【AI】Tensor基础入门
Tensor基础入门
import torch
import numpy as np
1. 初始化Tensor
- 直接来源于数据
data = [[1, 2],[3, 4]]
x_data = torch.tensor(data)
x_data
tensor([[1, 2],
[3, 4]])
- 使用Numpy数组
np_array = np.array(data)
x_np = torch.from_numpy(np_array)
x_np
tensor([[1, 2],
[3, 4]], dtype=torch.int32)
- 利用其他tensor
x_ones = torch.ones_like(x_data) # retains the properties of x_data
print(f"Ones Tensor: \n {x_ones} \n")
x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data
print(f"Random Tensor: \n {x_rand} \n")
Ones Tensor:
tensor([[1, 1],
[1, 1]])
Random Tensor:
tensor([[0.9375, 0.0723],
[0.7438, 0.0384]])
- 使用随机生成或常量生成
shape = (2,3,)
rand_tensor = torch.rand(shape)
ones_tensor = torch.ones(shape)
zeros_tensor = torch.zeros(shape)
print(f"Random Tensor: \n {rand_tensor} \n")
print(f"Ones Tensor: \n {ones_tensor} \n")
print(f"Zeros Tensor: \n {zeros_tensor}")
Random Tensor:
tensor([[0.0201, 0.9371, 0.0226],
[0.4742, 0.3425, 0.8761]])
Ones Tensor:
tensor([[1., 1., 1.],
[1., 1., 1.]])
Zeros Tensor:
tensor([[0., 0., 0.],
[0., 0., 0.]])
2.Tensor的属性
Tensor具有shape，datatype和device三个属性
tensor = torch.rand(3,4)
print(f"Shape of tensor: {tensor.shape}")
print(f"Datatype of tensor: {tensor.dtype}")
print(f"Device tensor is stored on: {tensor.device}")
Shape of tensor: torch.Size([3, 4])
Datatype of tensor: torch.float32
Device tensor is stored on: cpu
3.Tensor的操作
Tensor有超过100多种操作，包括算术运算，线性代数，矩阵运算，采样和更多其他操作，具体可以参考[]:(https://pytorch.org/docs/stable/torch.html)
通常Tensor是在cpu上创建的，当我们需要在GPU上使用时，可以使用to方法来操作
# We move our tensor to the GPU if available
if torch.cuda.is_available():
tensor = tensor.to("cuda")
- 像numpy一样索引和分割
tensor = torch.ones(4, 4)
print(f"First row: {tensor[0]}")
print(f"First column: {tensor[:, 0]}")
print(f"Last column: {tensor[..., -1]}")
tensor[:,1] = 0
print(tensor)
First row: tensor([1., 1., 1., 1.])
First column: tensor([1., 1., 1., 1.])
Last column: tensor([1., 1., 1., 1.])
tensor([[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.]])
- 连接tensors(joining tensors)
t1 = torch.cat([tensor, tensor, tensor], dim=1)
print(t1)
t2 = torch.cat([tensor, tensor, tensor], dim=0)
print(t2)
tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],
[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])
tensor([[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.]])
- 算术操作
# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value
# ``tensor.T`` returns the transpose of a tensor
y1 = tensor @ tensor.T
y2 = tensor.matmul(tensor.T)
y3 = torch.rand_like(y1)
torch.matmul(tensor, tensor.T, out=y3)
# This computes the element-wise product. z1, z2, z3 will have the same value
z1 = tensor * tensor
z2 = tensor.mul(tensor)
z3 = torch.rand_like(tensor)
torch.mul(tensor, tensor, out=z3)
tensor([[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.]])
- 单元素tensors
可以使用item()方法来将其转换为python的算术值
agg = tensor.sum()
agg_item = agg.item()
print(agg_item, type(agg_item))
12.0
- 原位操作
使用“_” 下划线后缀的方法是原位操作符，可以减少内存占用
print(f"{tensor} \n")
tensor.add_(5)
print(tensor)
tensor([[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.],
[1., 0., 1., 1.]])
tensor([[6., 5., 6., 6.],
[6., 5., 6., 6.],
[6., 5., 6., 6.],
[6., 5., 6., 6.]])
4. Bridge with Numpy
在cpu上的tensors可以和numpy arrays共享存储位置
- tensors转numpy 数组
t = torch.ones(5)
print(f"t: {t}")
n = t.numpy()
print(f"n: {n}")
print(f"t: {t}")
t: tensor([1., 1., 1., 1., 1.])
n: [1. 1. 1. 1. 1.]
t: tensor([1., 1., 1., 1., 1.])
修改tensors也会修改对应的numpy数组
t.add_(1)
print(f"t: {t}")
print(f"n: {n}")
t: tensor([2., 2., 2., 2., 2.])
n: [2. 2. 2. 2. 2.]
- Numpy数组转Tensor
n = np.ones(5)
t = torch.from_numpy(n)
np.add(n, 1, out=n)
print(f"t: {t}")
print(f"n: {n}")
t: tensor([2., 2., 2., 2., 2.], dtype=torch.float64)
n: [2. 2. 2. 2. 2.]