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体验新版 GitCode,发现更多精彩内容 >>
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e6654c1c
编写于
6月 17, 2018
作者:
Y
Yu Yang
提交者:
GitHub
6月 17, 2018
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Merge pull request #11489 from wanghaoshuang/whs_doc1
Fix doc of warpctc, array_read, edit_distance and sequence_reshape.
上级
c2e8f40d
24766a17
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
137 addition
and
65 deletion
+137
-65
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+27
-8
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+110
-57
未找到文件。
python/paddle/fluid/layers/control_flow.py
浏览文件 @
e6654c1c
...
...
@@ -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
(
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
e6654c1c
...
...
@@ -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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