python机器学习pytorch 张量基础教程

# 导入需要的包
import torch
import numpy as np

# 创建python list 数据
data = [[1, 2],[3, 4]]
# 初始化张量
x_data = torch.tensor(data)

# 创建numpy 数组
np_array = np.array(data)
# from_numpy初始化张量
x_np = torch.from_numpy(np_array)

# 创建和x_data （形状shape、数据类型datatype）一样的张量并全部初始化为1
x_ones = torch.ones_like(x_data) # retains the properties of x_data
print(f"Ones Tensor: \n {x_ones} \n")
# 创建和x_data （形状shape）一样的张量并随机初始化，覆盖其数据类型datatype 为torch.float
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([[0.5001, 0.2973],
        [0.8085, 0.9395]])

# 定义形状元组
shape = (2,3,)
# 随机初始化
rand_tensor = torch.rand(shape)
# 全部初始化为1
ones_tensor = torch.ones(shape)
# 全部初始化为0
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([[0.0032, 0.5302, 0.2832],
        [0.0826, 0.3679, 0.8727]])
Ones Tensor:
 tensor([[1., 1., 1.],
        [1., 1., 1.]])
Zeros Tensor:
 tensor([[0., 0., 0.],
        [0., 0., 0.]])

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

# We move our tensor to the GPU if available
if torch.cuda.is_available():
    tensor = tensor.to("cuda")

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.]])

t1 = torch.cat([tensor, tensor, tensor], dim=1)
print(t1)

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.]])

# This computes the matrix multiplication between two tensors. y1, y2, y3 will have the same value
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.]])

agg = tensor.sum()
agg_item = agg.item()
print(agg_item, type(agg_item))

12.0 <class 'float'>

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.]])

t = torch.ones(5)
print(f"t: {t}")
n = t.numpy()
print(f"n: {n}")

t: tensor([1., 1., 1., 1., 1.])
n: [1. 1. 1. 1. 1.]

t.add_(1)
print(f"t: {t}")
print(f"n: {n}")

t: tensor([2., 2., 2., 2., 2.])
n: [2. 2. 2. 2. 2.]

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.]