Google_Stock_Price_Prediction.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Google_Stock_Price_Prediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkzC-9nRKujn",
        "colab_type": "code",
        "outputId": "79d71f56-22ca-4d92-f1b0-542741bf58c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTgbPmR3JytA",
        "colab_type": "text"
      },
      "source": [
        "# Simple Stock Price Prediction with LSTM in Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JKBrv6PCNcia",
        "colab_type": "text"
      },
      "source": [
        "## 1. Training Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uthEx6AaO6d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the dependencies\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbDPUn0ILBEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading the training dataset\n",
        "\n",
        "dataset_train = pd.read_csv('/content/gdrive/My Drive/deeplearing/documents/week5/Google_Stock_Price_Train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WD_LNWGtPKfZ",
        "colab_type": "code",
        "outputId": "30be8a8d-bd57-4bec-b4b5-e0cb72bf707c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "# Showing the first 3 rows of dataset\n",
        "\n",
        "dataset_train.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1/3/2012</td>\n",
              "      <td>325.25</td>\n",
              "      <td>332.83</td>\n",
              "      <td>324.97</td>\n",
              "      <td>663.59</td>\n",
              "      <td>7,380,500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/4/2012</td>\n",
              "      <td>331.27</td>\n",
              "      <td>333.87</td>\n",
              "      <td>329.08</td>\n",
              "      <td>666.45</td>\n",
              "      <td>5,749,400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/5/2012</td>\n",
              "      <td>329.83</td>\n",
              "      <td>330.75</td>\n",
              "      <td>326.89</td>\n",
              "      <td>657.21</td>\n",
              "      <td>6,590,300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Date    Open    High     Low   Close     Volume\n",
              "0  1/3/2012  325.25  332.83  324.97  663.59  7,380,500\n",
              "1  1/4/2012  331.27  333.87  329.08  666.45  5,749,400\n",
              "2  1/5/2012  329.83  330.75  326.89  657.21  6,590,300"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9sxJJXOuDqL",
        "colab_type": "code",
        "outputId": "8f717b66-a133-435b-95f5-284cb836046f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "# Checking variables info\n",
        "\n",
        "dataset_train.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1258 entries, 0 to 1257\n",
            "Data columns (total 6 columns):\n",
            "Date      1258 non-null object\n",
            "Open      1258 non-null float64\n",
            "High      1258 non-null float64\n",
            "Low       1258 non-null float64\n",
            "Close     1258 non-null object\n",
            "Volume    1258 non-null object\n",
            "dtypes: float64(3), object(3)\n",
            "memory usage: 59.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv1Me3v9ub4E",
        "colab_type": "code",
        "outputId": "fa072166-2f3c-4b3b-f795-3e6907ed4721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Slicing the training set \n",
        "\"\"\"\n",
        "If we use dataset_train.iloc[:, 1].values instead, it will give us 1 dimensional array. \n",
        "\n",
        "     df = dataset_train.iloc[:, 1].values\n",
        "     print(\"Dimensions of df:{} & shape:{}\".format( df.ndim, df.shape ))\n",
        "\n",
        "O/P: Dimensions of df:1 & shape:(1258,)\n",
        "\n",
        "But next scalling function expects the object to be as a matrix of rows and columns.\n",
        "\n",
        "\"\"\"\n",
        "training_set = dataset_train.iloc[:, 1:2].values\n",
        "\n",
        "print(\"Dimensions of training_set:{} & shape:{}\".format( training_set.ndim, training_set.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of training_set:2 & shape:(1258, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgk6u3SBLFvD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalizing the training set(scalling in range of 0 to 1)\n",
        "\n",
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = sc.fit_transform(training_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3A8zPUML0mz",
        "colab_type": "code",
        "outputId": "2d43e940-12cf-4ca7-8aff-31266e4c224b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Constructing the training set\n",
        "\n",
        "X_train = []\n",
        "y_train = []\n",
        "\n",
        "# Define the the time step size\n",
        "time_step = 60\n",
        "for i in range(time_step, training_set_scaled.shape[0]):\n",
        "    X_train.append(training_set_scaled[i-time_step:i, 0])\n",
        "    y_train.append(training_set_scaled[i, 0])\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "\n",
        "print(\"Dimensions of X_train:{} & shape:{}\".format( X_train.ndim, X_train.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of X_train:2 & shape:(1198, 60)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VScTfTybpqV",
        "colab_type": "code",
        "outputId": "4abe402d-1689-4f92-fb7f-f174730f0c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Adding 1 more dimension to X_train as LSTM layers expects it`s inputs to be of 3 dimensions\n",
        "\"\"\"\n",
        "3 Dimensions are:\n",
        "  1st : Sample of inputs\n",
        "  2nd : Time steps (here we have taken 60 timesteps in single consideration)\n",
        "  3rd : number of features (here it`s 1 as we are dealing with only 'Open' column)\n",
        "\n",
        "\"\"\"\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
        "\n",
        "print(\"Dimensions of X_train:{} & shape:{}\".format( X_train.ndim, X_train.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of X_train:3 & shape:(1198, 60, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIZvHrxtiBzq",
        "colab_type": "text"
      },
      "source": [
        "##2. Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRQ4iYpYL4YD",
        "colab_type": "code",
        "outputId": "1a9a4f6b-09cc-4535-eb2d-cc1aa03c33a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Importing the model dependencies\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_mn7QC8L7ZD",
        "colab_type": "code",
        "outputId": "004bb937-a634-451a-eceb-eca348613888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "# Initializing the sequential model of Keras\n",
        "\n",
        "regressor = Sequential()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0723 16:18:02.259157 139832105531264 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpzgR5S8L-6D",
        "colab_type": "code",
        "outputId": "c782e28c-4ccd-4813-9c34-5b8f90743e66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "# Adding 1st LSTM layer & dropout layer where input shape details(Time step & number of features) has to be mentioned\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0723 16:18:02.305189 139832105531264 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0723 16:18:02.315176 139832105531264 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0723 16:18:02.579873 139832105531264 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "W0723 16:18:02.594267 139832105531264 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: 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"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yIl8zreMCQD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding 2nd LSTM layer & dropout layer\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_-M_8CKMffC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding 3rd LSTM layer & dropout layer\n",
        "\n",
        "regressor.add(LSTM(units = 50, return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7E3MhmbiMiBD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding 4th LSTM layer & dropout layer\n",
        "\n",
        "regressor.add(LSTM(units = 50))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihv64TvNMjbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding final output layer\n",
        "\n",
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Et-K5cVTeUAw",
        "colab_type": "code",
        "outputId": "9770de3e-d2ae-44f3-ec68-4005fe0b3d53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "# Compiling the sequential model\n",
        "\n",
        "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0723 16:18:03.461133 139832105531264 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EdEwKxLnrAX",
        "colab_type": "code",
        "outputId": "5c92d802-6337-4880-8a2c-4310d49c8c3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "# Showing the model details\n",
        "\n",
        "print(regressor.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 60, 50)            10400     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 60, 50)            20200     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 60, 50)            20200     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "lstm_4 (LSTM)                (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jedN0W2MeRpR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Saving the image of model structure\n",
        "\n",
        "from keras.utils import plot_model\n",
        "plot_model(regressor, to_file='LSTM_model_Google_stock.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PP1_0sykd3fH",
        "colab_type": "text"
      },
      "source": [
        "##3. Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0N_VfnnMoLj",
        "colab_type": "code",
        "outputId": "74751acb-3cb4-4a90-f632-b55aafb198ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Fitting the model with training data\n",
        "\n",
        "fitting_metadata = regressor.fit(X_train, y_train, epochs =100 , batch_size = 32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 2/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 3/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0015 - acc: 0.0017\n",
            "Epoch 4/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 5/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0015 - acc: 0.0017\n",
            "Epoch 6/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0017 - acc: 0.0017\n",
            "Epoch 7/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0016 - acc: 0.0017\n",
            "Epoch 8/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 9/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 10/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 11/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 12/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0015 - acc: 0.0017\n",
            "Epoch 13/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 14/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 15/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 16/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 17/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 18/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 19/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 20/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 21/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 22/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 23/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 24/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 25/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 26/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 27/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 28/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0015 - acc: 0.0017\n",
            "Epoch 29/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 30/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 31/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 32/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 33/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 34/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 35/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 36/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 37/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 38/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 39/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 40/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 41/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 42/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 43/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 44/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 45/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 46/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 47/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 48/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 49/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 50/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 51/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 52/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 53/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 54/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0014 - acc: 0.0017\n",
            "Epoch 55/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 56/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 57/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 58/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 59/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 60/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 61/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 62/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 63/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 64/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 65/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 66/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 67/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.5101e-04 - acc: 0.0017\n",
            "Epoch 68/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 69/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 70/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 71/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 72/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 73/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 74/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 75/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.5974e-04 - acc: 0.0017\n",
            "Epoch 76/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 77/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 78/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 79/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.9672e-04 - acc: 0.0017\n",
            "Epoch 80/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.5123e-04 - acc: 0.0017\n",
            "Epoch 81/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 82/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.2775e-04 - acc: 0.0017\n",
            "Epoch 83/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.7146e-04 - acc: 0.0017\n",
            "Epoch 84/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.4057e-04 - acc: 0.0017\n",
            "Epoch 85/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 86/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 87/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 88/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 89/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 90/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 91/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0012 - acc: 0.0017\n",
            "Epoch 92/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0013 - acc: 0.0017\n",
            "Epoch 93/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 94/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 95/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.3967e-04 - acc: 0.0017\n",
            "Epoch 96/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 97/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0011 - acc: 0.0017\n",
            "Epoch 98/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 99/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0010 - acc: 0.0017\n",
            "Epoch 100/100\n",
            "1198/1198 [==============================] - 7s 6ms/step - loss: 9.6318e-04 - acc: 0.0017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUlxDjEBWxxd",
        "colab_type": "code",
        "outputId": "27e83612-eeec-4357-97ca-c12d6ff7b513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Summarizing the history for accuracy\n",
        "\n",
        "plt.plot(fitting_metadata.history['acc'])\n",
        "#plt.plot(fitting_metadata.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NeLCP/d0OHA8gaTowGnhyL20V6Z+ZmQ2i+lo1HBHdki4A7gDqgNaIWCPpcqA9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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSo-Ef14Wyod",
        "colab_type": "code",
        "outputId": "7b069098-fa43-472f-9cc2-ceb37adfc427",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Summarize the history for loss\n",
        "\n",
        "plt.plot(fitting_metadata.history['loss'])\n",
        "#plt.plot(fitting_metadata.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NbWBWiJhdB7OwmzJoBaPRaBoPbWBWiFjSCvIXVzA6yK/RaBoNbWBWCCvIX9iuP7TGg/yD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\nxhhjAmEBxhhjTCD+H7C2Hampg7OAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBKY7SsZe52H",
        "colab_type": "text"
      },
      "source": [
        "## 4. Test Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvo7qyGaMqR0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading the test dataset \n",
        "\n",
        "dataset_test = pd.read_csv('/content/gdrive/My Drive/deeplearing/documents/week5/Google_Stock_Price_Test.csv')\n",
        "real_stock_price = dataset_test.iloc[:, 1:2].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRmkQ4SfYLX0",
        "colab_type": "code",
        "outputId": "dede4241-0a54-4e22-cbeb-749a33ee87ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Slicing the test set\n",
        "\n",
        "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
        "test_set = dataset_total[len(dataset_total) - len(dataset_test) - time_step:].values\n",
        "print(\"Dimensions of test_set:{} & shape:{}\".format( test_set.ndim, test_set.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of test_set:1 & shape:(80,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfVC_CYHg2QA",
        "colab_type": "code",
        "outputId": "c73626b7-7adb-4d0c-c2a5-06249e81e8de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Reshaping the test data\n",
        "'''\n",
        "As our scalling function expects the object to be as a matrix of rows and columns\n",
        "\n",
        "'''\n",
        "\n",
        "test_set = test_set.reshape(-1,1)\n",
        "print(\"Dimensions of test_set:{} & shape:{}\".format( test_set.ndim, test_set.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of test_set:2 & shape:(80, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pJPBI5_ciuh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalizing the test set\n",
        "\n",
        "test_set_scalled = sc.transform(test_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLn9XRd0jOO3",
        "colab_type": "code",
        "outputId": "9ef1a1b2-2072-40ea-d9aa-b9c1120c9d93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Constructing the test set\n",
        "\n",
        "X_test = []\n",
        "for i in range(time_step, test_set.shape[0]):\n",
        "    X_test.append(test_set_scalled[i-time_step:i, 0])\n",
        "X_test = np.array(X_test)\n",
        "\n",
        "print(\"Dimensions of X_test:{} & shape:{}\".format( X_test.ndim, X_test.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of X_test:2 & shape:(20, 60)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjxxEmyKj3jT",
        "colab_type": "code",
        "outputId": "13fc8a0b-8d22-41f5-cd3a-cc4020d3eb22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Making X_test 3 dimensional to be expected as input of LSTM layers\n",
        "\n",
        "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "print(\"Dimensions of X_test:{} & shape:{}\".format( X_test.ndim, X_test.shape ))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dimensions of X_test:3 & shape:(20, 60, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h924bFzRliCA",
        "colab_type": "text"
      },
      "source": [
        "## 5. Predicting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaymblnZYsOu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting with test data\n",
        "\n",
        "predicted_stock_price = regressor.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDXU0UvamdSg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reverse scalling of predicted data\n",
        "\n",
        "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzmZcyagYUcF",
        "colab_type": "code",
        "outputId": "fb715855-2089-44f6-c78a-6435c6da9a71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Plotting the predicted data\n",
        "\n",
        "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
        "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
        "plt.title('Google Stock Price Prediction')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Google Stock Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JxKvqTAARGQX8BdQGUjySFYGt3rmJQDxQLn2/InKniCwSkUVxcXGZje3fi3E4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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G09akLznRhv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}