【Task5(2天)】PyTorch实现L1,L2正则化以及Dropout
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2022-07-13 10:37:40
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【Task5(2天)】PyTorch实现L1,L2正则化以及Dropout
- 了解知道Dropout原理
- 用代码实现正则化(L1、L2、Dropout)
- Dropout的numpy实现
- PyTorch中实现dropout
了解知道Dropout原理
Dropout是防止过拟合的一种方法(过拟合overfitting指:模型在训练数据上损失函数较小,预测准确率较高;但是在测试数据上损失函数比较大,预测准确率较低。) 训练神经网络模型时,如果训练样本较少,为了防止模型过拟合,Dropout可以作为一种优化方法。
Dropout是指在神经网络的每次训练中以一个参数p为概率,使部分隐层部分神经元失活,以此来解决过拟合问题,效果可以当作用多个不同的神经网络模型在同一训练集上进行训练,最后集成求平均。Dropout还可以消除某些神经元之间的联系,增强模型的鲁棒性。
用代码实现正则化(L1、L2、Dropout)
-
L1范数
L1范数是参数矩阵W中元素的绝对值之和,L1范数相对于L0范数不同点在于,L0范数求解是NP问题,而L1范数是L0范数的最优凸近似,求解较为容易。L1常被称为LASSO.
regularization_loss = 0 for param in model.parameters(): regularization_loss += torch.sum(abs(param)) for epoch in range(EPOCHS): y_pred = model(x_train) classify_loss = criterion(y_pred, y_train.float().view(-1, 1)) loss = classify_loss + 0.001 * regularization_loss # 引入L1正则化项
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L2范数
L2范数是参数矩阵W中元素的平方之和,这使得参数矩阵中的元素更稀疏,与前两个范数不同的是,它不会让参数变为0,而是使得参数大部分都接近于0。L1追求稀疏化,从而丢弃了一部分特征(参数为0),而L2范数只是使参数尽可能为0,保留了特征。L2被称为Rigde.
optimizer = torch.optim.SGD(model.parameters(), lr=1e-1, momentum=0.9, weight_decay=0.001)
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Dropout
import numpy as np X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ]) y = np.array([[0,1,1,0]]).T alpha,hidden_dim,dropout_percent,do_dropout = (0.5,4,0.2,True) synapse_0 = 2*np.random.random((3,hidden_dim)) - 1 synapse_1 = 2*np.random.random((hidden_dim,1)) - 1 for j in xrange(60000): layer_1 = (1/(1+np.exp(-(np.dot(X,synapse_0))))) if(do_dropout): layer_1 *= np.random.binomial([np.ones((len(X),hidden_dim))],1-dropout_percent)[0] * (1.0/(1-dropout_percent)) layer_2 = 1/(1+np.exp(-(np.dot(layer_1,synapse_1)))) layer_2_delta = (layer_2 - y)*(layer_2*(1-layer_2)) layer_1_delta = layer_2_delta.dot(synapse_1.T) * (layer_1 * (1-layer_1)) synapse_1 -= (alpha * layer_1.T.dot(layer_2_delta)) synapse_0 -= (alpha * X.T.dot(layer_1_delta))
PyTorch中实现Dropout
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
# torch.manual_seed(1) # reproducible
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, test_y = Variable(test_x, volatile=True), 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), # drop 50% of the neuron
torch.nn.ReLU(),
torch.nn.Linear(N_HIDDEN, N_HIDDEN),
torch.nn.Dropout(0.5), # drop 50% of the neuron
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
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:
# change to eval mode in order to fix drop out effect
net_overfitting.eval()
net_dropped.eval() # parameters for dropout differ from train mode
# plotting
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)
# change back to train mode
net_overfitting.train()
net_dropped.train()
plt.ioff()
plt.show()