def test_sparse_softmax_cross_entropy_pytorch(self):
"""Test SparseSoftmaxCrossEntropy."""
loss = losses.SparseSoftmaxCrossEntropy()
y = np.array([[0.1, 0.8], [0.4, 0.6]])
outputs = torch.tensor(y)
labels = torch.tensor([1, 0])
result = loss._create_pytorch_loss()(outputs, labels).numpy()
softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1)
expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])]
assert np.allclose(expected, result)
def test_sparse_softmax_cross_entropy_tf(self):
"""Test SparseSoftmaxCrossEntropy."""
loss = losses.SparseSoftmaxCrossEntropy()
y = np.array([[0.1, 0.8], [0.4, 0.6]])
outputs = tf.constant(y)
labels = tf.constant([1, 0])
result = loss._compute_tf_loss(outputs, labels).numpy()
softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1)
expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])]
assert np.allclose(expected, result)
labels = tf.constant([[1], [0]])
result = loss._compute_tf_loss(outputs, labels).numpy()
softmax = np.exp(y) / np.expand_dims(np.sum(np.exp(y), axis=1), 1)
expected = [-np.log(softmax[0, 1]), -np.log(softmax[1, 0])]
assert np.allclose(expected, result)
