How to find Number of parameters of a keras model?

Models and layers have special method for that purpose:

model.count_params()

Also, to get a short summary of each layer dimensions and parameters, you might find useful the following method

model.summary()

import keras.backend as K

def size(model): # Compute number of params in a model (the actual number of floats)
    return sum([np.prod(K.get_value(w).shape) for w in model.trainable_weights])

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.