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RNN.py
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69 lines (49 loc) · 2.15 KB
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import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
#from __future__ import print_function
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(1)
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
lr = 0.001
training_iters = 100000
batch_size = 128
n_inputs = 28
n_steps = 28
n_hidden_units = 128
n_classes = 10
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])
weights = { 'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))}
biases = { 'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units,])),
'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))}
def RNN(X, weights, biases):
# hidden layer for input to cell
# transpose the inputs shape from
X = tf.reshape(X, [-1, n_inputs])
X_in = tf.matmul(X, weights['in']) + biases['in']
X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
# cell
cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units)
init_state = cell.zero_state(batch_size, dtype=tf.float32)
outputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False)
outputs = tf.unstack(tf.transpose(outputs, [1, 0, 2]))
results = tf.matmul(outputs[-1], weights['out']) + biases['out']
return results
pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
step = 0
while step * batch_size < training_iters:
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
sess.run([train_op], feed_dict={x: batch_xs, y: batch_ys,})
if step % 20 == 0:
print(sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys}))
step += 1