visualize-convnet.py

print('visualize convnet')

from hack import hack
hack()

from keras.models import load_model
model = load_model('cats_and_dogs_small.h5')

# model.summary()

img_path = '../catsdogssmall/test/cats/cat.1700.jpg'

from keras.preprocessing import image
import numpy as np 

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.

print(img_tensor.shape)

import matplotlib.pyplot as plt 

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

from keras import models

layer_outputs = [layer.output for layer in model.layers[:8]]
activation_model = models.Model(inputs=model.input, outputs=layer_outputs)

activations = activation_model.predict(img_tensor)

# first_layer_activation = activations[1]
# print(first_layer_activation.shape)

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

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() + 0.00001) # so it's not zero ever!
            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')

plt.show()