2
$\begingroup$

How to define a custom performance metric in Keras?

I am trying to use it but I can not see the metrics values on each epoch.

clf51.compile(optimizer=sgd51, loss='binary_crossentropy', metrics=["accuracy"])

clf51.fit(X_train, Y_train, batch_size=384, epochs=5, callbacks=[metrics], validation_split=0.30, verbose=2)

As you can see:

Train on 139554 samples, validate on 59810 samples

Epoch 1/5 - 4s - loss: 0.3576 - acc: 0.9885 - val_loss: 0.0531 - val_acc: 0.9989

Epoch 2/5 - 3s - loss: 0.0261 - acc: 0.9987 - val_loss: 0.0135 - val_acc: 0.9987

Am I doing something wrong? I would like to make a earlystop using f1s.

$\endgroup$
1
$\begingroup$

The callback you are using isn't for displaying the desired metrics, just recording them. For example if you want to access the F1-score you need to type: metrics.f1s. This is useful, let's say, if you want to make a graph on how the F1-score reduced during training.

To use the EarlyStopping callback, however, f1-score needs to be a metric not a callback like you have it!

You need to write (or find) a function that calculates the F1-score through keras' backend functions. You might want to check if this works for you.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.