def test_sgd_sequential():
# tests that requesting train_iteration_mode = 'sequential'
# works
dim = 1
batch_size = 3
m = 5 * batch_size
dataset = ArangeDataset(m)
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
visited = [False] * m
def visit(X):
assert X.shape[1] == 1
assert np.all(X[1:] == X[0:-1] + 1)
start = int(X[0, 0])
if start > 0:
assert visited[start - 1]
for i in xrange(batch_size):
assert not visited[start + i]
visited[start + i] = 1
data_specs = (model.get_input_space(), model.get_input_source())
cost = CallbackCost(visit, data_specs)
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
algorithm = SGD(learning_rate,
cost,
batch_size=5,
train_iteration_mode='sequential',
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
algorithm.train(dataset)
assert all(visited)
def test_determinism():
# Verifies that running SGD twice results in the same examples getting visited
# in the same order
for mode in _iteration_schemes:
dim = 1
batch_size = 3
num_batches = 5
m = num_batches * batch_size
dataset = ArangeDataset(m)
model = SoftmaxModel(dim)
learning_rate = 1e-3
batch_size = 5
visited = [[-1] * m]
def visit(X):
mx = max(visited[0])
counter = mx + 1
for i in X[:, 0]:
i = int(i)
assert visited[0][i] == -1
visited[0][i] = counter
counter += 1
data_specs = (model.get_input_space(), model.get_input_source())
cost = CallbackCost(visit, data_specs)
# We need to include this so the test actually stops running at some point
termination_criterion = EpochCounter(5)
def run_algorithm():
unsupported_modes = ['random_slice', 'random_uniform']
algorithm = SGD(learning_rate,
cost,
batch_size=5,
train_iteration_mode=mode,
monitoring_dataset=None,
termination_criterion=termination_criterion,
update_callbacks=None,
init_momentum=None,
set_batch_size=False)
algorithm.setup(dataset=dataset, model=model)
raised = False
try:
algorithm.train(dataset)
except ValueError:
print mode
assert mode in unsupported_modes
raised = True
if mode in unsupported_modes:
assert raised
return True
return False
if run_algorithm():
continue
visited.insert(0, [-1] * m)
del model.monitor
run_algorithm()
for v in visited:
assert len(v) == m
for elem in range(m):
assert elem in v
assert len(visited) == 2
print visited[0]
print visited[1]
assert np.all(np.asarray(visited[0]) == np.asarray(visited[1]))