argmax(dim=-1, keepdim=True), 1.0) actions = (actions_hard - actions).detach() + actions actions = clamp_grad(actions, -0.5, 0.5) else: actions, gumbel_noise = cat_distr.rsample(gumbel_noise=gumbel_noise) else: actions = torch.zeros_like(cat_distr.probs) actions.scatter_(-1, ...
参数dim=1,相当于调用了 squeeze(1)这个操作,最后就得到结果是一个size为4的vector。 注:如果dim=0,则返回每列的最大值。 所以一定不要混淆!这里的dim是指的 the dimension to reduce!并不是在这个dimension上去返回最大值!!! 用torch.argmax()这个函数似乎更直观,更好理解一些。
b = torch.argmax(z, dim=1)#.view(B,1)logprob = cat.log_prob(b).view(B,1)# czs = []# for j in range(1):# z = sample_relax_z(logits)# surr_input = torch.cat([z, x, logits.detach()], dim=1)# cz = surrogate.net(surr_input)# czs.append(cz)# czs = torch.stack(...
output.append(int(Y.argmax(dim=1).item()))return''.join([idx_to_char[i]foriinoutput]) 开发者ID:wdxtub,项目名称:deep-learning-note,代码行数:20,代码来源:utils.py 示例4: calculate_outputs_and_gradients ▲点赞 6▼ # 需要导入模块: import torch [as 别名]# 或者: from torch importargma...
argmax(dim=1).item())) return ''.join([idx_to_char[i] for i in output]) Example #30Source File: train.py From MomentumContrast.pytorch with MIT License 5 votes def initialize_queue(model_k, device, train_loader): queue = torch.zeros((0, 128), dtype=torch.float) queue = ...
🐛 Describe the bug Calling torch.argmax on a mps tensor results in -9223372036854775808 for all outputs. torch.argmax works fine on cpu. No exception is raised during execution. I've tested this after reinstalling in a new anaconda env a...
torch.multinomial和torch.argmax的区别 reference: torch.view() 先按行flatten之后再按照所需维度进行取数 import torch a = torch.randn(2,3,4) a Out[4]: tensor([[[ 0.0331, -1.1727, -0.2692, -1.6970], [-1.7191, -2.1063, 3.2157, 0.4572], ...
zeros(5) >>> a[3:] = 1 >>> a.argmax() tensor(3) >>> a.max(dim=-1).indices tensor(3) >>> a.argmax(-1) tensor(3) >>> a.max(0).indices tensor(3) >>> Collaborator kshitij12345 commented Nov 4, 2020 Also, a small note, the doc for torch.argmax has a copy-...
target = torch.argmax(target, dim=1) # 计算准确率 accuracy = accuracy(output, target) print(f'Validation Accuracy: {accuracy.item()}') 跟踪度量标准:如果您希望在训练过程中跟踪多个度量标准,可以使用torchmetrics.MetricCollection类。例如,跟踪准确率和损失: Python: fromtorchmetricsimportMetricCollection...
torch.sparse.mm(a,b)用法上类似torch.mm(a,b),但a是稀疏矩阵,b是普通矩阵或稀疏矩阵 这是一个空的稀疏矩阵 稀疏矩阵的定义,要求指出稀疏矩阵中非零元素的位置和值 import torch indices = torch.LongTensor([[0,0], [1,1], [2,2]])#稀疏矩阵中非零元素的坐标 indices = indices.t() #一定要转置...