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sentimentAnalysisUsingNeuralNetwork.py
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sentimentAnalysisUsingNeuralNetwork.py
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import tensorflow as tf
import numpy as np
from createSentimentFeatureSets import create_feature_sets_and_labels
# creating the training input, output and testing inout output from the data stored in data
train_x, train_y, test_x, test_y = create_feature_sets_and_labels('data/pos.txt', 'data/neg.txt')
n_nodes_hl1 = 1000
n_nodes_hl2 = 1000
n_nodes_hl3 = 1000
n_classes = 2
batch_size = 100
# input height x weight
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]))}
# the model structure: (input * weights) + biases
l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
l1 = tf.nn.relu(l1) # relu is the activation function
l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
l2 = tf.nn.relu(l2) # relu is the activation function
l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])
l3 = tf.nn.relu(l3) # relu is the activation function
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(prediction, y))
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
hm_epochs = 10
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: ", accuracy.eval({x: test_x, y: test_y}))
if __name__ == '__main__':
train_neural_network(x)