如何解决在多种情况下尽早停车
我正在为推荐器系统（项目推荐）进行多类分类，并且我目前正在使用sparse_categorical_crossentropy损失来训练我的网络。因此，通过监视我的验证损失EarlyStopping来执行val_loss是合理的：
tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)
可正常使用。但是，网络（推荐系统）的性能由“平均精度为10”来衡量，并在训练过程中作为指标average_precision_at_k10进行跟踪。因此，我还可以使用以下指标来提前停止：
tf.keras.callbacks.EarlyStopping(monitor='average_precision_at_k10',patience=10)
也可以按预期工作。
我的问题： 有时，验证损失会增加，而平均精度为10的情况会有所改善，反之亦然。因此，我需要监视两者，并尽早停止，当且仅当都恶化时。我想做什么：
tf.keras.callbacks.EarlyStopping(monitor=['val_loss','average_precision_at_k10'],patience=10)
显然不起作用。有什么想法可以做到吗？
解决方法
在上面Gerry P的指导下，我设法创建了自己的自定义EarlyStopping回调，并以为可以在其他人希望实现类似功能的情况下将其发布在此处。
如果两者验证损失 和均 em的平均精度对于{{1 }}个时代，尽早停止。
patience然后用作：
class CustomEarlyStopping(keras.callbacks.Callback):
def __init__(self,patience=0):
super(CustomEarlyStopping,self).__init__()
self.patience = patience
self.best_weights = None
def on_train_begin(self,logs=None):
# The number of epoch it has waited when loss is no longer minimum.
self.wait = 0
# The epoch the training stops at.
self.stopped_epoch = 0
# Initialize the best as infinity.
self.best_v_loss = np.Inf
self.best_map10 = 0
def on_epoch_end(self,epoch,logs=None):
v_loss=logs.get('val_loss')
map10=logs.get('val_average_precision_at_k10')
# If BOTH the validation loss AND map10 does not improve for 'patience' epochs,stop training early.
if np.less(v_loss,self.best_v_loss) and np.greater(map10,self.best_map10):
self.best_v_loss = v_loss
self.best_map10 = map10
self.wait = 0
# Record the best weights if current results is better (less).
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience:
self.stopped_epoch = epoch
self.model.stop_training = True
print("Restoring model weights from the end of the best epoch.")
self.model.set_weights(self.best_weights)
def on_train_end(self,logs=None):
if self.stopped_epoch > 0:
print("Epoch %05d: early stopping" % (self.stopped_epoch + 1))
您可以通过创建自定义回调来实现。有关如何执行此操作的信息位于here.下面是一些代码，这些代码说明了您可以在自定义回调中执行的操作。我参考的文档显示了许多其他选项。
class LRA(keras.callbacks.Callback): # subclass the callback class
# create class variables as below. These can be accessed in your code outside the class definition as LRA.my_class_variable,LRA.best_weights
my_class_variable=something # a class variable
best_weights=model.get_weights() # another class variable
# define an initialization function with parameters you want to feed to the callback
def __init__(self,param1,param2,etc):
super(LRA,self).__init__()
self.param1=param1
self.param2=param2
etc for all parameters
# write any initialization code you need here
def on_epoch_end(self,logs=None): # method runs on the end of each epoch
v_loss=logs.get('val_loss') # example of getting log data at end of epoch the validation loss for this epoch
acc=logs.get('accuracy') # another example of getting log data
LRA.best_weights=model.get_weights() # example of setting class variable value
print(f'Hello epoch {epoch} has just ended') # print a message at the end of every epoch
lr=float(tf.keras.backend.get_value(self.model.optimizer.lr)) # get the current learning rate
if v_loss > self.param1:
new_lr=lr * self.param2
tf.keras.backend.set_value(model.optimizer.lr,new_lr) # set the learning rate in the optimizer
# write whatever code you need
我建议您创建自己的回调。 在下面的内容中，我添加了一个同时监视准确性和损失的解决方案。您可以使用自己的指标替换acc：
class CustomCallback(keras.callbacks.Callback):
acc = {}
loss = {}
best_weights = None
def __init__(self,patience=None):
super(CustomCallback,self).__init__()
self.patience = patience
def on_epoch_end(self,logs=None):
epoch += 1
self.loss[epoch] = logs['loss']
self.acc[epoch] = logs['accuracy']
if self.patience and epoch > self.patience:
# best weight if the current loss is less than epoch-patience loss. Simiarly for acc but when larger
if self.loss[epoch] < self.loss[epoch-self.patience] and self.acc[epoch] > self.acc[epoch-self.patience]:
self.best_weights = self.model.get_weights()
else:
# to stop training
self.model.stop_training = True
# Load the best weights
self.model.set_weights(self.best_weights)
else:
# best weight are the current weights
self.best_weights = self.model.get_weights()
请记住，如果要控制监视数量（即min_delta）的最小变化，则必须将其集成到代码中。
以下是有关如何建立自定义回调的文档：custom_callback
,这时，进行自定义循环并仅使用if语句会更加简单。例如：
<script>这是使用此方法的简单自定义训练循环：
def main(epochs=50):
for epoch in range(epochs):
fit(epoch)
if test_acc.result() > .8 and topk_acc.result() > .9:
print(f'\nEarly stopping. Test acc is above 80% and TopK acc is above 90%.')
break
if __name__ == '__main__':
main(epochs=100)
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