06_build_nn_quickly.py

"""
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Dependencies:
torch: 0.1.11
"""
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt

torch.manual_seed(1)    # reproducible

# fake data
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 = torch.autograd.Variable(x, requires_grad=False), Variable(y, requires_grad=False)


# replace following code with an easy sequential network
"""
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 = torch.nn.Sequential(
    torch.nn.Linear(1, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 1)
)
print(net)  # net architecture


optimizer = torch.optim.SGD(net.parameters(), lr=0.5)

plt.ion()   # something about plotting
plt.show()

for t in range(100):
    prediction = net(x)     # input x and predict based on x

    loss_func = torch.nn.MSELoss()     # this is for regression mean squared loss
    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 % 5 == 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.pause(0.1)

plt.ioff()
plt.show()