tensorflow39《TensorFlow实战》笔记-07-03 TensorFlow实现Bidirectional LSTM Classifier code

# 《TensorFlow实战》07 TensorFlow实现循环神经网络及Word2Vec
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:sz07.03.py # TensorFlow实现Bidirectional LSTM Classifier

# 源码位置
# https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py 
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
learning_rate = 0.01
max_samples = 400000
batch_size = 128
display_step=10
n_input = 28
n_steps = 28
n_hidden = 256
n_classes = 10

x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])
weights = tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
biases = tf.Variable(tf.random_normal([n_classes]))

def BiRNN(x, weights, biases):
x = tf.transpose(x, [1, 0, 2])
x = tf.reshape(x, [-1, n_input])
x = tf.split(x, n_steps)

lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32)
return tf.matmul(outputs[-1], weights) + biases

pred = BiRNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
step = 1
while step * batch_size < max_samples:
batch_x, batch_y = mnist.train.next_batch(batch_size)
batch_x = batch_x.reshape((batch_size, n_steps, n_input))
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
if step %display_step == 0:
acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})
loss = sess.run(cost, feed_dict={x:batch_x, y:batch_y})
print("Iter " + str(step*batch_size) + ", Minibatch Loss = " +
"{:.6f}".format(loss) + ", Training Accuracy= " +
"{:.5f}".format(acc))
step += 1
print("Optimization Finished!")

test_len = 10000
test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))
test_label = mnist.test.labels[:test_len]
print("Testing Accuracy:", sess.run(accuracy, feed_dict={x:test_data, y:test_label}))
'''
Iter 1280, Minibatch Loss = 2.086039, Training Accuracy= 0.17188
Iter 2560, Minibatch Loss = 1.309406, Training Accuracy= 0.46094
Iter 3840, Minibatch Loss = 1.036413, Training Accuracy= 0.63281
...
Iter 395520, Minibatch Loss = 0.006523, Training Accuracy= 1.00000
Iter 396800, Minibatch Loss = 0.014398, Training Accuracy= 0.99219
Iter 398080, Minibatch Loss = 0.014382, Training Accuracy= 0.99219
Iter 399360, Minibatch Loss = 0.018454, Training Accuracy= 0.99219
Optimization Finished!
Testing Accuracy: 0.9863
'''