如何在Keras中获得多标签准确性？

Sklearn将多标签问题的（）计算为子集精度：如果每个预测标签都是正确的，则每个预测都被视为正确的。Keras的默认

“精度”

是二进制精度：

def accuracy(y_true, y_pred):
    return K.mean(K.equal(y_true, K.round(y_pred)), axis=-1)

---

值太高。我认为

K.equal

计算每个元素的相等，对吗？如何计算每个向量的等式？

我想你需要为此编写一个函数。这里有一个例子

import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import Callback

from sklearn.metrics.classification import accuracy_score

n_labels = 3
n_samples = 20
n_feats = 10

x = np.random.normal(size=(n_samples, n_feats))
y = np.random.randint(2, size=(n_samples, n_labels))

model = Sequential()
model.add(Dense(50, input_shape=(n_feats, )))
model.add(Dense(n_labels, activation='sigmoid'))

model.compile(
    loss='binary_crossentropy', 
    optimizer='rmsprop', 
    metrics=['accuracy']
)

class SkLearnHistory(Callback):
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def on_train_begin(self, logs={}):
        self.accuracy = []

    def on_epoch_end(self, epoch, logs={}):
        y_pred = np.round(self.model.predict(self.x))
        y_true = self.y

        score = accuracy_score(y_true, y_pred)
        self.accuracy.append(score)

        return

sklearn_history = SkLearnHistory(x, y)
keras_history = model.fit(x, y, epochs=5, verbose=0, callbacks=[sklearn_history])

keras_history.history['acc']

# [0.46666663885116577,
#  0.48333334922790527,
#  0.51666665077209473,
#  0.58333337306976318,
#  0.60000002384185791]

sklearn_history.accuracy

# [0.14999999999999999, 0.20000000000000001, 0.25, 0.25, 0.25]