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- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
- from tensorflow.python.ops import rnn, rnn_cell
- mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
- hm_epochs = 3
- n_classes = 10
- 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(labels=y,logits=prediction) )
- optimizer = tf.train.AdamOptimizer().minimize(cost)
- with tf.Session() as sess:
- sess.run(tf.initialize_all_variables())
- for epoch in range(hm_epochs):
- epoch_loss = 0
- for _ in range(int(mnist.train.num_examples/batch_size)):
- epoch_x, epoch_y = mnist.train.next_batch(batch_size)
- epoch_x = epoch_x.reshape((batch_size,n_chunks,chunk_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:mnist.test.images.reshape((-1, n_chunks, chunk_size)), y:mnist.test.labels}))
- train_neural_network(x)
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