mindspore.numpy.logaddexp2

mindspore.numpy.logaddexp2(x1, x2, dtype=None)[source]

Logarithm of the sum of exponentiations of the inputs in base of 2.

Calculates log2(2**x1 + 2**x2). This function is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases the base-2 logarithm of the calculated probability can be used instead. This function allows adding probabilities stored in such a fashion.

Note

Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported.

Parameters

x1 (Tensor) – Input tensor.
x2 (Tensor) – Input tensor. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
dtype (mindspore.dtype) – Default: None. Overrides the dtype of the output Tensor.

Returns

Tensor or scalar. This is a scalar if both x1 and x2 are scalars.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> x1 = np.array([2, 4, 8]).astype('float16')
>>> x2 = np.array(2).astype('float16')
>>> output = np.logaddexp2(x1, x2)
>>> print(output)
[3. 4.32 8.02]