Add notes about using sonnet (fix #222)

README.md

@@ -33,8 +33,9 @@ The examples are not only for demonstration of the framework -- you can train th
 
 It's Yet Another TF wrapper, but different in:
 1. Not focus on models.
-	+ It includes only a few common models, and helpful tools such as `LinearWrap` to simplify large models.
-	  But you can use any other TF wrappers here, such as slim/tflearn/tensorlayer.
+	+ There are already too many symbolic function wrappers.
+		Tensorpack includes only a few common models, and helpful tools such as `LinearWrap` to simplify large models.
+	  But you can use any other wrappers within tensorpack, such as sonnet/Keras/slim/tflearn/tensorlayer/....
 
 2. Focus on large datasets.
 	+ __DataFlow__ allows you to process large datasets such as ImageNet in Python without blocking the training.

docs/tutorial/model.md

@@ -21,7 +21,7 @@ Basically, `_get_inputs` should define the metainfo of all the possible placehol
 the argument `input_tensors` is the list of input tensors matching `_get_inputs`.
 
 You can use any symbolic functions in `_build_graph`, including TensorFlow core library
-functions, TensorFlow slim layers, or functions in other packages such as tflean, tensorlayer.
+functions and other symbolic libraries (see below).
 
 tensorpack also contains a small collection of common model primitives,
 such as conv/deconv, fc, batch normalization, pooling layers, and some custom loss functions.
@@ -62,12 +62,23 @@ l = FullyConnected('fc1', l, 10, nl=tf.identity)
 
 ### Use Models outside Tensorpack
 
-You can use the tensorpack models alone as a simple symbolic function library, and write your own
+You can use tensorpack models alone as a simple symbolic function library, and write your own
 training code instead of using tensorpack trainers.
 
 To do this, just enter a [TowerContext](http://tensorpack.readthedocs.io/en/latest/modules/tfutils.html#tensorpack.tfutils.TowerContext)
 when you define your model:
 ```python
 with TowerContext('', is_training=True):
-	# call any tensorpack symbolic functions
+	# call any tensorpack layer
 ```
+
+### Use Other Symbolic Libraries within Tensorpack
+
+When defining the model you can construct the graph using whatever library you feel comfortable with.
+
+Usually, slim/tflearn/tensorlayer are just symbolic functions, calling them is nothing different
+from calling `tf.add`. However it's a bit different to use sonnet/Keras.
+
+sonnet/Keras manages the variable scope by their own model classes, and calling their symbolic functions
+always creates new variable scope. See the [Keras example](../examples/mnist-keras.py) for how to
+use them within tensorpack.

examples/mnist-keras.py

@@ -32,7 +32,7 @@ class Model(ModelDesc):
                 InputDesc(tf.int32, (None,), 'label'),
                 ]
 
-    @memoized   # this is necessary for Keras to work under tensorpack
+    @memoized   # this is necessary for sonnet/Keras to work under tensorpack
     def _build_keras_model(self):
         M = Sequential()
         M.add(KL.Conv2D(32, 3, activation='relu', input_shape=[IMAGE_SIZE, IMAGE_SIZE, 1], padding='same'))
@@ -83,6 +83,7 @@ class Model(ModelDesc):
         return tf.train.AdamOptimizer(lr)
 
 
+# Keras needs an extra input
 class KerasCallback(Callback):
     def __init__(self, isTrain):
         self._isTrain = isTrain