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rnn.py
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rnn.py
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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.ops import rnn, rnn_cell
from data import create_dataset
import numpy as np
train_x, train_y, test_x, test_y=create_dataset()
hm_epochs = 10
n_classes = 40
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128
x = tf.placeholder('float', [None, n_chunks,chunk_size])
y = tf.placeholder('float')
def recurrent_neural_network(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size,n_classes])),
'biases':tf.Variable(tf.random_normal([n_classes]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
lstm_cell = rnn_cell.BasicLSTMCell(rnn_size,state_is_tuple=True)
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1],layer['weights']) + layer['biases']
return output
def train_neural_network(x):
prediction = recurrent_neural_network(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
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
_, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
epoch_loss += c
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)