use named arguments in PredictConfig.

CHANGES.md

+
+## Breaking API changes.
+
+tensorpack is still in early development, and API changes can happen.
+Usually the backward compatibilty is preserved for several month, with a deprecation warning.
+If you are an early bird to try out this library, you might need to occasionally update your code.
+
+Here are a list of things that were changed, starting from an early version.
+TensorFlow itself also changes API and those are not listed here.
+
+
+* 2017/01/06. `summary.add_moving_summary` now takes any number of positional arguments instead of a list.
+	See [commit](https://github.com/ppwwyyxx/tensorpack/commit/bbf41d9e58053f843d0471e6d2d87ff714a79a90) to change your code.
+* 2017/01/05. The argument `TrainConfig(dataset=)` is renamed to `TrainConfig(dataflow=)`.
+	See [commit](https://github.com/ppwwyyxx/tensorpack/commit/651a5aea8f9aacad7147542021dcf106fc824bc2) to change your code.
+* 2016/11/06. The inferencer `ClassificationError` now expects the vector tensor returned by
+	`prediction_incorrect` instead of the "wrong" tensor. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/740e9d8ca146af5a911f68a369dd7348243a2253)
+	to make changes.
+* 2016/10/17. `Conv2D` and `FullyConnect` use `tf.identity` by default instead of `tf.nn.relu`.
+	See [commit](https://github.com/ppwwyyxx/tensorpack/commit/6eb0bebe60d6f38bcad9ddb3e6091b0b154a09cf).
+* 2016/09/01. The method `_build_graph` of `ModelDesc` doesn't takes `is_training` argument anymore.
+	The `is_training` attribute can be obtained from tower context. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/fc9e45b0208ff09daf454d3bd910c540735b7f83).
+* 2016/05/15. The method `_get_cost` of `ModelDesc` is replaced by `_build_graph`. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/e69034b5c9b588db9fb52295b1e63c89e8b42654).
+
+

tensorpack/callbacks/steps.py

@@ -21,9 +21,13 @@ __all__ = ['StepStatPrinter', 'SummaryMovingAverage', 'ProgressBar']
 class StepStatPrinter(Callback):
     """ It prints the value of some tensors in each step.
     It's just a demo of how trigger_step works but you should in general use
-    :func:`print_stat` or :func:`tf.Print` instead. """
+    :func:`symbolic_functions.print_stat` or :func:`tf.Print` instead. """
 
     def __init__(self, names):
+        """
+        Args:
+            names(list): list of string, the names of the tensor to print.
+        """
         names = [get_op_tensor_name(n)[1] for n in names]
         logger.warn("Using print_stat or tf.Print in the graph is much faster than StepStatPrinter!")
         self._names = names
@@ -79,4 +83,4 @@ class ProgressBar(Callback):
             self._bar = tqdm.trange(self._total, **self._tqdm_args)
         self._bar.update()
         if self.step_num == self._total - 1:
-            self._bar.__exit__()
+            self._bar.close()

tensorpack/models/model_desc.py

@@ -17,6 +17,8 @@ from ..tfutils.tower import get_current_tower_context
 __all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph']
 
 
+# TODO "variable" is not a right name to use across this file.
+
 class InputVar(object):
     """ Store metadata about input placeholders. """
     def __init__(self, type, shape, name, sparse=False):

tensorpack/predict/config.py

@@ -4,7 +4,7 @@
 
 import six
 
-from tensorpack.models import ModelDesc
+from ..models import ModelDesc
 from ..tfutils import get_default_sess_config
 from ..tfutils.sessinit import SessionInit, JustCurrentSession
 
@@ -12,12 +12,19 @@ __all__ = ['PredictConfig']
 
 
 class PredictConfig(object):
-    def __init__(self, **kwargs):
+    def __init__(self, model, session_init=None,
+                 session_config=get_default_sess_config(0.4),
+                 input_names=None,
+                 output_names=None,
+                 return_input=False):
         """
         Args:
-            session_init (SessionInit): how to initialize variables of the session.
             model (ModelDesc): the model to use.
-            input_names (list): a list of input tensor names.
+            session_init (SessionInit): how to initialize variables of the session.
+                Defaults to do nothing.
+            session_config]
+            input_names (list): a list of input tensor names. Defaults to all
+                inputs of the model.
             output_names (list): a list of names of the output tensors to predict, the
                 tensors can be any computable tensor in the graph.
             return_input: same as in :attr:`PredictorBase.return_input`.
@@ -25,34 +32,29 @@ class PredictConfig(object):
         # TODO use the name "tensor" instead of "variable"
         def assert_type(v, tp):
             assert isinstance(v, tp), v.__class__
+        self.model = model
+        assert_type(self.model, ModelDesc)
+
         # XXX does it work? start with minimal memory, but allow growth.
         # allow_growth doesn't seem to work very well in TF.
-        self.session_config = kwargs.pop('session_config', get_default_sess_config(0.4))
-        self.session_init = kwargs.pop('session_init', JustCurrentSession())
+        self.session_config = session_config
+        if session_init is None:
+            session_init = JustCurrentSession()
+        self.session_init = session_init
         assert_type(self.session_init, SessionInit)
-        self.model = kwargs.pop('model')
-        assert_type(self.model, ModelDesc)
 
         # inputs & outputs
-        # TODO add deprecated warning later
-        self.input_names = kwargs.pop('input_names', None)
-        if self.input_names is None:
-            self.input_names = kwargs.pop('input_var_names', None)
-            if self.input_names is not None:
-                pass
-                # logger.warn("[Deprecated] input_var_names is deprecated in PredictConfig. Use input_names instead!")
+        self.input_names = input_names
         if self.input_names is None:
             # neither options is set, assume all inputs
             raw_vars = self.model.get_input_vars_desc()
             self.input_names = [k.name for k in raw_vars]
-        self.output_names = kwargs.pop('output_names', None)
-        if self.output_names is None:
-            self.output_names = kwargs.pop('output_var_names')
-            # logger.warn("[Deprecated] output_var_names is deprecated in PredictConfig. Use output_names instead!")
+        self.output_names = output_names
+        assert_type(self.output_names, list)
+        assert_type(self.input_names, list)
         assert len(self.input_names), self.input_names
         for v in self.input_names:
             assert_type(v, six.string_types)
         assert len(self.output_names), self.output_names
 
-        self.return_input = kwargs.pop('return_input', False)
-        assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))
+        self.return_input = bool(return_input)