Not able to obtain Predicted values in Bayesian neural network

Questions
3

Thanks simon… I produced predicted values as you said. But the accuracy is coming out to be very poor.
This is my code:

def construct_nn(ann_input, ann_output):
    n_hidden = 5

    # Initialize random weights between each layer
    init_1 = np.random.randn(X.shape[1], n_hidden).astype(floatX)
    init_2 = np.random.randn(n_hidden, n_hidden).astype(floatX)
    init_out = np.random.randn(n_hidden).astype(floatX)
     # Initialize random biases in each layer
    init_b_1 = np.random.randn(n_hidden).astype(floatX)
    init_b_2 = np.random.randn(n_hidden).astype(floatX)
    init_b_out = np.random.randn(1).astype(floatX)

    with pm.Model() as neural_network:
        # Weights from input to hidden layer
        weights_in_1 = pm.Normal('w_in_1', 0, sd=1,
                                 shape=(X.shape[1], n_hidden),
                                 testval=init_1)
        bias_1 = pm.Normal('b_1', mu=0, sd=1, shape=(n_hidden), testval=init_b_1)
        # Weights from 1st to 2nd layer
        weights_1_2 = pm.Normal('w_1_2', 0, sd=1,
                                shape=(n_hidden, n_hidden),
                                testval=init_2)
        bias_2 = pm.Normal('b_2', mu=0, sd=1, shape=(n_hidden), testval=init_b_2)
        # Weights from hidden layer to output
        weights_2_out = pm.Normal('w_2_out', 0, sd=1,
                                  shape=(n_hidden,),
                                  testval=init_out)
        bias_out = pm.Normal('b_out', mu=0, sd=1, shape=(1), testval=init_b_out)

        # Build neural-network using tanh activation function
        act_1 = pm.math.tanh(pm.math.dot(ann_input, weights_in_1) + bias_1)
        act_2 = pm.math.tanh(pm.math.dot(act_1, weights_1_2) + bias_2)
        act_out = pm.math.dot(act_2, weights_2_out) + bias_out
        sd = pm.HalfNormal('sd', sd=1)
        out = pm.Normal('out',mu=act_out,sd=sd, observed=ann_output)
                          
    return neural_network

ann_input = theano.shared(X_train)
ann_output = theano.shared(Y_train)
neural_network = construct_nn(ann_input, ann_output)

from pymc3.theanof import set_tt_rng, MRG_RandomStreams
set_tt_rng(MRG_RandomStreams(42))


with neural_network:
    inference = pm.ADVI()
    approx = pm.fit(n=3000, method=inference)
    
    
trace = approx.sample(draws=5000)  
pm.summary(trace)
plt.plot(-inference.hist)
plt.ylabel('ELBO')
plt.xlabel('iteration');
plt.show()

# posterior predictive checks
predANN = Y_train
#with neural_network:
ppc = pm.sample_ppc(trace, model=neural_network, samples=5000)

pred_ppc=ppc['y']

plt.plot(Y_train, pred_ppc, 'o')
plt.plot(Y_train, predANN, 'o')
plt.ylabel('Predicted')
plt.xlabel('Actual')
plt.show()