def create_output_directory():
os.makedirs(cfg.output_dir, exist_ok=True)
output_subdir = create_result_subdir(cfg.output_dir)
print('Output directory: ' + output_subdir)
files = [f for f in os.listdir('.') if os.path.isfile(f)]
for f in files:
shutil.copy(f, output_subdir)
return output_subdir
def train():
result_subdir = create_result_subdir(result_dir)
real_words = read_words(real_words_path)
fake_words = read_words(fake_words_path)
real_words = [
word for word in [convert_to_char_seq(word) for word in real_words]
if word != []
]
fake_words = [
word for word in [convert_to_char_seq(word) for word in fake_words]
if word != []
]
words = real_words + fake_words
words = pad_words(words)
words = np.array(words)[:, :, np.newaxis]
print(words.shape)
labels = np.concatenate(
[np.ones(len(real_words)),
np.zeros(len(fake_words))])
words_train, words_val, labels_train, labels_val = train_test_split(
words, labels, test_size=0.2, random_state=42)
model = simple_model()
opt = Adam(0.01)
model.compile(loss=binary_crossentropy,
optimizer=opt,
metrics=[binary_accuracy])
model.summary()
checkpoint = ModelCheckpoint(os.path.join(
result_subdir, 'model.{epoch:03d}-{val_loss:.2f}.h5'),
monitor='val_loss')
model.fit(words_train,
labels_train,
batch_size=32,
epochs=10,
verbose=1,
validation_data=(words_val, labels_val),
callbacks=[])
def create_output_directory():
os.makedirs('eval', exist_ok=True)
output_subdir = create_result_subdir('eval')
print('Output directory: ' + output_subdir)
return output_subdir
num_gpus = distribution_strategy.num_replicas_in_sync
print("Num gpus:", num_gpus)
# Compute global batch size using number of replicas.
global_batch_size = batch_size_per_gpu * num_gpus
dataset = make_dataset().batch(global_batch_size)
total_n_examples = 60_000
steps_per_epoch = total_n_examples // global_batch_size
results_dir = "results"
resume_run_id = 1
if resume_run_id:
config.log_dir = f"{results_dir}/{resume_run_id:02}-mnist"
else:
config.log_dir = utils.create_result_subdir(results_dir, "mnist")
config.checkpoint_dir = config.log_dir + "/checkpoints"
# strategy = tf.distribute.MirroredStrategy()
strategy = tf.distribute.get_strategy()
with strategy.scope():
gen = DCGANGenerator()
disc = DCGANDiscriminator()
gan = blurred_gan.BlurredWGANGP(gen,
disc,
hyperparams=hyperparameters,
config=config)
checkpoint = tf.train.Checkpoint(gan=gan)
manager = tf.train.CheckpointManager(checkpoint,
# TODO: Make multi-GPU training work
# distribution_strategy = tf.distribute.MirroredStrategy()
distribution_strategy = tf.distribute.get_strategy()
num_gpus = distribution_strategy.num_replicas_in_sync
print("Num gpus:", num_gpus)
# Compute global batch size using number of replicas.
global_batch_size = batch_size_per_gpu * num_gpus
dataset = make_dataset().batch(global_batch_size)
total_n_examples = 202_599
steps_per_epoch = total_n_examples // global_batch_size
results_dir = "results"
config.log_dir = utils.create_result_subdir(results_dir, "celeba")
config.checkpoint_dir = config.log_dir + "/checkpoints"
gen = DCGANGenerator()
disc = DCGANDiscriminator()
gan = blurred_gan.BlurredWGANGP(gen,
disc,
hyperparams=hyperparameters,
config=config)
checkpoint = tf.train.Checkpoint(gan=gan)
manager = tf.train.CheckpointManager(checkpoint,
directory=config.checkpoint_dir,
max_to_keep=5,
keep_checkpoint_every_n_hours=1)
