使用上區別

# @rnn.static_cnn
#    input: a num_step-length list of tensors with shape (batch,embed_size)
#    output: 
#       outputs: a num_step-length list of tensors whose shape is (batch,state_size),
#                which is the last layer output of each time_step/num_step
#       stats: a num_layer-length tuple with shape of its each item (batch,state_size),
 

#              which is every layer output of the last time_step
#
#  @rnn.dynamic_cnn
#     input: a tensor with shape (batch,num_step,embed_size))
#     output:
#        outputs: a tensor with shape (batch,num_step,state_size),
#                 which is the last layer output of each time_step/num_step
 

#        stats: a num_layer-length tuple with shape of its each item (batch,state_size),
#               which is every layer output of the last time_step

執行上的區別

# @rnn.dynamic_cnn:
#	the time_step of different batch can be different,
#	which means the rnn will stop when time arrives at time_step
#	and it runs faster than static_cnn
 

# @rnn.static_cnn:
#	the time_step of different batch must be same.