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Paddle
未验证 提交 0a15b0db 编写于 作者: Y yuchen202 提交者： GitHub
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[xdoctest] reformat example code with google style in No.36-43 (#56440)

上级 71e28b12
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Showing with 843 addition and 650 deletion
python/paddle/nn/functional/activation.py
浏览文件 @ 0a15b0db
......@@ -49,12 +49,15 @@ def celu(x, alpha=1.0, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
out = F.celu(x, alpha=0.2)
# [[-0.19865242, 6. ],
# [ 1. , 15.60000038]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
>>> out = F.celu(x, alpha=0.2)
>>> print(out)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.19865242, 6. ],
[ 1. , 15.60000038]])
"""
if alpha == 0:
raise ZeroDivisionError("alpha cannot be 0 for celu")
......@@ -100,13 +103,15 @@ def elu(x, alpha=1.0, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
out = F.elu(x, alpha=0.2)
# [[-0.12642411 6. ]
# [ 1. 15.6 ]]
>>> x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
>>> out = F.elu(x, alpha=0.2)
>>> print(out)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.12642412, 6. ],
[ 1. , 15.60000038]])
"""
if in_dynamic_mode():
......@@ -168,16 +173,20 @@ def gelu(x, approximate=False, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([[-1, 0.5], [1, 1.5]])
out1 = F.gelu(x)
# [[-0.15865529, 0.34573123],
# [ 0.84134471, 1.39978933]]
out2 = F.gelu(x, True)
# [[-0.15880799, 0.34571400],
# [ 0.84119201, 1.39957154]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = paddle.to_tensor([[-1, 0.5], [1, 1.5]])
>>> out1 = F.gelu(x)
>>> print(out1)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.15865529, 0.34573123],
[ 0.84134471, 1.39978933]])
>>> out2 = F.gelu(x, True)
>>> print(out2)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.15880796, 0.34571400],
[ 0.84119201, 1.39957154]])
"""
if in_dynamic_mode():
......@@ -223,11 +232,15 @@ def hardshrink(x, threshold=0.5, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = paddle.to_tensor([-1, 0.3, 2.5])
>>> out = F.hardshrink(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-1. , 0. , 2.50000000])
x = paddle.to_tensor([-1, 0.3, 2.5])
out = F.hardshrink(x) # [-1., 0., 2.5]
"""
if in_dynamic_mode():
......@@ -274,11 +287,14 @@ def hardtanh(x, min=-1.0, max=1.0, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-1.5, 0.3, 2.5])
out = F.hardtanh(x) # [-1., 0.3, 1.]
>>> x = paddle.to_tensor([-1.5, 0.3, 2.5])
>>> out = F.hardtanh(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-1. , 0.30000001, 1. ])
"""
if in_dynamic_mode():
......@@ -338,11 +354,14 @@ def hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-4., 5., 1.])
out = F.hardsigmoid(x) # [0., 1., 0.666667]
>>> x = paddle.to_tensor([-4., 5., 1.])
>>> out = F.hardsigmoid(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 1. , 0.66666669])
"""
if in_dynamic_mode():
......@@ -390,11 +409,14 @@ def hardswish(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-4., 5., 1.])
out = F.hardswish(x) # [0., 5., 0.666667]
>>> x = paddle.to_tensor([-4., 5., 1.])
>>> out = F.hardswish(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0. , 5. , 0.66666669])
"""
if in_dynamic_mode():
return _C_ops.hardswish(x)
......@@ -442,13 +464,14 @@ def leaky_relu(x, negative_slope=0.01, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-2., 0., 1.])
out = F.leaky_relu(x)
print(out)
# [-0.02, 0., 1.]
>>> x = paddle.to_tensor([-2., 0., 1.])
>>> out = F.leaky_relu(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.02000000, 0. , 1. ])
"""
if in_dynamic_mode():
......@@ -502,25 +525,26 @@ def prelu(x, weight, data_format="NCHW", name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
data = paddle.to_tensor([[[[-2.0, 3.0, -4.0, 5.0],
[ 3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[ 1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[ 6.0, 7.0, 8.0, 9.0]]]], dtype='float32')
w = paddle.to_tensor([0.25], dtype='float32')
out = F.prelu(data, w)
print(out)
# [[[[-0.5 , 3. , -1. , 5. ],
# [ 3. , -1. , 5. , -1.5 ],
# [-1.75, -2. , 8. , 9. ]],
# [[ 1. , -0.5 , -0.75, 4. ],
# [-1.25, 6. , 7. , -2. ],
# [ 6. , 7. , 8. , 9. ]]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> data = paddle.to_tensor([[[[-2.0, 3.0, -4.0, 5.0],
... [ 3.0, -4.0, 5.0, -6.0],
... [-7.0, -8.0, 8.0, 9.0]],
... [[ 1.0, -2.0, -3.0, 4.0],
... [-5.0, 6.0, 7.0, -8.0],
... [ 6.0, 7.0, 8.0, 9.0]]]], dtype='float32')
>>> w = paddle.to_tensor([0.25], dtype='float32')
>>> out = F.prelu(data, w)
>>> print(out)
Tensor(shape=[1, 2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[-0.50000000, 3. , -1. , 5. ],
[ 3. , -1. , 5. , -1.50000000],
[-1.75000000, -2. , 8. , 9. ]],
[[ 1. , -0.50000000, -0.75000000, 4. ],
[-1.25000000, 6. , 7. , -2. ],
[ 6. , 7. , 8. , 9. ]]]])
"""
assert (
len(weight.shape) == 0 or len(weight.shape) == 1
......@@ -634,24 +658,24 @@ def rrelu(x, lower=1.0 / 8.0, upper=1.0 / 3.0, training=True, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input_tensor = paddle.to_tensor([[[[-2.0, 3.0, -4.0, 5.0],
[ 3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[ 1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[ 6.0, 7.0, 8.0, 9.0]]]], dtype='float32')
out = F.rrelu(input_tensor, 0.1, 0.3)
print(out)
#[[[[-0.20000899 3. -0.8810822 5. ]
# [ 3. -0.55175185 5. -1.0776101 ]
# [-1.0680687 -1.9896201 8. 9. ]]
# [[ 1. -0.5238267 -0.65515125 4. ]
# [-1.3766339 6. 7. -2.3465784 ]
# [ 6. 7. 8. 9. ]]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(1)
>>> input_tensor = paddle.to_tensor([[[[-2.0, 3.0, -4.0, 5.0],
... [ 3.0, -4.0, 5.0, -6.0],
... [-7.0, -8.0, 8.0, 9.0]],
... [[ 1.0, -2.0, -3.0, 4.0],
... [-5.0, 6.0, 7.0, -8.0],
... [ 6.0, 7.0, 8.0, 9.0]]]], dtype='float32')
>>> out = F.rrelu(input_tensor, 0.1, 0.3)
>>> print(out)
Tensor(shape=[1, 2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[-0.20715050, 3. , -1.01193857, 5. ],
[ 3. , -0.94084597, 5. , -0.65544695],
[-1.24268556, -2.34339547, 8. , 9. ]],
[[ 1. , -0.44942653, -0.68969047, 4. ],
[-1.03736508, 6. , 7. , -0.95799232],
[ 6. , 7. , 8. , 9. ]]]])
"""
if not isinstance(lower, float) or not isinstance(upper, float):
raise TypeError(
......@@ -722,13 +746,14 @@ def relu(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-2, 0, 1], dtype='float32')
out = F.relu(x)
print(out)
# [0., 0., 1.]
>>> x = paddle.to_tensor([-2, 0, 1], dtype='float32')
>>> out = F.relu(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0., 0., 1.])
"""
if in_dynamic_mode():
......@@ -770,11 +795,14 @@ def log_sigmoid(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
out = F.log_sigmoid(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
>>> x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
>>> out = F.log_sigmoid(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.31326166, -0.12692805, -0.04858733, -0.01814996])
"""
if in_dynamic_mode():
......@@ -830,20 +858,25 @@ def maxout(x, groups, axis=1, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.rand([1, 2, 3, 4])
# [[[[0.5002636 0.22272532 0.17402348 0.2874594 ]
# [0.95313174 0.6228939 0.7129065 0.7087491 ]
# [0.02879342 0.88725346 0.61093384 0.38833922]]
# [[0.5231306 0.03807496 0.91661984 0.15602879]
# [0.666127 0.616567 0.30741522 0.24044901]
# [0.7142536 0.7351477 0.31588817 0.23782359]]]]
out = F.maxout(x, groups=2)
# [[[[0.5231306 0.22272532 0.91661984 0.2874594 ]
# [0.95313174 0.6228939 0.7129065 0.7087491 ]
# [0.7142536 0.88725346 0.61093384 0.38833922]]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(2023)
>>> x = paddle.rand([1, 2, 3, 4])
>>> print(x)
Tensor(shape=[1, 2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[0.86583614, 0.52014720, 0.25960937, 0.90525323],
[0.42400089, 0.40641287, 0.97020894, 0.74437362],
[0.51785129, 0.73292869, 0.97786582, 0.04315904]],
[[0.42639419, 0.71958369, 0.20811461, 0.19731510],
[0.38424349, 0.14603184, 0.22713774, 0.44607511],
[0.21657862, 0.67685395, 0.46460176, 0.92382854]]]])
>>> out = F.maxout(x, groups=2)
>>> print(out)
Tensor(shape=[1, 1, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[0.86583614, 0.71958369, 0.25960937, 0.90525323],
[0.42400089, 0.40641287, 0.97020894, 0.74437362],
[0.51785129, 0.73292869, 0.97786582, 0.92382854]]]])
"""
if in_dynamic_mode():
return _C_ops.maxout(x, groups, axis)
......@@ -888,13 +921,14 @@ def relu6(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-1, 0.3, 6.5])
out = F.relu6(x)
print(out)
# [0, 0.3, 6]
>>> x = paddle.to_tensor([-1, 0.3, 6.5])
>>> out = F.relu6(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.30000001, 6. ])
"""
threshold = 6.0
if in_dynamic_mode():
......@@ -945,13 +979,15 @@ def selu(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([[0.0, 1.0],[2.0, 3.0]])
out = F.selu(x)
print(out)
# [[0, 1.050701],[2.101402, 3.152103]]
>>> x = paddle.to_tensor([[0.0, 1.0],[2.0, 3.0]])
>>> out = F.selu(x)
>>> print(out)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 1.05070102],
[2.10140204, 3.15210295]])
"""
if scale <= 1.0:
raise ValueError(
......@@ -1000,11 +1036,14 @@ def silu(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
out = F.silu(x) # [ 0.731059, 1.761594, 2.857722, 3.928055 ]
>>> x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
>>> out = F.silu(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.73105860, 1.76159406, 2.85772228, 3.92805505])
"""
if in_dynamic_mode():
......@@ -1111,25 +1150,35 @@ def softmax(x, axis=-1, dtype=None, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0],
[3.0, 4.0, 5.0, 6.0],
[7.0, 8.0, 8.0, 9.0]],
[[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[6.0, 7.0, 8.0, 9.0]]],dtype='float32')
out1 = F.softmax(x)
out2 = F.softmax(x, dtype='float64')
# out1's data type is float32; out2's data type is float64
# out1 and out2's value is as follows:
# [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
# [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
# [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
# [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
# [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
# [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = paddle.to_tensor([[[2.0, 3.0, 4.0, 5.0],
... [3.0, 4.0, 5.0, 6.0],
... [7.0, 8.0, 8.0, 9.0]],
... [[1.0, 2.0, 3.0, 4.0],
... [5.0, 6.0, 7.0, 8.0],
... [6.0, 7.0, 8.0, 9.0]]],dtype='float32')
>>> out1 = F.softmax(x)
>>> out2 = F.softmax(x, dtype='float64')
>>> #out1's data type is float32; out2's data type is float64
>>> #out1 and out2's value is as follows:
>>> print(out1)
>>> print(out2)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[0.03205860, 0.08714432, 0.23688284, 0.64391428],
[0.03205860, 0.08714432, 0.23688284, 0.64391428],
[0.07232949, 0.19661194, 0.19661194, 0.53444666]],
[[0.03205860, 0.08714432, 0.23688284, 0.64391428],
[0.03205860, 0.08714432, 0.23688284, 0.64391428],
[0.03205860, 0.08714432, 0.23688284, 0.64391428]]])
Tensor(shape=[2, 3, 4], dtype=float64, place=Place(cpu), stop_gradient=True,
[[[0.03205860, 0.08714432, 0.23688282, 0.64391426],
[0.03205860, 0.08714432, 0.23688282, 0.64391426],
[0.07232949, 0.19661193, 0.19661193, 0.53444665]],
[[0.03205860, 0.08714432, 0.23688282, 0.64391426],
[0.03205860, 0.08714432, 0.23688282, 0.64391426],
[0.03205860, 0.08714432, 0.23688282, 0.64391426]]])
"""
if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
......@@ -1214,11 +1263,14 @@ def softplus(x, beta=1, threshold=20, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3], dtype='float32')
out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
>>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3], dtype='float32')
>>> out = F.softplus(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.51301527, 0.59813893, 0.74439669, 0.85435522])
"""
if in_dynamic_mode():
......@@ -1264,14 +1316,14 @@ def softshrink(x, threshold=0.5, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-0.9, -0.2, 0.1, 0.8])
out = F.softshrink(x)
print(out)
# Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-0.39999998, 0. , 0. , 0.30000001])
>>> x = paddle.to_tensor([-0.9, -0.2, 0.1, 0.8])
>>> out = F.softshrink(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.39999998, 0. , 0. , 0.30000001])
"""
if threshold < 0:
raise ValueError(
......@@ -1315,14 +1367,14 @@ def softsign(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.softsign(x)
print(out)
# Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-0.28571430, -0.16666666, 0.09090909, 0.23076925])
>>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
>>> out = F.softsign(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.28571430, -0.16666666, 0.09090909, 0.23076925])
"""
if in_dynamic_mode():
return _C_ops.softsign(x)
......@@ -1354,14 +1406,14 @@ def swish(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-2., 0., 1.])
out = F.swish(x)
print(out)
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-0.23840584, 0. , 0.73105854])
>>> x = paddle.to_tensor([-2., 0., 1.])
>>> out = F.swish(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.23840584, 0. , 0.73105860])
"""
if in_dynamic_mode():
return _C_ops.swish(x)
......@@ -1403,11 +1455,14 @@ def mish(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-5., 0., 5.])
out = F.mish(x) # [-0.03357624, 0., 4.99955208]
>>> x = paddle.to_tensor([-5., 0., 5.])
>>> out = F.mish(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.03357624, 0. , 4.99955177])
"""
if in_dynamic_mode():
return _C_ops.mish(x, 20)
......@@ -1439,14 +1494,14 @@ def tanhshrink(x, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
out = F.tanhshrink(x)
print(out)
# Tensor(shape=[4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-0.02005106, -0.00262468, 0.00033200, 0.00868741])
>>> x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
>>> out = F.tanhshrink(x)
>>> print(out)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.02005100, -0.00262472, 0.00033201, 0.00868741])
"""
if in_dynamic_mode():
return _C_ops.tanh_shrink(x)
......@@ -1488,14 +1543,14 @@ def thresholded_relu(x, threshold=1.0, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.to_tensor([2., 0., 1.])
out = F.thresholded_relu(x)
print(out)
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [2., 0., 0.])
>>> x = paddle.to_tensor([2., 0., 1.])
>>> out = F.thresholded_relu(x)
>>> print(out)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[2., 0., 0.])
"""
if in_dynamic_mode():
......@@ -1561,26 +1616,35 @@ def log_softmax(x, axis=-1, dtype=None, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = [[[-2.0, 3.0, -4.0, 5.0],
[3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[6.0, 7.0, 8.0, 9.0]]]
x = paddle.to_tensor(x)
out1 = F.log_softmax(x)
out2 = F.log_softmax(x, dtype='float64')
# out1's data type is float32; out2's data type is float64
# out1 and out2's value is as follows:
# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
# [[ -3.0518122 -6.051812 -7.051812 -0.051812 ]
# [-12.313267 -1.3132664 -0.3132665 -15.313267 ]
# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = [[[-2.0, 3.0, -4.0, 5.0],
... [3.0, -4.0, 5.0, -6.0],
... [-7.0, -8.0, 8.0, 9.0]],
... [[1.0, -2.0, -3.0, 4.0],
... [-5.0, 6.0, 7.0, -8.0],
... [6.0, 7.0, 8.0, 9.0]]]
>>> x = paddle.to_tensor(x)
>>> out1 = F.log_softmax(x)
>>> out2 = F.log_softmax(x, dtype='float64')
>>> #out1's data type is float32; out2's data type is float64
>>> #out1 and out2's value is as follows:
>>> print(out1)
Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-7.12783957 , -2.12783957 , -9.12783909 , -0.12783945 ],
[-2.12705135 , -9.12705135 , -0.12705141 , -11.12705135],
[-16.31326103, -17.31326103, -1.31326187 , -0.31326184 ]],
[[-3.05181193 , -6.05181217 , -7.05181217 , -0.05181199 ],
[-12.31326675, -1.31326652 , -0.31326646 , -15.31326675],
[-3.44018984 , -2.44018984 , -1.44018972 , -0.44018975 ]]])
>>> print(out2)
Tensor(shape=[2, 3, 4], dtype=float64, place=Place(cpu), stop_gradient=True,
[[[-7.12783948 , -2.12783948 , -9.12783948 , -0.12783948 ],
[-2.12705141 , -9.12705141 , -0.12705141 , -11.12705141],
[-16.31326180, -17.31326180, -1.31326180 , -0.31326180 ]],
[[-3.05181198 , -6.05181198 , -7.05181198 , -0.05181198 ],
[-12.31326640, -1.31326640 , -0.31326640 , -15.31326640],
[-3.44018970 , -2.44018970 , -1.44018970 , -0.44018970 ]]])
"""
if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
......@@ -1655,17 +1719,16 @@ def glu(x, axis=-1, name=None):
Examples:
.. code-block:: python
import paddle
from paddle.nn import functional as F
x = paddle.to_tensor(
[[-0.22014759, -1.76358426, 0.80566144, 0.04241343],
[-1.94900405, -1.89956081, 0.17134808, -1.11280477]]
)
print(F.glu(x))
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[-0.15216254, -0.90048921],
# [-1.05778778, -0.46985325]])
>>> import paddle
>>> from paddle.nn import functional as F
>>> x = paddle.to_tensor(
... [[-0.22014759, -1.76358426, 0.80566144, 0.04241343],
... [-1.94900405, -1.89956081, 0.17134808, -1.11280477]]
... )
>>> print(F.glu(x))
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.15216254, -0.90048921],
[-1.05778778, -0.46985325]])
"""
check_variable_and_dtype(
......@@ -1727,18 +1790,19 @@ def gumbel_softmax(x, temperature=1.0, hard=False, axis=-1, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
logits = paddle.randn([4, 6])
temperature = 0.01
gumbel_softmax = F.gumbel_softmax(logits, temperature)
print(gumbel_softmax)
# out's value is as follows:
# [[0.00000001, 1. , 0.00000000, 0.00000000, 0.00000006, 0.00000000],
# [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 1. ],
# [0.00000062, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.99999940],
# [0.00000000, 0.00000000, 0.00000000, 0.00001258, 0.99998736, 0.00000000]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(2023)
>>> logits = paddle.randn([4, 6])
>>> temperature = 0.01
>>> gumbel_softmax = F.gumbel_softmax(logits, temperature)
>>> print(gumbel_softmax)
Tensor(shape=[4, 6], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.00000000, 1. , 0.00000000, 0.00000000, 0.00000000, 0.00000000],
[0.00000000, 0.00000000, 1. , 0.00000000, 0.00000000, 0.00000000],
[0.00000000, 0.00000004, 0.00000000, 0.00000000, 1. , 0.00000000],
[0.00000000, 1. , 0.00000000, 0.00000000, 0.00000000, 0.00000000]])
"""
if in_dynamic_mode():
......
python/paddle/nn/functional/common.py
浏览文件 @ 0a15b0db
......@@ -95,11 +95,11 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.randn((100,3,224,224))
y = F.unfold(x, [3, 3], 1, 1, 1)
>>> x = paddle.randn((100,3,224,224))
>>> y = F.unfold(x, [3, 3], 1, 1, 1)
"""
helper = LayerHelper("unfold", **locals())
......@@ -348,23 +348,21 @@ def interpolate(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
output_1 = F.interpolate(x=input_data, size=[12,12])
print(output_1.shape)
# [2L, 3L, 12L, 12L]
# given scale
output_2 = F.interpolate(x=input_data, scale_factor=[2,1])
print(output_2.shape)
# [2L, 3L, 12L, 10L]
# bilinear interp
output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear")
print(output_2.shape)
# [2L, 3L, 12L, 10L]
>>> import paddle
>>> import paddle.nn.functional as F
>>> input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
>>> output_1 = F.interpolate(x=input_data, size=[12,12])
>>> print(output_1.shape)
[2, 3, 12, 12]
>>> # given scale
>>> output_2 = F.interpolate(x=input_data, scale_factor=[2,1])
>>> print(output_2.shape)
[2, 3, 12, 10]
>>> # bilinear interp
>>> output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear")
>>> print(output_2.shape)
[2, 3, 12, 10]
"""
data_format = data_format.upper()
resample = mode.upper()
......@@ -877,15 +875,14 @@ def upsample(
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
upsample_out = paddle.nn.Upsample(size=[12,12])
>>> import paddle
>>> import paddle.nn as nn
output = upsample_out(x=input_data)
print(output.shape)
# [2L, 3L, 12L, 12L]
>>> input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32)
>>> upsample_out = paddle.nn.Upsample(size=[12,12])
>>> output = upsample_out(x=input_data)
>>> print(output.shape)
[2, 3, 12, 12]
"""
return interpolate(
......@@ -913,17 +910,16 @@ def bilinear(x1, x2, weight, bias=None, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x1 = paddle.randn((5, 5)).astype(paddle.float32)
x2 = paddle.randn((5, 4)).astype(paddle.float32)
w = paddle.randn((1000, 5, 4)).astype(paddle.float32)
b = paddle.randn((1, 1000)).astype(paddle.float32)
result = F.bilinear(x1, x2, w, b)
print(result.shape)
# [5, 1000]
>>> x1 = paddle.randn((5, 5)).astype(paddle.float32)
>>> x2 = paddle.randn((5, 4)).astype(paddle.float32)
>>> w = paddle.randn((1000, 5, 4)).astype(paddle.float32)
>>> b = paddle.randn((1, 1000)).astype(paddle.float32)
>>> result = F.bilinear(x1, x2, w, b)
>>> print(result.shape)
[5, 1000]
"""
if in_dynamic_mode():
......@@ -1061,39 +1057,38 @@ def dropout(
.. code-block:: python
import paddle
x = paddle.to_tensor([[1,2,3], [4,5,6]]).astype(paddle.float32)
y_train = paddle.nn.functional.dropout(x, 0.5)
y_test = paddle.nn.functional.dropout(x, 0.5, training=False)
y_0 = paddle.nn.functional.dropout(x, axis=0)
y_1 = paddle.nn.functional.dropout(x, axis=1)
y_01 = paddle.nn.functional.dropout(x, axis=[0,1])
print(x)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[1., 2., 3.],
# [4., 5., 6.]])
print(y_train)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[2. , 0. , 6. ],
# [8. , 0. , 12.]])
print(y_test)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[1., 2., 3.],
# [4., 5., 6.]])
print(y_0)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0. , 0. , 0. ],
# [8. , 10., 12.]])
print(y_1)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[2. , 0. , 6. ],
# [8. , 0. , 12.]])
print(y_01)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[0. , 0. , 0. ],
# [8. , 0. , 12.]])
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.to_tensor([[1,2,3], [4,5,6]]).astype(paddle.float32)
>>> y_train = paddle.nn.functional.dropout(x, 0.5)
>>> y_test = paddle.nn.functional.dropout(x, 0.5, training=False)
>>> y_0 = paddle.nn.functional.dropout(x, axis=0)
>>> y_1 = paddle.nn.functional.dropout(x, axis=1)
>>> y_01 = paddle.nn.functional.dropout(x, axis=[0,1])
>>> print(x)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1., 2., 3.],
[4., 5., 6.]])
>>> print(y_train)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2., 4., 0.],
[8., 0., 0.]])
>>> print(y_test)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1., 2., 3.],
[4., 5., 6.]])
>>> print(y_0)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2., 4., 6.],
[8. , 10., 12.]])
>>> print(y_1)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2. , 4. , 6. ],
[8. , 10., 12.]])
>>> print(y_01)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0., 0., 6.],
[0., 0., 0.]])
"""
if not isinstance(p, (float, int, Variable)):
raise TypeError("p argument should be a number or Variable")
......@@ -1258,17 +1253,106 @@ def dropout2d(x, p=0.5, training=True, data_format='NCHW', name=None):
Examples:
.. code-block:: python
import paddle
x = paddle.randn(shape=(2, 3, 4, 5)).astype(paddle.float32)
y_train = paddle.nn.functional.dropout2d(x) #train
y_test = paddle.nn.functional.dropout2d(x, training=False) #test
for i in range(2):
for j in range(3):
print(x[i,j,:,:])
print(y_train[i,j,:,:]) # may all 0
print(y_test[i,j,:,:])
>>> import paddle
>>> paddle.seed(1)
>>> x = paddle.randn(shape=(2, 3, 4, 5)).astype(paddle.float32)
>>> y_train = paddle.nn.functional.dropout2d(x) #train
>>> y_test = paddle.nn.functional.dropout2d(x, training=False) #test
>>> for i in range(2):
... for j in range(3):
... print(x[i,j,:,:])
... print(y_train[i,j,:,:]) # may all 0
... print(y_test[i,j,:,:])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.30557564, 0.11855337, 0.41220093, -0.09968963, 1.50014710],
[ 1.24004936, -0.92485696, 0.08612321, 1.15149164, -0.09276631],
[ 1.22873247, -1.46587241, -1.30802727, 0.19496460, 1.73776841],
[ 0.40092674, 0.67630458, 0.72265440, 1.31720388, -1.41899264]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.61115128, 0.23710674, 0.82440186, -0.19937925, 3.00029421],
[ 2.48009872, -1.84971392, 0.17224643, 2.30298328, -0.18553263],
[ 2.45746493, -2.93174481, -2.61605453, 0.38992921, 3.47553682],
[ 0.80185348, 1.35260916, 1.44530880, 2.63440776, -2.83798528]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.30557564, 0.11855337, 0.41220093, -0.09968963, 1.50014710],
[ 1.24004936, -0.92485696, 0.08612321, 1.15149164, -0.09276631],
[ 1.22873247, -1.46587241, -1.30802727, 0.19496460, 1.73776841],
[ 0.40092674, 0.67630458, 0.72265440, 1.31720388, -1.41899264]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.88350385, -1.14767575, 0.51043051, -0.10051888, -0.61305630],
[-0.12084112, 0.48506257, -1.13189507, 0.62806708, -0.80003673],
[ 0.51513153, -0.08890446, 0.22753835, 0.11557858, 0.78117645],
[ 1.47505593, 0.84618902, -0.38528305, -1.05887091, 0.16592593]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 1.76700771, -2.29535151, 1.02086103, -0.20103776, -1.22611260],
[-0.24168225, 0.97012514, -2.26379013, 1.25613415, -1.60007346],
[ 1.03026307, -0.17780893, 0.45507669, 0.23115715, 1.56235290],
[ 2.95011187, 1.69237804, -0.77056611, -2.11774182, 0.33185187]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.88350385, -1.14767575, 0.51043051, -0.10051888, -0.61305630],
[-0.12084112, 0.48506257, -1.13189507, 0.62806708, -0.80003673],
[ 0.51513153, -0.08890446, 0.22753835, 0.11557858, 0.78117645],
[ 1.47505593, 0.84618902, -0.38528305, -1.05887091, 0.16592593]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-1.46668839, -0.38117948, 1.18678427, 0.38740095, 0.29117522],
[-0.13538910, -0.14527084, -0.04912176, -0.26063353, 0.23640174],
[ 0.45643106, 0.60587281, -1.03242552, -0.45319262, -1.57911122],
[-0.08732958, -0.75898546, 0.14563090, -1.73751652, -0.89109969]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0., -0., 0. , 0. , 0. ],
[-0., -0., -0., -0., 0. ],
[0. , 0. , -0., -0., -0.],
[-0., -0., 0. , -0., -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-1.46668839, -0.38117948, 1.18678427, 0.38740095, 0.29117522],
[-0.13538910, -0.14527084, -0.04912176, -0.26063353, 0.23640174],
[ 0.45643106, 0.60587281, -1.03242552, -0.45319262, -1.57911122],
[-0.08732958, -0.75898546, 0.14563090, -1.73751652, -0.89109969]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.32110816, -0.76044011, 0.34456784, -0.39410326, 0.37896338],
[ 0.52747023, 0.72711533, 0.29204839, 0.72493637, 0.31128070],
[ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693],
[ 0.96669912, 0.43670532, -0.38109761, 0.78405094, -2.17882323]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0., -0., 0. , -0., 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , -0., -0., -0., -0.],
[0. , 0. , -0., 0. , -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.32110816, -0.76044011, 0.34456784, -0.39410326, 0.37896338],
[ 0.52747023, 0.72711533, 0.29204839, 0.72493637, 0.31128070],
[ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693],
[ 0.96669912, 0.43670532, -0.38109761, 0.78405094, -2.17882323]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.17168395, 0.45112833, 0.63307828, 2.38763475, -1.27247131],
[ 0.56171960, -1.09584677, 0.38300961, -0.57512099, 0.31011426],
[-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209],
[ 2.22819090, 1.12403083, -0.04198794, -1.51167727, -0.42699185]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 0. , 0. , 0. , -0.],
[0. , -0., 0. , -0., 0. ],
[-0., -0., -0., -0., -0.],
[0. , 0. , -0., -0., -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.17168395, 0.45112833, 0.63307828, 2.38763475, -1.27247131],
[ 0.56171960, -1.09584677, 0.38300961, -0.57512099, 0.31011426],
[-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209],
[ 2.22819090, 1.12403083, -0.04198794, -1.51167727, -0.42699185]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.62503546, -0.20989063, -0.22046235, -0.38679042, -1.02590704],
[ 1.04561794, 1.08428383, -0.52219963, -1.56003857, 0.89213932],
[-0.16578521, 0.14524542, -0.45563069, 0.48180851, 1.35843253],
[ 1.07669640, -0.84535235, -1.18651557, 0.79144061, -0.45565742]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , -0., -0., -0., -0.],
[0. , 0. , -0., -0., 0. ],
[-0., 0. , -0., 0. , 0. ],
[0. , -0., -0., 0. , -0.]])
Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.62503546, -0.20989063, -0.22046235, -0.38679042, -1.02590704],
[ 1.04561794, 1.08428383, -0.52219963, -1.56003857, 0.89213932],
[-0.16578521, 0.14524542, -0.45563069, 0.48180851, 1.35843253],
[ 1.07669640, -0.84535235, -1.18651557, 0.79144061, -0.45565742]])
"""
input_shape = x.shape
if len(input_shape) != 4:
......@@ -1317,14 +1401,14 @@ def dropout3d(x, p=0.5, training=True, data_format='NCDHW', name=None):
Examples:
.. code-block:: python
import paddle
>>> import paddle
x = paddle.randn(shape=(2, 3, 4, 5, 6)).astype(paddle.float32)
y_train = paddle.nn.functional.dropout3d(x) #train
y_test = paddle.nn.functional.dropout3d(x, training=False) #test
print(x[0,0,:,:,:])
print(y_train[0,0,:,:,:]) # may all 0
print(y_test[0,0,:,:,:])
>>> x = paddle.randn(shape=(2, 3, 4, 5, 6)).astype(paddle.float32)
>>> y_train = paddle.nn.functional.dropout3d(x) #train
>>> y_test = paddle.nn.functional.dropout3d(x, training=False) #test
>>> print(x[0,0,:,:,:])
>>> print(y_train[0,0,:,:,:]) # may all 0
>>> print(y_test[0,0,:,:,:])
"""
......@@ -1371,19 +1455,19 @@ def alpha_dropout(x, p=0.5, training=True, name=None):
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32)
y_train = paddle.nn.functional.alpha_dropout(x, 0.5)
y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False)
print(y_train)
# Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[-0.10721093, -0.77919382],
# [-0.10721093, 1.66559887]]) (randomly)
print(y_test)
# Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
# [[-1., 1.],
# [-1., 1.]])
>>> import paddle
>>> paddle.seed(1)
>>> x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32)
>>> y_train = paddle.nn.functional.alpha_dropout(x, 0.5)
>>> y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False)
>>> print(y_train)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.77919382, 1.66559887],
[-0.10721093, -0.77919382]])
>>> print(y_test)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-1., 1.],
[-1., 1.]])
"""
if not isinstance(p, (float, int)):
raise TypeError("p argument should be a float or int")
......@@ -1516,32 +1600,35 @@ def pad(x, pad, mode='constant', value=0.0, data_format="NCHW", name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
# example 1
x_shape = (1, 1, 3)
x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
y = F.pad(x, [0, 0, 0, 0, 2, 3], value=1, mode='constant', data_format="NCL")
print(y)
# [[[1. 1. 1. 2. 3. 1. 1. 1.]]]
# example 2
x_shape = (1, 1, 3)
x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
y = F.pad(x, [2, 3], value=1, mode='constant', data_format="NCL")
print(y)
# [[[1. 1. 1. 2. 3. 1. 1. 1.]]]
# example 3
x_shape = (1, 1, 2, 3)
x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
y = F.pad(x, [1, 2, 1, 1], value=1, mode='circular')
print(y)
# [[[[6. 4. 5. 6. 4. 5.]
# [3. 1. 2. 3. 1. 2.]
# [6. 4. 5. 6. 4. 5.]
# [3. 1. 2. 3. 1. 2.]]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> # example 1
>>> x_shape = (1, 1, 3)
>>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
>>> y = F.pad(x, [0, 0, 0, 0, 2, 3], value=1, mode='constant', data_format="NCL")
>>> print(y)
Tensor(shape=[1, 1, 8], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 1., 1., 2., 3., 1., 1., 1.]]])
>>> # example 2
>>> x_shape = (1, 1, 3)
>>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
>>> y = F.pad(x, [2, 3], value=1, mode='constant', data_format="NCL")
>>> print(y)
Tensor(shape=[1, 1, 8], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[1., 1., 1., 2., 3., 1., 1., 1.]]])
>>> # example 3
>>> x_shape = (1, 1, 2, 3)
>>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1
>>> y = F.pad(x, [1, 2, 1, 1], value=1, mode='circular')
>>> print(y)
Tensor(shape=[1, 1, 4, 6], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[6., 4., 5., 6., 4., 5.],
[3., 1., 2., 3., 1., 2.],
[6., 4., 5., 6., 4., 5.],
[3., 1., 2., 3., 1., 2.]]]])
"""
assert mode in [
'reflect',
......@@ -1713,16 +1800,18 @@ def zeropad2d(x, padding, data_format="NCHW", name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x_shape = paddle.to_tensor([1, 1, 2, 3])
x = paddle.arange(paddle.prod(x_shape), dtype="float32").reshape(x_shape) + 1
y = F.zeropad2d(x, [1, 2, 1, 1])
print(y)
# [[[[0. 0. 0. 0. 0. 0.]
# [0. 1. 2. 3. 0. 0.]
# [0. 4. 5. 6. 0. 0.]
# [0. 0. 0. 0. 0. 0.]]]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> x_shape = paddle.to_tensor([1, 1, 2, 3])
>>> x = paddle.arange(paddle.prod(x_shape), dtype="float32").reshape(x_shape) + 1
>>> y = F.zeropad2d(x, [1, 2, 1, 1])
>>> print(y)
Tensor(shape=[1, 1, 4, 6], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[0., 0., 0., 0., 0., 0.],
[0., 1., 2., 3., 0., 0.],
[0., 4., 5., 6., 0., 0.],
[0., 0., 0., 0., 0., 0.]]]])
"""
return pad(
......@@ -1767,16 +1856,17 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8):
Code Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
>>> import paddle
>>> import paddle.nn as nn
paddle.seed(1)
x1 = paddle.randn(shape=[2, 3])
x2 = paddle.randn(shape=[2, 3])
>>> paddle.seed(1)
>>> x1 = paddle.randn(shape=[2, 3])
>>> x2 = paddle.randn(shape=[2, 3])
result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0)
print(result)
# [0.97689527, 0.99996042, -0.55138415]
>>> result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0)
>>> print(result)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[ 0.97689527, 0.99996042, -0.55138415])
"""
w12 = sum(paddle.multiply(x1, x2), axis=axis)
......@@ -1822,21 +1912,29 @@ def linear(x, weight, bias=None, name=None):
Examples:
.. code-block:: python
import paddle
x = paddle.randn((3, 2), dtype="float32")
# x: [[-0.32342386 -1.200079 ]
# [ 0.7979031 -0.90978354]
# [ 0.40597573 1.8095392 ]]
weight = paddle.full(shape=[2, 4], fill_value="0.5", dtype="float32", name="weight")
# weight: [[0.5 0.5 0.5 0.5]
# [0.5 0.5 0.5 0.5]]
bias = paddle.ones(shape=[4], dtype="float32", name="bias")
# bias: [1. 1. 1. 1.]
y = paddle.nn.functional.linear(x, weight, bias)
# y: [[0.23824859 0.23824859 0.23824859 0.23824859]
# [0.9440598 0.9440598 0.9440598 0.9440598 ]
# [2.1077576 2.1077576 2.1077576 2.1077576 ]]
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.randn((3, 2), dtype="float32")
>>> print(x)
Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.06132207, 1.11349595],
[ 0.41906244, -0.24858207],
[-1.85169315, -1.50370061]])
>>> weight = paddle.full(shape=[2, 4], fill_value="0.5", dtype="float32", name="weight")
>>> print(weight)
Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.50000000, 0.50000000, 0.50000000, 0.50000000],
[0.50000000, 0.50000000, 0.50000000, 0.50000000]])
>>> bias = paddle.ones(shape=[4], dtype="float32", name="bias")
>>> print(bias)
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[1., 1., 1., 1.])
>>> y = paddle.nn.functional.linear(x, weight, bias)
>>> print(y)
Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 1.58740902, 1.58740902, 1.58740902, 1.58740902],
[ 1.08524013, 1.08524013, 1.08524013, 1.08524013],
[-0.67769694, -0.67769694, -0.67769694, -0.67769694]])
"""
if in_dynamic_mode():
# TODO(jiabin): using addmm for fast forward route
......@@ -1921,17 +2019,17 @@ def label_smooth(label, prior_dist=None, epsilon=0.1, name=None):
Examples:
.. code-block:: python
import paddle
paddle.disable_static()
>>> import paddle
>>> paddle.disable_static()
x = paddle.to_tensor([[[0, 1, 0],
[ 1, 0, 1]]], dtype="float32", stop_gradient=False)
>>> x = paddle.to_tensor([[[0, 1, 0],
>>> [ 1, 0, 1]]], dtype="float32", stop_gradient=False)
output = paddle.nn.functional.label_smooth(x)
print(output)
# Tensor(shape=[1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=False,
# [[[0.03333334, 0.93333334, 0.03333334],
# [0.93333334, 0.03333334, 0.93333334]]])
>>> output = paddle.nn.functional.label_smooth(x)
>>> print(output)
Tensor(shape=[1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=False,
[[[0.03333334, 0.93333334, 0.03333334],
[0.93333334, 0.03333334, 0.93333334]]])
"""
if epsilon > 1.0 or epsilon < 0.0:
raise ValueError("The value of epsilon must be between 0 and 1.")
......@@ -2002,67 +2100,64 @@ def class_center_sample(label, num_classes, num_samples, group=None):
.. code-block:: python
:name: code-example1
# CPU or single GPU
import paddle
num_classes = 20
batch_size = 10
num_samples = 6
label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
# the output is
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19])
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [4, 3, 0, 2, 5, 1, 6, 8, 7, 8])
#Tensor(shape=[9], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19])
>>> # CPU or single GPU
>>> import paddle
>>> num_classes = 20
>>> batch_size = 10
>>> num_samples = 6
>>> paddle.seed(2023)
>>> label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
>>> remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples)
>>> print(label)
Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True,
[17, 10, 5 , 18, 8 , 8 , 19, 14, 10, 14])
>>> print(remapped_label)
Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True,
[4, 2, 0, 5, 1, 1, 6, 3, 2, 3])
>>> print(sampled_class_index)
Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True,
[5 , 8 , 10, 14, 17, 18, 19])
.. code-block:: python
:name: code-example2
# required: distributed
# Multi GPU, test_class_center_sample.py
import paddle
import paddle.distributed as dist
strategy = dist.fleet.DistributedStrategy()
dist.fleet.init(is_collective=True, strategy=strategy)
batch_size = 10
num_samples = 6
rank_id = dist.get_rank()
# num_classes of each GPU can be different, e.g num_classes_list = [10, 8]
num_classes_list = [10, 10]
num_classes = paddle.sum(paddle.to_tensor(num_classes_list))
label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64')
label_list = []
dist.all_gather(label_list, label)
label = paddle.concat(label_list, axis=0)
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
#python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py
# rank 0 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [0, 2, 4, 8, 9, 3])
# rank 1 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [0, 1, 2, 3, 5, 7, 8])
>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> # required: distributed
>>> # Multi GPU, test_class_center_sample.py
>>> import paddle
>>> import paddle.distributed as dist
>>> strategy = dist.fleet.DistributedStrategy()
>>> dist.fleet.init(is_collective=True, strategy=strategy)
>>> batch_size = 10
>>> num_samples = 6
>>> rank_id = dist.get_rank()
>>> # num_classes of each GPU can be different, e.g num_classes_list = [10, 8]
>>> num_classes_list = [10, 10]
>>> num_classes = paddle.sum(paddle.to_tensor(num_classes_list))
>>> label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64')
>>> label_list = []
>>> dist.all_gather(label_list, label)
>>> label = paddle.concat(label_list, axis=0)
>>> remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples)
>>> print(label)
>>> print(remapped_label)
>>> print(sampled_class_index)
>>> #python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py
>>> # rank 0 output:
Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
[10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
[6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
[0, 2, 4, 8, 9, 3])
>>> # rank 1 output:
Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
[10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
[6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
[0, 1, 2, 3, 5, 7, 8])
"""
if not (group is False or group is None or hasattr(group, 'is_member')):
raise ValueError(
......@@ -2216,12 +2311,15 @@ def fold(
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.randn([2,3*2*2,12])
y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2)
# y.shape = [2,3,4,5]
>>> x = paddle.randn([2,3*2*2,12])
>>> y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2)
>>> x = paddle.randn([2,3*2*2,12])
>>> y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2)
>>> print(y.shape)
[2, 3, 4, 5]
"""
......
python/paddle/nn/functional/conv.py
浏览文件 @ 0a15b0db
......@@ -368,24 +368,24 @@ def conv1d(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
x = paddle.to_tensor([[[4, 8, 1, 9],
[7, 2, 0, 9],
[6, 9, 2, 6]]], dtype="float32")
w = paddle.to_tensor([[[9, 3, 4],
[0, 0, 7],
[2, 5, 6]],
[[0, 3, 4],
[2, 9, 7],
[5, 6, 8]]], dtype="float32")
y = F.conv1d(x, w)
print(y)
# Tensor(shape=[1, 2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[133., 238.],
# [160., 211.]]])
>>> import paddle
>>> import paddle.nn.functional as F
>>> x = paddle.to_tensor([[[4, 8, 1, 9],
... [7, 2, 0, 9],
... [6, 9, 2, 6]]], dtype="float32")
>>> w = paddle.to_tensor([[[9, 3, 4],
... [0, 0, 7],
... [2, 5, 6]],
... [[0, 3, 4],
... [2, 9, 7],
... [5, 6, 8]]], dtype="float32")
>>> y = F.conv1d(x, w)
>>> print(y)
Tensor(shape=[1, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[133., 238.],
[160., 211.]]])
"""
cudnn_version = get_cudnn_version()
if cudnn_version is not None:
......@@ -632,16 +632,16 @@ def conv2d(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
w_var = paddle.randn((6, 3, 3, 3), dtype='float32')
>>> x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
>>> w_var = paddle.randn((6, 3, 3, 3), dtype='float32')
y_var = F.conv2d(x_var, w_var)
>>> y_var = F.conv2d(x_var, w_var)
print(y_var.shape)
# [2, 6, 6, 6]
>>> print(y_var.shape)
[2, 6, 6, 6]
"""
# entry checks
if data_format not in ["NCHW", "NHWC"]:
......@@ -887,20 +887,20 @@ def conv1d_transpose(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
# shape: (1, 2, 4)
x = paddle.to_tensor([[[4, 0, 9, 7],
[8, 0, 9, 2,]]], dtype="float32")
# shape: (2, 1, 2)
w = paddle.to_tensor([[[7, 0]],
[[4, 2]]], dtype="float32")
>>> # shape: (1, 2, 4)
>>> x = paddle.to_tensor([[[4, 0, 9, 7],
>>> [8, 0, 9, 2,]]], dtype="float32")
>>> # shape: (2, 1, 2)
>>> w = paddle.to_tensor([[[7, 0]],
>>> [[4, 2]]], dtype="float32")
y = F.conv1d_transpose(x, w)
print(y)
# Tensor(shape=[1, 1, 5], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[60., 16., 99., 75., 4. ]]])
>>> y = F.conv1d_transpose(x, w)
>>> print(y)
Tensor(shape=[1, 1, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[60., 16., 99., 75., 4. ]]])
"""
cudnn_version = get_cudnn_version()
if cudnn_version is not None:
......@@ -1183,16 +1183,16 @@ def conv2d_transpose(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
>>> x_var = paddle.randn((2, 3, 8, 8), dtype='float32')
>>> w_var = paddle.randn((3, 6, 3, 3), dtype='float32')
y_var = F.conv2d_transpose(x_var, w_var)
>>> y_var = F.conv2d_transpose(x_var, w_var)
print(y_var.shape)
# [2, 6, 10, 10]
>>> print(y_var.shape)
[2, 6, 10, 10]
"""
if data_format not in ['NCHW', 'NHWC']:
......@@ -1476,16 +1476,16 @@ def conv3d(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
w_var = paddle.randn((6, 3, 3, 3, 3), dtype='float32')
>>> x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
>>> w_var = paddle.randn((6, 3, 3, 3, 3), dtype='float32')
y_var = F.conv3d(x_var, w_var)
>>> y_var = F.conv3d(x_var, w_var)
print(y_var.shape)
# [2, 6, 6, 6, 6]
>>> print(y_var.shape)
[2, 6, 6, 6, 6]
"""
# entry check
if data_format not in ["NCDHW", "NDHWC"]:
......@@ -1688,18 +1688,18 @@ def conv3d_transpose(
variable storing transposed convolution and non-linearity activation result.
Examples:
.. code-block:: python
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
>>> x_var = paddle.randn((2, 3, 8, 8, 8), dtype='float32')
>>> w_var = paddle.randn((3, 6, 3, 3, 3), dtype='float32')
y_var = F.conv3d_transpose(x_var, w_var)
>>> y_var = F.conv3d_transpose(x_var, w_var)
print(y_var.shape)
# [2, 6, 10, 10, 10]
>>> print(y_var.shape)
[2, 6, 10, 10, 10]
"""
# entry checks
if data_format not in ["NCDHW", "NDHWC"]:
......
python/paddle/nn/functional/distance.py
浏览文件 @ 0a15b0db
......@@ -59,14 +59,13 @@ def pairwise_distance(x, y, p=2.0, epsilon=1e-6, keepdim=False, name=None):
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1., 3.], [3., 5.]], dtype=paddle.float64)
y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
distance = paddle.nn.functional.pairwise_distance(x, y)
print(distance)
# Tensor(shape=[2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
# [4.99999860, 4.99999860])
>>> import paddle
>>> x = paddle.to_tensor([[1., 3.], [3., 5.]], dtype=paddle.float64)
>>> y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
>>> distance = paddle.nn.functional.pairwise_distance(x, y)
>>> print(distance)
Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
[4.99999860, 4.99999860])
"""
if in_dynamic_mode():
sub = _C_ops.subtract(x, y)
......
python/paddle/nn/functional/extension.py
浏览文件 @ 0a15b0db
......@@ -55,48 +55,46 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
diag_embed_input = paddle.arange(6)
diag_embed_output1 = F.diag_embed(diag_embed_input)
print(diag_embed_output1)
# Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True,
# [[0, 0, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0],
# [0, 0, 2, 0, 0, 0],
# [0, 0, 0, 3, 0, 0],
# [0, 0, 0, 0, 4, 0],
# [0, 0, 0, 0, 0, 5]])
diag_embed_output2 = F.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 )
print(diag_embed_output2)
# Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True,
# [[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0, 0],
# [0, 0, 2, 0, 0, 0, 0],
# [0, 0, 0, 3, 0, 0, 0],
# [0, 0, 0, 0, 4, 0, 0],
# [0, 0, 0, 0, 0, 5, 0]])
diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3])
print(diag_embed_input_2dim)
# Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
# [[0, 1, 2],
# [3, 4, 5]])
diag_embed_output3 = F.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 )
print(diag_embed_output3)
# Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
# [[[0, 0, 0],
# [3, 0, 0]],
# [[0, 1, 0],
# [0, 4, 0]],
# [[0, 0, 2],
# [0, 0, 5]]])
>>> import paddle
>>> import paddle.nn.functional as F
>>> diag_embed_input = paddle.arange(6)
>>> diag_embed_output1 = F.diag_embed(diag_embed_input)
>>> print(diag_embed_output1)
Tensor(shape=[6, 6], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0],
[0, 0, 0, 3, 0, 0],
[0, 0, 0, 0, 4, 0],
[0, 0, 0, 0, 0, 5]])
>>> diag_embed_output2 = F.diag_embed(diag_embed_input, offset=-1, dim1=0,dim2=1 )
>>> print(diag_embed_output2)
Tensor(shape=[7, 7], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0, 0],
[0, 0, 0, 3, 0, 0, 0],
[0, 0, 0, 0, 4, 0, 0],
[0, 0, 0, 0, 0, 5, 0]])
>>> diag_embed_input_2dim = paddle.reshape(diag_embed_input,[2,3])
>>> print(diag_embed_input_2dim)
Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[0, 1, 2],
[3, 4, 5]])
>>> diag_embed_output3 = F.diag_embed(diag_embed_input_2dim,offset= 0, dim1=0, dim2=2 )
>>> print(diag_embed_output3)
Tensor(shape=[3, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
[[[0, 0, 0],
[3, 0, 0]],
[[0, 1, 0],
[0, 4, 0]],
[[0, 0, 2],
[0, 0, 5]]])
"""
if not isinstance(input, Variable):
input = assign(input)
......@@ -200,16 +198,16 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
Examples:
.. code-block:: python
import paddle
>>> import paddle
lengths = paddle.to_tensor([10, 9, 8])
mask = paddle.nn.functional.sequence_mask(lengths)
>>> lengths = paddle.to_tensor([10, 9, 8])
>>> mask = paddle.nn.functional.sequence_mask(lengths)
print(mask)
# Tensor(shape=[3, 10], dtype=int64, place=Place(gpu:0), stop_gradient=True,
# [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
# [1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
>>> print(mask)
Tensor(shape=[3, 10], dtype=int64, place=Place(cpu), stop_gradient=True,
[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])
"""
......@@ -296,14 +294,24 @@ def gather_tree(ids, parents):
Examples:
.. code-block:: python
import paddle
>>> import paddle
ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]])
>>> ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]])
parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]])
>>> parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]])
>>> final_sequences = paddle.nn.functional.gather_tree(ids, parents)
>>> [[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]]
>>> final_sequences = paddle.nn.functional.gather_tree(ids, parents)
>>> print(final_sequences)
Tensor(shape=[3, 2, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
[[[2, 2],
[1, 6]],
[[3, 3],
[6, 1]],
[[0, 1],
[9, 0]]])
final_sequences = paddle.nn.functional.gather_tree(ids, parents)
# [[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]]
"""
if ids.ndim != 3:
......@@ -388,11 +396,11 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None, data_format="NCHW"):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
input = paddle.randn([6, 4, 2, 2])
out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
>>> input = paddle.randn([6, 4, 2, 2])
>>> out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
"""
if data_format not in ["NCHW", "NHWC"]:
raise ValueError(
......
python/paddle/nn/functional/flash_attention.py
浏览文件 @ 0a15b0db
......@@ -181,13 +181,12 @@ def flash_attention(
Examples:
.. code-block:: python
# required: skiptest
import paddle
>>> import paddle
q = paddle.rand((1, 128, 2, 16), dtype=paddle.float16)
>>> paddle.seed(1)
>>> q = paddle.rand((1, 128, 2, 16))
output = paddle.nn.functional.flash_attention(q, q, q, 0.9, False, False)
print(output)
>>> output = paddle.nn.functional.flash_attention.flash_attention(q, q, q, 0.9, False, False)
"""
head_dim = query.shape[3]
sdp_func_name = _select_sdp(head_dim)
......@@ -340,13 +339,12 @@ def flash_attn_unpadded(
Examples:
.. code-block:: python
# required: skiptest
import paddle
q = paddle.rand((1, 128, 2, 16), dtype=paddle.float16)
>>> import paddle
>>> paddle.seed(1)
>>> q = paddle.rand((1, 128, 2, 16))
output = paddle.nn.functional.flash_attn_unpadded(q, q, q, 0.9, False, False)
print(output)
>>> output = paddle.nn.functional.flash_attention.flash_attn_unpadded(q, q, q, 0.9, False, False)
>>> print(output)
"""
if in_dynamic_mode():
(
......
python/paddle/nn/initializer/uniform.py
浏览文件 @ 0a15b0db
......@@ -158,24 +158,33 @@ class Uniform(UniformInitializer):
Examples:
.. code-block:: python
import paddle
data = paddle.ones(shape=[3, 1, 2], dtype='float32')
weight_attr = paddle.framework.ParamAttr(
name="linear_weight",
initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5))
bias_attr = paddle.framework.ParamAttr(
name="linear_bias",
initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5))
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
# linear.weight: [[-0.46245047 0.05260676]
# [ 0.38054508 0.29169726]]
# linear.bias: [-0.2734719 0.23939109]
res = linear(data)
# res: [[[-0.3553773 0.5836951]]
# [[-0.3553773 0.5836951]]
# [[-0.3553773 0.5836951]]]
>>> import paddle
>>> paddle.seed(1)
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5))
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.Uniform(low=-0.5, high=0.5))
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Parameter containing:
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[-0.48212373, 0.26492310],
[ 0.17605734, -0.45379421]])
>>> print(linear.bias)
Parameter containing:
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
[-0.11236754, 0.46462214])
>>> res = linear(data)
>>> print(res)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[[-0.41843393, 0.27575102]],
[[-0.41843393, 0.27575102]],
[[-0.41843393, 0.27575102]]])
"""
def __init__(self, low=-1.0, high=1.0, name=None):
......
python/paddle/nn/initializer/xavier.py
浏览文件 @ 0a15b0db
......@@ -214,24 +214,33 @@ class XavierNormal(XavierInitializer):
Examples:
.. code-block:: python
import paddle
data = paddle.ones(shape=[3, 1, 2], dtype='float32')
weight_attr = paddle.framework.ParamAttr(
name="linear_weight",
initializer=paddle.nn.initializer.XavierNormal())
bias_attr = paddle.framework.ParamAttr(
name="linear_bias",
initializer=paddle.nn.initializer.XavierNormal())
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
# inear.weight: [[ 0.06910077 -0.18103665]
# [-0.02546741 -1.0402188 ]]
# linear.bias: [-0.5012929 0.12418364]
res = linear(data)
# res: [[[-0.4576595 -1.0970719]]
# [[-0.4576595 -1.0970719]]
# [[-0.4576595 -1.0970719]]]
>>> import paddle
>>> paddle.seed(1)
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.XavierNormal())
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.XavierNormal())
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Parameter containing:
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[-0.21607460, 0.08382989],
[ 0.29147008, -0.07049121]])
>>> print(linear.bias)
Parameter containing:
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
[1.06076419, 0.87684733])
>>> res = linear(data)
>>> print(res)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[[1.13615966, 0.89018601]],
[[1.13615966, 0.89018601]],
[[1.13615966, 0.89018601]]])
"""
def __init__(self, fan_in=None, fan_out=None, name=None):
......@@ -266,24 +275,32 @@ class XavierUniform(XavierInitializer):
Examples:
.. code-block:: python
import paddle
data = paddle.ones(shape=[3, 1, 2], dtype='float32')
weight_attr = paddle.framework.ParamAttr(
name="linear_weight",
initializer=paddle.nn.initializer.XavierUniform())
bias_attr = paddle.framework.ParamAttr(
name="linear_bias",
initializer=paddle.nn.initializer.XavierUniform())
linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
# linear.weight: [[-0.04229349 -1.1248565 ]
# [-0.10789523 -0.5938053 ]]
# linear.bias: [ 1.1983747 -0.40201235]
res = linear(data)
# res: [[[ 1.0481861 -2.1206741]]
# [[ 1.0481861 -2.1206741]]
# [[ 1.0481861 -2.1206741]]]
>>> import paddle
>>> paddle.seed(1)
>>> data = paddle.ones(shape=[3, 1, 2], dtype='float32')
>>> weight_attr = paddle.framework.ParamAttr(
... name="linear_weight",
... initializer=paddle.nn.initializer.XavierUniform())
>>> bias_attr = paddle.framework.ParamAttr(
... name="linear_bias",
... initializer=paddle.nn.initializer.XavierUniform())
>>> linear = paddle.nn.Linear(2, 2, weight_attr=weight_attr, bias_attr=bias_attr)
>>> print(linear.weight)
Parameter containing:
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[-1.18095720, 0.64892638],
[ 0.43125069, -1.11156428]])
>>> print(linear.bias)
Parameter containing:
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=False,
[-0.27524316, 1.13808715])
>>> res = linear(data)
>>> print(res)
Tensor(shape=[3, 1, 2], dtype=float32, place=Place(cpu), stop_gradient=False,
[[[-1.02494967, 0.67544925]],
[[-1.02494967, 0.67544925]],
[[-1.02494967, 0.67544925]]])
"""
def __init__(self, fan_in=None, fan_out=None, name=None):
......
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