srch

torch.any(input, dim, keepdim=False, *, out=None) -> Tensor 功能： For each row of input in the given dimension dim, returns True if any element in the row evaluates to True and False otherwise. If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1.

torch.any(input, dim, keepdim=False, *, out=None) → Tensor 对于每一行 input 在给定尺寸 dim ，返回 True 如果行中的任何元素评估为 True 和 False 否则。 如果 keepdim 为 True ，则输出张量的大小与 input 大小相同，但尺寸为1的 dim 除外。

any() - .. function:: any() -> bool Returns True if any elements in the tensor are non-zero, False otherwise. Example:: >>> a = torch.randn(…

08.01.2018 · torch.isnan(your_tensor).any() Share. Improve this answer. Follow edited Jun 5 '20 at 21:15. Jeremy Caney. 5,400 24 24 gold badges 39 39 silver badges 64 64 bronze badges. answered Jun 5 '20 at 20:22. crypdick crypdick. 7,359 4 4 …

Python torch.any怎么用？Python torch.any使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch的用法示例。 在下文中一共展示了torch.any方法的11个代码示例

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有时候张量中存在 nan 值，这种情况多半出现在 “除以了 零” 然而程序并不会报错. 用 torch.isnan() 来查看 张量中的 nan 值. 查看x中的空值：

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Feature Motivation Current torch.all/torch.any and Tensor.all, Tensor.any only support bool/uint8. This is inconvenient when trying to write ...

12.01.2020 · 1、安装完成后，不能import torch，提示 ImportError: dlopen: cannot load any more object with static TLS 解决办法：有很多答案都说是将import torch放在import cv2之前，但我试了之后还是不能解决，最后是通过在jupyter notebook中可以直接import torch。我是通 …

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Jan 09, 2018 · Starting with PyTorch 0.4.1 there is the detect_anomaly context manager, which automatically inserts assertions equivalent to assert not torch.isnan(grad).any() between all steps of backward propagation. It's very useful when issues arise during backward pass.

torch.any(input, dim, keepdim=False, *, out=None) → Tensor. For each row of input in the given dimension dim , returns True if any element in the row evaluate to True and False otherwise. If keepdim is True, the output tensor is of the same size as input except in …

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torch.any ... Tests if any element in input evaluates to True . ... This function matches the behaviour of NumPy in returning output of dtype bool for all supported ...

torch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...

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torch.any. input ( Tensor) – the input tensor. Tests if any element in input evaluates to True. This function matches the behaviour of NumPy in returning output of dtype bool for all supported dtypes except uint8 . For uint8 the dtype of output is uint8 itself. For each row of input in the given dimension dim , returns True if any element in ...

torch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...

You may also want to check out all available functions/classes of the module torch , or try the search function . Example 1 ...

__contains__, (self,), self, element) if isinstance(element, (torch. ... b = tensor([[1, 1, 1], [4, 5, 6]]) >>> check_in = lambda x: torch.any(x == b.T).