import tensorflow as tf
def lstm_cell():
return tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.BasicLSTMCell(128), output_keep_prob=0.7)
cell= tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(2)])
initial_state= cell.zero_state(1, tf.float32)
layer = tf.placeholder(tf.float32, [1,1,36])
outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=layer, initial_state=initial_state)
outputs = tf.reshape(outputs, shape=[1, -1])
outputs = tf.layers.dense(outputs, 36,\
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
outputs = tf.reshape(outputs, shape=[1, 1, -1])
rnn_outputs_sequence=outputs
print(outputs)
for i in range(1, 16, 1):
outputs, state=tf.nn.dynamic_rnn(cell=cell, inputs=outputs, initial_state=state)
outputs = tf.reshape(outputs, shape=[1, -1])
outputs = tf.layers.dense(outputs, 36,\
kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
outputs = tf.reshape(outputs, shape=[1, 1, -1])
print(outputs)
rnn_outputs_sequence=tf.concat((rnn_outputs_sequence, outputs),axis=1)
print(rnn_outputs_sequence)