Visualize outputs of activations with Tensorflow 2.0

activations_visualization.py

import numpy as np
import tensorflow as tf

layers_name = ['activation_1']
IMAGE_PATH = './cat.jpg'

# Model to examine
model = tf.keras.applications.resnet50.ResNet50(weights='imagenet', include_top=True)

# Image to pass as input
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
img = tf.keras.preprocessing.image.img_to_array(img)

# Get the outputs of layers we want to inspect
outputs = [
    layer.output for layer in model.layers
    if layer.name in layers_name
]

# Create a connection between the input and those target outputs
activations_model = tf.keras.models.Model(model.inputs, outputs=outputs)
activations_model.compile(optimizer='adam', loss='categorical_crossentropy')

# Get their outputs
activations_1 = activations_model.predict(np.array([img]))