def test_multiple_monitoring_datasets():
# tests that DefaultTrainingAlgorithm can take multiple
# monitoring datasets.
BATCH_SIZE = 2
BATCHES = 3
NUM_FEATURES = 4
dim = 3
m = 10
rng = np.random.RandomState([2014, 02, 25])
X = rng.randn(m, dim)
Y = rng.randn(m, dim)
train = DenseDesignMatrix(X=X)
test = DenseDesignMatrix(X=Y)
algorithm = DefaultTrainingAlgorithm(
batch_size=BATCH_SIZE,
batches_per_iter=BATCHES,
monitoring_dataset={'train': train, 'test': test})
model = S3C(nvis=NUM_FEATURES, nhid=1,
irange=.01, init_bias_hid=0., init_B=1.,
min_B=1., max_B=1., init_alpha=1.,
min_alpha=1., max_alpha=1., init_mu=0.,
m_step=Grad_M_Step(learning_rate=0.),
e_step=E_Step(h_new_coeff_schedule=[1.]))
algorithm.setup(model=model, dataset=train)
algorithm.train(dataset=train)
def test_counting():
BATCH_SIZE = 2
BATCHES = 3
NUM_FEATURES = 4
num_examples = BATCHES * BATCH_SIZE
dataset = DummyDataset(num_examples=num_examples,
num_features=NUM_FEATURES)
algorithm = DefaultTrainingAlgorithm(batch_size=BATCH_SIZE,
batches_per_iter=BATCHES)
model = S3C(nvis=NUM_FEATURES,
nhid=1,
irange=.01,
init_bias_hid=0.,
init_B=1.,
min_B=1.,
max_B=1.,
init_alpha=1.,
min_alpha=1.,
max_alpha=1.,
init_mu=0.,
m_step=Grad_M_Step(learning_rate=0.),
e_step=E_Step(h_new_coeff_schedule=[1.]))
algorithm.setup(model=model, dataset=dataset)
algorithm.train(dataset=dataset)
if not (model.monitor.get_batches_seen() == BATCHES):
raise AssertionError('Should have seen '+str(BATCHES) + \
' batches but saw '+str(model.monitor.get_batches_seen()))
assert model.monitor.get_examples_seen() == num_examples
assert isinstance(model.monitor.get_examples_seen(), py_integer_types)
assert isinstance(model.monitor.get_batches_seen(), py_integer_types)
def set_training_criteria(self,
learning_rate=0.05,
batch_size=10,
max_epochs=10):
self.training_alg = DefaultTrainingAlgorithm(
batch_size=batch_size,
monitoring_dataset=self.datasets,
termination_criterion=EpochCounter(max_epochs))
def test_train_batch():
# Just tests that train_batch can be called without crashing
m = 1
dim = 2
rng = np.random.RandomState([2014, 03, 17])
X = rng.randn(m, dim)
train = DenseDesignMatrix(X=X)
rbm = RBM(nvis=dim, nhid=3)
trainer = DefaultTrainingAlgorithm(batch_size=1, batches_per_iter=10)
trainer.setup(rbm, train)
trainer.train(train)
def test_unspecified_batch_size():
# Test that failing to specify the batch size results in a
# NoBatchSizeError
m = 1
dim = 2
rng = np.random.RandomState([2014, 03, 17])
X = rng.randn(m, dim)
train = DenseDesignMatrix(X=X)
rbm = RBM(nvis=dim, nhid=3)
trainer = DefaultTrainingAlgorithm()
try:
trainer.setup(rbm, train)
except NoBatchSizeError:
return
raise AssertionError("Missed the lack of a batch size")
