model.py 文件源码

def tf_bivariate_normal(y, mu, sigma, rho, n_mixtures, batch_size):
    mu = tf.verify_tensor_all_finite(mu, "Mu not finite!")
    y = tf.verify_tensor_all_finite(y, "Y not finite!")
    delta = tf.sub(tf.tile(tf.expand_dims(y, 1), [1, n_mixtures, 1]), mu)
    delta = tf.verify_tensor_all_finite(delta, "Delta not finite!")
    sigma = tf.verify_tensor_all_finite(sigma, "Sigma not finite!")
    s = tf.reduce_prod(sigma, 2)
    s = tf.verify_tensor_all_finite(s, "S not finite!")
    # -1 <= rho <= 1
    z = tf.reduce_sum(tf.square(tf.div(delta, sigma + epsilon) + epsilon), 2) - \
        2 * tf.div(tf.mul(rho, tf.reduce_prod(delta, 2)), s + epsilon)

    z = tf.verify_tensor_all_finite(z, "Z not finite!")
    # 0 < negRho <= 1
    rho = tf.verify_tensor_all_finite(rho, "rho in bivariate normal not finite!")
    negRho = tf.clip_by_value(1 - tf.square(rho), epsilon, 1.0)
    negRho = tf.verify_tensor_all_finite(negRho, "negRho not finite!")
    # Note that if negRho goes near zero, or z goes really large, this explodes.
    negRho = tf.verify_tensor_all_finite(negRho, "negRho in bivariate normal not finite!")

    result = tf.clip_by_value(tf.exp(tf.div(-z, 2 * negRho)), 1.0e-8, 1.0e8)
    result = tf.verify_tensor_all_finite(result, "Result in bivariate normal not finite!")
    denom = 2 * np.pi * tf.mul(s, tf.sqrt(negRho))
    denom = tf.verify_tensor_all_finite(denom, "Denom in bivariate normal not finite!")
    result = tf.clip_by_value(tf.div(result, denom + epsilon), epsilon, 1.0)
    result = tf.verify_tensor_all_finite(result, "Result2 in bivariate normal not finite!")
    return result, delta