def create_SNN_hparams(hidden_layers=[30], learning_rate=0.001, epochs=100, batch_size=64, pair_size=32,
                       activation='relu',
                       optimizer='Adam', singlenet_l1=0, singlenet_l2=0, reg_term=0, feature_vector_dim=10, dropout=0,
                       fp_length=100, fp_number=3, conv_width=8, conv_number=2,
                       conv_activation='relu', conv_l1=0, conv_l2=0,
                       verbose=1):
    """
    Creates hparam dict for input into create_DNN_model or other similar functions. Contain Hyperparameter info
    :return: hparam dict
    """
    names = ['hidden_layers', 'learning_rate', 'epochs', 'batch_size', 'pair_size', 'activation', 'optimizer',
             'singlenet_l1', 'singlenet_l2',
             'reg_term', 'dropout', 'feature_vector_dim', 'fp_length', 'fp_number', 'conv_width', 'conv_number',
             'conv_activation', 'conv_l1', 'conv_l2',
             'verbose']
    values = [hidden_layers, learning_rate, epochs, batch_size, pair_size, activation, optimizer,
              singlenet_l1, singlenet_l2,
              reg_term, dropout, feature_vector_dim, fp_length, fp_number, conv_width, conv_number,
              conv_activation, conv_l1, conv_l2,
              verbose]
    hparams = dict(zip(names, values))
    return hparams