Early stopping conditioned on metric val_loss which is not available

[HypergeneticSpacekid]

Hi,

I am getting the following errors, when executing

history = model.fit(x_train, y_train, epochs=params['epochs'], verbose=0,
                    batch_size=64, shuffle=True,
                    validation_data=(x_cv, y_cv),
                    callbacks=[es, mcp, rlp]
                    , sample_weight=sample_weights)

returns:

"WARNING:tensorflow:Early stopping conditioned on metric val_loss which is not available. Available metrics are:
WARNING:tensorflow:Can save best model only with val_loss available, skipping.
WARNING:tensorflow:Reduce LR on plateau conditioned on metric val_loss which is not available. Available metrics are: lr"

Do you know why this might be happening?

best wishes and thanks,
Alex

[nayash]

I never faced this warning while training. Also, I have not used Keras in a while now, so hard to say (without looking at rest of the code) what's happening here. But you may wanna look at this.

[via986]

@HypergeneticSpacekid
i got almost same error in mcp, solved:

replaced this line:

mcp = ModelCheckpoint(best_model_path, monitor='val_f1_metric', verbose=1, save_best_only=True, save_weights_only=False, mode='max', period=1)

to:

mcp = ModelCheckpoint(best_model_path, monitor='val_f1_metric', verbose=1, save_best_only=True, save_weights_only=False, mode='max', save_freq="epoch")