How to get the total parameters number of different operation in tensorflow?












0















I know how to get the trainable variables number in tensorflow. However, in some cases( seq2seq, video caption), the train_op works a little differently from the predict_op. I want to know how many parameters are related to the train_op, and how many parameters are related to the predict_op?



Here is the code to get the number of trainable variable parameters.



def get_num_params():
print('==='* 30)
num_params = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
print(variable.name, shape)
num_params += reduce(mul, [dim.value for dim in shape], 1)
print('total param number', num_params)
print('===' * 30)









share|improve this question



























    0















    I know how to get the trainable variables number in tensorflow. However, in some cases( seq2seq, video caption), the train_op works a little differently from the predict_op. I want to know how many parameters are related to the train_op, and how many parameters are related to the predict_op?



    Here is the code to get the number of trainable variable parameters.



    def get_num_params():
    print('==='* 30)
    num_params = 0
    for variable in tf.trainable_variables():
    shape = variable.get_shape()
    print(variable.name, shape)
    num_params += reduce(mul, [dim.value for dim in shape], 1)
    print('total param number', num_params)
    print('===' * 30)









    share|improve this question

























      0












      0








      0








      I know how to get the trainable variables number in tensorflow. However, in some cases( seq2seq, video caption), the train_op works a little differently from the predict_op. I want to know how many parameters are related to the train_op, and how many parameters are related to the predict_op?



      Here is the code to get the number of trainable variable parameters.



      def get_num_params():
      print('==='* 30)
      num_params = 0
      for variable in tf.trainable_variables():
      shape = variable.get_shape()
      print(variable.name, shape)
      num_params += reduce(mul, [dim.value for dim in shape], 1)
      print('total param number', num_params)
      print('===' * 30)









      share|improve this question














      I know how to get the trainable variables number in tensorflow. However, in some cases( seq2seq, video caption), the train_op works a little differently from the predict_op. I want to know how many parameters are related to the train_op, and how many parameters are related to the predict_op?



      Here is the code to get the number of trainable variable parameters.



      def get_num_params():
      print('==='* 30)
      num_params = 0
      for variable in tf.trainable_variables():
      shape = variable.get_shape()
      print(variable.name, shape)
      num_params += reduce(mul, [dim.value for dim in shape], 1)
      print('total param number', num_params)
      print('===' * 30)






      tensorflow seq2seq






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Jan 2 at 3:28









      user3787704user3787704

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