Pytorch Tutorial 2

simple linear regression with auto gradient method in pytorch

1. @ means inner dot
2. .t() means transpose matrix
3. .numel() means number of element in matrix
4. with torch.no_grad() means code insider this block will not track gradients to save memory and computation time

import torch
import numpy as np

inputs = np.array([[0, 0, 3],
                   [0, 1, 9],
                   [1, 0, 8],
                   [1, 1, 28]], dtype='float32')

outputs = np.array([[0, 1],
                    [9, 4],
                    [7, 3],
                    [6, 7]], dtype='float32')

inputs = torch.from_numpy(inputs)
outputs = torch.from_numpy(outputs)

w = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, requires_grad=True)

# print(b)

def model(x):
    # the b is the vector, when the matrix plus b, the b will be copy bunch of data to make it as the matrix
    return x @ w.t() + b

def mse(t1, t2):
    return torch.sum((t1 - t2) ** 2) / t1.numel()

learning_rate = 1e-5
for t in range(500):
    y_pred = model(inputs)
    loss = mse(y_pred, outputs)
    loss.backward()
    with torch.no_grad():
        w -= learning_rate * w.grad
        b -= learning_rate * b.grad
        w.grad.zero_()
        b.grad.zero_()
    print(loss.item())