Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
P
Paddle
“f6c9f56394838021af5db26d046f3c90606a17fc”上不存在“develop/doc/api/v2/fluid/executor.html”
提交 439bfe8c 编写于 作者: S silingtong123 提交者： liuwei1031
浏览文件
操作

fix doc, updates API documents (#20554) 

* fix doc,  updates API documents of uniform_random and  uniform_random_batch_size_like (#20316)

*  fix doc, updates document of sequence_softmax, sequence_scatter, sequence_unpad (#20269)
上级 f978fbce
显示空白变更内容
内联 并排
Showing with 169 addition and 99 deletion
paddle/fluid/API.spec
浏览文件 @ 439bfe8c
...@@ -140,7 +140,7 @@ paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter ...@@ -140,7 +140,7 @@ paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter
paddle.fluid.layers.conv2d (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, 'NCHW')), ('document', 'e91c63b8ac8c35982c0ac518537e44bf')) paddle.fluid.layers.conv2d (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, 'NCHW')), ('document', 'e91c63b8ac8c35982c0ac518537e44bf'))
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', '423d8e89236962a1fe7358548b993c64'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'f7d6a5173c92c23f9a25cbc58a0eb577')) 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'))
...@@ -155,7 +155,7 @@ paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'out ...@@ -155,7 +155,7 @@ paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'out
paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '10e122eb755c2bd1f78ef2332b28f1a0')) paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '10e122eb755c2bd1f78ef2332b28f1a0'))
paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '858c432e7cbd8bb952cc2eb555457d50')) paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '858c432e7cbd8bb952cc2eb555457d50'))
paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62')) paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62'))
paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e478180d5bc010a84f35af958cafa62c')) paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'af40ffc8474eaec0112c8fb55a88439a'))
paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', 'f5a878b6166f34878376a58d7e6fa95c')) paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', 'f5a878b6166f34878376a58d7e6fa95c'))
paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'ecb55075fdf89a866bcede85e60aebad')) paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'ecb55075fdf89a866bcede85e60aebad'))
paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '968c9b17affaf714e5021c3dc8d68c73')) paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '968c9b17affaf714e5021c3dc8d68c73'))
...@@ -213,7 +213,7 @@ paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs= ...@@ -213,7 +213,7 @@ paddle.fluid.layers.gather_nd (ArgSpec(args=['input', 'index', 'name'], varargs=
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'))
paddle.fluid.layers.scatter_nd (ArgSpec(args=['index', 'updates', 'shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e43f1d3a938b35da246aea3e72a020ec')) paddle.fluid.layers.scatter_nd (ArgSpec(args=['index', 'updates', 'shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e43f1d3a938b35da246aea3e72a020ec'))
paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'abe3f714120117a5a3d3e639853932bf')) paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8e3fb174970251a1840c58ebb12b795f'))
paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '44f35002962cf24e14dd2958f6584e3d')) paddle.fluid.layers.random_crop (ArgSpec(args=['x', 'shape', 'seed'], varargs=None, keywords=None, defaults=(None,)), ('document', '44f35002962cf24e14dd2958f6584e3d'))
paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'dea29c0c3cdbd5b498afef60e58c9d7c')) paddle.fluid.layers.mean_iou (ArgSpec(args=['input', 'label', 'num_classes'], varargs=None, keywords=None, defaults=None), ('document', 'dea29c0c3cdbd5b498afef60e58c9d7c'))
paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c46011e3d848bc3dc650772b25fbbf10')) paddle.fluid.layers.relu (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c46011e3d848bc3dc650772b25fbbf10'))
...@@ -253,7 +253,7 @@ paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam ...@@ -253,7 +253,7 @@ paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6fc5d7492830d60c7fa61b3bc8f0d7e7')) paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6fc5d7492830d60c7fa61b3bc8f0d7e7'))
paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '7bc28b6e147067a220f8e00b9055f62e')) paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '7bc28b6e147067a220f8e00b9055f62e'))
paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '0035541f94bddb5ad472f9788384ca6a')) paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '0035541f94bddb5ad472f9788384ca6a'))
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', '571c963b9b49f1a323d2ea2343f10dd2'))
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', '9ac9bdc45be94494d8543b8cec5c26e0')) paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '9ac9bdc45be94494d8543b8cec5c26e0'))
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.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'))
...@@ -307,7 +307,7 @@ paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_ta ...@@ -307,7 +307,7 @@ paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_ta
paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779')) paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a')) paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064')) paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '126ede8ce0e751244b1b54cd359c89d7')) paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545')) paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd')) paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd'))
paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '556fa82daf62cbb0fb393f4125daba77')) paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '556fa82daf62cbb0fb393f4125daba77'))
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 439bfe8c
...@@ -2327,10 +2327,13 @@ def sequence_conv(input, ...@@ -2327,10 +2327,13 @@ def sequence_conv(input,
def sequence_softmax(input, use_cudnn=False, name=None): def sequence_softmax(input, use_cudnn=False, name=None):
""" """
This function computes the softmax activation among all time-steps for each **Note**:
sequence. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either :math:`[N, 1]` or :math:`[N]`, where :math:`N` **The input type of the OP must be LoDTensor. For Tensor, use:** :ref:`api_fluid_layers_softmax`
is the sum of the length of all sequences.
A LoD-tensor can be regarded as several sequences, and this op apply softmax algo on each sequence.
The shape of input Tensor can be :math:`[N, 1]` or :math:`[N]`, where :math:`N`
is the sum of the length of all sequences. Recommended usage: :math:`[N]`.
For i-th sequence in a mini-batch: For i-th sequence in a mini-batch:
...@@ -2338,29 +2341,56 @@ def sequence_softmax(input, use_cudnn=False, name=None): ...@@ -2338,29 +2341,56 @@ def sequence_softmax(input, use_cudnn=False, name=None):
Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))} Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))}
For example, for a mini-batch of 3 sequences with variable-length, For example, for a LoD-Tensor with 6 sequences ([3, 2, 4, 1, 2, 3] - sequence length list in order),
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], the lod in the runtime is [[0, 3, 5, 9, 10, 12, 15]],
then softmax will be computed among :math:`X[0:2, :]`, :math:`X[2:5, :]`, then softmax will be computed among :math:`X[0:3,:],X[3:5,:],X[5:9,:],X[9:10,:],X[10:12,:],X[12:15,:]`,
:math:`X[5:7, :]`, and :math:`N` turns out to be 7. and :math:`N` turns out to be 15.
.. code-block:: text
*Case 1:
Given:
input.data = [0.7, 1, 0.6,
1.5, 1.1,
1.2, 0.2, 0.6, 1.9,
3.1,
2.5, 0.8,
0.1, 2.4, 1.3]
input.lod = [[0, 3, 5, 9, 10, 12, 15]]
then:
output.data = [0.30724832, 0.41474187, 0.2780098,
0.59868765, 0.40131235,
0.2544242, 0.09359743, 0.13963096, 0.5123474,
1.,
0.84553474, 0.15446526,
0.06995796, 0.69777346, 0.23226859]
output.lod = [[0, 3, 5, 9, 10, 12, 15]]
Args: Args:
input (Variable): The input variable which is a LoDTensor. input (Variable):A LoDTensor with shape of :math:`[N, 1]` or :math:`[N]`, Recommended usage: :math:`[N]`.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \ Supported data types: float32, float64.
library is installed. Default: False. use_cudnn (bool, optional): Use cudnn kernel or not. Effective only when the cudnn version of the paddle
name (str|None): A name for this layer(optional). If set None, the layer library is installed and GPU is used for training or reasoning. Default: False.
will be named automatically. Default: None. name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: output of sequence_softmax Variable: A LoD-Tensor which has the same shape and data type with input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[7, 1], x = fluid.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_sequence_softmax_1 = fluid.layers.sequence_softmax(input=x)
y = fluid.data(name='y', shape=[7],
dtype='float32', lod_level=1) dtype='float32', lod_level=1)
x_sequence_softmax = fluid.layers.sequence_softmax(input=x) x_sequence_softmax_2 = fluid.layers.sequence_softmax(input=y)
""" """
assert not in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
...@@ -5590,15 +5620,15 @@ def sequence_pad(x, pad_value, maxlen=None, name=None): ...@@ -5590,15 +5620,15 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
def sequence_unpad(x, length, name=None): def sequence_unpad(x, length, name=None):
""" """
**Sequence Unpad Layer** **Note**:
This layer removes the padding data in the input sequences and convert **The input of the OP is Tensor and the output is LoDTensor. For padding operation, See:** :ref:`api_fluid_layers_sequence_pad`
them into sequences with actual length as output, identitied by lod
information. The OP removes the padding data from the input based on the length information and returns a LoDTensor.
.. code-block:: text .. code-block:: text
Example: Case 1:
Given input Variable **x**: Given input Variable **x**:
x.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0], x.data = [[ 1.0, 2.0, 3.0, 4.0, 5.0],
...@@ -5613,18 +5643,18 @@ def sequence_unpad(x, length, name=None): ...@@ -5613,18 +5643,18 @@ def sequence_unpad(x, length, name=None):
after unpadding, the output Variable will be: after unpadding, the output Variable will be:
out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]] out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
out.lod = [[2, 3, 4]] out.lod = [[0, 2, 5, 9]]
Args: Args:
x(Variable): Input Variable which contains the padded sequences with x(Variable): A Tensor which contains padding data, and its shape size can not be less than 2.
equal length. Supported data types: float32, float64, int32, int64.
length(Variable): The Variable that specifies the actual ength of length(Variable): A 1D Tensor that stores the actual length of each sample, and the Tensor
sequences after unpadding. has the same shape with the 0th dimension of the X . Supported data types: int64.
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): The default value is None. Normally there is no need for user to set this property.
will be named automatically. For more information, please refer to :ref:`api_guide_Name`
Returns: Returns:
Variable: The Variable contains the unpadded sequences. Variable: A LoDTensor whose recursive sequence length is consistent with the information of the length parameter and it has the same data type with input.
Examples: Examples:
.. code-block:: python .. code-block:: python
...@@ -5633,11 +5663,11 @@ def sequence_unpad(x, length, name=None): ...@@ -5633,11 +5663,11 @@ def sequence_unpad(x, length, name=None):
import numpy import numpy
# pad data # pad data
x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32', lod_level=1) x = fluid.data(name='x', shape=[10, 5], dtype='float32', lod_level=1)
pad_value = fluid.layers.assign(input=numpy.array([0.0], dtype=numpy.float32)) pad_value = fluid.layers.assign(input=numpy.array([0.0], dtype=numpy.float32))
pad_data, len = fluid.layers.sequence_pad(x=x, pad_value=pad_value) pad_data, len = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
# upad data # unpad data
unpad_data = fluid.layers.sequence_unpad(x=pad_data, length=len) unpad_data = fluid.layers.sequence_unpad(x=pad_data, length=len)
""" """
...@@ -10694,18 +10724,28 @@ def scatter_nd(index, updates, shape, name=None): ...@@ -10694,18 +10724,28 @@ def scatter_nd(index, updates, shape, name=None):
def sequence_scatter(input, index, updates, name=None): def sequence_scatter(input, index, updates, name=None):
""" """
**Sequence Scatter Layer** **Note**:
**The index and updates parameters of the OP must be LoDTensor.**
This operator scatters the Updates tensor to the input X. It uses the LoD Plus the updates data to the correspoding input according to the index.
information of Ids to select the rows to update, and use the values in Ids as
the columns to update in each row of X.
Here is an example: The updated algorithm is as follows: output[instance_index][index [pos]] = input[instance_index][index [pos]] + updates[pos],
where instance_idx is the K sample corresponding to pos in batch.
Given the following input: The value of output[i][j] depends on whether j can be found in the i+1th interval of the index. If found,
out[i][j] = input[i][j] + update[m] [n], otherwise, out[i][j] = input[i][j].
For example, in the following example, the lod information for index is divided into three sequences. Among
them, because the element 0 can be found in the first interval of the index, it is updated with the value of
the corresponding position of the updates, out[0][0] = input[0][0]+updates[0][0] . Because element 1 cannot
be found in the third interval of index, out[2][1] = input[2][1].
.. code-block:: text .. code-block:: text
*Case 1:
Given:
input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]] [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
...@@ -10717,37 +10757,32 @@ def sequence_scatter(input, index, updates, name=None): ...@@ -10717,37 +10757,32 @@ def sequence_scatter(input, index, updates, name=None):
updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]] updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]]
updates.lod = [[ 0, 3, 8, 12]] updates.lod = [[ 0, 3, 8, 12]]
Then we have the output: Then:
.. code-block:: text
out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0], out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.4, 1.3, 1.2, 1.1], [1.0, 1.0, 1.4, 1.3, 1.2, 1.1],
[1.0, 1.0, 1.3, 1.2, 1.4, 1.1]] [1.0, 1.0, 1.3, 1.2, 1.4, 1.1]]
out.dims = X.dims = [3, 6] out.dims = X.dims = [3, 6]
Args: Args:
input (Variable): The source input with rank>=1. input (Variable): A Tensor with shape of :math:`[N, k_1... k_n]`. Supported data types: float32, float64, int32, int64.
index (Variable): A LoD Tensor. The index input of sequence scatter op index (Variable): A LoDTensor contains index information. Its LoD level must be 1 and its data type must be int64.
where input will be updated. The index input with rank=1. Its dtype updates (Variable): A LodTensor contains updates information. It has the same LoD level with the index and has the
should be int32 or int64 as it is used as indexes. same data type with the input. Supported data types: float32, float64, int32, int64.
updates (Variable): A LoD Tensor. The values to scatter to the input name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information,
tensor X, must be a LoDTensor with the same LoD information as index. please refer to :ref:`api_guide_Name`
name (str|None): The output variable name. Default None.
Returns: Returns:
Variable: The output is a tensor with the same shape as input. Variable: A Tensor which has been updated. It has the same shape and data type with input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers
input = layers.data( name="x", shape=[3, 6], append_batch_size=False, dtype='float32' ) input = fluid.data( name="x", shape=[None, 3, 6], dtype='float32' )
index = layers.data( name='index', shape=[1], dtype='int32') index = fluid.data( name='index', shape=[12, 1], dtype='int64', lod_level=1)
updates = layers.data( name='updates', shape=[1], dtype='float32') updates = fluid.data( name='updates', shape=[12, 1], dtype='float32', lod_level=1)
output = fluid.layers.sequence_scatter(input, index, updates) output = fluid.layers.sequence_scatter(input, index, updates)
""" """
...@@ -12605,28 +12640,64 @@ def uniform_random_batch_size_like(input, ...@@ -12605,28 +12640,64 @@ def uniform_random_batch_size_like(input,
max=1.0, max=1.0,
seed=0): seed=0):
""" """
${comment} This OP initializes a variable with random values sampled from a
uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension.
.. code-block:: text
*Case 1:
Given:
input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3]
shape=[2,4]
result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 0,
input_dim_idx = 0,
result.shape[0] = input.shape[0],
then:
result=[[ 0.3443427 , -0.23056602, 0.3477049 , 0.06139076]] # result.shape=[1,4]
*Case 2:
Given:
input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3]
shape=[2,4]
input_dim_idx=1
output_dim_idx=1
result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 1,
input_dim_idx = 1,
result.shape[1] = input.shape[1],
then:
result=[[-0.23133647, -0.84195036, 0.21441269],
[-0.08774924, 0.25605237, -0.09403259]] # result.shape=[2,3]
Args: Args:
input (Variable): ${input_comment} input (Variable): A Tensor. Supported data types: float32, float64.
shape (tuple|list): ${shape_comment} shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
input_dim_idx (Int): ${input_dim_idx_comment} input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default 0.
output_dim_idx (Int): ${output_dim_idx_comment} output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
min (Float): ${min_comment} min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (Float): ${max_comment} max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
seed (Int): ${seed_comment} seed (int, optional): Random seed used for generating samples. 0 means use a seed generated by the system.Note that if seed is not 0, this operator will always generate the same random numbers every time.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32.
Returns: Returns:
out (Variable): ${out_comment} Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers
input = layers.data(name="input", shape=[13, 11], dtype='float32') # example 1:
out = layers.uniform_random_batch_size_like(input, [-1, 11]) input = fluid.data(name="input", shape=[1, 3], dtype='float32')
out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
# example 2:
out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
""" """
helper = LayerHelper('uniform_random_batch_size_like', **locals()) helper = LayerHelper('uniform_random_batch_size_like', **locals())
...@@ -17039,8 +17110,8 @@ def mse_loss(input, label): ...@@ -17039,8 +17110,8 @@ def mse_loss(input, label):
@templatedoc() @templatedoc()
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
""" """
This operator initializes a variable with random values sampled from a This OP initializes a variable with random values sampled from a
uniform distribution. The random result is in set [min, max). uniform distribution in the range [min, max).
Examples: Examples:
:: ::
...@@ -17052,24 +17123,23 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): ...@@ -17052,24 +17123,23 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
result=[[0.8505902, 0.8397286]] result=[[0.8505902, 0.8397286]]
Args: Args:
shape (list|tuple|Variable): The shape of the output tensor, the data type of the integer is int, shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple,
and if the shape type is list or tuple, its elements can be an integer its elements can be an integer
or a tensor with the shape [1], the data type of the tensor is int64. or a Tensor with the shape [1], and the type of the Tensor is int64.
If the shape type is Variable,it ia a 1D tensor, the data type of the tensor is int64. If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor is int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of the output tensor, such as float32, float64. dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
Default: float32. Default: float32.
min (float, optional): Minimum value of uniform random, It's a closed interval. Default -1.0. min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (float, optional): Maximun value of uniform random, It's an open interval. Default 1.0. max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means use a seed (int, optional): Random seed used for generating samples. 0 means use a
seed generated by the system. Note that if seed is not 0, this seed generated by the system. Note that if seed is not 0, this
operator will always generate the same random numbers every time. operator will always generate the same random numbers every time.
Default 0. Default 0.
Returns: a Tensor with randomly initialized results whose data type is determined by the dtype parameter Returns:
and whose dimension is determined by the shape parameter. Variable: A Tensor of the specified shape filled with uniform_random values.
Return type: Variable
Throw exception: Raises:
TypeError: The shape type should be list or tupple or variable. TypeError: The shape type should be list or tupple or variable.
Examples: Examples:
...@@ -17088,7 +17158,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0): ...@@ -17088,7 +17158,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
# example 3: # example 3:
# attr shape is a Variable, the data type must be int64 # attr shape is a Variable, the data type must be int64
var_shape = fluid.layers.data(name='var_shape',shape=[2],append_batch_size=False) var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.uniform_random(var_shape) result_3 = fluid.layers.uniform_random(var_shape)
""" """
......
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