# 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...
「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 ...
torch.ones_like()与torch.ones()功能:创建全1张量,用法与zero相同。 torch.full() 和 torch.full_like() torch.full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) torch.full_like(input, fill_value, dtype=None, layout=None, device=None, req...
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...
tensor3=torch.randn_like(tensor2,dtype=torch.float)print('tensor3: ',tensor3) 输出结果,这里是根据上个方法声明的tensor2变量来声明新的变量,可以看出尺寸大小都是 5*3,但是数值类型是改变了的。 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 ...
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...
```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...
():p.grad = torch.zeros_like(p)def get_model(self):return self.model@staticmethod@rpc.functions.async_executiondef update_and_fetch_model(ps_rref, grads):# Using the RRef to retrieve the local PS instanceself = ps_rref.local_value()with self.lock:self.curr_update_size += 1# ...
such as prioritized replay buffers and the like.) To install the library locally, start by cloning the repo: git clone https://github.com/pytorch/rl and don't forget to check out the branch or tag you want to use for the build: git checkout v0.4.0 Go to the directory where you ha...
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() ...