Day44_train_facial_keypoint_Sample.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
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    "colab": {
      "name": "Day44_train_facial_keypoint_Sample.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VX9THIs0i0EG",
        "colab_type": "text"
      },
      "source": [
        "### 範例\n",
        "接下來的程式碼會示範如何定義一個簡單的 CNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-Vdxiqni0EH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOBybLLdi0EK",
        "colab_type": "code",
        "outputId": "22db06cc-152e-4a0b-faca-d7ebab498042",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# 使用 colab 環境的同學請執行以下程式碼\n",
        "%tensorflow_version 1.x # 確保 colob 中使用的 tensorflow 是 1.x 版本而不是 tensorflow 2\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "import os\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive') # 將 google drive 掛載在 colob，\n",
        "%cd 'gdrive/My Drive'\n",
        "# os.system(\"mkdir cupoy_cv_part4\") # 可以自己改路徑\n",
        "# %cd cupoy_cv_part4 # 可以自己改路徑"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "`%tensorflow_version` only switches the major version: 1.x or 2.x.\n",
            "You set: `1.x # 確保 colob 中使用的 tensorflow 是 1.x 版本而不是 tensorflow 2`. This will be interpreted as: `1.x`.\n",
            "\n",
            "\n",
            "TensorFlow 1.x selected.\n",
            "1.15.2\n",
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n",
            "/content/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sACAriwJi0EN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 讀取資料集以及做前處理的函數\n",
        "def load_data(dirname):\n",
        "    # 讀取 csv 文件\n",
        "    data = pd.read_csv(dirname)\n",
        "    # 過濾有缺失值的 row\n",
        "    data = data.dropna()\n",
        "\n",
        "    # 將圖片像素值讀取為 numpy array 的形態\n",
        "    data['Image'] = data['Image'].apply(lambda img: np.fromstring(img, sep=' ')).values \n",
        "\n",
        "    # 單獨把圖像 array 抽取出來\n",
        "    imgs = np.vstack(data['Image'].values)/255\n",
        "    # reshape 為 96 x 96\n",
        "    imgs = imgs.reshape(data.shape[0], 96, 96)\n",
        "    # 轉換為 float\n",
        "    imgs = imgs.astype(np.float32)\n",
        "    \n",
        "    # 提取坐標的部分\n",
        "    points = data[data.columns[:-1]].values\n",
        "\n",
        "    # 轉換為 float\n",
        "    points = points.astype(np.float32)\n",
        "\n",
        "    # normalize 坐標值到 [-0.5, 0.5]\n",
        "    points = points/96 - 0.5\n",
        "    \n",
        "    return imgs, points"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86BbuFj-i0EP",
        "colab_type": "code",
        "outputId": "b8f8af79-5a9a-4693-c88d-83a551b1a1ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# 讀取資料\n",
        "imgs_train, points_train = load_data(dirname = 'training.csv')\n",
        "print(\"圖像資料:\", imgs_train.shape, \"\\n關鍵點資料:\", points_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "圖像資料: (2140, 96, 96) \n",
            "關鍵點資料: (2140, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_BodxS8i0ER",
        "colab_type": "code",
        "outputId": "b851b509-30ff-4e6a-d87a-56cdede83322",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9VkMcocpi0EU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 回傳定義好的 model 的函數\n",
        "def get_model():\n",
        "    # 定義人臉關鍵點檢測網路\n",
        "    model = Sequential()\n",
        "\n",
        "    # 定義神經網路的輸入\n",
        "    model.add(Conv2D(filters=16, kernel_size=3, activation='relu', input_shape=(96, 96, 1)))\n",
        "    model.add(MaxPooling2D(pool_size=2))\n",
        "\n",
        "    model.add(Conv2D(filters=32, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=2))\n",
        "\n",
        "    model.add(Conv2D(filters=64, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=2))\n",
        "\n",
        "    model.add(Conv2D(filters=128, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=2))\n",
        "\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "    model.add(Dense(512, activation='relu'))\n",
        "    model.add(Dropout(0.2))\n",
        "\n",
        "    # 最後輸出 30 維的向量，也就是 15 個關鍵點的值\n",
        "    model.add(Dense(30))\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rnf4jmN7i0EW",
        "colab_type": "code",
        "outputId": "03ef2b8e-222e-489b-8406-7ab6449b3dc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "model = get_model()\n",
        "# 配置 loss funtion 和 optimizer\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY8ueTOLi0EZ",
        "colab_type": "code",
        "outputId": "d690ce27-dc9f-44fc-df8b-6eb425c91958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "# 印出網路結構\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 94, 94, 16)        160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 47, 47, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 45, 45, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 22, 22, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 20, 20, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1049088   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 30)                15390     \n",
            "=================================================================\n",
            "Total params: 1,424,286\n",
            "Trainable params: 1,424,286\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGbwfR3fi0Eb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint, History\n",
        "# model checkpoint \n",
        "checkpoint = ModelCheckpoint('best_weights.h5', verbose=1, save_best_only=True)\n",
        "hist = History()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfKZdro8i0Ed",
        "colab_type": "code",
        "outputId": "c7304b4e-24f6-477a-eb13-0b626bc0823f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# training the model\n",
        "hist_model = model.fit(imgs_train.reshape(-1, 96, 96, 1), \n",
        "                       points_train, \n",
        "                       validation_split=0.2, batch_size=64, callbacks=[checkpoint, hist],\n",
        "                       shuffle=True, epochs=150, verbose=1)\n",
        "# save the model weights\n",
        "model.save_weights('weights.h5')\n",
        "# save the model\n",
        "model.save('model.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "Train on 1712 samples, validate on 428 samples\n",
            "Epoch 1/150\n",
            "1712/1712 [==============================] - 15s 8ms/step - loss: 0.0068 - val_loss: 0.0023\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.00228, saving model to best_weights.h5\n",
            "Epoch 2/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0018 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.00228 to 0.00195, saving model to best_weights.h5\n",
            "Epoch 3/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0014 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.00195 to 0.00187, saving model to best_weights.h5\n",
            "Epoch 4/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0012 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.00187 to 0.00185, saving model to best_weights.h5\n",
            "Epoch 5/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0011 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.00185 to 0.00182, saving model to best_weights.h5\n",
            "Epoch 6/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.7900e-04 - val_loss: 0.0017\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00182 to 0.00172, saving model to best_weights.h5\n",
            "Epoch 7/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.3423e-04 - val_loss: 0.0016\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.00172 to 0.00160, saving model to best_weights.h5\n",
            "Epoch 8/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 7.3522e-04 - val_loss: 0.0014\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00160 to 0.00136, saving model to best_weights.h5\n",
            "Epoch 9/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 6.4001e-04 - val_loss: 0.0013\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.00136 to 0.00129, saving model to best_weights.h5\n",
            "Epoch 10/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.8189e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.00129 to 0.00119, saving model to best_weights.h5\n",
            "Epoch 11/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.3194e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.00119 to 0.00111, saving model to best_weights.h5\n",
            "Epoch 12/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.0445e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.00111 to 0.00111, saving model to best_weights.h5\n",
            "Epoch 13/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.8059e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.00111 to 0.00106, saving model to best_weights.h5\n",
            "Epoch 14/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.4517e-04 - val_loss: 0.0010\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.00106 to 0.00101, saving model to best_weights.h5\n",
            "Epoch 15/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.3025e-04 - val_loss: 9.9167e-04\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.00101 to 0.00099, saving model to best_weights.h5\n",
            "Epoch 16/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.0655e-04 - val_loss: 9.5599e-04\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.00099 to 0.00096, saving model to best_weights.h5\n",
            "Epoch 17/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.7237e-04 - val_loss: 9.0295e-04\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.00096 to 0.00090, saving model to best_weights.h5\n",
            "Epoch 18/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.5490e-04 - val_loss: 8.7469e-04\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.00090 to 0.00087, saving model to best_weights.h5\n",
            "Epoch 19/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.5381e-04 - val_loss: 9.1569e-04\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.00087\n",
            "Epoch 20/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.3899e-04 - val_loss: 8.6632e-04\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.00087 to 0.00087, saving model to best_weights.h5\n",
            "Epoch 21/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.2973e-04 - val_loss: 8.3971e-04\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.00087 to 0.00084, saving model to best_weights.h5\n",
            "Epoch 22/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.0809e-04 - val_loss: 8.6480e-04\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.00084\n",
            "Epoch 23/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.0950e-04 - val_loss: 8.4337e-04\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.00084\n",
            "Epoch 24/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.0347e-04 - val_loss: 8.4452e-04\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.00084\n",
            "Epoch 25/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.9578e-04 - val_loss: 8.1688e-04\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.00084 to 0.00082, saving model to best_weights.h5\n",
            "Epoch 26/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.8266e-04 - val_loss: 8.2726e-04\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.00082\n",
            "Epoch 27/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.7257e-04 - val_loss: 8.2128e-04\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.00082\n",
            "Epoch 28/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.6190e-04 - val_loss: 7.8566e-04\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.00082 to 0.00079, saving model to best_weights.h5\n",
            "Epoch 29/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.5832e-04 - val_loss: 7.7519e-04\n",
            "\n",
            "Epoch 00029: val_loss improved from 0.00079 to 0.00078, saving model to best_weights.h5\n",
            "Epoch 30/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.5471e-04 - val_loss: 7.9596e-04\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.00078\n",
            "Epoch 31/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.5031e-04 - val_loss: 7.8474e-04\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.00078\n",
            "Epoch 32/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.5070e-04 - val_loss: 8.4260e-04\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.00078\n",
            "Epoch 33/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.4362e-04 - val_loss: 7.8409e-04\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.00078\n",
            "Epoch 34/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.3662e-04 - val_loss: 7.8148e-04\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.00078\n",
            "Epoch 35/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.3361e-04 - val_loss: 7.8269e-04\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.00078\n",
            "Epoch 36/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2980e-04 - val_loss: 8.1937e-04\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.00078\n",
            "Epoch 37/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2909e-04 - val_loss: 8.0138e-04\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.00078\n",
            "Epoch 38/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2200e-04 - val_loss: 7.8610e-04\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.00078\n",
            "Epoch 39/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.1599e-04 - val_loss: 7.6355e-04\n",
            "\n",
            "Epoch 00039: val_loss improved from 0.00078 to 0.00076, saving model to best_weights.h5\n",
            "Epoch 40/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0993e-04 - val_loss: 7.7851e-04\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.00076\n",
            "Epoch 41/150\n",
            "1712/1712 [==============================] - 17s 10ms/step - loss: 2.0748e-04 - val_loss: 7.6008e-04\n",
            "\n",
            "Epoch 00041: val_loss improved from 0.00076 to 0.00076, saving model to best_weights.h5\n",
            "Epoch 42/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.1315e-04 - val_loss: 7.6037e-04\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.00076\n",
            "Epoch 43/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0695e-04 - val_loss: 8.0033e-04\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.00076\n",
            "Epoch 44/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9783e-04 - val_loss: 7.7453e-04\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.00076\n",
            "Epoch 45/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9839e-04 - val_loss: 7.8700e-04\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.00076\n",
            "Epoch 46/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9316e-04 - val_loss: 7.8305e-04\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.00076\n",
            "Epoch 47/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9523e-04 - val_loss: 7.6574e-04\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.00076\n",
            "Epoch 48/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9620e-04 - val_loss: 7.9676e-04\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.00076\n",
            "Epoch 49/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8748e-04 - val_loss: 7.3749e-04\n",
            "\n",
            "Epoch 00049: val_loss improved from 0.00076 to 0.00074, saving model to best_weights.h5\n",
            "Epoch 50/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8751e-04 - val_loss: 7.5978e-04\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.00074\n",
            "Epoch 51/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8408e-04 - val_loss: 7.5465e-04\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.00074\n",
            "Epoch 52/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8211e-04 - val_loss: 7.3452e-04\n",
            "\n",
            "Epoch 00052: val_loss improved from 0.00074 to 0.00073, saving model to best_weights.h5\n",
            "Epoch 53/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8443e-04 - val_loss: 7.6115e-04\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.00073\n",
            "Epoch 54/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7475e-04 - val_loss: 7.4510e-04\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.00073\n",
            "Epoch 55/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7246e-04 - val_loss: 7.5907e-04\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.00073\n",
            "Epoch 56/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7364e-04 - val_loss: 7.5511e-04\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.00073\n",
            "Epoch 57/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6847e-04 - val_loss: 7.4791e-04\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.00073\n",
            "Epoch 58/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6895e-04 - val_loss: 7.5148e-04\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.00073\n",
            "Epoch 59/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6342e-04 - val_loss: 7.5943e-04\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.00073\n",
            "Epoch 60/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6162e-04 - val_loss: 7.5880e-04\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.00073\n",
            "Epoch 61/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5838e-04 - val_loss: 7.7189e-04\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.00073\n",
            "Epoch 62/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5962e-04 - val_loss: 7.3181e-04\n",
            "\n",
            "Epoch 00062: val_loss improved from 0.00073 to 0.00073, saving model to best_weights.h5\n",
            "Epoch 63/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6581e-04 - val_loss: 7.4282e-04\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.00073\n",
            "Epoch 64/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5578e-04 - val_loss: 7.5863e-04\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.00073\n",
            "Epoch 65/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5342e-04 - val_loss: 7.7605e-04\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.00073\n",
            "Epoch 66/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5242e-04 - val_loss: 7.3338e-04\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.00073\n",
            "Epoch 67/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5226e-04 - val_loss: 7.4860e-04\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.00073\n",
            "Epoch 68/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5451e-04 - val_loss: 7.4425e-04\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.00073\n",
            "Epoch 69/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5480e-04 - val_loss: 7.7082e-04\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.00073\n",
            "Epoch 70/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5151e-04 - val_loss: 7.6599e-04\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.00073\n",
            "Epoch 71/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4610e-04 - val_loss: 7.7062e-04\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.00073\n",
            "Epoch 72/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4313e-04 - val_loss: 7.6311e-04\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.00073\n",
            "Epoch 73/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4247e-04 - val_loss: 7.7360e-04\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.00073\n",
            "Epoch 74/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4579e-04 - val_loss: 7.8738e-04\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.00073\n",
            "Epoch 75/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3981e-04 - val_loss: 7.8494e-04\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.00073\n",
            "Epoch 76/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3954e-04 - val_loss: 7.6986e-04\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.00073\n",
            "Epoch 77/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3843e-04 - val_loss: 8.3956e-04\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.00073\n",
            "Epoch 78/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3758e-04 - val_loss: 7.6064e-04\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.00073\n",
            "Epoch 79/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3488e-04 - val_loss: 7.7991e-04\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.00073\n",
            "Epoch 80/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3805e-04 - val_loss: 7.8468e-04\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.00073\n",
            "Epoch 81/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3718e-04 - val_loss: 7.7866e-04\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.00073\n",
            "Epoch 82/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2934e-04 - val_loss: 7.6024e-04\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.00073\n",
            "Epoch 83/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3224e-04 - val_loss: 7.7224e-04\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.00073\n",
            "Epoch 84/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3057e-04 - val_loss: 7.5885e-04\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.00073\n",
            "Epoch 85/150\n",
            "1712/1712 [==============================] - 18s 10ms/step - loss: 1.3069e-04 - val_loss: 7.6513e-04\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.00073\n",
            "Epoch 86/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3089e-04 - val_loss: 7.5615e-04\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.00073\n",
            "Epoch 87/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2984e-04 - val_loss: 7.4145e-04\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.00073\n",
            "Epoch 88/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2822e-04 - val_loss: 7.6162e-04\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.00073\n",
            "Epoch 89/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2719e-04 - val_loss: 7.9512e-04\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.00073\n",
            "Epoch 90/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2642e-04 - val_loss: 7.8919e-04\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.00073\n",
            "Epoch 91/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2631e-04 - val_loss: 7.3371e-04\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.00073\n",
            "Epoch 92/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2351e-04 - val_loss: 7.5828e-04\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.00073\n",
            "Epoch 93/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2635e-04 - val_loss: 7.6801e-04\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 0.00073\n",
            "Epoch 94/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2365e-04 - val_loss: 7.4820e-04\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.00073\n",
            "Epoch 95/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1930e-04 - val_loss: 7.6475e-04\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.00073\n",
            "Epoch 96/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1935e-04 - val_loss: 7.5016e-04\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.00073\n",
            "Epoch 97/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2242e-04 - val_loss: 7.3869e-04\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.00073\n",
            "Epoch 98/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1780e-04 - val_loss: 7.8313e-04\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.00073\n",
            "Epoch 99/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2225e-04 - val_loss: 7.4350e-04\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.00073\n",
            "Epoch 100/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1954e-04 - val_loss: 7.5289e-04\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.00073\n",
            "Epoch 101/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1510e-04 - val_loss: 7.3750e-04\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.00073\n",
            "Epoch 102/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1420e-04 - val_loss: 7.4058e-04\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.00073\n",
            "Epoch 103/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1285e-04 - val_loss: 7.4745e-04\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.00073\n",
            "Epoch 104/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1493e-04 - val_loss: 7.5516e-04\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.00073\n",
            "Epoch 105/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1443e-04 - val_loss: 7.9260e-04\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.00073\n",
            "Epoch 106/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1855e-04 - val_loss: 7.5445e-04\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.00073\n",
            "Epoch 107/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1360e-04 - val_loss: 7.7333e-04\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.00073\n",
            "Epoch 108/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1159e-04 - val_loss: 7.6464e-04\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.00073\n",
            "Epoch 109/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1223e-04 - val_loss: 7.6306e-04\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.00073\n",
            "Epoch 110/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0710e-04 - val_loss: 7.3408e-04\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.00073\n",
            "Epoch 111/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0611e-04 - val_loss: 7.2523e-04\n",
            "\n",
            "Epoch 00111: val_loss improved from 0.00073 to 0.00073, saving model to best_weights.h5\n",
            "Epoch 112/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0549e-04 - val_loss: 7.6101e-04\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.00073\n",
            "Epoch 113/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0205e-04 - val_loss: 7.4869e-04\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.00073\n",
            "Epoch 114/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0594e-04 - val_loss: 7.3606e-04\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.00073\n",
            "Epoch 115/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0694e-04 - val_loss: 7.3401e-04\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.00073\n",
            "Epoch 116/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0289e-04 - val_loss: 7.6070e-04\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.00073\n",
            "Epoch 117/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0223e-04 - val_loss: 7.3369e-04\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.00073\n",
            "Epoch 118/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0351e-04 - val_loss: 7.2935e-04\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.00073\n",
            "Epoch 119/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.9464e-05 - val_loss: 7.5284e-04\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.00073\n",
            "Epoch 120/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0465e-04 - val_loss: 7.5867e-04\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.00073\n",
            "Epoch 121/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0095e-04 - val_loss: 7.4649e-04\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.00073\n",
            "Epoch 122/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0040e-04 - val_loss: 7.3386e-04\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.00073\n",
            "Epoch 123/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0129e-04 - val_loss: 7.3713e-04\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.00073\n",
            "Epoch 124/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.8131e-05 - val_loss: 7.5985e-04\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.00073\n",
            "Epoch 125/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.5179e-05 - val_loss: 7.1848e-04\n",
            "\n",
            "Epoch 00125: val_loss improved from 0.00073 to 0.00072, saving model to best_weights.h5\n",
            "Epoch 126/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.8159e-05 - val_loss: 7.5056e-04\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.00072\n",
            "Epoch 127/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.7414e-05 - val_loss: 7.4520e-04\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.00072\n",
            "Epoch 128/150\n",
            "1712/1712 [==============================] - 18s 11ms/step - loss: 9.6170e-05 - val_loss: 7.7208e-04\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.00072\n",
            "Epoch 129/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.4942e-05 - val_loss: 7.6938e-04\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.00072\n",
            "Epoch 130/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.7848e-05 - val_loss: 7.3636e-04\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.00072\n",
            "Epoch 131/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.4272e-05 - val_loss: 7.3665e-04\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.00072\n",
            "Epoch 132/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.4558e-05 - val_loss: 7.4778e-04\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.00072\n",
            "Epoch 133/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3747e-05 - val_loss: 7.3463e-04\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.00072\n",
            "Epoch 134/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.2855e-05 - val_loss: 7.3428e-04\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.00072\n",
            "Epoch 135/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.4272e-05 - val_loss: 7.4801e-04\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.00072\n",
            "Epoch 136/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.5727e-05 - val_loss: 7.6938e-04\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.00072\n",
            "Epoch 137/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3930e-05 - val_loss: 7.8246e-04\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.00072\n",
            "Epoch 138/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.2306e-05 - val_loss: 7.3691e-04\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.00072\n",
            "Epoch 139/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.2232e-05 - val_loss: 7.5503e-04\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.00072\n",
            "Epoch 140/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.2277e-05 - val_loss: 7.4273e-04\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.00072\n",
            "Epoch 141/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.9950e-05 - val_loss: 7.7092e-04\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.00072\n",
            "Epoch 142/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.9713e-05 - val_loss: 7.7737e-04\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.00072\n",
            "Epoch 143/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.9746e-05 - val_loss: 7.4335e-04\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.00072\n",
            "Epoch 144/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.1187e-05 - val_loss: 7.7873e-04\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.00072\n",
            "Epoch 145/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3278e-05 - val_loss: 7.7324e-04\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.00072\n",
            "Epoch 146/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3488e-05 - val_loss: 7.4923e-04\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.00072\n",
            "Epoch 147/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.7561e-05 - val_loss: 7.5922e-04\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.00072\n",
            "Epoch 148/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.8712e-05 - val_loss: 7.4157e-04\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.00072\n",
            "Epoch 149/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.6883e-05 - val_loss: 7.5346e-04\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.00072\n",
            "Epoch 150/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.8523e-05 - val_loss: 7.8356e-04\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.00072\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIGoZ7Aii0Ef",
        "colab_type": "code",
        "outputId": "c735a309-9c21-40e6-f6e7-a628d4db97f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# loss 值的圖\n",
        "plt.title('Optimizer : Adam', fontsize=10)\n",
        "plt.ylabel('Loss', fontsize=16)\n",
        "plt.plot(hist_model.history['loss'], color='b', label='Training Loss')\n",
        "plt.plot(hist_model.history['val_loss'], color='r', label='Validation Loss')\n",
        "plt.legend(loc='upper right')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd6d6f62390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NATUCLji0Eh",
        "colab_type": "text"
      },
      "source": [
        "### 觀察 model 在 testing 上的結果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g7hrR4Ui0Ei",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 讀取測試資料集\n",
        "imgs_test, _ = load_data(dirname = 'test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERQRLjD8i0Ek",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 在灰階圖像上畫關鍵點的函數\n",
        "def plot_keypoints(img, points):\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    for i in range(0,30,2):\n",
        "        plt.scatter((points[i] + 0.5)*96, (points[i+1]+0.5)*96, color='red')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp9_9J_8i0Em",
        "colab_type": "code",
        "outputId": "fc57dad0-c76d-4686-9cf6-bb2d14308795",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "source": [
        "fig = plt.figure(figsize=(15,15))\n",
        "# 在測試集圖片上用剛剛訓練好的模型做關鍵點的預測\n",
        "points_test = model.predict(imgs_test.reshape(imgs_test.shape[0], 96, 96, 1))\n",
        "\n",
        "for i in range(16):\n",
        "    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n",
        "    plot_keypoints(imgs_test[i], np.squeeze(points_test[i]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOTZAEeGi0Eo",
        "colab_type": "text"
      },
      "source": [
        "目前為止，大致可以觀察到，直接使用簡單的模型以及訓練方式在這組數據上應該可以在訓練集和測試集上都得到一個還不錯的結果，說明這組資料其實不會很難。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VN331q9Ki0Eo",
        "colab_type": "text"
      },
      "source": [
        "### 作業\n",
        "請嘗試使用 flip (左右翻轉) 來做 augmentation 以降低人臉關鍵點檢測的 loss\n",
        "\n",
        "Note: 圖像 flip 之後，groundtruth 的關鍵點也要跟著 flip 哦\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8fgiUqvi0Ep",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_with_augment = get_model()\n",
        "model_with_augment.compile(loss='mean_squared_error', optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmx1Srp3i0Er",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your code\n",
        "\n",
        "# 實現圖片以及關機點 label 做左右翻轉的函數\n",
        "def augment_data(img, points):\n",
        "    rows, cols = img.shape\n",
        "    new_img = np.copy(img)\n",
        "    new_img = np.fliplr(new_img)\n",
        "            \n",
        "    # 翻轉關鍵點 label\n",
        "    new_points = np.copy(points)\n",
        "    for i in range(0,30,2):\n",
        "        new_points[i] = -points[i]\n",
        "    \n",
        "    # 調整對稱的 label\n",
        "    new_points_temp = np.copy(new_points)\n",
        "    new_points[0:2] = new_points_temp[2:4]\n",
        "    new_points[2:4] = new_points_temp[0:2]\n",
        "    new_points[4:6] = new_points_temp[8:10]\n",
        "    new_points[6:8] = new_points_temp[10:12]\n",
        "    new_points[8:10] = new_points_temp[4:6]\n",
        "    new_points[10:12] = new_points_temp[6:8]\n",
        "    new_points[12:14] = new_points_temp[16:18]\n",
        "    new_points[14:16] = new_points_temp[18:20]\n",
        "    new_points[16:18] = new_points_temp[12:14]\n",
        "    new_points[18:20] = new_points_temp[14:16]\n",
        "    new_points[22:24] = new_points_temp[24:26]\n",
        "    new_points[24:26] = new_points_temp[22:24]\n",
        "        \n",
        "    return new_img, new_points\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rDa5ZSWsJ-E",
        "colab_type": "code",
        "outputId": "9a4eb226-fa1c-45cc-a659-74ede9de0348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "flip_img, flip_points = augment_data(imgs_train[0], points_train[0])\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(1, 2, 1, xticks=[], yticks=[])\n",
        "plot_keypoints(imgs_train[0], points_train[0]) # 原來的圖片\n",
        "ax = fig.add_subplot(1, 2, 2, xticks=[], yticks=[])\n",
        "plot_keypoints(flip_img, flip_points) # 翻轉後的圖片"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ns4d-ToYsKGa",
        "colab_type": "code",
        "outputId": "91e1c5ab-fbb4-4ffc-91c1-2d985753d5a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# 創建 list\n",
        "aug_imgs_train = []\n",
        "aug_points_train = []\n",
        "\n",
        "# 對所有原始資料做 augmentation\n",
        "for i in range(imgs_train.shape[0]):\n",
        "    # 做左右翻轉\n",
        "    aug_img, aug_point = augment_data(imgs_train[i], points_train[i])\n",
        "    \n",
        "    # append 原始資料\n",
        "    aug_imgs_train.append(imgs_train[i])\n",
        "    aug_points_train.append(points_train[i]) \n",
        "    \n",
        "    # append 做過 augmentation 後的資料\n",
        "    aug_imgs_train.append(aug_img)\n",
        "    aug_points_train.append(aug_point) \n",
        "\n",
        "# convert to numpy\n",
        "aug_imgs_train = np.array(aug_imgs_train)   \n",
        "aug_points_train = np.copy(aug_points_train)\n",
        "\n",
        "print(aug_imgs_train.shape)\n",
        "print(aug_points_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4280, 96, 96)\n",
            "(4280, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WT4E1QYdsKM6",
        "colab_type": "code",
        "outputId": "ce6cecd5-986a-4137-eb6a-9d91e50af7c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# model checkpoint \n",
        "checkpoint_aug = ModelCheckpoint('best_weights_augment.h5', verbose=1, save_best_only=True)\n",
        "# training the model\n",
        "hist_model_aug = model_with_augment.fit(aug_imgs_train.reshape(aug_imgs_train.shape[0], 96, 96, 1), aug_points_train, \n",
        "                           validation_split=0.2, batch_size=64, callbacks=[checkpoint_aug, hist],\n",
        "                           shuffle=True, epochs=150, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3424 samples, validate on 856 samples\n",
            "Epoch 1/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 0.0056 - val_loss: 0.0020\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.00202, saving model to best_weights_augment.h5\n",
            "Epoch 2/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 0.0014 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.00202 to 0.00193, saving model to best_weights_augment.h5\n",
            "Epoch 3/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 0.0012 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.00193 to 0.00187, saving model to best_weights_augment.h5\n",
            "Epoch 4/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 0.0010 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.00187 to 0.00183, saving model to best_weights_augment.h5\n",
            "Epoch 5/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9111e-04 - val_loss: 0.0017\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.00183 to 0.00166, saving model to best_weights_augment.h5\n",
            "Epoch 6/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 7.1347e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00166 to 0.00124, saving model to best_weights_augment.h5\n",
            "Epoch 7/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 5.8494e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.00124 to 0.00115, saving model to best_weights_augment.h5\n",
            "Epoch 8/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 5.1624e-04 - val_loss: 0.0010\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00115 to 0.00101, saving model to best_weights_augment.h5\n",
            "Epoch 9/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 4.7219e-04 - val_loss: 9.5929e-04\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.00101 to 0.00096, saving model to best_weights_augment.h5\n",
            "Epoch 10/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 4.3431e-04 - val_loss: 9.6566e-04\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.00096\n",
            "Epoch 11/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 4.0471e-04 - val_loss: 8.8774e-04\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.00096 to 0.00089, saving model to best_weights_augment.h5\n",
            "Epoch 12/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.8637e-04 - val_loss: 8.6188e-04\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.00089 to 0.00086, saving model to best_weights_augment.h5\n",
            "Epoch 13/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.6239e-04 - val_loss: 7.9048e-04\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.00086 to 0.00079, saving model to best_weights_augment.h5\n",
            "Epoch 14/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.4271e-04 - val_loss: 7.8359e-04\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.00079 to 0.00078, saving model to best_weights_augment.h5\n",
            "Epoch 15/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.2844e-04 - val_loss: 7.4413e-04\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.00078 to 0.00074, saving model to best_weights_augment.h5\n",
            "Epoch 16/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.1679e-04 - val_loss: 7.8941e-04\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.00074\n",
            "Epoch 17/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.9993e-04 - val_loss: 7.5542e-04\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.00074\n",
            "Epoch 18/150\n",
            "3424/3424 [==============================] - 32s 9ms/step - loss: 2.9316e-04 - val_loss: 7.4760e-04\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.00074\n",
            "Epoch 19/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.8691e-04 - val_loss: 7.5353e-04\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.00074\n",
            "Epoch 20/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.8141e-04 - val_loss: 6.9571e-04\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.00074 to 0.00070, saving model to best_weights_augment.h5\n",
            "Epoch 21/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.7403e-04 - val_loss: 7.2424e-04\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.00070\n",
            "Epoch 22/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.6382e-04 - val_loss: 6.8022e-04\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.00070 to 0.00068, saving model to best_weights_augment.h5\n",
            "Epoch 23/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.6015e-04 - val_loss: 6.6015e-04\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.00068 to 0.00066, saving model to best_weights_augment.h5\n",
            "Epoch 24/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.4648e-04 - val_loss: 7.3454e-04\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.00066\n",
            "Epoch 25/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.4317e-04 - val_loss: 6.7063e-04\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.00066\n",
            "Epoch 26/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.3564e-04 - val_loss: 6.6297e-04\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.00066\n",
            "Epoch 27/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.3190e-04 - val_loss: 6.5178e-04\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.00066 to 0.00065, saving model to best_weights_augment.h5\n",
            "Epoch 28/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.2997e-04 - val_loss: 6.7955e-04\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.00065\n",
            "Epoch 29/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.1827e-04 - val_loss: 6.9608e-04\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.00065\n",
            "Epoch 30/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.2054e-04 - val_loss: 6.6576e-04\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.00065\n",
            "Epoch 31/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.0806e-04 - val_loss: 6.5931e-04\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.00065\n",
            "Epoch 32/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.0939e-04 - val_loss: 6.6810e-04\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.00065\n",
            "Epoch 33/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.0250e-04 - val_loss: 6.7078e-04\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.00065\n",
            "Epoch 34/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.0075e-04 - val_loss: 6.4591e-04\n",
            "\n",
            "Epoch 00034: val_loss improved from 0.00065 to 0.00065, saving model to best_weights_augment.h5\n",
            "Epoch 35/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.9364e-04 - val_loss: 6.4411e-04\n",
            "\n",
            "Epoch 00035: val_loss improved from 0.00065 to 0.00064, saving model to best_weights_augment.h5\n",
            "Epoch 36/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.9138e-04 - val_loss: 6.5142e-04\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.00064\n",
            "Epoch 37/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.8819e-04 - val_loss: 6.5842e-04\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.00064\n",
            "Epoch 38/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.8658e-04 - val_loss: 6.4465e-04\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.00064\n",
            "Epoch 39/150\n",
            "3424/3424 [==============================] - 31s 9ms/step - loss: 1.8048e-04 - val_loss: 6.6786e-04\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.00064\n",
            "Epoch 40/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.8026e-04 - val_loss: 6.4411e-04\n",
            "\n",
            "Epoch 00040: val_loss improved from 0.00064 to 0.00064, saving model to best_weights_augment.h5\n",
            "Epoch 41/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.7528e-04 - val_loss: 6.5720e-04\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.00064\n",
            "Epoch 42/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.7530e-04 - val_loss: 6.9290e-04\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.00064\n",
            "Epoch 43/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.7180e-04 - val_loss: 6.7853e-04\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.00064\n",
            "Epoch 44/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.7379e-04 - val_loss: 6.9180e-04\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.00064\n",
            "Epoch 45/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.6465e-04 - val_loss: 6.3330e-04\n",
            "\n",
            "Epoch 00045: val_loss improved from 0.00064 to 0.00063, saving model to best_weights_augment.h5\n",
            "Epoch 46/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.6331e-04 - val_loss: 6.4625e-04\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.00063\n",
            "Epoch 47/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.6232e-04 - val_loss: 6.3982e-04\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.00063\n",
            "Epoch 48/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.5886e-04 - val_loss: 6.2062e-04\n",
            "\n",
            "Epoch 00048: val_loss improved from 0.00063 to 0.00062, saving model to best_weights_augment.h5\n",
            "Epoch 49/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.5369e-04 - val_loss: 6.5011e-04\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.00062\n",
            "Epoch 50/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.5433e-04 - val_loss: 6.2392e-04\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.00062\n",
            "Epoch 51/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.5231e-04 - val_loss: 6.4133e-04\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.00062\n",
            "Epoch 52/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4812e-04 - val_loss: 6.3486e-04\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.00062\n",
            "Epoch 53/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.4919e-04 - val_loss: 6.6066e-04\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.00062\n",
            "Epoch 54/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.4792e-04 - val_loss: 6.5836e-04\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.00062\n",
            "Epoch 55/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4361e-04 - val_loss: 6.4738e-04\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.00062\n",
            "Epoch 56/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4496e-04 - val_loss: 6.7409e-04\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.00062\n",
            "Epoch 57/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4384e-04 - val_loss: 6.6950e-04\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.00062\n",
            "Epoch 58/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4630e-04 - val_loss: 6.3585e-04\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.00062\n",
            "Epoch 59/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3740e-04 - val_loss: 6.3841e-04\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.00062\n",
            "Epoch 60/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.3830e-04 - val_loss: 6.2771e-04\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.00062\n",
            "Epoch 61/150\n",
            "3424/3424 [==============================] - 31s 9ms/step - loss: 1.3592e-04 - val_loss: 6.3881e-04\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.00062\n",
            "Epoch 62/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.3602e-04 - val_loss: 6.7074e-04\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.00062\n",
            "Epoch 63/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.3321e-04 - val_loss: 6.2832e-04\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.00062\n",
            "Epoch 64/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2764e-04 - val_loss: 6.5660e-04\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.00062\n",
            "Epoch 65/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3035e-04 - val_loss: 6.5654e-04\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.00062\n",
            "Epoch 66/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2766e-04 - val_loss: 6.3541e-04\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.00062\n",
            "Epoch 67/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2962e-04 - val_loss: 6.4267e-04\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.00062\n",
            "Epoch 68/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2682e-04 - val_loss: 6.3735e-04\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.00062\n",
            "Epoch 69/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2606e-04 - val_loss: 6.3432e-04\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.00062\n",
            "Epoch 70/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2549e-04 - val_loss: 6.1384e-04\n",
            "\n",
            "Epoch 00070: val_loss improved from 0.00062 to 0.00061, saving model to best_weights_augment.h5\n",
            "Epoch 71/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2248e-04 - val_loss: 6.3613e-04\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.00061\n",
            "Epoch 72/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2076e-04 - val_loss: 6.7522e-04\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.00061\n",
            "Epoch 73/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.1945e-04 - val_loss: 6.2461e-04\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.00061\n",
            "Epoch 74/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2010e-04 - val_loss: 6.3415e-04\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.00061\n",
            "Epoch 75/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1937e-04 - val_loss: 6.1237e-04\n",
            "\n",
            "Epoch 00075: val_loss improved from 0.00061 to 0.00061, saving model to best_weights_augment.h5\n",
            "Epoch 76/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1693e-04 - val_loss: 6.4521e-04\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.00061\n",
            "Epoch 77/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1499e-04 - val_loss: 6.4386e-04\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.00061\n",
            "Epoch 78/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1481e-04 - val_loss: 6.5143e-04\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.00061\n",
            "Epoch 79/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1429e-04 - val_loss: 6.5682e-04\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.00061\n",
            "Epoch 80/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.1342e-04 - val_loss: 6.2806e-04\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.00061\n",
            "Epoch 81/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1125e-04 - val_loss: 6.6109e-04\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.00061\n",
            "Epoch 82/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1254e-04 - val_loss: 6.7969e-04\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.00061\n",
            "Epoch 83/150\n",
            "3424/3424 [==============================] - 31s 9ms/step - loss: 1.0935e-04 - val_loss: 6.3334e-04\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.00061\n",
            "Epoch 84/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1199e-04 - val_loss: 6.4098e-04\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.00061\n",
            "Epoch 85/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1090e-04 - val_loss: 6.2115e-04\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.00061\n",
            "Epoch 86/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0834e-04 - val_loss: 6.6573e-04\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.00061\n",
            "Epoch 87/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0799e-04 - val_loss: 6.5017e-04\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.00061\n",
            "Epoch 88/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1034e-04 - val_loss: 6.3109e-04\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.00061\n",
            "Epoch 89/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0621e-04 - val_loss: 6.1710e-04\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.00061\n",
            "Epoch 90/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0728e-04 - val_loss: 6.1383e-04\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.00061\n",
            "Epoch 91/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0576e-04 - val_loss: 6.5869e-04\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.00061\n",
            "Epoch 92/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.0129e-04 - val_loss: 6.2183e-04\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.00061\n",
            "Epoch 93/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0434e-04 - val_loss: 6.2692e-04\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 0.00061\n",
            "Epoch 94/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0462e-04 - val_loss: 6.1482e-04\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.00061\n",
            "Epoch 95/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0090e-04 - val_loss: 6.3728e-04\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.00061\n",
            "Epoch 96/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0163e-04 - val_loss: 6.1375e-04\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.00061\n",
            "Epoch 97/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0041e-04 - val_loss: 6.2654e-04\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.00061\n",
            "Epoch 98/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0017e-04 - val_loss: 6.4359e-04\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.00061\n",
            "Epoch 99/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0254e-04 - val_loss: 6.3063e-04\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.00061\n",
            "Epoch 100/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9842e-05 - val_loss: 6.4056e-04\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.00061\n",
            "Epoch 101/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.8313e-05 - val_loss: 6.2531e-04\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.00061\n",
            "Epoch 102/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.8399e-05 - val_loss: 6.1558e-04\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.00061\n",
            "Epoch 103/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.7032e-05 - val_loss: 6.2437e-04\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.00061\n",
            "Epoch 104/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.6870e-05 - val_loss: 6.1683e-04\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.00061\n",
            "Epoch 105/150\n",
            "3424/3424 [==============================] - 32s 9ms/step - loss: 9.8505e-05 - val_loss: 6.4662e-04\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.00061\n",
            "Epoch 106/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9124e-05 - val_loss: 6.1651e-04\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.00061\n",
            "Epoch 107/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9923e-05 - val_loss: 6.3482e-04\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.00061\n",
            "Epoch 108/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.7710e-05 - val_loss: 6.4118e-04\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.00061\n",
            "Epoch 109/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.7234e-05 - val_loss: 6.5323e-04\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.00061\n",
            "Epoch 110/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.5416e-05 - val_loss: 6.4290e-04\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.00061\n",
            "Epoch 111/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4151e-05 - val_loss: 6.3094e-04\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 0.00061\n",
            "Epoch 112/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4623e-05 - val_loss: 6.1226e-04\n",
            "\n",
            "Epoch 00112: val_loss improved from 0.00061 to 0.00061, saving model to best_weights_augment.h5\n",
            "Epoch 113/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.3752e-05 - val_loss: 6.4550e-04\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.00061\n",
            "Epoch 114/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 9.2532e-05 - val_loss: 6.5713e-04\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.00061\n",
            "Epoch 115/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 9.1280e-05 - val_loss: 6.2295e-04\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.00061\n",
            "Epoch 116/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1623e-05 - val_loss: 6.1488e-04\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.00061\n",
            "Epoch 117/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.3101e-05 - val_loss: 6.4998e-04\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.00061\n",
            "Epoch 118/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2977e-05 - val_loss: 6.3565e-04\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.00061\n",
            "Epoch 119/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4322e-05 - val_loss: 6.3669e-04\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.00061\n",
            "Epoch 120/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.6142e-05 - val_loss: 6.3238e-04\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.00061\n",
            "Epoch 121/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4176e-05 - val_loss: 6.2731e-04\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.00061\n",
            "Epoch 122/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2625e-05 - val_loss: 6.6586e-04\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.00061\n",
            "Epoch 123/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.5604e-05 - val_loss: 6.3570e-04\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.00061\n",
            "Epoch 124/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1947e-05 - val_loss: 6.3403e-04\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.00061\n",
            "Epoch 125/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1092e-05 - val_loss: 6.3677e-04\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.00061\n",
            "Epoch 126/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.0184e-05 - val_loss: 6.3971e-04\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.00061\n",
            "Epoch 127/150\n",
            "3424/3424 [==============================] - 33s 10ms/step - loss: 8.7513e-05 - val_loss: 6.3782e-04\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.00061\n",
            "Epoch 128/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6913e-05 - val_loss: 6.2958e-04\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.00061\n",
            "Epoch 129/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.0366e-05 - val_loss: 6.2966e-04\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.00061\n",
            "Epoch 130/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8430e-05 - val_loss: 6.2551e-04\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.00061\n",
            "Epoch 131/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.0495e-05 - val_loss: 6.3631e-04\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.00061\n",
            "Epoch 132/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9432e-05 - val_loss: 6.3875e-04\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.00061\n",
            "Epoch 133/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9122e-05 - val_loss: 6.2357e-04\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.00061\n",
            "Epoch 134/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8110e-05 - val_loss: 6.2839e-04\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.00061\n",
            "Epoch 135/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9161e-05 - val_loss: 6.3554e-04\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.00061\n",
            "Epoch 136/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8055e-05 - val_loss: 6.2445e-04\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.00061\n",
            "Epoch 137/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6841e-05 - val_loss: 6.5351e-04\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.00061\n",
            "Epoch 138/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7340e-05 - val_loss: 6.5009e-04\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.00061\n",
            "Epoch 139/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.5505e-05 - val_loss: 6.4507e-04\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.00061\n",
            "Epoch 140/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6955e-05 - val_loss: 6.3267e-04\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.00061\n",
            "Epoch 141/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9175e-05 - val_loss: 6.3942e-04\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.00061\n",
            "Epoch 142/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7385e-05 - val_loss: 6.4679e-04\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.00061\n",
            "Epoch 143/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7111e-05 - val_loss: 6.3865e-04\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.00061\n",
            "Epoch 144/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8087e-05 - val_loss: 6.3451e-04\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.00061\n",
            "Epoch 145/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3745e-05 - val_loss: 6.1992e-04\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.5384e-05 - val_loss: 6.2695e-04\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.00061\n",
            "Epoch 147/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3495e-05 - val_loss: 6.2991e-04\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.00061\n",
            "Epoch 148/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3104e-05 - val_loss: 6.4750e-04\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.00061\n",
            "Epoch 149/150\n",
            "3424/3424 [==============================] - 31s 9ms/step - loss: 8.3384e-05 - val_loss: 6.3144e-04\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.00061\n",
            "Epoch 150/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6410e-05 - val_loss: 6.0838e-04\n",
            "\n",
            "Epoch 00150: val_loss improved from 0.00061 to 0.00061, saving model to best_weights_augment.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cO2Nr1bsYI6",
        "colab_type": "code",
        "outputId": "b3de9781-c50a-488b-ff53-03d094f794fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# loss 值的圖\n",
        "plt.title('Optimizer : Adam', fontsize=10)\n",
        "plt.ylabel('Loss', fontsize=16)\n",
        "plt.plot(hist_model_aug.history['loss'], color='b', label='Training Loss')\n",
        "plt.plot(hist_model_aug.history['val_loss'], color='r', label='Validation Loss')\n",
        "plt.legend(loc='upper right')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd6d271d780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}