def setUp(self):
fake_dataset = Dataset.get_fake(self.input_size, 1)
self.model = MADE(fake_dataset,
hidden_sizes=self.hidden_sizes,
batch_size=fake_dataset['train']['data'].shape[0],
hidden_activation=theano.tensor.nnet.sigmoid,
use_cond_mask=self.use_cond_mask,
direct_input_connect=self.direct_input_connect,
direct_output_connect=self.direct_output_connect)
self.nb_shuffle = 50
def _get_fake_model(self, in_size, hidden_sizes):
fake_dataset = Dataset.get_fake(in_size, 1)
return MADE(
fake_dataset,
# learning_rate=trainingparams['learning_rate'],
# decrease_constant=trainingparams['decrease_constant'],
hidden_sizes=hidden_sizes,
# random_seed=hyperparams['random_seed'],
# batch_size=trainingparams['batch_size'],
hidden_activation=lambda x: x,
use_cond_mask=False,
direct_input_connect="Output",
direct_output_connect=False)
def _get_conditioning_mask_model(dataset, input_size, hidden_sizes):
import theano.tensor as T
return MADE(
dataset,
# learning_rate=trainingparams['learning_rate'],
# decrease_constant=trainingparams['decrease_constant'],
hidden_sizes=hidden_sizes,
# random_seed=hyperparams['random_seed'],
# batch_size=trainingparams['batch_size'],
#hidden_activation=lambda x: x,
hidden_activation=T.nnet.sigmoid,
use_cond_mask=True,
direct_input_connect="None",
direct_output_connect=False,
weights_initialization="Diagonal")
def build_model(dataset, trainingparams, hyperparams, hidden_sizes):
print '\n### Initializing MADE ... ',
start_time = t.time()
model = MADE(dataset,
learning_rate=trainingparams['learning_rate'],
decrease_constant=trainingparams['decrease_constant'],
hidden_sizes=hidden_sizes,
random_seed=hyperparams['random_seed'],
batch_size=trainingparams['batch_size'],
hidden_activation=activation_functions[
hyperparams['hidden_activation']],
use_cond_mask=hyperparams['use_cond_mask'],
direct_input_connect=hyperparams['direct_input_connect'],
direct_output_connect=hyperparams['direct_output_connect'],
update_rule=trainingparams['update_rule'],
dropout_rate=trainingparams['dropout_rate'],
weights_initialization=hyperparams['weights_initialization'],
mask_distribution=hyperparams['mask_distribution'])
print utils.get_done_text(start_time), "###"
return model
def setUp(self):
### Testing that the sum of all prob is equal to 1 ###
# This test has to be run in 64bit for accuracy
self._old_theano_config_floatX = theano.config.floatX
theano.config.floatX = 'float64'
self.nb_test = 15
self._shuffling_type = "Full"
fake_dataset = Dataset.get_permutation(self.input_size)
self.model = MADE(fake_dataset,
hidden_sizes=self.hidden_sizes,
batch_size=fake_dataset['train']['data'].shape[0],
hidden_activation=theano.tensor.nnet.sigmoid,
use_cond_mask=self.use_cond_mask,
direct_input_connect=self.direct_input_connect,
direct_output_connect=self.direct_output_connect)
# Train the model to have more accurate results
for i in range(2 * self.input_size):
self.model.shuffle(self._shuffling_type)
self.model.learn(i, True)
