def test_uniformly_weighted(self):
# Uniform weights of size batch size
w1 = np.ones(self.y.shape[0], dtype='f')
loss_w1 = compute_weighted_value_loss(
self.eltwise_losses, self.y.shape[0], w1,
batch_accumulator=self.batch_accumulator).array
if self.batch_accumulator == 'mean':
eltwise_loss = self.eltwise_losses.sum(axis=1).mean()
else:
eltwise_loss = self.eltwise_losses.sum()
self.assertAlmostEqual(loss_w1, eltwise_loss, places=5)
def test_randomly_weighted(self):
# Random weights
wu = np.random.uniform(low=0, high=2, size=self.y.shape[0]).astype('f')
loss_wu = compute_weighted_value_loss(
self.eltwise_losses,
self.y.shape[0], wu,
batch_accumulator=self.batch_accumulator).array
if self.batch_accumulator == 'mean':
eltwise_loss = (self.eltwise_losses.sum(axis=1) * wu).mean()
else:
eltwise_loss = (self.eltwise_losses * wu[:, None]).sum()
self.assertAlmostEqual(loss_wu, eltwise_loss, places=5)