import torch
import matplotlib.pyplot as plt

x = torch.linspace(0, 100, 100).type(torch.float32)
y = x + torch.randn(100) * 10

learning_rate = 0.0001

weight = torch.rand(1, requires_grad=True)
bias = torch.rand(1, requires_grad=True)

for i in range(1000):
    predictions = weight.expand_as(x) * x + bias.expand_as(x)
    loss = torch.mean((predictions - y) ** 2)
    print('loss:', loss.item())
    loss.backward()
    weight.data.add_(-learning_rate * weight.grad.data)
    bias.data.add_(-learning_rate * bias.grad.data)
    weight.grad.data.zero_()
    bias.grad.data.zero_()

plt.figure(figsize=(10, 8))
xplot, = plt.plot(x, y, 'o')
yplot, = plt.plot(x, weight.data * x + bias.data)
plt.xlabel('X')
plt.ylabel('Y')
str1 = str(weight.data[0]) + 'x + ' + str(bias.data[0])
plt.legend([xplot, yplot], ['Data', str1])
plt.show()

import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt

x = torch.linspace(0, 100, 100).type(torch.float32)
y = x + torch.randn(100) * 10

x_train = x.reshape(-1, 1)
y_train = y.reshape(-1, 1)

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(1, 1)

    def forward(self, x):
        return self.fc(x)

model = Model()

criterion = nn.MSELoss()

optimizer = optim.SGD(model.parameters(), lr=0.0001)

for i in range(1000):
    predictions = model(x_train)
    loss = criterion(predictions, y_train)
    print('loss:', loss.item())

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

weight = model.fc.weight[0, 0].item()
bias = model.fc.bias[0].item()

plt.figure(figsize=(10, 8))
xplot, = plt.plot(x, y, 'o')
yplot, = plt.plot(x, weight * x + bias)
plt.xlabel('X')
plt.ylabel('Y')
str1 = str(weight) + 'x + ' + str(bias)
plt.legend([xplot, yplot], ['Data', str1])
plt.show()