MachineIntelligence/SentimentTensorFlow.py at master · Artperkitny/MachineIntelligence · GitHub

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import tensorflow as tf
from create_sentiment_featureset import create_feature_sets_and_labels
import pickle
import numpy as np

train_x, train_y, test_x, test_y = pickle.load(open("sentiment_set.pickle","rb"))

n_node_hl1 = 500
n_node_hl2 = 1500
n_node_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_node_hl1])),
                      'biases':tf.Variable(tf.random_normal([n_node_hl1]))}

    hidden_2_layer = {'weights':tf.Variable(tf.random_normal([n_node_hl1, n_node_hl2])),
                      'biases':tf.Variable(tf.random_normal([n_node_hl2]))}

    hidden_3_layer = {'weights':tf.Variable(tf.random_normal([n_node_hl2, n_node_hl3])),
                      'biases':tf.Variable(tf.random_normal([n_node_hl3]))}

    output_layer = {'weights':tf.Variable(tf.random_normal([n_node_hl3, n_classes])),
                      '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)

    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_3_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 = 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, ' 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)