Visualize activation filters

visualize.py

# -*- coding: utf-8 -*-
"""
Created on Wed Apr 15 17:20:25 2020

@author: Nanda Velugoti
"""

import numpy as np
import matplotlib.pyplot as plt
from keras.preprocessing import image
from keras.models import load_model, Model

img_path = 'train/Cat/123.jpg'
img = image.load_img(img_path, target_size = (150, 150))
img_tensor = image.img_to_array(img)
img_tensor = np.expand_dims(img_tensor, axis=0)
img_tensor /= 255

plt.imshow(img_tensor[0])
plt.show()

model = load_model('cats_dogs_6.h5')
layer_outputs = [layer.output for layer in model.layers[:8]]
activation_model = Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(img_tensor)

first_layer_activation = activations[0]

plt.matshow(first_layer_activation[0, :, :, 4], cmap='viridis')
plt.matshow(first_layer_activation[0, :, :, 7], cmap='viridis')

layer_names = []
for layer in model.layers[:8]:
    layer_names.append(layer.name)
    
images_per_row = 16
for layer_name, layer_activation in zip(layer_names, activations):
    n_features = layer_activation.shape[-1]
    size = layer_activation.shape[1]
    n_cols = n_features // images_per_row
    display_grid = np.zeros((size*n_cols, images_per_row*size))
    for col in range(n_cols):
        for row in range(images_per_row):
            channel_image = layer_activation[0, :, :, col*images_per_row + row]
            channel_image -= channel_image.mean()
            channel_image -= channel_image.std()
            channel_image *= 64
            channel_image += 128
            channel_image = np.clip(channel_image, 0, 255).astype('uint8')
            display_grid[col*size : (col+1)*size, row*size: (row+1)*size] = channel_image
            
    scale = 1./size
    plt.figure(figsize=(scale*display_grid.shape[1], scale * display_grid.shape[0]))
    plt.title(layer_name)
    plt.grid(False)
    plt.imshow(display_grid, aspect='auto', cmap='viridis')