machine learning - How can I get biases from a trained model in Keras?

Question

I have built a simple neural network,

model = Sequential()
model.add(Dense(20, input_dim=5, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))

and I would get its weights by:

summary = model.summary()
W_Input_Hidden = model.layers[0].get_weights()[0]
W_Output_Hidden = model.layers[1].get_weights()[0]

print(summary)
print('INPUT-HIDDEN LAYER WEIGHTS:')
print(W_Input_Hidden)
print('HIDDEN-OUTPUT LAYER WEIGHTS:')
print(W_Output_Hidden)

but, in this way, I only get the weights matrices (5x20 , 1x20) without the biases. How can I get the biases values?

Answer 1

Quite simple, its just the second element in the array returned by get_weights() (For Dense layers):

B_Input_Hidden = model.layers[0].get_weights()[1]
B_Output_Hidden = model.layers[1].get_weights()[1]