pytorch学习1 安装和入门

安装

conda和pip都可以，pip快些
自行下载，在终端输入即可
链接: link.
vscode用调试，用了jypter notebook或者ipython

入门（参考莫烦python）

torch 和numpy转换

import torch
import numpy as np
np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
    '\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]
    '\ntorch tensor:', torch_data,      #  0  1  2 \n 3  4  5    [torch.LongTensor of size 2x3]
    '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)

tensor

tensor是pytorch的一种数据结构，类似高维数组
1.5*3数组

x=t.tensor(5,3)

2.随机二维数组

x=t.rand(5,3)
print(x.size(),x.size()[0],x.size(1))#查看列

3.add

t.add(x,y)
x+y
result=t.Tensor(5,3)
t.add(x,y,out=result)
print(result)
y.add(x)
print(y,"y")
##inplace add,y 改变，下划线的都会修改tensor本身
y.add_(x)
print(y,"y")

4.五个一行的

##随机小于2
x[:,2]
a=t.ones(5)
print(a)

5.numpy和tensor转换

import numpy as np
np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
    '\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]
    '\ntorch tensor:', torch_data,      #  0  1  2 \n 3  4  5    [torch.LongTensor of size 2x3]
    '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)

6.gpu

if t.cuda.is_available():    
    x=x.cuda()

7.others

# abs 绝对值计算
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data)  # 转换成32位浮点 tensor
print(
    '\nabs',
    '\nnumpy: ', np.abs(data),          # [1 2 1 2]
    '\ntorch: ', torch.abs(tensor)      # [1 2 1 2]
)

# sin   三角函数 sin
print(
    '\nsin',
    '\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]
    '\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]
)

# mean  均值
print(
    '\nmean',
    '\nnumpy: ', np.mean(data),         # 0.0
    '\ntorch: ', torch.mean(tensor)     # 0.0
)

# matrix multiplication
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data)  # 32-bit floating point
# correct method
print(
    '\nmatrix multiplication (matmul)',
    '\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
    '\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
)
# incorrect method
data = np.array(data)
print(
    '\nmatrix multiplication (dot)',
    '\nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]]
    '\ntorch: ', tensor.dot(tensor)     # this will convert tensor to [1,2,3,4], you'll get 30.0
)