Extract activations of a VGG16 model

Question

I'm using Tensorflow 2.0 and a pre-trained VGG16 model. I would like to visualize the activations. Therefore I want to extract them.

Currently I'm doing the following:

model = tf.keras.applications.VGG16(input_shape=(224, 224, 3), weights='imagenet')
model.outputs = [layer.output for layer in model.layers]
model.build(input_shape=(1, 224, 224 ,3))
activations = model(image_data)

However, I'm getting the following error when I try to call the last line:

ValueError: Structure is a scalar but len(flat_sequence) == 23 > 1

Answer 1

Would this approach: https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/

work for TF 2.0?

from keras.applications.vgg16 import VGG16
from matplotlib import pyplot
# load the model
model = VGG16()
# retrieve weights from the second hidden layer
filters, biases = model.layers[1].get_weights()
# normalize filter values to 0-1 so we can visualize them
f_min, f_max = filters.min(), filters.max()
filters = (filters - f_min) / (f_max - f_min)