#载入数据集
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
#输入图片是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#隐藏单元
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#计算一共有多少个批次
#这里的none表示第一个维度可以是任意的长度
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))
#初始化偏置量
biases=tf.Variable(tf.constant(0.1,shape=[n_classes]))
#定义RNN网络
def RNN(X,weights,biases):
inputs=tf.reshape(X,[-1,max_time,n_inputs])
#定义LSTM基本CELL
lstm_cell=tf.contrib.rnn.core_rnn_cell.BasicLSTMCell(lstm_size)
outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
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)
#结果存放在一个布尔型列表中
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,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))#把correct_prediction变为float32类型
# 初始化
init=tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for epoch in range(6):
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.image,y:mnist.test.labels})
print("Iter"+str(epoch)+",Testing Accuracy="+str(acc))