xx=torch.from_numpy(data)print(xx)#print(x.shape)print(x.numel())#占用内存print(x.dim())#维度 三:创建tensor(datatype包) 1.创建 从numpy中 直接传入list的方式 非初始化的api #numpydata = np.ones(4) xx=torch.from_numpy(data)#直接传入y = torch.tensor([2, 3])print(y) 2.默认类型...
import torch # 创建一个示例的张量 tensor = torch.tensor([1, 2, 3]) # 查看张量的数据类型 data_type = tensor.dtype print("张量的数据类型:", data_type) 在这个示例中,我们创建了一个示例的张量 tensor,然后使用 .dtype 属性来获取其数据类型,并将其打印出来。 PyTorch 支持多种数据类型,例如 tor...
1 torch.Tensor Atorch.Tensoris a multi-dimensional matrix containing elements of a single data type. torch.Tensor是包含单一数据类型的多维矩阵 2 Data types Torch定义了10种不同CPU和GPU的张量类型,下面摘录常用的几种 torch.Tensoris an alias for the default tensor type (torch.FloatTensor). torch.Te...
torch.utils.data.ConcatDataset: 用于连接多个ConcatDataset数据集 torch.utils.data.ChainDataset: 用于连接多个IterableDataset数据集,在IterableDataset的__add__()方法中被调用 torch.utils.data.Subset: 用于获取指定一个索引序列对应的子数据集 代码语言:javascript 代码运行次数:0 运行 AI代码解释 classSubset(Datase...
(input, target):optimizer.zero_grad()output=model(input)loss=loss_fn(output, target)loss.backward()optimizer.step()returnloss# 使用 torchdynamo.optimize 包装训练步骤optimized_training_step=torchdynamo.optimize(training_step)# 训练循环forinp...
Sequence): return [pin_memory(sample) for sample in data] elif hasattr(data, "pin_memory"): return data.pin_memory() else: return data 默认情况下,如果固定逻辑看到一个属于自定义类型 (custom type) 的batch(如果有一个 collate_fn 返回自定义批处理类型的批处理,则会发生),或者如果该批处理的...
type(torch.DoubleTensor)) # Prints "tensor([0., 1., 2., 3.], dtype=torch.float64)" print(x.type(torch.LongTensor)) # Cast back to int-64, prints "tensor([0, 1, 2, 3])" 3在GPU上运算 这是上一节学过的,你还记得么? 下面的例子,讲解了几种非常有用的操作,切换 tensor 的运算...
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from values. device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_defau...
print(data) # type(data) = pandas.core.frame.DataFrame # NumRooms Alley Price 0 NaN Pave 127500 1 2.0 NaN 106000 2 4.0 NaN 178100 3 NaN NaN 140000 # 读取第0行数据 data.loc[0] NumRooms NaN Alley Pave Price 127500 Name: 0, dtype: object ...
()) mme = MultiDataModel( name = endpoint_name, model_data_prefix = output_path, model = model, sagemaker_session = smsess) mme.deploy( initial_instance_count = 1, instance_type = "ml.g4dn.xlarge", serializer=sagemaker.serializers.JSONSerializer(), deserializer=sagemaker.deserializers....