def train_entangled_pong_network():
# inputs
input_shape = (1, 84, 84)
filters = 32
kernel_size = 6
beta = 1.0 # entangled latent space
epochs = 10
batch_size = 1
# define filename
name = 'cvae_atari_entangled_pong'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'kernel_size': kernel_size,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = PongEntangledConvolutionalLatentVAE(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=1e-1)
vae.compile(optimizer=optimizer)
# get dataset
train_directory = './atari_agents/record/train/'
test_directory = './atari_agents/record/test/'
train_generator = utils.atari_generator(train_directory,
batch_size=batch_size)
test_generator = utils.atari_generator(test_directory,
batch_size=batch_size)
train_size = utils.count_images(train_directory)
test_size = utils.count_images(test_directory)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
def train_shallow(beta):
# inputs
input_shape = (1, 28, 28)
epochs = 20
batch_size = 1
filters = 32
kernel_size = 6
pre_latent_size = 128
latent_size = 32
# define filename
name = 'shallow'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'kernel_size': kernel_size,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = ShallowDenseMNIST(input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
pre_latent_size=pre_latent_size,
latent_size=latent_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=1e-4)
vae.compile(optimizer=optimizer)
# get dataset
(X_train, _), (X_test, _) = utils.load_mnist()
train_generator = utils.make_generator(X_train, batch_size=batch_size)
test_generator = utils.make_generator(X_test, batch_size=batch_size)
train_size = len(X_train)
test_size = len(X_test)
# print summaries
vae.print_model_summaries()
# fit VAE
steps_per_epoch = int(train_size / batch_size)
validation_steps = int(test_size / batch_size)
vae.fit_generator(train_generator,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_generator,
validation_steps=validation_steps)
