import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim


x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
y = x.pow(2) + 0.2 * torch.rand(x.size())

# plt.scatter(x.numpy(), y.numpy())
# plt.show()


class Net(nn.Module):
    def __init__(self, n_features, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = nn.Linear(n_features, n_hidden)
        self.predict = nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x


net = Net(1, 20, 1)
print(net)

plt.ion()
plt.show()

optimizer = optim.SGD(net.parameters(), lr=0.02, momentum=0.9)
loss_function = nn.MSELoss()

for t in range(200):
    prediction = net(x)

    loss = loss_function(prediction, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (t % 5) == 0:
        print("Loss : %f" % loss.data)
        # plot and show learning process
        plt.cla()
        plt.scatter(x.numpy(), y.numpy())
        plt.plot(x.numpy(), prediction.data.numpy(), 'r-', lw=5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.data, fontdict={'size': 20, 'color': 'red'})
        plt.pause(0.1)

plt.ioff()
plt.show()
