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Merge pull request #11489 from wanghaoshuang/whs_doc1

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Fix doc of warpctc, array_read, edit_distance and sequence_reshape.
wangkuiyi-patch-1
Yu Yang 7 years ago committed by GitHub
parent c2e8f40dcb 24766a170d
commit e6654c1c1d
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2 changed files with 137 additions and 65 deletions
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35
python/paddle/fluid/layers/control_flow.py
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@ -1055,19 +1055,38 @@ def equal(x, y, cond=None, **ignored):
def array_read(array, i):
"""This function performs the operation to read the data in as an
"""
This function performs the operation to read the data in as an
LOD_TENSOR_ARRAY.
.. code-block:: text
Given:
array = [0.6, 0.1, 0.3, 0.1]
And:
i = 2
Then:
output = 0.3
Args:
array (Variable|list): The input tensor that will be written to an array.
i (Variable|list): The subscript index in tensor array, that points the
place where data will be written to.
array (Variable|list): The input tensor that store data to be read.
i (Variable|list): The index of the data to be read from input array.
Returns:
Variable: The tensor type variable that has the data written to it.
Examples:
.. code-block::python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = layers.array_read(tmp, i=i)
.. code-block:: python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = fluid.layers.array_read(tmp, i=i)
"""
helper = LayerHelper('array_read', **locals())
if not isinstance(

167
python/paddle/fluid/layers/nn.py
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@ -25,21 +25,74 @@ import utils
import random
__all__ = [
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d', 'conv3d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'pool3d',
'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'conv3d_transpose',
'sequence_expand', 'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max',
'reduce_min', 'reduce_prod', 'sequence_first_step', 'sequence_last_step',
'dropout', 'split', 'ctc_greedy_decoder', 'edit_distance', 'l2_normalize',
'matmul', 'topk', 'warpctc', 'sequence_reshape', 'transpose', 'im2sequence',
'nce', 'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
'image_resize_short', 'resize_bilinear', 'gather', 'random_crop',
'mean_iou', 'relu', 'log'
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'lod_reset',
'lrn',
'pad',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'random_crop',
'mean_iou',
'relu',
'log',
]
@ -3257,8 +3310,7 @@ def topk(input, k, name=None):
return values, indices
def edit_distance(input, label, normalized=True, ignored_tokens=None,
name=None):
def edit_distance(input, label, normalized=True, ignored_tokens=None):
"""
EditDistance operator computes the edit distances between a batch of
hypothesis strings and their references. Edit distance, also called
@ -3272,21 +3324,21 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None,
"kitten" -> "sitten" -> "sittin" -> "sitting"
Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with
The input is a LoDTensor consisting of all the hypothesis strings with
the total number denoted by `batch_size`, and the separation is specified
by the LoD information. And the `batch_size` reference strings are arranged
in order in the same way in the LoDTensor Input(Refs).
in order in the same way in the input LoDTensor.
Output(Out) contains the `batch_size` results and each stands for the edit
The output contains the `batch_size` results and each stands for the edit
distance for a pair of strings respectively. If Attr(normalized) is true,
the edit distance will be divided by the length of reference string.
Args:
input(Variable): The indices for hypothesis strings.
label(Variable): The indices for reference strings.
normalized(bool): Indicated whether to normalize the edit distance by
normalized(bool, default True): Indicated whether to normalize the edit distance by
the length of reference string.
ignored_tokens(list of int): Tokens that should be removed before
ignored_tokens(list<int>, default None): Tokens that should be removed before
calculating edit distance.
name (str): The name of this layer. It is optional.
@ -3298,7 +3350,6 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None,
x = fluid.layers.data(name='x', shape=[8], dtype='float32')
y = fluid.layers.data(name='y', shape=[7], dtype='float32')
cost = fluid.layers.edit_distance(input=x,label=y)
"""
helper = LayerHelper("edit_distance", **locals())
@ -3418,35 +3469,33 @@ def warpctc(input, label, blank=0, norm_by_times=False):
input tensor.
Args:
input(Variable): (LodTensor, default: LoDTensor<float>),
the unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
of variable-length sequence, which is a 2-D Tensor with LoD
information. It is of the shape [Lg, 1], where Lg is th sum of
all labels' length.
blank (int): default 0, the blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times (bool): default false, whether to normalize
the gradients by the number of time-step, which is also the
sequence's length. There is no need to normalize the gradients
if warpctc layer was follewed by a mean_op.
input (Variable): The unscaled probabilities of variable-length sequences,
which is a 2-D Tensor with LoD information.
It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
sequences' length and num_classes is the true number of classes.
(not including the blank label).
label (Variable): The ground truth of variable-length sequence,
which is a 2-D Tensor with LoD information. It is of the shape [Lg, 1],
where Lg is th sum of all labels' length.
blank (int, default 0): The blank label index of Connectionist
Temporal Classification (CTC) loss, which is in the
half-opened interval [0, num_classes + 1).
norm_by_times(bool, default false): Whether to normalize the gradients
by the number of time-step, which is also the sequence's length.
There is no need to normalize the gradients if warpctc layer was
follewed by a mean_op.
Returns:
Variable: The Connectionist Temporal Classification (CTC) loss,
which is a 2-D Tensor of the shape [batch_size, 1].
Examples:
.. code-block:: python
y = layers.data(
name='y', shape=[11, 8], dtype='float32', lod_level=1)
y_predict = layers.data(
name='y_predict', shape=[11, 1], dtype='float32')
cost = layers.warpctc(input=y_predict, label=y)
label = fluid.layers.data(shape=[11, 8], dtype='float32', lod_level=1)
predict = fluid.layers.data(shape=[11, 1], dtype='float32')
cost = fluid.layers.warpctc(input=predict, label=label)
"""
helper = LayerHelper('warpctc', **locals())
@ -3475,17 +3524,21 @@ def sequence_reshape(input, new_dim):
.. code-block:: text
x is a LoDTensor:
x.lod = [[2, 4]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.lod = [[0, 2, 6]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8],
[9, 10], [11, 12]]
x.dims = [6, 2]
set new_dim = 4
then out is a LoDTensor:
out.lod = [[1, 2]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8], [9, 10, 11, 12]]
out.lod = [[0, 1, 3]]
out.data = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]]
out.dims = [3, 4]
Currently, only 1-level LoDTensor is supported and please make sure
@ -3493,19 +3546,19 @@ def sequence_reshape(input, new_dim):
no remainder for each sequence.
Args:
input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
with shape being [N, M] where M for dimension.
new_dim (int): New dimension which the input LoDTensor is reshaped to.
input (Variable): A 2-D LoDTensor with shape being [N, M] where M for dimension.
new_dim (int): New dimension that the input LoDTensor is reshaped to.
Returns:
Variable: Reshaped LoDTensor according to new dimension.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 20],
dtype='float32', lod_level=1)
x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
"""
helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())

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