How to get the total parameters number of different operation in tensorflow?
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
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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
add a comment |
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
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
tensorflow seq2seq
asked Jan 2 at 3:28
user3787704user3787704
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