PyTorch基础

红圈圈里的数据类型比较重要

瞎贴一波：

一、数据类型

a = torch.randn(2,3)
print(a)
print(type(a))
print(isinstance(a, torch.FloatTensor))#合法化检验
print(isinstance(a, torch.cuda.FloatTensor))#合法化检验

a = a.cuda()#返回一个GPU上的引用
print(isinstance(a, torch.cuda.FloatTensor))#合法化检验

>>>
tensor([[ 1.0300, -0.7234, -0.5679],
        [-0.7928, -0.2508, -0.9085]])
<class ‘torch.Tensor‘>
True
False
True

torch.tensor(1.)

>>>tensor(1.)

torch.tensor(1.3)

>>>tensor(1.3000)


torch.tensor([1.])
#torch.tensor([1.])

>>>tensor([1.])


a = torch.tensor(2.2)
a.shape

>>>torch.Size([])

len(a.shape)

>>>0

a.size()

>>>torch.Size([])

torch.tensor([1.1])

>>>tensor([1.1000])

torch.tensor([1.1,2.2])

>>>tensor([1.1000, 2.2000])

torch.FloatTensor(1)

>>>tensor([1.4574e-43])

torch.FloatTensor(2)

>>>tensor([0., 0.])

data = np.ones(2)
data

>>>array([1., 1.])

torch.from_numpy(data)

>>>tensor([1., 1.], dtype=torch.float64)

a = torch.ones(2)
a.shape

>>>torch.Size([2])

a = torch.randn(2,3)
a

>>>tensor([[ 0.5736,  0.4078, -0.8879],
        [-0.0126,  0.4756, -0.0214]])


a.shape

>>>torch.Size([2, 3])

a.size(0)

>>>2

a.size(1)

>>>3

a = torch.rand(2,1,4,4)
a

>>>tensor([[[[0.4224, 0.5601, 0.3603, 0.7440],
          [0.2749, 0.4182, 0.5837, 0.1956],
          [0.9536, 0.9231, 0.6720, 0.4501],
          [0.0255, 0.0731, 0.7247, 0.3907]]],


        [[[0.0651, 0.7923, 0.1018, 0.3250],
          [0.7650, 0.5583, 0.5320, 0.9807],
          [0.2130, 0.2525, 0.7932, 0.0258],
          [0.7981, 0.6380, 0.1390, 0.2147]]]])


a.numel() #元素个数

>>>32

a.dim()  #维度

>>>4

二、创建Tensor

1.import from numpy，从numpy数据中载入

a = np.array([2,3.3])
torch.from_numpy(a)

>>>tensor([2.0000, 3.3000], dtype=torch.float64)

a = np.ones([2,3])
torch.from_numpy(a)

>>>tensor([[1., 1., 1.],
        [1., 1., 1.]], dtype=torch.float64)

2.import from List，从列表类型数据中载入

torch.tensor([2., 3.2]) #直接接受数据，经常使用，注意是小写
>>>tensor([2.0000, 3.2000])

torch.FloatTensor(4,3)#设定维度，经常使用
>>>tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])


torch.FloatTensor([2.,3.2]) #不轻易用，容易和小写的tensor混淆
>>>tensor([2.0000, 3.2000])

torch.IntTensor(2,3) #未初始化，数据分布差异太大
>>>tensor([[0, 0, 1],
        [0, 1, 0]], dtype=torch.int32)

torch.FloatTensor(2,3) #未初始化
>>>tensor([[7.4715e+37, 6.6001e-43, 8.4078e-45],
        [0.0000e+00, 1.4013e-45, 0.0000e+00]])


torch.tensor([1.2, 3]).type()
>>>‘torch.FloatTensor‘    #默认情况是FloatTensor

torch.set_default_tensor_type(torch.DoubleTensor)     #修改默认情况
torch.tensor([1.2, 3]).type()
>>>‘torch.DoubleTensor‘