callbacks._set_model(model) callbacks._set_params({ 'batch_size': batch_size, 'nb_epoch': nb_epoch, 'nb_sample': nb_train_sample, 'verbose': 1, 'do_validation': do_validation, 'metrics': metrics, }) ########################## # TRAINING ########################## callbacks.on_train_begin() model.stop_training = False for epoch in range(nb_epoch): callbacks.on_epoch_begin(epoch) if shuffle_on_epoch_start: X_train, y_train = util.shuffle_data(X_train, y_train) # train util.train_on_batch(model, X_train, y_train, nb_classes, callbacks=callbacks, normalize=normalize_data, batch_size=batch_size, class_weight=class_weight,
callbacks._set_model(model) callbacks._set_params({ 'batch_size': batch_size, 'nb_epoch': nb_epoch, 'nb_sample': nb_train_sample, 'verbose': 1, 'do_validation': do_validation, 'metrics': metrics, }) ########################## # TRAINING ########################## callbacks.on_train_begin() model.stop_training = False for epoch in range(nb_epoch): callbacks.on_epoch_begin(epoch) if shuffle_on_epoch_start: X_train, y_train = util.shuffle_data(X_train, y_train) # train util.train_on_batch(model, X_train, y_train, nb_classes, callbacks=callbacks, normalize=normalize_data, batch_size=batch_size, class_weight=class_weight, shuffle=False) epoch_logs = {}