# Torch Code: torch.IntTensor([1,2,3,4,5,6,7,8]) #output: #tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=torch.int32) # PaddlePaddle Code: paddle.to_tensor([1,2,3,4,5,6,7,8.8],dtype='int32') #output: #Tensor(shape=[8], dtype=int32, place=Place(cpu), stop_...
torch.Tensor是默认的tensor类型(torch.FlaotTensor)的简称。 一个张量tensor可以从Python的list或序列构建: >>>torch.FloatTensor([[1, 2, 3], [4, 5, 6]]) 1 2 3 4 5 6 [torch.FloatTensor of size 2x3] 一个空张量tensor可以通过规定其大小来构建: >>>torch.IntTensor(2, 4).zero_() 0 0 ...
torch.numel(input)→ int Returns the total number of elements in the input tensor. Parameters input (Tensor)– the input tensor. Example: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 复制 >>> a = torch.randn(1, 2, 3, 4, 5) >>> torch.numel(a) 120 >>> a = torch.zeros(4...
torch.int32torch.int64tensor([1.,2.,3.])torch.float32torch.float64 方式-2 importtorcha=torch.tensor([1,2,3],dtype=torch.int32)print(a)a=torch.tensor([1,2,3],dtype=torch.int64)print(a)a=torch.tensor([1,2,3],dtype=torch.float32)print(a)a=torch.tensor([1,2,3],dtype=torch....
import torch # 创建一个示例的浮点数张量 float_tensor = torch.tensor([1.5, 2.7, 3.2], dtype=torch.float32) # 将浮点数张量转换为整数类型(int64) int_tensor = float_tensor.to(torch.int64) print("浮点数张量:", float_tensor) print("整数类型张量:", int_tensor) 在这个示例中,我们首先创建...
问如何判断torch.tensor类型是否为int？EN在实际的工作当中，我们难免要与空值打交道，相信不少初学者...
可以看出输出的数据类型都为整形(torch.int32) 1.3 torch.rand 用于生成数据类型为浮点型且维度指定的随机Tensor,和在NumPy中使用numpy.rand生成随机数的方法类似,随机生成的浮点数据在 0~1 区间均匀分布。 import torch a = torch.rand(2, 3) 1.
torch.set_default_tensor_type(t)[source] torch.numel(input) → int torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None, sci_mode=None)[source] torch.set_flush_denormal(mode) → bool Creation Ops ...
大量的Api,Tensor运算,神经网络 二:线性回归(linera包) 1.线性回归的小例子 importnumpy as np#线性回归#求loss#loss = (wx +b - y) ** 2defcompute_error_for_line_given_point(b, w, points): totalError=0foriinrange(0, len(points)): ...
Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0,1)[0, 1)[0,1) . torch.rand_like(input) is equivalent to torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device). ...