da329da89ad1d5029093d7faea05784c43b86d00,textClassification/models.py,,getModel,#Any#Any#Any#,594

Before Change

        model = cnn(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)
    if (modelName == "cnn2"):
        model = cnn2(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)
    if (modelName == "cnn3"):
        model = cnn3(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)
    if (modelName == "lstm_cnn"):
        model = lstm_cnn(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)
    if (modelName == "conv"):
        model = dpcnn(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)

After Change

    elif (model_type == "gru_simple"):
        model = gru_simple(maxlen, max_features, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, embedding_vector, nb_classes)
    else:
        raise (OSError("The model type "+model_type+" is unknown"))
    return model


def train_model(model, list_classes, batch_size, max_epoch, use_roc_auc, train_x, train_y, val_x, val_y):

Instances