Pytorch 0.4 更新学习笔记 - 好文

一.Tensor 和 Variable合并为Tensor。

    Tensor可以追踪计算图。所以没必要使用Variable了。

    type(x) -> x.type()

    使用detach()来获取Tensor的数值。

二.弃用volatile

    使用torch.no_grad()/torch.set_grad_enabled(grad_mode)来屏蔽计算图。

>>> x = torch.zeros(1, requires_grad=True) >>> with torch.no_grad(): ... y = x
* 2 >>> y.requires_grad False >>> >>> is_train = False >>> with torch.
set_grad_enabled(is_train): ... y = x * 2 >>> y.requires_grad False
三.三个类torch.dtype/torch.device/torch.layout

    torch.dtype(使用Tensor的的dtype属性获取数据类型)

Data type torch.dtype Tensor types
32-bit floating point torch.float32 or torch.float torch.*.FloatTensor
64-bit floating point torch.float64 or torch.double torch.*.DoubleTensor
16-bit floating point torch.float16 or torch.half torch.*.HalfTensor
8-bit integer (unsigned) torch.uint8 torch.*.ByteTensor
8-bit integer (signed) torch.int8 torch.*.CharTensor
16-bit integer (signed) torch.int16 or torch.short torch.*.ShortTensor
32-bit integer (signed) torch.int32 or torch.int torch.*.IntTensor
64-bit integer (signed) torch.int64 or torch.long torch.*.LongTensor
    torch.device(使用Tensor的device属性获取Tensor的位置，cpu或gpu)

    torch.device('cpu')/torch.device('cuda')

    创建Tensor的方法

>>> device = torch.device("cuda:1") >>> x = torch.randn(3, 3, dtype=torch.
float64, device=device

>>> cuda = torch.device("cuda") >>> torch.tensor([[1], [2], [3]], dtype=torch.
half, device=cuda) >>> x = torch.randn(3, dtype=torch.float64) >>> torch.
zeros_like(x) tensor([ 0., 0., 0.], dtype=torch.float64) >>> x = torch.randn(3,
dtype=torch.float64) >>> x.new_ones(2) tensor([ 1., 1.], dtype=torch.float64)
目前有用的是这些。其他的以后有需要再补充

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