Simple Keras DNN with probabilistic Gaussian output (mean + variance)

keras_prob_dnn.py

import numpy as np

from keras.layers import Input, Dense
from keras.models import Model, Sequential


def dnn(n_inputs, n_outputs, n_hidden_layers=3, hlayer_size=128, probabilistic=True):
    """Defines simple DNN model
    """
    x_input = Input(shape=[n_inputs])
    model = Sequential()

    for i in range(0, n_hidden_layers):
        model.add(Dense(hlayer_size, activation='relu'))

    if probabilistic:
        model.add(Dense(n_outputs * 2, activation='linear'))
    else:
        model.add(Dense(n_outputs, activation='linear'))

    model = Model(x_input, model(x_input))
    model.summary()

    return model


def predict_prob(model, x, batch_size=2048):
    """Make predictions given model and 2d data
    """

    ypred = model.predict(x, batch_size=batch_size, verbose=1)
    n_outs = int(ypred.shape[1] / 2)
    mean = ypred[:, 0:n_outs]
    sigma = np.exp(ypred[:, n_outs:])

    return mean, sigma

how can i use mean, sigma value to predict new input?