How do I get the weights of a layer in Keras?

Question

I am using Windows 10, Python 3.5, and tensorflow 1.1.0. I have the following script:

import tensorflow as tf
import tensorflow.contrib.keras.api.keras.backend as K
from tensorflow.contrib.keras.api.keras.layers import Dense

tf.reset_default_graph()
init = tf.global_variables_initializer()
sess =  tf.Session()
K.set_session(sess) # Keras will use this sesssion to initialize all variables

input_x = tf.placeholder(tf.float32, [None, 10], name='input_x')    
dense1 = Dense(10, activation='relu')(input_x)

sess.run(init)

dense1.get_weights()

I get the error: AttributeError: 'Tensor' object has no attribute 'weights'

What am I doing wrong, and how do I get the weights of dense1? I have look at this and this SO post, but I still can't make it work.

Answer 1

If you want to get weights and biases of all layers, you can simply use:

for layer in model.layers: print(layer.get_config(), layer.get_weights())

This will print all information that's relevant.

If you want the weights directly returned as numpy arrays, you can use:

first_layer_weights = model.layers[0].get_weights()[0]
first_layer_biases  = model.layers[0].get_weights()[1]
second_layer_weights = model.layers[1].get_weights()[0]
second_layer_biases  = model.layers[1].get_weights()[1]

etc.