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))
Gets root mse between the logarithms of the prediction and the truth.
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))