Updating forkMerge branch 'master' of https://github.com/deepchem/deepchem into sparse_adam

    return torch.nn.L1Loss(reduction='none')

class HuberLoss(Loss):

  """Modified version of L1 Loss, also known as Smooth L1 loss.

  Less sensitive to small errors, linear for larger errors.

  Huber loss is generally better for cases where are are both large outliers as well as small, as compared to the L1 loss.

  By default, Delta = 1.0 and reduction = 'none'.

  """

  def _compute_tf_loss(self, output, labels):

    import tensorflow as tf

    output, labels = _make_tf_shapes_consistent(output, labels)

    return tf.keras.losses.Huber(reduction='none')(output, labels)

  def _create_pytorch_loss(self):

    import torch

    return torch.nn.SmoothL1Loss(reduction='none')

class L2Loss(Loss):

  """The squared difference between the true and predicted values."""

    expected = [[0.1, 0.2], [0.6, 0.6]]

    assert np.allclose(expected, result)

  @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed')

  def test_huber_loss_tf(self):

    """Test HuberLoss."""

    loss = losses.HuberLoss()

    outputs = tf.constant([[0.1, 0.8], [0.4, 0.6]])

    labels = tf.constant([[1.0, -1.0], [-1.0, 1.0]])

    result = np.mean(loss._compute_tf_loss(outputs, labels).numpy())

    expected = 0.67125

    assert np.allclose(expected, result)

  @unittest.skipIf(not has_pytorch, 'PyTorch is not installed')

  def test_huber_loss_pytorch(self):

    """Test HuberLoss."""

    loss = losses.HuberLoss()

    outputs = torch.tensor([[0.1, 0.8], [0.4, 0.6]])

    labels = torch.tensor([[1.0, -1.0], [-1.0, 1.0]])

    result = np.mean(loss._create_pytorch_loss()(outputs, labels).numpy())

    expected = 0.67125

    assert np.allclose(expected, result)

  @unittest.skipIf(not has_tensorflow, 'TensorFlow is not installed')

  def test_l2_loss_tf(self):

    """Test L2Loss."""

.. autoclass:: deepchem.models.losses.L1Loss

  :members:

.. autoclass:: deepchem.models.losses.HuberLoss

  :members:

.. autoclass:: deepchem.models.losses.L2Loss

  :members: