def test_make_one_hot_works(self):
n_classes = 10
for i in range(n_classes):
one_hot_lab = du.make_onehot(i, n_classes)
one_hot_lab_true = [0. for _ in range(n_classes)]
one_hot_lab_true[i] = 1.
one_hot_lab_true = tf.constant(one_hot_lab_true)
self.assertAllClose(one_hot_lab, one_hot_lab_true)
def get_loss(self, batch_x, batch_y, return_preds=False):
if not self.onehot:
batch_y = dataset_utils.make_onehot(batch_y, self.n_classes)
return self.get_loss_with_onehot_labels(batch_x, batch_y, return_preds)