google_stock.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of google_stock_1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bE8w9Lwxb3OM",
        "colab_type": "code",
        "outputId": "11ff0981-67d0-4e70-b70f-bb32ac35d1c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "import warnings\n",
        "import tensorflow as tf\n",
        "from keras_utils import SGDwithLR,AdamwithClip,RMSpropwithClip\n",
        "from keras import models\n",
        "from keras_focused import SimpleFocusedRNN\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import sequence\n",
        "from keras import layers\n",
        "from keras.layers import Flatten,SimpleRNN\n",
        "import numpy as np\n",
        "import numpy\n",
        "from keras import backend as K\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The name for Record Variable has changed, use RecordWeights or RecordTensor instead\n",
            "Not working!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPMK5xXGcCNv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "dataset_train = pd.read_csv(\"trainset.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71sWjoXitgwD",
        "colab_type": "code",
        "outputId": "c74c57ab-5b48-47b5-acdf-d61d519b3698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "dataset_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>357.385559</td>\n",
              "      <td>361.151062</td>\n",
              "      <td>355.959839</td>\n",
              "      <td>359.288177</td>\n",
              "      <td>359.288177</td>\n",
              "      <td>5115500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013-01-03</td>\n",
              "      <td>360.122742</td>\n",
              "      <td>363.600128</td>\n",
              "      <td>358.031342</td>\n",
              "      <td>359.496826</td>\n",
              "      <td>359.496826</td>\n",
              "      <td>4666500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-01-04</td>\n",
              "      <td>362.313507</td>\n",
              "      <td>368.339294</td>\n",
              "      <td>361.488861</td>\n",
              "      <td>366.600616</td>\n",
              "      <td>366.600616</td>\n",
              "      <td>5562800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013-01-07</td>\n",
              "      <td>365.348755</td>\n",
              "      <td>367.301056</td>\n",
              "      <td>362.929504</td>\n",
              "      <td>365.001007</td>\n",
              "      <td>365.001007</td>\n",
              "      <td>3332900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013-01-08</td>\n",
              "      <td>365.393463</td>\n",
              "      <td>365.771027</td>\n",
              "      <td>359.874359</td>\n",
              "      <td>364.280701</td>\n",
              "      <td>364.280701</td>\n",
              "      <td>3373900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1254</th>\n",
              "      <td>2017-12-22</td>\n",
              "      <td>1061.109985</td>\n",
              "      <td>1064.199951</td>\n",
              "      <td>1059.439941</td>\n",
              "      <td>1060.119995</td>\n",
              "      <td>1060.119995</td>\n",
              "      <td>755100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1255</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>1058.069946</td>\n",
              "      <td>1060.119995</td>\n",
              "      <td>1050.199951</td>\n",
              "      <td>1056.739990</td>\n",
              "      <td>1056.739990</td>\n",
              "      <td>760600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1256</th>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>1057.390015</td>\n",
              "      <td>1058.369995</td>\n",
              "      <td>1048.050049</td>\n",
              "      <td>1049.369995</td>\n",
              "      <td>1049.369995</td>\n",
              "      <td>1271900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1257</th>\n",
              "      <td>2017-12-28</td>\n",
              "      <td>1051.599976</td>\n",
              "      <td>1054.750000</td>\n",
              "      <td>1044.770020</td>\n",
              "      <td>1048.140015</td>\n",
              "      <td>1048.140015</td>\n",
              "      <td>837100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1258</th>\n",
              "      <td>2017-12-29</td>\n",
              "      <td>1046.719971</td>\n",
              "      <td>1049.699951</td>\n",
              "      <td>1044.900024</td>\n",
              "      <td>1046.400024</td>\n",
              "      <td>1046.400024</td>\n",
              "      <td>887500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1259 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date         Open         High  ...        Close    Adj Close   Volume\n",
              "0     2013-01-02   357.385559   361.151062  ...   359.288177   359.288177  5115500\n",
              "1     2013-01-03   360.122742   363.600128  ...   359.496826   359.496826  4666500\n",
              "2     2013-01-04   362.313507   368.339294  ...   366.600616   366.600616  5562800\n",
              "3     2013-01-07   365.348755   367.301056  ...   365.001007   365.001007  3332900\n",
              "4     2013-01-08   365.393463   365.771027  ...   364.280701   364.280701  3373900\n",
              "...          ...          ...          ...  ...          ...          ...      ...\n",
              "1254  2017-12-22  1061.109985  1064.199951  ...  1060.119995  1060.119995   755100\n",
              "1255  2017-12-26  1058.069946  1060.119995  ...  1056.739990  1056.739990   760600\n",
              "1256  2017-12-27  1057.390015  1058.369995  ...  1049.369995  1049.369995  1271900\n",
              "1257  2017-12-28  1051.599976  1054.750000  ...  1048.140015  1048.140015   837100\n",
              "1258  2017-12-29  1046.719971  1049.699951  ...  1046.400024  1046.400024   887500\n",
              "\n",
              "[1259 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl1tpwt2tqGz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainset = dataset_train.iloc[:,1:2].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fzjevDqttN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0,1))\n",
        "training_scaled = sc.fit_transform(trainset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTIpv6uftwUL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = []\n",
        "y_train = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W66KRlC7tzTT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "for i in range(60,1259):\n",
        "    x_train.append(training_scaled[i-60:i, 0])\n",
        "    y_train.append(training_scaled[i,0])\n",
        "x_train,y_train = np.array(x_train),np.array(y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXZUEIf8xKa1",
        "colab_type": "code",
        "outputId": "20e591f8-2110-4683-e06a-0a05a9753f3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1199, 60)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_r8MttXt7d0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hg3VaokuDhk",
        "colab_type": "code",
        "outputId": "7e963f6e-b5ca-4349-c4ba-53886c3e6036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "from keras.layers import Dropout\n",
        "K.clear_session()\n",
        "regressor = Sequential()\n",
        "regressor.add(SimpleFocusedRNN(units = 50,name='focus-1',kernel_initializer='he_normal',\n",
        "                                  init_sigma_current=0.1,\n",
        "                                  init_sigma_prev=0.1,\n",
        "                                   initer='Glorot',\n",
        "                                   distribution='uniform',\n",
        "                                    return_sequences = True,input_shape = (x_train.shape[1],1)))\n",
        "regressor.add(Dropout(0.2))\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape (None, 1)\n",
            "self.input_dim=  1\n",
            "initilizer=  Glorot\n",
            "distribution uniform\n",
            "u_current Tensor(\"focus-1/transpose_2:0\", shape=(1, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-1/transpose_3:0\", shape=(50, 50), dtype=float32)\n",
            "u_current Tensor(\"focus-1/while/transpose:0\", shape=(1, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-1/while/transpose_1:0\", shape=(50, 50), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJrcG2isuHXb",
        "colab_type": "code",
        "outputId": "3c9cbef1-3336-4bc7-ce16-755d6ae2bffc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "regressor.add(SimpleFocusedRNN(units = 50,name='focus-2',kernel_initializer='he_normal',\n",
        "                                  init_sigma_current=0.1,\n",
        "                                  init_sigma_prev=0.1,\n",
        "                                   initer='Glorot',\n",
        "                                   distribution='uniform',\n",
        "                                   return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape (None, 50)\n",
            "self.input_dim=  50\n",
            "initilizer=  Glorot\n",
            "distribution uniform\n",
            "u_current Tensor(\"focus-2/transpose_2:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-2/transpose_3:0\", shape=(50, 50), dtype=float32)\n",
            "u_current Tensor(\"focus-2/while/transpose:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-2/while/transpose_1:0\", shape=(50, 50), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DM5EZ7QuJ2D",
        "colab_type": "code",
        "outputId": "20b33fdf-7da6-42ba-d772-816724188c69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "regressor.add(SimpleFocusedRNN(units = 50,name='focus-3',kernel_initializer='he_normal',\n",
        "                                  init_sigma_current=0.1,\n",
        "                                  init_sigma_prev=0.1,\n",
        "                                   initer='Glorot',\n",
        "                                   distribution='uniform',\n",
        "                                   return_sequences = True))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape (None, 50)\n",
            "self.input_dim=  50\n",
            "initilizer=  Glorot\n",
            "distribution uniform\n",
            "u_current Tensor(\"focus-3/transpose_2:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-3/transpose_3:0\", shape=(50, 50), dtype=float32)\n",
            "u_current Tensor(\"focus-3/while/transpose:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-3/while/transpose_1:0\", shape=(50, 50), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drh3MxVouMUk",
        "colab_type": "code",
        "outputId": "368dedae-d9c2-42ae-bcf0-04272db82927",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "regressor.add(SimpleFocusedRNN(units = 50,name='focus-4',kernel_initializer='he_normal',\n",
        "                                  init_sigma_current=0.1,\n",
        "                                  init_sigma_prev=0.1,\n",
        "                                   initer='Glorot',\n",
        "                                   distribution='uniform'))\n",
        "regressor.add(Dropout(0.2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape (None, 50)\n",
            "self.input_dim=  50\n",
            "initilizer=  Glorot\n",
            "distribution uniform\n",
            "u_current Tensor(\"focus-4/transpose_2:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-4/transpose_3:0\", shape=(50, 50), dtype=float32)\n",
            "u_current Tensor(\"focus-4/while/transpose:0\", shape=(50, 50), dtype=float32)\n",
            "u_previous Tensor(\"focus-4/while/transpose_1:0\", shape=(50, 50), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxgN4PPGuOhT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense\n",
        "regressor.add(Dense(units = 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEwcpjgsuQZE",
        "colab_type": "code",
        "outputId": "c1f8553d-8dd4-41a9-fed0-62d4df7d0335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "opt=AdamwithClip()\n",
        "regressor.compile(optimizer = opt,loss = 'mean_squared_error')\n",
        "print(regressor.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "focus-1 (SimpleFocusedRNN)   (None, 60, 50)            2800      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "focus-2 (SimpleFocusedRNN)   (None, 60, 50)            5250      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "focus-3 (SimpleFocusedRNN)   (None, 60, 50)            5250      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 60, 50)            0         \n",
            "_________________________________________________________________\n",
            "focus-4 (SimpleFocusedRNN)   (None, 50)                5250      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 18,601\n",
            "Trainable params: 18,601\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YF8Ym06uSn8",
        "colab_type": "code",
        "outputId": "7d136029-8d68-4488-fc4e-8cffcc6d9e5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "regressor.fit(x_train,y_train,epochs = 100, batch_size = 32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1199/1199 [==============================] - 10s 8ms/step - loss: 0.2179\n",
            "Epoch 2/100\n",
            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0534\n",
            "Epoch 3/100\n",
            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0362\n",
            "Epoch 4/100\n",
            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0228\n",
            "Epoch 5/100\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0095\n",
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            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0095\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0081\n",
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            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0077\n",
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            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0061\n",
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            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0056\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0065\n",
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            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0050\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0046\n",
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            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0044\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0036\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0037\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0033\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0031\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0032\n",
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            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0029\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0029\n",
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            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0028\n",
            "Epoch 28/100\n",
            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0029\n",
            "Epoch 29/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0027\n",
            "Epoch 30/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0027\n",
            "Epoch 31/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0029\n",
            "Epoch 32/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0024\n",
            "Epoch 33/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0030\n",
            "Epoch 34/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0024\n",
            "Epoch 35/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0027\n",
            "Epoch 36/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0026\n",
            "Epoch 37/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0023\n",
            "Epoch 38/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0026\n",
            "Epoch 39/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0020\n",
            "Epoch 40/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0022\n",
            "Epoch 41/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0020\n",
            "Epoch 42/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0022\n",
            "Epoch 43/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0022\n",
            "Epoch 44/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0020\n",
            "Epoch 45/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 46/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 47/100\n",
            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0021\n",
            "Epoch 48/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 49/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 50/100\n",
            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0018\n",
            "Epoch 51/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 52/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0018\n",
            "Epoch 53/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 54/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0018\n",
            "Epoch 55/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 56/100\n",
            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0018\n",
            "Epoch 57/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 58/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0016\n",
            "Epoch 59/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 60/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0016\n",
            "Epoch 61/100\n",
            "1199/1199 [==============================] - 7s 5ms/step - loss: 0.0018\n",
            "Epoch 62/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 63/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0016\n",
            "Epoch 64/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0015\n",
            "Epoch 65/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0015\n",
            "Epoch 66/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 67/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0016\n",
            "Epoch 68/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 69/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0017\n",
            "Epoch 70/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0015\n",
            "Epoch 71/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 72/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0019\n",
            "Epoch 73/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 74/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 75/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 76/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 77/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 78/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0015\n",
            "Epoch 79/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0012\n",
            "Epoch 80/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 81/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 82/100\n",
            "1199/1199 [==============================] - 7s 6ms/step - loss: 0.0013\n",
            "Epoch 83/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 84/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 85/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 86/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 87/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0014\n",
            "Epoch 88/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 89/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 90/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 91/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 92/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0012\n",
            "Epoch 93/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 94/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0012\n",
            "Epoch 95/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0011\n",
            "Epoch 96/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 97/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 98/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0012\n",
            "Epoch 99/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n",
            "Epoch 100/100\n",
            "1199/1199 [==============================] - 6s 5ms/step - loss: 0.0013\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9f04eb4630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1Wv9XTDv1bm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_test =pd.read_csv(\"testset.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1s5hktCv41M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "real_stock_price = dataset_test.iloc[:,1:2].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SdqiHLqv60D",
        "colab_type": "code",
        "outputId": "b203572b-7e13-41fc-ec55-6a6297a630a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "dataset_total = pd.concat((dataset_train['Open'],dataset_test['Open']),axis = 0)\n",
        "dataset_total"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       357.385559\n",
              "1       360.122742\n",
              "2       362.313507\n",
              "3       365.348755\n",
              "4       365.393463\n",
              "          ...     \n",
              "120    1143.599976\n",
              "121    1128.000000\n",
              "122    1121.339966\n",
              "123    1102.089966\n",
              "124    1120.000000\n",
              "Name: Open, Length: 1384, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MV-ZFB4LwDnt",
        "colab_type": "code",
        "outputId": "6e2cd346-849d-4dd9-c49c-a1bb031819f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        }
      },
      "source": [
        "inputs = dataset_total[len(dataset_total) - len(dataset_test)-60:].values\n",
        "inputs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       1020.      , 1016.799988, 1026.439941, 1027.98999 , 1025.040039,\n",
              "       1040.880005, 1037.      , 1051.369995, 1077.430054, 1069.400024,\n",
              "       1082.      , 1077.859985, 1052.      , 1025.52002 , 1029.51001 ,\n",
              "       1046.      , 1030.01001 , 1013.659973, 1028.099976, 1019.      ,\n",
              "       1016.900024, 1049.22998 , 1058.540039, 1058.099976, 1086.030029,\n",
              "       1093.599976, 1100.      , 1090.      , 1077.310059, 1079.890015,\n",
              "       1061.859985, 1074.060059, 1083.560059, 1065.130005, 1079.      ,\n",
              "       1079.02002 , 1064.890015, 1063.030029, 1067.560059, 1099.349976,\n",
              "       1122.329956, 1140.98999 , 1142.170044, 1131.319946, 1118.180054,\n",
              "       1118.599976, 1131.069946, 1141.119995, 1143.849976, 1148.859985,\n",
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              "       1143.599976, 1128.      , 1121.339966, 1102.089966, 1120.      ])"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 38
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko20aHnZwGks",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs = inputs.reshape(-1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kTB4yPewI6d",
        "colab_type": "code",
        "outputId": "629b7cc7-b764-4e84-a7ee-797e18d46f8d",
        "colab": {
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          "height": 1000
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      "source": [
        "inputs"
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      "execution_count": 0,
      "outputs": [
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          "metadata": {
            "tags": []
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          "execution_count": 40
        }
      ]
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    {
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      "metadata": {
        "id": "1lvkLlc9wLut",
        "colab_type": "code",
        "outputId": "f61a8d19-1cda-43e4-c93c-786ceeb940d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "inputs = sc.transform(inputs)\n",
        "inputs.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(185, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-j9JrZ6EwNq0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test = []\n",
        "for i in range(60,185):\n",
        "    x_test.append(inputs[i-60:i,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Oj_64OCwPWN",
        "colab_type": "code",
        "outputId": "aa72d17b-39fe-4e19-ff82-469f692812d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_test = np.array(x_test)\n",
        "x_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(125, 60)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbPEJ7BfwR0O",
        "colab_type": "code",
        "outputId": "e3c26f05-c9e8-410c-f372-7fe9b9d80998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_test = np.reshape(x_test, (x_test.shape[0],x_test.shape[1],1))\n",
        "x_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(125, 60, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7D4SW4lfwTl9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_price = regressor.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfSnh0O2wVUk",
        "colab_type": "code",
        "outputId": "97273458-dcee-4b6e-a0e6-5f1781758929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "predicted_price = sc.inverse_transform(predicted_price)\n",
        "predicted_price"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1100.9076 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4Vuap4_wXt2",
        "colab_type": "code",
        "outputId": "b9abb16d-1e94-4133-f964-40e3056b0180",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot(real_stock_price,color = 'red', label = 'Real Price')\n",
        "plt.plot(predicted_price, color = 'blue', label = 'Predicted 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": {
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avCEgx57Slm3VPdk8I3kVaCMRFhUi0h0wACIyAYig+0d0iEh/4F3gGmNMnrve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\nxScqFoqiKIpP/g8xWbN487WvbwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}