莫烦pytorch(15)——过拟合
1.构造数据集
import matplotlib.pyplot as plt
N_SAMPLES = 20
N_HIDDEN = 300
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))
# 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))
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()
2.构造dropout和一般的两个网络进行对比
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) # net architecture
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() # something about plotting
dropout可以放在activation之前,也可以放在之后,大多数是放在之前,只需要torch.nn.drop(xxx)
,xxx代表的是百分之多少的units起作用。
3.训练
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:
#因为在测试的过程中是不用dropout,所以在测试过程这个需要加eval()把dropout去掉
net_overfitting.eval()
net_dropped.eval()
test_pred_ofit = net_overfitting(test_x)
test_pred_drop = net_dropped(test_x)
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.numpy(), fontdict={'size': 20, 'color': 'red'})
plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(),
fontdict={'size': 20, 'color': 'blue'})
plt.legend(loc='upper left');
plt.ylim((-2.5, 2.5));
plt.pause(0.1)
net_overfitting.train()
net_overfitting.train()
plt.ioff()
plt.show()
明显看出用了dropout的loss更低,并且更为平滑
这里有一个注意点,就是训练集是需要dropout的,但是测试集是不用的,所以在测试过程中,需要用到net_overfitting.eval()
和net_dropped.eval()
(其实net_overfitting不需要eval,但是为了保险起见,都写一遍,在测试结束接着训练时,在加上net_overfitting.train()
和net_overfitting.train()
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