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Dependencies:
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
%matplotlib inline
torch.manual_seed(1) # reproducible
<torch._C.Generator at 0x1074602a0>
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)
# torch can only train on Variable, so convert them to Variable
x, y = Variable(x), Variable(y)
plt.scatter(x.data.numpy(), y.data.numpy())
plt.show()
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
self.predict = torch.nn.Linear(n_hidden, n_output) # output layer
def forward(self, x):
x = F.relu(self.hidden(x)) # activation function for hidden layer
x = self.predict(x) # linear output
return x
net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network
print(net) # net architecture
Net ( (hidden): Linear (1 -> 10) (predict): Linear (10 -> 1) )
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
loss_func = torch.nn.MSELoss() # this is for regression mean squared loss
plt.ion() # something about plotting
for t in range(100):
prediction = net(x) # input x and predict based on x
loss = loss_func(prediction, y) # must be (1. nn output, 2. target)
optimizer.zero_grad() # clear gradients for next train
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if t % 10 == 0:
# plot and show learning process
plt.cla()
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
plt.text(0.5, 0, 'Loss=%.4f' % loss.data[0], fontdict={'size': 20, 'color': 'red'})
plt.show()
plt.pause(0.2)
plt.ioff()