Visualise activation in CNN

visualise_activation.py

from keras.models import Model

layer_outputs = [layer.output for layer in model.layers] 
activation_model = Model(inputs=model.input, outputs=layer_outputs) 
activations = activation_model.predict(X_train[10].reshape(1,28,28,1))

def display_activation(activations, col_size, row_size, act_index):
  activation = activations[act_index]
  activation_index=0
  fig, ax = plt.subplots(row_size, col_size, figsize=(row_size*2.5,col_size*1.5)) 
  for row in range(0,row_size):
    for col in range(0,col_size):
      ax[row][col].imshow(activation[0, :, :, activation_index], cmap='gray')
      activation_index += 1