def __init__(self, model, criterion,
replicate_in_memory=True):
self.model = model
self.criterion = criterion
self.metrics = dict()
self.add_metric('loss', lambda: Loss())
self.replicate_in_memory = replicate_in_memory
def test_loss():
loss_calculator = Loss(average=True)
loss = list()
loss_calculator.reset()
for _ in range(10):
epoch_loss = np.random.uniform(0, 1)
loss.append(epoch_loss)
loss_calculator.update(epoch_loss)
assert np.around(np.array(loss).mean(),
decimals=4) == np.around(loss_calculator.value,
decimals=4)
loss_calculator = Loss(average=False)
loss = list()
loss_calculator.reset()
for _ in range(10):
epoch_loss = np.random.uniform(0, 1)
loss.append(epoch_loss)
loss_calculator.update(epoch_loss)
assert np.allclose(np.array(loss), loss_calculator.value)
def __init__(self, model, criterion):
self.model = model
self.criterion = criterion
self.metrics = dict()
self.add_metric('loss', lambda: Loss())