pytorch 实现小demo
from __future__ import print_function
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
import numpy as np
X=Variable(torch.randn(100,1),requires_grad=False)
Y=3*X+5
w=Variable(torch.randn(1,1),requires_grad=True)
b=Variable(torch.randn(1,1),requires_grad=True)
def gradient_descent():
for i in range(100):
y_ = X @ w + b
loss=torch.mean((Y-y_)**2)
loss.backward()
print ("w.grad: ",w.grad)
w.data = w.data - 0.1 * w.grad.data
b.data = b.data - 0.1 * b.grad.data
w.grad.data.zero_()
b.grad.data.zero_()
print ("w is :%3.2f , b is :%3.2f, loss is :%3.2f"%(w.mean(),b.mean(),loss))
def stochastic_gradient_descent():
for i in range(100):
for x,y in zip(X,Y):
y_ = x @ w + b
loss = torch.mean((y - y_) ** 2)
loss.backward()
print("w.grad: ", w.grad)
w.data = w.data - 0.1 * w.grad.data
b.data = b.data - 0.1 * b.grad.data
w.grad.data.zero_()
b.grad.data.zero_()
print("w is :%3.2f , b is :%3.2f, loss is :%3.2f" % (w.mean(), b.mean(), loss))
stochastic_gradient_descent()
