Visualice activations in convolutions

app.py

from keras.layers import Conv2D,Flatten
model = Sequential()

# Add a convolutional layer of 32 filters of size 3x3
model.add(Conv2D(32, kernel_size = 3, input_shape = (28, 28, 1), activation = 'relu'))

# Add a convolutional layer of 16 filters of size 3x3
model.add(Conv2D(16, kernel_size =3, activation = 'relu'))

# Flatten the previous layer output
model.add(Flatten())

# Add as many outputs as classes with softmax activation
model.add(Dense(10, activation = 'softmax'))

#TRAIN...

# Obtain a reference to the outputs of the first layer
first_layer_output = model.layers[0].output

# Build a model using the model's input and the first layer output
first_layer_model = Model(inputs = model.layers[0].input, outputs = first_layer_output)

# Use this model to predict on X_test
activations = first_layer_model.predict(X_test)

# Plot the activations of first digit of X_test for the 15th filter
axs[0].matshow(activations[0,:,:,14], cmap = 'viridis')

# Do the same but for the 18th filter now
axs[1].matshow(activations[0,:,:,17], cmap = 'viridis')
plt.show()