人到中年有点甜
在 kerasEarlyStopping中，当受监控的数量停止改善时，您会停止。从您的问题来看，您不清楚要停止的条件是什么。如果您只想监视一个值，EarlyStopping但只想在一批后停止，如果该值没有提高，您可以重写EarlyStopping类并实现逻辑 inon_batch_end而不是on_epoch_end：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如果您有其他逻辑，则可以根据您的逻辑使用on_batch_end和设置，但我认为您明白了。self.model.stop_training = True
红颜莎娜
您可以使用model.stop_training参数来停止训练。例如，如果我们想在第 2 轮第 3 批次停止训练，那么您可以执行如下操作。import kerasfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGDimport numpy as npimport pandas as pdclass 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 = TrueX_train = np.random.random((100, 3))y_train = pd.get_dummies(np.argmax(X_train[:, :3], axis=1)).valuesclf = 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 1/10100/100 [==============================] - 0s 337us/step - loss: 1.0860Epoch 2/10 16/100 [===>..........................] - ETA: 0s - loss: 1.0830Stopping at Epoch 1, Batch 3<keras.callbacks.callbacks.History at 0x7ff2e3eeee10>