Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2298
Star 20931
Fork 5422
P
Paddle
提交 0b321c8a 编写于 作者: Z zhongpu 提交者： Jiabin Yang
浏览文件
操作

fix APIs, to_variable、NCE、PRelu、softmax、rankloss for dygraph,... 

fix APIs, to_variable、NCE、PRelu、softmax、rankloss for dygraph, test=document_fix, test=develop (#20142)
上级 508127b1
显示空白变更内容
内联 并排
Showing with 165 addition and 120 deletion
paddle/fluid/API.spec
浏览文件 @ 0b321c8a
...@@ -141,7 +141,7 @@ paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', ...@@ -141,7 +141,7 @@ paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size',
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'feff9c8ebb4d4d0be5345f9042f57c8e')) paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'feff9c8ebb4d4d0be5345f9042f57c8e'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', '5a709f7ef3fdb8fc819d09dc4fbada9a')) paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', '5a709f7ef3fdb8fc819d09dc4fbada9a'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711')) paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '7ccaea1b93fe4f7387a6036692986c6b')) paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'f7d6a5173c92c23f9a25cbc58a0eb577'))
paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', 'daf9ae55b2d54bd5f35acb397fd1e1b5')) paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', 'daf9ae55b2d54bd5f35acb397fd1e1b5'))
paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'df8edcb8dd020fdddf778c9f613dc650')) paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'df8edcb8dd020fdddf778c9f613dc650'))
paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', 'd873fdd73bcd74f9203d347cfb90de75')) paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', 'd873fdd73bcd74f9203d347cfb90de75'))
...@@ -187,7 +187,7 @@ paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, k ...@@ -187,7 +187,7 @@ paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, k
paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', '678de6d6d0c93da74189990b039daae8')) paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', '678de6d6d0c93da74189990b039daae8'))
paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '87dd4b818f102bc1a780e1804c28bd38')) paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '87dd4b818f102bc1a780e1804c28bd38'))
paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '7b3d14d6707d878923847ec617d7d521')) paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '7b3d14d6707d878923847ec617d7d521'))
paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '54e1675aa0364f4a78fa72804ec0f413')) paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '6992e4140d667fdf816d0617648b5c00'))
paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88')) paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88'))
paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cdf5dc2078f1e20dc61dd0bec7e28a29')) paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cdf5dc2078f1e20dc61dd0bec7e28a29'))
paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', 'd016c137beb9a4528b7378b437d00151')) paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', 'd016c137beb9a4528b7378b437d00151'))
...@@ -221,7 +221,7 @@ paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs= ...@@ -221,7 +221,7 @@ paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=
paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '02f668664e3bfc4df6c00d7363467140')) paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '02f668664e3bfc4df6c00d7363467140'))
paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '32196a194f757b4da114a595a5bc6414')) paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '32196a194f757b4da114a595a5bc6414'))
paddle.fluid.layers.crop_tensor (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'd460aaf35afbbeb9beea4789aa6e4343')) paddle.fluid.layers.crop_tensor (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'd460aaf35afbbeb9beea4789aa6e4343'))
paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8eb36596bb43d7a907d3397c7aedbdb3')) paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6d49ba251e23f32cb09df54a851bb960'))
paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '1a177f30e5013fae7ee6c45860cf4946')) paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '1a177f30e5013fae7ee6c45860cf4946'))
paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '9af1926c06711eacef9e82d7a9e4d308')) paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '9af1926c06711eacef9e82d7a9e4d308'))
paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '538fc860b2a1734e118b94e4a1a3ee67')) paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '538fc860b2a1734e118b94e4a1a3ee67'))
...@@ -586,7 +586,7 @@ paddle.fluid.dygraph.Layer.sublayers (ArgSpec(args=['self', 'include_sublayers'] ...@@ -586,7 +586,7 @@ paddle.fluid.dygraph.Layer.sublayers (ArgSpec(args=['self', 'include_sublayers']
paddle.fluid.dygraph.Layer.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Layer.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.__impl__ (ArgSpec(args=['func'], varargs=None, keywords=None, defaults=()), ('document', '75d1d3afccc8b39cdebf05cb1f5969f9')) paddle.fluid.dygraph.__impl__ (ArgSpec(args=['func'], varargs=None, keywords=None, defaults=()), ('document', '75d1d3afccc8b39cdebf05cb1f5969f9'))
paddle.fluid.dygraph.guard (ArgSpec(args=['place'], varargs=None, keywords=None, defaults=(None,)), ('document', '7071320ffe2eec9aacdae574951278c6')) paddle.fluid.dygraph.guard (ArgSpec(args=['place'], varargs=None, keywords=None, defaults=(None,)), ('document', '7071320ffe2eec9aacdae574951278c6'))
paddle.fluid.dygraph.to_variable (ArgSpec(args=['value', 'block', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0e69fa3666f15dd01b6e3e270b9371cd')) paddle.fluid.dygraph.to_variable (ArgSpec(args=['value', 'block', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '7df6297d66295bdc933e3982caa6f1a8'))
paddle.fluid.dygraph.Conv2D ('paddle.fluid.dygraph.nn.Conv2D', ('document', '10915f3c643e232d9c6789ce20a96869')) paddle.fluid.dygraph.Conv2D ('paddle.fluid.dygraph.nn.Conv2D', ('document', '10915f3c643e232d9c6789ce20a96869'))
paddle.fluid.dygraph.Conv2D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'dtype'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.Conv2D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'dtype'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.Conv2D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.Conv2D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
...@@ -723,7 +723,7 @@ paddle.fluid.dygraph.LayerNorm.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc ...@@ -723,7 +723,7 @@ paddle.fluid.dygraph.LayerNorm.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc
paddle.fluid.dygraph.LayerNorm.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.LayerNorm.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.LayerNorm.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.LayerNorm.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.LayerNorm.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.LayerNorm.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NCE ('paddle.fluid.dygraph.nn.NCE', ('document', '993aeea9be436e9c709a758795cb23e9')) paddle.fluid.dygraph.NCE ('paddle.fluid.dygraph.nn.NCE', ('document', '148e58ba1698e0cd60a3490fd4188d04'))
paddle.fluid.dygraph.NCE.__init__ (ArgSpec(args=['self', 'name_scope', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, 'uniform', None, 0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NCE.__init__ (ArgSpec(args=['self', 'name_scope', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, 'uniform', None, 0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.NCE.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.NCE.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.NCE.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.NCE.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
...@@ -740,7 +740,7 @@ paddle.fluid.dygraph.NCE.set_dict (ArgSpec(args=['self', 'stat_dict', 'include_s ...@@ -740,7 +740,7 @@ paddle.fluid.dygraph.NCE.set_dict (ArgSpec(args=['self', 'stat_dict', 'include_s
paddle.fluid.dygraph.NCE.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac')) paddle.fluid.dygraph.NCE.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
paddle.fluid.dygraph.NCE.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) paddle.fluid.dygraph.NCE.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
paddle.fluid.dygraph.NCE.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.NCE.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PRelu ('paddle.fluid.dygraph.nn.PRelu', ('document', 'da956af1676b08bf15553751a3643b55')) paddle.fluid.dygraph.PRelu ('paddle.fluid.dygraph.nn.PRelu', ('document', '58141577833fedf619f2f324eea57e00'))
paddle.fluid.dygraph.PRelu.__init__ (ArgSpec(args=['self', 'name_scope', 'mode', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.dygraph.PRelu.__init__ (ArgSpec(args=['self', 'name_scope', 'mode', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.dygraph.PRelu.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) paddle.fluid.dygraph.PRelu.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
paddle.fluid.dygraph.PRelu.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) paddle.fluid.dygraph.PRelu.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
......
python/paddle/fluid/dygraph/base.py
浏览文件 @ 0b321c8a
...@@ -150,15 +150,15 @@ def _print_debug_msg(limit=5, is_test=False): ...@@ -150,15 +150,15 @@ def _print_debug_msg(limit=5, is_test=False):
@framework.dygraph_only @framework.dygraph_only
def to_variable(value, block=None, name=None): def to_variable(value, block=None, name=None):
""" """
This function will create a variable from ndarray The API will create a ``Variable`` object from numpy\.ndarray or Variable object.
Args: Parameters:
value(ndarray): the numpy value need to be convert value(ndarray): The numpy\.ndarray object that needs to be converted, it can be multi-dimension, and the data type is one of numpy\.{float16, float32, float64, int16, int32, int64, uint8, uint16}.
block(fluid.Block|None): which block this variable will be in block(fluid.Block, optional): Which block this variable will be in. Default: None.
name(str|None): Name of Variable 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`
return: Returns:
Variable: The variable created from given numpy Variable: ``Tensor`` created from the specified numpy\.ndarray object, data type and shape is the same as ``value`` .
Examples: Examples:
......
python/paddle/fluid/dygraph/nn.py
浏览文件 @ 0b321c8a
...@@ -1838,39 +1838,43 @@ class GRUUnit(layers.Layer): ...@@ -1838,39 +1838,43 @@ class GRUUnit(layers.Layer):
class NCE(layers.Layer): class NCE(layers.Layer):
""" """
Compute and return the noise-contrastive estimation training loss. See This interface is used to construct a callable object of the ``NCE`` class.
For more details, refer to code examples.
It implements the function of the ``NCE`` loss function.
By default this function uses a uniform distribution for sampling, and it
compute and return the noise-contrastive estimation training loss. See
`Noise-contrastive estimation: A new estimation principle for unnormalized statistical models <http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`_ . `Noise-contrastive estimation: A new estimation principle for unnormalized statistical models <http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`_ .
By default this operator uses a uniform distribution for sampling.
Parameters: Parameters:
name_scope(str): The name of this class. name_scope(str): The name of this class.
num_total_classes (int): Total number of classes in all samples num_total_classes (int): Total number of classes in all samples
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter)
of nce. If it is set to None or one attribute of ParamAttr, nce of nce. If it is set to None or one attribute of ParamAttr, nce
will create ParamAttr as param_attr. If the Initializer of the param_attr will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None. is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of nce. bias_attr (ParamAttr or bool, optional): The attribute for the bias of nce.
If it is set to False, no bias will be added to the output units. If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, nce If it is set to None or one attribute of ParamAttr, nce
will create ParamAttr as bias_attr. If the Initializer of the bias_attr will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None. is not set, the bias is initialized zero. Default: None.
num_neg_samples (int): The number of negative classes. The default value is 10. num_neg_samples (int, optional): The number of negative classes. The default value is 10.
sampler (str): The sampler used to sample class from negtive classes. sampler (str, optional): The sampler used to sample class from negtive classes.
It can be 'uniform', 'log_uniform' or 'custom_dist'. It can be 'uniform', 'log_uniform' or 'custom_dist'.
default: 'uniform'. default: 'uniform'.
custom_dist (float[]|None): A float[] with size=num_total_classes. custom_dist (float[], optional): A float[] with size=num_total_classes.
It is used when sampler is set to 'custom_dist'. It is used when sampler is set to 'custom_dist'.
custom_dist[i] is the probability of i-th class to be sampled. custom_dist[i] is the probability of i-th class to be sampled.
Default: None. Default: None.
seed (int): The seed used in sampler. Default: 0. seed (int, optional): The seed used in sampler. Default: 0.
is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default: False. is_sparse(bool, optional): The flag indicating whether to use sparse update. If is_sparse is True, the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default: False.
Attributes: Attribute:
weight (Parameter): the learnable weights of this layer. **weight** (Parameter): the learnable weights of this layer.
bias (Parameter|None): the learnable bias of this layer.
**bias** (Parameter or None): the learnable bias of this layer.
Returns: Returns:
Variable: The output nce loss. None
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -2087,6 +2091,10 @@ class NCE(layers.Layer): ...@@ -2087,6 +2091,10 @@ class NCE(layers.Layer):
class PRelu(layers.Layer): class PRelu(layers.Layer):
""" """
This interface is used to construct a callable object of the ``PRelu`` class.
For more details, refer to code examples.
It implements three activation methods of the ``PRelu`` activation function.
Equation: Equation:
.. math:: .. math::
...@@ -2098,30 +2106,32 @@ class PRelu(layers.Layer): ...@@ -2098,30 +2106,32 @@ class PRelu(layers.Layer):
and element. all: all elements share same weight and element. all: all elements share same weight
channel:elements in a channel share same weight channel:elements in a channel share same weight
element:each element has a weight element:each element has a weight
param_attr(ParamAttr|None): The parameter attribute for the learnable param_attr(ParamAttr, optional): The parameter attribute for the learnable
weight (alpha). weight (alpha). Default: None.
Attributes: Attribute:
weight (Parameter): the learnable weights of this layer. **weight** (Parameter): the learnable weights of this layer.
Returns: Returns:
Variable: The output tensor with the same shape as input. None
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
import numpy as np import numpy as np
inp_np = np.ones([5, 200, 100, 100]).astype('float32') inp_np = np.ones([5, 200, 100, 100]).astype('float32')
with fluid.dygraph.guard(): with fluid.dygraph.guard():
inp_np = to_variable(inp_np)
mode = 'channel' mode = 'channel'
prelu = fluid.PRelu( prelu = fluid.PRelu(
'prelu', 'prelu',
mode=mode, mode=mode,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0))) param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np)) dy_rlt = prelu(inp_np)
""" """
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 0b321c8a
...@@ -2324,11 +2324,11 @@ def sequence_softmax(input, use_cudnn=False, name=None): ...@@ -2324,11 +2324,11 @@ def sequence_softmax(input, use_cudnn=False, name=None):
def softmax(input, use_cudnn=False, name=None, axis=-1): def softmax(input, use_cudnn=False, name=None, axis=-1):
""" """
The input of the softmax operator is a tensor of any rank. The output tensor This operator implements the softmax layer. The calculation process is as follows:
has the same shape as the input.
The dimension :attr:`axis` of the input tensor will be permuted to the last. 1. The dimension :attr:`axis` of the ``input`` will be permuted to the last.
Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
2. Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is the same as the dimension :attr:`axis` of the input second dimension(row length) is the same as the dimension :attr:`axis` of the input
tensor, and the first dimension(column length) is the product of all other tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator dimensions of the input tensor. For each row of the matrix, the softmax operator
...@@ -2336,6 +2336,9 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -2336,6 +2336,9 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1. K-dimensional vector of real values in the range [0, 1] that add up to 1.
3. After the softmax operation is completed, the inverse operations of steps 1 and 2
are performed to restore the two-dimensional matrix to the same dimension as the ``input``.
It computes the exponential of the given dimension and the sum of exponential It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input. values of all the other dimensions in the K-dimensional vector input.
Then the ratio of the exponential of the given dimension and the sum of Then the ratio of the exponential of the given dimension and the sum of
...@@ -2348,20 +2351,66 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -2348,20 +2351,66 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
Out[i, j] = \\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])} Out[i, j] = \\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}
Example:
.. code-block:: text
Case 1:
Input:
X.shape = [2, 3, 4]
X.data = [[[2.0, 3.0, 4.0, 5.0],
[3.0, 4.0, 5.0, 6.0],
[7.0, 8.0, 8.0, 9.0]],
[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[6.0, 7.0, 8.0, 9.0]]]
Attrs:
axis = -1
Output:
Out.shape = [2, 3, 4]
Out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
[0.07232949, 0.19661193, 0.19661193, 0.53444665]],
[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
[0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]
Case 2:
Input:
X.shape = [2, 3, 4]
X.data = [[[2.0, 3.0, 4.0, 5.0],
[3.0, 4.0, 5.0, 6.0],
[7.0, 8.0, 8.0, 9.0]],
[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[6.0, 7.0, 8.0, 9.0]]]
Attrs:
axis = 1
Output:
Out.shape = [2, 3, 4]
Out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783],
[0.01786798, 0.01786798, 0.04661262, 0.04661262],
[0.97555875, 0.97555875, 0.93623955, 0.93623955]],
[[0.00490169, 0.00490169, 0.00490169, 0.00490169],
[0.26762315, 0.26762315, 0.26762315, 0.26762315],
[0.72747516, 0.72747516, 0.72747516, 0.72747516]]]
Args: Args:
input (Variable): The input variable. A LoDTensor or Tensor with type input (Variable): The input variable. A multi-dimension ``Tensor`` with type float32 or float64.
float32, float64. use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn \
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
library is installed. To improve numerical stablity, set use_cudnn to \ library is installed. To improve numerical stablity, set use_cudnn to \
False by default. Default: False False by default.
name (str|None): A name for this layer(optional). If set None, the layer 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` . Default: None.
will be named automatically. Default: None. will be named automatically. Default: None.
axis (int): The index of dimension to perform softmax calculations, it should axis (int, optional): The index of dimension to perform softmax calculations, it should
be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of
input variable. Default: -1. -1 means the last dimension. input variable. Default: -1. -1 means the last dimension.
Returns: Returns:
Variable: output of softmax. A Tensor with type float32, float64. Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input`` .
Examples: Examples:
...@@ -2379,7 +2428,6 @@ def softmax(input, use_cudnn=False, name=None, axis=-1): ...@@ -2379,7 +2428,6 @@ def softmax(input, use_cudnn=False, name=None, axis=-1):
output= exe.run(feed={"input": x}, output= exe.run(feed={"input": x},
fetch_list=[result[0]]) fetch_list=[result[0]])
print(output) print(output)
#array([0.22595254, 0.39276356, 0.38128382], dtype=float32)]
""" """
helper = LayerHelper('softmax', **locals()) helper = LayerHelper('softmax', **locals())
if not isinstance(input, Variable): if not isinstance(input, Variable):
...@@ -7804,12 +7852,9 @@ def softmax_with_cross_entropy(logits, ...@@ -7804,12 +7852,9 @@ def softmax_with_cross_entropy(logits,
return_softmax=False, return_softmax=False,
axis=-1): axis=-1):
""" """
**Softmax With Cross Entropy Operator.** This operator implements the cross entropy loss function with softmax. This function
combines the calculation of the softmax operation and the cross entropy loss function
Cross entropy loss with softmax is used as the output layer extensively. This to provide a more numerically stable gradient.
operator computes the softmax normalized values for dimension :attr:`axis` of
the input tensor, after which cross-entropy loss is computed. This provides
a more numerically stable gradient.
Because this operator performs a softmax on logits internally, it expects Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of unscaled logits. This operator should not be used with the output of
...@@ -7826,59 +7871,58 @@ def softmax_with_cross_entropy(logits, ...@@ -7826,59 +7871,58 @@ def softmax_with_cross_entropy(logits,
.. math:: .. math::
loss_j = -\\text{logit}_{label_j} + loss_j = -\\text{logits}_{label_j} +
\\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logits}_i)\\right), j = 1,..., K
2) Soft label (each sample can have a distribution over all classes) 2) Soft label (each sample can have a distribution over all classes)
.. math:: .. math::
loss_j = -\\sum_{i=0}^{K}\\text{label}_i loss_j = -\\sum_{i=0}^{K}\\text{label}_i
\\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K} \\left(\\text{logits}_i - \\log\\left(\\sum_{i=0}^{K}
\\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K \\exp(\\text{logits}_i)\\right)\\right), j = 1,...,K
3) If :attr:`numeric_stable_mode` is :attr:`True`, softmax is calculated 3) If :attr:`numeric_stable_mode` is :attr:`True`, softmax is calculated first by:
first by:
.. math:: .. math::
max_j &= \\max_{i=0}^{K}{\\text{logit}_i} max_j &= \\max_{i=0}^{K}{\\text{logits}_i}
log\\_max\\_sum_j &= \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j) log\\_max\\_sum_j &= \\log\\sum_{i=0}^{K}\\exp(logits_i - max_j)
softmax_j &= \\exp(logit_j - max_j - {log\\_max\\_sum}_j) softmax_j &= \\exp(logits_j - max_j - {log\\_max\\_sum}_j)
and then cross entropy loss is calculated by softmax and label. and then cross entropy loss is calculated by softmax and label.
Args: Args:
logits (Variable): The input tensor of unscaled log probabilities. logits (Variable): A multi-dimension ``Tensor`` , and the data type is float32 or float64. The input tensor of unscaled log probabilities.
label (Variable): The ground truth tensor. If :attr:`soft_label` label (Variable): The ground truth ``Tensor`` , data type is the same
is set to :attr:`True`, Label is a Tensor<float/double> in the as the ``logits`` . If :attr:`soft_label` is set to :attr:`True`,
same shape with :attr:`logits`. If :attr:`soft_label` is set to Label is a ``Tensor`` in the same shape with :attr:`logits`.
:attr:`True`, Label is a Tensor<int64> in the same shape with If :attr:`soft_label` is set to :attr:`True`, Label is a ``Tensor``
:attr:`logits` expect shape in dimension :attr:`axis` as 1. in the same shape with :attr:`logits` expect shape in dimension :attr:`axis` as 1.
soft_label (bool): A flag to indicate whether to interpretate the given soft_label (bool, optional): A flag to indicate whether to interpretate the given
labels as soft labels. Default False. labels as soft labels. Default False.
ignore_index (int): Specifies a target value that is ignored and does ignore_index (int, optional): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid not contribute to the input gradient. Only valid
if :attr:`soft_label` is set to :attr:`False`. if :attr:`soft_label` is set to :attr:`False`.
Default: kIgnoreIndex Default: kIgnoreIndex(-100).
numeric_stable_mode (bool): A flag to indicate whether to use a more numeric_stable_mode (bool, optional): A flag to indicate whether to use a more
numerically stable algorithm. Only valid numerically stable algorithm. Only valid
when :attr:`soft_label` is :attr:`False` when :attr:`soft_label` is :attr:`False`
and GPU is used. When :attr:`soft_label` and GPU is used. When :attr:`soft_label`
is :attr:`True` or CPU is used, the is :attr:`True` or CPU is used, the
algorithm is always numerically stable. algorithm is always numerically stable.
Note that the speed may be slower when use Note that the speed may be slower when use
stable algorithm. Default: True stable algorithm. Default: True.
return_softmax (bool): A flag indicating whether to return the softmax return_softmax (bool, optional): A flag indicating whether to return the softmax
along with the cross entropy loss. Default: False along with the cross entropy loss. Default: False.
axis (int): The index of dimension to perform softmax calculations. It axis (int, optional): The index of dimension to perform softmax calculations. It
should be in range :math:`[-1, rank - 1]`, while :math:`rank` should be in range :math:`[-1, rank - 1]`, while :math:`rank`
is the rank of input :attr:`logits`. Default: -1. is the rank of input :attr:`logits`. Default: -1.
Returns: Returns:
Variable or Tuple of two Variables: Return the cross entropy loss if \ ``Variable`` or Tuple of two ``Variable`` : Return the cross entropy loss if \
`return_softmax` is False, otherwise the tuple \ `return_softmax` is False, otherwise the tuple \
(loss, softmax), softmax is in the same shape \ (loss, softmax), softmax is in the same shape \
with input logits and cross entropy loss is in \ with input logits and cross entropy loss is in \
...@@ -7890,8 +7934,8 @@ def softmax_with_cross_entropy(logits, ...@@ -7890,8 +7934,8 @@ def softmax_with_cross_entropy(logits,
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[128], dtype='float32') data = fluid.data(name='data', shape=[-1, 128], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
fc = fluid.layers.fc(input=data, size=100) fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.softmax_with_cross_entropy( out = fluid.layers.softmax_with_cross_entropy(
logits=fc, label=label) logits=fc, label=label)
...@@ -11039,54 +11083,45 @@ def affine_grid(theta, out_shape, name=None): ...@@ -11039,54 +11083,45 @@ def affine_grid(theta, out_shape, name=None):
def rank_loss(label, left, right, name=None): def rank_loss(label, left, right, name=None):
""" """
This operator implements the sort loss layer in the RankNet model. RankNet is a pairwise ranking model
**Rank loss layer for RankNet** with a training sample consisting of a pair of documents (A and B), The label (P)
indicates whether A is ranked higher than B or not. Please refer to more details:
`RankNet <http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf>`_ `RankNet <http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf>`_
is a pairwise ranking model with a training sample consisting of a pair
of documents, A and B. Label P indicates whether A is ranked higher than B
or not:
P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
about the rank of the input pair.
Rank loss layer takes three inputs: left ( :math:`o_i` ), right ( :math:`o_j` ) and Rank loss layer takes three inputs: left ( :math:`o_i` ), right ( :math:`o_j` ) and
label ( :math:`P_{i,j}` ). The inputs respectively represent RankNet's output scores label ( :math:`P_{i,j}` ). The inputs respectively represent RankNet's output scores
for documents A and B and the value of label P. The following equation for documents A and B and the value of label P. Rank loss layer takes batch inputs
computes rank loss C_{i,j} from the inputs: with size batch_size (batch_size >= 1), P = {0, 1} or {0, 0.5, 1},
where 0.5 means that there is no information about the rank of the input pair.
The following equation computes rank loss C_{i,j} from the inputs:
.. math:: .. math::
C_{i,j} &= -\\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\\\ C_{i,j} &= -\\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\\\
.. math::
o_{i,j} &= o_i - o_j \\\\ o_{i,j} &= o_i - o_j \\\\
.. math::
\\tilde{P_{i,j}} &= \\left \{0, 0.5, 1 \\right \} \ or \ \\left \{0, 1 \\right \} \\tilde{P_{i,j}} &= \\left \{0, 0.5, 1 \\right \} \ or \ \\left \{0, 1 \\right \}
Parameters:
Rank loss layer takes batch inputs with size batch_size (batch_size >= 1). label (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32, batch indicates the size of the data. Indicats whether A ranked higher than B or not.
left (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32. RankNet's output score for doc A.
Args: right (Variable): 2-D ``Tensor`` with the shape of :math:`[batch,1]`, the data type is float32. RankNet's output score for doc B.
label (Variable): Indicats whether A ranked higher than B or not. name(str|None): 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` .
left (Variable): RankNet's output score for doc A.
right (Variable): RankNet's output score for doc B.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns: Returns:
list: The value of rank loss. Variable: ``Tensor`` indicating the output value of the sort loss layer, the data type is float32, and the return value's shape is :math:`[batch,1]` .
Raises: Raises:
ValueError: Any of label, left, and right is not a variable. ValueError: Any of label, left, and right is not a ``Variable`` .
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
label = fluid.layers.data(name="label", shape=[-1, 1], dtype="float32") label = fluid.data(name="label", shape=[-1, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[-1, 1], dtype="float32") left = fluid.data(name="left", shape=[-1, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[-1, 1], dtype="float32") right = fluid.data(name="right", shape=[-1, 1], dtype="float32")
out = fluid.layers.rank_loss(label, left, right) out = fluid.layers.rank_loss(label, left, right)
""" """
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或