自动求导机制+线性回归试水笔记（Pytorch）

自动求导机制+线性回归试水笔记（Pytorch） 手动定义求导矩阵

方法一：x=torch.randn(3,4,requires_grad=True)

方法二： x=torch.randn(3,4)
               x.requires_grad=True

计算流程

x=torch.rand(1)
b=torch.rand(1,requires_grad=True)
w=torch.rand(1,requires_grad=True)

y=x*w
z=y+b

#反向传播计算
z.backward(retain_graph=True)    #若不清零，会自动累加
w.grad
b.grad

线性回归试水

import torch
import numpy as np
import torch.nn as nn

#构造一组x和对应标签y
x_val=[i for i in range(11)]
x_train=np.array(x_val,dtype=np.float32)
x_train=x_train.reshape(-1,1)
x_train.shape

y_val=[2*i+1 for i in x_val]
y_train=np.array(y_val,dtype=np.float32)
y_train=y_train.reshape(-1,1)
y_train.shape

#线性回归模型
#线性回归可以理解为一个不加激活函数的全连接层
class LinearRegressionModel(nn.Module):
    def __init__(self,input_dim,output_dim):
        super(LinearRegressionModel,self).__init__()
        self.linear = nn.Linear(input_dim,output_dim)

    def forward(self,x):
        out =self.linear (x)
        return out

input_dim=1
output_dim=1
model = LinearRegressionModel (input_dim,output_dim)

#指定参数和损失
epochs=1000
learning_rate=0.01
optimizer = torch.optim.SGD(model.parameters()，lr=learning_rate)
criterion = nn.MSELoss()        #分类用交叉熵，回归用MSE（一般情况下）

#训练模型
for epoch in range(epochs):
    epoch += 1
    # 注意转行成tensor
    inputs = torch.from_numpy(x_train)
    labels = torch.from_numpy(y_train)

    # 梯度要清零每一次迭代
    optimizer.zero_grad() 

    # 前向传播
    outputs = model(inputs)

    # 计算损失
    loss = criterion(outputs, labels)

    # 返向传播
    loss.backward()

    # 更新权重参数
    optimizer.step()
    if epoch % 50 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))

#模型预测结果
predicted = model(torch.from_numpy(x_train).requires_grad_()).data.numpy()

#模型的保存和读取
torch.save(model.state_dict(), 'model.pkl')
model.load_state_dict(torch.load('model.pkl'))


##GPU训练
import torch
import torch.nn as nn
import numpy as np


class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)  

    def forward(self, x):
        out = self.linear(x)
        return out

input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim, output_dim)


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)


criterion = nn.MSELoss()


learning_rate = 0.01

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 1000
for epoch in range(epochs):
    epoch += 1
    inputs = torch.from_numpy(x_train).to(device)
    labels = torch.from_numpy(y_train).to(device)

    optimizer.zero_grad() 

    outputs = model(inputs)

    loss = criterion(outputs, labels)

    loss.backward()

    optimizer.step()

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

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