关于stock-prediction2的问题

[beathahahaha]

input=tf.reshape(X,[-1,input_size])
input_rnn=tf.matmul(input,w_in)+b_in
input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit])

output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32)

综合网上教程，我觉的dynamic_cnn中的input_rnn维度应该是[-1,time_step,input_size],tensorflow中是封装好的（参考：https://www.cnblogs.com/zyly/p/9029591.html），但作者您自己写了input 的w 和b，将input_rnn的维度改成了[-1,time_step,rnn_unit]，感觉有点奇怪。

我自己写的代码如下：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)
print("start")
print(tf.version)
print(mnist)

n_inputs=28 #input_size
max_time=28 #也即time_step
lstm_size=100 #num_units
n_classes=10
batch_size=50
n_batch=mnist.train.num_examples //batch_size

x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

w=tf.Variable(tf.truncated_normal([lstm_size,n_classes],stddev=0.1))
b=tf.Variable(tf.constant(0.1,shape=[n_classes]))

def RNN(X,w,b):
inputs=tf.reshape(X,[-1,max_time,n_inputs])
lstm_cell=tf.contrib.rnn.BasicLSTMCell(lstm_size)
outputs,final_state=tf.nn.dynamic_rnn(lstm_cell,inputs,dtype=tf.float32)
#final_state[0]=cell state
#final_state[1]=hidden state
results=tf.nn.softmax(tf.matmul(final_state[1],w)+b)
return results

prediction=RNN(x,w,b)
cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #hinton建议设置为1e-3，代表初始学习率

correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

n=0
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
for epoch in range(6):
print("n:",n)
n+=1
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})
print("epoch:",epoch)

    acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
    print(str(epoch)+" times,accuracy:"+str(acc))

print("over")