【pytorch_3】簡単なリニア回帰
11865 ワード
簡単な線形回帰モデルを構築し、コードをpytorch版に変換します.目的:通常コードとpytorchコードの違いを比較する.
# （ ）
import numpy as np
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
w = 1.0
def forward(x):
return x*w
def loss(x,y):
y_pred = forward(x)
return (y_pred-y)**2
def gradient(x,y):
return 2*x*(x*w-y)
print('Predict (before training)',4, forward(4))
for epoch in range(100):
for x,y in zip(x_data,y_data):
grad = gradient(x,y)
w = w - 0.01*grad
print('\tgrad:',x,y,grad)
l = loss(x,y)
print('Epoch:',epoch,'w-',w,'loss=',l)
print('Predict (after training)',4,forward(4))
# （pytorch）
import torch
x_data = torch.Tensor([[1.0], [2.0], [3.0]])
y_data = torch.Tensor([[2.0], [4.0], [6.0]])
w = torch.Tensor([1.0])
w.requires_grad = True
def forward(x):
return x*w
def loss(x,y):
y_pred = forward(x)
return (y_pred-y)**2
print('Predict (before training)',4, forward(4).item())
for epoch in range(100):
for x,y in zip(x_data,y_data):
l = loss(x,y)
l.backward()
print('\tgrad:',x,y,w.grad.item())
w.data = w.data - 0.01*w.grad.data
w.grad.data.zero_()
print('progress:',epoch,'w-',w.item(),'loss=',l.item())
print('Predict (after training)',4,forward(4).item())