我就废话不多说了,大家还是直接看代码吧!

def iou(y_true, y_pred, label: int):  """  Return the Intersection overunion(IoU) for a given label.  Args:    y_true: the expected y values as a one-hot    y_pred: the predicted y values as a one-hot or softmax output    label: the label to return the IoU for  Returns:    the IoU for the given label  """  # extract the label values using the argmax operator then  # calculate equality of the predictions and truths to the label  y_true = K.cast(K.equal(K.argmax(y_true), label), K.floatx())  y_pred = K.cast(K.equal(K.argmax(y_pred), label), K.floatx())  # calculate the |intersection| (AND) of the labels  intersection = K.sum(y_true * y_pred)  # calculate the |union| (OR) of the labelsunion= K.sum(y_true) + K.sum(y_pred) - intersection  # avoid pide by zero - if theunionis zero, return 1  # otherwise, return the intersection overunionreturn K.switch(K.equal(union, 0), 1.0, intersection / union) def mean_iou(y_true, y_pred):  """  Return the Intersection overunion(IoU) score.  Args:    y_true: the expected y values as a one-hot    y_pred: the predicted y values as a one-hot or softmax output  Returns:    the scalar IoU value (mean over all labels)  """  # get number of labels to calculate IoU for  num_labels = K.int_shape(y_pred)[-1] - 1  # initialize a variable to store total IoU in  mean_iou = K.variable(0)    # iterate over labels to calculate IoU for  for label in range(num_labels):    mean_iou = mean_iou + iou(y_true, y_pred, label)      # pide total IoU by number of labels to get mean IoU  return mean_iou / num_labels

补充知识:keras 自定义评估函数和损失函数loss训练模型后加载模型出现ValueError: Unknown metric function:fbeta_score

keras自定义评估函数

有时候训练模型,现有的评估函数并不足以科学的评估模型的好坏,这时候就需要自定义一些评估函数,比如样本分布不均衡是准确率accuracy评估无法判定一个模型的好坏,这时候需要引入精确度和召回率作为评估标准,不幸的是keras没有这些评估函数。

以下是参考别的文章摘取的两个自定义评估函数

召回率:

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

精确度:

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

自定义了评估函数,一般在编译模型阶段加入即可:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy', precision, recall])

自定义了损失函数focal_loss一般也在编译阶段加入:

model.compile(optimizer=Adam(lr=0.0001), loss=[focal_loss],
metrics=['accuracy',fbeta_score], )

其他的没有特别要注意的点,直接按照原来的思路训练一版模型出来就好了,关键的地方在于加载模型这里,自定义的函数需要特殊的加载方式,不然会出现加载没有自定义函数的问题:ValueError: Unknown loss function:focal_loss

解决方案:

model_name = 'test_calssification_model.h6'model_dfcw = load_model(model_name,            custom_objects={'focal_loss': focal_loss,'fbeta_score':fbeta_score})

注意点:将自定义的损失函数和评估函数都加入到custom_objects里,以上就是在自定义一个损失函数从编译模型阶段到加载模型阶段出现的所有的问题。

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