activation_maximization_cifar10.py

'''
  Visualizing how layers represent classes with keras-vis Activation Maximization.
'''

# =============================================
# Model to be visualized
# =============================================
import keras
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras import activations

# Model configuration
img_width, img_height = 32, 32
batch_size = 250
no_epochs = 100
no_classes = 10
validation_split = 0.2
verbosity = 1

# Load MNIST dataset
(input_train, target_train), (input_test, target_test) = cifar10.load_data()

# Reshape data based on channels first / channels last strategy.
# This is dependent on whether you use TF, Theano or CNTK as backend.
# Source: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
if K.image_data_format() == 'channels_first':
    input_train = input_train.reshape(input_train.shape[0],3, img_width, img_height)
    input_test = input_test.reshape(input_test.shape[0], 3, img_width, img_height)
    input_shape = (3, img_width, img_height)
else:
    input_train = input_train.reshape(input_train.shape[0], img_width, img_height, 3)
    input_test = input_test.reshape(input_test.shape[0], img_width, img_height, 3)
    input_shape = (img_width  , img_height, 3)

# Parse numbers as floats
input_train = input_train.astype('float32')
input_test = input_test.astype('float32')

# Convert them into black or white: [0, 1].
input_train = input_train / 255
input_test = input_test / 255

# Convert target vectors to categorical targets
target_train = keras.utils.to_categorical(target_train, no_classes)
target_test = keras.utils.to_categorical(target_test, no_classes)

# Create the model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(no_classes, activation='softmax', name='visualized_layer'))

# Compile the model
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])

# Fit data to model
model.fit(input_train, target_train,
          batch_size=batch_size,
          epochs=no_epochs,
          verbose=verbosity,
          validation_split=validation_split)

# Generate generalization metrics
score = model.evaluate(input_test, target_test, verbose=0)
print(f'Test loss: {score[0]} / Test accuracy: {score[1]}')

# =============================================
# Activation Maximization code
# =============================================
from vis.visualization import visualize_activation
from vis.utils import utils
import matplotlib.pyplot as plt

# Find the index of the to be visualized layer above
layer_index = utils.find_layer_idx(model, 'visualized_layer')

# Swap softmax with linear
model.layers[layer_index].activation = activations.linear
model = utils.apply_modifications(model)  

# Classes to visualize
classes_to_visualize = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
classes = {
  0: 'airplane',
  1: 'automobile',
  2: 'bird',
  3: 'cat',
  4: 'deer',
  5: 'dog',
  6: 'frog',
  7: 'horse',
  8: 'ship',
  9: 'truck'
}

# Visualize
for number_to_visualize in classes_to_visualize:
  visualization = visualize_activation(model, layer_index, filter_indices=number_to_visualize, input_range=(0., 1.))
  plt.imshow(visualization)
  plt.title(f'CIFAR10 target = {classes[number_to_visualize]}')
  plt.show()