数据挖掘 - 如何计算 keras 序列模型的准确率、准确率和召回率以及 F1 分数？ - 吾爱随笔录

我想计算多类分类问题的准确率、准确率和召回率以及 F1 分数。我正在使用下面提到的这些代码行。

from keras import backend as K
def precision(y_true, y_pred, average='None'):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

def recall(y_true, y_pred, average='micro'):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall

def f1(y_true, y_pred, average='weighted'):
    def recall(y_true, y_pred):
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + K.epsilon())
        return recall

    def precision(y_true, y_pred):
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision

    precision = precision(y_true, y_pred)
    recall = recall(y_true, y_pred)
    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))

不知道为什么测试和训练的召回值都是 1。请帮我计算使用 Keras 模型进行多类分类的准确率、准确率和召回率以及 F1 分数。

def recall(y_true, y_pred,class_to_analyse): pred = K.argmax(y_pred) true = K.argmax(y_true) p = K.cast(K.equal(pred,class_to_analyse),'int32') t = K.cast(K.equal(true,class_to_analyse),'int32') # Compute the true positive common = K.sum(K.dot(K.reshape(t,(1,-1)),K.reshape(p,(-1,1)))) # divide by all positives in t recall = common/ (K.sum(t) + K.epsilon) return recall def precision(y_true, y_pred,class_to_analyse): pred = K.argmax(y_pred) true = K.argmax(y_true) p = K.cast(K.equal(pred,class_to_analyse),'int32') t = K.cast(K.equal(true,class_to_analyse),'int32') # Compute the true positive common = K.sum(K.dot(K.reshape(t,(1,-1)),K.reshape(p,(-1,1)))) # divide by all positives in t precision = common/ (K.sum(p) + K.epsilon) return precision def fbeta(y_true, y_pred,class_to_analyse): beta = 1 # for f1 score precision = precision(y_true, y_pred,class_to_analyse) recall = recall(y_true, y_pred,class_to_analyse) beta_squared = beta ** 2 return (beta_squared + 1) * (precision * recall) / (beta_squared * precision + recall)