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linear_regration.py
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linear_regration.py
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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
"""
our model class should be inherit from nn.Module, which is base class for all neural network modules.
member methods __init__() and forward() have to be implemented
class nn.linear contain two member Tensors: weight and bias
class nn.Linear has implemented the magic method __call__(),which enable the instance of the class can
be called just like a function.Normally the forward() will be called
"""
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')
# model.parameters()自动完成参数的初始化操作,这个地方我可能理解错了
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# 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())
# the grad computer by .backward() will be accumulated. so before backward, remember set the grad to zero
optimizer.zero_grad()
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)