machine-learning - keras 中是否有基于精度或召回率而不是损失的优化器？

Question

我正在开发一个只有两个类的分割神经网络，0 和 1(0 是背景，1 是我想在图像上找到的对象)。在每张图像上，大约有 80% 为 1，20% 为 0。如您所见，数据集不平衡，导致结果错误。我的准确率是 85%，损失也很低，但这只是因为我的模型擅长寻找背景!

我希望优化器基于另一个指标，例如精度或召回率，这在这种情况下更有用。

有人知道如何实现吗？

Answer 1

您不使用精度或召回率来进行优化。您只需将它们作为有效分数进行跟踪即可获得最佳权重。不要混合损失、优化器、指标和其他。它们的目的不同。

THRESHOLD = 0.5
def precision(y_true, y_pred, threshold_shift=0.5-THRESHOLD):

    # just in case 
    y_pred = K.clip(y_pred, 0, 1)

    # shifting the prediction threshold from .5 if needed
    y_pred_bin = K.round(y_pred + threshold_shift)

    tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
    fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)))

    precision = tp / (tp + fp)
    return precision


def recall(y_true, y_pred, threshold_shift=0.5-THRESHOLD):

    # just in case 
    y_pred = K.clip(y_pred, 0, 1)

    # shifting the prediction threshold from .5 if needed
    y_pred_bin = K.round(y_pred + threshold_shift)

    tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
    fn = K.sum(K.round(K.clip(y_true - y_pred_bin, 0, 1)))

    recall = tp / (tp + fn)
    return recall


def fbeta(y_true, y_pred, beta = 2, threshold_shift=0.5-THRESHOLD):   
    # just in case 
    y_pred = K.clip(y_pred, 0, 1)

    # shifting the prediction threshold from .5 if needed
    y_pred_bin = K.round(y_pred + threshold_shift)

    tp = K.sum(K.round(y_true * y_pred_bin)) + K.epsilon()
    fp = K.sum(K.round(K.clip(y_pred_bin - y_true, 0, 1)))
    fn = K.sum(K.round(K.clip(y_true - y_pred, 0, 1)))

    precision = tp / (tp + fp)
    recall = tp / (tp + fn)

    beta_squared = beta ** 2
    return (beta_squared + 1) * (precision * recall) / (beta_squared * precision + recall) 


def model_fit(X,y,X_test,y_test):
    class_weight={
    1: 1/(np.sum(y) / len(y)),
    0:1}
    np.random.seed(47)
    model = Sequential()
    model.add(Dense(1000, input_shape=(X.shape[1],)))
    model.add(Activation('relu'))
    model.add(Dropout(0.35))
    model.add(Dense(500))
    model.add(Activation('relu'))
    model.add(Dropout(0.35))
    model.add(Dense(250))
    model.add(Activation('relu'))
    model.add(Dropout(0.35))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy', optimizer='adamax',metrics=[fbeta,precision,recall])
    model.fit(X, y,validation_data=(X_test,y_test), epochs=200, batch_size=50, verbose=2,class_weight = class_weight)
    return model