keras_simple_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Main Imports\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# Additional Imports\n",
    "from sklearn.datasets import load_wine\n",
    "from tensorflow.contrib.keras import models, layers, losses, optimizers, metrics, activations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiating wine dataset from sklearn datasets\n",
    "wine_data = load_wine()\n",
    "\n",
    "# Showing the keys associated with the dataset\n",
    "wine_data.keys();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting features and labels\n",
    "\n",
    "feat_data = wine_data['data']\n",
    "labels = wine_data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset into training and testing using sklearn.model_selection.train_test_split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(feat_data, labels, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rescaling the data with MinMaxScaler from sklearn.preprocessing\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaled_x_train = scaler.fit_transform(x_train)\n",
    "scaled_x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setting up the model\n",
    "\n",
    "dnn_keras_model = models.Sequential()\n",
    "dnn_keras_model.add(layers.Dense(units=13,input_dim=13,activation='relu'))\n",
    "dnn_keras_model.add(layers.Dense(units=13,activation='relu'))\n",
    "dnn_keras_model.add(layers.Dense(units=13,activation='relu'))\n",
    "dnn_keras_model.add(layers.Dense(units=3,activation='softmax'))\n",
    "\n",
    "dnn_keras_model.compile(optimizer = 'adam',\n",
    "                       loss = 'sparse_categorical_crossentropy',\n",
    "                       metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_keras_model.fit(scaled_x_train, y_train, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating predictions\n",
    "\n",
    "predictions = dnn_keras_model.predict_classes(scaled_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating classification report using sklearn.metrics\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(predictions, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}