深度学习实践5 （pytorch相关API）

PyTorch Fashion(风格)

1、prepare dataset

2、design model using Class # 目的是为了前向传播forward，即计算y-hat(预测值)

3、Construct loss and optimizer (using PyTorch API) 其中，计算loss是为了进行反向传播，optimizer是为了更新梯度。

4、Training cycle (forward,backward,update)

epoch的训练过程：

①前向传播，求y hat （输入的预测值）

②根据y_hat和y_label(y_data)计算loss

③反向传播 backward (计算梯度)

④根据梯度，更新参数

import torch

# prepare dataset
# x,y是矩阵，3行1列 也就是说总共有3个数据，每个数据只有1个特征
x_data = torch.tensor([[1.0], [2.0], [3.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0]])

# design model using class

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        # (1,1)是指输入x和输出y的特征维度，这里数据集中的x和y的特征都是1维的
        # 该线性层需要学习的参数是w和b  获取w/b的方式分别是~linear.weight/linear.bias
        self.linear = torch.nn.Linear(1, 1)

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


model = LinearModel()

# construct loss and optimizer
# criterion = torch.nn.MSELoss(size_average = False)
criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # model.parameters()自动完成参数的初始化操作

# training cycle forward, backward, update
for epoch in range(100):
    y_pred = model(x_data)  # forward:predict
    loss = criterion(y_pred, y_data)  # forward: loss
    print(epoch, loss.item())

    optimizer.zero_grad()  # the grad computer by .backward() will be accumulated. so before backward, remember set the grad to zero
    loss.backward()  # backward: autograd，自动计算梯度
    optimizer.step()  # update 参数，即更新w和b的值

print('w = ', model.linear.weight.item())
print('b = ', model.linear.bias.item())

x_test = torch.tensor([[4.0]])
y_test = model(x_test)
print('y_pred = ', y_test.data)

标签：loss,linear,torch,pytorch,API,深度,forward,model,backward
来源： https://www.cnblogs.com/zc-dn/p/16370185.html