Python – tensorflow.math.betainc() 方法

TensorFlow 是由 Google 设计的开源Python库，用于开发机器学习模型和深度学习神经网络。 betainc()是 tensorflow 数学模块中用于计算正则化不完全 beta 积分I _x (a,b) 的方法。

Regularized incomplete beta integral Ix(a,b)  is defines as:

 Ix(a,b) = B(x;a,b)/B(a,b)

where,

B(x;a,b) is incomplete beta function and is defined as:

B(x;a,b) = ?^x_o t^a-1(1-t)^b-1 dt

and B(a,b) is complete beta function.

Syntax: tensorflow.math.betainc(  a, b, x, name)
 

Parameters:

• a: It’s a Tensor. Allowed types are float32 and float64.
• b: It’s a Tensor of type same as a.
• x: It’s also a Tensor of type same as a.
• name: It’s an optional parameter that defines the name for the operation.
   

Return: Returns a tensor of type same as a.
 

示例 1：

Python3

# importing the library
import tensorflow as tf
  
# initializing the constant tensors
a = tf.constant([1,2,3,4,5], dtype = tf.float64)
b = tf.constant([1.5,2.7,3.4,4.9,5.6], dtype = tf.float64)
x = tf.constant( [1,1,1,1,1], dtype = tf.float64)
  
# printing the input tensors
print('Input a: ',a)
print('Input b: ',b)
print('Input x: ',x)
  
# calculating the regularized incomplete beta integral 
ribi = tf.math.betainc(a,b,x)
  
# printing the result
print('regularized incomplete beta integral: ',ribi)

输出：

Input a:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5,), dtype=float64)
Input b:  tf.Tensor([1.5 2.7 3.4 4.9 5.6], shape=(5,), dtype=float64)
Input x:  tf.Tensor([1. 1. 1. 1. 1.], shape=(5,), dtype=float64)
regularized incomplete beta integral:  tf.Tensor([1. 1. 1. 1. 1.], shape=(5,), dtype=float64)

示例 2：此示例尝试使用不允许的 dtype 张量评估正则化不完全 beta 积分。这将引发 NotFoundError。

Python3

# importing the library
import tensorflow as tf
  
# initializing the constant tensors
a = tf.constant([1,2,3,4,5], dtype = tf.complex128)
b = tf.constant([1.5,2.7,3.4,4.9,5.6], dtype = tf.complex128)
x = tf.constant( [1,1,1,1,1], dtype = tf.complex128)
  
# printing the input tensors
print('Input a: ',a)
print('Input b: ',b)
print('Input x: ',x)
  
# calculating the regularized incomplete beta integral 
ribi = tf.math.betainc(a,b,x)

输出：

Input a:  tf.Tensor([1.+0.j 2.+0.j 3.+0.j 4.+0.j 5.+0.j], shape=(5,), dtype=complex128)
Input b:  tf.Tensor([1.5+0.j 2.7+0.j 3.4+0.j 4.9+0.j 5.6+0.j], shape=(5,), dtype=complex128)
Input x:  tf.Tensor([1.+0.j 1.+0.j 1.+0.j 1.+0.j 1.+0.j], shape=(5,), dtype=complex128)

NotFoundError                             Traceback (most recent call last)

 in ()
      1 # calculating the regularized incomplete beta integral
----> 2 ribi = tf.math.betainc(a,b,x)

2 frames

/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)

NotFoundError: Could not find valid device for node.
Node:{{node Betainc}}
All kernels registered for op Betainc :
  device='XLA_CPU_JIT'; T in [DT_FLOAT, DT_DOUBLE]
  device='XLA_GPU_JIT'; T in [DT_FLOAT, DT_DOUBLE]
  device='XLA_CPU'; T in [DT_FLOAT, DT_DOUBLE]
  device='XLA_GPU'; T in [DT_FLOAT, DT_DOUBLE]
  device='GPU'; T in [DT_DOUBLE]
  device='GPU'; T in [DT_FLOAT]
  device='CPU'; T in [DT_DOUBLE]
  device='CPU'; T in [DT_FLOAT]