core.py 文件源码

def fit(self, dataloader, nb_iter=None, nb_epoch=None, iter_per_epoch=None,
            callbacks=[], verbose=0):
        """Trains the underlying Keras model.

        Args:
            dataloader (StandardDataLoader): Manages the loading of data to
                model.
            nb_iter (int): The number of iterations to train the model.
            nb_epoch (int): The number of epochs to train the model.
            iter_per_epoch (int): Defines the number of iterations per epoch.
            callbacks (list): List of Keras callbacks to run during training.
        """
        nb_iter, iter_per_epoch = self._get_iterations(
            nb_iter, nb_epoch, iter_per_epoch)
        callbacks = CallbackList(callbacks)
        callbacks._set_model(self)
        callbacks.on_train_begin()

        try:
            epoch = 0
            self.stop_training = False
            for i in xrange(nb_iter):
                # Begin epoch
                if i % iter_per_epoch == 0:
                    callbacks.on_epoch_begin(epoch)

                # Execution
                callbacks.on_batch_begin(i)

                if verbose > 0:
                    import time
                    time.sleep(0.001)
                    j = i % iter_per_epoch
                    perc = int(100 * (j + 1) /iter_per_epoch)
                    prog = ''.join(['='] * (perc/2))
                    string = "[{:50s}] {:3d}%\r".format(prog, perc)
                    sys.stdout.write(string); sys.stdout.flush()

                losses = self.keras_model.train_on_batch(
                    *dataloader.get_training_batch())

                callbacks.on_batch_end(i)

                # End epoch
                if (i + 1) % iter_per_epoch == 0:
                    callbacks.on_epoch_end(epoch, logs={'losses': losses})
                    epoch += 1
                if self.stop_training:
                    break
        except KeyboardInterrupt:
            print "\n[BayesNet] Abort: KeyboardInterrupt"
            raise

        callbacks.on_train_end()