我正在為推薦系統(tǒng)（項(xiàng)目推薦）進(jìn)行多類分類，目前正在使用sparse_categorical_crossentropy損失來訓(xùn)練我的網(wǎng)絡(luò)。EarlyStopping因此，通過監(jiān)控我的驗(yàn)證損失來執(zhí)行是合理的，val_loss如下所示：tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)其按預(yù)期工作。然而，網(wǎng)絡(luò)（推薦系統(tǒng)）的性能是通過 Average-Precision-at-10 來衡量的，并在訓(xùn)練期間作為指標(biāo)進(jìn)行跟蹤，如average_precision_at_k10。因此，我還可以使用此指標(biāo)執(zhí)行提前停止：tf.keras.callbacks.EarlyStopping(monitor='average_precision_at_k10', patience=10)這也按預(yù)期工作。我的問題： 有時(shí)驗(yàn)證損失會增加，而 10 處的平均精度會提高，反之亦然。因此，當(dāng)且僅當(dāng)兩者都惡化時(shí)，我需要監(jiān)測兩者，并盡早停止。我想做的事：tf.keras.callbacks.EarlyStopping(monitor=['val_loss', 'average_precision_at_k10'], patience=10)這顯然不起作用。有什么想法可以做到這一點(diǎn)嗎？
4 回答
守著星空守著你
TA貢獻(xiàn)1799條經(jīng)驗(yàn) 獲得超8個贊
我成功創(chuàng)建了自己的自定義 EarlyStopping 回調(diào)，并認(rèn)為我將其發(fā)布在這里，以防其他人想要實(shí)現(xiàn)類似的東西。
如果驗(yàn)證損失和10 時(shí)的?平均精度對于epoch 數(shù)沒有改善，則執(zhí)行早期停止。patience
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))
然后將其用作：
model.fit(
? ? x_train,
? ? y_train,
? ? batch_size=64,
? ? steps_per_epoch=5,
? ? epochs=30,
? ? verbose=0,
? ? callbacks=[CustomEarlyStopping(patience=10)],
)
呼如林
TA貢獻(xiàn)1798條經(jīng)驗(yàn) 獲得超3個贊
您可以通過創(chuàng)建自定義回調(diào)來實(shí)現(xiàn)此目的。下面是一些代碼，說明了您可以在自定義回調(diào)中執(zhí)行哪些操作。我引用的文檔顯示了許多其他選項(xiàng)。
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
哈士奇WWW
TA貢獻(xiàn)1799條經(jīng)驗(yàn) 獲得超6個贊
我建議您創(chuàng)建自己的回調(diào)。下面我添加了一個監(jiān)控準(zhǔn)確性和損失的解決方案。您可以將 acc 替換為您自己的指標(biāo)：
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()
請記住，如果您想控制受監(jiān)控?cái)?shù)量的最小變化（又名 min_delta），您必須將其集成到代碼中。
蕪湖不蕪
TA貢獻(xiàn)1796條經(jīng)驗(yàn) 獲得超7個贊
此時(shí)，創(chuàng)建自定義循環(huán)并僅使用 if 語句會更簡單。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)
這是使用此方法的簡單自定義訓(xùn)練循環(huán)：
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)
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