tensorflow2.0

I have models built from uncompiled keras code and am trying to run them through a custom training loop. The TF 2.0 eager (by default) code runs about 30s on a CPU (laptop). When I create a keras model with wrapped tf.function call methods, it is running much, much slower and appears to take a very long time to start, particularly the "first" time. For example, in the tf.function code the initial train on 10 samples takes 40s, and the follow up one on 10 samples takes 2s. On 20 samples, the initial takes 50s and the follow up takes 4s. The first train on 1 sample takes 2s and follow up takes

I've ran into serious incompatibility problems for the same code ran with one vs. the other; e.g.: Getting value of tensor Compiling model Saving optimizer Looking into the Github source , the modules and their imports look fairly identical, and tf.keras even imports from tf.python.keras . In tutorials, I see both being used time to time. As an example, code below will fail with tf.python.keras . What's the deal? What is the difference, and when should I use one or the other? from tensorflow.keras.layers import Input, Dense from tensorflow.keras.models import Model from tensorflow.keras

My end goal is to simulate likelihood ratio test statistics, however, the core problem I am having is that I do not understand how to get TensorFlow 2 to perform many optimizations for different data inputs. Here is my attempt, hopefully, it gives you the idea of what I am trying: import tensorflow as tf import tensorflow_probability as tfp from tensorflow_probability import distributions as tfd import numpy as np # Bunch of independent Poisson distributions that we want to combine poises0 = [tfp.distributions.Poisson(rate = 10) for i in range(5)] # Construct joint distributions joint0 = tfd