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
# limitations under the License.
# ============================================================================
import math
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
from mindspore.common.tensor import Tensor
from ..cell import Cell
from ...common import dtype as mstype
from ..._checkparam import Validator as validator
from ..._checkparam import Rel

__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace']

[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 = nn.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
[docs]class Range(Cell): r""" Creates a sequence of numbers. Args: start (Union[int, float]): If `limit` is `None`, the value acts as limit in the range and first entry defaults to `0`. Otherwise, it acts as first entry in the range. limit (Union[int, float]): Acts as upper limit of sequence. If `None`, defaults to the value of `start` while set the first entry of the range to `0`. It can not be equal to `start`. delta (Union[int, float]): Increment of the range. It can not be equal to zero. Default: 1. Outputs: Tensor, the dtype is int if the dtype of `start`, `limit` and `delta` all are int. Otherwise, dtype is float. Examples: >>> net = nn.Range(1, 8, 2) >>> out = net() [1, 3, 5, 7] """ def __init__(self, start, limit=None, delta=1): super(Range, self).__init__() validator.check_value_type("start", start, [int, float], self.cls_name) validator.check_value_type("delta", delta, [int, float], self.cls_name) if delta == 0: raise ValueError("The input of `delta` can not be equal to zero.") if limit is not None: validator.check_value_type("limit", limit, [int, float], self.cls_name) if isinstance(start, int) and isinstance(limit, int) and isinstance(delta, int): self.dtype = mstype.int32 else: self.dtype = mstype.float32 else: if isinstance(start, int) and isinstance(delta, int): self.dtype = mstype.int32 else: self.dtype = mstype.float32 if isinstance(start, int): start = float(start) if isinstance(limit, int): limit = float(limit) if isinstance(delta, int): delta = float(delta) self.range_x = inner.Range(start, limit, delta) if limit is None: length_input = math.ceil(start / delta) else: length_input = math.ceil((limit - start) / delta) self.input_tensor = Tensor(list(range(length_input)), self.dtype) def construct(self): range_out = self.range_x(self.input_tensor) return range_out
[docs]class LinSpace(Cell): r""" Generates values in an interval. Args: start (Union[int, float]): The start of interval. With shape of 0-D. stop (Union[int, float]): The end of interval. With shape of 0-D. num (int): ticks number in the interval, the ticks include start and stop value. With shape of 0-D. Outputs: Tensor, With type same as `start`. The shape is 1-D with length of `num`. Examples: >>> linspace = nn.LinSpace(1, 10, 5) >>> output = linspace() [1, 3.25, 5.5, 7.75, 10] """ def __init__(self, start, stop, num): super(LinSpace, self).__init__() validator.check_value_type("start", start, [int, float], self.cls_name) validator.check_value_type("stop", stop, [int, float], self.cls_name) validator.check_value_type("num", num, [int], self.cls_name) validator.check_integer("num", num, 0, Rel.GT, self.cls_name) self.is_single = bool(num == 1) self.lin_space = inner.LinSpace() self.start = Tensor(start, mstype.float32) self.stop = Tensor(stop, mstype.float32) self.assist = Tensor(list(range(num)), mstype.float32) self.num = Tensor(num, mstype.int32) self.start_array = Tensor([start], mstype.float32) def construct(self): if self.is_single: return self.start_array lin_space_out = self.lin_space(self.assist, self.start, self.stop, self.num) return lin_space_out