DNN_USING_KERAS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DNN_USING_KERAS.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPMxLBpb7WUMr7+P5uddUVj",
      "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/PRAJWAL2108/DEEP-LEARNING/blob/main/DNN_USING_KERAS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOKxo3CjpIda"
      },
      "source": [
        "# first neural network with keras make predictions\n",
        "from numpy import loadtxt\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EN1XAAcCpJ2a"
      },
      "source": [
        "# load the dataset\n",
        "dataset = loadtxt('pima-indians-diabetes.csv', delimiter=',')\n",
        "# split into input (X) and output (y) variables\n",
        "X = dataset[:,0:8]\n",
        "y = dataset[:,8]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E62rZnlSpRv0"
      },
      "source": [
        "# define the keras model\n",
        "model = Sequential()\n",
        "model.add(Dense(12, input_dim=8, activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXymgEkupT_8"
      },
      "source": [
        "# compile the keras model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxo9VuuSpWkv",
        "outputId": "07598168-8132-46fc-98ab-2e4f57a08976"
      },
      "source": [
        "# fit the keras model on the dataset\n",
        "model.fit(X, y, epochs=150, batch_size=10, verbose=0)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f378574e110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGB0wrtppX6l",
        "outputId": "2b777ddf-cbc0-4348-9fd7-647f283281e7"
      },
      "source": [
        "# make class predictions with the model\n",
        "predictions = model.predict_classes(X)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiUC0VtmpCvL",
        "outputId": "35820e0f-ae94-481b-feca-a42ab2a9f206"
      },
      "source": [
        "# summarize the first 5 cases\n",
        "for i in range(5):\n",
        "\tprint('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6.0, 148.0, 72.0, 35.0, 0.0, 33.6, 0.627, 50.0] => 1 (expected 1)\n",
            "[1.0, 85.0, 66.0, 29.0, 0.0, 26.6, 0.351, 31.0] => 0 (expected 0)\n",
            "[8.0, 183.0, 64.0, 0.0, 0.0, 23.3, 0.672, 32.0] => 1 (expected 1)\n",
            "[1.0, 89.0, 66.0, 23.0, 94.0, 28.1, 0.167, 21.0] => 0 (expected 0)\n",
            "[0.0, 137.0, 40.0, 35.0, 168.0, 43.1, 2.288, 33.0] => 1 (expected 1)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}