import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
regularization_loss = 0
for param in model.parameters():
regularization_loss += torch.sum(abs(param))
calssify_loss = criterion(pred,target)
loss = classify_loss + lamda * regularization_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
optimizer = torch.optim.SGD(model.parameters(),lr=0.01,weight_decay=0.001)
torch.manual_seed(1) # Sets the seed for generating random numbers.reproducible
N_SAMPLES = 20
N_HIDDEN = 300
x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
print('x.size()',x.size())
y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')
plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')
plt.legend(loc='upper left')
plt.ylim((-2.5, 2.5))
plt.show()
x.size() torch.Size([20, 1])
