IMDB binary clasification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMhNVElna7k0bgOWFPpVRLk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sainisatish/DeepLearningFromScratch/blob/main/IMDB%20binary%20clasification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below code is an example of book ( Deep learning with Python )\n"
      ],
      "metadata": {
        "id": "mwrW9Lm1AV9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "J0wb4_zgyDCw"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import imdb"
      ],
      "metadata": {
        "id": "UamQbQrUmuYl"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_data,train_labels),(test_data,test_labels) = imdb.load_data(num_words=10000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWs7jwaRmv_D",
        "outputId": "d409c530-43a3-4fa3-c47a-140e6bebadbc"
      },
      "execution_count": 3,
      "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": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict([(value,key) for (key,value) in word_index.items() ])"
      ],
      "metadata": {
        "id": "hjfK8eG5mxHS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "037b45e1-3250-4e7b-8a76-6c4ebc9eaa10"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1641221/1641221 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decoded_review = \" \".join( reverse_word_index.get(i-3,\"?\") for i in train_data[0] )"
      ],
      "metadata": {
        "id": "d16y5CzloNlh"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def vectorize_sequences(sequences, dimension=10000):   # multi hot encoding\n",
        "    results = np.zeros((len(sequences), dimension))   \n",
        "    for i, sequence in enumerate(sequences):\n",
        "        for j in sequence:\n",
        "            results[i, j] = 1.                        \n",
        "    return results\n",
        "x_train = vectorize_sequences(train_data)             \n",
        "x_test = vectorize_sequences(test_data)"
      ],
      "metadata": {
        "id": "xOzX_Lgkxs78"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test  = np.asarray(test_labels).astype(\"float32\")"
      ],
      "metadata": {
        "id": "wZe0bZZT3XRd"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "6mLc94hg4F-i"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(16,activation=\"relu\"),\n",
        "    layers.Dense(16,activation=\"relu\"),\n",
        "    layers.Dense(1 ,activation=\"sigmoid\")\n",
        "                        ])"
      ],
      "metadata": {
        "id": "xN-vnS3l4W1G"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "ne15SGnQ4csa"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_val = x_train[:10000]\n",
        "partial_x_train = x_train[10000:]\n",
        "y_val = y_train[:10000]\n",
        "partial_y_train = y_train[10000:]"
      ],
      "metadata": {
        "id": "hxyu0bsE4ckL"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(partial_x_train,partial_y_train,epochs=20,batch_size=512,validation_data=(x_val,y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aniNHCDY4cOT",
        "outputId": "b12444ca-dbe0-425e-aaf6-1d805eb5d903"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 2s 30ms/step - loss: 0.5198 - accuracy: 0.7936 - val_loss: 0.3966 - val_accuracy: 0.8714\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.3154 - accuracy: 0.9047 - val_loss: 0.3106 - val_accuracy: 0.8899\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.2344 - accuracy: 0.9271 - val_loss: 0.2888 - val_accuracy: 0.8872\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.1858 - accuracy: 0.9413 - val_loss: 0.2778 - val_accuracy: 0.8892\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 1s 19ms/step - loss: 0.1526 - accuracy: 0.9524 - val_loss: 0.2756 - val_accuracy: 0.8902\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 1s 19ms/step - loss: 0.1260 - accuracy: 0.9631 - val_loss: 0.3074 - val_accuracy: 0.8829\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.1068 - accuracy: 0.9680 - val_loss: 0.3039 - val_accuracy: 0.8854\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0898 - accuracy: 0.9741 - val_loss: 0.3161 - val_accuracy: 0.8818\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 1s 21ms/step - loss: 0.0728 - accuracy: 0.9815 - val_loss: 0.3330 - val_accuracy: 0.8834\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0592 - accuracy: 0.9854 - val_loss: 0.3564 - val_accuracy: 0.8778\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0502 - accuracy: 0.9886 - val_loss: 0.3801 - val_accuracy: 0.8783\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0400 - accuracy: 0.9919 - val_loss: 0.4040 - val_accuracy: 0.8790\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0318 - accuracy: 0.9939 - val_loss: 0.4724 - val_accuracy: 0.8630\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0260 - accuracy: 0.9958 - val_loss: 0.4579 - val_accuracy: 0.8761\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0196 - accuracy: 0.9975 - val_loss: 0.4976 - val_accuracy: 0.8727\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0160 - accuracy: 0.9980 - val_loss: 0.5257 - val_accuracy: 0.8734\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0116 - accuracy: 0.9987 - val_loss: 0.5612 - val_accuracy: 0.8723\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0096 - accuracy: 0.9987 - val_loss: 0.5913 - val_accuracy: 0.8715\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0069 - accuracy: 0.9994 - val_loss: 0.6210 - val_accuracy: 0.8669\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0041 - accuracy: 0.9999 - val_loss: 0.7249 - val_accuracy: 0.8529\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict = history.history"
      ],
      "metadata": {
        "id": "aWQWglnd8tEZ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_dict.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akH1spGZ899g",
        "outputId": "ea73a928-cc46-427b-ce50-15ef3e31e7fd"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history_dict = history.history\n",
        "loss_values = history_dict[\"loss\"]\n",
        "val_loss_values = history_dict[\"val_loss\"]\n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")        \n",
        "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")   \n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LnamGbzi9CuT",
        "outputId": "f86e9a78-0614-4851-b83c-fa1bea2a90dc"
      },
      "execution_count": 18,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.clf()                           \n",
        "acc = history_dict[\"accuracy\"]\n",
        "val_acc = history_dict[\"val_accuracy\"]\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.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DewceW2h9HdR",
        "outputId": "0a1bc8fb-0e1a-4232-8f93-613474a9c85f"
      },
      "execution_count": 20,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ucsiVuix9SZ0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}