def testRunTraining(self):
training.run_training(
dataset='mnist',
output_type='bernoulli',
n_y=10,
n_y_active=1,
training_data_type='sequential',
n_concurrent_classes=1,
lr_init=1e-3,
lr_factor=1.,
lr_schedule=[1],
blend_classes=False,
train_supervised=False,
n_steps=1000,
report_interval=1000,
knn_values=[3],
random_seed=1,
encoder_kwargs={
'encoder_type': 'multi',
'n_enc': [1200, 600, 300, 150],
'enc_strides': [1],
},
decoder_kwargs={
'decoder_type': 'single',
'n_dec': [500, 500],
'dec_up_strides': None,
},
n_z=32,
dynamic_expansion=True,
ll_thresh=-200.0,
classify_with_samples=False,
gen_replay_type='fixed',
use_supervised_replay=False,
)
def main(unused_argv):
if FLAGS.dataset == 'mnist':
n_y = 25
n_y_active = 25
n_z = 50
else: # omniglot
n_y = 100
n_y_active = 100
n_z = 100
training.run_training(
dataset=FLAGS.dataset,
n_y=n_y,
n_y_active=n_y_active,
n_z=n_z,
output_type='bernoulli',
training_data_type='iid',
n_concurrent_classes=1,
lr_init=5e-4,
lr_factor=1.,
lr_schedule=[1],
blend_classes=False,
train_supervised=False,
n_steps=100000,
report_interval=10000,
knn_values=[3, 5, 10],
random_seed=1,
encoder_kwargs={
'encoder_type': 'multi',
'n_enc': [500, 500],
'enc_strides': [1],
},
decoder_kwargs={
'decoder_type': 'single',
'n_dec': [500],
'dec_up_strides': None,
},
dynamic_expansion=False,
ll_thresh=-0.0,
classify_with_samples=True,
gen_replay_type=None,
use_supervised_replay=False,
)