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pytorch Dropout過擬合

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import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt

torch.manual_seed(1)

N_SAMPLES = 20
N_HIDDEN = 300

# training data
x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
y = x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
x, y = Variable
(x), Variable(y) # test data 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)) test_x = Variable(test_x, volatile=True) test_y = Variable(test_y, volatile=True) # show data # 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() net_overfitting = torch.nn.Sequential( torch.nn.Linear(1, N_HIDDEN), torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, N_HIDDEN), torch.nn.ReLU
(), torch.nn.Linear(N_HIDDEN, 1), ) net_dropped = torch.nn.Sequential( torch.nn.Linear(1, N_HIDDEN), torch.nn.Dropout(0.5), torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, N_HIDDEN), torch.nn.Dropout(0.5), torch.nn.ReLU(), torch.nn.Linear(N_HIDDEN, 1), ) print(net_overfitting) print(net_dropped) optimizer_ofit = torch.optim.Adam( net_overfitting.parameters(), lr = 0.01, ) optimizer_drop = torch.optim.Adam( net_dropped.parameters(), lr = 0.01, ) loss_func = torch.nn.MSELoss() plt.ion() for t in range(500): pred_ofit = net_overfitting(x) pred_drop = net_dropped(x) loss_ofit = loss_func(pred_ofit, y) loss_drop = loss_func(pred_drop, y) optimizer_ofit.zero_grad() optimizer_drop.zero_grad() loss_ofit.backward() loss_drop.backward() optimizer_ofit.step() optimizer_drop.step() if t % 10 == 0: net_overfitting.eval() net_dropped.eval() plt.cla() test_pred_ofit = net_overfitting(test_x) test_pred_drop = net_dropped(test_x) plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test') plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting') plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)') plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 20, 'color': 'red'}) plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 20, 'color': 'blue'}) plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1) net_overfitting.train() net_dropped.train() plt.ioff() plt.show()