torch.randint(0, self.output_dim, (self.input_dim,),dtype=torch.long), torch.randint(0, self.output_dim, (self.input_dim,),dtype=torch.long) ] self.weights_ = [ (2* torch.randint(0,2, (self.input_dim,)) -1).float(), (2* torch.randint(0,2, (self.input_dim,)) -1).fl...
Expand、repeat 2.view、reshape 使用方式一样 3.squeeze与unsqueeze importtorch#unsqueezedata = torch.rand(4, 2, 28, 28)#在位置上插入print(data.unsqueeze(0).shape)#torch.Size([1, 4, 2, 28, 28])print(data.unsqueeze(1).shape)#torch.Size([4, 1, 2, 28, 28])#squeeze#指定删除的维度,...
repeat(self.walks_per_node * self.num_negative_samples) rws = [batch] for i in range(self.walk_length): keys = self.metapath[i % len(self.metapath)] batch = torch.randint(0, self.num_nodes_dict[keys[-1]], (batch.size(0), ), dtype=torch.long) rws.append(batch) rw = torch...
运行程序,就可以看到所有的函数、方法 import torch s = dir(torch) for i in s: print(i) 1. 2. 3. 4. 输出有一千多个结果 AVG AggregationType AnyType Argument ArgumentSpec BFloat16Storage BFloat16Tensor BenchmarkConfig BenchmarkExecutionStats Block BoolStorage BoolTensor BoolType BufferDict Byte...
torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1, torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1, torch.ravel: lambda input: -1, torch.real: lambda input, out=None: -1, to...
copy_idx = np.random.randint(0, ncidx.size(0)) t_feat[j] = t_feat[ncidx[copy_idx]]returnscores 开发者ID:RobinROAR,项目名称:TensorflowTutorialsCode,代码行数:31,代码来源:experiments.py 示例15: test ▲点赞 1▼ deftest(self):ifopt['model'] =='CharCNN': ...
torch.randint(2,3,(2,2)) #整数分布 torch.randn(2,3) # 标准正态分布 torch.randperm(10) # 不重复随机数 torch.linspace(0,10,1) # 线性间距 torch.arange(12) torch.poisson(torch.rand(3,5)*5) # poisson分布 import matplotlib.pyplot as plt ...
此外,torch.cat不会增加张量的总维度数量,它仅仅是在一个指定的维度上扩展了张量的大小。...data1= torch.randint(0, 10, [2, 3]) data2= torch.randint(0, 10, [2, 3]) new_data = torch.stack([data1 15310 【colab pytorch】张量操作 tensor.detach.clone()() # | New | No | 13、张量...
[dim]) # Randomize sign sign = torch.randint(0, 1, shape, device=device) * 2 - 1 return (t * sign).to(dtype) def define_custom_op_for_test(id_, fn_cpu, fn_cuda, fn_xpu, fn_meta, tags=()): global libtest global ids if id_ not in ids: libtest.define(f...
randint(0, img.size()[1] - h) y1 = random.randint(0, img.size()[2] - w) if img.size()[0] == 3: #img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1) #img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1) #img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1...