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sentiment_NN.py
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sentiment_NN.py
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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)