Source code for mindspore.nn.layer.math

# Copyright 2020 Huawei Technologies Co., Ltd
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ============================================================================
from mindspore.ops import operations as P
from ..cell import Cell
from ..._checkparam import Validator as validator

__all__ = ['ReduceLogSumExp']

[docs]class ReduceLogSumExp(Cell): r""" Reduce a dimension of a tensor by calculating exponential for all elements in the dimension, then calculate logarithm of the sum. The dtype of the tensor to be reduced is number. Args: keep_dims (bool): If True, keep these reduced dimensions and the length is 1. If False, don't keep these dimensions. Default : False. Inputs: - **input_x** (Tensor[Number]) - The input tensor. - **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Outputs: Tensor, has the same dtype as the 'input_x'. - If axis is (), and keep_dims is false, the output is a 0-D tensor representing the sum of all elements in the input tensor. - If axis is int, set as 2, and keep_dims is false, the shape of output is :math:`(x_1, x_3, ..., x_R)`. - If axis is tuple(int), set as (2, 3), and keep_dims is false, the shape of output is :math:`(x_1, x_4, ..., x_R)`. Examples: >>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32)) >>> op = P.ReduceLogSumExp(keep_dims=True) >>> output = op(input_x, 1) """ def __init__(self, axis, keep_dims=False): super(ReduceLogSumExp, self).__init__() validator.check_value_type('axis', axis, [int, list, tuple], self.cls_name) validator.check_value_type('keep_dims', keep_dims, [bool], self.cls_name) self.axis = axis self.exp = P.Exp() self.sum = P.ReduceSum(keep_dims) self.log = P.Log() def construct(self, input_x): exp = self.exp(input_x) sumexp = self.sum(exp, self.axis) logsumexp = self.log(sumexp) return logsumexp