据我所知，model.fit(epochs=NUM_EPOCHS)不会在每个epoch后重置指标。我的指标和model.fit()的代码如下（简化版）：
import tensorflow as tffrom tensorflow.keras import applicationsNUM_CLASSES = 4INPUT_SHAPE = (256, 256, 3)MODELS = { 'DenseNet121': applications.DenseNet121, 'DenseNet169': applications.DenseNet169}REDUCE_LR_PATIENCE = 2REDUCE_LR_FACTOR = 0.7EARLY_STOPPING_PATIENCE = 4for modelName, model in MODELS.items(): loadedModel = model(include_top=False, weights='imagenet', pooling='avg', input_shape=INPUT_SHAPE) sequentialModel = tf.keras.models.Sequential() sequentialModel.add(loadedModel) sequentialModel.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')) aucCurve = tf.keras.metrics.AUC(curve = 'ROC', multi_label = True) categoricalAccuracy = tf.keras.metrics.CategoricalAccuracy() F1Score = tfa.metrics.F1Score(num_classes = NUM_CLASSES, average = 'macro', threshold = None) metrics = [aucCurve, categoricalAccuracy, F1Score] sequentialModel.compile(metrics=metrics) callbacks = [ tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=REDUCE_LR_PATIENCE, verbose=1, factor=REDUCE_LR_FACTOR), tf.keras.callbacks.EarlyStopping(monitor='val_loss', verbose=1, patience=EARLY_STOPPING_PATIENCE), tf.keras.callbacks.ModelCheckpoint(filepath=modelName + '_epoch-{epoch:02d}.h5', monitor='val_loss', save_best_only=False, verbose=1), tf.keras.callbacks.CSVLogger(modelName + '_training.csv')] sequentialModel.fit(epochs=NUM_EPOCHS)或许我可以通过在NUM_EPOCHS范围内进行for循环，并在循环中初始化指标来重置指标，但我并不确定这是否是一个好的解决方案。此外，我有ModelCheckpoint和CSVLogger回调，它们需要从model.fit()获取epoch编号，因此如果我使用for循环，这实际上是行不通的。
您对如何在每个epoch后重置指标有什么建议吗？在NUM_EPOCHS范围内进行for循环是这里唯一的解决方案吗？谢谢您。
回答：
不，指标是按每个epoch计算的。它们不是在epoch之间进行平均，而是每个epoch内的批次进行平均。你会看到指标在每个epoch后不断改善，因为你的模型正在被训练。
