python - Keras 回调 ModelCheckpoint 不保存权重

Question

这里建议的解决方案没有解决我的问题this question .

我正在尝试使用回调 ModelCheckpoint 和 EarlyStopping 在提前停止时保存最佳权重。在第一个纪元之后，我收到运行时警告，但代码运行剩余的纪元没有错误，但仍然没有包含权重的文件出现。第一个纪元后的警告如下:

RuntimeWarning: Can save best model only with val_acc available, skipping. 'skipping.' % (self.monitor), RuntimeWarning) RuntimeWarning: Early stopping conditioned on metric val_acc which is not available. Available metrics are: val_loss,val_accuracy,loss,accuracy (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning

我在 fit() 函数中添加了验证数据，所以我不确定为什么。

filepath = "weights_best.hdf5"

model.compile(loss="mean_squared_error",
                  metrics=['accuracy'],
                  optimizer=optimizer)
batchSize = 64 
numEpochs = 75
validation_data = (data.x_valid, data.y_valid)

callbackCheckpoint = keras.callbacks.callbacks.ModelCheckpoint(filepath,
                                                                   monitor='val_acc',
                                                                   save_best_only=True,
                                                                   save_weights_only= True,
                                                                   mode='max')

callbackEarlyStop = keras.callbacks.callbacks.EarlyStopping(monitor='val_acc',
                                                                min_delta=0,
                                                                patience=7,
                                                                verbose=0,
                                                                mode='auto')
callbacks = [callbackCheckpoint, callbackEarlyStop]
model.fit(data.x_train, data.y_train, batchSize, numEpochs, callbacks=callbacks,
              validation_data=validation_data)

如有任何帮助，我们将不胜感激!

Answer 1

将 monitor='val_acc' 更改为 monitor='val_accuracy' 或将 metrics=['accuracy'] 更改为 metrics= ['acc']