def prior()

in model.py [0:0]

• 24 lines of code
• 5 McCabe index (conditional complexity)

def prior(name, y_onehot, hps):

    with tf.variable_scope(name):
        n_z = hps.top_shape[-1]

        h = tf.zeros([tf.shape(y_onehot)[0]]+hps.top_shape[:2]+[2*n_z])
        if hps.learntop:
            h = Z.conv2d_zeros('p', h, 2*n_z)
        if hps.ycond:
            h += tf.reshape(Z.linear_zeros("y_emb", y_onehot,
                                           2*n_z), [-1, 1, 1, 2 * n_z])

        pz = Z.gaussian_diag(h[:, :, :, :n_z], h[:, :, :, n_z:])

    def logp(z1):
        objective = pz.logp(z1)
        return objective

    def sample(eps=None, eps_std=None):
        if eps is not None:
            # Already sampled eps. Don't use eps_std
            z = pz.sample2(eps)
        elif eps_std is not None:
            # Sample with given eps_std
            z = pz.sample2(pz.eps * tf.reshape(eps_std, [-1, 1, 1, 1]))
        else:
            # Sample normally
            z = pz.sample

        return z

    def eps(z1):
        return pz.get_eps(z1)

    return logp, sample, eps