add documentation about KerasModel (#1036)

docs/modules/index.rst

@@ -16,4 +16,4 @@ API Documentation
   predict
   tfutils
   utils
-
+  contrib

tensorpack/contrib/keras.py

@@ -93,6 +93,8 @@ class KerasModelCaller(object):
 
             with clear_tower0_name_scope():
                 model = self.cached_model = self.get_model(*input_tensors)
+                assert isinstance(model, tf.keras.Model), \
+                    "Your get_model function should return a `tf.keras.Model`!"
             outputs = model.outputs
         elif reuse:
             # use the cached Keras model to mimic reuse
@@ -110,11 +112,16 @@ class KerasModelCaller(object):
         return outputs
 
 
-# Keras needs an extra input if learning_phase is used by the model
-# This cb will be used by
-# 1. trainer with isTrain=True
-# 2. InferenceRunner with isTrain=False, in the form of hooks
 class KerasPhaseCallback(Callback):
+    """
+    Keras needs an extra input if learning_phase is used by the model
+    This callback will be used:
+    1. By the trainer with isTrain=True
+    2. By InferenceRunner with isTrain=False, in the form of hooks
+
+    If you use :class:`KerasModel` or :func:`setup_keras_trainer`,
+    this callback will be automatically added when needed.
+    """
     def __init__(self, isTrain):
         assert isinstance(isTrain, bool), isTrain
         self._isTrain = isTrain
@@ -221,7 +228,7 @@ class KerasModel(object):
         """
         Args:
             get_model (input1, input2, ... -> keras.Model):
-                Takes tensors and returns a Keras model. Will be part of the tower function.
+                A function which takes tensors and returns a Keras model. Will be part of the tower function.
             inputs_desc ([InputDesc]):
             targets_desc ([InputDesc]):
             input (InputSource | DataFlow):
@@ -229,6 +236,7 @@ class KerasModel(object):
                 GPUs and use them all.
         """
         self.get_model = get_model
+        assert callable(get_model), get_model
         self.inputs_desc = inputs_desc
         self.targets_desc = targets_desc
         if trainer is None: