Bayesian regression using Gaussian mixture model

I am trying to perform a Bayesian regression for a 2-D dataset.
Refer 2D-data.

I want to formulate it as a sum of Gaussians and for this purpose, I’m using Gaussian Mixture Models with PyMC3. The code snippet is shown below.

nbr_gauss = 15
data = np.column_stack((x_train, y_train))

# standardize the data
x_train = normalize(x_train,norm='max', axis=0)
y_train = normalize(y_train,norm='max',axis = 0)

with pm.Model() as multiVarModel:
    # Proportion of each component (Prior) -> Mixture weights
    p = pm.Dirichlet('p', a=np.ones(nbr_gauss))

    # Prior on means (mu_k)
    mu = pm.Normal(
        mu=np.linspace(x_train.min(), 1, nbr_gauss),
        testval=np.linspace(x_train.min(), 1, nbr_gauss)

    # Prior on the precision matrix (or the inv of covariance matrix)
    tau= pm.Gamma('tau', alpha=10, beta=1.0, shape=(nbr_gauss,))

    # Likelihood
    Y_obs = pm.NormalMixture('Y_obs', w=p, mu=mu, tau=tau, observed=y_train)

    prior_checks = pm.sample_prior_predictive(samples=50, random_seed=seed)

    # Start the sampler
    trace = pm.sample(draws=3000, tune=1000, step=pm.NUTS(), chains=1, cores=1)

    # sample posterior predictive samples
    ppc_trace = pm.sample_posterior_predictive(trace,
                                               var_names=["mu", "tau", "p", "Y_obs"],

I am currently failing to retrieve the original curve after having estimated the parameters (p, mu, tau). Is the implementation correct? Thanks.