def main():
# inputs
input_shape = (1, 84, 84)
filters = 32
kernel_size = 6
epochs = 20
batch_size = 1
# log directory
run = 'cvae_atari_pong_no_batch_norm_16_May_17_15_27_batch_size_1_beta_1_epochs_20_filters_32_kernel_size_6_loss_vae_loss_lr_0.0001_optimizer_adam'
log_dir = './summaries/' + experiment + '/' + run + '/'
# define model
vae = PongEntangledConvolutionalLatentNoBatchNormVAE(
input_shape, log_dir, filters=filters, kernel_size=kernel_size)
# load weights
vae.load_model()
# extract models
model = vae.get_model()
decoder = vae.get_decoder()
encoder = vae.get_encoder()
# load testing data
test_directory = './atari_agents/record/test/'
test_generator = utils.atari_generator(test_directory, batch_size=1)
X_test_size = 100
X_test = np.asarray(
[next(test_generator)[0][0] for i in range(X_test_size)])
# show original and reconstruction
sampling.encode_decode_sample(X_test, model)
# plot filters
sampling.show_convolutional_layers(X_test, encoder, 8, 8)
def train_space_invaders_network_no_batch_norm(beta):
# inputs
input_shape = (1, 84, 84)
filters = 32
kernel_size = 6
epochs = 10
batch_size = 1
lr = 1e-4
# define filename
name = 'cvae_atari_space_invaders_no_batch_norm'
# builder hyperparameter dictionary
hp_dictionary = {
'epochs': epochs,
'batch_size': batch_size,
'beta': beta,
'filters': filters,
'kernel_size': kernel_size,
'lr': lr,
'loss': 'vae_loss',
'optimizer': 'adam'
}
# define log directory
log_dir = './summaries/' + experiment + '/' + utils.build_hyperparameter_string(
name, hp_dictionary) + '/'
# make VAE
vae = PongEntangledConvolutionalLatentNoBatchNormVAE(
input_shape,
log_dir,
filters=filters,
kernel_size=kernel_size,
beta=beta)
# compile VAE
from keras import optimizers
optimizer = optimizers.Adam(lr=lr)
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)