# 需要导入模块: import torch [as 别名]# 或者: from torch importget_rng_state[as 别名]defsave_states(self):"""Saves the states inside a checkpoint associated with ``epoch``."""checkpoint_data = dict()ifisinstance(self.model, torch.nn.DataParallel): checkpoint_data['model'] = self.model...
返回生成随机数的原始种子(pathon long) 3.torch.get_rng_state() 返回随机生成器状态(Byte Tensor) 4.torch.set_rng_state(new_state) 设定随机生成器状态 参数:new_state(torch.Byte Tensor)-期望的状态 5.torch.default_generator=<torch._C.Generator object> 6.torch.bernoulli(input,out=None)->Tensor ...
torch.get_rng_state 是 torch.set_rng_state 是 torch.bernoulli 是 支持fp32 torch.multinomial 是 支持fp16,fp32 torch.normal 是 支持fp16,fp32 torch.poisson 否 torch.rand 是 torch.rand_like 是 支持uint8,int8,int16,int32,int64 torch.randint ...
1、torch.cuda.get_rng_state(device=device(type='cuda'))[SOURCE] Returns the random number generator state of the current GPU as a ByteTensor. Parameters:device(torch.deviceorint,optional) – The device to return the RNG state of. Default:torch.device('cuda')(i.e., the current CUDA dev...
Please visit changelog for the brownouts and more details: https://github.blog/changelog/2024-05-16-new-dates-for-actions-larger-runner-multi-label-deprecation/. Show more Check labels The following actions uses Node.js version which is deprecated and will be forced to run on node20: mal...
Support for multiple labels on Larger Runners will be removed after June 17th, 2024. Please visit changelog for the brownouts and more details: https://github.blog/changelog/2024-05-16-new-dates-for-actions-larger-runner-multi-label-deprecation/. Show more ...
torch.cuda.get_rng_state(device=-1) 以ByteTensor的形式返回当前GPU的随机数生成器的状态。 参数:device(int,可选的) – 需要返回RNG状态的目标设备。默认:-1 (例如,使用当前设备)。 警告 此函数会立即初始化CUDA。 torch.cuda.set_rng_state(new_state,device=-1) ...
torch.random.set_rng_state(new_state) torch.sparse 这一节是关于稀疏矩阵的,要注意一下哪些内容有用,哪些内容没用。in beta. Pytorch 提供了 torch.Tensor 来表示包含单个数据类型元素的多维数组。默认情况,数组连续存储。 稀疏矩阵的特点:绝大部分元素为0。这意味着近存储或处理非0(fill value)元素(specified...
该模块提供了一系列的方法来保存和设置随机数生成器的状态,包括使用get_rng_state函数获取当前随机数生成器的状态,set_rng_state函数设置当前随机数生成器状态,并且可以使用manual_seed函数来设置随机种子,也可以使用initial_seed函数来得到程序初始的随机种子。因为神经网络的训练是一个随机的过程,包括数据的输入、权重的...
torch.get_rng_state()[source] Returns the random number generator state as a torch.ByteTensor. torch.set_rng_state(new_state)[source] Sets the random number generator state. Parameters new_state(torch.ByteTensor) – The desired state