Weather_Prediction_Test_2.ipynb

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  "metadata": {
    "colab": {
      "name": "Weather Predicition Test 2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.7.6 64-bit"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.6"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNE6GFKq8vRU"
      },
      "source": [
        "# Imports\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from keras.layers import Dense, LSTM, Bidirectional, Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from scipy.ndimage import gaussian_filter1d\n",
        "from scipy.signal import medfilt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mE3wSTRR8075",
        "outputId": "3ca78517-351b-4097-cbba-23ae2fe447d4"
      },
      "source": [
        "# Load Data\n",
        "\n",
        "data = pd.read_csv(\"data/weatherHistory.csv\")\n",
        "dates = pd.DataFrame(data.iloc[:,0:1].values)\n",
        "temp_data = data.iloc[:,3:4].values #temperature column only\n",
        "temp_data\n",
        "print(type(dates))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KH6TVt6rL1Tz"
      },
      "source": [
        "# Optional Filter\n",
        "\n",
        "filter_active = 0\n",
        "if filter_active == True:\n",
        "  temp_data = medfilt(temp_data, 3)\n",
        "  temp_data = gaussian_filter1d(temp_data, 1.2)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEPp6ocn82dm"
      },
      "source": [
        "# Encode the data\n",
        "\n",
        "mms = MinMaxScaler(feature_range = (0,1))\n",
        "temp_data = mms.fit_transform(temp_data)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XrTZqQD9bHj"
      },
      "source": [
        "# Train data (OPTION 1)\n",
        "\n",
        "x_train = []\n",
        "y_train = []\n",
        "n_future = 7     #7 days temperature forecast\n",
        "n_past = 30      #Past 30 days \n",
        "\n",
        "for i in range(0,len(temp_data)-n_past-n_future+1):\n",
        "    x_train.append(temp_data[i : i + n_past , 0])     \n",
        "    y_train.append(temp_data[i + n_past : i + n_past + n_future , 0 ])\n",
        "\n",
        "x_train , y_train = np.array(x_train), np.array(y_train)\n",
        "x_train = np.reshape(x_train, (x_train.shape[0] , x_train.shape[1], 1) )\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "# Train Data (OPTION 2)\n",
        "\n",
        "# x_train, y_train = train_test_split(temp_data, \n",
        "#                                     test_size=0.25, \n",
        "#                                     random_state=0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WW9SIGA69tsC"
      },
      "source": [
        "# LSTM Model Defninition\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Bidirectional(LSTM(units=50, \n",
        "                             activation= \"relu\", \n",
        "                             return_sequences=True, \n",
        "                             input_shape = (x_train.shape[1], 1))))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units= 50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units= 50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units= 50))\n",
        "model.add(Dense(units = n_future , activation=\"relu\"))  "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ht92v0P9_E4h"
      },
      "source": [
        "# Extra Model Functionality\n",
        "\n",
        "# Model Checkpoint \n",
        "checkpoint_filepath = '/checkpoints'\n",
        "checkpoint = ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='loss',\n",
        "    mode='max',\n",
        "    save_best_only=True)\n",
        "\n",
        "\n",
        "# Early Stopping\n",
        "stopping = EarlyStopping(monitor='loss', patience=5)\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aG7TdawB-DNP",
        "outputId": "f47e0831-f5e5-44a5-8536-7e87d7446d5e"
      },
      "source": [
        "\n",
        "# Train Model\n",
        "\n",
        "model.compile(optimizer='adam', \n",
        "              loss = 'mean_squared_error', \n",
        "              metrics='accuracy')\n",
        "history = model.fit(x_train, \n",
        "          y_train, \n",
        "          epochs=10, \n",
        "          batch_size=128,\n",
        "          verbose=1,\n",
        "          callbacks=[checkpoint, stopping]\n",
        "          )\n",
        "\n",
        "# Save model\n",
        "model.save('lstm_model.h5')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "754/754 [==============================] - ETA: 0s - loss: 0.0529 - accuracy: 0.2347"
          ]
        },
        {
          "output_type": "error",
          "ename": "PermissionDeniedError",
          "evalue": "/checkpoints_temp_b8b626421f7f4330b0b3ebdd1206f3ff; Read-only file system [Op:SaveV2]",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mPermissionDeniedError\u001b[0m                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-8da317d51970>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m           \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m           \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstopping\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m           )\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1135\u001b[0m           \u001b[0mepoch_logs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1137\u001b[0;31m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1138\u001b[0m         \u001b[0mtraining_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m    410\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_supports_tf_logs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnumpy_logs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Only convert once.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m   1247\u001b[0m     \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_freq\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'epoch'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1249\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1251\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_should_save_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py\u001b[0m in \u001b[0;36m_save_model\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m   1297\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights_only\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m                 self.model.save_weights(\n\u001b[0;32m-> 1299\u001b[0;31m                     filepath, overwrite=True, options=self._options)\n\u001b[0m\u001b[1;32m   1300\u001b[0m               \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36msave_weights\u001b[0;34m(self, filepath, overwrite, save_format, options)\u001b[0m\n\u001b[1;32m   2099\u001b[0m              'saved.\\n\\nConsider using a TensorFlow optimizer from `tf.train`.')\n\u001b[1;32m   2100\u001b[0m             % (optimizer,))\n\u001b[0;32m-> 2101\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trackable_saver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2102\u001b[0m       \u001b[0;31m# Record this checkpoint so it's visible from tf.train.latest_checkpoint.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2103\u001b[0m       checkpoint_management.update_checkpoint_state_internal(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, file_prefix, checkpoint_number, session, options)\u001b[0m\n\u001b[1;32m   1198\u001b[0m     \u001b[0mfile_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecursive_create_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1199\u001b[0m     save_path, new_feed_additions = self._save_cached_when_graph_building(\n\u001b[0;32m-> 1200\u001b[0;31m         file_prefix_tensor, object_graph_tensor, options)\n\u001b[0m\u001b[1;32m   1201\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnew_feed_additions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1202\u001b[0m       \u001b[0mfeed_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_feed_additions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/tracking/util.py\u001b[0m in \u001b[0;36m_save_cached_when_graph_building\u001b[0;34m(self, file_prefix, object_graph_tensor, options)\u001b[0m\n\u001b[1;32m   1143\u001b[0m         or context.executing_eagerly() or ops.inside_function()):\n\u001b[1;32m   1144\u001b[0m       \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctional_saver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiDeviceSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnamed_saveable_objects\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1145\u001b[0;31m       \u001b[0msave_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1146\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/cpu:0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1147\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msave_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, file_prefix, options)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mtf_function_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0msave_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36msave_fn\u001b[0;34m()\u001b[0m\n\u001b[1;32m    267\u001b[0m           \u001b[0;31m# initial read operations should be placed on the SaveableObject's\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m           \u001b[0;31m# device.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m           \u001b[0msharded_saves\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshard_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msharded_saves\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/saving/functional_saver.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, file_prefix, options)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0msave_device\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_io_device\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"cpu:0\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msave_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mio_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_slices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_prefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/gen_io_ops.py\u001b[0m in \u001b[0;36msave_v2\u001b[0;34m(prefix, tensor_names, shape_and_slices, tensors, name)\u001b[0m\n\u001b[1;32m   1729\u001b[0m       return save_v2_eager_fallback(\n\u001b[1;32m   1730\u001b[0m           \u001b[0mprefix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape_and_slices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1731\u001b[0;31m           ctx=_ctx)\n\u001b[0m\u001b[1;32m   1732\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SymbolicException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1733\u001b[0m       \u001b[0;32mpass\u001b[0m  \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/gen_io_ops.py\u001b[0m in \u001b[0;36msave_v2_eager_fallback\u001b[0;34m(prefix, tensor_names, shape_and_slices, tensors, name, ctx)\u001b[0m\n\u001b[1;32m   1749\u001b[0m   \u001b[0m_attrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"dtypes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_attr_dtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1750\u001b[0m   _result = _execute.execute(b\"SaveV2\", 0, inputs=_inputs_flat, attrs=_attrs,\n\u001b[0;32m-> 1751\u001b[0;31m                              ctx=ctx, name=name)\n\u001b[0m\u001b[1;32m   1752\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1753\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mPermissionDeniedError\u001b[0m: /checkpoints_temp_b8b626421f7f4330b0b3ebdd1206f3ff; Read-only file system [Op:SaveV2]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTNquCCEVB1r"
      },
      "source": [
        "\n",
        "# Visualize Metrics\n",
        "\n",
        "# Loss\n",
        "def visualize_loss(history, title):\n",
        "    loss = history.history[\"loss\"]\n",
        "    epochs = range(len(loss))\n",
        "    plt.figure()\n",
        "    plt.plot(epochs, loss, \"ro--\", label=\"Training loss\")\n",
        "    plt.title(title)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.show()\n",
        "\n",
        "# Accuracy\n",
        "def visualize_acc(history, title):\n",
        "    acc = history.history[\"accuracy\"]\n",
        "    epochs = range(len(acc))\n",
        "    plt.figure()\n",
        "    plt.plot(epochs, acc, \"ro--\", label=\"Training loss\")\n",
        "    plt.title(title)\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "visualize_loss(history, \"Training Loss\")\n",
        "visualize_acc(history, \"Training Accuracy\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4C9_xglf-LEf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43829ef7-c9f0-4282-a515-5d5f2d2bc2ac"
      },
      "source": [
        "# Predictions and Plots\n",
        "temp_data\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.50697507],\n",
              "       [0.50508505],\n",
              "       [0.50544505],\n",
              "       ...,\n",
              "       [0.71055711],\n",
              "       [0.70218702],\n",
              "       [0.68463685]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12F7fyNdhkRD"
      },
      "source": [
        "# Prediction\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm4BMriujm8Y"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2AzlmOQjmuD"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}