PPC with Minibatch ADVI

Questions
4

@Pflip
What does your eventual code look like? I am trying to do the same, I am not sure I understand the casting that @ferrine was talking about. Could you post a sample code snippet? Thank you,

Here is where I am:

#theano shared vars
minibatch_x = pm.Minibatch(X_train, batch_size=500, dtype='float64')
minibatch_y = pm.Minibatch(y_train, batch_size=500, dtype='float64')

def build_ann(init, in_var, out_var):
    with pm.Model() as cnn:
        network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28), input_var=in_var)
        network = lasagne.layers.DenseLayer(network, num_units=25, nonlinearity=lasagne.nonlinearities.tanh,
                                          b=init,
                                          W=init)
        network = lasagne.layers.DenseLayer(network, num_units=25, nonlinearity=lasagne.nonlinearities.tanh,
                                          b=init,
                                          W=init)
        #Final layer of 10 units, softmax across 10 labels
        network = lasagne.layers.DenseLayer(network, num_units=10, nonlinearity=lasagne.nonlinearities.softmax,
                                         b=init,
                                         W=init)
        prediction = lasagne.layers.get_output(network)
        #categorical distribution
        out = pm.Categorical('out', p=prediction, observed=out_var, total_size=y_train.shape[0])
    return cnn

#This needs to be corrected !!! do not pass minibatch here
cnn = build_ann(GaussianWeights(), minibatch_x, minibatch_y)

with cnn:
    # Needs to be corrected !!!! how to supply more_replacements?
    approx = pm.fit(50, method='advi', 
                   # more_replacements={in_var:minibatch_x, out_var:minibatch_y}
                   )
    trace = approx.sample(draws=620)
    ppc = pm.sample_ppc(trace, samples=100)
    
    #Predict on test data
    input_var.set_value(X_test)
    target_var.set_value(y_test)
    y_pred = mode(ppc['out'], axis=0).mode[0, :]