On Keras: >>On the other hand, it tends to make it harder to customize models, especially during training. More importantly, the static computation graph on the

On Keras: >>On the other hand, it tends to make it harder to customize models, especially during training. More importantly, the static computation graph on the backend, along with Keras’ need for an extra compile() phase, means that it’s hard to customize a model’s behaviour once it’s built. What does that mean, customize models during training? Also, how are dynamic-graph architectures performing vs. models where the architecture doesn't change? Are they winning competitions?