pytorch框架使用基础1

代码教程：https://www.bilibili.com/video/BV1nV41177ko

import torch
import torch.nn as nn
import numpy
from torch import tensor

x = torch.empty(5,3)#创建一个shape（5，3）的0tensor
print(x)

y = torch.rand(5,3)#创建一个shape（5，3）的随机tensor
print(y)  #输出为tensor，tensor是一个框架，也是底层，类似与举证

x = torch.tensor([5.5,3])
print(x)#传值，内丝于numpy

x = x.new_ones(5,3,dtype=torch.double)#创建一个元素都为1的tensor
print(x)
x = torch.rand_like(x,dtype = torch.double)
print(x)
print(x.size())#打印大小，类似shape

#view操作可以改变举证的维度
x = torch.randn(4,4)
y = x.view(16)
z = x.view(-1,4)#-1表示自动计算
print(x.size(), y.size(), z.size())


#与numpy的协同操作
#1.tensor转numpy
a = torch.ones(5)
b = a.numpy()
print(b)
#2.numpy转tensor
a = numpy.array([1,2])
b = torch.from_numpy(a)
print(b)

#设置require_grad会自动求导
x = torch.ones(3,4,requires_grad=True)
b = torch.randn(3,4,requires_grad=True)
t = x*x*x + b
print(t.requires_grad) #也支持求导
y = t.sum()
print(y)
y.backward()#反向传播，但是如果不清零会累加
#y.backward(retain_graph=True) retain_qraph允许代码重复执行

print(x.grad)

#x.is_leaf 是否为叶子

#例子：线性回归于模型
#1.初始化训练数据和标签
x_values = [i for i in range(11)]
x_train = numpy.array(x_values,dtype=numpy.float32)
x_train = x_train.reshape(-1,1)

y_values = [i*2+1 for i in range(11)]
y_train = numpy.array(x_values,dtype=numpy.float32)
y_train = x_train.reshape(-1,1)

2.#设计一个类，继承torch.nn的Module
class LinearRegressModel(nn.Module):
    def __init__(self,input_dim,output_dim):
        #调用父类的构造方法
        super(LinearRegressModel, self).__init__()
        #创建模型
        self.linear = nn.Linear(input_dim,output_dim)

    #向前传播
    def forward(self,x):
        out = torch.sigmoid(self.linear(x))

        #out是tensor
        return out

input_dim = 1
output_dim = 1

model = LinearRegressModel(input_dim, output_dim)
epochs = 1000
learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)
criterion = nn.MSELoss() #损失函数

x = torch.tensor([1,2,3,4,5,6,7,8,9,10,11], dtype=torch.double)
print(x)
x = x.view(-1,1)

for epoch in range(epochs):
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)

    optimizer.zero_grad()

    outputs = model(inputs)

    loss = criterion(outputs,labels)

    loss.backward()

    optimizer.step() #刷新

    if epoch % 50 == 0:
        print("epoch {}, loss {}".format(epoch, loss))

#Scalar
x = tensor(42.)
print(x)
print(x.dim()) #打印维度

print(x.item()) #打印

x = tensor([[2,3,4],[4,5,6]])
try:
    print(x.item())
except:
    print("错误：只有一个元素张量可以转换为向量标量")
#Vector
#例如：[-5,2,0],在深度学习中通常指特征，列入词向量特征，某一维度特征等
v = tensor([1.5,-0.5,3.0])
print(v.size())
print(v.dim())

#Matrix
M = tensor([[1,2],[3,4]])
print(M.matmul(M))