我正在使用Keras（带有Tensorflow 后端）进行二元分类，我的准确率约为 76%，召回率约为 70%。现在我想尝试使用决策阈值。据我所知Keras 使用决策阈值 0.5。 Keras 有没有办法使用自定义阈值来实现决策精度和召回率？
感谢您的宝贵时间！
【问题讨论】：
标签： python machine-learning tensorflow classification keras
像这样创建自定义指标：
感谢@Marcin 编辑：创建以threshold_value 作为参数返回所需指标的函数
def precision_threshold(threshold=0.5):
def precision(y_true, y_pred):
"""Precision metric.
Computes the precision over the whole batch using threshold_value.
"""
threshold_value = threshold
# Adaptation of the "round()" used before to get the predictions. Clipping to make sure that the predicted raw values are between 0 and 1.
y_pred = K.cast(K.greater(K.clip(y_pred, 0, 1), threshold_value), K.floatx())
# Compute the number of true positives. Rounding in prevention to make sure we have an integer.
true_positives = K.round(K.sum(K.clip(y_true * y_pred, 0, 1)))
# count the predicted positives
predicted_positives = K.sum(y_pred)
# Get the precision ratio
precision_ratio = true_positives / (predicted_positives + K.epsilon())
return precision_ratio
return precision
def recall_threshold(threshold = 0.5):
def recall(y_true, y_pred):
"""Recall metric.
Computes the recall over the whole batch using threshold_value.
"""
threshold_value = threshold
# Adaptation of the "round()" used before to get the predictions. Clipping to make sure that the predicted raw values are between 0 and 1.
y_pred = K.cast(K.greater(K.clip(y_pred, 0, 1), threshold_value), K.floatx())
# Compute the number of true positives. Rounding in prevention to make sure we have an integer.
true_positives = K.round(K.sum(K.clip(y_true * y_pred, 0, 1)))
# Compute the number of positive targets.
possible_positives = K.sum(K.clip(y_true, 0, 1))
recall_ratio = true_positives / (possible_positives + K.epsilon())
return recall_ratio
return recall
现在你可以使用它们了
model.compile(..., metrics = [precision_threshold(0.1), precision_threshold(0.2),precision_threshold(0.8), recall_threshold(0.2,...)])
我希望这会有所帮助:)
【讨论】：
y_pred 中的kth 最大值动态计算threshold_value：我在这里问过这个问题：stackoverflow.com/questions/45720458/…