>>> print("numpy array:\n",arr) >>> print("tensor:\n",t) numpy array: [[1 2 3] [4 5 6]] tensor: tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.int32) >>> print("\n修改arr") >>> arr[0,0] = 0 >>> print("numpy array:\n",arr) >>> print("tensor:\n",t...
0.0],[1.0,1.0]])y=torch.tensor([3.0,5.0,4.0,6.0])optimizer=torch.optim.SGD(model.parameters(),lr=0.1)withtorchsnooper.snoop():for_inrange(10):optimizer.zero_grad()pred=model(x)squared_diff=(y-pred)**2loss=squared_diff.mean()print(loss.item())loss.backward()optimizer.step()...
step=1)c=torch.arange(0,10,1);print(c)#初始化指定值的Tensore=torch.full((2,3),2);print(e)#***随机初始化***#初始化为[0,1)内的均匀分布随机数a=torch.rand((2,3));print(a)#初始化为[0,1)内的均匀分布随机数
# Create tensors via torch.from_numpy(ndarray)arr=np.array([[1,2,3],[4,5,6]])t=torch.from_numpy(arr)print("numpy array: ",arr)print("tensor : ",t)print("\n修改arr")arr[0,0]=0print("numpy array: ",arr)print("tensor : ",t)print("\n修改tensor")t[0,0]=-1print("num...
# 输出:Tensor: tensor([[-0.6643, -1.7207, -0.7312], # [-0.9627, -0.5519, -0.7359]]) print("Tensor of type:",a.type()) # 输出:Tensor of type: torch.FloatTensor # 在深度学习中,我们经常会用到参数的合理化检验,一般使用下面这个方法 ...
2.5 torch. full() 2.6 torch.full_like() 功能:依据input 形状创建指定数据的张量 size : 张量的形状 , 如 (3,3) fill_value : 张量的值 code t = torch.full((3, 3), 5) print(t) tensor([[5, 5, 5], [5, 5, 5], [5, 5, 5]]) ...
>>>print(matrix) tensor([[2,3,4], [5,6,9]]) 3,使用torch.Tensor.item()或者int()方法从只有一个值的 Tensor中获取 Python Number: >>>x = torch.tensor([[4.5]]) >>>x tensor([[4.5000]]) >>>x.item() 4.5 >>>int(x)
以下是使用torch.tensor()创建张量的基本示例: 复制 importnumpyasnpimporttorch arr=np.ones((3,3))'''[[1.1.1.][1.1.1.][1.1.1.]]'''print(arr)# ndarray的数据类型:float64print("ndarray的数据类型:",arr.dtype)t=torch.tensor(arr)'''tensor([[1.,1.,1.],[1.,1.,1.],[1.,1.,1....
print(x) #全1张良 x=torch.ones((3,3)) print(x) tensor([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) #元素为随机数的张量 x=torch.rand((3,3)) print(x) tensor([[0.4704, 0.6278, 0.2294], [0.1838, 0.4951, 0.8452], ...
print(a)print(torch.flip(a, dims=[2, 1])) print(a)print(a.shape)out = torch.rot90(a, -1, dims=[0, 2])#顺时针旋转90°print(out)print(out.shape) 06 Tensor的填充操作 torch.full((2,3),3.14) 07 Tensor的频谱操作(傅里叶变换)...