How to get featuremap of convolutional layer in keras

Question

I have a model that I loaded using Keras. I need to be able to find individual feature maps (print values of each feature map). I was able to print weights. Following is my code:

for layer in model.layers:
    g=layer.get_config()
    h=layer.get_weights()

    print g

    print h

The model consists of one convlayer which has total 384 neurons. First 128 have filter size 3, next 4 and last 128 have filter size 5. Then, there are relu and maxpool layers and then it is fed into softmax layer. I want to be able to find outputs (values not shapes) of convlayer, relu and maxpool. I have seen codes online but I'm unable to comprehend on how to map them to my situation.

Answer 1

If you are looking for a way to find the activation (ie feature map or output) of a layer given one or more input samples, you can simply define a backend function that takes the input array(s) and gives the activation(s) as its output. Here is an example for illustration (ie you may need to adapt it to your needs and your model architecture):

from keras import backend as K

# define a function to get the activation of all layers
outputs = [layer.output for layer in model.layers]
active_func = K.function([model.input], [outputs])

# you can use it like this
activations = active_func([my_input_array])