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提交 175b847f 编写于 作者: M minqiyang
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Add API example for logical ops and clip ops 

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Showing with 149 addition and 101 deletion
python/paddle/fluid/layers/nn.py
浏览文件 @ 175b847f
......@@ -726,11 +726,11 @@ def dynamic_gru(input,
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, dynamic_gru will create ParamAttr as
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, dynamic_gru will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
is_reverse(bool): Whether to compute reversed GRU, default
......@@ -847,11 +847,11 @@ def gru_unit(input,
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, gru_unit will create ParamAttr as
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, gru_unit will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
activation (string): The activation type for cell (actNode).
......@@ -1064,9 +1064,9 @@ def dropout(x,
inference: out = input
(make is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
dropout op can be removed from the program.
dropout op can be removed from the program.
the program will be efficient
Returns:
......@@ -2149,7 +2149,7 @@ def pool2d(input,
ceil_mode (bool): ${ceil_mode_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
exclusive (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
Returns:
......@@ -2240,7 +2240,7 @@ def pool3d(input,
ceil_mode (bool): ${ceil_mode_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
exclusive (bool): Whether to exclude padding points in average pooling
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
Returns:
......@@ -4342,7 +4342,7 @@ def nce(input,
sampler (str): The sampler used to sample class from negtive classes.
It can be 'uniform', 'log_uniform' or 'custom_dist'.
default: 'uniform'.
custom_dist (Variable): A tensor with shape [num_total_classes].
custom_dist (Variable): A tensor with shape [num_total_classes].
It is used when sampler is set to 'custom_dist'.
custom_dist[i] is the probsbility of i-th class to be sampled.
default: None.
......@@ -4385,7 +4385,7 @@ def nce(input,
num_neg_samples=3,
sampler="custom_dist",
custom_dist=dist)
"""
helper = LayerHelper('nce', **locals())
assert isinstance(input, Variable)
......@@ -4556,9 +4556,9 @@ def transpose(x, perm, name=None):
Examples:
.. code-block:: python
# use append_batch_size=False to avoid prepending extra
# use append_batch_size=False to avoid prepending extra
# batch size in shape
x = fluid.layers.data(name='x', shape=[5, 10, 15],
x = fluid.layers.data(name='x', shape=[5, 10, 15],
dtype='float32', append_batch_size=False)
x_transposed = layers.transpose(x, perm=[1, 0, 2])
"""
......@@ -4835,7 +4835,7 @@ def softmax_with_cross_entropy(logits,
3) If numeric_stable_mode is True, softmax is calculated first by:
.. math::
max_j = \\max_{i=0}^{K}{\\text{logit}_i}
log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
......@@ -4858,18 +4858,18 @@ def softmax_with_cross_entropy(logits,
numeric_stable_mode (bool): A flag to indicate whether to use a more
numerically stable algorithm. Only valid
when soft_label is False and GPU is used.
When soft_label is True or CPU is used,
the algorithm is always numerically stable.
Note that the speed may be slower when use
When soft_label is True or CPU is used,
the algorithm is always numerically stable.
Note that the speed may be slower when use
stable algorithm. Default: False
return_softmax (bool): A flag indicating whether to return the softmax
return_softmax (bool): A flag indicating whether to return the softmax
along with the cross entropy loss. Default: False
Returns:
Variable or Tuple of two Variables: Return the cross entropy loss if
`return_softmax` is False, otherwise the tuple
(loss, softmax), where the cross entropy loss is
a 2-D tensor with shape [N x 1], and softmax is a
Variable or Tuple of two Variables: Return the cross entropy loss if
`return_softmax` is False, otherwise the tuple
(loss, softmax), where the cross entropy loss is
a 2-D tensor with shape [N x 1], and softmax is a
2-D tensor with shape [N x K].
Examples:
......@@ -5756,20 +5756,20 @@ def image_resize(input,
Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
currently.
Default: 'BILINEAR'
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
:attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
......@@ -5780,7 +5780,7 @@ def image_resize(input,
Raises:
TypeError: out_shape should be a list or tuple or Variable.
TypeError: actual_shape should either be Variable or None.
ValueError: The 'resample' of image_resize can only be 'BILINEAR'
ValueError: The 'resample' of image_resize can only be 'BILINEAR'
or 'NEAREST' currently.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 2.
......@@ -5852,17 +5852,17 @@ def resize_bilinear(input,
name=None,
actual_shape=None):
"""
Resize input by performing bilinear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
Resize input by performing bilinear interpolation based on given
output shape which specified by actual_shape, out_shape and scale
in priority order.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this op) on a rectilinear 2D grid. The key idea is
to perform linear interpolation first in one direction, and then
again in the other direction.
For details of bilinear interpolation, please refer to Wikipedia:
For details of bilinear interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
Args:
......@@ -5875,17 +5875,17 @@ def resize_bilinear(input,
a higher priority than scale. Default: None.
name(str|None): The output variable name.
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
:attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
......@@ -5909,11 +5909,11 @@ def resize_nearest(input,
actual_shape=None):
"""
Resize input by performing nearest neighbor interpolation in both the
3rd dimention(in height direction) and the 4th dimention(in width
direction) based on given output shape which specified by actual_shape,
3rd dimention(in height direction) and the 4th dimention(in width
direction) based on given output shape which specified by actual_shape,
out_shape and scale in priority order.
For details of nearest neighbor interpolation, please refer to Wikipedia:
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
Args:
......@@ -5926,17 +5926,17 @@ def resize_nearest(input,
a higher priority than scale. Default: None.
name(str|None): The output variable name.
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
actual_shape(Variable): An optional input to specify output shape
dynamically. If provided, image resize
according to this given shape rather than
:attr:`out_shape` and :attr:`scale` specifying
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
shape. That is to say actual_shape has the
highest priority. It is recommended to use
actual_shape instead of :attr:`out_shape` if you
want to specify output shape dynamically. When
using actual_shape to specify output shape, one of
:attr:`out_shape` and :attr:`scale` should also be
set, otherwise errors would be occured in graph
constructing stage.
Default: None
......@@ -6446,15 +6446,15 @@ def affine_grid(theta, out_shape, name=None):
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
out_shape = [2, 3, 5, 5]
Step 1:
Generate normalized coordinates according to out_shape.
The values of the normalized coordinates are in the interval between -1 and 1.
The shape of the normalized coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
......@@ -7702,6 +7702,15 @@ def logical_and(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_and(x=left, y=right)
"""
return _logical_op(
......@@ -7721,6 +7730,15 @@ def logical_or(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_or(x=left, y=right)
"""
return _logical_op(
......@@ -7740,6 +7758,15 @@ def logical_xor(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_xor(x=left, y=right)
"""
return _logical_op(
......@@ -7758,6 +7785,13 @@ def logical_not(x, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
result = fluid.layers.logical_not(x=left)
"""
return _logical_op(
......@@ -7777,6 +7811,13 @@ def clip(x, min, max, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
input = fluid.layers.data(
name='data', shape=[1], dtype='float32')
reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
"""
helper = LayerHelper("clip", **locals())
......@@ -7809,6 +7850,13 @@ def clip_by_norm(x, max_norm, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
input = fluid.layers.data(
name='data', shape=[1], dtype='float32')
reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
"""
helper = LayerHelper("clip_by_norm", **locals())
......@@ -7954,19 +8002,19 @@ def maxout(x, groups, name=None):
def space_to_depth(x, blocksize, name=None):
"""
Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the
input LoDtensor where values from the height and width dimensions are moved to the channel dimension.
This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the
input LoDtensor where values from the height and width dimensions are moved to the channel dimension.
The attr blocksize indicates the input block size.
space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according
space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according
to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
space_to_depth is used to This operation is useful for resizing the activations between convolutions
space_to_depth is used to This operation is useful for resizing the activations between convolutions
(but keeping all data)
- Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
- The depth of the output tensor is block_size * block_size * input channel
- The depth of the output tensor is block_size * block_size * input channel
- The Y, X coordinates within each block of the input become the high order component of the output channel index
- channel should be divisible by square of blocksize
- height, width should be divsible by blocksize
......@@ -8013,7 +8061,7 @@ def space_to_depth(x, blocksize, name=None):
@templatedoc()
def sequence_reverse(x, name=None):
"""
"""
${comment}
Args:
......@@ -8080,21 +8128,21 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
def similarity_focus(input, axis, indexes, name=None):
"""
"""
SimilarityFocus Operator
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
2. For each index, find the largest numbers in the tensor T, so that the same
row and same column has at most one number(what it means is that if the
largest number has been found in the i-th row and the j-th column, then
the numbers in the i-th row or j-th column will be skipped. And then the
next largest number will be selected from the remaining numbers. Obviously
there will be min(B, C) numbers), and mark the corresponding position of the
3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
......@@ -8150,16 +8198,16 @@ def similarity_focus(input, axis, indexes, name=None):
[1.0, 0.0]]]]
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].
axis(int): Indicating the dimension to be selected. It can only be
1, 2 or 3.
indexes(list): Indicating the indexes of the selected dimension.
Returns:
Variable: A tensor variable with the same shape and same type
Variable: A tensor variable with the same shape and same type
as the input.
Examples:
.. code-block:: python
data = fluid.layers.data(
......@@ -8262,12 +8310,12 @@ def hash(input, hash_size, num_hash=1, name=None):
@templatedoc()
def grid_sampler(x, grid, name=None):
"""
This operation samples input X by using bilinear interpolation based on
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points.
Step 1:
......@@ -8277,7 +8325,7 @@ def grid_sampler(x, grid, name=None):
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
Step 2:
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
interpolate point value by 4 nearest points.
wn ------- y_n ------- en
......@@ -8314,7 +8362,7 @@ def grid_sampler(x, grid, name=None):
name (str, default None): The name of this layer.
Returns:
out(Variable): Output of shape [N, C, H, W] data samples input X
out(Variable): Output of shape [N, C, H, W] data samples input X
using bilnear interpolation based on input grid.
Exmples:
......
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