def test_iterator():
"""
Tests whether SparseDataset can be loaded and
initializes iterator
"""
x = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
ds = SparseDataset(from_scipy_sparse_dataset=x)
it = ds.iterator(mode='sequential', batch_size=1)
it.next()
def test_iterator():
"""
Tests whether SparseDataset can be loaded and
initializes iterator
"""
x = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
ds = SparseDataset(from_scipy_sparse_dataset=x)
it = ds.iterator(mode='sequential', batch_size=1)
it.next()
def test_training_a_model():
"""
tests wether SparseDataset can be trained
with a dummy model.
"""
dim = 3
m = 10
rng = np.random.RandomState([22, 4, 2014])
X = rng.randn(m, dim)
ds = csr_matrix(X)
dataset = SparseDataset(from_scipy_sparse_dataset=ds)
model = SoftmaxModel(dim)
learning_rate = 1e-1
batch_size = 5
epoch_num = 2
termination_criterion = EpochCounter(epoch_num)
cost = DummyCost()
algorithm = SGD(learning_rate,
cost,
batch_size=batch_size,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
train = Train(dataset,
model,
algorithm,
save_path=None,
save_freq=0,
extensions=None)
train.main_loop()