提交 76a58197 编写于 作者: D DuYao 提交者: hong

update English document (#20330)

* update English document, test=document_fix

* update api.spec, test=document_fix

* update api.spec, test=document_fix

* update, test=document_fix
上级 e34fccbc
...@@ -199,7 +199,7 @@ paddle.fluid.layers.lod_append (ArgSpec(args=['x', 'level'], varargs=None, keywo ...@@ -199,7 +199,7 @@ paddle.fluid.layers.lod_append (ArgSpec(args=['x', 'level'], varargs=None, keywo
paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', 'fa565b65fb98d3ca82361c79f41b06b2')) paddle.fluid.layers.lrn (ArgSpec(args=['input', 'n', 'k', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(5, 1.0, 0.0001, 0.75, None)), ('document', 'fa565b65fb98d3ca82361c79f41b06b2'))
paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '46b3ada86dd2c79042dca90a55e08f66')) paddle.fluid.layers.pad (ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '46b3ada86dd2c79042dca90a55e08f66'))
paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '89aa122a50dc20ee116ae49d66854d20')) paddle.fluid.layers.pad_constant_like (ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None)), ('document', '89aa122a50dc20ee116ae49d66854d20'))
paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '214f1dfbe95a628600bbe99e836319cf')) paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None)), ('document', '70b6f4ab59e60650231b1ead4ad46222'))
paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '6fc9bae94518bbf3e1a9e479f38f6537')) paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', '6fc9bae94518bbf3e1a9e479f38f6537'))
paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3885fd76e122ac0563fa8369bcab7363')) paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '3885fd76e122ac0563fa8369bcab7363'))
paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-05, None)), ('document', '08d94daffbea3935178810bdc1633f07')) paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-05, None)), ('document', '08d94daffbea3935178810bdc1633f07'))
...@@ -604,7 +604,7 @@ paddle.fluid.dygraph.Conv2D.set_dict (ArgSpec(args=['self', 'stat_dict', 'includ ...@@ -604,7 +604,7 @@ paddle.fluid.dygraph.Conv2D.set_dict (ArgSpec(args=['self', 'stat_dict', 'includ
paddle.fluid.dygraph.Conv2D.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.Conv2D.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Conv2D.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.Conv2D.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Conv2D.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Conv2D.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3D ('paddle.fluid.dygraph.nn.Conv3D', ('document', '50412bd3fbf3557a8ef48e25c6517025')) paddle.fluid.dygraph.Conv3D ('paddle.fluid.dygraph.nn.Conv3D', ('document', 'f81dee6781d6c18d0e7f5ca66b2fb010'))
paddle.fluid.dygraph.Conv3D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Conv3D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.Conv3D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.Conv3D.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.Conv3D.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
...@@ -689,7 +689,7 @@ paddle.fluid.dygraph.Embedding.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc ...@@ -689,7 +689,7 @@ paddle.fluid.dygraph.Embedding.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc
paddle.fluid.dygraph.Embedding.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.Embedding.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Embedding.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.Embedding.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Embedding.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Embedding.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.GRUUnit ('paddle.fluid.dygraph.nn.GRUUnit', ('document', '389e860e455b67aab1f4d472ac9d7e49')) paddle.fluid.dygraph.GRUUnit ('paddle.fluid.dygraph.nn.GRUUnit', ('document', 'f0e648f0a8d3389f755698dde488dc93'))
paddle.fluid.dygraph.GRUUnit.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.GRUUnit.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.GRUUnit.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.GRUUnit.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.GRUUnit.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.GRUUnit.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
...@@ -757,7 +757,7 @@ paddle.fluid.dygraph.PRelu.set_dict (ArgSpec(args=['self', 'stat_dict', 'include ...@@ -757,7 +757,7 @@ paddle.fluid.dygraph.PRelu.set_dict (ArgSpec(args=['self', 'stat_dict', 'include
paddle.fluid.dygraph.PRelu.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.PRelu.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.PRelu.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.PRelu.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.PRelu.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PRelu.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.BilinearTensorProduct ('paddle.fluid.dygraph.nn.BilinearTensorProduct', ('document', 'be70d0f6d43729d9cb80c9a34ed5f26b')) paddle.fluid.dygraph.BilinearTensorProduct ('paddle.fluid.dygraph.nn.BilinearTensorProduct', ('document', 'ddea5bc0668a636ded7db09538511c20'))
paddle.fluid.dygraph.BilinearTensorProduct.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'name', 'act', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.BilinearTensorProduct.__init__ (ArgSpec(args=['self', 'name_scope', 'size', 'name', 'act', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.BilinearTensorProduct.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.BilinearTensorProduct.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.BilinearTensorProduct.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.BilinearTensorProduct.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
...@@ -791,7 +791,7 @@ paddle.fluid.dygraph.Conv2DTranspose.set_dict (ArgSpec(args=['self', 'stat_dict' ...@@ -791,7 +791,7 @@ paddle.fluid.dygraph.Conv2DTranspose.set_dict (ArgSpec(args=['self', 'stat_dict'
paddle.fluid.dygraph.Conv2DTranspose.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.Conv2DTranspose.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.Conv2DTranspose.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.Conv2DTranspose.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.Conv2DTranspose.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Conv2DTranspose.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3DTranspose ('paddle.fluid.dygraph.nn.Conv3DTranspose', ('document', '91ba132bc690eaf76eabdbde8f87e4a0')) paddle.fluid.dygraph.Conv3DTranspose ('paddle.fluid.dygraph.nn.Conv3DTranspose', ('document', '0ef981fd6a74aaff21673f9925736ac7'))
paddle.fluid.dygraph.Conv3DTranspose.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Conv3DTranspose.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv3DTranspose.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.Conv3DTranspose.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.Conv3DTranspose.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.Conv3DTranspose.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
...@@ -870,31 +870,31 @@ paddle.fluid.dygraph.Tracer.train_mode (ArgSpec(args=['self'], varargs=None, key ...@@ -870,31 +870,31 @@ paddle.fluid.dygraph.Tracer.train_mode (ArgSpec(args=['self'], varargs=None, key
paddle.fluid.dygraph.prepare_context (ArgSpec(args=['strategy'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.prepare_context (ArgSpec(args=['strategy'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.save_dygraph (ArgSpec(args=['state_dict', 'model_path'], varargs=None, keywords=None, defaults=None), ('document', '7c2bd58a69f9bca3b884f44154c84569')) paddle.fluid.dygraph.save_dygraph (ArgSpec(args=['state_dict', 'model_path'], varargs=None, keywords=None, defaults=None), ('document', '7c2bd58a69f9bca3b884f44154c84569'))
paddle.fluid.dygraph.load_dygraph (ArgSpec(args=['model_path'], varargs=None, keywords=None, defaults=None), ('document', 'd6d98002c39d2484835f4748e35b761c')) paddle.fluid.dygraph.load_dygraph (ArgSpec(args=['model_path'], varargs=None, keywords=None, defaults=None), ('document', 'd6d98002c39d2484835f4748e35b761c'))
paddle.fluid.dygraph.NoamDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NoamDecay', ('document', '9ccfea97dbf15134d406a23aae1e1fa2')) paddle.fluid.dygraph.NoamDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NoamDecay', ('document', '3441619381487db8d1929a205f3c6d41'))
paddle.fluid.dygraph.NoamDecay.__init__ (ArgSpec(args=['self', 'd_model', 'warmup_steps', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NoamDecay.__init__ (ArgSpec(args=['self', 'd_model', 'warmup_steps', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NoamDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.NoamDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.NoamDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NoamDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PiecewiseDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PiecewiseDecay', ('document', '8f4d37eaad4e2f5b12850f3663856758')) paddle.fluid.dygraph.PiecewiseDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PiecewiseDecay', ('document', '0fccf303b94a13ae670fb3dd51931f73'))
paddle.fluid.dygraph.PiecewiseDecay.__init__ (ArgSpec(args=['self', 'boundaries', 'values', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PiecewiseDecay.__init__ (ArgSpec(args=['self', 'boundaries', 'values', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PiecewiseDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.PiecewiseDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.PiecewiseDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PiecewiseDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NaturalExpDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NaturalExpDecay', ('document', '94bed58b392a5a71b6d1abd39eed7111')) paddle.fluid.dygraph.NaturalExpDecay ('paddle.fluid.dygraph.learning_rate_scheduler.NaturalExpDecay', ('document', '5fef27468d49ca8ca6c6a9635ad0f5c1'))
paddle.fluid.dygraph.NaturalExpDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NaturalExpDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NaturalExpDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.NaturalExpDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.NaturalExpDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NaturalExpDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.ExponentialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.ExponentialDecay', ('document', 'a259689c649c5f82636536386ce2ef19')) paddle.fluid.dygraph.ExponentialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.ExponentialDecay', ('document', '846eb564df136d8a8917bf16b5b8ac9b'))
paddle.fluid.dygraph.ExponentialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.ExponentialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.ExponentialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.ExponentialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.ExponentialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.ExponentialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.InverseTimeDecay ('paddle.fluid.dygraph.learning_rate_scheduler.InverseTimeDecay', ('document', '6a868b2c7cc0f09f57ef71902bbc93ca')) paddle.fluid.dygraph.InverseTimeDecay ('paddle.fluid.dygraph.learning_rate_scheduler.InverseTimeDecay', ('document', '1a74f0370e2e64f9e786d3c336526e6d'))
paddle.fluid.dygraph.InverseTimeDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.InverseTimeDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.InverseTimeDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.InverseTimeDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.InverseTimeDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.InverseTimeDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PolynomialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PolynomialDecay', ('document', 'bb90314cee58952f13522dcd571ca832')) paddle.fluid.dygraph.PolynomialDecay ('paddle.fluid.dygraph.learning_rate_scheduler.PolynomialDecay', ('document', 'e222a066a2bcf31bc52a14271048e034'))
paddle.fluid.dygraph.PolynomialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PolynomialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PolynomialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.PolynomialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.PolynomialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PolynomialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.CosineDecay ('paddle.fluid.dygraph.learning_rate_scheduler.CosineDecay', ('document', '46dadadee1a8a92d70bd277d9345bfb0')) paddle.fluid.dygraph.CosineDecay ('paddle.fluid.dygraph.learning_rate_scheduler.CosineDecay', ('document', '0d7fe2b87492a0eb5cde60dbe268ea17'))
paddle.fluid.dygraph.CosineDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'step_each_epoch', 'epochs', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.CosineDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'step_each_epoch', 'epochs', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.CosineDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) paddle.fluid.dygraph.CosineDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866'))
paddle.fluid.dygraph.CosineDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.CosineDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
...@@ -69,7 +69,7 @@ class LearningRateDecay(object): ...@@ -69,7 +69,7 @@ class LearningRateDecay(object):
class PiecewiseDecay(LearningRateDecay): class PiecewiseDecay(LearningRateDecay):
""" """
piecewise decay scheduler Piecewise decay scheduler.
The algorithm can be described as the code below. The algorithm can be described as the code below.
...@@ -77,22 +77,25 @@ class PiecewiseDecay(LearningRateDecay): ...@@ -77,22 +77,25 @@ class PiecewiseDecay(LearningRateDecay):
boundaries = [10000, 20000] boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1] values = [1.0, 0.5, 0.1]
if step < 10000: if global_step < 10000:
learning_rate = 1.0 learning_rate = 1.0
elif 10000 <= step < 20000: elif 10000 <= global_step < 20000:
learning_rate = 0.5 learning_rate = 0.5
else: else:
learning_rate = 0.1 learning_rate = 0.1
Args:
boundaries: A list of steps numbers. Parameters:
values: A list of learning rate values that will be picked during boundaries(list): A list of steps numbers. The type of element in the list is python int.
different step boundaries. values(list): A list of learning rate values that will be picked during
begin: The begin step to initilize the self.step_num different step boundaries. The type of element in the list is python float.
step: The step_size using when calculate the new step_num (Defalult is 1) begin(int): The begin step to initilize the global_step in the description above.
dtype: The dtype used to create the learning rate variable step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -125,25 +128,40 @@ class NaturalExpDecay(LearningRateDecay): ...@@ -125,25 +128,40 @@ class NaturalExpDecay(LearningRateDecay):
""" """
Applies natural exponential decay to the initial learning rate. Applies natural exponential decay to the initial learning rate.
.. code-block:: python The algorithm can be described as following.
if not staircase: .. math::
decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
else:
decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
Args: decayed\_learning\_rate = learning\_rate * e^{y}
learning_rate: A scalar float32 value or a Variable. This
will be the initial learning rate during training If staircase is set to False, then:
decay_steps: A Python `int32` number.
decay_rate: A Python `float` number. .. math::
staircase: Boolean. If set true, decay the learning rate every decay_steps.
begin: A Python 'int32' number, the begin step (Default is 0) y = - decay\_rate * \\frac{global\_step}{decay\_steps}
step: A Python 'int32' number, the step size (Default is 1)
dtype: A Python 'str', the dtype used to create learning rate variable (Default is 'float32') If staircase is set to True, then:
.. math::
y = - decay\_rate * math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(int): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -189,29 +207,41 @@ class ExponentialDecay(LearningRateDecay): ...@@ -189,29 +207,41 @@ class ExponentialDecay(LearningRateDecay):
""" """
Applies exponential decay to the learning rate. Applies exponential decay to the learning rate.
When training a model, it is often recommended to lower the learning rate as the The algorithm can be described as following.
training progresses. By using this function, the learning rate will be decayed by
'decay_rate' every 'decay_steps' steps.
.. code-block:: python .. math::
if staircase == True: decayed\_learning\_rate = learning\_rate * decay\_rate ^ y
decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
else:
decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
Args: If staircase is set to False, then:
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above. .. math::
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals. y = \\frac{global\_step}{decay\_steps}
Default: False
begin(int): The begin step (default is 0) If staircase is set to True, then:
step(int): The step size (default is 1)
dtype(str): The dtype used to create learning rate (default is 'float32') .. math::
y = math.floor(\\frac{global\_step}{decay\_steps})
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -257,27 +287,35 @@ class InverseTimeDecay(LearningRateDecay): ...@@ -257,27 +287,35 @@ class InverseTimeDecay(LearningRateDecay):
""" """
Applies inverse time decay to the initial learning rate. Applies inverse time decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as the The algorithm can be described as following.
training progresses. By using this function, an inverse decay function will be If staircase is set to False, then:
applied to the initial learning rate.
.. math::
>>> if staircase == True: decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * \\frac{global\_step}{decay\_step}}
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
>>> else:
>>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
Args: If staircase is set to True, then:
learning_rate(Variable|float): The initial learning rate.
decay_steps(int): See the decay computation above. .. math::
decay_rate(float): The decay rate. See the decay computation above.
staircase(Boolean): If True, decay the learning rate at discrete intervals. decayed\_learning\_rate = \\frac{learning\_rate}{1 + decay\_rate * math.floor(\\frac{global\_step}{decay\_step})}
Default: False
begin(int): The begin step (default is 0) Parameters:
step(int): The step size (default is 1) learning_rate(Variable|float): The initial learning rate. If the type
dtype(str): The dtype used to create learning rate (default is 'float32') is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int): The decay step size. It determines the decay cycle.
decay_rate(float): The decay rate.
staircase(bool, optional): If set to True, decay the learning rate at discrete intervals. The
default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -323,28 +361,40 @@ class PolynomialDecay(LearningRateDecay): ...@@ -323,28 +361,40 @@ class PolynomialDecay(LearningRateDecay):
""" """
Applies polynomial decay to the initial learning rate. Applies polynomial decay to the initial learning rate.
.. code-block:: text The algorithm can be described as following.
if cycle: If cycle is set to True, then:
decay_steps = decay_steps * ceil(global_step / decay_steps)
else:
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ power + end_learning_rate
Args: .. math::
learning_rate(Variable|float32): A scalar float32 value or a Variable. This
will be the initial learning rate during training. decay\_steps & = decay\_steps * math.ceil(\\frac{global\_step}{decay\_steps})
decay_steps(int32): A Python `int32` number.
end_learning_rate(float): A Python `float` number. decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
power(float): A Python `float` number.
cycle(bool): If set true, decay the learning rate every decay_steps. If cycle is set to False, then:
begin(int): The begin step (default is 0)
step(int): The step size (default is 1) .. math::
dtype(str): The dtype used to create learning rate (default is 'float32')
global\_step & = min(global\_step, decay\_steps)
decayed\_learning\_rate & = (learning\_rate-end\_learning\_rate)*(1-\\frac{global\_step}{decay\_steps})^{power}+end\_learning\_rate
Parameters:
learning_rate(Variable|float): The initial learning rate. If the type
is Variable, it's a tensor with shape [1], the data type can be
float32 or float64. It also can be set to python int number.
decay_steps(int32): The decay step size. It determines the decay cycle.
end_learning_rate(float, optional): The minimum final learning rate. The default value is 0.0001.
power(float, optional): Power of polynomial. The default value is 1.0.
cycle(bool, optional): If set true, decay the learning rate every decay_steps. The default value is False.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -401,24 +451,26 @@ class CosineDecay(LearningRateDecay): ...@@ -401,24 +451,26 @@ class CosineDecay(LearningRateDecay):
""" """
Applies cosine decay to the learning rate. Applies cosine decay to the learning rate.
when training a model, it is often recommended to lower the learning rate as the The algorithm can be described as following.
training progresses. By using this function, the learning rate will be decayed by
following cosine decay strategy.
.. math:: .. math::
decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \\frac{math.pi}{epochs} ) + 1) decayed\_learning\_rate = learning\_rate * 0.5 * (math.cos(global\_step * \\frac{math.pi}{step\_each\_epoch} ) + 1)
Args: Parameters:
learning_rate(Variable|float): The initial learning rate. learning_rate(Variable|float): The initial learning rate. If the type
step_each_epoch(int): the number of steps in an epoch. is Variable, it's a tensor with shape [1], the data type can be
epochs(int): the number of epochs. float32 or float64. It also can be set to python int number.
begin(int): The begin step (default is 0). step_each_epoch(int): The number of steps in an epoch.
step(int): The step size (default is 1). epochs(int): The number of epochs.
dtype(str): The dtype used to create learning rate (default is 'float32'). begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -453,33 +505,29 @@ class CosineDecay(LearningRateDecay): ...@@ -453,33 +505,29 @@ class CosineDecay(LearningRateDecay):
class NoamDecay(LearningRateDecay): class NoamDecay(LearningRateDecay):
""" """
Noam decay method. The numpy implementation of noam decay as follows. Applies Noam decay to the initial learning rate.
.. code-block:: python The algorithm can be described as following.
import numpy as np .. math::
# set hyper parameters
d_model = 2
current_steps = 20
warmup_steps = 200
# compute
lr_value = np.power(d_model, -0.5) * np.min([
np.power(current_steps, -0.5),
np.power(warmup_steps, -1.5) * current_steps])
Please reference `attention is all you need decayed\_learning\_rate = d_{model}^{-0.5} * min(global\_step^{-0.5}, global\_step * warmup\_steps^{-1.5})
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Args: Please reference `attention is all you need <https://arxiv.org/pdf/1706.03762.pdf>`_
d_model(Variable): The dimensionality of input and output of model.
warmup_steps(Variable): A super parameter. Parameters:
begin(int): The begin step (default is 0) d$_{model}$(Variable|int): The dimensionality of input and output feature vector of model. If type is Variable,
step(int): The step size (default is 1) it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
dtype(str): The dtype used to create learning rate (default is 'float32') warmup_steps(Variable|int): The number of warmup steps. A super parameter. If type is Variable,
it's a tensor with shape [1] and the data type can be int32 or int64. The type can also be python int.
begin(int, optional): The begin step. The initial value of global_step described above. The default value is 0.
step(int, optional): The step size used to calculate the new global_step in the description above.
The defalult value is 1.
dtype(str, optional): The data type used to create the learning rate variable. The data type can be set as
'float32', 'float64'. The default value is 'float32'.
Returns: Returns:
The decayed learning rate. None.
Examples: Examples:
.. code-block:: python .. code-block:: python
......
此差异已折叠。
...@@ -8937,8 +8937,8 @@ def label_smooth(label, ...@@ -8937,8 +8937,8 @@ def label_smooth(label,
dtype="float32", dtype="float32",
name=None): name=None):
""" """
Label smoothing is a mechanism to regularize the classifier layer and is Label smoothing is a mechanism to regularize the classifier layer and is called
called label-smoothing regularization (LSR). label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident, Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may since optimizing the log-likelihood of the correct label directly may
...@@ -8957,19 +8957,23 @@ def label_smooth(label, ...@@ -8957,19 +8957,23 @@ def label_smooth(label,
See more details about label smoothing in https://arxiv.org/abs/1512.00567. See more details about label smoothing in https://arxiv.org/abs/1512.00567.
Args: Parameters:
label(Variable): The input variable containing the label data. The label(Variable): The input variable containing the label data. The
label data should use one-hot representation. label data should use one-hot representation. It's
prior_dist(Variable): The prior distribution to be used to smooth a multidimensional tensor with a shape of
:math:`[N_1, ..., Depth]`, where Depth is class number.
prior_dist(Variable, optional): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution labels. If not provided, an uniform distribution
is used. The shape of :attr:`prior_dist` should is used. It's a multidimensional tensor with a shape of
be :math:`(1, class\_num)`. :math:`[1, class\_num]` . The default value is None.
epsilon(float): The weight used to mix up the original ground-truth epsilon(float, optional): The weight used to mix up the original ground-truth
distribution and the fixed distribution. distribution and the fixed distribution. The default value is
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, 0.1.
float_64, int etc. dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can be set
name(str|None): A name for this layer(optional). If set None, the layer as 'float32', 'float64'. The default value is 'float32'.
will be named automatically. name(str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to
:ref:`api_guide_Name`.
Returns: Returns:
Variable: The tensor variable containing the smoothed labels. Variable: The tensor variable containing the smoothed labels.
......
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