提交 5fff20c2 编写于 作者: M minqiyang

Change name to huber loss

test=develop
上级 ab98101c
...@@ -199,7 +199,7 @@ paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None ...@@ -199,7 +199,7 @@ paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1)) paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.psroi_pool ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.huber_regression_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None) paddle.fluid.layers.huber_loss ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)) paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)) paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None) paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
...@@ -174,7 +174,7 @@ __all__ = [ ...@@ -174,7 +174,7 @@ __all__ = [
'get_tensor_from_selected_rows', 'get_tensor_from_selected_rows',
'lstm', 'lstm',
'psroi_pool', 'psroi_pool',
'huber_regression_loss', 'huber_loss',
] ]
kIgnoreIndex = -100 kIgnoreIndex = -100
...@@ -9180,7 +9180,7 @@ def psroi_pool(input, ...@@ -9180,7 +9180,7 @@ def psroi_pool(input,
return out return out
def huber_regression_loss(input, label, delta): def huber_loss(input, label, delta):
""" """
Huber regression loss is a loss function used in robust regression. Huber regression loss is a loss function used in robust regression.
Huber regression loss can evaluate the fitness of input to label. Huber regression loss can evaluate the fitness of input to label.
...@@ -9212,9 +9212,9 @@ def huber_regression_loss(input, label, delta): ...@@ -9212,9 +9212,9 @@ def huber_regression_loss(input, label, delta):
.. code-block:: python .. code-block:: python
predictions = fluid.layers.softmax(x) predictions = fluid.layers.softmax(x)
loss = fluid.layers.huber_regression_loss(input=predictions, label=label, 1.0) loss = fluid.layers.huber_loss(input=predictions, label=label, 1.0)
""" """
helper = LayerHelper('huber_regression_loss', **locals()) helper = LayerHelper('huber_loss', **locals())
residual = helper.create_variable_for_type_inference( residual = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()) dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype()) out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
......
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