sentiment_NN.py

import tensorflow as tf
import numpy as np
from sentiment import create_feature_sets_and_labels


train_x, train_y, test_x, test_y = create_feature_sets_and_labels('pos.txt', 'neg.txt')

n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500

n_classes = 2
batch_size = 100



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

def neural_network_model(data):


	hidden_1_layer = {'weights' : tf.Variable(tf.truncated_normal([len(train_x[0]), n_nodes_hl1],stddev=0.1)) , 'biases' : tf.Variable(tf.random_normal([n_nodes_hl1]))}
	
	hidden_2_layer = {'weights' : tf.Variable(tf.truncated_normal([n_nodes_hl1, n_nodes_hl2],stddev=0.1)) , 'biases' : tf.Variable(tf.random_normal([n_nodes_hl2]))}
	
	hidden_3_layer = {'weights' : tf.Variable(tf.truncated_normal([n_nodes_hl2, n_nodes_hl3],stddev=0.1)) , 'biases' : tf.Variable(tf.random_normal([n_nodes_hl3]))}
	
	output_layer = {'weights' : tf.Variable(tf.truncated_normal([n_nodes_hl3, n_classes],stddev=0.1)) , 'biases' : tf.Variable(tf.random_normal([n_classes]))}
	
	l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
	l1 = tf.nn.relu(l1)  #passing it through activation funct

	l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
	l2 = tf.nn.relu(l2)

	l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_2_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 #cycels FF+BP

	with tf.Session() as sess:
		sess.run(tf.initialize_all_variables())

		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; ', 100*accuracy.eval({x:test_x, y:test_y})) 

train_neural_network(x)