lstm_options.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/beinghorizontal/Quantext/blob/main/lstm_options.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "\n",
        "# !pip install yfinance\n",
        "# !pip install pandas_ta\n",
        "# !pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n",
        "# !pip install keras==3.0.0"
      ],
      "metadata": {
        "id": "5a2cf7ecd15a94b1"
      },
      "id": "5a2cf7ecd15a94b1",
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import essential libraries"
      ],
      "metadata": {
        "collapsed": false,
        "id": "a17be93e31eef16b"
      },
      "id": "a17be93e31eef16b"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "from preprocess_options_colab import get_options\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "import torch\n",
        "from keras.layers import LSTM, Dense, Dropout, BatchNormalization\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.utils import plot_model"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:20:44.693459Z",
          "start_time": "2024-03-14T13:20:40.127739Z"
        },
        "id": "f682831893363ad3"
      },
      "id": "f682831893363ad3",
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get option chain data from preprocess_options.py"
      ],
      "metadata": {
        "collapsed": false,
        "id": "65ce73d0cb820883"
      },
      "id": "65ce73d0cb820883"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "optionslist = get_options()\n",
        "\n",
        "df = optionslist[0]"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:20:49.760938Z",
          "start_time": "2024-03-14T13:20:49.184583Z"
        },
        "id": "f641e6d2212871a9"
      },
      "id": "f641e6d2212871a9",
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download the historical data from Yahoo finance for 15 years data, replace  'days=15' to change the years accordingly"
      ],
      "metadata": {
        "collapsed": false,
        "id": "ea567ffe036910c0"
      },
      "id": "ea567ffe036910c0"
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:20:55.113689Z",
          "start_time": "2024-03-14T13:20:55.094773Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4276d5cda9a9cef6",
        "outputId": "81c4045a-abd3-431b-958f-39dab81d92b9"
      },
      "id": "4276d5cda9a9cef6",
      "execution_count": 3
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:20:57.962363Z",
          "start_time": "2024-03-14T13:20:57.943755Z"
        },
        "id": "59a9983c9c3ab6e0"
      },
      "id": "59a9983c9c3ab6e0",
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['MX_PEOI', 'MX_CEOI', 'Nifty_Close', 'Nifty_V', 'CEIV', 'PEIV', 'PCR_OI', 'PCR_WT', 'ActivePEOI', 'ActiveCEOI', 'Long_Sum', 'Short_Sum', 'LongLiq_Sum', 'ShortCover_Sum', 'CE_Unwind_Sum', 'PE_Unwind_Sum', 'MaxPain', 'ActivePE', 'ActiveCE', 'MX_PE', 'MX_CE', 'peIvRank', 'ceIvRank', 'Close', 'MX_PEOI_mean', 'MX_CEOI_mean', 'Nifty_Close_mean', 'Nifty_V_mean', 'CEIV_mean', 'PEIV_mean', 'PCR_OI_mean', 'PCR_WT_mean', 'ActivePEOI_mean', 'ActiveCEOI_mean', 'Long_Sum_mean', 'Short_Sum_mean', 'LongLiq_Sum_mean', 'ShortCover_Sum_mean', 'CE_Unwind_Sum_mean', 'PE_Unwind_Sum_mean', 'MaxPain_mean', 'ActivePE_mean', 'ActiveCE_mean', 'MX_PE_mean', 'MX_CE_mean', 'peIvRank_mean', 'ceIvRank_mean']\n"
          ]
        }
      ],
      "source": [
        "print(df.columns.to_list())"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:00.034987Z",
          "start_time": "2024-03-14T13:21:00.024389Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b19277d4408a683e",
        "outputId": "48441050-809f-46f9-f319-86a7d07478be"
      },
      "id": "b19277d4408a683e",
      "execution_count": null
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "columns_to_norm = ['MX_PEOI', 'MX_CEOI', 'Nifty_Close', 'Nifty_V', 'CEIV', 'PEIV', 'PCR_OI', 'PCR_WT', 'ActivePEOI', 'ActiveCEOI', 'Long_Sum', 'Short_Sum', 'LongLiq_Sum', 'ShortCover_Sum', 'CE_Unwind_Sum', 'PE_Unwind_Sum', 'MaxPain', 'ActivePE', 'ActiveCE', 'MX_PE', 'MX_CE', 'peIvRank', 'ceIvRank', 'Close', 'MX_PEOI_mean', 'MX_CEOI_mean', 'Nifty_Close_mean', 'Nifty_V_mean', 'CEIV_mean', 'PEIV_mean', 'PCR_OI_mean', 'PCR_WT_mean', 'ActivePEOI_mean', 'ActiveCEOI_mean', 'Long_Sum_mean', 'Short_Sum_mean', 'LongLiq_Sum_mean', 'ShortCover_Sum_mean', 'CE_Unwind_Sum_mean', 'PE_Unwind_Sum_mean', 'MaxPain_mean', 'ActivePE_mean', 'ActiveCE_mean', 'MX_PE_mean', 'MX_CE_mean', 'peIvRank_mean', 'ceIvRank_mean']\n",
        "\n",
        "for col in columns_to_norm:\n",
        "    df[[col]] = scaler.fit_transform(df[[col]])\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:06.360565Z",
          "start_time": "2024-03-14T13:21:06.249925Z"
        },
        "id": "665d6240d190c049"
      },
      "id": "665d6240d190c049",
      "execution_count": 5
    },
    {
      "cell_type": "markdown",
      "source": [
        "# check what dataframe looks like because we are going to normalize the remaining data later\n",
        "# Most of the values have positive and negative numbers. So we will use minmax scaler range is equal to -1, 1"
      ],
      "metadata": {
        "collapsed": false,
        "id": "16e1266d11bf14ab"
      },
      "id": "16e1266d11bf14ab"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create 5 day sma for target"
      ],
      "metadata": {
        "collapsed": false,
        "id": "5c438ec74cbca7cc"
      },
      "id": "5c438ec74cbca7cc"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "df['5sma'] = df['Close'].shift(-5).rolling(5).mean()\n",
        "# create binary target\n",
        "df['target'] = (df['5sma'] > df['Close']).astype(int)\n",
        "df = df.dropna()\n",
        "df = df.drop(['Close','5sma'],axis=1)\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:17.543407Z",
          "start_time": "2024-03-14T13:21:17.532406Z"
        },
        "id": "590213a661131013"
      },
      "id": "590213a661131013",
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create target and features"
      ],
      "metadata": {
        "collapsed": false,
        "id": "280dade0b5b2b1ca"
      },
      "id": "280dade0b5b2b1ca"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "features = df.drop('target', axis=1).values\n",
        "target = df['target'].values"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:20.974039Z",
          "start_time": "2024-03-14T13:21:20.962705Z"
        },
        "id": "cc520691a66eb24f"
      },
      "id": "cc520691a66eb24f",
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "def create_sequences(features, target, sequence_length):\n",
        "    X = []\n",
        "    y = []\n",
        "    for i in range(len(features) - sequence_length):\n",
        "        X.append(features[i:i+sequence_length])\n",
        "        y.append(target[i+(sequence_length-1)])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "sequence_length = 25\n",
        "X, y = create_sequences(features, target, sequence_length)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:22.924226Z",
          "start_time": "2024-03-14T13:21:22.904051Z"
        },
        "id": "c5d1e5bb0a0ce395"
      },
      "id": "c5d1e5bb0a0ce395",
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0.42198057 0.30314841 0.63408386 ... 0.69793011 0.89940804 0.80754182]\n",
            "  [0.32819226 0.31024391 0.59351039 ... 0.68820593 0.79881609 0.70535655]\n",
            "  [0.32405326 0.32963161 0.58451852 ... 0.67719477 0.70078466 0.60619241]\n",
            "  ...\n",
            "  [0.34397492 0.3275503  0.55199417 ... 0.61135009 0.40896511 0.49938488]\n",
            "  [0.31015057 0.34386963 0.56070223 ... 0.63773048 0.41767178 0.57573704]\n",
            "  [0.47242241 0.33629256 0.62439049 ... 0.67634894 0.50016994 0.53606315]]\n",
            "\n",
            " [[0.32819226 0.31024391 0.59351039 ... 0.68820593 0.79881609 0.70535655]\n",
            "  [0.32405326 0.32963161 0.58451852 ... 0.67719477 0.70078466 0.60619241]\n",
            "  [0.36923767 0.32873398 0.55975292 ... 0.65844118 0.60169591 0.50400714]\n",
            "  ...\n",
            "  [0.31015057 0.34386963 0.56070223 ... 0.63773048 0.41767178 0.57573704]\n",
            "  [0.47242241 0.33629256 0.62439049 ... 0.67634894 0.50016994 0.53606315]\n",
            "  [0.336143   0.3222011  0.56020314 ... 0.64757018 0.57104505 0.58886906]]\n",
            "\n",
            " [[0.32405326 0.32963161 0.58451852 ... 0.67719477 0.70078466 0.60619241]\n",
            "  [0.36923767 0.32873398 0.55975292 ... 0.65844118 0.60169591 0.50400714]\n",
            "  [0.32465596 0.31665645 0.5803149  ... 0.6374869  0.50110396 0.40182187]\n",
            "  ...\n",
            "  [0.47242241 0.33629256 0.62439049 ... 0.67634894 0.50016994 0.53606315]\n",
            "  [0.336143   0.3222011  0.56020314 ... 0.64757018 0.57104505 0.58886906]\n",
            "  [0.10653422 0.16724199 0.54883044 ... 0.66695864 0.57029983 0.66805014]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0.3481897  0.33982573 0.54732586 ... 0.63530376 0.24097429 0.18973267]\n",
            "  [0.20234045 0.18666284 0.53960032 ... 0.61591234 0.14038233 0.12987361]\n",
            "  [0.38163218 0.35056147 0.5528045  ... 0.64696654 0.03979038 0.05189195]\n",
            "  ...\n",
            "  [0.42198801 0.34937678 0.62183624 ... 0.64059974 0.45766401 0.82219572]\n",
            "  [0.35556556 0.34148572 0.56430376 ... 0.65875083 0.37869996 0.82648921]\n",
            "  [0.34888782 0.33928512 0.5983367  ... 0.68314801 0.37135619 0.82648921]]\n",
            "\n",
            " [[0.20234045 0.18666284 0.53960032 ... 0.61591234 0.14038233 0.12987361]\n",
            "  [0.38163218 0.35056147 0.5528045  ... 0.64696654 0.03979038 0.05189195]\n",
            "  [0.39200394 0.33326705 0.65506404 ... 0.6278375  0.0527789  0.09047711]\n",
            "  ...\n",
            "  [0.35556556 0.34148572 0.56430376 ... 0.65875083 0.37869996 0.82648921]\n",
            "  [0.34888782 0.33928512 0.5983367  ... 0.68314801 0.37135619 0.82648921]\n",
            "  [0.34065104 0.3319142  0.58113184 ... 0.70026545 0.41005105 0.85568501]]\n",
            "\n",
            " [[0.38163218 0.35056147 0.5528045  ... 0.64696654 0.03979038 0.05189195]\n",
            "  [0.39200394 0.33326705 0.65506404 ... 0.6278375  0.0527789  0.09047711]\n",
            "  [0.38177329 0.38422908 0.60200919 ... 0.60669764 0.0527789  0.11238563]\n",
            "  ...\n",
            "  [0.34888782 0.33928512 0.5983367  ... 0.68314801 0.37135619 0.82648921]\n",
            "  [0.34065104 0.3319142  0.58113184 ... 0.70026545 0.41005105 0.85568501]\n",
            "  [0.37386264 0.35004938 0.48808822 ... 0.73617362 0.40561896 0.85498787]]]\n",
            "[1 1 1 1 1 0 0 0 0 0]\n",
            "Training shape: (1299, 25, 46)\n",
            "Validation shape: (867, 25, 46)\n",
            "46\n"
          ]
        }
      ],
      "source": [
        "#prompt print first 10 values of X\n",
        "#print(X[:10])\n",
        "\n",
        "#prompt print first 10 values of y\n",
        "#print(y[:10])\n",
        "\n",
        "# split X and Y into training and validation sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.4,shuffle=False)\n",
        "\n",
        "print(\"Training shape:\", X_train.shape)\n",
        "print(\"Validation shape:\", X_val.shape)\n",
        "num_features = X_train.shape[2]\n",
        "print(num_features)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:21:29.379987Z",
          "start_time": "2024-03-14T13:21:29.364405Z"
        },
        "id": "b70ac2070e6c5d88",
        "outputId": "0e5506eb-fca6-45ab-b633-865506c9c34b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "b70ac2070e6c5d88",
      "execution_count": 9
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_4 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_5 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ],
      "source": [
        "from keras.optimizers import Adam\n",
        "from keras.regularizers import l1_l2,l1,l2\n",
        "from keras.layers import LeakyReLU\n",
        "# inputs = Input(shape=(sequence_length, X_train.sha))  # Replace with appropriate values\n",
        "\n",
        "\n",
        "# Build the LSTM model\n",
        "model = Sequential()\n",
        "#model.add(LSTM(152, return_sequences=True, input_shape=(sequence_length, num_features), activation='relu', kernel_regularizer=l1_l2(l1=0.01, l2=0.01)))\n",
        "#model.add(LSTM(64, return_sequences=True, input_shape=(sequence_length, num_features), activation='relu', kernel_regularizer=l1(0.001)))\n",
        "model.add(LSTM(82, return_sequences=True, input_shape=(sequence_length, num_features), activation='relu'))\n",
        "#model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.2))\n",
        "model.add(LSTM(52, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "# #model.add(BatchNormalization())\n",
        "# #model.add(Dropout(0.5))\n",
        "model.add(LSTM(32, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "# #model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.2))\n",
        "model.add(LSTM(22,return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "# #model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.2))\n",
        "model.add(LSTM(32, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "# #model.add(BatchNormalization())\n",
        "# model.add(Dropout(0.4))\n",
        "model.add(LSTM(16, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "#model.add(BatchNormalization())\n",
        "#model.add(Dropout(0.4))\n",
        "model.add(LSTM(8))  # Add an extra LSTM layer\n",
        "# model.add(Dense(1, activation='sigmoid',  kernel_regularizer=l1_l2(l1=0.01, l2=0.01)))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "optimizer = Adam(learning_rate=0.001) # default is 0.001\n",
        "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:39:32.508185Z",
          "start_time": "2024-03-14T13:39:32.353624Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cbb792350d1bb7d",
        "outputId": "60584051-67c8-42c3-816b-8dadc21d578f"
      },
      "id": "2cbb792350d1bb7d",
      "execution_count": 10
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss',    # Monitor the val loss\n",
        "                               patience = 250,           # Num epochs; if no improvement training will be stopped\n",
        "                               verbose=1,\n",
        "                               mode='min',            # The training will stop when the quantity monitored has stopped decreasing\n",
        "                               restore_best_weights=True) # Restores model weights from the epoch with the best value of the monitored quantity.\n",
        "\n",
        "# Model checkpoint callback\n",
        "model_checkpoint = ModelCheckpoint('/content/best_model.keras',   # Path where to save the model\n",
        "                                   monitor='val_loss',   # Monitor the validation loss\n",
        "                                   save_best_only=True,  # The latest best model according to the quantity monitored will not be overwritten\n",
        "                                   mode='min',           # The training will save the model when the quantity monitored has decreased\n",
        "                                   verbose=1)\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:39:35.496875Z",
          "start_time": "2024-03-14T13:39:35.478095Z"
        },
        "id": "66861e4281e8b142"
      },
      "id": "66861e4281e8b142",
      "execution_count": 11
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "!pip install livelossplot"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:31:07.650421Z",
          "start_time": "2024-03-14T13:31:03.799754Z"
        },
        "id": "1c17a3cc5cba8d06"
      },
      "id": "1c17a3cc5cba8d06",
      "execution_count": null
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "\n",
        "plot_losses = PlotLossesKeras()  # Create the live plot callback"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:39:41.625514Z",
          "start_time": "2024-03-14T13:39:41.618850Z"
        },
        "id": "7873735035e2ac1"
      },
      "id": "7873735035e2ac1",
      "execution_count": 13
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.484, max:    0.554, cur:    0.554)\n",
            "\tvalidation       \t (min:    0.524, max:    0.595, cur:    0.554)\n",
            "Loss\n",
            "\ttraining         \t (min:    0.686, max:    0.699, cur:    0.686)\n",
            "\tvalidation       \t (min:    0.680, max:    0.708, cur:    0.691)\n"
          ]
        }
      ],
      "source": [
        "model.fit(X_train, y_train,\n",
        "                    epochs=1000,\n",
        "                    batch_size=32,\n",
        "                    validation_data=(X_val, y_val),\n",
        "                    callbacks=[early_stopping, model_checkpoint, plot_losses],\n",
        "                    verbose=False)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "e98dfc99b826ca1e",
        "outputId": "8869fbcd-5b81-4647-e02d-efe580460f73"
      },
      "id": "e98dfc99b826ca1e",
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "# plot val and train accuracy"
      ],
      "metadata": {
        "collapsed": false,
        "id": "e54834962e8a7b39"
      },
      "id": "e54834962e8a7b39"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "# plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "# plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "# plt.xlabel('Epochs')\n",
        "# plt.ylabel('Accuracy')\n",
        "# plt.title('Training and Validation Accuracy')\n",
        "# plt.legend()\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-14T13:26:14.188142Z",
          "start_time": "2024-03-14T13:26:13.998002Z"
        },
        "id": "61feb195ea067404"
      },
      "id": "61feb195ea067404",
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": [
        "#plot model architecture"
      ],
      "metadata": {
        "collapsed": false,
        "id": "8fc8550975af0302"
      },
      "id": "8fc8550975af0302"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "# plot_model(model, to_file='lstm_model.png', show_shapes=True, show_layer_names=True)\n",
        "\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-13T15:26:29.428095Z",
          "start_time": "2024-03-13T15:26:29.193013Z"
        },
        "id": "d8b65a9608b0f851"
      },
      "id": "d8b65a9608b0f851",
      "execution_count": null
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "# print(model.summary())"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-13T15:30:14.728733Z",
          "start_time": "2024-03-13T15:30:14.706529Z"
        },
        "id": "9975027641af22ac"
      },
      "id": "9975027641af22ac",
      "execution_count": null
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}