How to tell Keras stop training based on loss value?

Question

Currently I use the following code:

callbacks = [     EarlyStopping(monitor='val_loss', patience=2, verbose=0),     ModelCheckpoint(kfold_weights_path, monitor='val_loss', save_best_only=True, verbose=0), ] model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,       shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),       callbacks=callbacks) 

It tells Keras to stop training when loss didn't improve for 2 epochs. But I want to stop training after loss became smaller than some constant "THR":

if val_loss < THR:     break 

I've seen in documentation there are possibility to make your own callback: http://keras.io/callbacks/ But nothing found how to stop training process. I need an advice.

Answer 1

I found the answer. I looked into Keras sources and find out code for EarlyStopping. I made my own callback, based on it:

class EarlyStoppingByLossVal(Callback):     def __init__(self, monitor='val_loss', value=0.00001, verbose=0):         super(Callback, self).__init__()         self.monitor = monitor         self.value = value         self.verbose = verbose      def on_epoch_end(self, epoch, logs={}):         current = logs.get(self.monitor)         if current is None:             warnings.warn("Early stopping requires %s available!" % self.monitor, RuntimeWarning)          if current < self.value:             if self.verbose > 0:                 print("Epoch %05d: early stopping THR" % epoch)             self.model.stop_training = True 

And usage:

callbacks = [     EarlyStoppingByLossVal(monitor='val_loss', value=0.00001, verbose=1),     # EarlyStopping(monitor='val_loss', patience=2, verbose=0),     ModelCheckpoint(kfold_weights_path, monitor='val_loss', save_best_only=True, verbose=0), ] model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,       shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),       callbacks=callbacks)