How to tell Keras stop training based on loss value?

The keras.callbacks.EarlyStopping callback does have a min_delta argument. From Keras documentation:

min_delta: minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.

I solved the same problem using custom callback.

In the following custom callback code assign THR with the value at which you want to stop training and add the callback to your model.

from keras.callbacks import Callback

class stopAtLossValue(Callback):

        def on_batch_end(self, batch, logs={}):
            THR = 0.03 #Assign THR with the value at which you want to stop training.
            if logs.get('loss') <= THR:
                 self.model.stop_training = True

One solution is to call model.fit(nb_epoch=1, ...) inside a for loop, then you can put a break statement inside the for loop and do whatever other custom control flow you want.

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)