def main():
opts = {}
# Utility
opts['random_seed'] = 66
opts['dataset'] = 'mnist' # gmm, circle_gmm, mnist, mnist3 ...
opts['data_dir'] = 'mnist'
opts['trained_model_path'] = None #'models'
opts[
'mnist_trained_model_file'] = None #'mnist_trainSteps_19999_yhat' # 'mnist_trainSteps_20000'
opts['work_dir'] = FLAGS.workdir
opts['ckpt_dir'] = 'checkpoints'
opts["verbose"] = 1
opts['tf_run_batch_size'] = 128
opts["early_stop"] = -1 # set -1 to run normally
opts["plot_every"] = 200
opts["save_every_epoch"] = 20
opts['gmm_max_val'] = 15.
# Datasets
opts['toy_dataset_size'] = 10000
opts['toy_dataset_dim'] = 2
opts['mnist3_dataset_size'] = 2 * 64 # 64 * 2500
opts['mnist3_to_channels'] = False # Hide 3 digits of MNIST to channels
opts['input_normalize_sym'] = False # Normalize data to [-1, 1]
opts['gmm_modes_num'] = 5
# AdaGAN parameters
opts['adagan_steps_total'] = 1
opts['samples_per_component'] = 1000
opts['is_bagging'] = FLAGS.is_bagging
opts['beta_heur'] = 'uniform' # uniform, constant
opts['weights_heur'] = 'theory_star' # theory_star, theory_dagger, topk
opts['beta_constant'] = 0.5
opts['topk_constant'] = 0.5
opts["mixture_c_epoch_num"] = 5
opts["eval_points_num"] = 25600
opts['digit_classification_threshold'] = 0.999
opts['inverse_metric'] = False # Use metric from the Unrolled GAN paper?
opts['inverse_num'] = 100 # Number of real points to inverse.
opts['objective'] = None
# Generative model parameters
opts["init_std"] = FLAGS.init_std
opts["init_bias"] = 0.0
opts['latent_space_distr'] = 'normal' # uniform, normal
opts['latent_space_dim'] = FLAGS.zdim
opts["gan_epoch_num"] = 100
opts['convolutions'] = True # If False then encoder is MLP of 3 layers
opts['d_num_filters'] = 1024
opts['d_num_layers'] = 4
opts['g_num_filters'] = 1024
opts['g_num_layers'] = 3
opts['e_is_random'] = False
opts['e_pretrain'] = False
opts['e_add_noise'] = False
opts['e_pretrain_bsize'] = 1000
opts['e_num_filters'] = 1024
opts['e_num_layers'] = 4
opts['g_arch'] = 'dcgan_mod'
opts['g_stride1_deconv'] = False
opts['g_3x3_conv'] = 0
opts['e_arch'] = 'dcgan'
opts['e_3x3_conv'] = 0
opts['conv_filters_dim'] = 4
# --GAN specific:
opts['conditional'] = False
opts['unrolled'] = FLAGS.unrolled # Use Unrolled GAN? (only for images)
opts['unrolling_steps'] = 5 # Used only if unrolled = True
# --VAE specific
opts['vae'] = FLAGS.vae
opts['vae_sigma'] = 0.01
# --POT specific
opts['pot'] = FLAGS.pot
opts['pot_pz_std'] = 2.
opts['pot_lambda'] = FLAGS.pot_lambda
opts['adv_c_loss'] = 'none'
opts['vgg_layer'] = 'pool2'
opts['adv_c_patches_size'] = 5
opts['adv_c_num_units'] = 32
opts['adv_c_loss_w'] = 1.0
opts['cross_p_w'] = 0.0
opts['diag_p_w'] = 0.0
opts['emb_c_loss_w'] = 1.0
opts['reconstr_w'] = 1.0
opts['z_test'] = 'gan'
opts['gan_p_trick'] = False
opts['pz_transform'] = False
opts['z_test_corr_w'] = 1.0
opts['z_test_proj_dim'] = 10
# Optimizer parameters
opts['optimizer'] = 'adam' # sgd, adam
opts["batch_size"] = 100
opts["d_steps"] = 1
opts['d_new_minibatch'] = False
opts["g_steps"] = 2
opts['batch_norm'] = True
opts['dropout'] = False
opts['dropout_keep_prob'] = 0.5
opts['recon_loss'] = 'cross_entropy'
# "manual" or number (float or int) giving the number of epochs to divide
# the learning rate by 10 (converted into an exp decay per epoch).
opts['decay_schedule'] = 'manual'
opts['opt_learning_rate'] = FLAGS.learning_rate
opts['opt_d_learning_rate'] = FLAGS.d_learning_rate
opts['opt_g_learning_rate'] = FLAGS.g_learning_rate
opts["opt_beta1"] = FLAGS.adam_beta1
opts['batch_norm_eps'] = 1e-05
opts['batch_norm_decay'] = 0.9
if opts['e_is_random']:
assert opts['latent_space_distr'] == 'normal',\
'Random encoders currently work only with Gaussian Pz'
# Data augmentation
opts['data_augm'] = False
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
utils.create_dir(opts['work_dir'])
utils.create_dir(os.path.join(opts['work_dir'], opts['ckpt_dir']))
with utils.o_gfile((opts['work_dir'], 'params.txt'), 'w') as text:
text.write('Parameters:\n')
for key in opts:
text.write('%s : %s\n' % (key, opts[key]))
data = DataHandler(opts)
assert data.num_points >= opts['batch_size'], 'Training set too small'
adagan = AdaGan(opts, data)
metrics = Metrics()
train_size = data.num_points
random_idx = np.random.choice(train_size, 4 * 320, replace=False)
metrics.make_plots(opts,
0,
data.data,
data.data[random_idx],
adagan._data_weights,
prefix='dataset_')
for step in range(opts["adagan_steps_total"]):
logging.info('Running step {} of AdaGAN'.format(step + 1))
adagan.make_step(opts, data)
num_fake = opts['eval_points_num']
logging.debug('Sampling fake points')
fake_points = adagan.sample_mixture(num_fake)
logging.debug('Sampling more fake points')
more_fake_points = adagan.sample_mixture(500)
logging.debug('Plotting results')
if opts['dataset'] == 'gmm':
metrics.make_plots(opts, step, data.data[:500], fake_points[0:100],
adagan._data_weights[:500])
logging.debug('Evaluating results')
(likelihood, C) = metrics.evaluate(opts,
step,
data.data[:500],
fake_points,
more_fake_points,
prefix='')
else:
metrics.make_plots(opts, step, data.data, fake_points[:320],
adagan._data_weights)
if opts['inverse_metric']:
logging.debug('Evaluating results')
l2 = np.min(adagan._invert_losses[:step + 1], axis=0)
logging.debug('MSE=%.5f, STD=%.5f' % (np.mean(l2), np.std(l2)))
res = metrics.evaluate(opts,
step,
data.data[:500],
fake_points,
more_fake_points,
prefix='')
logging.debug("AdaGan finished working!")
def main():
opts = {}
opts['random_seed'] = 821
opts['dataset'] = 'gmm' # gmm, circle_gmm, mnist, mnist3 ...
opts['unrolled'] = FLAGS.unrolled # Use Unrolled GAN? (only for images)
opts['unrolling_steps'] = 5 # Used only if unrolled = True
opts['data_dir'] = 'mnist'
opts['trained_model_path'] = 'models'
opts[
'mnist_trained_model_file'] = 'mnist_trainSteps_19999_yhat' # 'mnist_trainSteps_20000'
opts['gmm_max_val'] = 15.
opts['toy_dataset_size'] = 256 * 1000
opts['toy_dataset_dim'] = 2
opts['mnist3_dataset_size'] = 2 * 64 # 64 * 2500
opts['mnist3_to_channels'] = False # Hide 3 digits of MNIST to channels
opts['input_normalize_sym'] = True # Normalize data to [-1, 1]
opts['adagan_steps_total'] = 10
opts['samples_per_component'] = 5000 # 50000
opts['work_dir'] = FLAGS.workdir
opts['is_bagging'] = FLAGS.is_bagging
opts['beta_heur'] = 'uniform' # uniform, constant
opts['weights_heur'] = 'theory_star' # theory_star, theory_dagger, topk
opts['beta_constant'] = 0.5
opts['topk_constant'] = 0.5
opts["init_std"] = FLAGS.init_std
opts["init_bias"] = 0.0
opts['latent_space_distr'] = 'normal' # uniform, normal
opts['optimizer'] = 'adam' # sgd, adam
opts["batch_size"] = 256
opts["d_steps"] = 1
opts["g_steps"] = 1
opts["verbose"] = True
opts['tf_run_batch_size'] = 100
opts['objective'] = 'JS'
opts['gmm_modes_num'] = 3
opts['latent_space_dim'] = FLAGS.zdim
opts["gan_epoch_num"] = 15
opts["mixture_c_epoch_num"] = 5
opts['opt_learning_rate'] = FLAGS.learning_rate
opts['opt_d_learning_rate'] = FLAGS.d_learning_rate
opts['opt_g_learning_rate'] = FLAGS.g_learning_rate
opts["opt_beta1"] = FLAGS.adam_beta1
opts['batch_norm_eps'] = 1e-05
opts['batch_norm_decay'] = 0.9
opts['d_num_filters'] = 16
opts['g_num_filters'] = 16
opts['conv_filters_dim'] = 4
opts["early_stop"] = -1 # set -1 to run normally
opts["plot_every"] = 500 # set -1 to run normally
opts["eval_points_num"] = 1000 # 25600
opts['digit_classification_threshold'] = 0.999
opts['inverse_metric'] = False # Use metric from the Unrolled GAN paper?
opts['inverse_num'] = 1 # Number of real points to inverse.
if opts['verbose']:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(message)s')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
utils.create_dir(opts['work_dir'])
with utils.o_gfile((opts['work_dir'], 'params.txt'), 'w') as text:
text.write('Parameters:\n')
for key in opts:
text.write('%s : %s\n' % (key, opts[key]))
data = DataHandler(opts)
assert data.num_points >= opts['batch_size'], 'Training set too small'
adagan = AdaGan(opts, data)
metrics = Metrics()
for step in range(opts["adagan_steps_total"]):
logging.info('Running step {} of AdaGAN'.format(step + 1))
adagan.make_step(opts, data)
num_fake = opts['eval_points_num']
logging.debug('Sampling fake points')
fake_points = adagan.sample_mixture(num_fake)
logging.debug('Sampling more fake points')
more_fake_points = adagan.sample_mixture(500)
logging.debug('Plotting results')
metrics.make_plots(opts, step, data.data[:500], fake_points[0:100],
adagan._data_weights[:500])
logging.debug('Evaluating results')
(likelihood, C) = metrics.evaluate(opts,
step,
data.data[:500],
fake_points,
more_fake_points,
prefix='')
logging.debug("AdaGan finished working!")