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SentimentNeuralNetwork.py
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SentimentNeuralNetwork.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jul 11 16:13:44 2017
@author: aakash.chotrani
"""
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 5 19:10:34 2017
@author: aakash.chotrani
"""
import tensorflow as tf
from SentimentAnalysis import create_feature_sets_and_labels
import pickle
import numpy as np
#train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
train_x, train_y, test_x, test_y = pickle.load(open("sentiment_set.pickle","rb"))
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.random_normal([len(train_x[0]),n_nodes_hl1])),
'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer ={'weights': tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
'biases':tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_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]))}
#(input_data*weights) + biases
l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']),hidden_1_layer['biases'])#input goes through the sum box
l1 = tf.nn.relu(l1)#rectified linear is activation function applied to layer 1
l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']),hidden_2_layer['biases'])#input goes through the sum box
l2 = tf.nn.relu(l1)#rectified linear is activation function applied to layer 2
l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']),hidden_3_layer['biases'])#input goes through the sum box
l3 = tf.nn.relu(l3)#rectified linear is activation function applied to layer 3
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)
#cycles of feed forward and back propagation
hm_epochs = 10
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+1,'completed out of', hm_epochs,'loss:',epoch_loss)
correct = tf.equal(tf.arg_max(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)