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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