How to get output of hidden layer given an input, weights and biases of the hidden layer in keras?

Question

Suppose I have trained the model below for an epoch:

model = Sequential([
    Dense(32, input_dim=784), # first number is output_dim
    Activation('relu'),
    Dense(10), # output_dim, input_dim is taken for granted from above
    Activation('softmax'),
])

And I got the weights dense1_w, biases dense1_b of first hidden layer (named it dense1) and a single data sample sample.

How do I use these to get the output of dense1 on the sample in keras?

Thanks!

Answer 1

The easiest way is to use the keras backend. With the keras backend you can define a function that gives you the intermediate output of a keras model as defined here (https://keras.io/getting-started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer).

So in essence:

get_1st_layer_output = K.function([model.layers[0].input],
                                  [model.layers[1].output])
layer_output = get_1st_layer_output([X])