# 答案:x_zeros = torch.zeros_like(x) # torch.clamp(x, min=x_zeros) torch.where(x > 0, x, 0.) 6.2.6 index_select vs gather 都用于张量元素的选取和重塑,参数的命名也类似,但其功能截然不同。简要而言: 6.2.6.1 index_select index_select:沿着张量的某个dim方向,按照index选取指定位置的张量...
data.zero_() self.layer.weight.data += 1 def forward(self, x): return self.layer(x) net = Net() BS = 6 input = torch.zeros(BS, 1).to(torch.float32) + 1 output = net(input) target = torch.zeros_like(input) loss = (0.5 * (output - target) ** 2).sum() loss.backward(...
列表12.17 dsets.py:208,def getCtAugmentedCandidate if 'noise' in augmentation_dict: noise_t = torch.randn_like(augmented_chunk) noise_t *= augmentation_dict['noise'] augmented_chunk += noise_t 其他增强类型已经增加了我们数据集的有效大小。噪音使我们模型的工作更加困难。一旦我们看到一些训练结果...
「torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False):这个是创建与 input 同形状的全 0 张量」 代码语言:javascript 代码运行次数:0 运行 AI代码解释 t=torch.zeros_like(out_t)# 这里的input要是个张量print(t)tensor([[0,0,0],[0,0,0],[0,0,0]]) 除了全 ...
计算平均值并发送到其他节点forpinmodel.parameters():# 新建一个list存储各个节点的梯度grad_list = [torch.zeros_like(p.grad)for_inrange(4)]# 获取所有节点的梯度dist.gather(p.grad, grad_list,group=group, async_op=False)# 计算所有节点的平均梯度grad_sum = torch.zeros_like(p.grad)foriinrange...
zeros_like(v.data) self.nn.delta_control[k] = torch.zeros_like(v.data) self.nn.delta_y[k] = torch.zeros_like(v.data) self.nns = [] for i in range(self.K): temp = copy.deepcopy(self.nn) temp.name = self.clients[i] temp.control = copy.deepcopy(self.nn.control) # ci ...
```python y_true = torch.tensor([0,1]) y_hat = torch.tensor([[0.1,0.2,0.7],[0.3,0.5,0.2]]) y_one_hot = torch.zeros_like(y_hat) y_one_hot.scatter_(1, y_true.unsqueeze(1), 1) y_one_hot ``` tensor([[1., 0., 0.], [0., 1., 0.]]) 可以看出此时的y_one_hot和...
y=torch.ones_like(y,device=device)#cuda是英伟达的一个gpu运算库,此处将tensor放到cuda上(本电脑的gpu就是英伟达的) x=(device)#将x搬到gpu上去 z=x+y # 此处x和y都得是在gpu上 z 1. 2. 3. 4. 5. 6. 7. AI检测代码解析 tensor([1.9240, 1.4169, 1.3466], device='cuda:0') ...
198 g_optimizer.zero_grad() 199 200 _, fake_output = dis(generated_img) # 判别器输入生成图像 201 gen_loss = loss_sigmoid_fn(fake_output, 202 torch.ones_like(fake_output, 203 device=device)) 204 gen_loss.backward() 205 206 g_optimizer.step() ...
Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. Our trunk health (Continuous Integration signals) can be found athud...