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.

Answer 2

If you write:

dense1 = Dense(10, activation='relu')(input_x)

Then dense1 is not a layer, it's the output of a layer. The layer is Dense(10, activation='relu')

So it seems you meant:

dense1 = Dense(10, activation='relu') y = dense1(input_x) 

Here is a full snippet:

import tensorflow as tf from tensorflow.contrib.keras import layers  input_x = tf.placeholder(tf.float32, [None, 10], name='input_x')     dense1 = layers.Dense(10, activation='relu') y = dense1(input_x)  weights = dense1.get_weights()