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PyTorch Tutorial - Linear Regression With PyTorch

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Lecture5 -- Bilibili: PyTorch Tutorial; Lecturer: Hongpu Liu
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import torch

# step 1: prepare the dataset
x_data = torch.tensor([[1.0],[2.0],[3.0]])
y_data = torch.tensor([[2.0],[4.0],[6.0]])

# step2: design model
"""
Our model class should inherit from torch.nn.Module, which is the base class for all of the neural networks.
__init__() and forward() have to be implemented.
class nn.Linear contains two Tensor components: weight and bias
class nn.Linear has implemented the magic method __call__(), which enable the instance of the class to be called like a function.
therefore, model(data) can call the forward function
"""

class LinearModel(torch.nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        # (1, 1) is the sie of each input sample and output sample
        # parameters in this model: weights and bias
        # model: y = xm + b
        self.linear = torch.nn.Linear(1, 1)
    
    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

model = LinearModel()

# step3: construct loss and optimizer
criterion = torch.nn.MSELoss(size_average = False)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01) #iterable of parameters to optimizer. model.parameters() can get all the parameters in the model

# step4: training cycle - forward, backward, update
for epoch in range(1000):
    y_pred = model(x_data)
    loss = criterion(y_pred, y_data)
    print(epoch, loss.item())

    optimizer.zero_grad() # the grad will be accumulated, so before backward, we need to set the grad to zero
    loss.backward() # backward: autograd
    optimizer.step() # update w, b

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

# step5: test model
x_test = torch.tensor([4.0])
y_test = model(x_test)
print('y_pred= ', y_test.data)

# 980 3.610836074585677e-10
# 981 3.6504843592410907e-10
# 982 3.5726088754017837e-10
# 983 3.4162894735345617e-10
# 984 3.445990159889334e-10
# 985 3.355609123900649e-10
# 986 3.370388412804459e-10
# 987 3.249738256272394e-10
# 988 3.1610625228495337e-10
# 989 3.1587887860951014e-10
# 990 3.1248248433257686e-10
# 991 3.1373303954751464e-10
# 992 3.1042191039887257e-10
# 993 3.0178171073202975e-10
# 994 3.0526337013725424e-10
# 995 2.9184832328610355e-10
# 996 2.9478997021215037e-10
# 997 2.864908310584724e-10
# 998 2.8178703814774053e-10
# 999 2.765574436125462e-10
# w= 2.0000112056732178
# b= 2.0000112056732178
# y_pred=  tensor([8.0000])