LSTM.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNam4RsVVkS/b4D0c+8GhcJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RachitBansal/Power-Forecasting/blob/master/LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEhTYtONLxDZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "outputId": "6e872c18-a9e5-45fc-8b0a-b5ad66a7a512"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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 /gdrive\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AkO0qR6Lyh7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TXsKXshNMDV5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "items = os.listdir(\"My Drive/Greend\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i1rHqjCMEFr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "src_path = \"My Drive/Greend/\"\n",
        "dest_path = \"My Drive/Greend/LSTM_results/extra/\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rS9q4sjXMS2_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.externals import joblib\n",
        "from keras.layers import Dense ,LSTM\n",
        "from keras.models import Sequential \n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from math import sqrt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import RepeatVector\n",
        "from keras.layers import TimeDistributed\n",
        "from keras.layers.convolutional import Conv1D\n",
        "from keras.layers.convolutional import MaxPooling1D\n",
        "from keras.layers import Flatten\n",
        "from sklearn.metrics import r2_score as r2Conversations\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import keras.backend as K\n",
        "import os\n",
        "import argparse\n",
        "from keras.models import load_model\n",
        "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
        "\n",
        "        #parser = argparse.ArgumentParser(description=\"Run LSTM\")\n",
        "        #parser.add_argument('--num_equips', type='int')\n",
        "        #parser.add_argument('--train', type='bool')\n",
        "        #parser.add_argument('--src_dir')\n",
        "        #parser.add_argument('--tgt_dir')\n",
        "        #parser.add_argument('--n_test', type='int')\n",
        "        #parser.add_argument('--n_models', type = 'int')\n",
        "        #args = parser.parse_args()\n",
        "        #run_LSTM(args.num_equips, args.n_test, args.n_models, args.src_dir, args.tgt_dir, args.train)\n",
        "                                  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyzSCq0-PKN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "earlystop = EarlyStopping(monitor = \"val_loss\",patience = 5,restore_best_weights=True,mode = \"auto\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kSfy-5aMktV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fbd99a43-2402-4fc2-bd53-025547700bad"
      },
      "source": [
        "class LSTM_pipeline:\n",
        "    def __init__(self,equipment,n_members,src_path,trg_path):\n",
        "        self.eq = equipment\n",
        "        self.n_members = int(n_members)\n",
        "        self.s_path = src_path\n",
        "        self.t_path = trg_path\n",
        "        \n",
        "    def fit_lstm(self,x_train,y_train):\n",
        "        regressor = Sequential()\n",
        "        regressor.add(LSTM(units = 8,activation = \"relu\",input_shape= (128,1)))\n",
        "        regressor.add(Dense(units = 1))\n",
        "        regressor.compile(optimizer = \"adam\",loss = \"mean_squared_error\",metrics =[\"accuracy\"])\n",
        "        regressor.fit(x_train,y_train,epochs =10,batch_size = 128,validation_split = 0.1,callbacks = [earlystop])\n",
        "        return regressor\n",
        "\n",
        "    def load_data(self):\n",
        "        self.train_x = np.load(file=self.s_path+'x_greend_eq'+str(self.eq-1)+'.npy')/300\n",
        "        self.train_y = np.load(file=self.s_path+'y_greend_eq'+str(self.eq-1)+'.npy')/300\n",
        "        test_x = np.load(file=self.s_path+'xt_greend_eq'+str(self.eq-1)+'.npy')/300\n",
        "        test_y = np.load(file=self.s_path+'yt_greend_eq'+str(self.eq-1)+'.npy')/300\n",
        "        val_split = int(0.2*test_x.shape[0])\n",
        "        self.train_x = self.train_x.reshape(-1,128,1)\n",
        "        self.train_y  = self.train_y.reshape(-1,1)\n",
        "        self.val_x = test_x[-val_split:].reshape(-1,128,1)\n",
        "        self.val_y = test_y[-val_split:].reshape(-1,1)\n",
        "        self.test_x = test_x[:-val_split].reshape(-1,128,1)\n",
        "        self.test_y = test_y[:-val_split].reshape(-1,1)\n",
        "        print(self.val_x.shape, self.val_y.shape, self.test_x.shape, self.test_y.shape)\n",
        "        \n",
        "    def load_all_models(self):\n",
        "        all_models = list()\n",
        "        for i in range(self.n_members):\n",
        "            filename = self.t_path + str(self.eq-1) + '_model_' + str(i + 1) + '.h5'\n",
        "            model = load_model(filename)\n",
        "            all_models.append(model)\n",
        "            print('>>> loaded %s' % filename)\n",
        "        return all_models\n",
        "        \n",
        "    def metricss(self, n_test):\n",
        "        preds = self.preds\n",
        "        test_y = self.test_y[:n_test].reshape(-1)\n",
        "        error = mean_squared_error(test_y, preds)\n",
        "        print('Test MSE: %.3f' % error)\n",
        "        print(\"RMSE : %.3f\"%(np.sqrt(error)))\n",
        "        print(\"MAE : %.3f\"%(mean_absolute_error(test_y,preds)))\n",
        "      \n",
        "    def predictions(self, n_test, idx=0):\n",
        "        self.preds = None     \n",
        "        predicted = None\n",
        "        temp_x = self.test_x[:n_test]\n",
        "        temp_x = temp_x.reshape(-1,128,1)\n",
        "        predicted =  self.members[0].predict(temp_x)\n",
        "        predicted = predicted\n",
        "        self.preds = predicted.reshape(-1)\n",
        "        self.metricss(n_test)\n",
        "\n",
        "    def make_lstm_comparison_graph(self, n_test, idx=0):\n",
        "        self.predictions(n_test, idx)\n",
        "        temp1 = self.test_y[idx:idx+n_test].reshape(-1)\n",
        "        plt.plot(temp1, c = 'yellow', label='actual')\n",
        "        plt.plot(self.preds, c = 'red', label='LSTM')\n",
        "        plt.ylabel('Value')\n",
        "        plt.xlabel('Index of Time Series Data')\n",
        "        plt.legend()\n",
        "        plt.savefig(self.t_path + str(self.eq-1) + '_graph_' + '.png')\n",
        "        plt.show()\n",
        "\n",
        "    def pipeline(self, n_test, train=True):\n",
        "        self.load_data()\n",
        "        if(train):\n",
        "            for i in range(self.n_members):\n",
        "                print(self.train_x.shape)\n",
        "                model = self.fit_lstm(self.train_x, self.train_y)\n",
        "                filename = self.t_path + str(self.eq-1) + '_model_' + str(i + 1) + '.h5'\n",
        "                model.save(filename)\n",
        "                print('>>> Saved %s' % filename)\n",
        "\n",
        "        self.members = self.load_all_models()\n",
        "        print('Loaded %d models' % len(self.members))\n",
        "        idx = 0\n",
        "        self.make_lstm_comparison_graph(n_test, idx) \n",
        "        \n",
        "     \n",
        "''' hello world'''\n",
        "def run_LSTM(num_equips, n_test, n_models, src_dir, tgt_dir, train = True):\n",
        "    d = {}\n",
        "    for i in range(num_equips):\n",
        "        d[\"obj{0}\".format(i)] = LSTM_pipeline(i+1, n_models, src_dir, tgt_dir)\n",
        "        d[\"obj{0}\".format(i)].pipeline(n_test, train)\n",
        "    \n",
        "run_LSTM(9, 100, 1, src_path,dest_path, False)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 71s 789us/step - loss: 38053.1469 - acc: 0.1179 - val_loss: 39329.9602 - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: 37891.2605 - acc: 1.1058e-04 - val_loss: 38339.8168 - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: 37308.7226 - acc: 0.0000e+00 - val_loss: 37831.7559 - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 56s 623us/step - loss: 36609.7397 - acc: 0.0000e+00 - val_loss: 36977.9429 - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 56s 624us/step - loss: 4383135.3449 - acc: 5.8608e-04 - val_loss: 30898.4502 - val_acc: 9.9512e-05\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 56s 623us/step - loss: 29505.7076 - acc: 0.0000e+00 - val_loss: 30429.0173 - val_acc: 0.0000e+00\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 56s 622us/step - loss: 29234.7301 - acc: 3.3174e-05 - val_loss: 30064.7417 - val_acc: 0.0000e+00\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 57s 629us/step - loss: 29020.0083 - acc: 3.3174e-05 - val_loss: 30048.8636 - val_acc: 9.9512e-05\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: 29023.2806 - acc: 1.1058e-04 - val_loss: 29640.9878 - val_acc: 9.9512e-05\n",
            "Epoch 10/10\n",
            "90432/90432 [==============================] - 57s 633us/step - loss: 28496.3757 - acc: 1.6587e-04 - val_loss: 28921.5048 - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/0_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/0_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 28243.343\n",
            "RMSE : 168.058\n",
            "MAE : 107.339\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i7CMTU7CVQhZzKqQJiueNOMHveu2jNErBRwpBxJyEFMKe2fxJoRlJxy2IoErX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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 73s 808us/step - loss: 2792.7428 - acc: 0.0029 - val_loss: 352.3759 - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 58s 637us/step - loss: 93.2035 - acc: 1.1058e-05 - val_loss: 38.3537 - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 58s 636us/step - loss: 27.2784 - acc: 1.1058e-05 - val_loss: 23.5005 - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: 21.2782 - acc: 2.2116e-05 - val_loss: 22.0387 - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 57s 629us/step - loss: 20.4761 - acc: 0.0012 - val_loss: 20.2481 - val_acc: 0.0046\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 57s 627us/step - loss: 19.9723 - acc: 2.1010e-04 - val_loss: 22.0480 - val_acc: 0.0000e+00\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 60s 658us/step - loss: 19.7570 - acc: 4.6444e-04 - val_loss: 19.3396 - val_acc: 0.0000e+00\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 60s 659us/step - loss: 19.5741 - acc: 0.0000e+00 - val_loss: 18.7821 - val_acc: 0.0000e+00\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 59s 651us/step - loss: 20.1242 - acc: 6.6348e-05 - val_loss: 22.4912 - val_acc: 1.9902e-04\n",
            "Epoch 10/10\n",
            "90432/90432 [==============================] - 59s 655us/step - loss: 20.0252 - acc: 1.5481e-04 - val_loss: 19.3035 - val_acc: 1.9902e-04\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/1_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/1_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 28.450\n",
            "RMSE : 5.334\n",
            "MAE : 2.081\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Fy10nIClkRGGQ23Y07DCTajsVhNy8pxBrmuUaTTUkvgHgRSEpNAlSPnyUEYWOwkfh6KTa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\nPB5PBi8KHo/H48ngRcHj8Xg8Gf4/W/VrgwcZb7gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 71s 785us/step - loss: 42044.3592 - acc: 0.2888 - val_loss: 41444.8894 - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 57s 629us/step - loss: 41890.1197 - acc: 0.0000e+00 - val_loss: 41409.3284 - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 626us/step - loss: 41855.2786 - acc: 0.0000e+00 - val_loss: 41375.0247 - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 57s 625us/step - loss: 41821.5523 - acc: 0.0000e+00 - val_loss: 41341.1999 - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 56s 619us/step - loss: 24351.0758 - acc: 4.4232e-05 - val_loss: 20075.6744 - val_acc: 0.0000e+00\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 56s 622us/step - loss: 20618.9842 - acc: 0.0000e+00 - val_loss: 19523.5043 - val_acc: 0.0000e+00\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 56s 623us/step - loss: 20309.6731 - acc: 0.0000e+00 - val_loss: 19456.9117 - val_acc: 0.0000e+00\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 57s 627us/step - loss: 20256.2635 - acc: 0.0000e+00 - val_loss: 19410.7391 - val_acc: 0.0000e+00\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: 20221.8939 - acc: 0.0000e+00 - val_loss: 19432.9719 - val_acc: 0.0000e+00\n",
            "Epoch 10/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: 20299.1584 - acc: 0.0000e+00 - val_loss: 19668.6673 - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/2_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/2_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 9769.573\n",
            "RMSE : 98.841\n",
            "MAE : 24.242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sp7CQzBQWIiVPFz+SzBQWIiWvAeo8TCospB0KC5GSp7CQzBQWIiVPFz+SzLIOCzPrl8+K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\nJzqo7zhgVauy1whnzo4jnCk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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 73s 808us/step - loss: nan - acc: 8.4041e-04 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 57s 634us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 57s 631us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 57s 633us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/3_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/3_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 18237.646\n",
            "RMSE : 135.047\n",
            "MAE : 26.246\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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odWD00JYZVxbx+dMhMcCtCjFLCgvJl4HwazYZFjC+5/YBlUVLdDIdqb54q6cD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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 73s 809us/step - loss: 1553545.4320 - acc: 0.0096 - val_loss: 83306.9688 - val_acc: 0.0102\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 58s 637us/step - loss: 84240.8302 - acc: 0.0098 - val_loss: 83483.0298 - val_acc: 0.0107\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 58s 640us/step - loss: 64091.0154 - acc: 0.0035 - val_loss: 57526.1992 - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 58s 640us/step - loss: 46228.4023 - acc: 0.0000e+00 - val_loss: 47306.4772 - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 58s 642us/step - loss: nan - acc: 1.1058e-05 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 58s 644us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 58s 636us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 58s 637us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 57s 628us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/4_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/4_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 41547.421\n",
            "RMSE : 203.832\n",
            "MAE : 44.747\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n1w8Ang4dO+rLqhCRc0VkvYis37p1a1x29xcTBSjW67MIYt5d6FlMEYvcNB3c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\n6NwGEwvDMIzu46CDYO5cuO4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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 74s 822us/step - loss: nan - acc: 7.6300e-04 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 58s 644us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 633us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 57s 629us/step - loss: nan - acc: 0.0000e+00 - val_loss: nan - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/5_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/5_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 17.708\n",
            "RMSE : 4.208\n",
            "MAE : 2.059\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8UW7OCRwPfBA4WUQeMn9vBb4FnCYizwCnmuXwUk/hz5hKskTSI/xpcidUUwq/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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 74s 818us/step - loss: 674.9859 - acc: 0.8024 - val_loss: 702.9962 - val_acc: 0.8430\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: 541.5685 - acc: 0.8470 - val_loss: 687.8856 - val_acc: 0.8376\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 58s 641us/step - loss: 521.2325 - acc: 0.4799 - val_loss: 682.4898 - val_acc: 0.0534\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 58s 645us/step - loss: 509.3692 - acc: 0.0621 - val_loss: 661.1629 - val_acc: 0.0632\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 58s 639us/step - loss: 516.7601 - acc: 0.0642 - val_loss: 684.0552 - val_acc: 0.0516\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 59s 652us/step - loss: 514.2359 - acc: 0.0545 - val_loss: 682.5680 - val_acc: 0.0518\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 58s 645us/step - loss: 513.4578 - acc: 0.0465 - val_loss: 681.5086 - val_acc: 0.0477\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 58s 645us/step - loss: 513.3951 - acc: 0.0479 - val_loss: 681.6394 - val_acc: 0.0541\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 58s 640us/step - loss: 513.4100 - acc: 0.0467 - val_loss: 681.3492 - val_acc: 0.0613\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/6_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/6_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 1.155\n",
            "RMSE : 1.075\n",
            "MAE : 0.799\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v6XjhN4+/Y0z4oxQx4g/EPZrVE/5uOjT+Sy/4DY/fQrZcMi7i99dAPcNO9cZ5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\nBRqSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 74s 822us/step - loss: 1055.6736 - acc: 0.0053 - val_loss: 1029.4054 - val_acc: 0.0000e+00\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 58s 639us/step - loss: 1022.5308 - acc: 0.0000e+00 - val_loss: 1029.8626 - val_acc: 0.0000e+00\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 635us/step - loss: 1019.9703 - acc: 0.0000e+00 - val_loss: 1029.5659 - val_acc: 0.0000e+00\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 57s 629us/step - loss: 1019.2807 - acc: 0.0000e+00 - val_loss: 1028.9491 - val_acc: 0.0000e+00\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 56s 621us/step - loss: 1018.8198 - acc: 0.0000e+00 - val_loss: 1028.4130 - val_acc: 0.0000e+00\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 57s 633us/step - loss: 1018.4218 - acc: 0.0000e+00 - val_loss: 1028.3314 - val_acc: 0.0000e+00\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: 1018.2578 - acc: 0.0000e+00 - val_loss: 1027.9014 - val_acc: 0.0000e+00\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 57s 635us/step - loss: 1017.7613 - acc: 0.0000e+00 - val_loss: 1027.3794 - val_acc: 0.0000e+00\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 57s 633us/step - loss: 1017.1461 - acc: 0.0000e+00 - val_loss: 1026.7185 - val_acc: 0.0000e+00\n",
            "Epoch 10/10\n",
            "90432/90432 [==============================] - 57s 627us/step - loss: 1016.6335 - acc: 0.0000e+00 - val_loss: 1025.9144 - val_acc: 0.0000e+00\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/7_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/7_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 431.255\n",
            "RMSE : 20.767\n",
            "MAE : 6.070\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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63H47PPJIWFazZsHMmeHgeGtr+OddvjzMzzPPhPKfdFLopk8P/7jl5WGjNwtd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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(8612, 128, 1) (8612, 1) (34452, 128, 1) (34452, 1)\n",
            "(100481, 128, 1)\n",
            "Train on 90432 samples, validate on 10049 samples\n",
            "Epoch 1/10\n",
            "90432/90432 [==============================] - 76s 839us/step - loss: 1.4608 - acc: 0.9870 - val_loss: 3.6698 - val_acc: 0.9867\n",
            "Epoch 2/10\n",
            "90432/90432 [==============================] - 57s 626us/step - loss: 752.6268 - acc: 0.9874 - val_loss: 3.6684 - val_acc: 0.9866\n",
            "Epoch 3/10\n",
            "90432/90432 [==============================] - 57s 632us/step - loss: 1.4575 - acc: 0.9875 - val_loss: 3.6676 - val_acc: 0.9866\n",
            "Epoch 4/10\n",
            "90432/90432 [==============================] - 56s 622us/step - loss: 1.4573 - acc: 0.9875 - val_loss: 3.6670 - val_acc: 0.9867\n",
            "Epoch 5/10\n",
            "90432/90432 [==============================] - 56s 623us/step - loss: 1.4572 - acc: 0.9875 - val_loss: 3.6668 - val_acc: 0.9868\n",
            "Epoch 6/10\n",
            "90432/90432 [==============================] - 57s 630us/step - loss: 1.4571 - acc: 0.9876 - val_loss: 3.6665 - val_acc: 0.9868\n",
            "Epoch 7/10\n",
            "90432/90432 [==============================] - 56s 624us/step - loss: 1.4571 - acc: 0.9877 - val_loss: 3.6662 - val_acc: 0.9868\n",
            "Epoch 8/10\n",
            "90432/90432 [==============================] - 57s 625us/step - loss: 1.4571 - acc: 0.9877 - val_loss: 3.6665 - val_acc: 0.9869\n",
            "Epoch 9/10\n",
            "90432/90432 [==============================] - 56s 624us/step - loss: 1.4571 - acc: 0.9877 - val_loss: 3.6664 - val_acc: 0.9869\n",
            "Epoch 10/10\n",
            "90432/90432 [==============================] - 56s 620us/step - loss: 1.4570 - acc: 0.9877 - val_loss: 3.6662 - val_acc: 0.9869\n",
            ">>> Saved My Drive/Greend/LSTM_results/extra/8_model_1.h5\n",
            ">>> loaded My Drive/Greend/LSTM_results/extra/8_model_1.h5\n",
            "Loaded 1 models\n",
            "Test MSE: 6.968\n",
            "RMSE : 2.640\n",
            "MAE : 0.430\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jAk4EbkwXKeM+7w2cQHJfFSJie0T8gZK/1yS3L9gzvQvlJOApSvheR8Qakvvk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\nzKwmB4SZmdXkgDAzs5r+P3ZrgxbBIFvyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iojL_FQiNH8A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}