def test_randomly_weighted(self):
# Random weights
wu = np.random.uniform(low=0, high=2, size=self.y.size).astype('f')
loss_wu = compute_weighted_value_loss(
self.y, self.t, clip_delta=self.clip_delta,
batch_accumulator=self.batch_accumulator,
weights=wu).array
if self.batch_accumulator == 'mean':
gt_loss = (self.gt_losses * wu).mean()
else:
gt_loss = (self.gt_losses * wu).sum()
self.assertAlmostEqual(loss_wu, gt_loss, places=5)
def test_uniformly_weighted(self):
# Uniform weights
w1 = np.ones(self.y.size, dtype='f')
loss_w1 = compute_weighted_value_loss(
self.y, self.t, clip_delta=self.clip_delta,
batch_accumulator=self.batch_accumulator,
weights=w1).array
if self.batch_accumulator == 'mean':
gt_loss = self.gt_losses.mean()
else:
gt_loss = self.gt_losses.sum()
self.assertAlmostEqual(loss_w1, gt_loss, places=5)