model.predict does not work with stochastic output layers

model.predict doesn't work for non-Tensor outputs, including Tensor-convertible objects like ed.RandomVariable.

For now, the workaround is to replace model.predict as below with an explicit for loop over the data.

dataset_test = dataset_test.repeat().batch(batch_size)
test_steps = ds_info.splits['test'].num_examples // batch_size

predictions = model.predict(dataset_test, verbose=1, steps=test_steps)  # raises error
logits = predictions.distribution.logits  # predicted logits of full dataset

dataset_test = dataset_test.batch(batch_size)

logits = []
for features, _ in dataset_test:
  predictions = model(features)
  logits.append(predictions.distribution.logits)

logits = tf.concat(logits, axis=0)  # predicted logits of full dataset

Note to loop over tf data, you need to use TF 2.0 behavior; otherwise you need to use a tf.Session with the deprecated iterator design.

google / edward2

model.predict does not work with stochastic output layers #99