Advanced Deep Learning with Keras

Chapter 8
# instantiate vae model
outputs = decoder([encoder([inputs, y_labels])[2], y_labels])
cvae = Model([inputs, y_labels], outputs, name='cvae')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
help_ = "Load h5 model trained weights"
parser.add_argument("-w", "--weights", help=help_)
help_ = "Use mse loss instead of binary cross entropy (default)"
parser.add_argument("-m", "--mse", help=help_, action='store_
true')
help_ = "Specify a specific digit to generate"
parser.add_argument("-d", "--digit", type=int, help=help_)
help_ = "Beta in Beta-CVAE. Beta > 1. Default is 1.0 (CVAE)"
parser.add_argument("-b", "--beta", type=float, help=help_)
args = parser.parse_args()
models = (encoder, decoder)
data = (x_test, y_test)
if args.beta is None or args.beta < 1.0:
beta = 1.0
print("CVAE")
model_name = "cvae_cnn_mnist"
else:
beta = args.beta
print("Beta-CVAE with beta=", beta)
model_name = "beta-cvae_cnn_mnist"
# VAE loss = mse_loss or xent_loss + kl_loss
if args.mse:
reconstruction_loss = mse(K.flatten(inputs),
K.flatten(outputs))
else:
reconstruction_loss = binary_crossentropy(K.flatten(inputs),
K.flatten(outputs))
reconstruction_loss *= image_size * image_size
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5 * beta
cvae_loss = K.mean(reconstruction_loss + kl_loss)
cvae.add_loss(cvae_loss)
cvae.compile(optimizer='rmsprop')
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