integer_encoding_simplernn.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": null,
      "metadata": {
        "id": "SX1YuKQ4UDon"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "docs = ['go india',\n",
        "\t\t'india india',\n",
        "\t\t'hip hip hurray',\n",
        "\t\t'jeetega bhai jeetega india jeetega',\n",
        "\t\t'bharat mata ki jai',\n",
        "\t\t'kohli kohli',\n",
        "\t\t'sachin sachin',\n",
        "\t\t'dhoni dhoni',\n",
        "\t\t'modi ji ki jai',\n",
        "\t\t'inquilab zindabad']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "tokenizer = Tokenizer(oov_token='<nothing>')"
      ],
      "metadata": {
        "id": "pOJzJ3vzUOkh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(docs)"
      ],
      "metadata": {
        "id": "njO0da4-Unl8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.word_index"
      ],
      "metadata": {
        "id": "GrKpFbzRUucs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e95f024d-bc39-4ea7-ece9-20eac074a4ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'<nothing>': 1,\n",
              " 'india': 2,\n",
              " 'jeetega': 3,\n",
              " 'hip': 4,\n",
              " 'ki': 5,\n",
              " 'jai': 6,\n",
              " 'kohli': 7,\n",
              " 'sachin': 8,\n",
              " 'dhoni': 9,\n",
              " 'go': 10,\n",
              " 'hurray': 11,\n",
              " 'bhai': 12,\n",
              " 'bharat': 13,\n",
              " 'mata': 14,\n",
              " 'modi': 15,\n",
              " 'ji': 16,\n",
              " 'inquilab': 17,\n",
              " 'zindabad': 18}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.word_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNcKSYUYegGl",
        "outputId": "aa1f89a9-8fe0-4c11-aea0-f256cec847ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('go', 1),\n",
              "             ('india', 4),\n",
              "             ('hip', 2),\n",
              "             ('hurray', 1),\n",
              "             ('jeetega', 3),\n",
              "             ('bhai', 1),\n",
              "             ('bharat', 1),\n",
              "             ('mata', 1),\n",
              "             ('ki', 2),\n",
              "             ('jai', 2),\n",
              "             ('kohli', 2),\n",
              "             ('sachin', 2),\n",
              "             ('dhoni', 2),\n",
              "             ('modi', 1),\n",
              "             ('ji', 1),\n",
              "             ('inquilab', 1),\n",
              "             ('zindabad', 1)])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.document_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mx3SMLV3euuU",
        "outputId": "9f6724a8-af24-4083-dea1-fcd26aa8dd35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = tokenizer.texts_to_sequences(docs)\n",
        "sequences"
      ],
      "metadata": {
        "id": "xNrqCeiUU05s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a77d3d6-c800-4b15-a30c-4b40942e0dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[10, 2],\n",
              " [2, 2],\n",
              " [4, 4, 11],\n",
              " [3, 12, 3, 2, 3],\n",
              " [13, 14, 5, 6],\n",
              " [7, 7],\n",
              " [8, 8],\n",
              " [9, 9],\n",
              " [15, 16, 5, 6],\n",
              " [17, 18]]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import pad_sequences"
      ],
      "metadata": {
        "id": "Y15DVHzhVCEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = pad_sequences(sequences,padding='post')"
      ],
      "metadata": {
        "id": "H00D6kZlVOC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjL4mCzTV4rT",
        "outputId": "b43686a6-6657-4ab4-a01c-822f364ab908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10,  2,  0,  0,  0],\n",
              "       [ 2,  2,  0,  0,  0],\n",
              "       [ 4,  4, 11,  0,  0],\n",
              "       [ 3, 12,  3,  2,  3],\n",
              "       [13, 14,  5,  6,  0],\n",
              "       [ 7,  7,  0,  0,  0],\n",
              "       [ 8,  8,  0,  0,  0],\n",
              "       [ 9,  9,  0,  0,  0],\n",
              "       [15, 16,  5,  6,  0],\n",
              "       [17, 18,  0,  0,  0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.datasets import imdb\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense,SimpleRNN,Embedding,Flatten"
      ],
      "metadata": {
        "id": "f4cMDq3KDiaC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test) = imdb.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVqDOLaefsom",
        "outputId": "786c8081-afe2-4c09-af32-bb5e9e3da1c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTk54ntQfu6W",
        "outputId": "33ecc4aa-11be-4630-800e-f53e968e00a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              " 32]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9tGLS4ofzBu",
        "outputId": "56fdc724-b227-4b60-a57b-bf3fdceadb84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "141"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pad_sequences(X_train,padding='post',maxlen=50)\n",
        "X_test = pad_sequences(X_test,padding='post',maxlen=50)"
      ],
      "metadata": {
        "id": "uoEmzhEhf1aV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4kNirFSgDEe",
        "outputId": "256dc84b-8986-4174-b365-a9455fdfad69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2071,   56,   26,  141,    6,  194, 7486,   18,    4,  226,   22,\n",
              "         21,  134,  476,   26,  480,    5,  144,   30, 5535,   18,   51,\n",
              "         36,   28,  224,   92,   25,  104,    4,  226,   65,   16,   38,\n",
              "       1334,   88,   12,   16,  283,    5,   16, 4472,  113,  103,   32,\n",
              "         15,   16, 5345,   19,  178,   32], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(SimpleRNN(32,input_shape=(50,1),return_sequences=False))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MN_Fi3n6gFkp",
        "outputId": "0d228317-dd1e-48a2-a284-9bc574b754e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 32)                1088      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,121\n",
            "Trainable params: 1,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,y_train,epochs=5,validation_data=(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EArIjALcgPTh",
        "outputId": "2c603e18-08ba-4754-98f0-ce4a657c0ae5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "782/782 [==============================] - 10s 12ms/step - loss: 0.6929 - accuracy: 0.5061 - val_loss: 0.6955 - val_accuracy: 0.5048\n",
            "Epoch 2/5\n",
            "782/782 [==============================] - 9s 12ms/step - loss: 0.6931 - accuracy: 0.5031 - val_loss: 0.6945 - val_accuracy: 0.5021\n",
            "Epoch 3/5\n",
            "782/782 [==============================] - 9s 12ms/step - loss: 0.6927 - accuracy: 0.5085 - val_loss: 0.6967 - val_accuracy: 0.5008\n",
            "Epoch 4/5\n",
            "782/782 [==============================] - 9s 12ms/step - loss: 0.6927 - accuracy: 0.5090 - val_loss: 0.6942 - val_accuracy: 0.5043\n",
            "Epoch 5/5\n",
            "782/782 [==============================] - 9s 12ms/step - loss: 0.6928 - accuracy: 0.5056 - val_loss: 0.6957 - val_accuracy: 0.5048\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8dc97f8810>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N9ziz5wWgTLS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}