TensorFlow技术解析与实战 9.5 RNN
# -*- coding:utf-8 -*-
import sys
import importlib
importlib.reload(sys)
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
# 加载数据
mnist = input_data.read_data_sets("./", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels
trX = trX.reshape(-1, 28, 28, 1) # 28x28x1 input img
teX = teX.reshape(-1, 28, 28, 1) # 28x28x1 input img
# 设置训练的超参数
lr = 0.001
training_iters = 100000
batch_size = 128
# 神经网络的参数
n_inputs = 28 # 输入层的n
n_steps = 28 # 28长度
n_hidden_units = 128 # 隐藏层的神经元个数
n_classes = 10 # 输出的数量，即分类的类别，0~9个数字，共有10个
# 输入数据占位符
x = tf.placeholder("float", [None, n_steps, n_inputs])
y = tf.placeholder("float", [None, n_classes])
# 定义权重
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, ]))
}
#定义RNN模型
def RNN(X, weights, biases):
X = tf.reshape(X, [-1, n_inputs]) #把输入的X转换成X ==》（128 batch * 28 steps, 28 inputs）
# 进入隐藏层
X_in = tf.matmul(X, weights['in']) + biases['in'] # (128 batch * 28 steps, 128 hidden)
X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units]) # 128 batch , 28 steps, 128 hidden
# 这里采用基本的LSTM循环网络单元：basic LSTM Cell
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units, forget_bias=1.0, state_is_tuple=True)
init_state = lstm_cell.zero_state(batch_size, dtype=tf.float32) #lstm单元由两个部分组成：（c_state, h_state）
# dynamic_rnn接收张量(batch, steps, inputs)或者（steps, batch, inputs）作为X_in
outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, X_in, initial_state=init_state, time_major=False)
results = tf.matmul(final_state[1], weights['out']) + biases['out']
return results
# 定义损失函数和优化器，优化器采用AdamOptimizer
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)
# 定义模型预测结果及准确率计算方法
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 训练数据及评估模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
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
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