spam_classifier_deep_nn.py

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 30 13:23:34 2018

@author: Aditya
"""

import tensorflow as tf
import numpy as np

import warnings
warnings.filterwarnings("ignore")

from create_sentiment_features import create_feature_sets_and_labels

train_x,train_y,test_x,test_y = create_feature_sets_and_labels('spam.txt','ham.txt')

n_nodes_hl1 = 1000
n_nodes_hl2 = 1000
n_nodes_hl3 = 1000

n_classes = 2
batch_size = 100

x = tf.placeholder('float', [None, len(train_x[0])])
y = tf.placeholder('float')

def neural_network_model(data):
    
    hidden1_layer = {'weights':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
    
    hidden2_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}
    
    hidden3_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                     'biases':tf.Variable(tf.random_normal([n_nodes_hl3]))}
    
    output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                     'biases':tf.Variable(tf.random_normal([n_classes]))}
    
    l1 = tf.add(tf.matmul(data, hidden1_layer['weights']) , hidden1_layer['biases'])
    l1 = tf.nn.relu(l1)
    
    l2 = tf.add(tf.matmul(l1, hidden2_layer['weights']) , hidden2_layer['biases'])
    l2 = tf.nn.relu(l2)
    
    l3 = tf.add(tf.matmul(l2, hidden3_layer['weights']) , hidden3_layer['biases'])
    l3 = tf.nn.relu(l3)
    
    output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
    
    return output

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)
    
    hm_epochs = 20
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(hm_epochs):
            epoch_loss = 0
            i = 0
            while i < len(train_x):
                start = i
                end = i + batch_size
                
                batch_x = np.array(train_x[start:end])
                batch_y = np.array(train_y[start:end])
                _, c = sess.run([optimizer, cost], feed_dict ={x: batch_x, y: batch_y})
                epoch_loss += c
                i += batch_size
            print('Epoch', epoch, 'completed out of', hm_epochs,'loss: ',epoch_loss)
        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
        
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))
        
train_neural_network(x)