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fix doc of alpha_dropout/dropout/dropout2d/dropout3d/npair_loss (#29136)

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* fix en doc, test=document_fix

* add blank after code declare, test=document_fix

* refine doc of dropout, test=document_fix

* refine npair_loss and dropout, test=document_fix
release/2.0-rc1
huangjun12 4 years ago committed by GitHub
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18
python/paddle/fluid/layers/loss.py
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@ -1651,16 +1651,15 @@ from .control_flow import equal
def npair_loss(anchor, positive, labels, l2_reg=0.002): def npair_loss(anchor, positive, labels, l2_reg=0.002):
r''' """
Read `Improved Deep Metric Learning with Multi class N pair Loss Objective\
<http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/\
papers/nips16_npairmetriclearning.pdf>`_ .
Npair loss requires paired data. Npair loss has two parts: the first part is L2 Npair loss requires paired data. Npair loss has two parts: the first part is L2
regularizer on the embedding vector; the second part is cross entropy loss which regularizer on the embedding vector; the second part is cross entropy loss which
takes the similarity matrix of anchor and positive as logits. takes the similarity matrix of anchor and positive as logits.
For more information, please refer to:
`Improved Deep Metric Learning with Multi class N pair Loss Objective <http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf>`_
Args: Args:
anchor(Tensor): embedding vector for the anchor image. shape=[batch_size, embedding_dims], anchor(Tensor): embedding vector for the anchor image. shape=[batch_size, embedding_dims],
the data type is float32 or float64. the data type is float32 or float64.
@ -1669,11 +1668,12 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
labels(Tensor): 1-D tensor. shape=[batch_size], the data type is float32 or float64 or int64. labels(Tensor): 1-D tensor. shape=[batch_size], the data type is float32 or float64 or int64.
l2_reg(float32): L2 regularization term on embedding vector, default: 0.002. l2_reg(float32): L2 regularization term on embedding vector, default: 0.002.
Returns: Returns:
A Tensor representing the npair loss, the data type is the same as A Tensor representing the npair loss, the data type is the same as anchor, the shape is [1].
anchor, the shape is [1].
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
@ -1685,9 +1685,9 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
labels = paddle.rand(shape=(18,), dtype=DATATYPE) labels = paddle.rand(shape=(18,), dtype=DATATYPE)
npair_loss = paddle.nn.functional.npair_loss(anchor, positive, labels, l2_reg = 0.002) npair_loss = paddle.nn.functional.npair_loss(anchor, positive, labels, l2_reg = 0.002)
print(npair_loss.numpy()) print(npair_loss)
''' """
check_variable_and_dtype(anchor, 'anchor', ['float32', 'float64'], check_variable_and_dtype(anchor, 'anchor', ['float32', 'float64'],
'npair_loss') 'npair_loss')
check_variable_and_dtype(positive, 'positive', ['float32', 'float64'], check_variable_and_dtype(positive, 'positive', ['float32', 'float64'],

59
python/paddle/nn/functional/common.py
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@ -769,7 +769,7 @@ def dropout(x,
p (float | int): Probability of setting units to zero. Default 0.5. p (float | int): Probability of setting units to zero. Default 0.5.
axis (int | list): The axis along which the dropout is performed. Default None. axis (int | list): The axis along which the dropout is performed. Default None.
training (bool): A flag indicating whether it is in train phrase or not. Default True. training (bool): A flag indicating whether it is in train phrase or not. Default True.
mode(str): ['upscale_in_train'(default) | 'downscale_in_infer'] mode(str): ['upscale_in_train'(default) | 'downscale_in_infer'].
1. upscale_in_train(default), upscale the output at training time 1. upscale_in_train(default), upscale the output at training time
@ -785,9 +785,14 @@ def dropout(x,
Returns: Returns:
A Tensor representing the dropout, has same shape and data type as `x` . A Tensor representing the dropout, has same shape and data type as `x` .
Examples: Examples:
We use ``p=0.5`` in the following description for simplicity. We use ``p=0.5`` in the following description for simplicity.
1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly. 1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly.
.. code-block:: text
Let's see a simple case when x is a 2d tensor with shape 2*3: Let's see a simple case when x is a 2d tensor with shape 2*3:
[[1 2 3] [[1 2 3]
[4 5 6]] [4 5 6]]
@ -813,7 +818,12 @@ def dropout(x,
[[0.5 1. 1.5] [[0.5 1. 1.5]
[2. 2.5 3. ]] [2. 2.5 3. ]]
2. When ``axis!=None`` , this is useful for dropping whole channels from an image or sequence. 2. When ``axis!=None`` , this is useful for dropping whole channels from an image or sequence.
.. code-block:: text
Let's see the simple case when x is a 2d tensor with shape 2*3 again: Let's see the simple case when x is a 2d tensor with shape 2*3 again:
[[1 2 3] [[1 2 3]
[4 5 6]] [4 5 6]]
@ -853,18 +863,15 @@ def dropout(x,
[[0 0 0] [[0 0 0]
[0 0 0]] [0 0 0]]
Actually this is not what we want because all elements may set to zero~ Actually this is not what we want because all elements may set to zero~
When x is a 4d tensor with shape `NCHW`, we can set ``axis=[0,1]`` and the dropout will be performed
in channel `N` and `C`, `H` and `W` is tied, i.e. When x is a 4d tensor with shape `NCHW`, we can set ``axis=[0,1]`` and the dropout will be performed in channel `N` and `C`, `H` and `W` is tied, i.e. paddle.nn.dropout(x, p, axis=[0,1]) . Please refer to ``paddle.nn.functional.dropout2d`` for more details.
paddle.nn.dropout(x, p, axis=[0,1]) Similarly, when x is a 5d tensor with shape `NCDHW`, we can set ``axis=[0,1]`` to perform dropout3d. Please refer to ``paddle.nn.functional.dropout3d`` for more details.
Please refer to ``paddle.nn.functional.dropout2d`` for more details.
Similarly, when x is a 5d tensor with shape `NCDHW`, we can set ``axis=[0,1]`` to perform
dropout3d. Please refer to ``paddle.nn.functional.dropout3d`` for more details.
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.array([[1,2,3], [4,5,6]]).astype('float32') x = np.array([[1,2,3], [4,5,6]]).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
y_train = paddle.nn.functional.dropout(x, 0.5) y_train = paddle.nn.functional.dropout(x, 0.5)
@ -872,12 +879,12 @@ def dropout(x,
y_0 = paddle.nn.functional.dropout(x, axis=0) y_0 = paddle.nn.functional.dropout(x, axis=0)
y_1 = paddle.nn.functional.dropout(x, axis=1) y_1 = paddle.nn.functional.dropout(x, axis=1)
y_01 = paddle.nn.functional.dropout(x, axis=[0,1]) y_01 = paddle.nn.functional.dropout(x, axis=[0,1])
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
print(y_test.numpy()) print(y_test)
print(y_0.numpy()) print(y_0)
print(y_1.numpy()) print(y_1)
print(y_01.numpy()) print(y_01)
""" """
if not isinstance(p, (float, int)): if not isinstance(p, (float, int)):
@ -987,21 +994,19 @@ def dropout2d(x, p=0.5, training=True, data_format='NCHW', name=None):
The data type is float32 or float64. The data type is float32 or float64.
p (float): Probability of setting units to zero. Default 0.5. p (float): Probability of setting units to zero. Default 0.5.
training (bool): A flag indicating whether it is in train phrase or not. Default True. training (bool): A flag indicating whether it is in train phrase or not. Default True.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from `NCHW` or `NHWC` . The default is `NCHW` . When it is `NCHW` , the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
will be consistent with that of the input. An optional string from:
`NCHW` , `NHWC` . The default is `NCHW` . When it is `NCHW` , the data is
stored in the order of: [batch_size, input_channels, input_height, input_width].
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
A Tensor representing the dropout2d, has same shape and data type as `x` . A Tensor representing the dropout2d, has same shape and data type as `x` .
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.random.random(size=(2, 3, 4, 5)).astype('float32') x = np.random.random(size=(2, 3, 4, 5)).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
y_train = paddle.nn.functional.dropout2d(x) #train y_train = paddle.nn.functional.dropout2d(x) #train
@ -1044,21 +1049,19 @@ def dropout3d(x, p=0.5, training=True, data_format='NCDHW', name=None):
The data type is float32 or float64. The data type is float32 or float64.
p (float): Probability of setting units to zero. Default 0.5. p (float): Probability of setting units to zero. Default 0.5.
training (bool): A flag indicating whether it is in train phrase or not. Default True. training (bool): A flag indicating whether it is in train phrase or not. Default True.
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from ``NCDHW`` or ``NDHWC``. The default is ``NCDHW`` . When it is ``NCDHW`` , the data is stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width].
will be consistent with that of the input. An optional string from:
``NCDHW``, ``NDHWC``. The default is ``NCDHW`` . When it is ``NCDHW`` , the data is
stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width].
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
A Tensor representing the dropout3d, has same shape and data type with `x` . A Tensor representing the dropout3d, has same shape and data type with `x` .
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.random.random(size=(2, 3, 4, 5, 6)).astype('float32') x = np.random.random(size=(2, 3, 4, 5, 6)).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
y_train = paddle.nn.functional.dropout3d(x) #train y_train = paddle.nn.functional.dropout3d(x) #train
@ -1105,18 +1108,18 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.array([[-1, 1], [-1, 1]]).astype('float32') x = np.array([[-1, 1], [-1, 1]]).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
y_train = paddle.nn.functional.alpha_dropout(x, 0.5) y_train = paddle.nn.functional.alpha_dropout(x, 0.5)
y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False) y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False)
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
# [[-0.10721093, 1.6655989 ], [-0.7791938, -0.7791938]] (randomly) # [[-0.10721093, 1.6655989 ], [-0.7791938, -0.7791938]] (randomly)
print(y_test.numpy()) print(y_test)
""" """
if not isinstance(p, (float, int)): if not isinstance(p, (float, int)):
raise TypeError("p argument should be a float or int") raise TypeError("p argument should be a float or int")

45
python/paddle/nn/layer/common.py
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@ -655,21 +655,22 @@ class Dropout(layers.Layer):
- input: N-D tensor. - input: N-D tensor.
- output: N-D tensor, the same shape as input. - output: N-D tensor, the same shape as input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.array([[1,2,3], [4,5,6]]).astype('float32') x = np.array([[1,2,3], [4,5,6]]).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
m = paddle.nn.Dropout(p=0.5) m = paddle.nn.Dropout(p=0.5)
y_train = m(x) y_train = m(x)
m.eval() # switch the model to test phase m.eval() # switch the model to test phase
y_test = m(x) y_test = m(x)
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
print(y_test.numpy()) print(y_test)
""" """
def __init__(self, p=0.5, axis=None, mode="upscale_in_train", name=None): def __init__(self, p=0.5, axis=None, mode="upscale_in_train", name=None):
@ -705,31 +706,29 @@ class Dropout2D(layers.Layer):
Parameters: Parameters:
p (float, optional): Probability of setting units to zero. Default: 0.5 p (float, optional): Probability of setting units to zero. Default: 0.5
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from `NCHW` or `NHWC`. The default is `NCHW`. When it is `NCHW`, the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
will be consistent with that of the input. An optional string from:
`NCHW`, `NHWC`. The default is `NCHW`. When it is `NCHW`, the data is
stored in the order of: [batch_size, input_channels, input_height, input_width].
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Shape: Shape:
- input: 4-D tensor. - input: 4-D tensor.
- output: 4-D tensor, the same shape as input. - output: 4-D tensor, the same shape as input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.random.random(size=(2, 3, 4, 5)).astype('float32') x = np.random.random(size=(2, 3, 4, 5)).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
m = paddle.nn.Dropout2D(p=0.5) m = paddle.nn.Dropout2D(p=0.5)
y_train = m(x) y_train = m(x)
m.eval() # switch the model to test phase m.eval() # switch the model to test phase
y_test = m(x) y_test = m(x)
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
print(y_test.numpy()) print(y_test)
""" """
def __init__(self, p=0.5, data_format='NCHW', name=None): def __init__(self, p=0.5, data_format='NCHW', name=None):
@ -763,31 +762,29 @@ class Dropout3D(layers.Layer):
Parameters: Parameters:
p (float | int): Probability of setting units to zero. Default: 0.5 p (float | int): Probability of setting units to zero. Default: 0.5
data_format (str, optional): Specify the data format of the input, and the data format of the output data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from `NCDHW` or `NDHWC`. The default is `NCDHW`. When it is `NCDHW`, the data is stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width].
will be consistent with that of the input. An optional string from:
`NCDHW`, `NDHWC`. The default is `NCDHW`. When it is `NCDHW`, the data is
stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width].
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Shape: Shape:
- input: 5-D tensor. - input: 5-D tensor.
- output: 5-D tensor, the same shape as input. - output: 5-D tensor, the same shape as input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.random.random(size=(2, 3, 4, 5, 6)).astype('float32') x = np.random.random(size=(2, 3, 4, 5, 6)).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
m = paddle.nn.Dropout3D(p=0.5) m = paddle.nn.Dropout3D(p=0.5)
y_train = m(x) y_train = m(x)
m.eval() # switch the model to test phase m.eval() # switch the model to test phase
y_test = m(x) y_test = m(x)
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
print(y_test.numpy()) print(y_test)
""" """
def __init__(self, p=0.5, data_format='NCDHW', name=None): def __init__(self, p=0.5, data_format='NCDHW', name=None):
@ -829,20 +826,20 @@ class AlphaDropout(layers.Layer):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np import numpy as np
paddle.disable_static()
x = np.array([[-1, 1], [-1, 1]]).astype('float32') x = np.array([[-1, 1], [-1, 1]]).astype('float32')
x = paddle.to_tensor(x) x = paddle.to_tensor(x)
m = paddle.nn.AlphaDropout(p=0.5) m = paddle.nn.AlphaDropout(p=0.5)
y_train = m(x) y_train = m(x)
m.eval() # switch the model to test phase m.eval() # switch the model to test phase
y_test = m(x) y_test = m(x)
print(x.numpy()) print(x)
print(y_train.numpy()) print(y_train)
# [[-0.10721093, 1.6655989 ], [-0.7791938, -0.7791938]] (randomly) # [[-0.10721093, 1.6655989 ], [-0.7791938, -0.7791938]] (randomly)
print(y_test.numpy()) print(y_test)
""" """
def __init__(self, p=0.5, name=None): def __init__(self, p=0.5, name=None):

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