PyTorch_01.py

import torch

x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]

#learning rate
lr = 1e-2

#initialize weight with a random value
w = 1.0

#model 
def forward(x):
    return x * w

#loss function
def loss(x,y):
    y_pred = forward(x)
    return (y_pred -y) ** 2

#compute gradient
def gradient(x,y):
    return 2 * x * (x * w -y)

#before training
print("predict (before training)",4,forward(4))

#training loop
for epoch in range(10):
    for x_val,y_val in zip(x_data,y_data):
        grad = gradient(x_val,y_val)
        w = w - lr * grad
        print("\tgrad: ",x_val,y_val,grad)
        l = loss(x_val,y_val)
    print("progress: ",epoch,"w= ",w,"loss=",l)

#after training
print("predict (after training)",4,forward(4))