pytorch学习第二篇：张量

tensor与numpy

import torch
import numpy as np

numpy数组 到 张量tensor

n = np.ones(5)
t = torch.from_numpy(n)

NumPy 数组中的变化反映在张量中

np.add(n, 1, out=n)
print(f"t: {t}")
print(f"n: {n}")

张量到 NumPy 数

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

张量的变化反映在 NumPy 数组中。

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

torch

data = [[1, 2],[3, 4]]
x_data = torch.tensor(data)

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")

shape = (2,3,)
rand_tensor = torch.rand(shape)
ones_tensor = torch.ones(shape)
zeros_tensor = torch.zeros(shape)

张量属性

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}")

CPU转GPU

if torch.cuda.is_available():
    tensor = tensor.to("cuda")

索引和切片

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)

张量连接

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

算数运算

矩阵乘法，对矩阵不了解的，可以看这篇文章

y1 = tensor @ tensor.T
y2 = tensor.matmul(tensor.T)

y3 = torch.rand_like(y1)
torch.matmul(tensor, tensor.T, out=y3)

普通乘法，下面是计算元素积。z1, z2, z3的值是一样的

z1 = tensor * tensor
z2 = tensor.mul(tensor)

z3 = torch.rand_like(tensor)
torch.mul(tensor, tensor, out=z3)