Keras - get weight of trained layer
keras, keras-layer, neural-network, python, tensorflow
Solution
If you use the `tensorflow` backend, you can evaluate the value of a tensor using the current session `sess` and feeding the correct input
import keras.backend as K
input_value = np.zeros(size=(batch_size, input_dim))
sess = K.get_session()
output = sess.run(layer_output, feed_dict={model.input: input_value})
If you just want to retrieve the weights, you can evaluate the weights of a layers using:
weights = [w.eval(K.get_session) for w in layer_dict['model_1'][layer_name].weights]
Problem
I'm trying to get the values of a layer in a trained network. I can get the layer as a TensorFlow Tensor, but I'm unable to access its values in an array shape: ``` from keras.models import load_model model = load_model('./model.h5') layer_dict = dict([(layer.name, layer) for layer in model.layers]) layer_name = 'block5_sepconv1_act' filter_index = 0 layer_output = layer_dict['model_1'][layer_name].output # <tf.Tensor 'block5_sepconv1_act/Relu:0' shape=(?, 16, 16, 728) dtype=float32> layer_filter = layer_output[:, :, :, filter_index] # <tf.Tensor 'strided_slice_11:0' shape=(?, 16, 16) dtype=float32> # how do I get the 16x16 values ?? ```