def get_rmse_log(net, X_train, y_train):
    """Gets root mse between the logarithms of the prediction and the truth."""
    num_train = X_train.shape[0]
    clipped_preds = nd.clip(net(X_train), 1, float('inf'))
    return np.sqrt(2 * nd.sum(square_loss(
        nd.log(clipped_preds), nd.log(y_train))).asscalar() / num_train)
def get_rmse_log ( net , X_train , y_train ) : num_train = X_train . shape [ 0 ] clipped_preds = nd . clip ( net ( X_train ) , 1 , float ( 'inf' ) ) return np . sqrt ( 2 * nd . sum ( square_loss ( nd . log ( clipped_preds ) , nd . log ( y_train ) ) ) . asscalar ( ) / num_train )
Gets root mse between the logarithms of the prediction and the truth.