tf.nn.dynamic_rnn和MultiRNNCell构建多层动态LSTM

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[code]import tensorflow as tf;
import numpy as np;

X = tf.random_normal(shape=[3, 5, 6], dtype=tf.float32)
X = tf.reshape(X, [-1, 5, 6])
stacked_rnn=[]
for i in range(3):
stacked_rnn.append(tf.contrib.rnn.BasicLSTMCell(24))
# cell = tf.nn.rnn_cell.BasicLSTMCell(10)  # 也可以换成别的，比如GRUCell，BasicRNNCell等等
lstm_multi = tf.contrib.rnn.MultiRNNCell(stacked_rnn)
# state = lstm_multi.zero_state(3, tf.float32)
output, state = tf.nn.dynamic_rnn(lstm_multi, X, time_major=False,dtype=tf.float32)

# with tf.Session() as sess:
#     sess.run(tf.initialize_all_variables())
#     print(output.get_shape())
#     print(sess.run(state))

print(output.shape)

print(len(state))#三个LSTM隐藏层
#第一个LSTM隐藏层
print(state[0].h.shape)#LSTM中的h状态
print(state[0].c.shape)#LSTM中的c状态
#第二个LSTM隐藏层
print(state[1].h.shape)
print(state[1].c.shape)
#第三个LSTM隐藏层
print(state[2].h.shape)
print(state[2].c.shape)

 

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