Design your model using class with Variables
Construct loss and optimizer(select from PyTorch API)
Training cycle (forward, backward, update)
import torch
from torch.autograd import Variable
# Data definition (3x1)
x_data = Variable(torch.Tensor([[1.0], [2.0], [3.0]]))
y_data = Variable(torch.Tensor([[2.0], [4.0], [6.0]]))
########################################################################
# 1.Design your model using class with Variables
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) # One in and one out
def forward(self, x):
"""
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()
########################################################################
# 2.Construct loss and optimizer (select from PyTorch API)
# 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 model.
criterion = torch.nn.MSELoss(size_average=False)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
########################################################################
# 3. Training: forward, loss, backward, step
# Training loop
for epoch in range(100):
# Forward pass: Compute predicted y by passing x to the model
y_pred = model(x_data)
# Compute and print loss
loss = criterion(y_pred, y_data)
# print("epoch:", epoch + 1, " loss:", loss.data[0])
print("epoch:", epoch+1, " loss:%.3f" % loss.data.item())
# 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])