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Paddle
未验证 提交 bd14926e 编写于 作者: C cyberslack_lee 提交者： GitHub
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[xdoctest] reformat example code with google style in No.91-94 (#55832) 

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Showing with 421 addition and 391 deletion
python/paddle/nn/decode.py
浏览文件 @ bd14926e
......@@ -189,20 +189,20 @@ class BeamSearchDecoder(Decoder):
.. code-block:: python
import numpy as np
import paddle
from paddle.nn import BeamSearchDecoder, dynamic_decode
from paddle.nn import GRUCell, Linear, Embedding
trg_embeder = Embedding(100, 32)
output_layer = Linear(32, 32)
decoder_cell = GRUCell(input_size=32, hidden_size=32)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
>>> import numpy as np
>>> import paddle
>>> from paddle.nn import BeamSearchDecoder, dynamic_decode
>>> from paddle.nn import GRUCell, Linear, Embedding
>>> trg_embeder = Embedding(100, 32)
>>> output_layer = Linear(32, 32)
>>> decoder_cell = GRUCell(input_size=32, hidden_size=32)
>>> decoder = BeamSearchDecoder(decoder_cell,
... start_token=0,
... end_token=1,
... beam_size=4,
... embedding_fn=trg_embeder,
... output_fn=output_layer)
...
"""
def __init__(
......@@ -1054,22 +1054,24 @@ def dynamic_decode(
.. code-block:: python
import paddle
from paddle.nn import BeamSearchDecoder, dynamic_decode
from paddle.nn import GRUCell, Linear, Embedding
trg_embeder = Embedding(100, 32)
output_layer = Linear(32, 32)
decoder_cell = GRUCell(input_size=32, hidden_size=32)
decoder = BeamSearchDecoder(decoder_cell,
start_token=0,
end_token=1,
beam_size=4,
embedding_fn=trg_embeder,
output_fn=output_layer)
encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
outputs = dynamic_decode(decoder=decoder,
inits=decoder_cell.get_initial_states(encoder_output),
max_step_num=10)
>>> import paddle
>>> from paddle.nn import BeamSearchDecoder, dynamic_decode
>>> from paddle.nn import GRUCell, Linear, Embedding
>>> trg_embeder = Embedding(100, 32)
>>> output_layer = Linear(32, 32)
>>> decoder_cell = GRUCell(input_size=32, hidden_size=32)
>>> decoder = BeamSearchDecoder(decoder_cell,
... start_token=0,
... end_token=1,
... beam_size=4,
... embedding_fn=trg_embeder,
... output_fn=output_layer)
>>> encoder_output = paddle.ones((4, 8, 32), dtype=paddle.get_default_dtype())
>>> outputs = dynamic_decode(decoder=decoder,
... inits=decoder_cell.get_initial_states(encoder_output),
... max_step_num=10)
>>> print(outputs[0].shape)
[4, 11, 4]
"""
if in_dynamic_mode():
return _dynamic_decode_imperative(
......
python/paddle/nn/layer/loss.py
浏览文件 @ bd14926e
......@@ -91,15 +91,15 @@ class BCEWithLogitsLoss(Layer):
.. code-block:: python
import paddle
>>> import paddle
logit = paddle.to_tensor([5.0, 1.0, 3.0], dtype="float32")
label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32")
bce_logit_loss = paddle.nn.BCEWithLogitsLoss()
output = bce_logit_loss(logit, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.45618814)
>>> logit = paddle.to_tensor([5.0, 1.0, 3.0], dtype="float32")
>>> label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32")
>>> bce_logit_loss = paddle.nn.BCEWithLogitsLoss()
>>> output = bce_logit_loss(logit, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.45618808)
"""
......@@ -303,50 +303,47 @@ class CrossEntropyLoss(Layer):
.. 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(2023)
>>> 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,
5.33697682)
.. 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(2023)
>>> 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,
1.14554912)
"""
......@@ -439,21 +436,25 @@ class HSigmoidLoss(Layer):
Examples:
.. code-block:: python
import paddle
paddle.set_device('cpu')
input = paddle.uniform([4, 3])
# [[0.56194401 -0.22450298 -0.10741806] # random
# [0.36136317 0.23556745 0.88748658] # random
# [0.18151939 0.80947340 -0.31078976] # random
# [0.68886101 -0.14239830 -0.41297770]] # random
label = paddle.to_tensor([0, 1, 4, 5])
m = paddle.nn.HSigmoidLoss(3, 5)
out = m(input, label)
# [[2.42524505]
# [1.74917245]
# [3.14571381]
# [2.34564662]]
>>> import paddle
>>> 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])
>>> m = paddle.nn.HSigmoidLoss(3, 6)
>>> out = m(input, label)
>>> print(out)
Tensor(shape=[4, 1], dtype=float32, place=Place(cpu), stop_gradient=False,
[[1.94512916],
[2.26129627],
[2.36135936],
[2.97453213]])
"""
def __init__(
......@@ -558,13 +559,14 @@ class MSELoss(Layer):
.. 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)
"""
......@@ -642,29 +644,29 @@ class L1Loss(Layer):
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.L1Loss()
output = l1_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.34999999)
>>> l1_loss = paddle.nn.L1Loss()
>>> output = l1_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.34999999)
l1_loss = paddle.nn.L1Loss(reduction='sum')
output = l1_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 1.39999998)
>>> l1_loss = paddle.nn.L1Loss(reduction='sum')
>>> output = l1_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.39999998)
l1_loss = paddle.nn.L1Loss(reduction='none')
output = l1_loss(input, label)
print(output)
# 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.L1Loss(reduction='none')
>>> output = l1_loss(input, label)
>>> print(output)
Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0.20000005, 0.19999999],
[0.20000000, 0.79999995]])
"""
......@@ -740,15 +742,15 @@ class BCELoss(Layer):
Examples:
.. code-block:: python
import paddle
>>> import paddle
input = paddle.to_tensor([0.5, 0.6, 0.7])
label = paddle.to_tensor([1.0, 0.0, 1.0])
bce_loss = paddle.nn.BCELoss()
output = bce_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.65537101)
>>> input = paddle.to_tensor([0.5, 0.6, 0.7])
>>> label = paddle.to_tensor([1.0, 0.0, 1.0])
>>> bce_loss = paddle.nn.BCELoss()
>>> output = bce_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.65537095)
"""
......@@ -840,20 +842,22 @@ class NLLLoss(Layer):
Examples:
.. code-block:: python
import paddle
>>> import paddle
nll_loss = paddle.nn.loss.NLLLoss()
log_softmax = paddle.nn.LogSoftmax(axis=1)
>>> nll_loss = paddle.nn.loss.NLLLoss()
>>> 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)
>>> 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)
"""
......@@ -929,12 +933,15 @@ class PoissonNLLLoss(Layer):
Examples:
.. code-block:: python
import paddle
poisson_nll_loss = paddle.nn.loss.PoissonNLLLoss()
input = paddle.randn([5, 2], dtype=paddle.float32)
label = paddle.randn([5, 2], dtype=paddle.float32)
loss = poisson_nll_loss(input, label)
>>> import paddle
>>> paddle.seed(2023)
>>> poisson_nll_loss = paddle.nn.loss.PoissonNLLLoss()
>>> input = paddle.randn([5, 2], dtype=paddle.float32)
>>> label = paddle.randn([5, 2], dtype=paddle.float32)
>>> loss = poisson_nll_loss(input, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.52983975)
"""
......@@ -1017,32 +1024,36 @@ class KLDivLoss(Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
shape = (5, 20)
x = 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 []
kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
pred_loss = kldiv_criterion(x, target)
# shape=[]
# 'mean' reduction, loss shape will be []
kldiv_criterion = nn.KLDivLoss(reduction='mean')
pred_loss = kldiv_criterion(x, target)
# shape=[]
# 'sum' reduction, loss shape will be []
kldiv_criterion = nn.KLDivLoss(reduction='sum')
pred_loss = kldiv_criterion(x, target)
# shape=[]
# 'none' reduction, loss shape is same with X shape
kldiv_criterion = nn.KLDivLoss(reduction='none')
pred_loss = kldiv_criterion(x, target)
# shape=[5, 20]
>>> import paddle
>>> import paddle.nn as nn
>>> shape = (5, 20)
>>> x = 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 []
>>> kldiv_criterion = nn.KLDivLoss(reduction='batchmean')
>>> pred_loss = kldiv_criterion(x, target)
>>> print(pred_loss.shape)
[]
>>> # 'mean' reduction, loss shape will be []
>>> kldiv_criterion = nn.KLDivLoss(reduction='mean')
>>> pred_loss = kldiv_criterion(x, target)
>>> print(pred_loss.shape)
[]
>>> # 'sum' reduction, loss shape will be []
>>> kldiv_criterion = nn.KLDivLoss(reduction='sum')
>>> pred_loss = kldiv_criterion(x, target)
>>> print(pred_loss.shape)
[]
>>> # 'none' reduction, loss shape is same with X shape
>>> kldiv_criterion = nn.KLDivLoss(reduction='none')
>>> pred_loss = kldiv_criterion(x, target)
>>> print(pred_loss.shape)
[5, 20]
"""
......@@ -1099,16 +1110,16 @@ class MarginRankingLoss(Layer):
.. code-block:: python
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")
margin_rank_loss = paddle.nn.MarginRankingLoss()
loss = margin_rank_loss(input, other, label)
>>> import paddle
print(loss)
# 0.75
>>> 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")
>>> margin_rank_loss = paddle.nn.MarginRankingLoss()
>>> loss = margin_rank_loss(input, other, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.75000000)
"""
def __init__(self, margin=0.0, reduction='mean', name=None):
......@@ -1155,50 +1166,41 @@ class CTCLoss(Layer):
.. code-block:: python
# declarative mode
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 = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels,
input_lengths,
label_lengths)
print(loss)
# Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [3.91798496, 2.90765190])
loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels,
input_lengths,
label_lengths)
print(loss)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 1.13760614)
>>> # declarative mode
>>> 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 = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels, input_lengths, label_lengths)
>>> print(loss)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[3.91798496, 2.90765214])
>>> loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels, input_lengths, label_lengths)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.13760614)
"""
def __init__(self, blank=0, reduction='mean'):
......@@ -1247,33 +1249,33 @@ class RNNTLoss(Layer):
Examples:
.. code-block:: python
# declarative mode
import numpy as np
import paddle
from paddle.nn import RNNTLoss
fn = RNNTLoss(reduction='sum', fastemit_lambda=0.0)
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 numpy as np
>>> import paddle
>>> from paddle.nn import RNNTLoss
>>> fn = RNNTLoss(reduction='sum', fastemit_lambda=0.0)
>>> 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 __init__(
......@@ -1346,13 +1348,15 @@ class SmoothL1Loss(Layer):
Examples:
.. code-block:: python
import paddle
input = paddle.rand([3, 3]).astype("float32")
label = paddle.rand([3, 3]).astype("float32")
loss = paddle.nn.SmoothL1Loss()
output = loss(input, label)
print(output)
# 0.049606
>>> import paddle
>>> paddle.seed(2023)
>>> input = paddle.rand([3, 3]).astype("float32")
>>> label = paddle.rand([3, 3]).astype("float32")
>>> loss = paddle.nn.SmoothL1Loss()
>>> output = loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.08307374)
"""
def __init__(self, reduction='mean', delta=1.0, name=None):
......@@ -1414,21 +1418,23 @@ class MultiLabelSoftMarginLoss(Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
>>> import paddle
>>> import paddle.nn as nn
input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
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 = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32)
multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='none')
loss = multi_label_soft_margin_loss(input, label)
print(loss)
# Tensor([3.49625897, 0.71111226, 0.43989015])
>>> multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='none')
>>> loss = multi_label_soft_margin_loss(input, label)
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[3.49625897, 0.71111226, 0.43989015])
multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='mean')
loss = multi_label_soft_margin_loss(input, label)
print(loss)
# Tensor(1.54908717)
>>> multi_label_soft_margin_loss = nn.MultiLabelSoftMarginLoss(reduction='mean')
>>> loss = multi_label_soft_margin_loss(input, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.54908717)
"""
def __init__(self, weight=None, reduction="mean", name=None):
......@@ -1512,24 +1518,26 @@ class HingeEmbeddingLoss(Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
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)
hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='none')
loss = hinge_embedding_loss(input, label)
print(loss)
# Tensor([[0., -2., 0.],
# [0., -1., 2.],
# [1., 1., 1.]])
hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')
loss = hinge_embedding_loss(input, label)
print(loss)
# Tensor(0.22222222)
>>> import paddle
>>> import paddle.nn as nn
>>> 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)
>>> hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='none')
>>> loss = hinge_embedding_loss(input, label)
>>> print(loss)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0., -2., 0.],
[ 0., -1., 2.],
[ 1., 1., 1.]])
>>> hinge_embedding_loss = nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')
>>> loss = hinge_embedding_loss(input, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.22222222)
"""
def __init__(self, margin=1.0, reduction="mean", name=None):
......@@ -1595,23 +1603,29 @@ class CosineEmbeddingLoss(Layer):
Examples:
.. code-block:: python
import paddle
>>> 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')
>>> 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')
cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='mean')
output = cosine_embedding_loss(input1, input2, label)
print(output) # 0.21155193
>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='mean')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.21155193)
cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='sum')
output = cosine_embedding_loss(input1, input2, label)
print(output) # 0.42310387
>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='sum')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.42310387)
cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='none')
output = cosine_embedding_loss(input1, input2, label)
print(output) # [0.42310387, 0. ]
>>> cosine_embedding_loss = paddle.nn.CosineEmbeddingLoss(margin=0.5, reduction='none')
>>> output = cosine_embedding_loss(input1, input2, label)
>>> print(output)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.42310387, 0. ])
"""
......@@ -1703,21 +1717,23 @@ class TripletMarginWithDistanceLoss(Layer):
Examples:
.. code-block:: python
import paddle
from paddle.nn import TripletMarginWithDistanceLoss
>>> import paddle
>>> from paddle.nn import TripletMarginWithDistanceLoss
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)
triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='none')
loss = triplet_margin_with_distance_loss(input, positive, negative,)
print(loss)
# Tensor([0. , 0.57496738, 0. ])
>>> 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)
>>> triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='none')
>>> loss = triplet_margin_with_distance_loss(input, positive, negative,)
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.57496595, 0. ])
triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='mean')
loss = triplet_margin_with_distance_loss(input, positive, negative,)
print(loss)
# Tensor(0.19165580)
>>> triplet_margin_with_distance_loss = TripletMarginWithDistanceLoss(reduction='mean')
>>> loss = triplet_margin_with_distance_loss(input, positive, negative,)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.19165532)
"""
......@@ -1812,20 +1828,22 @@ class TripletMarginLoss(Layer):
Examples:
.. code-block:: python
import paddle
>>> import paddle
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)
triplet_margin_loss = paddle.nn.TripletMarginLoss(reduction='none')
loss = triplet_margin_loss(input, positive, negative)
print(loss)
# Tensor([0. , 0.57496738, 0. ])
>>> 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)
>>> triplet_margin_loss = paddle.nn.TripletMarginLoss(reduction='none')
>>> loss = triplet_margin_loss(input, positive, negative)
>>> print(loss)
Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True,
[0. , 0.57496595, 0. ])
triplet_margin_loss = paddle.nn.TripletMarginLoss(margin=1.0, swap=True, reduction='mean', )
loss = triplet_margin_loss(input, positive, negative,)
print(loss)
# Tensor(0.19165580)
>>> triplet_margin_loss = paddle.nn.TripletMarginLoss(margin=1.0, swap=True, reduction='mean')
>>> loss = triplet_margin_loss(input, positive, negative)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
2.40039468)
"""
......@@ -1924,15 +1942,17 @@ class MultiMarginLoss(Layer):
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
>>> import paddle
>>> import paddle.nn as nn
input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
label = paddle.to_tensor([0, 1, 2], dtype=paddle.int32)
>>> input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32)
>>> label = paddle.to_tensor([0, 1, 2], dtype=paddle.int32)
multi_margin_loss = nn.MultiMarginLoss(reduction='mean')
loss = multi_margin_loss(input, label)
print(loss)
>>> multi_margin_loss = nn.MultiMarginLoss(reduction='mean')
>>> loss = multi_margin_loss(input, label)
>>> print(loss)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
1.11111104)
"""
def __init__(
......@@ -2003,31 +2023,30 @@ class SoftMarginLoss(Layer):
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')
soft_margin_loss = paddle.nn.SoftMarginLoss()
output = soft_margin_loss(input, label)
print(output)
# Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# 0.64022040)
input_np = paddle.uniform(shape=(5, 5), min=0.1, max=0.8, dtype="float64")
label_np = paddle.randint(high=2, shape=(5, 5), dtype="int64")
label_np[label_np==0]=-1
input = paddle.to_tensor(input_np)
label = paddle.to_tensor(label_np)
soft_margin_loss = paddle.nn.SoftMarginLoss(reduction='none')
output = soft_margin_loss(input, label)
print(output)
# Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True,
# [[0.61739663, 0.51405668, 1.09346100, 0.42385561, 0.91602303],
# [0.76997038, 1.01977148, 0.98971722, 1.13976032, 0.88152088],
# [0.55476735, 1.10505384, 0.89923519, 0.45018155, 1.06587511],
# [0.37998142, 0.48067240, 0.47791212, 0.55664053, 0.98581399],
# [0.78571653, 0.59319711, 0.39701841, 0.76172109, 0.83781742]])
>>> 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')
>>> soft_margin_loss = paddle.nn.SoftMarginLoss()
>>> output = soft_margin_loss(input, label)
>>> print(output)
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
0.64022040)
>>> input_np = paddle.uniform(shape=(5, 5), min=0.1, max=0.8, dtype="float64")
>>> label_np = paddle.randint(high=2, shape=(5, 5), dtype="int64")
>>> label_np[label_np==0]=-1
>>> input = paddle.to_tensor(input_np)
>>> label = paddle.to_tensor(label_np)
>>> soft_margin_loss = paddle.nn.SoftMarginLoss(reduction='none')
>>> output = soft_margin_loss(input, label)
>>> print(output)
Tensor(shape=[5, 5], dtype=float64, place=Place(cpu), stop_gradient=True,
[[1.10725628, 0.48778139, 0.56217249, 1.12581404, 0.51430043],
[0.90375795, 0.37761249, 0.43007557, 0.95089798, 0.43288319],
[1.16043599, 0.63015939, 0.51362715, 0.43617541, 0.57783301],
[0.81927846, 0.52558369, 0.59713908, 0.83100696, 0.50811616],
[0.82684205, 1.02064907, 0.50296995, 1.13461733, 0.93222519]])
"""
def __init__(self, reduction='mean', name=None):
......@@ -2100,16 +2119,23 @@ class GaussianNLLLoss(Layer):
Examples::
.. code-block:: python
import paddle
import paddle.nn as nn
input = paddle.randn([5, 2], dtype=paddle.float32)
label = paddle.randn([5, 2], dtype=paddle.float32)
variance = paddle.ones([5, 2], dtype=paddle.float32)
gs_nll_loss = nn.GaussianNLLLoss(full=False, epsilon=1e-6, reduction='none')
loss = gs_nll_loss(input, label, variance)
print(loss)
>>> import paddle
>>> import paddle.nn as nn
>>> 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)
>>> gs_nll_loss = nn.GaussianNLLLoss(full=False, epsilon=1e-6, reduction='none')
>>> loss = gs_nll_loss(input, label, variance)
>>> 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]])
Note:
The clamping of ``variance`` is ignored with respect to autograd, and so the
......
python/paddle/nn/quant/format.py
浏览文件 @ bd14926e
......@@ -149,29 +149,31 @@ class ConvertibleQuantedLayer(Layer, metaclass=abc.ABCMeta):
It defines some functions to convert quantizers and observers to quantize
or dequantize operators that maintain the quantization parameters used
during inference.
Examples:
.. code-block:: python
# Given codes in ./customized_quanter.py
class CustomizedQuantedLayer(ConvertibleQuantedLayer):
def __init__(self):
super().__init__()
self.weight_a = paddle.create_parameter(shape=[1], dtype='float32')
self.weight_b = paddle.create_parameter(shape=[1], dtype='float32')
self.quanter_for_weight_a = None
self.activation_weight = None
def forward(self, input):
qweight_a = self.quanter_for_weight_a(self.weight_a)
weight_b = self.weight_b
qinput = self.activation_weight(input)
// compute with qweight_a, weight_b and qinput.
return qweight * qinput + weight_b
def weights_to_quanters(self):
return [('weight_a', 'quanter_for_weight_a')]
def activation_quanters(self):
return ['activation_weight']
.. code-block:: python
>>> # Given codes in ./customized_quanter.py
>>> class CustomizedQuantedLayer(ConvertibleQuantedLayer):
... def __init__(self):
... super().__init__()
... self.weight_a = paddle.create_parameter(shape=[1], dtype='float32')
... self.weight_b = paddle.create_parameter(shape=[1], dtype='float32')
... self.quanter_for_weight_a = None
... self.activation_weight = None
...
... def forward(self, input):
... qweight_a = self.quanter_for_weight_a(self.weight_a)
... weight_b = self.weight_b
... qinput = self.activation_weight(input)
... # compute with qweight_a, weight_b and qinput.
... return qweight * qinput + weight_b
...
... def weights_to_quanters(self):
... return [('weight_a', 'quanter_for_weight_a')]
...
... def activation_quanters(self):
... return ['activation_weight']
"""
def __init__(self):
......
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