def test_simulate_lrs_batch_step(self, policy):
lr_sch = LRScheduler(policy,
base_lr=1,
max_lr=5,
step_size_up=4,
step_every='batch')
lrs = lr_sch.simulate(11, 1)
expected = np.array([1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3])
assert np.allclose(expected, lrs)
def test_lr_scheduler_record_epoch_step(self, classifier_module,
classifier_data, policy, kwargs):
epochs = 3
scheduler = LRScheduler(policy, **kwargs)
lrs = scheduler.simulate(epochs, initial_lr=123.)
net = NeuralNetClassifier(classifier_module,
max_epochs=epochs,
lr=123.,
callbacks=[('scheduler', scheduler)])
net.fit(*classifier_data)
assert np.all(net.history[:, 'event_lr'] == lrs)
def test_lr_scheduler_record_batch_step(self, classifier_module,
classifier_data):
X, y = classifier_data
batch_size = 128
scheduler = LRScheduler(TorchCyclicLR,
base_lr=1,
max_lr=5,
step_size_up=4)
net = NeuralNetClassifier(classifier_module,
max_epochs=1,
lr=123.,
batch_size=batch_size,
callbacks=[('scheduler', scheduler)])
net.fit(X, y)
new_lrs = scheduler.simulate(
net.history[-1, 'train_batch_count'],
initial_lr=123.,
)
assert np.all(net.history[-1, 'batches', :, 'event_lr'] == new_lrs)
def test_simulate_lrs_batch_step(self):
lr_policy = LRScheduler(CyclicLR, base_lr=1, max_lr=5, step_size_up=4)
lrs = lr_policy.simulate(11, 1)
expected = np.array([1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3])
assert np.allclose(expected, lrs)
def test_simulate_lrs_epoch_step(self):
lr_policy = LRScheduler(StepLR, step_size=2)
lrs = lr_policy.simulate(6, 1)
expected = np.array([1.0, 1.0, 0.1, 0.1, 0.01, 0.01])
assert np.allclose(expected, lrs)