网站建设学生作业成品,网站开发方案目录,苏州网站设计公司兴田德润i网址多少,网络营销类网站分类目录#xff1a;《深入浅出Pytorch函数》总目录 相关文章#xff1a; 机器学习中的数学——激活函数#xff1a;Softmax函数 深入浅出Pytorch函数——torch.softmax/torch.nn.functional.softmax 深入浅出Pytorch函数——torch.nn.Softmax 将Softmax函数应用于沿dim的…分类目录《深入浅出Pytorch函数》总目录 相关文章 · 机器学习中的数学——激活函数Softmax函数 · 深入浅出Pytorch函数——torch.softmax/torch.nn.functional.softmax · 深入浅出Pytorch函数——torch.nn.Softmax 将Softmax函数应用于沿dim的所有切片并将重新缩放它们使元素位于 [ 0 , 1 ] [0, 1] [0,1]的范围内并和为1。
语法
torch.softmax(input, dim, *, dtypeNone) - Tensor
torch.nn.functional.softmax(input, dimNone, _stacklevel3, dtypeNone) - Tensor参数
input[Tensor] 输入张量dim[int] Softmax函数将沿着dim轴计算即沿dim的每个切片的和为1dtype[可选, torch.dtype] 想要返回张量的数据类型。如果指定则在执行操作之前将输入张量强制转换为dtype。这对于防止数据类型溢出非常有用。默认值为None
返回值
与input具有相同形状且值在[01]范围内的Tensor。
实例 x torch.randn(4, 5)torch.nn.functional.softmax(x, dim0)
tensor([[0.4259, 0.5448, 0.1935, 0.3904, 0.1963],[0.1370, 0.1053, 0.1966, 0.2625, 0.4343],[0.0540, 0.2823, 0.5101, 0.2082, 0.0905],[0.3832, 0.0676, 0.0998, 0.1390, 0.2789]]) torch.nn.functional.softmax(x, dim1)
tensor([[0.0728, 0.1720, 0.1233, 0.5541, 0.0779],[0.1139, 0.0667, 0.2677, 0.4361, 0.1156],[0.2576, 0.0811, 0.0671, 0.2975, 0.2968],[0.1874, 0.0358, 0.2240, 0.4470, 0.1057]])函数实现
def softmax(input: Tensor, dim: Optional[int] None, _stacklevel: int 3, dtype: Optional[DType] None) - Tensor:rApplies a softmax function.Softmax is defined as::math:\text{Softmax}(x_{i}) \frac{\exp(x_i)}{\sum_j \exp(x_j)}It is applied to all slices along dim, and will re-scale them so that the elementslie in the range [0, 1] and sum to 1.See :class:~torch.nn.Softmax for more details.Args:input (Tensor): inputdim (int): A dimension along which softmax will be computed.dtype (:class:torch.dtype, optional): the desired data type of returned tensor.If specified, the input tensor is casted to :attr:dtype before the operationis performed. This is useful for preventing data type overflows. Default: None... note::This function doesnt work directly with NLLLoss,which expects the Log to be computed between the Softmax and itself.Use log_softmax instead (its faster and has better numerical properties).if has_torch_function_unary(input):return handle_torch_function(softmax, (input,), input, dimdim, _stacklevel_stacklevel, dtypedtype)if dim is None:dim _get_softmax_dim(softmax, input.dim(), _stacklevel)if dtype is None:ret input.softmax(dim)else:ret input.softmax(dim, dtypedtype)return ret
