diff --git a/paddle/fluid/operators/huber_loss_op.cc b/paddle/fluid/operators/huber_loss_op.cc index 4ecd8634ff41ff4eba6b5ed1d0fc78068190dce5..253b65a5f33308fc2c94537641b0fa19378b0cc9 100644 --- a/paddle/fluid/operators/huber_loss_op.cc +++ b/paddle/fluid/operators/huber_loss_op.cc @@ -124,8 +124,9 @@ REGISTER_OPERATOR(huber_loss, ops::HuberLossOp, ops::HuberLossOpMaker, paddle::framework::DefaultGradOpDescMaker); REGISTER_OPERATOR(huber_loss_grad, ops::HuberLossGradOp); REGISTER_OP_CPU_KERNEL( - huber_loss, - ops::HuberLossKernel); + huber_loss, ops::HuberLossKernel, + ops::HuberLossKernel); REGISTER_OP_CPU_KERNEL( huber_loss_grad, - ops::HuberLossGradKernel); + ops::HuberLossGradKernel, + ops::HuberLossGradKernel); diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 4df74edfcebe4e8da7172c89f3958f3df2fd2c1f..fb1ae7b753d69447b00635c0a6c0ae8f040f5ad9 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -169,6 +169,7 @@ __all__ = [ 'log_loss', 'add_position_encoding', 'bilinear_tensor_product', + 'huber_regression_loss', ] @@ -4595,7 +4596,7 @@ def hsigmoid(input, """ The hierarchical sigmoid operator is used to accelerate the training process of language model. This operator organizes the classes into a - complete binary tree, or you can use is_custom to pass your own tree to + complete binary tree, or you can use is_custom to pass your own tree to implement hierarchical. Each leaf node represents a class(a word) and each internal node acts as a binary classifier. For each word there's a unique path from root to it's leaf node, hsigmoid calculate the cost for each @@ -4611,7 +4612,7 @@ def hsigmoid(input, 2. build a dict to store word_id -> word's leaf to root path, we call it path_table. 3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code means label of each binary classification, using 1 indicate true, 0 indicate false. - 4. now, each word should has its path and code along the path, you can pass a batch of path and code + 4. now, each word should has its path and code along the path, you can pass a batch of path and code related to the same batch of inputs. @@ -4621,8 +4622,8 @@ def hsigmoid(input, and :math:`D` is the feature size. label (Variable): The tensor variable contains labels of training data. It's a tensor with shape is :math:`[N \\times 1]`. - num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set, - it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num + num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set, + it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num which indicates the num of classes using by binary classify. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid @@ -4635,15 +4636,15 @@ def hsigmoid(input, is not set, the bias is initialized zero. Default: None. name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None. - path_table: (Variable|None) this variable can store each batch of samples' path to root, + path_table: (Variable|None) this variable can store each batch of samples' path to root, it should be in leaf -> root order - path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like - structure and each element in this array is indexes in parent nodes' Weight Matrix. - path_code: (Variable|None) this variable can store each batch of samples' code, + path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like + structure and each element in this array is indexes in parent nodes' Weight Matrix. + path_code: (Variable|None) this variable can store each batch of samples' code, each code consist with every code of parent nodes. it should be in leaf -> root order - is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is + is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is set you need to set path_table/path_code/num_classes, otherwise num_classes should be set - is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient + is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient of W and input will be sparse. Returns: @@ -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