layerwise_output.py

#importing required libraries and functions
from keras.models import Model
#defining names of layers from which we will take the output
layer_names = ['block1_conv1','block2_conv1','block3_conv1','block4_conv2']
outputs = []
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
#extracting the output and appending to outputs
for layer_name in layer_names:
    intermediate_layer_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output)
    intermediate_output = intermediate_layer_model.predict(image)
    outputs.append(intermediate_output)
#plotting the outputs
fig,ax = plt.subplots(nrows=4,ncols=5,figsize=(20,20))

for i in range(4):
    for z in range(5):
        ax[i][z].imshow(outputs[i][0,:,:,z])
        ax[i][z].set_title(layer_names[i])
        ax[i][z].set_xticks([])
        ax[i][z].set_yticks([])
plt.savefig('layerwise_output.jpg')