Skip to content

P
Paddle

wmsofts / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 0
Fork 0
P
Paddle

体验新版 GitCode，发现更多精彩内容 >>

未验证 提交 899ab30d 编写于 作者: Y Yibing Liu 提交者： GitHub
浏览文件
操作

Fix several api docs (#20282) 

* Fix several api docs

test=develop, test=document_fix
上级 6f9e64d4
隐藏空白更改
内联 并排
Showing with 169 addition and 146 deletion
paddle/fluid/API.spec
浏览文件 @ 899ab30d
...@@ -130,7 +130,7 @@ paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', ' ...@@ -130,7 +130,7 @@ paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', '
paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3')) paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False)), ('document', '83617c165827e030636c80486d5de6f3'))
paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e')) paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e'))
paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b28bdb43160e9667be2a3457d19d9f5b')) paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b28bdb43160e9667be2a3457d19d9f5b'))
paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '933b7e268c4ffa3d5c3ef953a5ee9f0b')) paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '708ce0348b74d3e0c7885c2c524b7fa7'))
paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '48ec1ba2d75c4e2faf8d9a47350462ae')) paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '48ec1ba2d75c4e2faf8d9a47350462ae'))
paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', 'd1985a930a59c3bd41a7c1d72594f5b9')) paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', 'd1985a930a59c3bd41a7c1d72594f5b9'))
paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae57e6e5136dade436f0df1f11770afa')) paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae57e6e5136dade436f0df1f11770afa'))
...@@ -178,7 +178,7 @@ paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_t ...@@ -178,7 +178,7 @@ paddle.fluid.layers.warpctc (ArgSpec(args=['input', 'label', 'blank', 'norm_by_t
paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'eeb1591cfc854c6ffdac77b376313c44')) paddle.fluid.layers.sequence_reshape (ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None), ('document', 'eeb1591cfc854c6ffdac77b376313c44'))
paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa')) paddle.fluid.layers.transpose (ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e72db173d4c082e27cb11f31d8c9bfa'))
paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', 'fe352915a543cec434f74e9b32ac49da')) paddle.fluid.layers.im2sequence (ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None)), ('document', 'fe352915a543cec434f74e9b32ac49da'))
paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '83d4ca6dfb957912807f535756e76992')) paddle.fluid.layers.nce (ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False)), ('document', '38297567127888e01542857839058d52'))
paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', 'd4435a63d34203339831ee6a86ef9242')) paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0)), ('document', 'd4435a63d34203339831ee6a86ef9242'))
paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', 'b83e7dfa81059b39bb137922dc914f50')) paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', 'b83e7dfa81059b39bb137922dc914f50'))
paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', '1270395ce97a4e1b556104abbb14f096')) paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', '1270395ce97a4e1b556104abbb14f096'))
...@@ -190,7 +190,7 @@ paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', ...@@ -190,7 +190,7 @@ paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters',
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', '54e1675aa0364f4a78fa72804ec0f413'))
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', '98e7927f09ee2270535b29f048e481ec')) 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.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd7a6d59e464a7ef1184eb6caefeb49f1')) paddle.fluid.layers.reshape (ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd7a6d59e464a7ef1184eb6caefeb49f1'))
paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbac07662a6e22e8e299ced880c7775')) paddle.fluid.layers.squeeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbac07662a6e22e8e299ced880c7775'))
paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b9bd3129d36a70e7c4385df51ff71c62')) paddle.fluid.layers.unsqueeze (ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b9bd3129d36a70e7c4385df51ff71c62'))
...@@ -208,7 +208,7 @@ paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', ...@@ -208,7 +208,7 @@ paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len',
paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCHW')), ('document', '44da7890c8a362a83a1c0902a1dc1e4d')) paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCHW')), ('document', '44da7890c8a362a83a1c0902a1dc1e4d'))
paddle.fluid.layers.resize_trilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCDHW')), ('document', '5b4d0f823f94c260fe5e6f7eec60a797')) paddle.fluid.layers.resize_trilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1, 'NCDHW')), ('document', '5b4d0f823f94c260fe5e6f7eec60a797'))
paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 'NCHW')), ('document', '0107a5cbae1aef3f381d3d769a6068eb')) paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'data_format'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 'NCHW')), ('document', '0107a5cbae1aef3f381d3d769a6068eb'))
paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', 'f985c9b66e3aec96fa753a8eb44c991c')) paddle.fluid.layers.gather (ArgSpec(args=['input', 'index', 'overwrite'], varargs=None, keywords=None, defaults=(True,)), ('document', '5c52e8512f97a84608bc3b8b3250fc70'))
paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7d625028525167b138106f574dffdf9')) paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7d625028525167b138106f574dffdf9'))
paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '3f94c3348dc79b7b40a839d31a3eaa84')) paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name', 'overwrite'], varargs=None, keywords=None, defaults=(None, True)), ('document', '3f94c3348dc79b7b40a839d31a3eaa84'))
paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2607b5c9369fbc52f208de066a80fc25')) paddle.fluid.layers.scatter_nd_add (ArgSpec(args=['ref', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2607b5c9369fbc52f208de066a80fc25'))
...@@ -222,7 +222,7 @@ paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None ...@@ -222,7 +222,7 @@ paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None
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', '8eb36596bb43d7a907d3397c7aedbdb3'))
paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '6fc86ed23b420c8a0f6c043563cf3937')) 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'))
paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '00d437d1e0d9450ea75a0495b93b54a7')) paddle.fluid.layers.pow (ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '00d437d1e0d9450ea75a0495b93b54a7'))
...@@ -256,7 +256,7 @@ paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', ...@@ -256,7 +256,7 @@ paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act',
paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28')) paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f')) paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe')) paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', 'b24d0b21361c4bb8ef2cec8c26fb12b2')) paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310')) paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34')) paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '8c622791994a0d657d8c6c9cefa5bf34'))
paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '340d8d656272ea396b441aab848429a2')) paddle.fluid.layers.strided_slice (ArgSpec(args=['input', 'axes', 'starts', 'ends', 'strides'], varargs=None, keywords=None, defaults=None), ('document', '340d8d656272ea396b441aab848429a2'))
...@@ -277,12 +277,12 @@ paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], var ...@@ -277,12 +277,12 @@ paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], var
paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '315b50c1cbd9569375b098c56f1e91c9')) paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '315b50c1cbd9569375b098c56f1e91c9'))
paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3')) paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ecc4b1323028bde0518d666882d03515')) paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ecc4b1323028bde0518d666882d03515'))
paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '18ec2e3afeb90e70c8b73d2b71c40fdb')) paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '57256fcb1119dc35ae031889fa601d61'))
paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3')) paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3'))
paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '90c74742f48c70b103f1fbb9eb129066')) paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '90c74742f48c70b103f1fbb9eb129066'))
paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'e3993a477c94729526040ff65d95728e')) paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'ef1701e11d60508fe8f02dd2a8c60bdf'))
paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937')) paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937'))
paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '45fc3652a8e1aeffbe4eba371c54f756')) paddle.fluid.layers.bilinear_tensor_product (ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '6755168c4b2308e1e4f54cb56fa7dcb2'))
paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2b0e5d5c155ce24bafc38b78cd0b164')) paddle.fluid.layers.merge_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2b0e5d5c155ce24bafc38b78cd0b164'))
paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c568321feb4d16c41a83df43f95089d')) paddle.fluid.layers.get_tensor_from_selected_rows (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c568321feb4d16c41a83df43f95089d'))
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)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391')) 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)), ('document', '5193cf1113f9d8d8f682ee5a5fc8b391'))
...@@ -921,7 +921,7 @@ paddle.fluid.transpiler.DistributeTranspilerConfig ('paddle.fluid.transpiler.dis ...@@ -921,7 +921,7 @@ paddle.fluid.transpiler.DistributeTranspilerConfig ('paddle.fluid.transpiler.dis
paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.transpiler.DistributeTranspilerConfig.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', '5e89c978199c4ecce2b26d5fed1ec52b')) paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'pool_size', 'pool_stride', 'pool_padding', 'pool_type', 'global_pooling', 'conv_stride', 'conv_padding', 'conv_dilation', 'conv_groups', 'param_attr', 'bias_attr', 'act', 'use_cudnn'], varargs=None, keywords=None, defaults=(0, 'max', False, 1, 0, 1, 1, None, None, None, True)), ('document', '5e89c978199c4ecce2b26d5fed1ec52b'))
paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', 'b2d435f782ac8ea3ca480b8d24e7f5b4')) paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', 'b2d435f782ac8ea3ca480b8d24e7f5b4'))
paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', 'b87bacfc70dd3477ed25ef14aa01389a')) paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3efe197c8e3e75f84a4c464d8b74e943'))
paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', 'b1a07a0000eb9103e3a143ca8c13de5b')) paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', 'b1a07a0000eb9103e3a143ca8c13de5b'))
paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '6033b78da39b8b0ed302fbb0f67da502')) paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '6033b78da39b8b0ed302fbb0f67da502'))
paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('document', 'c3c8dd3193d991adf8bda505560371d6')) paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('document', 'c3c8dd3193d991adf8bda505560371d6'))
...@@ -1043,7 +1043,7 @@ paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args ...@@ -1043,7 +1043,7 @@ paddle.fluid.optimizer.DGCMomentumOptimizer.get_opti_var_name_list (ArgSpec(args
paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304')) paddle.fluid.optimizer.DGCMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.DGCMomentumOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17')) paddle.fluid.optimizer.DGCMomentumOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17'))
paddle.fluid.optimizer.DGCMomentumOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8')) paddle.fluid.optimizer.DGCMomentumOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8'))
paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '7dd8b270156a52f1f6b4663336960893')) paddle.fluid.optimizer.LambOptimizer ('paddle.fluid.optimizer.LambOptimizer', ('document', '56b5b21dc8fb01174c3bdd0b24f8be4b'))
paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'exclude_from_weight_decay_fn', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.LambOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'lamb_weight_decay', 'beta1', 'beta2', 'epsilon', 'regularization', 'exclude_from_weight_decay_fn', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.01, 0.9, 0.999, 1e-06, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610')) paddle.fluid.optimizer.LambOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae')) paddle.fluid.optimizer.LambOptimizer.apply_optimize (ArgSpec(args=['self', 'loss', 'startup_program', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '5c46d1926a40f1f873ffe9f37ac89dae'))
...@@ -1052,9 +1052,9 @@ paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self ...@@ -1052,9 +1052,9 @@ paddle.fluid.optimizer.LambOptimizer.get_opti_var_name_list (ArgSpec(args=['self
paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304')) paddle.fluid.optimizer.LambOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'grad_clip'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '8387af01322a6defc92c1832faccd304'))
paddle.fluid.optimizer.LambOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17')) paddle.fluid.optimizer.LambOptimizer.set_dict (ArgSpec(args=['self', 'state_dict'], varargs=None, keywords=None, defaults=None), ('document', '36aa497a2d29abaa4147987d71721d17'))
paddle.fluid.optimizer.LambOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8')) paddle.fluid.optimizer.LambOptimizer.state_dict (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'deca1537945d33940b350923fb16ddf8'))
paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'a38b7d5b9f17a295ed15d4c1b9ab4cd0')) paddle.fluid.optimizer.ExponentialMovingAverage ('paddle.fluid.optimizer.ExponentialMovingAverage', ('document', 'c93a5e29890877d94c3180b3d5f2d464'))
paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.optimizer.ExponentialMovingAverage.__init__ (ArgSpec(args=['self', 'decay', 'thres_steps', 'name'], varargs=None, keywords=None, defaults=(0.999, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '30f494752ac8921dc5835a63637f453a')) paddle.fluid.optimizer.ExponentialMovingAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '178fa374c299906eb88c0df8ed21a337'))
paddle.fluid.optimizer.ExponentialMovingAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '8c8a1791608b02a1ede53d6dd3a4fcec')) paddle.fluid.optimizer.ExponentialMovingAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '8c8a1791608b02a1ede53d6dd3a4fcec'))
paddle.fluid.optimizer.ExponentialMovingAverage.update (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ea10f08af6d7aac3b7974aa976e4085f')) paddle.fluid.optimizer.ExponentialMovingAverage.update (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ea10f08af6d7aac3b7974aa976e4085f'))
paddle.fluid.optimizer.PipelineOptimizer ('paddle.fluid.optimizer.PipelineOptimizer', ('document', '6f85382abedb922387b08d98e8d0b69c')) paddle.fluid.optimizer.PipelineOptimizer ('paddle.fluid.optimizer.PipelineOptimizer', ('document', '6f85382abedb922387b08d98e8d0b69c'))
......
paddle/fluid/operators/crf_decoding_op.cc
浏览文件 @ 899ab30d
...@@ -21,45 +21,47 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -21,45 +21,47 @@ class CRFDecodingOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override { void Make() override {
AddInput( AddInput(
"Emission", "Emission",
"(Tensor<float>/LoDTensor<float>). For a LoDTensor input, its " "(Tensor/LoDTensor). For a LoDTensor input, its shape is [N x D] "
"shape is [N x D] where N is the total sequence length of the " "where N is the total sequence length of the mini-batch and D is "
"mini-batch and D is the total tag number. While for a tensor " "the total tag number. While for a tensor input, its shape is "
"input, its shape is [B X S X D] with B the batch size and S the " "[B X S X D] with B the batch size and S the sequence length of each "
"sequence length of each sample after padding. This input is the " "sample after padding. This input is the unscaled emission weight "
"unscaled emission weight matrix of the linear_chain_crf operator."); "matrix of the linear_chain_crf operator. The data type is float32 "
"or float64.");
AddInput( AddInput(
"Transition", "Transition",
"(Tensor<float>). A Tensor with shape [(D + 2) x D]. " "(Tensor). A Tensor with shape [(D + 2) x D]. "
"This input is the transition weights learned by the linear_chain_crf " "This input is the transition weights learned by the linear_chain_crf "
"operator, denoted as w. The 1st row of w are transition weights for " "operator, denoted as w. The 1st row of w are transition weights for "
"the start mask. The 2nd row of w are transition weights for the end " "the start mask. The 2nd row of w are transition weights for the end "
"mask. Transition weights between other tags begin from the 3rd row of " "mask. Transition weights between other tags begin from the 3rd row of "
"w. See more details in comments of the linear_chain_crf operator."); "w. See more details in comments of the linear_chain_crf operator. "
"The data type is the same as Input(Emission).");
AddInput( AddInput(
"Label", "Label",
"(Tensor<int64_t>/LoDTensor<int64_t>). The ground truth with shape " "(Tensor/LoDTensor). The ground truth with shape "
"[N x 1] (for LoDTensor) or [B x S] (for Tensor). This input is " "[N x 1] (for LoDTensor) or [B x S] (for Tensor). This input is "
"optional. See more details in the operator's comments.") "optional. See more details in the operator's comments. The data type "
"is int64.")
.AsDispensable(); .AsDispensable();
AddOutput( AddOutput(
"ViterbiPath", "ViterbiPath",
"(Tensor<int64_t>/LoDTensor<int64_t>). The decoding results. What to " "(Tensor/LoDTensor). The decoding results. What to "
"return changes depending on whether the Input(Label) (the ground " "return changes depending on whether the Input(Label) (the ground "
"truth) is given. See more details in the operator's comment."); "truth) is given. See more details in the operator's comment. "
"The data type is int64.");
AddInput("Length", AddInput("Length",
"(Tensor<int64_t>). The actual length of each sample before " "(Tensor). The actual length of each sample before "
"padding with shape [B x 1]. It means the Input(Emission), " "padding with shape [B x 1]. It means the Input(Emission), "
"Input(Label) " "Input(Label) and Output(ViterbiPath) are common tensors with "
"and Output(ViterbiPath) are common tensors with padding when " "padding when this input is given. The data type is int64.")
"this input "
"is given.")
.AsDispensable(); .AsDispensable();
AddComment(R"DOC( AddComment(R"DOC(
The crf_decoding operator reads the emission feature weights and the transition The crf_decoding operator reads the emission feature weights and the transition
feature weights learned by the linear_chain_crf operator. It implements the feature weights learned by the linear_chain_crf operator and performs decoding.
Viterbi algorithm which is a dynamic programming algorithm for finding the most It implements the Viterbi algorithm which is a dynamic programming algorithm
likely sequence of hidden states, called the Viterbi path, that results in a for finding the most likely sequence of hidden states, called the Viterbi path,
sequence of observed tags. that results in a sequence of observed tags.
The output of this operator changes according to whether Input(Label) is given: The output of this operator changes according to whether Input(Label) is given:
......
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
浏览文件 @ 899ab30d
...@@ -46,7 +46,7 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker { ...@@ -46,7 +46,7 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
AddAttr<int>("seed", AddAttr<int>("seed",
"(int, default 0) " "(int, default 0) "
"Random seed of generator." "Random seed of generator."
"0 means use system wide seed." "0 means don't specify random seed."
"Note that if seed is not 0, this operator will always " "Note that if seed is not 0, this operator will always "
"generate the same random numbers every time.") "generate the same random numbers every time.")
.SetDefault(0); .SetDefault(0);
...@@ -58,9 +58,9 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker { ...@@ -58,9 +58,9 @@ class GaussianRandomBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
AddComment(R"DOC( AddComment(R"DOC(
Used to initialize tensors with gaussian random generator. Used to initialize tensors with gaussian random generator.
The default mean of the distribution is 0. and default standard The default mean of the distribution is 0, and default standard
deviation (std) of the distribution is 1.. Uers can set mean and std deviation (std) of the distribution is 1.0. Uers can set mean and std
by input arguments. via input arguments.
)DOC"); )DOC");
} }
}; };
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 899ab30d
...@@ -1624,11 +1624,13 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -1624,11 +1624,13 @@ def crf_decoding(input, param_attr, label=None, length=None):
Args: Args:
input(${emission_type}): ${emission_comment} input(${emission_type}): ${emission_comment}
param_attr(ParamAttr): The parameter attribute for training. param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
label(${label_type}): ${label_comment} label(${label_type}, optional): ${label_comment}
label(${length_type}): ${length_comment} length(${length_type}, optional): ${length_comment}
Returns: Returns:
Variable: ${viterbi_path_comment} Variable: ${viterbi_path_comment}
...@@ -1640,8 +1642,8 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -1640,8 +1642,8 @@ def crf_decoding(input, param_attr, label=None, length=None):
# LoDTensor-based example # LoDTensor-based example
num_labels = 10 num_labels = 10
feature = fluid.layers.data(name='word_emb', shape=[784], dtype='float32', lod_level=1) feature = fluid.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64', lod_level=1) label = fluid.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1)
emission = fluid.layers.fc(input=feature, size=num_labels) emission = fluid.layers.fc(input=feature, size=num_labels)
crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label, crf_cost = fluid.layers.linear_chain_crf(input=emission, label=label,
...@@ -1651,9 +1653,9 @@ def crf_decoding(input, param_attr, label=None, length=None): ...@@ -1651,9 +1653,9 @@ def crf_decoding(input, param_attr, label=None, length=None):
# Common tensor example # Common tensor example
num_labels, max_len = 10, 20 num_labels, max_len = 10, 20
feature = fluid.layers.data(name='word_emb_pad', shape=[max_len, 784], dtype='float32') feature = fluid.data(name='word_emb_pad', shape=[-1, max_len, 784], dtype='float32')
label = fluid.layers.data(name='label_pad', shape=[max_len, 1], dtype='int64') label = fluid.data(name='label_pad', shape=[-1, max_len, 1], dtype='int64')
length = fluid.layers.data(name='length', shape=[1], dtype='int64') length = fluid.data(name='length', shape=[-1, 1], dtype='int64')
emission = fluid.layers.fc(input=feature, size=num_labels, emission = fluid.layers.fc(input=feature, size=num_labels,
num_flatten_dims=2) num_flatten_dims=2)
...@@ -7119,33 +7121,33 @@ def nce(input, ...@@ -7119,33 +7121,33 @@ def nce(input,
${comment} ${comment}
Args: Args:
input (Variable): input variable. input (Variable): Input variable, 2-D tensor with shape [batch_size, dim],
label (Variable): label. and data type is float32 or float64.
num_total_classes (int):${num_total_classes_comment} label (Variable): Input label, 2-D tensor with shape [batch_size, num_true_class],
and data type is int64.
num_total_classes (int):${num_total_classes_comment}.
sample_weight (Variable|None): A Variable of shape [batch_size, 1] sample_weight (Variable|None): A Variable of shape [batch_size, 1]
storing a weight for each sample. The default weight for each storing a weight for each sample. The default weight for each
sample is 1.0. sample is 1.0.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights param_attr (ParamAttr|None): To specify the weight parameter attribute.
of nce. If it is set to None or one attribute of ParamAttr, nce Default: None, which means the default weight parameter property is
will create ParamAttr as param_attr. If the Initializer of the param_attr used. See usage for details in :ref:`api_fluid_ParamAttr` .
is not set, the parameter is initialized with Xavier. Default: None. bias_attr (ParamAttr|None): To specify the bias parameter attribute.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of nce. Default: None, which means the default bias parameter property is
If it is set to False, no bias will be added to the output units. used. See usage for details in :ref:`api_fluid_ParamAttr` .
If it is set to None or one attribute of ParamAttr, nce num_neg_samples (int): ${num_neg_samples_comment}.
will create ParamAttr as bias_attr. If the Initializer of the bias_attr name(str|None): For detailed information, please refer to
is not set, the bias is initialized zero. Default: None. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
num_neg_samples (int): ${num_neg_samples_comment} sampler (str, optional): The sampler used to sample class from negtive classes.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
sampler (str): 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[]): A float[] with size=num_total_classes. custom_dist (nd.array|None): A numpy ndarray 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 probsbility of i-th class to be sampled. custom_dist[i] is the probsbility 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, means no random seed.
is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows. is_sparse(bool, optional): The flag indicating whether to use sparse update,
the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default False.
Returns: Returns:
Variable: The output nce loss. Variable: The output nce loss.
...@@ -7160,8 +7162,8 @@ def nce(input, ...@@ -7160,8 +7162,8 @@ def nce(input,
window_size = 5 window_size = 5
words = [] words = []
for i in xrange(window_size): for i in xrange(window_size):
words.append(fluid.layers.data( words.append(fluid.data(
name='word_{0}'.format(i), shape=[1], dtype='int64')) name='word_{0}'.format(i), shape=[-1, 1], dtype='int64'))
dict_size = 10000 dict_size = 10000
label_word = int(window_size / 2) + 1 label_word = int(window_size / 2) + 1
...@@ -8223,17 +8225,17 @@ def one_hot(input, depth, allow_out_of_range=False): ...@@ -8223,17 +8225,17 @@ def one_hot(input, depth, allow_out_of_range=False):
def autoincreased_step_counter(counter_name=None, begin=1, step=1): def autoincreased_step_counter(counter_name=None, begin=1, step=1):
""" """
Create an auto-increase variable Create an auto-increase variable. which will be automatically increased
which will be automatically increased by 1 every mini-batch by 1 in every iteration. By default, the first return of this counter is 1,
Return the run counter of the main program, default is started from 1. and the step size is 1.
Args: Args:
counter_name(str): The counter name, default is '@STEP_COUNTER@'. counter_name(str, optional): The counter name. Default '@STEP_COUNTER@'.
begin(int): The first value of this counter. begin(int, optional): The first return value of this counter. Default 1.
step(int): The increment step between each execution. step(int, optional): The step size. Default 1.
Returns: Returns:
Variable: The global run counter. Variable: The auto-increased Variable with data type int64.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -10045,9 +10047,11 @@ def gather(input, index, overwrite=True): ...@@ -10045,9 +10047,11 @@ def gather(input, index, overwrite=True):
[5, 6]] [5, 6]]
Args: Args:
input (Variable): The source input with rank>=1. input (Variable): The source input tensor with rank>=1. Supported data type is
index (Variable): The index input with rank=1. int32, int64, float32, float64 and uint8 (only for CPU),
overwrite (bool): The mode that updating the grad when has same index. float16 (only for GPU).
index (Variable): The index input tensor with rank=1. Data type is int32 or int64.
overwrite (bool, optional): The mode that updating the grad when has same index.
If True, use the overwrite mode to update the grad of the same index, If True, use the overwrite mode to update the grad of the same index,
if False, use the accumulate mode to update the grad of the same index. if False, use the accumulate mode to update the grad of the same index.
Default value is True. Default value is True.
...@@ -10062,8 +10066,8 @@ def gather(input, index, overwrite=True): ...@@ -10062,8 +10066,8 @@ def gather(input, index, overwrite=True):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[-1, 5], dtype='float32') x = fluid.data(name='x', shape=[-1, 5], dtype='float32')
index = fluid.layers.data(name='index', shape=[-1, 1], dtype='int32') index = fluid.data(name='index', shape=[-1, 1], dtype='int32')
output = fluid.layers.gather(x, index) output = fluid.layers.gather(x, index)
""" """
helper = LayerHelper('gather', **locals()) helper = LayerHelper('gather', **locals())
...@@ -11117,11 +11121,12 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None): ...@@ -11117,11 +11121,12 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
Args: Args:
label (Variable): Indicates whether the left is ranked higher than the right or not. label (Variable): Indicates whether the left is ranked higher than the right or not.
left (Variable): Ranking score for left. Data type is float32.
right (Variable): Ranking score for right. left (Variable): Ranking score for left. Data type float32.
right (Variable): Ranking score for right. Data type float32.
margin (float): Indicates the given margin. margin (float): Indicates the given margin.
name (str|None): A name for this layer (optional). If set None, the layer name(str|None): For detailed information, please refer to
will be named automatically. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns: Returns:
Variable: The ranking loss. Variable: The ranking loss.
...@@ -11134,9 +11139,9 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None): ...@@ -11134,9 +11139,9 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
.. 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.margin_rank_loss(label, left, right) out = fluid.layers.margin_rank_loss(label, left, right)
""" """
helper = LayerHelper('margin_rank_loss', **locals()) helper = LayerHelper('margin_rank_loss', **locals())
...@@ -12421,12 +12426,12 @@ def gaussian_random_batch_size_like(input, ...@@ -12421,12 +12426,12 @@ def gaussian_random_batch_size_like(input,
Args: Args:
input (Variable): ${input_comment} input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment} shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment} input_dim_idx (int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment} output_dim_idx (int): ${output_dim_idx_comment}
mean (Float): ${mean_comment} mean (float): ${mean_comment}
std (Float): ${std_comment} std (float): ${std_comment}
seed (Int): ${seed_comment} seed (int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc dtype(np.dtype|core.VarDesc.VarType|str): The type of output data, float32 or float_64.
Returns: Returns:
out (Variable): ${out_comment} out (Variable): ${out_comment}
...@@ -12435,7 +12440,7 @@ def gaussian_random_batch_size_like(input, ...@@ -12435,7 +12440,7 @@ def gaussian_random_batch_size_like(input,
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[13, 11], dtype='float32') input = fluid.data(name="input", shape=[13, 11], dtype='float32')
out = fluid.layers.gaussian_random_batch_size_like( out = fluid.layers.gaussian_random_batch_size_like(
input, shape=[-1, 11], mean=1.0, std=2.0) input, shape=[-1, 11], mean=1.0, std=2.0)
...@@ -14298,7 +14303,8 @@ def similarity_focus(input, axis, indexes, name=None): ...@@ -14298,7 +14303,8 @@ def similarity_focus(input, axis, indexes, name=None):
Args: Args:
input(Variable): The input tensor variable(default float). It should input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C]. be a 4-D tensor with shape [BatchSize, A, B, C]. Data type is
float32 or float64.
axis(int): Indicating the dimension to be selected. It can only be axis(int): Indicating the dimension to be selected. It can only be
1, 2 or 3. 1, 2 or 3.
indexes(list): Indicating the indexes of the selected dimension. indexes(list): Indicating the indexes of the selected dimension.
...@@ -14311,7 +14317,7 @@ def similarity_focus(input, axis, indexes, name=None): ...@@ -14311,7 +14317,7 @@ def similarity_focus(input, axis, indexes, name=None):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.data(
name='data', shape=[-1, 3, 2, 2], dtype='float32') name='data', shape=[-1, 3, 2, 2], dtype='float32')
fluid.layers.similarity_focus(input=data, axis=1, indexes=[0]) fluid.layers.similarity_focus(input=data, axis=1, indexes=[0])
""" """
...@@ -14524,13 +14530,15 @@ def log_loss(input, label, epsilon=1e-4, name=None): ...@@ -14524,13 +14530,15 @@ def log_loss(input, label, epsilon=1e-4, name=None):
- (1 - label) * \\log{(1 - input + \\epsilon)} - (1 - label) * \\log{(1 - input + \\epsilon)}
Args: Args:
input (Variable|list): a 2-D tensor with shape [N x 1], where N is the input (Variable|list): A 2-D tensor with shape [N x 1], where N is the
batch size. This input is a probability computed batch size. This input is a probability computed
by the previous operator. by the previous operator. Data type float32.
label (Variable|list): the ground truth which is a 2-D tensor with label (Variable|list): The ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size. shape [N x 1], where N is the batch size.
epsilon (float): epsilon Data type float32.
name (string): the name of log_loss epsilon (float, optional): A small number for numerical stability. Default 1e-4.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Returns: Returns:
Variable: A 2-D tensor with shape [N x 1], the negative log loss. Variable: A 2-D tensor with shape [N x 1], the negative log loss.
...@@ -14539,8 +14547,8 @@ def log_loss(input, label, epsilon=1e-4, name=None): ...@@ -14539,8 +14547,8 @@ def log_loss(input, label, epsilon=1e-4, name=None):
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
label = fluid.layers.data(name='label', shape=[1], dtype='int64') label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
prob = fluid.layers.data(name='prob', shape=[10], dtype='float32') prob = fluid.data(name='prob', shape=[-1, 10], dtype='float32')
cost = fluid.layers.log_loss(input=prob, label=label) cost = fluid.layers.log_loss(input=prob, label=label)
""" """
helper = LayerHelper('log_loss', **locals()) helper = LayerHelper('log_loss', **locals())
...@@ -14678,7 +14686,7 @@ def bilinear_tensor_product(x, ...@@ -14678,7 +14686,7 @@ def bilinear_tensor_product(x,
param_attr=None, param_attr=None,
bias_attr=None): bias_attr=None):
""" """
**Add Bilinear Tensor Product Layer** **Bilinear Tensor Product Layer**
This layer performs bilinear tensor product on two inputs. This layer performs bilinear tensor product on two inputs.
For example: For example:
...@@ -14689,31 +14697,34 @@ def bilinear_tensor_product(x, ...@@ -14689,31 +14697,34 @@ def bilinear_tensor_product(x,
In this formula: In this formula:
- :math:`x`: the first input contains M elements, shape is [batch_size, M]. - :math:`x`: the first input contains M elements, shape is [batch_size, M].
- :math:`y`: the second input contains N elements, shape is [batch_size, N]. - :math:`y`: the second input contains N elements, shape is [batch_size, N].
- :math:`W_{i}`: the i-th learned weight, shape is [M, N] - :math:`W_{i}`: the i-th learned weight, shape is [M, N].
- :math:`out_{i}`: the i-th element of out, shape is [batch_size, size]. - :math:`out_{i}`: the i-th element of out, shape is [batch_size, size].
- :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`. - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.
Args: Args:
x (Variable): 2-D input tensor with shape [batch_size, M] x (Variable): 2-D input tensor with shape [batch_size, M]. Data type
y (Variable): 2-D input tensor with shape [batch_size, N] is float32 or float64.
y (Variable): 2-D input tensor with shape [batch_size, N]. Data type
should be same as **x**.
size (int): The dimension of this layer. size (int): The dimension of this layer.
act (str, default None): Activation to be applied to the output of this layer. act (str|None): Activation to be applied to the output of this layer. Default None.
name (str, default None): The name of this layer. name(str|None): For detailed information, please refer to
param_attr (ParamAttr, default None): The parameter attribute for the learnable w. :ref:`api_guide_Name` . Usually name is no need to set and None by default.
parameters/weights of this layer. param_attr (ParamAttr|None): To specify the weight parameter attribute.
bias_attr (ParamAttr, default None): The parameter attribute for the bias Default: None, which means the default weight parameter property is
of this layer. If it is set to False, no bias will be added to the output units. used. See usage for details in :ref:`api_fluid_ParamAttr` .
If it is set to None, the bias is initialized zero. Default: None. bias_attr (ParamAttr|None): To specify the bias parameter attribute.
Default: None, which means the default bias parameter property is
used. See usage for details in :ref:`api_fluid_ParamAttr` .
Returns: Returns:
Variable: A 2-D Tensor of shape [batch_size, size]. Variable: A 2-D Tensor of shape [batch_size, size]. Data type is the same as input **x**.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
layer1 = fluid.layers.data("t1", shape=[-1, 5], dtype="float32") layer1 = fluid.data("t1", shape=[-1, 5], dtype="float32")
layer2 = fluid.layers.data("t2", shape=[-1, 4], dtype="float32") layer2 = fluid.data("t2", shape=[-1, 4], dtype="float32")
tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000) tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000)
""" """
helper = LayerHelper('bilinear_tensor_product', **locals()) helper = LayerHelper('bilinear_tensor_product', **locals())
......
python/paddle/fluid/nets.py
浏览文件 @ 899ab30d
...@@ -318,8 +318,9 @@ def sequence_conv_pool(input, ...@@ -318,8 +318,9 @@ def sequence_conv_pool(input,
def glu(input, dim=-1): def glu(input, dim=-1):
""" """
The Gated Linear Units(GLU) composed by split, sigmoid activation and element-wise The Gated Linear Units(GLU) composed by :ref:`api_fluid_layers_split` ,
multiplication. Specifically, Split the input into two equal sized parts, :ref:`api_fluid_layers_sigmoid` and :ref:`api_fluid_layers_elementwise_mul` .
Specifically, GLU will plit the input into two equal-sized parts,
:math:`a` and :math:`b`, along the given dimension and then compute as :math:`a` and :math:`b`, along the given dimension and then compute as
following: following:
...@@ -331,18 +332,20 @@ def glu(input, dim=-1): ...@@ -331,18 +332,20 @@ def glu(input, dim=-1):
<https://arxiv.org/pdf/1612.08083.pdf>`_. <https://arxiv.org/pdf/1612.08083.pdf>`_.
Args: Args:
input (Variable): The input variable which is a Tensor or LoDTensor. input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int): The dimension along which to split. If :math:`dim < 0`, the The supported data types include float32, float64
and float16 (only for GPU).
dim (int, optional): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`. Default -1. dimension to split along is :math:`rank(input) + dim`. Default -1.
Returns: Returns:
Variable: Variable with half the size of input. Variable: Variable with half the size and same data type of input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data( data = fluid.data(
name="words", shape=[-1, 6, 3, 9], dtype="float32") name="words", shape=[-1, 6, 3, 9], dtype="float32")
# shape of output: [-1, 3, 3, 9] # shape of output: [-1, 3, 3, 9]
output = fluid.nets.glu(input=data, dim=1) output = fluid.nets.glu(input=data, dim=1)
......
python/paddle/fluid/optimizer.py
浏览文件 @ 899ab30d
...@@ -2297,11 +2297,11 @@ class LambOptimizer(AdamOptimizer): ...@@ -2297,11 +2297,11 @@ class LambOptimizer(AdamOptimizer):
.. math:: .. math::
m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t \\ m_t &= \\beta_1 m_{t - 1}+ (1 - \\beta_1)g_t
v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2 \\ v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2
r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon} \\ r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon}
w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1}) w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1})
...@@ -2310,25 +2310,28 @@ class LambOptimizer(AdamOptimizer): ...@@ -2310,25 +2310,28 @@ class LambOptimizer(AdamOptimizer):
learning rate, :math:`\\lambda` the LAMB weight decay rate. learning rate, :math:`\\lambda` the LAMB weight decay rate.
Args: Args:
learning_rate (float|Variable): the learning rate used to update parameters. \ learning_rate (float|Variable, optional): the learning rate used to update parameters. \
Can be a float value or a Variable with one \ Can be a float value or a Variable with data type float32. Default 0.001.
float value as data element. lamb_weight_decay (float, optional): The LAMB weight decay rate. Default 0.01.
lamb_weight_decay (float): The LAMB weight decay rate. beta1 (float, optional): The exponential decay rate for the 1st moment estimates.
beta1 (float): The exponential decay rate for the 1st moment estimates. Default 0.9.
beta2 (float): The exponential decay rate for the 2nd moment estimates. beta2 (float, optional): The exponential decay rate for the 2nd moment estimates.
epsilon (float): A small float value for numerical stability. Default 0.999.
regularization (Regularizer): A Regularizer, such as epsilon (float, optional): A small float value for numerical stability. Default 1e-6.
fluid.regularizer.L1DecayRegularizer. regularization (Regularizer|None): A Regularizer, such as
exclude_from_weight_decay_fn (function): Exclude a parameter from weight fluid.regularizer.L1DecayRegularizer. Default None.
decay when **exclude_from_weight_decay_fn(parameter)** returns true. exclude_from_weight_decay_fn (function|None): Exclude a parameter from weight
name (str|None): An optional name prefix. decay when **exclude_from_weight_decay_fn(parameter)** returns true.
Default None.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[5], dtype='float32') data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10) hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden) cost = fluid.layers.mean(hidden)
...@@ -2781,10 +2784,13 @@ class ExponentialMovingAverage(object): ...@@ -2781,10 +2784,13 @@ class ExponentialMovingAverage(object):
Args: Args:
decay (float): The exponential decay rate, usually close to 1, such as decay (float, optional): The exponential decay rate, usually close to 1, such as
0.999, 0.9999, ... . 0.999, 0.9999, ... . Default 0.999.
thres_steps (Variable|None): If not `None`, schedule the decay rate. thres_steps (Variable|None): If not `None`, schedule the decay rate.
name (str|None): An optional name prefix. Default None.
name (str|None): For detailed information, please refer to
:ref:`api_guide_Name`. Usually name is no need to set and None by
default.
Examples: Examples:
...@@ -2795,7 +2801,7 @@ class ExponentialMovingAverage(object): ...@@ -2795,7 +2801,7 @@ class ExponentialMovingAverage(object):
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
data = fluid.layers.data(name='x', shape=[5], dtype='float32') data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10) hidden = fluid.layers.fc(input=data, size=10)
cost = fluid.layers.mean(hidden) cost = fluid.layers.mean(hidden)
...@@ -2953,7 +2959,8 @@ class ExponentialMovingAverage(object): ...@@ -2953,7 +2959,8 @@ class ExponentialMovingAverage(object):
Args: Args:
executor (Executor): The Executor to execute applying. executor (Executor): The Executor to execute applying.
need_restore (bool): Whether to restore parameters after applying. need_restore (bool, optional): Whether to restore parameters after
applying. Default True.
""" """
executor.run(self.apply_program) executor.run(self.apply_program)
try: try:
......
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