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fix nce, multinomial, Categorical, Normal, Uniform en doc (#28541)

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* fix Categorical en doc

* fix doc for apis

* remove numpy in sample code
musl/disable_test_yolov3_temporarily
pangyoki 5 years ago committed by GitHub
parent 9f53f3d09e
commit 6df685ab64
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 28 additions and 32 deletions
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26
python/paddle/distribution.py
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@ -197,11 +197,9 @@ class Uniform(Distribution):
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distribution import Uniform
paddle.disable_static()
# Without broadcasting, a single uniform distribution [3, 4]:
u1 = Uniform(low=3.0, high=4.0)
# 2 distributions [1, 3], [2, 4]
@ -214,8 +212,7 @@ class Uniform(Distribution):
u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = paddle.to_tensor(value_npdata)
value_tensor = paddle.to_tensor([0.8], dtype="float32")
uniform = Uniform([0.], [2.])
@ -419,11 +416,9 @@ class Normal(Distribution):
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distribution import Normal
paddle.disable_static()
# Define a single scalar Normal distribution.
dist = Normal(loc=0., scale=3.)
# Define a batch of two scalar valued Normals.
@ -437,8 +432,7 @@ class Normal(Distribution):
dist = Normal(loc=1., scale=[11., 22.])
# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = paddle.to_tensor(value_npdata)
value_tensor = paddle.to_tensor([0.8], dtype="float32")
normal_a = Normal([0.], [1.])
normal_b = Normal([0.5], [2.])
@ -672,13 +666,13 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
paddle.seed(200) # on CPU device
y = paddle.rand([6])
print(y.numpy())
print(y)
# [0.77663314 0.90824795 0.15685187
# 0.04279523 0.34468332 0.7955718 ]
@ -746,7 +740,7 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
@ -793,13 +787,13 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
paddle.seed(200) # on CPU device
y = paddle.rand([6])
print(y.numpy())
print(y)
# [0.77663314 0.90824795 0.15685187
# 0.04279523 0.34468332 0.7955718 ]
@ -844,7 +838,7 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
@ -889,7 +883,7 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
@ -955,7 +949,7 @@ class Categorical(Distribution):
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x.numpy())
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]

28
python/paddle/fluid/layers/loss.py
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@ -659,12 +659,12 @@ def nce(input,
${comment}
Args:
input (Variable): Input variable, 2-D tensor with shape [batch_size, dim],
input (Tensor): Input tensor, 2-D tensor with shape [batch_size, dim],
and data type is float32 or float64.
label (Variable): Input label, 2-D tensor with shape [batch_size, num_true_class],
label (Tensor): Input label, 2-D tensor with shape [batch_size, num_true_class],
and data type is int64.
num_total_classes (int):${num_total_classes_comment}.
sample_weight (Variable|None): A Variable of shape [batch_size, 1]
sample_weight (Tensor|None): A Tensor of shape [batch_size, 1]
storing a weight for each sample. The default weight for each
sample is 1.0.
param_attr (ParamAttr|None): To specify the weight parameter attribute.
@ -688,19 +688,21 @@ def nce(input,
the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default False.
Returns:
Variable: The output nce loss.
Tensor: The output nce loss.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import numpy as np
paddle.enable_static()
window_size = 5
words = []
for i in range(window_size):
words.append(fluid.data(
words.append(paddle.static.data(
name='word_{0}'.format(i), shape=[-1, 1], dtype='int64'))
dict_size = 10000
@ -711,18 +713,18 @@ def nce(input,
if i == label_word:
continue
emb = fluid.layers.embedding(input=words[i], size=[dict_size, 32],
param_attr='embed', is_sparse=True)
emb = paddle.static.nn.embedding(input=words[i], size=[dict_size, 32],
param_attr='embed', is_sparse=True)
embs.append(emb)
embs = fluid.layers.concat(input=embs, axis=1)
loss = fluid.layers.nce(input=embs, label=words[label_word],
num_total_classes=dict_size, param_attr='nce.w_0',
bias_attr='nce.b_0')
embs = paddle.concat(x=embs, axis=1)
loss = paddle.static.nn.nce(input=embs, label=words[label_word],
num_total_classes=dict_size, param_attr='nce.w_0',
bias_attr='nce.b_0')
#or use custom distribution
dist = np.array([0.05,0.5,0.1,0.3,0.05])
loss = fluid.layers.nce(input=embs, label=words[label_word],
loss = paddle.static.nn.nce(input=embs, label=words[label_word],
num_total_classes=5, param_attr='nce.w_1',
bias_attr='nce.b_1',
num_neg_samples=3,

6
python/paddle/tensor/random.py
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@ -113,13 +113,13 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
paddle.seed(100) # on CPU device
x = paddle.rand([2,4])
print(x.numpy())
print(x)
# [[0.5535528 0.20714243 0.01162981 0.51577556]
# [0.36369765 0.2609165 0.18905126 0.5621971 ]]
paddle.seed(200) # on CPU device
out1 = paddle.multinomial(x, num_samples=5, replacement=True)
print(out1.numpy())
print(out1)
# [[3 3 0 0 0]
# [3 3 3 1 0]]
@ -129,7 +129,7 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
paddle.seed(300) # on CPU device
out3 = paddle.multinomial(x, num_samples=3)
print(out3.numpy())
print(out3)
# [[3 0 1]
# [3 1 0]]

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