我如何用keras实现多标签分类神经网络

Question

我正在尝试使用 Keras 实现一个涉及多标签分类的问题的神经网络。我知道解决这个问题的一种方法是将它转化为几个二元分类问题。我已经实现了其中一个，但不确定如何继续其他的，主要是我如何将它们组合起来？我的数据集有 5 个输入变量和 5 个标签。通常单个数据样本会有 1-2 个标签。很少有超过两个标签。

这是我的代码（感谢 machinelearningmastery.com）：

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline


# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)


# load dataset
dataframe = pandas.read_csv("Realdata.csv", header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:5].astype(float)
Y = dataset[:,5]


# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)


# baseline model
def create_baseline():
    # create model
    model = Sequential()
    model.add(Dense(5, input_dim=5, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    scores = model.evaluate(X, encoded_Y)
    print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
    #Make predictions....change the model.predict to whatever you want instead of X
    predictions = model.predict(X)
    # round predictions
    rounded = [round(x[0]) for x in predictions]
    print(rounded)
    return model
    

# evaluate model with standardized dataset
estimator = KerasClassifier(build_fn=create_baseline, epochs=100, batch_size=5, verbose=0)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator, X, encoded_Y, cv=kfold)
print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

Answer 1

您所指的方法是 one-versus-all or the one-versus-one 多标签分类策略。但是，在使用神经网络时，对于具有 5 个标签的多标签分类问题，最简单的解决方案是使用具有 5 个输出节点的单个模型。使用喀拉斯：

model = Sequential()
model.add(Dense(5, input_dim=5, kernel_initializer='normal', activation='relu'))
model.add(Dense(5, kernel_initializer='normal', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd')

您可以将训练标签作为长度为 5 的二进制编码向量提供。例如，对应于类 2 和 3 的示例将具有标签 [0 1 1 0 0].

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