在多种情况下提前停止

Question

我正在为推荐系统（项目推荐）进行多类分类，我目前正在使用sparse_categorical_crossentropy loss 训练我的网络。因此，通过监控我的验证损失来执行EarlyStopping 是合理的，val_loss 如下：

tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

按预期工作。然而，网络（推荐系统）的性能是通过 Average-Precision-at-10 来衡量的，并在训练期间作为一个指标进行跟踪，如 average_precision_at_k10。因此，我也可以使用此指标执行提前停止：

tf.keras.callbacks.EarlyStopping(monitor='average_precision_at_k10', patience=10)

这也可以按预期工作。

我的问题： 有时验证损失会增加，而 10 处的平均精度正在提高，反之亦然。因此，我需要监控两者，并执行提前停止，当且仅当两者都在恶化。我想做的事：

tf.keras.callbacks.EarlyStopping(monitor=['val_loss', 'average_precision_at_k10'], patience=10)

这显然行不通。任何想法如何做到这一点？

Answer 1

我建议您创建自己的回调。 在下文中，我添加了一个同时监控准确性和损失的解决方案。您可以将 acc 替换为您自己的指标：

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, epoch, 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，如果您想控制监控量的最小变化（又名 min_delta），您必须将其集成到代码中。

这是有关如何构建客户回调的文档：custom_callback

Answer 2

您可以通过创建自定义回调来实现此目的。有关如何执行此操作的信息位于here. 下面是一些代码，说明了您可以在自定义回调中执行什么操作。我引用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, epoch, 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

Answer 3

在上述Gerry P 的指导下，我设法创建了自己的自定义 EarlyStopping 回调，并认为我将其发布在这里以防其他人希望实现类似的东西。

如果 both 验证损失 和 平均精度为 10 没有提高 patience epochs，提前停止。

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))

然后用作：

model.fit(
    x_train,
    y_train,
    batch_size=64,
    steps_per_epoch=5,
    epochs=30,
    verbose=0,
    callbacks=[CustomEarlyStopping(patience=10)],
)

Answer 4

此时，制作自定义循环并仅使用 if 语句会更简单。例如：

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)

这是一个使用此方法的简单自定义训练循环：

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow_datasets as tfds
import tensorflow as tf

data, info = tfds.load('iris', split='train',
                       as_supervised=True,
                       shuffle_files=True,
                       with_info=True)

def preprocessing(inputs, targets):
    scaled = tf.divide(inputs, tf.reduce_max(inputs, axis=0))
    return scaled, targets

dataset = data.filter(lambda x, y: tf.less_equal(y, 2)).\
    map(preprocessing).\
    shuffle(info.splits['train'].num_examples)

train_dataset = dataset.take(120).batch(4)
test_dataset = dataset.skip(120).take(30).batch(4)


model = tf.keras.Sequential([
    tf.keras.layers.Dense(8, activation='relu'),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(info.features['label'].num_classes, activation='softmax')
    ])


loss_object = tf.losses.SparseCategoricalCrossentropy(from_logits=True)

train_loss = tf.metrics.Mean()
test_loss = tf.metrics.Mean()

train_acc = tf.metrics.SparseCategoricalAccuracy()
test_acc = tf.metrics.SparseCategoricalAccuracy()

topk_acc = tf.metrics.SparseTopKCategoricalAccuracy(k=2)

opt = tf.keras.optimizers.Adam(learning_rate=1e-3)


@tf.function
def train_step(inputs, labels):
    with tf.GradientTape() as tape:
        logits = model(inputs)
        loss = loss_object(labels, logits)

    gradients = tape.gradient(loss, model.trainable_variables)
    opt.apply_gradients(zip(gradients, model.trainable_variables))
    train_loss(loss)
    train_acc(labels, logits)


@tf.function
def test_step(inputs, labels):
    logits = model(inputs)
    loss = loss_object(labels, logits)

    test_loss.update_state(loss)
    test_acc.update_state(labels, logits)

    topk_acc.update_state(labels, logits)

def fit(epoch):
    template = 'Epoch {:>2} Train Loss {:.3f} Test Loss {:.3f} ' \
               'Train Acc {:.2f} Test Acc {:.2f} Test TopK Acc {:.2f} '

    train_loss.reset_states()
    test_loss.reset_states()
    train_acc.reset_states()
    test_acc.reset_states()

    topk_acc.reset_states()

    for X_train, y_train in train_dataset:
        train_step(X_train, y_train)

    for X_test, y_test in test_dataset:
        test_step(X_test, y_test)

    print(template.format(
        epoch + 1,
        train_loss.result(),
        test_loss.result(),
        train_acc.result(),
        test_acc.result(),
        topk_acc.result()
    ))


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)