LSTM_using_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "vguU0GDSYjoa"
      },
      "outputs": [],
      "source": [
        "from keras.layers import LSTM\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding\n",
        "from keras.datasets import imdb\n",
        "from keras.preprocessing import sequence\n",
        "from keras.layers import Dense\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(input_train, y_train), (input_test, y_test) = imdb.load_data(\n",
        "num_words=10000)"
      ],
      "metadata": {
        "id": "P_UVsvT1YtMd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_features = 10000\n",
        "maxlen = 500\n",
        "batch_size = 32"
      ],
      "metadata": {
        "id": "uXEIgZ07Y1H1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_train = sequence.pad_sequences(input_train, maxlen=maxlen)\n",
        "input_test = sequence.pad_sequences(input_test, maxlen=maxlen)"
      ],
      "metadata": {
        "id": "4WP_TYB2ZyEd"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(max_features, 32))\n",
        "model.add(LSTM(32))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='rmsprop',\n",
        "loss='binary_crossentropy',\n",
        "metrics=['acc'])\n",
        "history = model.fit(input_train, y_train,\n",
        "epochs=10,\n",
        "batch_size=128,\n",
        "validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dW9LC4tZ0OI",
        "outputId": "7a28a310-0c7a-40e5-b02f-033fb3e0bcf9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "157/157 [==============================] - 58s 350ms/step - loss: 0.5022 - acc: 0.7576 - val_loss: 0.3547 - val_acc: 0.8540\n",
            "Epoch 2/10\n",
            "157/157 [==============================] - 55s 349ms/step - loss: 0.2936 - acc: 0.8863 - val_loss: 0.3714 - val_acc: 0.8520\n",
            "Epoch 3/10\n",
            "157/157 [==============================] - 55s 350ms/step - loss: 0.2337 - acc: 0.9116 - val_loss: 0.2853 - val_acc: 0.8810\n",
            "Epoch 4/10\n",
            "157/157 [==============================] - 54s 343ms/step - loss: 0.1980 - acc: 0.9267 - val_loss: 0.4111 - val_acc: 0.8312\n",
            "Epoch 5/10\n",
            "157/157 [==============================] - 53s 337ms/step - loss: 0.1774 - acc: 0.9358 - val_loss: 0.3173 - val_acc: 0.8736\n",
            "Epoch 6/10\n",
            "157/157 [==============================] - 55s 351ms/step - loss: 0.1556 - acc: 0.9437 - val_loss: 0.3444 - val_acc: 0.8808\n",
            "Epoch 7/10\n",
            "157/157 [==============================] - 54s 345ms/step - loss: 0.1406 - acc: 0.9495 - val_loss: 0.3441 - val_acc: 0.8884\n",
            "Epoch 8/10\n",
            "157/157 [==============================] - 55s 349ms/step - loss: 0.1276 - acc: 0.9556 - val_loss: 0.3709 - val_acc: 0.8852\n",
            "Epoch 9/10\n",
            "157/157 [==============================] - 55s 350ms/step - loss: 0.1231 - acc: 0.9564 - val_loss: 0.3554 - val_acc: 0.8788\n",
            "Epoch 10/10\n",
            "157/157 [==============================] - 55s 351ms/step - loss: 0.1092 - acc: 0.9620 - val_loss: 0.3415 - val_acc: 0.8794\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(acc) + 1)\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "IoQYAIqyaDEH",
        "outputId": "e45b9259-2c09-46f9-c1ad-514abcefe68f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yA7NX_TccaHi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}