基于tensorflow的循环神经网络RNN(LSTM)实现（MNIST数据集）
环境为windows10+python3+tensorflow，如无法运行，可能是tf版本更新发生变化。
训练文件train.py：
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #导入数据
lr=0.001#学习效率
training_iters=500000 #训练次数
batch_size=128 #每次抓取的数据量
n_inputs=28 #28列
n_steps=28 #28行
n_hidden_unis=128 #隐藏层单元个数
n_classes=10 #0-9
#进行网络结构的建立
with tf.name_scope('inputs'): #input
x = tf.placeholder(tf.float32,[None,n_steps,n_inputs],name='x_inputs') #得到传递进来的训练样本 图片
y = tf.placeholder(tf.float32,[None,n_classes],name='y_inputs') #标签
weights=\
{
'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_unis]),name='weights'), #从样本进行输入
'out':tf.Variable(tf.random_normal([n_hidden_unis,n_classes]),name='weights1') #输出至隐藏层
}
biases=\
{ #偏置
'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_unis,]),name='biases'), #(128,)
'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]),name='biases1') #(10,)
}
def RNN(X,weights,biases):#X（128batch,28steps,28inputs）
with tf.name_scope('layer'):
X = tf.reshape(X,[-1,n_inputs]) #重置为128*28,28的向量
X_in = tf.matmul(X,weights['in'])+biases['in'] #X与weight矩阵乘，加偏置
X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_unis]) #变换形式，置为n*28*128向量
tf.summary.histogram('X_in', X_in) #储存结果
#cell
with tf.name_scope('cell'):
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_unis,forget_bias=1.0, state_is_tuple=True) #该层128个cell，偏置 = 1
_init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32) #初始化
outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False) #输入至cell中
tf.summary.histogram('outsputs', outputs) #储存输出和状态
tf.summary.histogram('outsputs', states)
#输出层
with tf.name_scope('result'):
result = tf.matmul(states[1],weights['out'])+biases['out'] #计算结果，将状态与权重（出）相乘，加偏置（出）
tf.summary.histogram('result', result)
return result
pred = RNN(x,weights,biases) #调用RNN进行预测
with tf.name_scope('loss'):
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=pred),name='loss') #求平均值 交叉熵（训练成本）cost
tf.summary.scalar('loss', cost)
with tf.name_scope('train'): #进行优化
train_op = tf.train.AdamOptimizer(lr).minimize(cost) #以lr为学习速度最小化cost
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) #比较预测结果和正确答案是否相等，结果为向量
accurary = tf.reduce_mean(tf.cast(correct_pred,tf.float32)) #将correct_pred转化为float32类型，求均值
tf.summary.scalar('accurary', accurary)
init = tf.global_variables_initializer()
saver = tf.train.Saver() #保存模型参数
with tf.Session() as sess: #将sess指向要处理的地方
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs1/", sess.graph) #打开写文件指针，准备记录
sess.run(init) #激活，开始训练
step=0
while step<10000:
# step*batch_size
测试文件test.py：
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #导入已下载数据
batch_size = 128
n_inputs=28#mnist数据集是28*28像素，n_inputs代表的是每一行的28列
n_steps=28#代表28行
n_hidden_unis=128#隐藏层单元个数
n_classes=10#分类称10个，代表0-9
with tf.name_scope('inputs'):
x = tf.placeholder(tf.float32,[None,n_steps,n_inputs],name='x_inputs') #得到传递进来的训练样本 图片 ？？？？？？格式？？？
y = tf.placeholder(tf.float32,[None,n_classes],name='y_inputs') #标签
weights={
'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_unis]),name='weights'),
'out':tf.Variable(tf.random_normal([n_hidden_unis,n_classes]),name='weights1')
}
biases={
#(128,)
'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_unis,]),name='biases'),
#(10,)
'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]),name='biases1')
}
def RNN(X,weights,biases):#X（128batch,28steps,,28inputs）128个数字，每个都是28行28列
##隐藏层
#X(128*28,28)
with tf.name_scope('layer'):
X=tf.reshape(X,[-1,n_inputs])
#X(128*28,28)
X_in=tf.matmul(X,weights['in'])+biases['in']
#X(128,28,28)
X_in=tf.reshape(X_in,[-1,n_steps,n_hidden_unis])
tf.summary.histogram('X_in', X_in)
#cell
with tf.name_scope('cell'):
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_unis,forget_bias=1.0, state_is_tuple=True)
_init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
tf.summary.histogram('outsputs', outputs)
tf.summary.histogram('outsputs', states)
#输出层
with tf.name_scope('result'):
result=tf.matmul(states[1],weights['out'])+biases['out']
tf.summary.histogram('result', result)
return result
pred=RNN(x,weights,biases)
correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1)) #比较预测结果和正确答案是否相等，结果为向量
accurary = tf.reduce_mean(tf.cast(correct_pred,tf.float32)) #将correct_pred转化为float32类型，求均值
tf.summary.scalar('accurary', accurary)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, "my_net1/save_net.ckpt") #读取模型
step=0
while step*batch_size<10000:
batch_xs,batch_ys = mnist.test.next_batch(batch_size) #再取下一个batch块，返回image和label
batch_xs = batch_xs.reshape([batch_size,n_steps,n_inputs]) #同之前操作
if step%10==0: #每10步，进行一次记录
print(sess.run(accurary,feed_dict={x:batch_xs,y:batch_ys})) #正确率
step += 1运行train.py生成网络模型，再调用test.py对图片进行分类测试