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提交 66ea7184 编写于 作者: H haowang101779990
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en api improve format Dec 27 

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python/paddle/fluid/data_feeder.py
浏览文件 @ 66ea7184
......@@ -272,8 +272,7 @@ class DataFeeder(object):
dict: the result of conversion.
Raises:
ValueError: If drop_last is False and the data batch which cannot
fit for devices.
ValueError: If drop_last is False and the data batch which cannot fit for devices.
"""
def __reader_creator__():
......
python/paddle/fluid/framework.py
浏览文件 @ 66ea7184
......@@ -1646,8 +1646,8 @@ class Program(object):
parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need
to print.
Returns
(str): The debug string.
Returns:
str : The debug string.
Raises:
ValueError: If any of required fields is not set and throw_on_error is
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 66ea7184
......@@ -1452,6 +1452,7 @@ class DynamicRNN(object):
def step_input(self, x):
"""
Mark a sequence as a dynamic RNN input.
Args:
x(Variable): The input sequence.
......@@ -1505,6 +1506,7 @@ class DynamicRNN(object):
"""
Mark a variable as a RNN input. The input will not be scattered into
time steps.
Args:
x(Variable): The input variable.
......@@ -1629,13 +1631,11 @@ class DynamicRNN(object):
Args:
init(Variable|None): The initialized variable.
shape(list|tuple): The memory shape. NOTE the shape does not contain
batch_size.
shape(list|tuple): The memory shape. NOTE the shape does not contain batch_size.
value(float): the initalized value.
need_reorder(bool): True if the initialized memory depends on the
input sample.
need_reorder(bool): True if the initialized memory depends on the input sample.
dtype(str|numpy.dtype): The data type of the initialized memory.
......@@ -1714,6 +1714,7 @@ class DynamicRNN(object):
"""
Update the memory from ex_mem to new_mem. NOTE that the shape and data
type of :code:`ex_mem` and :code:`new_mem` must be same.
Args:
ex_mem(Variable): the memory variable.
new_mem(Variable): the plain variable generated in RNN block.
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 66ea7184
......@@ -65,7 +65,7 @@ def rpn_target_assign(bbox_pred,
rpn_negative_overlap=0.3,
use_random=True):
"""
** Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection. **
**Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection.**
This layer can be, for given the Intersection-over-Union (IoU) overlap
between anchors and ground truth boxes, to assign classification and
......@@ -135,19 +135,20 @@ def rpn_target_assign(bbox_pred,
Examples:
.. code-block:: python
bbox_pred = layers.data(name='bbox_pred', shape=[100, 4],
append_batch_size=False, dtype='float32')
cls_logits = layers.data(name='cls_logits', shape=[100, 1],
append_batch_size=False, dtype='float32')
anchor_box = layers.data(name='anchor_box', shape=[20, 4],
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
gt_boxes=gt_boxes)
bbox_pred = layers.data(name='bbox_pred', shape=[100, 4],
append_batch_size=False, dtype='float32')
cls_logits = layers.data(name='cls_logits', shape=[100, 1],
append_batch_size=False, dtype='float32')
anchor_box = layers.data(name='anchor_box', shape=[20, 4],
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
gt_boxes=gt_boxes)
"""
helper = LayerHelper('rpn_target_assign', **locals())
......@@ -1519,27 +1520,30 @@ def anchor_generator(input,
Args:
input(Variable): The input feature map, the format is NCHW.
anchor_sizes(list|tuple|float): The anchor sizes of generated anchors,
given in absolute pixels e.g. [64., 128., 256., 512.].
For instance, the anchor size of 64 means the area of this anchor equals to 64**2.
given in absolute pixels e.g. [64., 128., 256., 512.].
For instance, the anchor size of 64 means the area of this anchor equals to 64**2.
aspect_ratios(list|tuple|float): The height / width ratios of generated
anchors, e.g. [0.5, 1.0, 2.0].
anchors, e.g. [0.5, 1.0, 2.0].
variance(list|tuple): The variances to be used in box regression deltas.
Default:[0.1, 0.1, 0.2, 0.2].
stride(list|turple): The anchors stride across width and height,
e.g. [16.0, 16.0]
Default:[0.1, 0.1, 0.2, 0.2].
stride(list|turple): The anchors stride across width and height,e.g. [16.0, 16.0]
offset(float): Prior boxes center offset. Default: 0.5
name(str): Name of the prior box op. Default: None.
Returns:
Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4].
H is the height of input, W is the width of input,
num_anchors is the box count of each position.
Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
Variances(Variable): The expanded variances of anchors
with a layout of [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_anchors is the box count of each position.
Each variance is in (xcenter, ycenter, w, h) format.
Anchors(Variable),Variances(Variable):
two variables:
- Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4]. \
H is the height of input, W is the width of input, \
num_anchors is the box count of each position. \
Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
- Variances(Variable): The expanded variances of anchors \
with a layout of [H, W, num_priors, 4]. \
H is the height of input, W is the width of input \
num_anchors is the box count of each position. \
Each variance is in (xcenter, ycenter, w, h) format.
Examples:
......@@ -1748,35 +1752,35 @@ def generate_proposals(scores,
eta=1.0,
name=None):
"""
** Generate proposal Faster-RCNN **
This operation proposes RoIs according to each box with their probability to be a foreground object and
the box can be calculated by anchors. Bbox_deltais and scores to be an object are the output of RPN. Final proposals
could be used to train detection net.
For generating proposals, this operation performs following steps:
1. Transposes and resizes scores and bbox_deltas in size of (H*W*A, 1) and (H*W*A, 4)
2. Calculate box locations as proposals candidates.
3. Clip boxes to image
4. Remove predicted boxes with small area.
5. Apply NMS to get final proposals as output.
Args:
scores(Variable): A 4-D Tensor with shape [N, A, H, W] represents the probability for each box to be an object.
N is batch size, A is number of anchors, H and W are height and width of the feature map.
bbox_deltas(Variable): A 4-D Tensor with shape [N, 4*A, H, W] represents the differece between predicted box locatoin and anchor location.
im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin image information for N batch. Info contains height, width and scale
between origin image size and the size of feature map.
anchors(Variable): A 4-D Tensor represents the anchors with a layout of [H, W, A, 4]. H and W are height and width of the feature map,
num_anchors is the box count of each position. Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
variances(Variable): The expanded variances of anchors with a layout of [H, W, num_priors, 4]. Each variance is in (xcenter, ycenter, w, h) format.
pre_nms_top_n(float): Number of total bboxes to be kept per image before NMS. 6000 by default.
post_nms_top_n(float): Number of total bboxes to be kept per image after NMS. 1000 by default.
nms_thresh(float): Threshold in NMS, 0.5 by default.
min_size(float): Remove predicted boxes with either height or width < min_size. 0.1 by default.
eta(float): Apply in adaptive NMS, if adaptive threshold > 0.5, adaptive_threshold = adaptive_threshold * eta in each iteration.
**Generate proposal Faster-RCNN**
This operation proposes RoIs according to each box with their probability to be a foreground object and
the box can be calculated by anchors. Bbox_deltais and scores to be an object are the output of RPN. Final proposals
could be used to train detection net.
For generating proposals, this operation performs following steps:
1. Transposes and resizes scores and bbox_deltas in size of (H*W*A, 1) and (H*W*A, 4)
2. Calculate box locations as proposals candidates.
3. Clip boxes to image
4. Remove predicted boxes with small area.
5. Apply NMS to get final proposals as output.
Args:
scores(Variable): A 4-D Tensor with shape [N, A, H, W] represents the probability for each box to be an object.
N is batch size, A is number of anchors, H and W are height and width of the feature map.
bbox_deltas(Variable): A 4-D Tensor with shape [N, 4*A, H, W] represents the differece between predicted box locatoin and anchor location.
im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin image information for N batch. Info contains height, width and scale
between origin image size and the size of feature map.
anchors(Variable): A 4-D Tensor represents the anchors with a layout of [H, W, A, 4]. H and W are height and width of the feature map,
num_anchors is the box count of each position. Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
variances(Variable): The expanded variances of anchors with a layout of [H, W, num_priors, 4]. Each variance is in (xcenter, ycenter, w, h) format.
pre_nms_top_n(float): Number of total bboxes to be kept per image before NMS. 6000 by default.
post_nms_top_n(float): Number of total bboxes to be kept per image after NMS. 1000 by default.
nms_thresh(float): Threshold in NMS, 0.5 by default.
min_size(float): Remove predicted boxes with either height or width < min_size. 0.1 by default.
eta(float): Apply in adaptive NMS, if adaptive threshold > 0.5, adaptive_threshold = adaptive_threshold * eta in each iteration.
"""
helper = LayerHelper('generate_proposals', **locals())
......
python/paddle/fluid/layers/io.py
浏览文件 @ 66ea7184
......@@ -949,12 +949,11 @@ def shuffle(reader, buffer_size):
is determined by argument buf_size.
Args:
param reader: the original reader whose output will be shuffled.
type reader: callable
param buf_size: shuffle buffer size.
type buf_size: int
return: the new reader whose output is shuffled.
rtype: callable
reader(callable): the original reader whose output will be shuffled.
buf_size(int): shuffle buffer size.
Returns:
callable: the new reader whose output is shuffled.
"""
return __create_unshared_decorated_reader__(
'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)})
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 66ea7184
......@@ -233,7 +233,7 @@ def fc(input,
dimensions will be flatten to form the first dimension of the final matrix (height of
the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, suppose
`X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
`X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
......@@ -502,46 +502,48 @@ def lstm(input,
If Device is GPU, This op will use cudnn LSTM implementation
A four-gate Long Short-Term Memory network with no peephole connections.
In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
the cell input ct-1 and the previous layer input xt given matrices W, R and biases bW, bR from the following equations:
$$ i_t = \\sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i) $$
$$ f_t = \\sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + bx_f + bh_f) $$
$$ o_t = \\sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + bx_o + bh_o) $$
$$ \\tilde{c_t} = tanh(W_{cx}x_t + W_{ch}h_{t-1} + bx_c + bh_c) $$
$$ c_t = f_t \\odot c_{t-1} + i_t \\odot \\tilde{c_t} $$
$$ h_t = o_t \\odot tanh(c_t) $$
- W terms denote weight matrices (e.g. $W_{ix}$ is the matrix
.. math::
i_t &= \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i)
f_t &= \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + bx_f + bh_f)
o_t &= \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + bx_o + bh_o)
\\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + bx_c + bh_c)
c_t &= f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
h_t &= o_t \odot tanh(c_t)
- $W$ terms denote weight matrices (e.g. $W_{ix}$ is the matrix
of weights from the input gate to the input)
- The b terms denote bias vectors ($bx_i$ and $bh_i$ are the input gate bias vector).
- sigmoid is the logistic sigmoid function.
- $i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$.
- The $\odot$ is the element-wise product of the vectors.
- `tanh` is the activation functions.
- $\tilde{c_t}$ is also called candidate hidden state,
- The :math:`\odot` is the element-wise product of the vectors.
- :math:`tanh` is the activation functions.
- :math:`\\tilde{c_t}` is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
Where sigmoid is the sigmoid operator: sigmoid(x) = 1 / (1 + e^-x), * represents a point-wise multiplication,
Where sigmoid is the sigmoid operator: :math:`sigmoid(x) = 1 / (1 + e^{-x})` , * represents a point-wise multiplication,
X represensts a matrix multiplication
Args:
input (Variable): LSTM input tensor, shape MUST be ( seq_len x batch_size x input_size )
init_h(Variable): The initial hidden state of the LSTM
init_h(Variable): The initial hidden state of the LSTM
This is a tensor with shape ( num_layers x batch_size x hidden_size)
if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
init_c(Variable): The initial cell state of the LSTM.
This is a tensor with shape ( num_layers x batch_size x hidden_size )
if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len
max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len
hidden_size (int): hidden size of the LSTM
num_layers (int): total layers number of the LSTM
dropout_prob(float|0.0): dropout prob, dropout ONLY work between rnn layers, NOT between time steps
......@@ -556,14 +558,18 @@ def lstm(input,
Returns:
rnn_out(Tensor): result of LSTM hidden, shape is (seq_len x batch_size x hidden_size)
if is_bidirec set to True, shape will be ( seq_len x batch_sze x hidden_size*2)
last_h(Tensor): the hidden state of the last step of LSTM
shape is ( num_layers x batch_size x hidden_size )
if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
last_c(Tensor): the cell state of the last step of LSTM
shape is ( num_layers x batch_size x hidden_size )
if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
rnn_out(Tensor),last_h(Tensor),last_c(Tensor):
Three tensors, rnn_out, last_h, last_c:
- rnn_out is result of LSTM hidden, shape is (seq_len x batch_size x hidden_size) \
if is_bidirec set to True, shape will be ( seq_len x batch_sze x hidden_size*2)
- last_h is the hidden state of the last step of LSTM \
shape is ( num_layers x batch_size x hidden_size ) \
if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
- last_c(Tensor): the cell state of the last step of LSTM \
shape is ( num_layers x batch_size x hidden_size ) \
if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
Examples:
......@@ -1220,6 +1226,8 @@ def dropout(x,
probability) the outputs of some units to zero, while others are remain
unchanged.
dropout op can be removed from the program to make the program more efficient.
Args:
x (Variable): The input tensor variable.
dropout_prob (float): Probability of setting units to zero.
......@@ -1230,22 +1238,24 @@ def dropout(x,
units will be dropped. DO NOT use a fixed seed in training.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
dropout_implementation(string): ['downgrade_in_infer'(defauld)|'upscale_in_train']
dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train']
1. downgrade_in_infer(default), downgrade the outcome at inference
train: out = input * mask
inference: out = input * dropout_prob
(make is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
- train: out = input * mask
- inference: out = input * dropout_prob
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
2. upscale_in_train, upscale the outcome at training time
train: out = input * mask / ( 1.0 - dropout_prob )
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.
the program will be efficient
- train: out = input * mask / ( 1.0 - dropout_prob )
- inference: out = input
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
Returns:
Variable: A tensor variable is the shape with `x`.
......@@ -1333,11 +1343,15 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
A 2-D tensor with shape [N x 1], the cross entropy loss.
Raises:
`ValueError`: 1) the 1st dimension of `input` and `label` are not equal.
2) when `soft_label == True`, and the 2nd dimension of
`input` and `label` are not equal.
3) when `soft_label == False`, and the 2nd dimension of
`label` is not 1.
ValueError:
1. the 1st dimension of ``input`` and ``label`` are not equal.
2. when ``soft_label == True``, and the 2nd dimension of
``input`` and ``label`` are not equal.
3. when ``soft_label == False``, and the 2nd dimension of
``label`` is not 1.
Examples:
.. code-block:: python
......@@ -1457,8 +1471,8 @@ def chunk_eval(input,
This function computes and outputs the precision, recall and
F1-score of chunk detection.
For some basics of chunking, please refer to
'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
For some basics of chunking, please refer to
`Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>`_ .
ChunkEvalOp computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
......@@ -1823,7 +1837,7 @@ def conv2d(input,
of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv2d
......@@ -2276,7 +2290,7 @@ def sequence_slice(input, offset, length, name=None):
.. code-block:: text
- Case:
- Case:
Given the input Variable **input**:
......@@ -2292,7 +2306,8 @@ def sequence_slice(input, offset, length, name=None):
out.lod = [[2, 1]],
out.dims = (3, 2).
NOTE: The first dimension size of **input**, **offset** and **length**
Note:
The first dimension size of **input**, **offset** and **length**
should be equal. The **offset** should start from 0.
Args:
......@@ -3013,7 +3028,7 @@ def group_norm(input,
"""
**Group Normalization Layer**
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_ .
Args:
input(Variable): The input tensor variable.
......@@ -3140,8 +3155,8 @@ def conv2d_transpose(input,
H^\prime_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 \\\\
H_{out} \in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
W_{out} \in [ W^\prime_{out}, W^\prime_{out} + strides[1] )
H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] )
Args:
input(Variable): The input image with [N, C, H, W] format.
......@@ -4673,7 +4688,7 @@ def ctc_greedy_decoder(input, blank, name=None):
[0.5, 0.1, 0.3, 0.1]]
input.lod = [[4, 4]]
Computation:
step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get:
......@@ -4704,10 +4719,10 @@ def ctc_greedy_decoder(input, blank, name=None):
name (str): The name of this layer. It is optional.
Returns:
Variable: CTC greedy decode result which is a 2-D tensor with shape [Lp, 1].
'Lp' is the sum if all output sequences' length. If all the sequences
in result were empty, the result LoDTensor will be [-1] with
LoD [[]] and dims [1, 1].
Variable: CTC greedy decode result which is a 2-D tensor with shape [Lp, 1]. \
'Lp' is the sum if all output sequences' length. If all the sequences \
in result were empty, the result LoDTensor will be [-1] with \
LoD [[]] and dims [1, 1].
Examples:
.. code-block:: python
......@@ -5060,7 +5075,7 @@ def hsigmoid(input,
"""
The hierarchical sigmoid operator is used to accelerate the training
process of language model. This operator organizes the classes into a
complete binary tree, or you can use is_custom to pass your own tree to
complete binary tree, or you can use is_custom to pass your own tree to
implement hierarchical. Each leaf node represents a class(a word) and each
internal node acts as a binary classifier. For each word there's a unique
path from root to it's leaf node, hsigmoid calculate the cost for each
......@@ -5072,13 +5087,13 @@ def hsigmoid(input,
<http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
And if you want to use the costumed tree by set 'is_custom' as true you may need to do following things first:
1. using your word dict to build a binary tree, each leaf node should be an word of your word dict
2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
means label of each binary classification, using 1 indicate true, 0 indicate false.
4. now, each word should has its path and code along the path, you can pass a batch of path and code
related to the same batch of inputs.
1. using your word dict to build a binary tree, each leaf node should be an word of your word dict
2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
means label of each binary classification, using 1 indicate true, 0 indicate false.
4. now, each word should has its path and code along the path, you can pass a batch of path and code
related to the same batch of inputs.
Args:
input (Variable): The input tensor variable with shape
......@@ -5086,8 +5101,8 @@ def hsigmoid(input,
and :math:`D` is the feature size.
label (Variable): The tensor variable contains labels of training data.
It's a tensor with shape is :math:`[N \\times 1]`.
num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set,
it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num
num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set,
it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num
which indicates the num of classes using by binary classify.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid
......@@ -5100,15 +5115,15 @@ def hsigmoid(input,
is not set, the bias is initialized zero. Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
path_table: (Variable|None) this variable can store each batch of samples' path to root,
path_table: (Variable|None) this variable can store each batch of samples' path to root,
it should be in leaf -> root order
path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like
structure and each element in this array is indexes in parent nodes' Weight Matrix.
path_code: (Variable|None) this variable can store each batch of samples' code,
path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like
structure and each element in this array is indexes in parent nodes' Weight Matrix.
path_code: (Variable|None) this variable can store each batch of samples' code,
each code consist with every code of parent nodes. it should be in leaf -> root order
is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
set you need to set path_table/path_code/num_classes, otherwise num_classes should be set
is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
of W and input will be sparse.
Returns:
......@@ -5485,11 +5500,11 @@ def softmax_with_cross_entropy(logits,
.. math::
max_j = \\max_{i=0}^{K}{\\text{logit}_i}
max_j &= \\max_{i=0}^{K}{\\text{logit}_i}
log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
log\\_max\\_sum_j &= \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
softmax_j = \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
softmax_j &= \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
and then cross entropy loss is calculated by softmax and label.
......@@ -5515,11 +5530,11 @@ def softmax_with_cross_entropy(logits,
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
2-D tensor with shape [N x K].
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:
.. code-block:: python
......@@ -5792,21 +5807,27 @@ def squeeze(input, axes, name=None):
the single dimensions will be removed from the shape. If an axis is
selected with shape entry not equal to one, an error is raised.
Examples:
Case 1:
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
we get:
Out.shape = (3, 1, 5)
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
axes = []
we get:
Out.shape = (3, 5)
For example:
.. code-block:: text
Case 1:
Given
X.shape = (1, 3, 1, 5)
and
axes = [0]
we get:
Out.shape = (3, 1, 5)
Case 2:
Given
X.shape = (1, 3, 1, 5)
and
axes = []
we get:
Out.shape = (3, 5)
Args:
input (Variable): The input variable to be squeezed.
......@@ -5842,6 +5863,9 @@ def unsqueeze(input, axes, name=None):
Dimension indices in axes are as seen in the output tensor.
For example:
.. code-block:: text
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
......@@ -6729,8 +6753,11 @@ def sequence_scatter(input, index, updates, name=None):
the columns to update in each row of X.
Here is an example:
Given the following input:
.. code-block:: text
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]]
......@@ -6743,7 +6770,9 @@ def sequence_scatter(input, index, updates, name=None):
updates.lod = [[ 0, 3, 8, 12]]
Then we have the output:
.. code-block:: text
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, 1.3, 1.2, 1.4, 1.1]]
......@@ -6759,7 +6788,7 @@ def sequence_scatter(input, index, updates, name=None):
name (str|None): The output variable name. Default None.
Returns:
output (Variable): The output is a tensor with the same shape as input.
Variable: The output is a tensor with the same shape as input.
Examples:
......@@ -6933,7 +6962,7 @@ def mean_iou(input, label, num_classes):
.. math::
IOU = \\frac{true\_positiv}{(true\_positive + false\_positive + false\_negative)}.
IOU = \\frac{true\_positive}{(true\_positive + false\_positive + false\_negative)}.
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
......@@ -6946,9 +6975,13 @@ def mean_iou(input, label, num_classes):
num_classes (int): The possible number of labels.
Returns:
mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
out_wrong(Variable): A Tensor with shape [num_classes]. The wrong numbers of each class.
out_correct(Variable): A Tensor with shape [num_classes]. The correct numbers of each class.
mean_iou (Variable),out_wrong(Variable),out_correct(Variable):
Three variables:
- mean_iou : A Tensor representing the mean intersection-over-union with shape [1].
- out_wrong: A Tensor with shape [num_classes]. The wrong numbers of each class.
- out_correct: A Tensor with shape [num_classes]. The correct numbers of each class.
Examples:
......@@ -7143,8 +7176,8 @@ def affine_grid(theta, out_shape, name=None):
Args:
theta (Variable): A batch of affine transform parameters with shape [N, 2, 3].
out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
out_shape can be a Variable or a list or tuple.
out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
``out_shape`` can be a Variable or a list or tuple.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -7157,6 +7190,7 @@ def affine_grid(theta, out_shape, name=None):
Examples:
.. code-block:: python
theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
data = fluid.layers.affine_grid(theta, out_shape)
......@@ -7192,9 +7226,10 @@ def affine_grid(theta, out_shape, name=None):
def rank_loss(label, left, right, name=None):
"""
**Rank loss layer for RankNet**
RankNet(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
`RankNet <http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf>`_
is a pairwise ranking model with a training sample consisting of a pair
of documents, A and B. Label P indicates whether A is ranked higher than B
or not:
......@@ -7202,16 +7237,19 @@ def rank_loss(label, left, right, name=None):
P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
about the rank of the input pair.
Rank loss layer takes three inputs: left (o_i), right (o_j) and
label (P_{i,j}). The inputs respectively represent RankNet's output scores
Rank loss layer takes three inputs: left ( :math:`o_i` ), right ( :math:`o_j` ) and
label ( :math:`P_{i,j}` ). The inputs respectively represent RankNet's output scores
for documents A and B and the value of label P. The following equation
computes rank loss C_{i,j} from the inputs:
$$
C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
o_{i,j} = o_i - o_j \\
\tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
$$
.. math::
C_{i,j} &= -\\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\\\
o_{i,j} &= o_i - o_j \\\\
\\tilde{P_{i,j}} &= \\left \{0, 0.5, 1 \\right \} \ or \ \\left \{0, 1 \\right \}
Rank loss layer takes batch inputs with size batch_size (batch_size >= 1).
......@@ -7237,7 +7275,6 @@ def rank_loss(label, left, right, name=None):
right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
out = fluid.layers.rank_loss(label, left, right)
"""
helper = LayerHelper('rank_loss', **locals())
......@@ -7269,7 +7306,7 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
.. math::
rank\_loss &= max(0, -label * (left - right) + margin)
rank\_loss = max(0, -label * (left - right) + margin)
Args:
label (Variable): Indicates whether the left is ranked higher than the right or not.
......@@ -7278,12 +7315,17 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
margin (float): Indicates the given margin.
name (str|None): A name for this layer (optional). If set None, the layer
will be named automatically.
Returns:
Variable: The ranking loss.
Raises:
ValueError: Any of label, left, and right is not a Variable.
Examples:
.. code-block:: python
label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
......@@ -7587,7 +7629,8 @@ def prelu(x, mode, param_attr=None, name=None):
"""
Equation:
y = \max(0, x) + alpha * \min(0, x)
.. math::
y = \max(0, x) + \\alpha * \min(0, x)
Args:
x (Variable): The input tensor.
......@@ -7653,8 +7696,8 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
.. code-block:: python
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
"""
helper = LayerHelper('brelu', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -7683,8 +7726,8 @@ def leaky_relu(x, alpha=0.02, name=None):
.. code-block:: python
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.leaky_relu(x, alpha=0.01)
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.leaky_relu(x, alpha=0.01)
"""
helper = LayerHelper('leaky_relu', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -7712,8 +7755,8 @@ def soft_relu(x, threshold=40.0, name=None):
.. code-block:: python
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.soft_relu(x, threshold=20.0)
x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
y = fluid.layers.soft_relu(x, threshold=20.0)
"""
helper = LayerHelper('soft_relu', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
......@@ -7729,23 +7772,32 @@ def flatten(x, axis=1, name=None):
"""
**Flatten layer**
Flattens the input tensor into a 2D matrix.
For Example:
.. code-block:: text
Examples:
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
axis = 2
We get:
Out.shape = (3 * 100, 4 * 100)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
axis = 0
We get:
Out.shape = (1, 3 * 100 * 100 * 4)
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
axis = 2
We get:
Out.shape = (3 * 100, 4 * 100)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
axis = 0
We get:
Out.shape = (1, 3 * 100 * 100 * 4)
Args:
x (Variable): A tensor of rank >= axis.
......@@ -7759,9 +7811,9 @@ def flatten(x, axis=1, name=None):
will be named automatically.
Returns:
Variable: A 2D tensor with the contents of the input tensor, with input
dimensions up to axis flattened to the outer dimension of
the output and remaining input dimensions flattened into the
Variable: A 2D tensor with the contents of the input tensor, with input \
dimensions up to axis flattened to the outer dimension of \
the output and remaining input dimensions flattened into the \
inner dimension of the output.
Raises:
......@@ -7801,19 +7853,23 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
The enumerated sequence has the same 1st dimension with variable `input`, and
the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
Examples:
Case 1:
Input:
X.lod = [[0, 3, 5]]
X.data = [[1], [2], [3], [4], [5]]
X.dims = [5, 1]
Attrs:
win_size = 2
pad_value = 0
Output:
Out.lod = [[0, 3, 5]]
Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
Out.dims = [5, 2]
.. code-block:: text
Case 1:
Input:
X.lod = [[0, 3, 5]]
X.data = [[1], [2], [3], [4], [5]]
X.dims = [5, 1]
Attrs:
win_size = 2
pad_value = 0
Output:
Out.lod = [[0, 3, 5]]
Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
Out.dims = [5, 2]
Args:
input (Variable): The input variable which is a index sequence.
......@@ -8896,6 +8952,7 @@ 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
......@@ -8969,14 +9026,16 @@ def similarity_focus(input, axis, indexes, name=None):
indexes(list): Indicating the indexes of the selected dimension.
Returns:
Variable: A tensor variable with the same shape and same type
as the input.
Variable: A tensor variable with the same shape and same type \
as the input.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[2, 3, 2, 2], dtype='float32')
x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
"""
helper = LayerHelper('similarity_focus', **locals())
# check attrs
......@@ -9055,6 +9114,7 @@ def hash(input, hash_size, num_hash=1, name=None):
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
......@@ -9075,50 +9135,52 @@ def hash(input, hash_size, num_hash=1, name=None):
def grid_sampler(x, grid, name=None):
"""
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
flow field grid, which is usually gennerated by :code:`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
interpolation value of 4 nearest corner points.
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
.. code-block:: text
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
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
interpolate point value by 4 nearest points.
Step 2:
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
| | |
| d_n |
| | |
x_w --d_w-- grid--d_e-- x_e
| | |
| d_s |
| | |
ws ------- y_s ------- wn
wn ------- y_n ------- en
| | |
| d_n |
| | |
x_w --d_w-- grid--d_e-- x_e
| | |
| d_s |
| | |
ws ------- y_s ------- wn
x_w = floor(x) // west side x coord
x_e = x_w + 1 // east side x coord
y_n = floor(y) // north side y coord
y_s = y_s + 1 // south side y coord
x_w = floor(x) // west side x coord
x_e = x_w + 1 // east side x coord
y_n = floor(y) // north side y coord
y_s = y_s + 1 // south side y coord
d_w = grid_x - x_w // distance to west side
d_e = x_e - grid_x // distance to east side
d_n = grid_y - y_n // distance to north side
d_s = y_s - grid_y // distance to south side
d_w = grid_x - x_w // distance to west side
d_e = x_e - grid_x // distance to east side
d_n = grid_y - y_n // distance to north side
d_s = y_s - grid_y // distance to south side
wn = X[:, :, y_n, x_w] // north-west point value
en = X[:, :, y_n, x_e] // north-east point value
ws = X[:, :, y_s, x_w] // south-east point value
es = X[:, :, y_s, x_w] // north-east point value
wn = X[:, :, y_n, x_w] // north-west point value
en = X[:, :, y_n, x_e] // north-east point value
ws = X[:, :, y_s, x_w] // south-east point value
es = X[:, :, y_s, x_w] // north-east point value
output = wn * d_e * d_s + en * d_w * d_s
+ ws * d_e * d_n + es * d_w * d_n
output = wn * d_e * d_s + en * d_w * d_s
+ ws * d_e * d_n + es * d_w * d_n
Args:
x(Variable): Input data of shape [N, C, H, W].
......@@ -9126,16 +9188,18 @@ 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
Variable: Output of shape [N, C, H, W] data samples input X
using bilnear interpolation based on input grid.
Exmples:
.. code-block:: python
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
out = fluid.layers.grid_sampler(x=x, grid=grid)
x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
out = fluid.layers.grid_sampler(x=x, grid=grid)
"""
helper = LayerHelper("grid_sampler", **locals())
......@@ -9203,19 +9267,19 @@ def add_position_encoding(input, alpha, beta, name=None):
"""
**Add Position Encoding Layer**
This layer accepts an input 3D-Tensor of shape [N x M x P], and return an
This layer accepts an input 3D-Tensor of shape [N x M x P], and returns an
output Tensor of shape [N x M x P] with positional encoding value.
Refer to `Attention Is All You Need<http://arxiv.org/pdf/1706.03762.pdf>`_ .
Refer to `Attention Is All You Need <http://arxiv.org/pdf/1706.03762.pdf>`_ .
.. math::
PE(pos, 2i) = \\sin{(pos / 10000^{2i / P})} \\\\
PE(pos, 2i + 1) = \\cos{(pos / 10000^{2i / P})} \\\\
Out(:, pos, i) = \\alpha * input(:, pos, i) + \\beta * PE(pos, i)
PE(pos, 2i) &= \\sin{(pos / 10000^{2i / P})} \\\\
PE(pos, 2i + 1) &= \\cos{(pos / 10000^{2i / P})} \\\\
Out(:, pos, i) &= \\alpha * input(:, pos, i) + \\beta * PE(pos, i)
Where:
* PE(pos, 2i): the increment for the number at even position
* PE(pos, 2i + 1): the increment for the number at odd position
- :math:`PE(pos, 2i)` : the increment for the number at even position
- :math:`PE(pos, 2i + 1)` : the increment for the number at odd position
Args:
input (Variable): 3-D input tensor with shape [N x M x P]
......@@ -9230,6 +9294,7 @@ def add_position_encoding(input, alpha, beta, name=None):
.. code-block:: python
position_tensor = fluid.layers.add_position_encoding(input=tensor)
"""
helper = LayerHelper('add_position_encoding', **locals())
dtype = helper.input_dtype()
......@@ -9262,13 +9327,13 @@ def bilinear_tensor_product(x,
For example:
.. math::
out{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
In this formula:
- :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:`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}`.
Args:
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 66ea7184
......@@ -393,9 +393,6 @@ def fill_constant_batch_size_like(input,
It also sets *stop_gradient* to True.
>>> data = fluid.layers.fill_constant_batch_size_like(
>>> input=like, shape=[1], value=0, dtype='int64')
Args:
input(${input_type}): ${input_comment}.
......@@ -411,6 +408,14 @@ def fill_constant_batch_size_like(input,
Returns:
${out_comment}.
Examples:
.. code-block:: python
data = fluid.layers.fill_constant_batch_size_like(
input=like, shape=[1], value=0, dtype='int64')
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_variable_for_type_inference(dtype=dtype)
......
python/paddle/fluid/metrics.py
浏览文件 @ 66ea7184
......@@ -361,8 +361,8 @@ class ChunkEvaluator(MetricBase):
Accumulate counter numbers output by chunk_eval from mini-batches and
compute the precision recall and F1-score using the accumulated counter
numbers.
For some basics of chunking, please refer to
'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.
For some basics of chunking, please refer to
`Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>`_ .
ChunkEvalEvaluator computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
......@@ -391,6 +391,7 @@ class ChunkEvaluator(MetricBase):
def update(self, num_infer_chunks, num_label_chunks, num_correct_chunks):
"""
Update the states based on the layers.chunk_eval() ouputs.
Args:
num_infer_chunks(int|numpy.array): The number of chunks in Inference on the given minibatch.
num_label_chunks(int|numpy.array): The number of chunks in Label on the given mini-batch.
......@@ -450,9 +451,9 @@ class EditDistance(MetricBase):
distance, instance_error = distance_evaluator.eval()
In the above example:
'distance' is the average of the edit distance in a pass.
'instance_error' is the instance error rate in a pass.
- 'distance' is the average of the edit distance in a pass.
- 'instance_error' is the instance error rate in a pass.
"""
......@@ -567,12 +568,15 @@ class DetectionMAP(object):
Calculate the detection mean average precision (mAP).
The general steps are as follows:
1. calculate the true positive and false positive according to the input
of detection and labels.
of detection and labels.
2. calculate mAP value, support two versions: '11 point' and 'integral'.
Please get more information from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
Args:
......@@ -613,10 +617,12 @@ class DetectionMAP(object):
for data in batches:
loss, cur_map_v, accum_map_v = exe.run(fetch_list=fetch)
In the above example:
In the above example:
- 'cur_map_v' is the mAP of current mini-batch.
- 'accum_map_v' is the accumulative mAP of one pass.
'cur_map_v' is the mAP of current mini-batch.
'accum_map_v' is the accumulative mAP of one pass.
"""
def __init__(self,
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 66ea7184
......@@ -125,14 +125,23 @@ def slice_variable(var_list, slice_count, min_block_size):
class DistributeTranspilerConfig(object):
"""
Args:
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
min_block_size (int): Minimum splitted element number in block.
According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
.. py:attribute:: slice_var_up (bool)
Do Tensor slice for pservers, default is True.
.. py:attribute:: split_method (PSDispatcher)
RoundRobin or HashName can be used.
Try to choose the best method to balance loads for pservers.
.. py:attribute:: min_block_size (int)
Minimum number of splitted elements in block.
According to : https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
We can use bandwidth effiently when data size is larger than 2MB.If you
want to change it, please be sure you see the slice_variable function.
want to change it, please be sure you have read the slice_variable function.
"""
slice_var_up = True
......
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