PyTorch Lecture 05: Linear Regression in the PyTorch way

几个步骤要记住

1.Design your model using class、

2.Construct loss and optimizer  (select from PyTorch API)

3. Training cycle (forward, backward, update)

（同时写代码的时候，一定要对齐）

import torch
from torch.autograd import Variable

x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))


class Model(torch.nn.Module):

    def __init__(self):
        """
        In the constructor we instantiate two nn.linear module
        """
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # onr in and one out

    def forward(self, x):  # (自己犯的错误是 两个def未对齐，导致找了好长时间错误，一定要吸取教训)
        """
        In the forward function we accept a Variable of input data and we must return
    a Variable of output data. We can use Modules defined in the constructor as
    well as arbitrary operators on Variables.
        """
        y_pred = self.linear(x)
        return y_pred


# our model
model = Model()
# Construct our loss function and an Optimizer. The call to model.parameters()
# in the SGD constructor will contain the learnable parameters of the
# two nn.Linear modules which are members of the models.

criterion = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training loop
# Forward pass :Compute predicted y by passing x to the model
for epoch in range(500):
    y_pred = model(x_data)
    # Compute and print loss
    loss = criterion(y_pred, y_data)
    print(epoch, loss.data[0])

    # Zero gradients,perform a backward pass,and update the weights
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# After training
hour_var = Variable(torch.Tensor([[4.0]]))
y_pred = model(hour_var)
print("predict (after training)", 4, model(hour_var).data[0][0])

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