RNN_USING_KERAS.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "h7EgWtrD4ToG"
      },
      "outputs": [],
      "source": [
        "from keras.layers import SimpleRNN\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, SimpleRNN\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(10000, 32))\n",
        "model.add(SimpleRNN(32))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GckmTxtx4b7z",
        "outputId": "843f6d6b-bd61-412d-b4ef-2694d2dde854"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, None, 32)          320000    \n",
            "                                                                 \n",
            " simple_rnn (SimpleRNN)      (None, 32)                2080      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 322,080\n",
            "Trainable params: 322,080\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(10000, 32))\n",
        "model.add(SimpleRNN(32, return_sequences=True))\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oNfWT244lot",
        "outputId": "795e5e3d-cf89-4f3c-c7b4-a2869b3ab57d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, None, 32)          320000    \n",
            "                                                                 \n",
            " simple_rnn_2 (SimpleRNN)    (None, None, 32)          2080      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 322,080\n",
            "Trainable params: 322,080\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#It’s sometimes useful to stack several recurrent layers one after the other in order to\n",
        "#increase the representational power of a network. In such a setup, you have to get all\n",
        "#of the intermediate layers to return full sequence of outputs:\n",
        "model = Sequential()\n",
        "model.add(Embedding(10000, 32))\n",
        "model.add(SimpleRNN(32, return_sequences=True))\n",
        "model.add(SimpleRNN(32, return_sequences=True))\n",
        "\n",
        "model.add(SimpleRNN(32, return_sequences=True))\n",
        "model.add(SimpleRNN(32))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HBplrK64srP",
        "outputId": "521bc730-14c2-4ce0-def4-c7208ede20a1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_4 (Embedding)     (None, None, 32)          320000    \n",
            "                                                                 \n",
            " simple_rnn_7 (SimpleRNN)    (None, None, 32)          2080      \n",
            "                                                                 \n",
            " simple_rnn_8 (SimpleRNN)    (None, None, 32)          2080      \n",
            "                                                                 \n",
            " simple_rnn_9 (SimpleRNN)    (None, None, 32)          2080      \n",
            "                                                                 \n",
            " simple_rnn_10 (SimpleRNN)   (None, 32)                2080      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 328,320\n",
            "Trainable params: 328,320\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CoIROsdf5BnB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}