",
        "x=Dense(50,activation='relu')(x)\r\n",
        "x=Dense(10,activation='relu')(x)\r\n",
        "output=Dense(5)(x)\r\n",
        "\r\n",
        "classifier = Model(input_cla, output)\r\n",
        "adam = Adam(lr=1e-4)\r\n",
        "loss = MeanSquaredError()\r\n",
        "classifier.compile(optimizer=adam, loss=loss, metrics = [tf.keras.metrics.MeanSquaredError()])\r\n",
        "\r\n",
        "classifier.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 208, 208, 1)]     0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 208, 208, 16)      160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 104, 104, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 104, 104, 32)      4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 52, 52, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 52, 52, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 26, 26, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 256)       295168    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 7, 7, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 4, 4, 1024)        4719616   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 2, 2, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 2, 2, 2048)        18876416  \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 1, 1, 2048)        0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 50)                102450    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 50)                2550      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                510       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 5)                 55        \n",
            "=================================================================\n",
            "Total params: 25,274,077\n",
            "Trainable params: 25,274,077\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t7iDDjBom0b"
      },
      "source": [
        "callbacks = [ReduceLROnPlateau(factor=0.3, patience=5, verbose = 1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO0vPTudPp_S",
        "outputId": "ed221927-e6d4-41d4-b31a-28ff325ffa09"
      },
      "source": [
        "BATCH_SIZE = 64\r\n",
        "TRAIN_STEP_SIZE = len(X_train) // BATCH_SIZE\r\n",
        "history = classifier.fit(X_train, Y_train, batch_size = BATCH_SIZE, epochs = 50, steps_per_epoch = TRAIN_STEP_SIZE,\r\n",
        "                    shuffle = True, validation_data = (X_val, Y_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "439/439 [==============================] - 40s 91ms/step - loss: 0.1331 - mean_squared_error: 0.1331 - val_loss: 0.0643 - val_mean_squared_error: 0.0643\n",
            "Epoch 2/50\n",
            "439/439 [==============================] - 41s 93ms/step - loss: 0.0396 - mean_squared_error: 0.0396 - val_loss: 0.0273 - val_mean_squared_error: 0.0273\n",
            "Epoch 3/50\n",
            "439/439 [==============================] - 40s 91ms/step - loss: 0.0228 - mean_squared_error: 0.0228 - val_loss: 0.0235 - val_mean_squared_error: 0.0235\n",
            "Epoch 4/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0238 - val_mean_squared_error: 0.0238\n",
            "Epoch 5/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0236 - val_mean_squared_error: 0.0236\n",
            "Epoch 6/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0248 - val_mean_squared_error: 0.0248\n",
            "Epoch 7/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0238 - val_mean_squared_error: 0.0238\n",
            "Epoch 8/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0105 - mean_squared_error: 0.0105 - val_loss: 0.0243 - val_mean_squared_error: 0.0243\n",
            "Epoch 9/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0092 - mean_squared_error: 0.0092 - val_loss: 0.0241 - val_mean_squared_error: 0.0241\n",
            "Epoch 10/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0083 - mean_squared_error: 0.0083 - val_loss: 0.0236 - val_mean_squared_error: 0.0236\n",
            "Epoch 11/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0075 - mean_squared_error: 0.0075 - val_loss: 0.0240 - val_mean_squared_error: 0.0240\n",
            "Epoch 12/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0068 - mean_squared_error: 0.0068 - val_loss: 0.0243 - val_mean_squared_error: 0.0243\n",
            "Epoch 13/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0064 - mean_squared_error: 0.0064 - val_loss: 0.0235 - val_mean_squared_error: 0.0235\n",
            "Epoch 14/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0061 - mean_squared_error: 0.0061 - val_loss: 0.0233 - val_mean_squared_error: 0.0233\n",
            "Epoch 15/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0060 - mean_squared_error: 0.0060 - val_loss: 0.0231 - val_mean_squared_error: 0.0231\n",
            "Epoch 16/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0057 - mean_squared_error: 0.0057 - val_loss: 0.0231 - val_mean_squared_error: 0.0231\n",
            "Epoch 17/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0056 - mean_squared_error: 0.0056 - val_loss: 0.0236 - val_mean_squared_error: 0.0236\n",
            "Epoch 18/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0055 - mean_squared_error: 0.0055 - val_loss: 0.0231 - val_mean_squared_error: 0.0231\n",
            "Epoch 19/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0223 - val_mean_squared_error: 0.0223\n",
            "Epoch 20/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0052 - mean_squared_error: 0.0052 - val_loss: 0.0223 - val_mean_squared_error: 0.0223\n",
            "Epoch 21/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0053 - mean_squared_error: 0.0053 - val_loss: 0.0230 - val_mean_squared_error: 0.0230\n",
            "Epoch 22/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0051 - mean_squared_error: 0.0051 - val_loss: 0.0223 - val_mean_squared_error: 0.0223\n",
            "Epoch 23/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0050 - mean_squared_error: 0.0050 - val_loss: 0.0219 - val_mean_squared_error: 0.0219\n",
            "Epoch 24/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0217 - val_mean_squared_error: 0.0217\n",
            "Epoch 25/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0049 - mean_squared_error: 0.0049 - val_loss: 0.0217 - val_mean_squared_error: 0.0217\n",
            "Epoch 26/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0220 - val_mean_squared_error: 0.0220\n",
            "Epoch 27/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0214 - val_mean_squared_error: 0.0214\n",
            "Epoch 28/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0048 - mean_squared_error: 0.0048 - val_loss: 0.0213 - val_mean_squared_error: 0.0213\n",
            "Epoch 29/50\n",
            "439/439 [==============================] - 41s 92ms/step - loss: 0.0047 - mean_squared_error: 0.0047 - val_loss: 0.0212 - val_mean_squared_error: 0.0212\n",
            "Epoch 30/50\n",
            "439/439 [==============================] - 41s 92ms/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0213 - val_mean_squared_error: 0.0213\n",
            "Epoch 31/50\n",
            "439/439 [==============================] - 41s 92ms/step - loss: 0.0046 - mean_squared_error: 0.0046 - val_loss: 0.0211 - val_mean_squared_error: 0.0211\n",
            "Epoch 32/50\n",
            "439/439 [==============================] - 41s 92ms/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0211 - val_mean_squared_error: 0.0211\n",
            "Epoch 33/50\n",
            "439/439 [==============================] - 41s 92ms/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0211 - val_mean_squared_error: 0.0211\n",
            "Epoch 34/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
            "Epoch 35/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0045 - mean_squared_error: 0.0045 - val_loss: 0.0209 - val_mean_squared_error: 0.0209\n",
            "Epoch 36/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
            "Epoch 37/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
            "Epoch 38/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0044 - mean_squared_error: 0.0044 - val_loss: 0.0204 - val_mean_squared_error: 0.0204\n",
            "Epoch 39/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0043 - mean_squared_error: 0.0043 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
            "Epoch 40/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
            "Epoch 41/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0201 - val_mean_squared_error: 0.0201\n",
            "Epoch 42/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
            "Epoch 43/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0204 - val_mean_squared_error: 0.0204\n",
            "Epoch 44/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
            "Epoch 45/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0042 - mean_squared_error: 0.0042 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
            "Epoch 46/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
            "Epoch 47/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
            "Epoch 48/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0201 - val_mean_squared_error: 0.0201\n",
            "Epoch 49/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0041 - mean_squared_error: 0.0041 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
            "Epoch 50/50\n",
            "439/439 [==============================] - 40s 92ms/step - loss: 0.0040 - mean_squared_error: 0.0040 - val_loss: 0.0200 - val_mean_squared_error: 0.0200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGvaXM9XkWD3"
      },
      "source": [
        "model_json = classifier.to_json()\r\n",
        "with open(\"/content/gdrive/MyDrive/ipv2.json\", \"w\") as json_file:\r\n",
        "    json_file.write(model_json)\r\n",
        "classifier.save_weights(\"/content/gdrive/MyDrive/ipv2.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11cHAClJ17fn"
      },
      "source": [
        "drive.flush_and_unmount()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6-N4HMd1_rC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}