Python Keras：监控val_损失以便提前停止-误报问题

对于上述代码，据称监测的val_损失保持不变。事实并非如此。你知道为什么会这样吗

estop = EarlyStopping(monitor='val_loss', patience=10)
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='max')
reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=2, verbose=1, epsilon=1e-4, mode='min')  
history = model.fit_generator(train_manager,validation_data=valid_manager,callbacks=[checkpoint,estop,reduce_lr_loss],use_multiprocessing=True,

                                 epochs=epochs, #steps_per_epoch = seq_len,

                                 verbose=1,
                              max_queue_size=15)

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您使用的是最小模式，这意味着当监控数量停止减少时，lr将减少

我不太明白。日志显示，val_损失没有从6.32651改善，而val_损失实际上在每个时期都有所减少。为什么同一个val_损耗有两个不同的数字？是的，这有点令人困惑，但最小模式意味着当值增加时，它会得到改善。您应该尝试自动模式或最大模式。整洁！所以尽量减少损失，最大化其他东西…-谢谢。

Epoch 1/20
5155/5155 [==============================] - 207s 40ms/step - loss: 6.3960 - acc: 0.0612 - val_loss: 6.3265 - val_acc: 0.0646

Epoch 00001: val_loss improved from -inf to 6.32651, saving model to weights-improvement-01.hdf5
Epoch 2/20
5155/5155 [==============================] - 207s 40ms/step - loss: 6.2359 - acc: 0.0680 - val_loss: 6.2394 - val_acc: 0.0682

Epoch 00002: val_loss did not improve from 6.32651
Epoch 3/20
5155/5155 [==============================] - 207s 40ms/step - loss: 6.1534 - acc: 0.0712 - val_loss: 6.1957 - val_acc: 0.0702

Epoch 00003: val_loss did not improve from 6.32651
Epoch 4/20
5155/5155 [==============================] - 206s 40ms/step - loss: 6.1023 - acc: 0.0732 - val_loss: 6.1722 - val_acc: 0.0710

Epoch 00004: val_loss did not improve from 6.32651
Epoch 5/20
5155/5155 [==============================] - 207s 40ms/step - loss: 6.0678 - acc: 0.0744 - val_loss: 6.1593 - val_acc: 0.0715

Epoch 00005: val_loss did not improve from 6.32651
Epoch 6/20
5155/5155 [==============================] - 206s 40ms/step - loss: 6.0431 - acc: 0.0752 - val_loss: 6.1525 - val_acc: 0.0720

Epoch 00006: val_loss did not improve from 6.32651
Epoch 7/20
2372/5155 [============>.................] - ETA: 1:32 - loss: 6.0147 - acc: 0.0762


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