Potentially rewrite constraint functions for bayesian layers

[dustinvtran]

We currently adopt Keras' practice of unconstrained parameters followed by projected gradient descent. It's more common in probabilistic modeling code to constrain the parameter space itself, e.g., ed.Normal(0., tf.nn.softplus(tf.Variable(1.)) + tf.keras.backend.epsilon()). This is subtle but potentially an impactful change so we should be careful with our ablation studies if we want to change this behavior.