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Release Distribution base class and Add Normal, Uniform class #26355

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merged 20 commits into from
Aug 21, 2020

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pangyoki
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@pangyoki pangyoki commented Aug 17, 2020

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Implement Probability Distribution related classes.

  • Release Distribution abstract base class and its sample, log_prob, entropy, probs methods.
  • Move Normal class and Uniform class in paddle/fluid/layers/distributions.py path to paddle/distribution.py.
  • Add parameter name of each class API. name is initialized in __init__ function, and every function of the class has its own name.
  • Add probs method for Normal class and Uniform class.

@pangyoki pangyoki force-pushed the add_Distribution_class_branch branch 4 times, most recently from e9443c2 to 97ed688 Compare August 18, 2020 17:55
broadcasting (e.g., `high - low` is a valid operation).

Args:
low(float|list|numpy.ndarray|Variable): The lower boundary of uniform distribution.The data type is float32
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Variable -》 Tensor ,the following is the same

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pytorch文档没写支持int,但实际是支持的。考虑int的情况较常用,建议也支持一下

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Done

* :math:`Z`: is the normalizing constant.

The parameters `low` and `high` must be shaped in a way that supports
broadcasting (e.g., `high - low` is a valid operation).
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加一个broadcast semantic的链接

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Done

.. code-block:: python

import numpy as np
from paddle.fluid import layers
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避免出现paddle.fluid

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Done

# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
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layers接口替换为新版本的paddle接口

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Done, use paddle.to_tensor method

zero_tmp, zero_tmp.shape, min=0., max=1., seed=seed)
output = uniform_random_tmp * (zero_tmp + self.high - self.low
) + self.low
return nn.reshape(output, output_shape)
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return应该要加个name吧,return nn.reshape(output, output_shape, name=name)

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Done


"""
name = self.name + '_probs'
return ops.exp(self.log_prob(value))
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建议直接用概率定义计算

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Done

* :math:`Z`: is the normalization constant.

Args:
loc(float|list|numpy.ndarray|Variable): The mean of normal distribution.The data type is float32.
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同Uniform,Variable -》Tensor, 建议支持int

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Done

# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
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value_tensor建议用paddle.to_tensor创建

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Done


"""
name = self.name + '_probs'
return ops.exp(self.log_prob(value))
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同Uniform,建议直接计算pdf

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Done

self.assertEqual(uniform1.name, name)

uniform2 = Uniform(0.0, 1.0)
self.assertEqual(uniform2.name, 'Uniform')
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补充method name的单测用例

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Done

@pangyoki pangyoki force-pushed the add_Distribution_class_branch branch 2 times, most recently from 30b37f2 to 8232ad8 Compare August 19, 2020 09:36
@pangyoki pangyoki force-pushed the add_Distribution_class_branch branch from 8232ad8 to 8188031 Compare August 19, 2020 13:17
[broadcasting](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basic_concept/broadcasting_en.html) (e.g., `high - low` is a valid operation).

Args:
low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is float32 or int
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漏了list|numpy.ndarray|Tensor

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Done, refer to PR #26535


Args:
low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is float32 or int
high(int|float|list|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is float32 or int
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同上

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Done, refer to PR #26535

lb = tensor.cast(lb_bool, dtype=value.dtype)
ub = tensor.cast(ub_bool, dtype=value.dtype)
return elementwise_sub(
nn.log(lb * ub), nn.log(self.high - self.low), name=name)
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动态图不需要加name,cast和elementwise_sub最好用core.ops

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Done, refer to PR #26535

ub_bool = value < self.high
lb = tensor.cast(lb_bool, dtype=value.dtype)
ub = tensor.cast(ub_bool, dtype=value.dtype)
return elementwise_div((lb * ub), (self.high - self.low), name=name)
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同上

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Done, refer to PR #26535


Mathematical details

The probability density function (pdf) is,
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去掉逗号

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Done, refer to PR #26535


Args:
loc(int|float|list|numpy.ndarray|Tensor): The mean of normal distribution.The data type is float32 or int.
scale(int|float|list|numpy.ndarray|Tensor): The std of normal distribution.The data type is float32 or int.
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data type缺少list|numpy.ndarray|Tensor

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Done, refer to PR #26535

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@XiaoguangHu01 XiaoguangHu01 left a comment

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LGTM

@zhiqiu zhiqiu self-requested a review August 21, 2020 02:37
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@LutaoChu LutaoChu left a comment

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LGTM

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@saxon-zh saxon-zh left a comment

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LGTM

@zhiqiu zhiqiu merged commit 644dfd7 into PaddlePaddle:develop Aug 21, 2020
@pangyoki pangyoki changed the title Release Distribution base class and Normal, Uniform class Release Distribution base class and Add Normal, Uniform class Oct 16, 2020
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6 participants