def run_task(variant):
log_dir = logger.get_snapshot_dir()
report = HTMLReport(os.path.join(log_dir, 'report.html'),
images_per_row=2,
default_image_width=500)
report.add_header('Simple Circle Sampling')
report.add_text(format_dict(variant))
report.save()
gan = SimpleGAN(noise_size=5, tf_session=tf.Session())
rand_theta = np.random.uniform(0, 2 * np.pi, size=(5000, 1))
data = np.hstack([0.5 * np.cos(rand_theta), 0.5 * np.sin(rand_theta)])
data = data + np.random.normal(scale=0.05, size=data.shape)
report.add_image(plot_samples(data[:500, :]), 'Real data')
generated_samples, _ = gan.sample_generator(100)
report.add_image(plot_samples(generated_samples))
for outer_iter in range(30):
dloss, gloss = gan.train(
data,
outer_iters=variant['outer_iters'],
)
logger.log('Outer iteration: {}, disc loss: {}, gen loss: {}'.format(
outer_iter, dloss, gloss))
report.add_text(
'Outer iteration: {}, disc loss: {}, gen loss: {}'.format(
outer_iter, dloss, gloss))
generated_samples, _ = gan.sample_generator(50)
report.add_image(plot_samples(generated_samples))
report.add_image(plot_dicriminator(gan))
report.save()
def run_task(variant):
gan_configs = {
'batch_size': 64,
'generator_output_activation': 'tanh',
'generator_optimizer': tf.train.RMSPropOptimizer(variant['generator_learning_rate']),
'discriminator_optimizer': tf.train.RMSPropOptimizer(variant['discriminator_learning_rate']),
'batch_normalize_discriminator': False,
'batch_normalize_generator': False,
'gan_type': 'lsgan',
}
if variant['generator_init'] == 'xavier':
gan_configs['generator_weight_initializer'] = tf.contrib.layers.xavier_initializer()
else:
gan_configs['generator_weight_initializer'] = tflearn.initializations.truncated_normal(stddev=variant['generator_init'])
gan = FCGAN(
generator_output_size=2,
discriminator_output_size=1,
generator_layers=[200, 200],
discriminator_layers=[128, 128],
noise_size=5,
tf_session=tf.Session(),
configs=gan_configs,
)
log_dir = logger.get_snapshot_dir()
report = HTMLReport(
os.path.join(log_dir, 'report.html'), images_per_row=2,
default_image_width=500
)
report.add_header('Simple Circle Sampling')
report.add_text(format_dict(variant))
report.save()
rand_theta = np.random.uniform(0, 2 * np.pi, size=(5000, 1))
data = np.hstack([0.5 * np.cos(rand_theta), 0.5 * np.sin(rand_theta)])
data = data + np.random.normal(scale=0.05, size=data.shape)
report.add_image(
plot_samples(data[:500, :]), 'Real data'
)
# for outer_iter in range(30):
# loss = gan.train_discriminator(data, data[:, 0:1] < 0, 100)
# logger.log(str(loss))
# report.add_image(
# plot_dicriminator(gan)
# )
# report.save()
# logger.log('Now training generator')
# for outer_iter in range(30):
# loss = gan.train_generator(np.random.randn(1000, 2), 100)
# logger.log(str(loss))
# generated_samples, _ = gan.sample_generator(50)
# report.add_image(
# plot_samples(generated_samples)
# )
for outer_iter in range(30):
dloss, gloss = gan.train(
data, np.ones((data.shape[0], 1)),
outer_iters=variant['outer_iters'], generator_iters=variant['generator_iters'],
discriminator_iters=variant['discriminator_iters']
)
logger.log(
'Outer iteration: {}, disc loss: {}, gen loss: {}'.format(
outer_iter, dloss, gloss
)
)
report.add_text(
'Outer iteration: {}, disc loss: {}, gen loss: {}'.format(
outer_iter, dloss, gloss
)
)
generated_samples, _ = gan.sample_generator(50)
report.add_image(
plot_samples(generated_samples)
)
report.add_image(
plot_dicriminator(gan)
)
report.save()