Keras如何处理多标签分类? [英] How does Keras handle multilabel classification?

问题描述

我不确定在以下情况下如何解释Keras的默认行为:

I am unsure how to interpret the default behavior of Keras in the following situation:

我的Y(基本事实)是使用scikit-learn的MultilabelBinarizer()设置的.

My Y (ground truth) was set up using scikit-learn's MultilabelBinarizer().

因此，举一个随机的例子，我的y列的一行是按如下方式热编码的: [0,0,0,1,0,1,0,0,0,0,1].

Therefore, to give a random example, one row of my y column is one-hot encoded as such: [0,0,0,1,0,1,0,0,0,0,1].

因此，我有11个可以预测的类，并且不只一个是真的.因此，问题的多标签性质.此特定样本有三个标签.

So I have 11 classes that could be predicted, and more than one can be true; hence the multilabel nature of the problem. There are three labels for this particular sample.

我像对待非多标签问题(照常营业)一样训练模型，但没有任何错误.

I train the model as I would for a non multilabel problem (business as usual) and I get no errors.

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(5000, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(600, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(y_train.shape[1], activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy',])

model.fit(X_train, y_train,epochs=5,batch_size=2000)

score = model.evaluate(X_test, y_test, batch_size=2000)
score

当Keras遇到我的y_train并看到它是多"一次热编码时，意味着什么?这意味着y_train的每一行中都存在一个以上的"1"吗?基本上，Keras是否会自动执行多标签分类?得分指标的解释有何不同?

What does Keras do when it encounters my y_train and sees that it is "multi" one-hot encoded, meaning there is more than one 'one' present in each row of y_train? Basically, does Keras automatically perform multilabel classification? Any differences in the interpretation of the scoring metrics?

推荐答案

简而言之

不要使用softmax.

使用sigmoid激活输出层.

将binary_crossentropy用于损失功能.

使用predict进行评估.

在softmax中，当增加一个标签的得分时，所有其他标签的得分都会降低(这是概率分布).拥有多个标签时，您不希望这样做.

In softmax when increasing score for one label, all others are lowered (it's a probability distribution). You don't want that when you have multiple labels.

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(5000, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.1))
model.add(Dense(600, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(y_train.shape[1], activation='sigmoid'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy',
              optimizer=sgd)

model.fit(X_train, y_train, epochs=5, batch_size=2000)

preds = model.predict(X_test)
preds[preds>=0.5] = 1
preds[preds<0.5] = 0
# score = compare preds and y_test

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