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未验证 提交 875a4ea9 编写于 作者: iSerendipity's avatar iSerendipity 提交者： GitHub
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[xdoctest][task 79] reformat example code with google style in ... 

[xdoctest][task 79] reformat example code with google style in  `python/paddle/nn/functional/loss.py` (#56901)

* [Doctest] fix No.79, test=docs_preview

* Apply suggestions from code review

* fix gpu

* fix

* fix

---------
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>
上级 4dbe441c
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Showing with 613 addition and 537 deletion
python/paddle/nn/functional/loss.py
浏览文件 @ 875a4ea9
......@@ -66,13 +66,13 @@ def dice_loss(input, label, epsilon=0.00001, name=None):
Example:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
x = paddle.randn((3,224,224,2))
label = paddle.randint(high=2, shape=(3,224,224,1))
predictions = F.softmax(x)
loss = F.dice_loss(input=predictions, label=label)
>>> x = paddle.randn((3,224,224,2))
>>> label = paddle.randint(high=2, shape=(3,224,224,1))
>>> predictions = F.softmax(x)
>>> loss = F.dice_loss(input=predictions, label=label)
"""
assert input.dtype in (paddle.float32, paddle.float64)
assert label.dtype in (paddle.int32, paddle.int64)
......@@ -136,12 +136,12 @@ def log_loss(input, label, epsilon=1e-4, name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
label = paddle.randn((10,1))
prob = paddle.randn((10,1))
cost = F.log_loss(input=prob, label=label)
>>> label = paddle.randn((10,1))
>>> prob = paddle.randn((10,1))
>>> cost = F.log_loss(input=prob, label=label)
"""
if in_dynamic_mode():
return _C_ops.log_loss(input, label, epsilon)
......@@ -244,15 +244,16 @@ def fluid_softmax_with_cross_entropy(
Examples:
.. code-block:: python
import paddle
>>> import paddle
>>> paddle.seed(2023)
logits = paddle.to_tensor([0.4, 0.6, 0.9])
label = paddle.randint(high=2, shape=[1], dtype="int64")
>>> logits = paddle.to_tensor([0.4, 0.6, 0.9])
>>> label = paddle.randint(high=2, shape=[1], dtype="int64")
out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label)
print(out)
# Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [1.15328646])
>>> out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label)
>>> print(out)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[1.15328646])
"""
input_dims = len(list(logits.shape))
if input_dims == 0:
......@@ -333,16 +334,18 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
.. code-block:: python
import paddle
>>> import paddle
>>> DATATYPE = "float32"
>>> paddle.seed(2023)
DATATYPE = "float32"
>>> anchor = paddle.rand(shape=(18, 6), dtype=DATATYPE)
>>> positive = paddle.rand(shape=(18, 6), dtype=DATATYPE)
>>> labels = paddle.rand(shape=(18,), dtype=DATATYPE)
anchor = paddle.rand(shape=(18, 6), dtype=DATATYPE)
positive = paddle.rand(shape=(18, 6), dtype=DATATYPE)
labels = paddle.rand(shape=(18,), dtype=DATATYPE)
npair_loss = paddle.nn.functional.npair_loss(anchor, positive, labels, l2_reg = 0.002)
print(npair_loss)
>>> npair_loss = paddle.nn.functional.npair_loss(anchor, positive, labels, l2_reg = 0.002)
>>> print(npair_loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.94269347)
"""
if anchor.size == 0:
......@@ -410,12 +413,13 @@ def square_error_cost(input, label):
.. code-block:: python
import paddle
input = paddle.to_tensor([1.1, 1.9])
label = paddle.to_tensor([1.0, 2.0])
output = paddle.nn.functional.square_error_cost(input, label)
print(output)
# [0.01, 0.01]
>>> import paddle
>>> input = paddle.to_tensor([1.1, 1.9])
>>> label = paddle.to_tensor([1.0, 2.0])
>>> output = paddle.nn.functional.square_error_cost(input, label)
>>> print(output)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.01000000, 0.01000000])
"""
if in_dynamic_mode():
......@@ -496,29 +500,35 @@ def edit_distance(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.to_tensor([[1,2,3],[4,5,6],[4,4,4],[1,1,1]], dtype='int64')
label = paddle.to_tensor([[1,3,4,1],[4,5,8,1],[7,7,7,1],[1,1,1,1]], dtype='int64')
input_len = paddle.to_tensor([3,3,3,3], dtype='int64')
label_len = paddle.to_tensor([4,4,4,4], dtype='int64')
distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=False)
# print(distance)
# [[3.]
# [2.]
# [4.]
# [1.]]
# if set normalized to True
# [[0.75]
# [0.5 ]
# [1. ]
# [0.25]
#
# print(sequence_num)
# [4]
>>> import paddle
>>> import paddle.nn.functional as F
>>> input = paddle.to_tensor([[1,2,3],[4,5,6],[4,4,4],[1,1,1]], dtype='int64')
>>> label = paddle.to_tensor([[1,3,4,1],[4,5,8,1],[7,7,7,1],[1,1,1,1]], dtype='int64')
>>> input_len = paddle.to_tensor([3,3,3,3], dtype='int64')
>>> label_len = paddle.to_tensor([4,4,4,4], dtype='int64')
>>> distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=False)
>>> print(distance)
Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True,
[4])
>>> print(sequence_num)
Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[3.],
[2.],
[4.],
[1.]])
>>> distance, sequence_num = F.loss.edit_distance(input=input, label=label, input_length=input_len, label_length=label_len, normalized=True)
>>> print(distance)
Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True,
[4])
>>> print(sequence_num)
Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.75000000],
[0.50000000],
[1. ],
[0.25000000]])
"""
......@@ -629,12 +639,14 @@ def binary_cross_entropy(
Examples:
.. code-block:: python
import paddle
>>> import paddle
input = paddle.to_tensor([0.5, 0.6, 0.7], 'float32')
label = paddle.to_tensor([1.0, 0.0, 1.0], 'float32')
output = paddle.nn.functional.binary_cross_entropy(input, label)
print(output) # 0.65537095
>>> input = paddle.to_tensor([0.5, 0.6, 0.7], 'float32')
>>> label = paddle.to_tensor([1.0, 0.0, 1.0], 'float32')
>>> output = paddle.nn.functional.binary_cross_entropy(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.65537095)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -769,12 +781,14 @@ def binary_cross_entropy_with_logits(
.. code-block:: python
import paddle
>>> import paddle
logit = paddle.to_tensor([5.0, 1.0, 3.0])
label = paddle.to_tensor([1.0, 0.0, 1.0])
output = paddle.nn.functional.binary_cross_entropy_with_logits(logit, label)
print(output) # 0.45618808
>>> logit = paddle.to_tensor([5.0, 1.0, 3.0])
>>> label = paddle.to_tensor([1.0, 0.0, 1.0])
>>> output = paddle.nn.functional.binary_cross_entropy_with_logits(logit, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.45618808)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -933,29 +947,36 @@ def hsigmoid_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
paddle.set_device('cpu')
input = paddle.uniform([4, 3])
# [[0.45424712 -0.77296764 0.82943869] # random
# [0.85062802 0.63303483 0.35312140] # random
# [0.57170701 0.16627562 0.21588242] # random
# [0.27610803 -0.99303514 -0.17114788]] # random
label = paddle.to_tensor([0, 1, 4, 5])
num_classes = 5
weight=paddle.uniform([num_classes-1, 3])
# [[-0.64477652 0.24821866 -0.17456549] # random
# [-0.04635394 0.07473493 -0.25081766] # random
# [ 0.05986035 -0.12185556 0.45153677] # random
# [-0.66236806 0.91271877 -0.88088769]] # random
out=F.hsigmoid_loss(input, label, num_classes, weight)
# [[1.96709502]
# [2.40019274]
# [2.11009121]
# [1.92374969]]
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.set_device('cpu')
>>> paddle.seed(2023)
>>> input = paddle.uniform([4, 3])
>>> print(input)
Tensor(shape=[4, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.73167229, 0.04029441, -0.48078126],
[ 0.81050646, -0.15199822, -0.18717426],
[ 0.94041789, 0.48874724, 0.03570259],
[ 0.46585739, 0.95573163, -0.91368192]])
>>> label = paddle.to_tensor([0, 1, 4, 5])
>>> num_classes = 5
>>> weight = paddle.uniform([num_classes - 1, 3])
>>> print(weight)
Tensor(shape=[4, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.14721161, 0.43916738, -0.58377075],
[-0.60536981, -0.23151302, -0.70793629],
[-0.54572451, -0.10784978, -0.56684279],
[ 0.35370791, -0.07079649, 0.84765708]])
>>> out = F.hsigmoid_loss(input, label, num_classes, weight)
>>> print(out)
Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[2.23681736],
[1.97140026],
[1.66425037],
[2.54727197]])
"""
if num_classes < 2:
raise ValueError(f'Expected num_classes >= 2 (got {num_classes})')
......@@ -1067,13 +1088,16 @@ def smooth_l1_loss(input, label, reduction='mean', delta=1.0, name=None):
Examples:
.. code-block:: python
import paddle
>>> import paddle
>>> paddle.seed(2023)
>>> input = paddle.rand([3, 3]).astype('float32')
>>> label = paddle.rand([3, 3]).astype('float32')
>>> output = paddle.nn.functional.smooth_l1_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.08307374)
input = paddle.rand([3, 3]).astype('float32')
label = paddle.rand([3, 3]).astype('float32')
output = paddle.nn.functional.smooth_l1_loss(input, label)
print(output)
# 0.068004
"""
if in_dynamic_mode():
......@@ -1155,13 +1179,16 @@ def margin_ranking_loss(
.. code-block:: python
import paddle
>>> import paddle
>>> input = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32')
>>> other = paddle.to_tensor([[2, 1], [2, 4]], dtype='float32')
>>> label = paddle.to_tensor([[1, -1], [-1, -1]], dtype='float32')
>>> loss = paddle.nn.functional.margin_ranking_loss(input, other, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.75000000)
input = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32')
other = paddle.to_tensor([[2, 1], [2, 4]], dtype='float32')
label = paddle.to_tensor([[1, -1], [-1, -1]], dtype='float32')
loss = paddle.nn.functional.margin_ranking_loss(input, other, label)
print(loss) # 0.75
"""
if reduction not in ['sum', 'mean', 'none']:
raise ValueError(
......@@ -1271,26 +1298,26 @@ def l1_loss(input, label, reduction='mean', name=None):
Examples:
.. code-block:: python
import paddle
>>> import paddle
input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]])
label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]])
>>> input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]])
>>> label = paddle.to_tensor([[1.7, 1], [0.4, 0.5]])
l1_loss = paddle.nn.functional.l1_loss(input, label)
print(l1_loss)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.34999999)
>>> l1_loss = paddle.nn.functional.l1_loss(input, label)
>>> print(l1_loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.34999999)
l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='none')
print(l1_loss)
# Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[0.20000005, 0.19999999],
# [0.20000000, 0.79999995]])
>>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='none')
>>> print(l1_loss)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.20000005, 0.19999999],
[0.20000000, 0.79999995]])
l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='sum')
print(l1_loss)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 1.39999998)
>>> l1_loss = paddle.nn.functional.l1_loss(input, label, reduction='sum')
>>> print(l1_loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.39999998)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -1367,19 +1394,22 @@ def nll_loss(
Examples:
.. code-block:: python
import paddle
from paddle.nn.functional import nll_loss
log_softmax = paddle.nn.LogSoftmax(axis=1)
input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ],
[0.53331435, 0.07999352, 0.8549948 ],
[0.25879037, 0.39530203, 0.698465 ],
[0.73427284, 0.63575995, 0.18827209],
[0.05689114, 0.0862954 , 0.6325046 ]], "float32")
log_out = log_softmax(input)
label = paddle.to_tensor([0, 2, 1, 1, 0], "int64")
result = nll_loss(log_out, label)
print(result) # Tensor(shape=[], dtype=float32, place=CPUPlace, stop_gradient=True, 1.07202101)
>>> import paddle
>>> from paddle.nn.functional import nll_loss
>>> log_softmax = paddle.nn.LogSoftmax(axis=1)
>>> input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ],
... [0.53331435, 0.07999352, 0.8549948 ],
... [0.25879037, 0.39530203, 0.698465 ],
... [0.73427284, 0.63575995, 0.18827209],
... [0.05689114, 0.0862954 , 0.6325046 ]], "float32")
>>> log_out = log_softmax(input)
>>> label = paddle.to_tensor([0, 2, 1, 1, 0], "int64")
>>> result = nll_loss(log_out, label)
>>> print(result)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.07202101)
"""
if reduction not in ['sum', 'mean', 'none']:
raise ValueError(
......@@ -1502,15 +1532,24 @@ def poisson_nll_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.randn([5, 2], dtype=paddle.float32)
label = paddle.randn([5, 2], dtype=paddle.float32)
loss = F.poisson_nll_loss(input, label, log_input=True, reduction='none')
print(loss)
loss = F.poisson_nll_loss(input, label, reduction='mean')
print(loss)
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(2023)
>>> input = paddle.randn([5, 2], dtype=paddle.float32)
>>> label = paddle.randn([5, 2], dtype=paddle.float32)
>>> loss = F.poisson_nll_loss(input, label, log_input=True, reduction='none')
>>> print(loss)
Tensor(shape=[5, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 1.09998012, 3.68829036],
[ 1.95291090, 0.69603068],
[-0.39289063, -2.03713036],
[ 4.52518702, 1.28625548],
[ 3.94454789, 0.53521496]])
>>> loss = F.poisson_nll_loss(input, label, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.52983975)
"""
# check parameter values
......@@ -1606,31 +1645,36 @@ def kl_div(input, label, reduction='mean', name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(2023)
shape = (5, 20)
>>> shape = (5, 20)
# input(x) should be a distribution in the log space
x = F.log_softmax(paddle.randn(shape), axis=1).astype('float32')
>>> # input(x) should be a distribution in the log space
>>> x = F.log_softmax(paddle.randn(shape), axis=1).astype('float32')
target = paddle.uniform(shape, min=-10, max=10).astype('float32')
>>> target = paddle.uniform(shape, min=-10, max=10).astype('float32')
# 'batchmean' reduction, loss shape will be [], who is 0-D Tensor
pred_loss = F.kl_div(x, target, reduction='batchmean')
# shape=[]
>>> # 'batchmean' reduction, loss shape will be [], who is 0-D Tensor
>>> pred_loss = F.kl_div(x, target, reduction='batchmean')
>>> print(pred_loss.shape)
[]
# 'mean' reduction, loss shape will be [], who is 0-D Tensor
pred_loss = F.kl_div(x, target, reduction='mean')
# shape=[]
>>> # 'mean' reduction, loss shape will be [], who is 0-D Tensor
>>> pred_loss = F.kl_div(x, target, reduction='mean')
>>> print(pred_loss.shape)
[]
# 'sum' reduction, loss shape will be [], who is 0-D Tensor
pred_loss = F.kl_div(x, target, reduction='sum')
# shape=[]
>>> # 'sum' reduction, loss shape will be [], who is 0-D Tensor
>>> pred_loss = F.kl_div(x, target, reduction='sum')
>>> print(pred_loss.shape)
[]
# 'none' reduction, loss shape is same with input shape
pred_loss = F.kl_div(x, target, reduction='none')
# shape=[5, 20]
>>> # 'none' reduction, loss shape is same with input shape
>>> pred_loss = F.kl_div(x, target, reduction='none')
>>> print(pred_loss.shape)
[5, 20]
"""
# ugly type promotion
......@@ -1723,13 +1767,14 @@ def mse_loss(input, label, reduction='mean', name=None):
.. code-block:: python
import paddle
mse_loss = paddle.nn.loss.MSELoss()
input = paddle.to_tensor(1.5)
label = paddle.to_tensor(1.7)
output = mse_loss(input, label)
print(output)
# 0.04000002
>>> import paddle
>>> mse_loss = paddle.nn.loss.MSELoss()
>>> input = paddle.to_tensor(1.5)
>>> label = paddle.to_tensor(1.7)
>>> output = mse_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.04000002)
"""
......@@ -1791,56 +1836,54 @@ def ctc_loss(
.. code-block:: python
# declarative mode
import paddle.nn.functional as F
import paddle
# length of the longest logit sequence
max_seq_length = 4
#length of the longest label sequence
max_label_length = 3
# number of logit sequences
batch_size = 2
# class num
class_num = 3
log_probs = paddle.to_tensor([[[4.17021990e-01, 7.20324516e-01, 1.14374816e-04],
[3.02332580e-01, 1.46755889e-01, 9.23385918e-02]],
[[1.86260208e-01, 3.45560730e-01, 3.96767467e-01],
[5.38816750e-01, 4.19194520e-01, 6.85219526e-01]],
[[2.04452246e-01, 8.78117442e-01, 2.73875929e-02],
[6.70467496e-01, 4.17304814e-01, 5.58689833e-01]],
[[1.40386939e-01, 1.98101491e-01, 8.00744593e-01],
[9.68261600e-01, 3.13424170e-01, 6.92322612e-01]],
[[8.76389146e-01, 8.94606650e-01, 8.50442126e-02],
[3.90547849e-02, 1.69830427e-01, 8.78142476e-01]]],
dtype="float32")
labels = paddle.to_tensor([[1, 2, 2],
[1, 2, 2]], dtype="int32")
input_lengths = paddle.to_tensor([5, 5], dtype="int64")
label_lengths = paddle.to_tensor([3, 3], dtype="int64")
loss = F.ctc_loss(log_probs, labels,
input_lengths,
label_lengths,
blank=0,
reduction='none')
print(loss)
# Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [3.91798496, 2.90765190])
loss = F.ctc_loss(log_probs, labels,
input_lengths,
label_lengths,
blank=0,
reduction='mean')
print(loss)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 1.13760614)
>>> # declarative mode
>>> import paddle.nn.functional as F
>>> import paddle
>>> import numpy as np
>>> # length of the longest logit sequence
>>> max_seq_length = 4
>>> #length of the longest label sequence
>>> max_label_length = 3
>>> # number of logit sequences
>>> batch_size = 2
>>> # class num
>>> class_num = 3
>>> log_probs = paddle.to_tensor(np.array([
... [[4.17021990e-01, 7.20324516e-01, 1.14374816e-04],
... [3.02332580e-01, 1.46755889e-01, 9.23385918e-02]],
... [[1.86260208e-01, 3.45560730e-01, 3.96767467e-01],
... [5.38816750e-01, 4.19194520e-01, 6.85219526e-01]],
... [[2.04452246e-01, 8.78117442e-01, 2.73875929e-02],
... [6.70467496e-01, 4.17304814e-01, 5.58689833e-01]],
... [[1.40386939e-01, 1.98101491e-01, 8.00744593e-01],
... [9.68261600e-01, 3.13424170e-01, 6.92322612e-01]],
... [[8.76389146e-01, 8.94606650e-01, 8.50442126e-02],
... [3.90547849e-02, 1.69830427e-01, 8.78142476e-01]]
... ]), dtype="float32")
>>> labels = paddle.to_tensor([[1, 2, 2],
... [1, 2, 2]], dtype="int32")
>>> input_lengths = paddle.to_tensor([5, 5], dtype="int64")
>>> label_lengths = paddle.to_tensor([3, 3], dtype="int64")
>>> loss = F.ctc_loss(log_probs, labels,
... input_lengths,
... label_lengths,
... blank=0,
... reduction='none')
>>> print(loss)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[3.91798496, 2.90765190])
>>> loss = F.ctc_loss(log_probs, labels,
... input_lengths,
... label_lengths,
... blank=0,
... reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.13760614)
"""
......@@ -1940,34 +1983,37 @@ def rnnt_loss(
.. code-block:: python
# declarative mode
import paddle.nn.functional as F
import numpy as np
import paddle
import functools
fn = functools.partial(F.rnnt_loss, reduction='sum', fastemit_lambda=0.0, blank=0)
acts = np.array([[[[0.1, 0.6, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.6, 0.1, 0.1],
[0.1, 0.1, 0.2, 0.8, 0.1]],
[[0.1, 0.6, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.2, 0.1, 0.1],
[0.7, 0.1, 0.2, 0.1, 0.1]]]])
labels = [[1, 2]]
acts = paddle.to_tensor(acts, stop_gradient=False)
lengths = [acts.shape[1]] * acts.shape[0]
label_lengths = [len(l) for l in labels]
labels = paddle.to_tensor(labels, paddle.int32)
lengths = paddle.to_tensor(lengths, paddle.int32)
label_lengths = paddle.to_tensor(label_lengths, paddle.int32)
costs = fn(acts, labels, lengths, label_lengths)
print(costs)
# Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=False,
# 4.49566677)
>>> # declarative mode
>>> import paddle.nn.functional as F
>>> import numpy as np
>>> import paddle
>>> import functools
>>> fn = functools.partial(F.rnnt_loss, reduction='sum', fastemit_lambda=0.0, blank=0)
>>> acts = np.array([[
... [[0.1, 0.6, 0.1, 0.1, 0.1],
... [0.1, 0.1, 0.6, 0.1, 0.1],
... [0.1, 0.1, 0.2, 0.8, 0.1]],
... [[0.1, 0.6, 0.1, 0.1, 0.1],
... [0.1, 0.1, 0.2, 0.1, 0.1],
... [0.7, 0.1, 0.2, 0.1, 0.1]]
... ]])
>>> labels = [[1, 2]]
>>> acts = paddle.to_tensor(acts, stop_gradient=False)
>>> lengths = [acts.shape[1]] * acts.shape[0]
>>> label_lengths = [len(l) for l in labels]
>>> labels = paddle.to_tensor(labels, paddle.int32)
>>> lengths = paddle.to_tensor(lengths, paddle.int32)
>>> label_lengths = paddle.to_tensor(label_lengths, paddle.int32)
>>> costs = fn(acts, labels, lengths, label_lengths)
>>> print(costs)
Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=False,
-2.85042444)
"""
def warprnnt(
......@@ -2090,141 +2136,145 @@ def margin_cross_entropy(
.. code-block:: python
:name: code-example1
# required: gpu
# Single GPU
import paddle
m1 = 1.0
m2 = 0.5
m3 = 0.0
s = 64.0
batch_size = 2
feature_length = 4
num_classes = 4
label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
X = paddle.randn(
shape=[batch_size, feature_length],
dtype='float64')
X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
X = paddle.divide(X, X_l2)
W = paddle.randn(
shape=[feature_length, num_classes],
dtype='float64')
W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
W = paddle.divide(W, W_l2)
logits = paddle.matmul(X, W)
loss, softmax = paddle.nn.functional.margin_cross_entropy(
logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
print(logits)
print(label)
print(loss)
print(softmax)
#Tensor(shape=[2, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[ 0.85204151, -0.55557678, 0.04994566, 0.71986042],
# [-0.20198586, -0.35270476, -0.55182702, 0.09749021]])
#Tensor(shape=[2], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [2, 3])
#Tensor(shape=[2, 1], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[82.37059586],
# [12.13448420]])
#Tensor(shape=[2, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[0.99978819, 0.00000000, 0.00000000, 0.00021181],
# [0.99992995, 0.00006468, 0.00000000, 0.00000537]])
>>> # doctest: +REQUIRES(env:GPU)
>>> import paddle
>>> paddle.seed(2023)
>>> paddle.device.set_device('gpu')
>>> m1 = 1.0
>>> m2 = 0.5
>>> m3 = 0.0
>>> s = 64.0
>>> batch_size = 2
>>> feature_length = 4
>>> num_classes = 4
>>> label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
>>> X = paddle.randn(
... shape=[batch_size, feature_length],
... dtype='float64')
>>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
>>> X = paddle.divide(X, X_l2)
>>> W = paddle.randn(
... shape=[feature_length, num_classes],
... dtype='float64')
>>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
>>> W = paddle.divide(W, W_l2)
>>> logits = paddle.matmul(X, W)
>>> loss, softmax = paddle.nn.functional.margin_cross_entropy(
... logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
>>> print(logits)
Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
[[-0.59561850, 0.32797505, 0.80279214, 0.00144975],
[-0.16265212, 0.84155098, 0.62008629, 0.79126072]])
>>> print(label)
Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
[1, 0])
>>> print(loss)
Tensor(shape=[2, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
[[61.94391901],
[93.30853839]])
>>> print(softmax)
Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
[[0.00000000, 0.00000000, 1. , 0.00000000],
[0.00000000, 0.96152676, 0.00000067, 0.03847257]])
.. code-block:: python
:name: code-example2
# required: distributed
# Multi GPU, test_margin_cross_entropy.py
import paddle
import paddle.distributed as dist
strategy = dist.fleet.DistributedStrategy()
dist.fleet.init(is_collective=True, strategy=strategy)
rank_id = dist.get_rank()
m1 = 1.0
m2 = 0.5
m3 = 0.0
s = 64.0
batch_size = 2
feature_length = 4
num_class_per_card = [4, 8]
num_classes = paddle.sum(paddle.to_tensor(num_class_per_card))
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)
X = paddle.randn(
shape=[batch_size, feature_length],
dtype='float64')
X_list = []
dist.all_gather(X_list, X)
X = paddle.concat(X_list, axis=0)
X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
X = paddle.divide(X, X_l2)
W = paddle.randn(
shape=[feature_length, num_class_per_card[rank_id]],
dtype='float64')
W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
W = paddle.divide(W, W_l2)
logits = paddle.matmul(X, W)
loss, softmax = paddle.nn.functional.margin_cross_entropy(
logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
print(logits)
print(label)
print(loss)
print(softmax)
# python -m paddle.distributed.launch --gpus=0,1 test_margin_cross_entropy.py
## for rank0 input
#Tensor(shape=[4, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[ 0.32888934, 0.02408748, -0.02763289, 0.18173063],
# [-0.52893978, -0.10623845, -0.21596515, -0.06432517],
# [-0.00536345, -0.03924667, 0.66735314, -0.28640926],
# [-0.09907366, -0.48534973, -0.10365338, -0.39472322]])
#Tensor(shape=[4], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [11, 1 , 10, 11])
## for rank1 input
#Tensor(shape=[4, 8], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
# [[ 0.68654754, 0.28137170, 0.69694954, -0.60923933, -0.57077653, 0.54576703, -0.38709028, 0.56028204],
# [-0.80360371, -0.03042448, -0.45107338, 0.49559349, 0.69998950, -0.45411693, 0.61927630, -0.82808600],
# [ 0.11457570, -0.34785879, -0.68819499, -0.26189226, -0.48241491, -0.67685711, 0.06510185, 0.49660849],
# [ 0.31604851, 0.52087884, 0.53124749, -0.86176582, -0.43426329, 0.34786144, -0.10850784, 0.51566383]])
#Tensor(shape=[4], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [11, 1 , 10, 11])
## for rank0 output
#Tensor(shape=[4, 1], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[38.96608230],
# [81.28152394],
# [69.67229865],
# [31.74197251]])
#Tensor(shape=[4, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
# [[0.00000000, 0.00000000, 0.00000000, 0.00000000],
# [0.00000000, 0.00000000, 0.00000000, 0.00000000],
# [0.00000000, 0.00000000, 0.99998205, 0.00000000],
# [0.00000000, 0.00000000, 0.00000000, 0.00000000]])
## for rank1 output
#Tensor(shape=[4, 1], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
# [[38.96608230],
# [81.28152394],
# [69.67229865],
# [31.74197251]])
#Tensor(shape=[4, 8], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
# [[0.33943993, 0.00000000, 0.66051859, 0.00000000, 0.00000000, 0.00004148, 0.00000000, 0.00000000],
# [0.00000000, 0.00000000, 0.00000000, 0.00000207, 0.99432097, 0.00000000, 0.00567696, 0.00000000],
# [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00001795],
# [0.00000069, 0.33993085, 0.66006319, 0.00000000, 0.00000000, 0.00000528, 0.00000000, 0.00000000]])
>>> # doctest: +REQUIRES(env:DISTRIBUTED)
>>> # Multi GPU, test_margin_cross_entropy.py
>>> import paddle
>>> import paddle.distributed as dist
>>> paddle.seed(2023)
>>> strategy = dist.fleet.DistributedStrategy()
>>> dist.fleet.init(is_collective=True, strategy=strategy)
>>> rank_id = dist.get_rank()
>>> m1 = 1.0
>>> m2 = 0.5
>>> m3 = 0.0
>>> s = 64.0
>>> batch_size = 2
>>> feature_length = 4
>>> num_class_per_card = [4, 8]
>>> num_classes = paddle.sum(paddle.to_tensor(num_class_per_card))
>>> 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)
>>> X = paddle.randn(
... shape=[batch_size, feature_length],
... dtype='float64')
>>> X_list = []
>>> dist.all_gather(X_list, X)
>>> X = paddle.concat(X_list, axis=0)
>>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
>>> X = paddle.divide(X, X_l2)
>>> W = paddle.randn(
... shape=[feature_length, num_class_per_card[rank_id]],
... dtype='float64')
>>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
>>> W = paddle.divide(W, W_l2)
>>> logits = paddle.matmul(X, W)
>>> loss, softmax = paddle.nn.functional.margin_cross_entropy(
... logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
>>> print(logits)
>>> print(label)
>>> print(loss)
>>> print(softmax)
>>> # python -m paddle.distributed.launch --gpus=0,1 --log_dir log test_margin_cross_entropy.py
>>> # cat log/workerlog.0
>>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
>>> # [[-0.59561850, 0.32797505, 0.80279214, 0.00144975],
>>> # [-0.16265212, 0.84155098, 0.62008629, 0.79126072],
>>> # [-0.59561850, 0.32797505, 0.80279214, 0.00144975],
>>> # [-0.16265212, 0.84155098, 0.62008629, 0.79126072]])
>>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
>>> # [5, 4, 5, 4])
>>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
>>> # [[104.27437027],
>>> # [113.40243782],
>>> # [104.27437027],
>>> # [113.40243782]])
>>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
>>> # [[0.00000000, 0.00000000, 0.01210039, 0.00000000],
>>> # [0.00000000, 0.96152674, 0.00000067, 0.03847257],
>>> # [0.00000000, 0.00000000, 0.01210039, 0.00000000],
>>> # [0.00000000, 0.96152674, 0.00000067, 0.03847257]])
>>> # cat log/workerlog.1
>>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True,
>>> # [[-0.34913275, -0.35180883, -0.53976657, -0.75234331, 0.70534995,
>>> # 0.87157838, 0.31064437, 0.19537700],
>>> # [-0.63941012, -0.05631600, -0.02561853, 0.09363013, 0.56571130,
>>> # 0.13611246, 0.08849565, 0.39219619],
>>> # [-0.34913275, -0.35180883, -0.53976657, -0.75234331, 0.70534995,
>>> # 0.87157838, 0.31064437, 0.19537700],
>>> # [-0.63941012, -0.05631600, -0.02561853, 0.09363013, 0.56571130,
>>> # 0.13611246, 0.08849565, 0.39219619]])
>>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:1), stop_gradient=True,
>>> # [5, 4, 5, 4])
>>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:1), stop_gradient=True,
>>> # [[104.27437027],
>>> # [113.40243782],
>>> # [104.27437027],
>>> # [113.40243782]])
>>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True,
>>> # [[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593,
>>> # 0.00000000, 0.00000000],
>>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000,
>>> # 0.00000000, 0.00000000],
>>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593,
>>> # 0.00000000, 0.00000000],
>>> # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000,
>>> # 0.00000000, 0.00000000]])
"""
assert reduction in ['mean', 'sum', 'none', None]
......@@ -2422,15 +2472,16 @@ def softmax_with_cross_entropy(
Examples:
.. code-block:: python
import paddle
>>> import paddle
>>> logits = paddle.to_tensor([0.4, 0.6, 0.9], dtype="float32")
>>> label = paddle.to_tensor([1], dtype="int64")
logits = paddle.to_tensor([0.4, 0.6, 0.9], dtype="float32")
label = paddle.to_tensor([1], dtype="int64")
>>> out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label)
>>> print(out)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[1.15328646])
out = paddle.nn.functional.softmax_with_cross_entropy(logits=logits, label=label)
print(out)
# Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [1.15328646])
"""
return fluid_softmax_with_cross_entropy(
logits,
......@@ -2622,50 +2673,50 @@ def cross_entropy(
Examples:
.. code-block:: python
# hard labels
import paddle
paddle.seed(99999)
N=100
C=200
reduction='mean'
input = paddle.rand([N, C], dtype='float64')
label = paddle.randint(0, C, shape=[N], dtype='int64')
weight = paddle.rand([C], dtype='float64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction=reduction)
dy_ret = cross_entropy_loss(
input,
label)
print(dy_ret)
# Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=True,
# 5.34043430)
>>> # hard labels
>>> import paddle
>>> paddle.seed(99999)
>>> N=100
>>> C=200
>>> reduction='mean'
>>> input = paddle.rand([N, C], dtype='float64')
>>> label = paddle.randint(0, C, shape=[N], dtype='int64')
>>> weight = paddle.rand([C], dtype='float64')
>>> cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
... weight=weight, reduction=reduction)
>>> dy_ret = cross_entropy_loss(
... input,
... label)
>>> print(dy_ret)
Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True,
5.35419278)
.. code-block:: python
# soft labels
import paddle
paddle.seed(99999)
axis = -1
ignore_index = -100
N = 4
C = 3
shape = [N, C]
reduction='mean'
weight = None
logits = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
labels /= paddle.sum(labels, axis=axis, keepdim=True)
paddle_loss_mean = paddle.nn.functional.cross_entropy(
logits,
labels,
soft_label=True,
axis=axis,
weight=weight,
reduction=reduction)
print(paddle_loss_mean)
# Tensor(shape=[], dtype=float64, place=Place(gpu:0), stop_gradient=True,
# 1.11043464)
>>> # soft labels
>>> import paddle
>>> paddle.seed(99999)
>>> axis = -1
>>> ignore_index = -100
>>> N = 4
>>> C = 3
>>> shape = [N, C]
>>> reduction='mean'
>>> weight = None
>>> logits = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
>>> labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0)
>>> labels /= paddle.sum(labels, axis=axis, keepdim=True)
>>> paddle_loss_mean = paddle.nn.functional.cross_entropy(
... logits,
... labels,
... soft_label=True,
... axis=axis,
... weight=weight,
... reduction=reduction)
>>> print(paddle_loss_mean)
Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True,
1.12801195)
"""
......@@ -3022,15 +3073,17 @@ def sigmoid_focal_loss(
.. code-block:: python
import paddle
>>> import paddle
logit = paddle.to_tensor([[0.97, 0.91, 0.03], [0.55, 0.43, 0.71]], dtype='float32')
label = paddle.to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32')
one = paddle.to_tensor([1.], dtype='float32')
fg_label = paddle.greater_equal(label, one)
fg_num = paddle.sum(paddle.cast(fg_label, dtype='float32'))
output = paddle.nn.functional.sigmoid_focal_loss(logit, label, normalizer=fg_num)
print(output) # 0.65782464
>>> logit = paddle.to_tensor([[0.97, 0.91, 0.03], [0.55, 0.43, 0.71]], dtype='float32')
>>> label = paddle.to_tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype='float32')
>>> one = paddle.to_tensor([1.], dtype='float32')
>>> fg_label = paddle.greater_equal(label, one)
>>> fg_num = paddle.sum(paddle.cast(fg_label, dtype='float32'))
>>> output = paddle.nn.functional.sigmoid_focal_loss(logit, label, normalizer=fg_num)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.65782464)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -3177,17 +3230,20 @@ def multi_label_soft_margin_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
# label elements in {1., -1.}
label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
loss = F.multi_label_soft_margin_loss(input, label, reduction='none')
print(loss)
# Tensor([3.49625897, 0.71111226, 0.43989015])
loss = F.multi_label_soft_margin_loss(input, label, reduction='mean')
print(loss)
# Tensor(1.54908717)
>>> import paddle
>>> import paddle.nn.functional as F
>>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
>>> # label elements in {1., -1.}
>>> label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
>>> loss = F.multi_label_soft_margin_loss(input, label, reduction='none')
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[3.49625897, 0.71111226, 0.43989015])
>>> loss = F.multi_label_soft_margin_loss(input, label, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.54908717)
"""
if reduction not in ['sum', 'mean', 'none']:
raise ValueError(
......@@ -3296,22 +3352,24 @@ def hinge_embedding_loss(input, label, margin=1.0, reduction='mean', name=None):
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
# label elements in {1., -1.}
label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
>>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
>>> # label elements in {1., -1.}
>>> label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='none')
print(loss)
# Tensor([[0., -2., 0.],
# [0., -1., 2.],
# [1., 1., 1.]])
>>> loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='none')
>>> print(loss)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0., -2., 0.],
[ 0., -1., 2.],
[ 1., 1., 1.]])
>>> loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.22222222)
loss = F.hinge_embedding_loss(input, label, margin=1.0, reduction='mean')
print(loss)
# Tensor(0.22222222)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -3386,20 +3444,24 @@ def cosine_embedding_loss(
Examples:
.. code-block:: python
import paddle
input1 = paddle.to_tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]], 'float32')
input2 = paddle.to_tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]], 'float32')
label = paddle.to_tensor([1, -1], 'int64')
output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='mean')
print(output) # 0.21155193
output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='sum')
print(output) # 0.42310387
output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='none')
print(output) # [0.42310387, 0. ]
>>> import paddle
>>> input1 = paddle.to_tensor([[1.6, 1.2, -0.5], [3.2, 2.6, -5.8]], 'float32')
>>> input2 = paddle.to_tensor([[0.5, 0.5, -1.8], [2.3, -1.4, 1.1]], 'float32')
>>> label = paddle.to_tensor([1, -1], 'int64')
>>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='mean')
>>> print(output) # 0.21155193
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.21155193)
>>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='sum')
>>> print(output) # 0.42310387
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.42310387)
>>> output = paddle.nn.functional.cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='none')
>>> print(output) # [0.42310387, 0. ]
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.42310387, 0. ])
"""
if len(label.shape) != 1:
......@@ -3519,20 +3581,21 @@ def triplet_margin_with_distance_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
positive= paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='none')
print(loss)
# Tensor([0. , 0.57496738, 0. ])
>>> import paddle
>>> import paddle.nn.functional as F
>>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
>>> positive = paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
>>> negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
>>> loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='none')
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.57496595, 0. ])
loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='mean')
print(loss)
# Tensor(0.19165580)
>>> loss = F.triplet_margin_with_distance_loss(input, positive, negative, margin=1.0, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.19165532)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -3669,20 +3732,21 @@ def triplet_margin_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
positive= paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='none')
print(loss)
# Tensor([0. , 0.57496738, 0. ])
>>> import paddle
>>> import paddle.nn.functional as F
>>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
>>> positive = paddle.to_tensor([[5, 1, 2], [3, 2, 1], [3, -1, 1]], dtype=paddle.float32)
>>> negative = paddle.to_tensor([[2, 1, -3], [1, 1, -1], [4, -2, 1]], dtype=paddle.float32)
>>> loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='none')
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.57496595, 0. ])
loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='mean')
print(loss)
# Tensor(0.19165580)
>>> loss = F.triplet_margin_loss(input, positive, negative, margin=1.0, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.19165532)
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -3789,13 +3853,15 @@ def multi_margin_loss(
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
>>> import paddle
>>> import paddle.nn.functional as F
input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
label = paddle.to_tensor([1, 2, 1], dtype=paddle.int32)
loss = F.multi_margin_loss(input, label, margin=1.0, reduction='none')
print(loss)
>>> input = paddle.to_tensor([[1, 5, 3], [0, 3, 2], [1, 4, 1]], dtype=paddle.float32)
>>> label = paddle.to_tensor([1, 2, 1], dtype=paddle.int32)
>>> loss = F.multi_margin_loss(input, label, margin=1.0, reduction='none')
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.66666663, 0. ])
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -3895,27 +3961,28 @@ def soft_margin_loss(input, label, reduction='mean', name=None):
Examples:
.. code-block:: python
import paddle
input = paddle.to_tensor([[0.5, 0.6, 0.7],[0.3, 0.5, 0.2]], 'float32')
label = paddle.to_tensor([[1.0, -1.0, 1.0],[-1.0, 1.0, 1.0]], 'float32')
output = paddle.nn.functional.soft_margin_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.64022040)
input = paddle.uniform(shape=(5, 5), dtype="float32", min=0.1, max=0.8)
label = paddle.randint(0, 2, shape=(5, 5), dtype="int64")
label[label==0]=-1
output = paddle.nn.functional.soft_margin_loss(input, label, reduction='none')
print(output)
# Tensor(shape=[5, 5], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[1.09917796, 0.52613139, 0.56263304, 0.82736146, 0.38776723],
# [1.07179427, 1.11924267, 0.49877715, 1.10026348, 0.46184641],
# [0.84367639, 0.74795729, 0.44629076, 0.55123353, 0.77659678],
# [0.39465919, 0.76651484, 0.54485321, 0.76609844, 0.77166790],
# [0.51283568, 0.84757161, 0.78913331, 1.05268764, 0.45318675]])
>>> import paddle
>>> paddle.seed(2023)
>>> input = paddle.to_tensor([[0.5, 0.6, 0.7],[0.3, 0.5, 0.2]], 'float32')
>>> label = paddle.to_tensor([[1.0, -1.0, 1.0],[-1.0, 1.0, 1.0]], 'float32')
>>> output = paddle.nn.functional.soft_margin_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.64022040)
>>> input = paddle.uniform(shape=(5, 5), dtype="float32", min=0.1, max=0.8)
>>> label = paddle.randint(0, 2, shape=(5, 5), dtype="int64")
>>> label[label==0] = -1
>>> output = paddle.nn.functional.soft_margin_loss(input, label, reduction='none')
>>> print(output)
Tensor(shape=[5, 5], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1.10725629, 0.48778144, 0.56217247, 1.12581408, 0.51430041],
[0.90375793, 0.37761253, 0.43007556, 0.95089805, 0.43288314],
[1.16043591, 0.63015938, 0.51362717, 0.43617544, 0.57783306],
[0.81927848, 0.52558368, 0.59713912, 0.83100700, 0.50811619],
[0.82684207, 1.02064908, 0.50296998, 1.13461733, 0.93222517]])
"""
if reduction not in ['sum', 'mean', 'none']:
......@@ -4007,18 +4074,27 @@ def gaussian_nll_loss(
Examples::
.. code-block:: python
import paddle
import paddle.nn.functional as F
input = paddle.randn([5, 2], dtype=paddle.float32)
label = paddle.randn([5, 2], dtype=paddle.float32)
variance = paddle.ones([5, 2], dtype=paddle.float32)
loss = F.gaussian_nll_loss(input, label, variance, reduction='none')
print(loss)
loss = F.gaussian_nll_loss(input, label, variance, reduction='mean')
print(loss)
>>> import paddle
>>> import paddle.nn.functional as F
>>> paddle.seed(2023)
>>> input = paddle.randn([5, 2], dtype=paddle.float32)
>>> label = paddle.randn([5, 2], dtype=paddle.float32)
>>> variance = paddle.ones([5, 2], dtype=paddle.float32)
>>> loss = F.gaussian_nll_loss(input, label, variance, reduction='none')
>>> print(loss)
Tensor(shape=[5, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.21808575, 1.43013096],
[1.05245590, 0.00394560],
[1.20861185, 0.00000062],
[0.56946373, 0.73300570],
[0.37142906, 0.12038800]])
>>> loss = F.gaussian_nll_loss(input, label, variance, reduction='mean')
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.57075173)
Note:
The clamping of ``variance`` is ignored with respect to autograd, and so the
......
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