Add python interface for huber regression loss

test=develop

paddle/fluid/operators/huber_loss_op.cc

@@ -124,8 +124,9 @@ REGISTER_OPERATOR(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker<float>,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(huber_loss_grad, ops::HuberLossGradOp);
 REGISTER_OP_CPU_KERNEL(
-    huber_loss,
-    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>);
+    huber_loss, ops::HuberLossKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     huber_loss_grad,
-    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HuberLossGradKernel<paddle::platform::CPUDeviceContext, double>);

python/paddle/fluid/layers/nn.py

@@ -169,6 +169,7 @@ __all__ = [
     'log_loss',
     'add_position_encoding',
     'bilinear_tensor_product',
+    'huber_regression_loss',
 ]
 
 
@@ -8770,3 +8771,51 @@ def bilinear_tensor_product(x,
 
     # add activation
     return helper.append_activation(out)
+
+
+def huber_regression_loss(input, label, delta):
+    """
+    Huber regression loss is a loss function used in robust regression.
+    Huber regression loss can evaluate the fitness of input to label.
+    Different from MSE loss, Huber regression loss is more robust for outliers.
+
+    When the difference between input and label is large than delta
+    .. math::
+
+        huber\_regression\_loss = delta * (label - input) - 0.5 * delta * delta
+
+    When the difference between input and label is less than delta
+    .. math::
+
+        huber\_regression\_loss = 0.5 * (label - input) * (label - input)
+
+
+    Args:
+        input (Variable): This input is a probability computed by the previous operator.
+                          The first dimension is batch size, and the last dimension is 1.
+        label (Variable): The groud truth whose first dimension is batch size
+                          and last dimension is 1.
+        delta (float): The parameter of huber regression loss, which controls
+                       the range of outliers
+
+    Returns:
+        huber\_regression\_loss (Variable): The huber regression loss with shape [batch_size, 1].
+
+    Examples:
+        .. code-block:: python
+
+            predictions = fluid.layers.softmax(x)
+            loss = fluid.layers.huber_regression_loss(input=predictions, label=label, 1.0)
+    """
+    helper = LayerHelper('huber_regression_loss', **locals())
+    residual = helper.create_variable_for_type_inference(
+        dtype=helper.input_dtype())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+    helper.append_op(
+        type='huber_loss',
+        inputs={'X': input,
+                'Y': label},
+        outputs={'Out': out,
+                 'Residual': residual},
+        attrs={'delta': delta})
+    return out