homework/Day44_train_facial_keypoint_HW.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.4"
    },
    "colab": {
      "name": "Day44_train_facial_keypoint_HW.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmGVRExL46F-",
        "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": "markdown",
      "metadata": {
        "id": "L0wCnnO146GE",
        "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": "o7A9axFi46GH",
        "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": "bNyMNWX846GO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "4732817c-6531-4988-88c3-c3205cbe4aea"
      },
      "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": 4,
      "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",
            "1.15.0\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",
            "[Errno 2] No such file or directory: 'cupoy_cv_part4 # 可以自己改路徑'\n",
            "/content/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ektu-1846GT",
        "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": "yh3ZE9Wu46GX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d3eea9e5-9c17-463b-c1c6-b646c8e63896"
      },
      "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": 6,
      "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": "Us7-7bef46Gb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fa2cfd14-ad07-4cbb-c5ed-32a061afdc8b"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRDuCKYA46Gk",
        "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": "RSuYQQuz46Gr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "66e2e7e5-6b55-4e9e-da51-4155bf6c0b75"
      },
      "source": [
        "model = get_model()\n",
        "# 配置 loss funtion 和 optimizer\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFfWCb7H46Gw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "e278d528-e4fa-4e1f-8fa8-60e1686d8f9c"
      },
      "source": [
        "# 印出網路結構\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "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": "WivPomPM46G0",
        "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": "12ZfHNNB46G6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2b56acee-9d1b-437c-c17e-cb6f678cd154"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "Train on 1712 samples, validate on 428 samples\n",
            "Epoch 1/150\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "1712/1712 [==============================] - 15s 9ms/step - loss: 0.0079 - val_loss: 0.0024\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.00236, saving model to best_weights.h5\n",
            "Epoch 2/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0021 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.00236 to 0.00189, saving model to best_weights.h5\n",
            "Epoch 3/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0015 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.00189\n",
            "Epoch 4/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0013 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.00189 to 0.00187, saving model to best_weights.h5\n",
            "Epoch 5/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0012 - val_loss: 0.0019\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.00187\n",
            "Epoch 6/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 0.0011 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00187 to 0.00180, saving model to best_weights.h5\n",
            "Epoch 7/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.7597e-04 - val_loss: 0.0018\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.00180 to 0.00176, saving model to best_weights.h5\n",
            "Epoch 8/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.6944e-04 - val_loss: 0.0017\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00176 to 0.00169, saving model to best_weights.h5\n",
            "Epoch 9/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 7.5538e-04 - val_loss: 0.0015\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.00169 to 0.00147, saving model to best_weights.h5\n",
            "Epoch 10/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 6.3593e-04 - val_loss: 0.0014\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.00147 to 0.00139, saving model to best_weights.h5\n",
            "Epoch 11/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.9234e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.00139 to 0.00119, saving model to best_weights.h5\n",
            "Epoch 12/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.3806e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.00119 to 0.00116, saving model to best_weights.h5\n",
            "Epoch 13/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 5.0494e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.00116 to 0.00112, saving model to best_weights.h5\n",
            "Epoch 14/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.7417e-04 - val_loss: 0.0010\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.00112 to 0.00104, saving model to best_weights.h5\n",
            "Epoch 15/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.4926e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.00104\n",
            "Epoch 16/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.3332e-04 - val_loss: 0.0010\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.00104 to 0.00102, saving model to best_weights.h5\n",
            "Epoch 17/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 4.0634e-04 - val_loss: 9.8223e-04\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.00102 to 0.00098, saving model to best_weights.h5\n",
            "Epoch 18/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.8828e-04 - val_loss: 9.6259e-04\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.00098 to 0.00096, saving model to best_weights.h5\n",
            "Epoch 19/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.7313e-04 - val_loss: 9.4259e-04\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.00096 to 0.00094, saving model to best_weights.h5\n",
            "Epoch 20/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.6003e-04 - val_loss: 9.2082e-04\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.00094 to 0.00092, saving model to best_weights.h5\n",
            "Epoch 21/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.4035e-04 - val_loss: 9.1748e-04\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.00092 to 0.00092, saving model to best_weights.h5\n",
            "Epoch 22/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.3072e-04 - val_loss: 9.3596e-04\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.00092\n",
            "Epoch 23/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.1870e-04 - val_loss: 9.1035e-04\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.00092 to 0.00091, saving model to best_weights.h5\n",
            "Epoch 24/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.1707e-04 - val_loss: 9.0737e-04\n",
            "\n",
            "Epoch 00024: val_loss improved from 0.00091 to 0.00091, saving model to best_weights.h5\n",
            "Epoch 25/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 3.0425e-04 - val_loss: 9.1646e-04\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.00091\n",
            "Epoch 26/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.9398e-04 - val_loss: 9.2409e-04\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.00091\n",
            "Epoch 27/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.9010e-04 - val_loss: 8.7628e-04\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.00091 to 0.00088, saving model to best_weights.h5\n",
            "Epoch 28/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.7569e-04 - val_loss: 8.6192e-04\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.00088 to 0.00086, saving model to best_weights.h5\n",
            "Epoch 29/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.7040e-04 - val_loss: 8.7367e-04\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.00086\n",
            "Epoch 30/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.6335e-04 - val_loss: 8.6782e-04\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.00086\n",
            "Epoch 31/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.6556e-04 - val_loss: 8.7617e-04\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.00086\n",
            "Epoch 32/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.5072e-04 - val_loss: 8.8057e-04\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.00086\n",
            "Epoch 33/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.4956e-04 - val_loss: 8.5869e-04\n",
            "\n",
            "Epoch 00033: val_loss improved from 0.00086 to 0.00086, saving model to best_weights.h5\n",
            "Epoch 34/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.4919e-04 - val_loss: 8.5521e-04\n",
            "\n",
            "Epoch 00034: val_loss improved from 0.00086 to 0.00086, saving model to best_weights.h5\n",
            "Epoch 35/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.4015e-04 - val_loss: 9.1453e-04\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.00086\n",
            "Epoch 36/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.3779e-04 - val_loss: 9.0287e-04\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.00086\n",
            "Epoch 37/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2950e-04 - val_loss: 8.4602e-04\n",
            "\n",
            "Epoch 00037: val_loss improved from 0.00086 to 0.00085, saving model to best_weights.h5\n",
            "Epoch 38/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.3455e-04 - val_loss: 9.3184e-04\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.00085\n",
            "Epoch 39/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2632e-04 - val_loss: 8.4740e-04\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.00085\n",
            "Epoch 40/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.2459e-04 - val_loss: 8.5284e-04\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.00085\n",
            "Epoch 41/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.1448e-04 - val_loss: 8.5223e-04\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.00085\n",
            "Epoch 42/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.1119e-04 - val_loss: 8.6441e-04\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.00085\n",
            "Epoch 43/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.1313e-04 - val_loss: 8.3018e-04\n",
            "\n",
            "Epoch 00043: val_loss improved from 0.00085 to 0.00083, saving model to best_weights.h5\n",
            "Epoch 44/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0697e-04 - val_loss: 8.3172e-04\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.00083\n",
            "Epoch 45/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0850e-04 - val_loss: 8.3366e-04\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.00083\n",
            "Epoch 46/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0668e-04 - val_loss: 8.2714e-04\n",
            "\n",
            "Epoch 00046: val_loss improved from 0.00083 to 0.00083, saving model to best_weights.h5\n",
            "Epoch 47/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0231e-04 - val_loss: 8.1986e-04\n",
            "\n",
            "Epoch 00047: val_loss improved from 0.00083 to 0.00082, saving model to best_weights.h5\n",
            "Epoch 48/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 2.0222e-04 - val_loss: 8.2354e-04\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.00082\n",
            "Epoch 49/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9624e-04 - val_loss: 8.2647e-04\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.00082\n",
            "Epoch 50/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9306e-04 - val_loss: 8.0908e-04\n",
            "\n",
            "Epoch 00050: val_loss improved from 0.00082 to 0.00081, saving model to best_weights.h5\n",
            "Epoch 51/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9272e-04 - val_loss: 8.0785e-04\n",
            "\n",
            "Epoch 00051: val_loss improved from 0.00081 to 0.00081, saving model to best_weights.h5\n",
            "Epoch 52/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.9010e-04 - val_loss: 8.3076e-04\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.00081\n",
            "Epoch 53/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8655e-04 - val_loss: 8.1797e-04\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.00081\n",
            "Epoch 54/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8630e-04 - val_loss: 8.1163e-04\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.00081\n",
            "Epoch 55/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8762e-04 - val_loss: 8.0435e-04\n",
            "\n",
            "Epoch 00055: val_loss improved from 0.00081 to 0.00080, saving model to best_weights.h5\n",
            "Epoch 56/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7429e-04 - val_loss: 8.0426e-04\n",
            "\n",
            "Epoch 00056: val_loss improved from 0.00080 to 0.00080, saving model to best_weights.h5\n",
            "Epoch 57/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.8233e-04 - val_loss: 8.2469e-04\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.00080\n",
            "Epoch 58/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7391e-04 - val_loss: 8.2360e-04\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.00080\n",
            "Epoch 59/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7933e-04 - val_loss: 7.8791e-04\n",
            "\n",
            "Epoch 00059: val_loss improved from 0.00080 to 0.00079, saving model to best_weights.h5\n",
            "Epoch 60/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7511e-04 - val_loss: 8.0822e-04\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.00079\n",
            "Epoch 61/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7096e-04 - val_loss: 8.4642e-04\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.00079\n",
            "Epoch 62/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6766e-04 - val_loss: 8.2266e-04\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.00079\n",
            "Epoch 63/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.7016e-04 - val_loss: 8.0663e-04\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.00079\n",
            "Epoch 64/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6802e-04 - val_loss: 8.4131e-04\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.00079\n",
            "Epoch 65/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6566e-04 - val_loss: 8.1658e-04\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.00079\n",
            "Epoch 66/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5890e-04 - val_loss: 8.5906e-04\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.00079\n",
            "Epoch 67/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.6060e-04 - val_loss: 8.5354e-04\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.00079\n",
            "Epoch 68/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5949e-04 - val_loss: 8.5035e-04\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.00079\n",
            "Epoch 69/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5752e-04 - val_loss: 8.0969e-04\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.00079\n",
            "Epoch 70/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5804e-04 - val_loss: 8.3225e-04\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.00079\n",
            "Epoch 71/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5349e-04 - val_loss: 8.3756e-04\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.00079\n",
            "Epoch 72/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5409e-04 - val_loss: 9.2604e-04\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.00079\n",
            "Epoch 73/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.5992e-04 - val_loss: 8.2326e-04\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.00079\n",
            "Epoch 74/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4991e-04 - val_loss: 8.5127e-04\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.00079\n",
            "Epoch 75/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4435e-04 - val_loss: 8.1862e-04\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.00079\n",
            "Epoch 76/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4429e-04 - val_loss: 8.1715e-04\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.00079\n",
            "Epoch 77/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4446e-04 - val_loss: 8.7229e-04\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.00079\n",
            "Epoch 78/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4756e-04 - val_loss: 8.1594e-04\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.00079\n",
            "Epoch 79/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4303e-04 - val_loss: 7.7705e-04\n",
            "\n",
            "Epoch 00079: val_loss improved from 0.00079 to 0.00078, saving model to best_weights.h5\n",
            "Epoch 80/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.4181e-04 - val_loss: 7.8321e-04\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.00078\n",
            "Epoch 81/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3918e-04 - val_loss: 8.1236e-04\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.00078\n",
            "Epoch 82/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3878e-04 - val_loss: 8.0510e-04\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.00078\n",
            "Epoch 83/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3455e-04 - val_loss: 7.8710e-04\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.00078\n",
            "Epoch 84/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3509e-04 - val_loss: 7.8060e-04\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.00078\n",
            "Epoch 85/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3078e-04 - val_loss: 8.1460e-04\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.00078\n",
            "Epoch 86/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3061e-04 - val_loss: 7.6682e-04\n",
            "\n",
            "Epoch 00086: val_loss improved from 0.00078 to 0.00077, saving model to best_weights.h5\n",
            "Epoch 87/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3206e-04 - val_loss: 8.1203e-04\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.00077\n",
            "Epoch 88/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2899e-04 - val_loss: 8.0152e-04\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.00077\n",
            "Epoch 89/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.3065e-04 - val_loss: 8.1740e-04\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.00077\n",
            "Epoch 90/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2655e-04 - val_loss: 8.0493e-04\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.00077\n",
            "Epoch 91/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2716e-04 - val_loss: 8.2024e-04\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.00077\n",
            "Epoch 92/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2396e-04 - val_loss: 7.9367e-04\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.00077\n",
            "Epoch 93/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2972e-04 - val_loss: 8.2587e-04\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 0.00077\n",
            "Epoch 94/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2819e-04 - val_loss: 7.9190e-04\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.00077\n",
            "Epoch 95/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2428e-04 - val_loss: 8.2041e-04\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.00077\n",
            "Epoch 96/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2159e-04 - val_loss: 7.9378e-04\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.00077\n",
            "Epoch 97/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2445e-04 - val_loss: 8.1293e-04\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.00077\n",
            "Epoch 98/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2274e-04 - val_loss: 8.2000e-04\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.00077\n",
            "Epoch 99/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.2012e-04 - val_loss: 7.9313e-04\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.00077\n",
            "Epoch 100/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1592e-04 - val_loss: 8.2856e-04\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.00077\n",
            "Epoch 101/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1869e-04 - val_loss: 8.0626e-04\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.00077\n",
            "Epoch 102/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1746e-04 - val_loss: 8.3194e-04\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.00077\n",
            "Epoch 103/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1873e-04 - val_loss: 8.3375e-04\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.00077\n",
            "Epoch 104/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1754e-04 - val_loss: 7.9364e-04\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.00077\n",
            "Epoch 105/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1297e-04 - val_loss: 8.1635e-04\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.00077\n",
            "Epoch 106/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1137e-04 - val_loss: 8.0135e-04\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.00077\n",
            "Epoch 107/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1239e-04 - val_loss: 7.9605e-04\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.00077\n",
            "Epoch 108/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1167e-04 - val_loss: 8.3129e-04\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.00077\n",
            "Epoch 109/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1406e-04 - val_loss: 8.1267e-04\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.00077\n",
            "Epoch 110/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1215e-04 - val_loss: 7.8969e-04\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.00077\n",
            "Epoch 111/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.1182e-04 - val_loss: 8.2160e-04\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 0.00077\n",
            "Epoch 112/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0845e-04 - val_loss: 8.0656e-04\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.00077\n",
            "Epoch 113/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0810e-04 - val_loss: 8.2274e-04\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.00077\n",
            "Epoch 114/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0759e-04 - val_loss: 8.0730e-04\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.00077\n",
            "Epoch 115/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0911e-04 - val_loss: 8.2810e-04\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.00077\n",
            "Epoch 116/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0527e-04 - val_loss: 8.1779e-04\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.00077\n",
            "Epoch 117/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0443e-04 - val_loss: 8.2247e-04\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.00077\n",
            "Epoch 118/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0686e-04 - val_loss: 8.2763e-04\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.00077\n",
            "Epoch 119/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0673e-04 - val_loss: 8.4378e-04\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.00077\n",
            "Epoch 120/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0362e-04 - val_loss: 8.0872e-04\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.00077\n",
            "Epoch 121/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0324e-04 - val_loss: 8.1673e-04\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.00077\n",
            "Epoch 122/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0647e-04 - val_loss: 8.3263e-04\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.00077\n",
            "Epoch 123/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0497e-04 - val_loss: 7.9623e-04\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.00077\n",
            "Epoch 124/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0196e-04 - val_loss: 8.0634e-04\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.00077\n",
            "Epoch 125/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.9679e-05 - val_loss: 8.2072e-04\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.00077\n",
            "Epoch 126/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 1.0474e-04 - val_loss: 8.0312e-04\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.00077\n",
            "Epoch 127/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.9483e-05 - val_loss: 8.1370e-04\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.00077\n",
            "Epoch 128/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.7659e-05 - val_loss: 8.2138e-04\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.00077\n",
            "Epoch 129/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.6062e-05 - val_loss: 7.8320e-04\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.00077\n",
            "Epoch 130/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.5442e-05 - val_loss: 7.8342e-04\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.00077\n",
            "Epoch 131/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3951e-05 - val_loss: 8.3209e-04\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.00077\n",
            "Epoch 132/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.5922e-05 - val_loss: 8.2552e-04\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.00077\n",
            "Epoch 133/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.8190e-05 - val_loss: 8.1265e-04\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.00077\n",
            "Epoch 134/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3236e-05 - val_loss: 7.9546e-04\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.00077\n",
            "Epoch 135/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.6640e-05 - val_loss: 8.0089e-04\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.00077\n",
            "Epoch 136/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.6742e-05 - val_loss: 8.1987e-04\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.00077\n",
            "Epoch 137/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.5527e-05 - val_loss: 8.1080e-04\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.00077\n",
            "Epoch 138/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.1945e-05 - val_loss: 8.0242e-04\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.00077\n",
            "Epoch 139/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3864e-05 - val_loss: 8.1742e-04\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.00077\n",
            "Epoch 140/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.2009e-05 - val_loss: 8.0489e-04\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.00077\n",
            "Epoch 141/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.3246e-05 - val_loss: 7.9872e-04\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.00077\n",
            "Epoch 142/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.1934e-05 - val_loss: 7.9503e-04\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.00077\n",
            "Epoch 143/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.9784e-05 - val_loss: 8.2005e-04\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.00077\n",
            "Epoch 144/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.0116e-05 - val_loss: 7.8781e-04\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.00077\n",
            "Epoch 145/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.0808e-05 - val_loss: 8.0064e-04\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.00077\n",
            "Epoch 146/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.8439e-05 - val_loss: 7.7548e-04\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.00077\n",
            "Epoch 147/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.0778e-05 - val_loss: 7.8487e-04\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.00077\n",
            "Epoch 148/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.9276e-05 - val_loss: 7.9722e-04\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.00077\n",
            "Epoch 149/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 8.8024e-05 - val_loss: 8.2750e-04\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.00077\n",
            "Epoch 150/150\n",
            "1712/1712 [==============================] - 14s 8ms/step - loss: 9.0345e-05 - val_loss: 7.9494e-04\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.00077\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08JQAXMF46G-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "20996a20-77af-486a-8091-843a5f489d48"
      },
      "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": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa4d7545d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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88knKy8uZOXMmOTk5dOvWLWm387uyZMkS7rjjDmbMmEH79u25+OKL01pPTKz7eghd2Kdy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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CK1EDrqD46HE",
        "colab_type": "text"
      },
      "source": [
        "### 觀察 model 在 testing 上的結果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cw4Ez9z46HF",
        "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": "NxB01SwH46HJ",
        "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": "XSN0ZnUi46HQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "cd47ddb6-fff7-48e7-ef45-3e570860e64a"
      },
      "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": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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alpPvR5N0VqtVz3WKrM/q1asBwHMVOnHihGP7VP6cLILKoxcKBde+Nm/e3FA/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mdLNoD7iaJ7Z7TpWN4DUuOlSHOBMBxZmuAWcb5XIZ09PTaYHlolytVt0DiPZPu+EZdciy/xNW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fi8d8e5LpWh1/rH97CSdLvdLMbUU/nkcUnHrFJsK9/n2rVrqX2pCtkkQeNZeZ7G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w0iqsyoL5fD4S26A69eqLm8vlIpZdPfHymr6EcnpSJ/T9XC7njZuybaH5SqxlOJTrRdWo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\nQPrXVauwqbzhI24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1U+6Rl16KT+7cOTUJtLfj3nXr8PqyZZiLMJqNipLWMWS952/GxsYi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XBYfSmazWbSL3dcUC6XszpLqf5onXn+xqVSyWWt1Opl+42OL/YftieZqWKxmGOXPFhLnFrriRQb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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xC1SfVPe46HU",
        "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": "code",
      "metadata": {
        "id": "TG9ReEBz46HV",
        "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": "3TZGctMW46HZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your code"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfZRVvEzCGxW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 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",
        "    \n",
        "    # 翻轉圖片\n",
        "    for i in range(96):\n",
        "        for j in range(48):\n",
        "            temp = img[i][j]\n",
        "            new_img[i][j] = img[i][cols-j-1]\n",
        "            new_img[i][cols-j-1] = temp\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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uk_7c9vrCR7z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "5e9aa09c-a2db-452c-ac9a-e131c270afb8"
      },
      "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": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qKaNSrIFw9zYNwrQH4oh9CURJUfaTdGLXtO/MkS4NTHwPW6G2n8vlomCHxzMy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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWrX_yDpCWiA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "236475b4-2ff3-466c-a3c6-9c75eabf19b1"
      },
      "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": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4280, 96, 96)\n",
            "(4280, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1BcKQ7tChAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d61d22cb-7aeb-4d11-926b-1fef2113821e"
      },
      "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": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3424 samples, validate on 856 samples\n",
            "Epoch 1/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 0.0051 - val_loss: 0.0020\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.00198, 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 0.00198 to 0.00191, 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 0.00191 to 0.00190, 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 0.00190 to 0.00184, saving model to best_weights_augment.h5\n",
            "Epoch 5/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9540e-04 - val_loss: 0.0016\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.00184 to 0.00157, saving model to best_weights_augment.h5\n",
            "Epoch 6/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 6.9580e-04 - val_loss: 0.0012\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.00157 to 0.00121, saving model to best_weights_augment.h5\n",
            "Epoch 7/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 5.8321e-04 - val_loss: 0.0011\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.00121 to 0.00109, saving model to best_weights_augment.h5\n",
            "Epoch 8/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 5.0052e-04 - val_loss: 9.4425e-04\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.00109 to 0.00094, saving model to best_weights_augment.h5\n",
            "Epoch 9/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 4.5722e-04 - val_loss: 9.3929e-04\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.00094 to 0.00094, saving model to best_weights_augment.h5\n",
            "Epoch 10/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 4.2304e-04 - val_loss: 8.3864e-04\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.00094 to 0.00084, saving model to best_weights_augment.h5\n",
            "Epoch 11/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 3.9660e-04 - val_loss: 8.5899e-04\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.00084\n",
            "Epoch 12/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 3.7001e-04 - val_loss: 7.9371e-04\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.00084 to 0.00079, saving model to best_weights_augment.h5\n",
            "Epoch 13/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 3.4847e-04 - val_loss: 8.1953e-04\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.00079\n",
            "Epoch 14/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 3.3181e-04 - val_loss: 8.1408e-04\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.00079\n",
            "Epoch 15/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 3.2092e-04 - val_loss: 7.6987e-04\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.00079 to 0.00077, saving model to best_weights_augment.h5\n",
            "Epoch 16/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 3.0265e-04 - val_loss: 7.5843e-04\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.00077 to 0.00076, saving model to best_weights_augment.h5\n",
            "Epoch 17/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.9724e-04 - val_loss: 7.6003e-04\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.00076\n",
            "Epoch 18/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.8373e-04 - val_loss: 7.0104e-04\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.00076 to 0.00070, saving model to best_weights_augment.h5\n",
            "Epoch 19/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.7352e-04 - val_loss: 7.4652e-04\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.00070\n",
            "Epoch 20/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.5986e-04 - val_loss: 6.9441e-04\n",
            "\n",
            "Epoch 00020: val_loss improved from 0.00070 to 0.00069, saving model to best_weights_augment.h5\n",
            "Epoch 21/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.5581e-04 - val_loss: 6.8592e-04\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.00069 to 0.00069, saving model to best_weights_augment.h5\n",
            "Epoch 22/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.4919e-04 - val_loss: 6.8642e-04\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.00069\n",
            "Epoch 23/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.3977e-04 - val_loss: 6.5609e-04\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.00069 to 0.00066, saving model to best_weights_augment.h5\n",
            "Epoch 24/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.3475e-04 - val_loss: 6.8998e-04\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.00066\n",
            "Epoch 25/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.3130e-04 - val_loss: 6.5796e-04\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.00066\n",
            "Epoch 26/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.2714e-04 - val_loss: 6.9355e-04\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.00066\n",
            "Epoch 27/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.2071e-04 - val_loss: 6.7041e-04\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.00066\n",
            "Epoch 28/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.1648e-04 - val_loss: 6.2330e-04\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.00066 to 0.00062, saving model to best_weights_augment.h5\n",
            "Epoch 29/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.0738e-04 - val_loss: 6.3454e-04\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.00062\n",
            "Epoch 30/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 2.0830e-04 - val_loss: 6.3129e-04\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.00062\n",
            "Epoch 31/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 2.0334e-04 - val_loss: 6.5494e-04\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.00062\n",
            "Epoch 32/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.9959e-04 - val_loss: 6.3621e-04\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.00062\n",
            "Epoch 33/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.9363e-04 - val_loss: 6.4616e-04\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.00062\n",
            "Epoch 34/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.8892e-04 - val_loss: 5.9135e-04\n",
            "\n",
            "Epoch 00034: val_loss improved from 0.00062 to 0.00059, saving model to best_weights_augment.h5\n",
            "Epoch 35/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.8808e-04 - val_loss: 6.1714e-04\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.00059\n",
            "Epoch 36/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.8401e-04 - val_loss: 6.4455e-04\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.00059\n",
            "Epoch 37/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.8210e-04 - val_loss: 6.1515e-04\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.00059\n",
            "Epoch 38/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.7729e-04 - val_loss: 6.1059e-04\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.00059\n",
            "Epoch 39/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.7273e-04 - val_loss: 6.2853e-04\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.00059\n",
            "Epoch 40/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.6867e-04 - val_loss: 6.3210e-04\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.00059\n",
            "Epoch 41/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.6830e-04 - val_loss: 6.1289e-04\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.00059\n",
            "Epoch 42/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.6730e-04 - val_loss: 6.4304e-04\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.00059\n",
            "Epoch 43/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.6410e-04 - val_loss: 5.8238e-04\n",
            "\n",
            "Epoch 00043: val_loss improved from 0.00059 to 0.00058, saving model to best_weights_augment.h5\n",
            "Epoch 44/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.6316e-04 - val_loss: 5.9469e-04\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.00058\n",
            "Epoch 45/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.5938e-04 - val_loss: 6.1044e-04\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.00058\n",
            "Epoch 46/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.6011e-04 - val_loss: 6.1357e-04\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.00058\n",
            "Epoch 47/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.5728e-04 - val_loss: 6.0615e-04\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.00058\n",
            "Epoch 48/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.5423e-04 - val_loss: 6.2223e-04\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.00058\n",
            "Epoch 49/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.5070e-04 - val_loss: 6.0042e-04\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.00058\n",
            "Epoch 50/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.5265e-04 - val_loss: 6.0775e-04\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.00058\n",
            "Epoch 51/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4637e-04 - val_loss: 6.2390e-04\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.00058\n",
            "Epoch 52/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.4459e-04 - val_loss: 6.1835e-04\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.00058\n",
            "Epoch 53/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.4578e-04 - val_loss: 5.9433e-04\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.00058\n",
            "Epoch 54/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.4318e-04 - val_loss: 5.7175e-04\n",
            "\n",
            "Epoch 00054: val_loss improved from 0.00058 to 0.00057, saving model to best_weights_augment.h5\n",
            "Epoch 55/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.3920e-04 - val_loss: 6.0987e-04\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.00057\n",
            "Epoch 56/150\n",
            "3424/3424 [==============================] - 27s 8ms/step - loss: 1.3713e-04 - val_loss: 6.4555e-04\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.00057\n",
            "Epoch 57/150\n",
            "3424/3424 [==============================] - 29s 8ms/step - loss: 1.3706e-04 - val_loss: 6.0770e-04\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.00057\n",
            "Epoch 58/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3671e-04 - val_loss: 5.7146e-04\n",
            "\n",
            "Epoch 00058: val_loss improved from 0.00057 to 0.00057, saving model to best_weights_augment.h5\n",
            "Epoch 59/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3168e-04 - val_loss: 5.9792e-04\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 0.00057\n",
            "Epoch 60/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3217e-04 - val_loss: 6.3436e-04\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.00057\n",
            "Epoch 61/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2796e-04 - val_loss: 5.8777e-04\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.00057\n",
            "Epoch 62/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.3026e-04 - val_loss: 6.1394e-04\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.00057\n",
            "Epoch 63/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2500e-04 - val_loss: 5.8977e-04\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.00057\n",
            "Epoch 64/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2581e-04 - val_loss: 6.2377e-04\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.00057\n",
            "Epoch 65/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2557e-04 - val_loss: 6.0008e-04\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.00057\n",
            "Epoch 66/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2238e-04 - val_loss: 6.0883e-04\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.00057\n",
            "Epoch 67/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.2189e-04 - val_loss: 5.8711e-04\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.00057\n",
            "Epoch 68/150\n",
            "3424/3424 [==============================] - 29s 8ms/step - loss: 1.1739e-04 - val_loss: 6.0339e-04\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.00057\n",
            "Epoch 69/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1854e-04 - val_loss: 6.0917e-04\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.00057\n",
            "Epoch 70/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1878e-04 - val_loss: 5.8051e-04\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.00057\n",
            "Epoch 71/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1743e-04 - val_loss: 6.2332e-04\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.00057\n",
            "Epoch 72/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1772e-04 - val_loss: 5.6902e-04\n",
            "\n",
            "Epoch 00072: val_loss improved from 0.00057 to 0.00057, saving model to best_weights_augment.h5\n",
            "Epoch 73/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1537e-04 - val_loss: 5.8694e-04\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.00057\n",
            "Epoch 74/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1656e-04 - val_loss: 6.1097e-04\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.00057\n",
            "Epoch 75/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1602e-04 - val_loss: 5.9971e-04\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.00057\n",
            "Epoch 76/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1264e-04 - val_loss: 6.0731e-04\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.00057\n",
            "Epoch 77/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0992e-04 - val_loss: 5.8860e-04\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.00057\n",
            "Epoch 78/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.1058e-04 - val_loss: 6.0316e-04\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.00057\n",
            "Epoch 79/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0983e-04 - val_loss: 5.8545e-04\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.00057\n",
            "Epoch 80/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0938e-04 - val_loss: 6.0221e-04\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.00057\n",
            "Epoch 81/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0802e-04 - val_loss: 5.9991e-04\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.00057\n",
            "Epoch 82/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0986e-04 - val_loss: 5.9062e-04\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.00057\n",
            "Epoch 83/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0651e-04 - val_loss: 6.0045e-04\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.00057\n",
            "Epoch 84/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0770e-04 - val_loss: 5.8456e-04\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.00057\n",
            "Epoch 85/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0681e-04 - val_loss: 6.1040e-04\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.00057\n",
            "Epoch 86/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0336e-04 - val_loss: 6.0495e-04\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.00057\n",
            "Epoch 87/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0441e-04 - val_loss: 6.0163e-04\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.00057\n",
            "Epoch 88/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0292e-04 - val_loss: 6.1475e-04\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.00057\n",
            "Epoch 89/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0398e-04 - val_loss: 6.2715e-04\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.00057\n",
            "Epoch 90/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0238e-04 - val_loss: 5.8783e-04\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.00057\n",
            "Epoch 91/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0241e-04 - val_loss: 6.0026e-04\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.00057\n",
            "Epoch 92/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0286e-04 - val_loss: 6.5806e-04\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.00057\n",
            "Epoch 93/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0364e-04 - val_loss: 6.1864e-04\n",
            "\n",
            "Epoch 00093: val_loss did not improve from 0.00057\n",
            "Epoch 94/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 1.0167e-04 - val_loss: 6.1904e-04\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.00057\n",
            "Epoch 95/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.8789e-05 - val_loss: 6.0668e-04\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.00057\n",
            "Epoch 96/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9692e-05 - val_loss: 5.9573e-04\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.00057\n",
            "Epoch 97/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9110e-05 - val_loss: 6.0477e-04\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.00057\n",
            "Epoch 98/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.8638e-05 - val_loss: 6.3315e-04\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.00057\n",
            "Epoch 99/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.9438e-05 - val_loss: 6.3792e-04\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.00057\n",
            "Epoch 100/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.7409e-05 - val_loss: 6.1009e-04\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.00057\n",
            "Epoch 101/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.8408e-05 - val_loss: 6.0462e-04\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.00057\n",
            "Epoch 102/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.6816e-05 - val_loss: 5.9593e-04\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.00057\n",
            "Epoch 103/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.3840e-05 - val_loss: 6.3016e-04\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.00057\n",
            "Epoch 104/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.6197e-05 - val_loss: 6.1048e-04\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.00057\n",
            "Epoch 105/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2811e-05 - val_loss: 5.9671e-04\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.00057\n",
            "Epoch 106/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4187e-05 - val_loss: 6.1609e-04\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.00057\n",
            "Epoch 107/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.5124e-05 - val_loss: 6.2052e-04\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.00057\n",
            "Epoch 108/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.4014e-05 - val_loss: 6.0071e-04\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.00057\n",
            "Epoch 109/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2428e-05 - val_loss: 6.0204e-04\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.00057\n",
            "Epoch 110/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.3641e-05 - val_loss: 6.3775e-04\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.00057\n",
            "Epoch 111/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.3686e-05 - val_loss: 5.8465e-04\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 0.00057\n",
            "Epoch 112/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1787e-05 - val_loss: 5.9347e-04\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.00057\n",
            "Epoch 113/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2866e-05 - val_loss: 6.2078e-04\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.00057\n",
            "Epoch 114/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1170e-05 - val_loss: 6.1877e-04\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.00057\n",
            "Epoch 115/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9244e-05 - val_loss: 6.0390e-04\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.00057\n",
            "Epoch 116/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.2927e-05 - val_loss: 6.1106e-04\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.00057\n",
            "Epoch 117/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9757e-05 - val_loss: 5.8800e-04\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.00057\n",
            "Epoch 118/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8566e-05 - val_loss: 6.0114e-04\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.00057\n",
            "Epoch 119/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8616e-05 - val_loss: 6.3823e-04\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.00057\n",
            "Epoch 120/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8368e-05 - val_loss: 6.1707e-04\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.00057\n",
            "Epoch 121/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.1191e-05 - val_loss: 6.3678e-04\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.00057\n",
            "Epoch 122/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 9.0376e-05 - val_loss: 5.9967e-04\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.00057\n",
            "Epoch 123/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7873e-05 - val_loss: 6.1486e-04\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.00057\n",
            "Epoch 124/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7615e-05 - val_loss: 6.0145e-04\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.00057\n",
            "Epoch 125/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.9327e-05 - val_loss: 6.2742e-04\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.00057\n",
            "Epoch 126/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8338e-05 - val_loss: 6.1445e-04\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.00057\n",
            "Epoch 127/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.8635e-05 - val_loss: 5.9586e-04\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.00057\n",
            "Epoch 128/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6316e-05 - val_loss: 6.1744e-04\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.00057\n",
            "Epoch 129/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6478e-05 - val_loss: 6.2505e-04\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.00057\n",
            "Epoch 130/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6564e-05 - val_loss: 6.1900e-04\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.00057\n",
            "Epoch 131/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.4696e-05 - val_loss: 6.0780e-04\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.00057\n",
            "Epoch 132/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6988e-05 - val_loss: 6.0663e-04\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.00057\n",
            "Epoch 133/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7089e-05 - val_loss: 5.9895e-04\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.00057\n",
            "Epoch 134/150\n",
            "3424/3424 [==============================] - 29s 9ms/step - loss: 8.5033e-05 - val_loss: 6.0092e-04\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.00057\n",
            "Epoch 135/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6350e-05 - val_loss: 6.0433e-04\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.00057\n",
            "Epoch 136/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.4830e-05 - val_loss: 5.9618e-04\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.00057\n",
            "Epoch 137/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3577e-05 - val_loss: 6.1557e-04\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.00057\n",
            "Epoch 138/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.7533e-05 - val_loss: 5.9373e-04\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.00057\n",
            "Epoch 139/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3269e-05 - val_loss: 6.3203e-04\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.00057\n",
            "Epoch 140/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3541e-05 - val_loss: 6.2364e-04\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.00057\n",
            "Epoch 141/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3633e-05 - val_loss: 6.2131e-04\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.00057\n",
            "Epoch 142/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.5316e-05 - val_loss: 6.0197e-04\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.00057\n",
            "Epoch 143/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.6235e-05 - val_loss: 6.1450e-04\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.00057\n",
            "Epoch 144/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.4342e-05 - val_loss: 6.1750e-04\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.00057\n",
            "Epoch 145/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3726e-05 - val_loss: 6.1103e-04\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.00057\n",
            "Epoch 146/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.3631e-05 - val_loss: 6.2420e-04\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.00057\n",
            "Epoch 147/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.2509e-05 - val_loss: 6.2004e-04\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.00057\n",
            "Epoch 148/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.1987e-05 - val_loss: 6.1966e-04\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.00057\n",
            "Epoch 149/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.1897e-05 - val_loss: 6.0793e-04\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.00057\n",
            "Epoch 150/150\n",
            "3424/3424 [==============================] - 28s 8ms/step - loss: 8.1846e-05 - val_loss: 6.1501e-04\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.00057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5EFZdWeSsLc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7842ad23-a516-422d-8145-10421379183a"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fa4d7611358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IFBTAV1+Vm5GTE25EUclERJoIBZNaGDoUioth8eJyM5o3h8MPVzARkSZDwaQW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\n1hRMRESk1hRMRESk1hRMRESk1v4/3ekJzk+uzmgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}