How to find Number of parameters of a keras model?

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For a Feedforward Network (FFN), it is easy to compute the number of parameters. Given a CNN, LSTM etc is there a quick way to find the number of parameters in a keras model?

keras deep-learning
4 Answers

Tracing back the print_summary() function, Keras developers compute the number of trainable and non_trainable parameters of a given model as follows:

import keras.backend as K
import numpy as np

trainable_count = int(np.sum([K.count_params(p) for p in set(model.trainable_weights)]))

non_trainable_count = int(np.sum([K.count_params(p) for p in set(model.non_trainable_weights)]))

Given that K.count_params() is defined as np.prod(int_shape(x)), this solution is quite similar to the one of Anuj Gupta, except for the use of set() and the way the shape of the tensors are retrieved.

user_43678010

After creating your network add: model.summary
And it will give you a summary of your network and the number of parameters.

user_161334160
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