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1 损失函数
我们先用sklearn生成一个多标签分类数据集。
from sklearn.datasets import make_multilabel_classification
X, y = make_multilabel_classification(n_samples=1000,
n_features=10,
n_classes=3,
n_labels=2,
random_state=1)
print(X.shape, y.shape)
看一下标签长啥样。
每一行都是0、1标签,1可能有多个,这就是多标签了。
由于仍然是二分类(标签只有0和1),所以激活函数用Sigmoid(对输出的每一个维度用Sigmoid)。这个时候损失函数就是BCELoss。
如果是普通的二分类,Sigmoid的输出是一个值。用 N N N表示样本数量, p n p_n pn表示预测第 n n n个样本为正例的概率, y n y_n yn表示第 n n n个样本的标签,则BCELoss计算公式为:
l o s s = − 1 N ∑ n = 1 N y n × l o g ( p n ) + ( 1 − y n ) × l o g ( 1 − p n ) loss=-\frac{1}{N}\sum_{n=1}^{N}y_n×log(p_n)+(1-y_n)×log(1-p_n) loss=−N1n=1∑Nyn×log(pn)+(1−yn)×log(1−pn)
那么对于多标签分类呢?BCELoss会计算每一个维度上的损失然后求平均。
举个例子,假如模型某个输出是[0.2,0.6,0.8],真实值是[0,0,1],那么该样本损失可以计算如下:
a = 0 × l n ( 0.2 ) + 1 × l n ( 1 − 0.2 ) b = 0 × l n ( 0.6 ) + 1 × l n ( 1 − 0.6 ) c = 1 × l n ( 0.8 ) + 0 × l n ( 1 − 08 ) l o s s = ( a + b + c ) / 3 a=0×ln(0.2)+1×ln(1-0.2)\\ b=0×ln(0.6)+1×ln(1-0.6)\\ c=1×ln(0.8)+0×ln(1-08)\\ loss=(a+b+c)/3 a=0×ln(0.2)+1×ln(1−0.2)b=0×ln(0.6)+1×ln(1−0.6)c=1×ln(0.8)+0×ln(1−08)loss=(a+b+c)/3
这只是单个样本的损失,最后还需要求所有样本损失的平均值。但是你就不用管了,只需要知道多标签分类用Sigmoid+BCELoss就可以完成损失计算。还有一个函数叫BCEWithLogitsLoss,是Sigmoid和BCELoss的结合。如果损失函数用这个,Sigmoid就可以不用。
2 准确率计算
依然是上面的例子,模型的输出是[0.2,0.6,0.8],真实值是[0,0,1]。准确率该怎么计算呢?
pred = torch.tensor([0.2, 0.6, 0.8])
y = torch.tensor([0, 0, 1])
accuracy = (pred.ge(0.5) == y).all().int().item()
accuracy
# output : 0
首先ge函数将pred中大于等于0.5的转化为True,小于0.5的转化成False,再比较pred和y(必须所有维度都相同才算分类准确),最后将逻辑值转化为整数输出即可。
训练时都是按照一个batch计算的,那就写一个循环吧。
pred = torch.tensor([[0.2, 0.5, 0.8], [0.4, 0.7, 0.1]])
y = torch.tensor([[0, 0, 1], [0, 1, 0]])
accuracy = sum(row.all().int().item() for row in (pred.ge(0.5) == y))
accuracy
# output : 1
3 完整代码
from sklearn.datasets import make_multilabel_classification
import torch
from torch.utils.data import DataLoader
from sklearn.model_selection import train_test_split
def get_dataset():
X, y = make_multilabel_classification(n_samples=1000,
n_features=10,
n_classes=3,
n_labels=2,
random_state=1)
return X,y
n_inputs, n_outputs = X.shape[1], y.shape[1]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33,random_state=42)
X_train = torch.from_numpy(X_train).float()
X_test = torch.from_numpy(X_test).float()
y_train = torch.from_numpy(y_train).float()
y_test = torch.from_numpy(y_test).float()
train_data=[(X,y) for X,y in zip(X_train,y_train)]
train_loader = DataLoader(train_data, batch_size=64,shuffle=True)
class MLP(nn.Module):
def __init__(self, n_inputs, n_outputs, num_hiddens):
super(MLP, self).__init__()
self.linear_relu_stack = nn.Sequential(
nn.Linear(n_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, n_outputs),
nn.Sigmoid())
def forward(self, x):
outputs = self.linear_relu_stack(x)
return outputs
num_hiddens = 30
model = MLP(n_inputs, n_outputs, num_hiddens)
print(model)
loss = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
def evaluate_accuracy(X, y, model):
pred = model(X)
correct = sum(row.all().int().item() for row in (pred.ge(0.5) == y))
n = y.shape[0]
return correct / n
def train(train_loader, X_test, y_test, model, loss, num_epochs, batch_size,
optimizer):
batch_count = 0
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in train_loader:
pred = model(X)
l = loss(pred, y)
optimizer.zero_grad()
l.backward()
optimizer.step()
train_l_sum += l.item()
train_acc_sum += sum(row.all().int().item()
for row in (pred.ge(0.5) == y))
n += y.shape[0]
batch_count += 1
test_acc = evaluate_accuracy(X_test, y_test, model)
print(
'epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
% (epoch + 1, train_l_sum / batch_count, train_acc_sum / n,
test_acc))
num_epochs, batch_size = 20, 64
train(train_loader, X_test, y_test, model, loss, num_epochs, batch_size,optimizer)
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