I am trying to model some deep networks which I have already modelled with Keras (the usual way) and now I wish to see what would happen in the Bayesian context.

Suppose the model I have is as the following:

```
x_in = Input(shape=(X_train.shape[1:]))
h = Dense(10, use_bias=False)(x_in)
out = Activation('softmax')(h)
model = Model(x_in, out)
```

How would you go about putting priors on the weights using PyMC3. The only tutorial that I have found is this, which for my fairly limited Python knowledge seems fairly complex. I do realise that the above is a fairly simple linear model, however I can expand from here if I have some guidance.

Would highly appreciate it if someone could show me how to wrap this in a pymc3 model, if at all possible. I am using Keras 2.1.5. I can downgrade if necessary.