Question

我正在为tf.keras培训编写自定义的提早停止回调。为此，我可以在其中一个回调函数中设置变量self.model.stop_training = True，例如on_epoch_end()。但是，即使我在一个时期的训练中设置了该变量，例如在on_batch_end()中，Keras只会在当前时期完成时停止训练。

因此我的问题是：即使在当前时代的进展中，Keras是否有办法立即停止训练？

Answer 1

在喀拉拉邦，您使用EarlyStopping停止监视的数量停止改善。从您的问题尚不清楚您要停止什么条件。如果您只想监视EarlyStopping中的值，但只想在批处理后停止，则该值没有改善，您可以重写EarlyStopping类并在{{1 }}，而不是on_batch_end：

on_epoch_end

如果您有其他逻辑，则可以使用class EarlyBatchStopping(Callback): def __init__(self, monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None, restore_best_weights=False): super(EarlyStopping, self).__init__() self.monitor = monitor self.baseline = baseline self.patience = patience self.verbose = verbose self.min_delta = min_delta self.wait = 0 self.stopped_epoch = 0 self.restore_best_weights = restore_best_weights self.best_weights = None if mode not in ['auto', 'min', 'max']: warnings.warn('EarlyStopping mode %s is unknown, ' 'fallback to auto mode.' % mode, RuntimeWarning) mode = 'auto' if mode == 'min': self.monitor_op = np.less elif mode == 'max': self.monitor_op = np.greater else: if 'acc' in self.monitor: self.monitor_op = np.greater else: self.monitor_op = np.less if self.monitor_op == np.greater: self.min_delta *= 1 else: self.min_delta *= -1 def on_train_begin(self, logs=None): # Allow instances to be re-used self.wait = 0 self.stopped_epoch = 0 if self.baseline is not None: self.best = self.baseline else: self.best = np.Inf if self.monitor_op == np.less else -np.Inf def on_batch_end(self, epoch, logs=None): current = self.get_monitor_value(logs) if current is None: return if self.monitor_op(current - self.min_delta, self.best): self.best = current self.wait = 0 if self.restore_best_weights: 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 if self.restore_best_weights: if self.verbose > 0: 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 and self.verbose > 0: print('Epoch %05d: early stopping' % (self.stopped_epoch + 1)) def get_monitor_value(self, logs): monitor_value = logs.get(self.monitor) if monitor_value is None: warnings.warn( 'Early stopping conditioned on metric `%s` ' 'which is not available. Available metrics are: %s' % (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning ) return monitor_value并根据您的逻辑设置on_batch_end，但我想您已经明白了。

Answer 2

您可以使用model.stop_training参数来停止训练。

例如，如果我们要在第2个时期停止第3批次的训练，则可以执行以下操作。

import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np
import pandas as pd

class My_Callback(keras.callbacks.Callback):
    def on_epoch_begin(self, epoch, logs={}):
      self.epoch = epoch

    def on_batch_end(self, batch, logs={}):
        if self.epoch == 1 and batch == 3:
          print (f"\nStopping at Epoch {self.epoch}, Batch {batch}")
          self.model.stop_training = True


X_train = np.random.random((100, 3))
y_train = pd.get_dummies(np.argmax(X_train[:, :3], axis=1)).values

clf = Sequential()
clf.add(Dense(9, activation='relu', input_dim=3))
clf.add(Dense(3, activation='softmax'))
clf.compile(loss='categorical_crossentropy', optimizer=SGD())

clf.fit(X_train, y_train, epochs=10, batch_size=16, callbacks=[My_Callback()])

输出：

Epoch 1/10
100/100 [==============================] - 0s 337us/step - loss: 1.0860
Epoch 2/10
 16/100 [===>..........................] - ETA: 0s - loss: 1.0830
Stopping at Epoch 1, Batch 3
<keras.callbacks.callbacks.History at 0x7ff2e3eeee10>

Keras中有办法立即停止训练吗？

2 个答案: