Tensorflow-RNN循环网络-手写数字识别(MNIST数据集)

#RNN卷积神经网络-手写数字识别--MNIST数据集
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#载入数据
mnist = input_data.read_data_sets('C:/Users/lenovo/Desktop/TF/MNIST_data',one_hot=True)

#输入图片28*28
n_inputs = 28 #输入一行，一行有28个数据
max_time = 28 #一共28行
lstm_size = 100 #隐层单元
n_classes = 10 #10个分类
batch_size = 50 #每批次50个样本
n_batch = mnist.train.num_examples // batch_size #一共有多少个批次

#这里的None表示第一个维度可以实任意的长度
x = tf.placeholder(tf.float32, [None, 784])
#正确的标签
y = tf.placeholder(tf.float32, [None, 10])

#初始化权值
weights = tf.Variable(tf.truncated_normal([lstm_size, n_classes], stddev=0.1))
#初始化偏置值
biases = tf.Variable(tf.constant(0.1, shape=[n_classes]))

#定义RNN网络
def RNN(X, weights, biases):
#inputs =[batch_size, max_time, n_inputs])
inputs = tf.reshape(X, [-1, max_time, n_inputs])
#定义LSTM基本CELL
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(lstm_size)
#final_state[0]是cell state
#final_state[1]是hidden_state
outputs, final_state = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)  #传入inputs个是固定
#outputs, final_state维度

results = tf.nn.softmax(tf.matmul(final_state[1], weights)+biases)
return results
#计算RNN的返回结果
prediction = RNN(x, weights, biases)
#损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
#使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#结果存放在一个bool类型列表中
correct_prediction = tf.equal(tf.arg_max(prediction,1), tf.arg_max(y, 1)) #argmax返回一维张量中最大的值所在的位置
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
tf.global_variables_initializer().run()
for epoch in range(21):
for batch in range(n_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})

acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
print('Iter'+ str(epoch)+',Testing Accuracy=' + str(acc))

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