update docs

README.md

@@ -30,13 +30,13 @@ Examples are not only for demonstration of the framework -- you can train them a
 ## Features:
 
 It's Yet Another TF wrapper, but different in:
-1. Not focus on models.
+1. It's not a model wrapper.
 	+ There are already too many symbolic function wrappers.
 		Tensorpack includes only a few common models,
-	  but you can use any other wrappers within tensorpack, such as sonnet/Keras/slim/tflearn/tensorlayer/....
+	  but you can use any other model wrappers within tensorpack, such as sonnet/Keras/slim/tflearn/tensorlayer/....
 
 2. Focus on __training speed__.
-	+	Speed comes for free with tensorpack -- it uses TensorFlow in the correct way.
+	+	Speed comes for free with tensorpack -- it uses TensorFlow in the __correct way__.
 	  Even on a tiny CNN example, the training runs [1.6x faster](https://gist.github.com/ppwwyyxx/8d95da79f8d97036a7d67c2416c851b6) than the equivalent Keras code.
 
 	+ Data-parallel multi-GPU training is off-the-shelf to use. It is as fast as Google's [official benchmark](https://www.tensorflow.org/performance/benchmarks).

examples/ResNet/imagenet_resnet_utils.py

@@ -9,7 +9,6 @@ import tensorflow as tf
 from tensorflow.contrib.layers import variance_scaling_initializer
 
 
-import tensorpack as tp
 from tensorpack import imgaug, dataset
 from tensorpack.dataflow import (
     AugmentImageComponent, PrefetchDataZMQ,
@@ -134,6 +133,9 @@ def apply_preactivation(l, preact):
 
 
 def get_bn(zero_init=False):
+    """
+    Zero init gamma is good for resnet. See https://arxiv.org/abs/1706.02677.
+    """
     if zero_init:
         return lambda x, name: BatchNorm('bn', x, gamma_init=tf.zeros_initializer())
     else:
@@ -220,10 +222,10 @@ def eval_on_ILSVRC12(model, sessinit, dataflow):
     )
     pred = SimpleDatasetPredictor(pred_config, dataflow)
     acc1, acc5 = RatioCounter(), RatioCounter()
-    for o in pred.get_result():
-        batch_size = o[0].shape[0]
-        acc1.feed(o[0].sum(), batch_size)
-        acc5.feed(o[1].sum(), batch_size)
+    for top1, top5 in pred.get_result():
+        batch_size = top1.shape[0]
+        acc1.feed(top1.sum(), batch_size)
+        acc5.feed(top5.sum(), batch_size)
     print("Top1 Error: {}".format(acc1.ratio))
     print("Top5 Error: {}".format(acc5.ratio))
 

examples/mnist-keras.py

@@ -78,9 +78,10 @@ class Model(ModelDesc):
         return tf.train.AdamOptimizer(lr)
 
 
-# Keras needs an extra input if learning_phase is needed
+# Keras needs an extra input if learning_phase is used by the model
 class KerasCallback(Callback):
     def __init__(self, isTrain):
+        assert isinstance(isTrain, bool), isTrain
         self._isTrain = isTrain
         self._learning_phase = KB.learning_phase()
 

tensorpack/train/feedfree.py

@@ -11,8 +11,7 @@ from ..graph_builder.input_source import QueueInput, FeedfreeInput
 from .simple import SimpleTrainer
 from .base import Trainer
 
-__all__ = ['FeedfreeTrainerBase', 'SingleCostFeedfreeTrainer',
-           'SimpleFeedfreeTrainer', 'QueueInputTrainer']
+__all__ = ['FeedfreeTrainerBase', 'SingleCostFeedfreeTrainer', 'QueueInputTrainer']
 
 
 # TODO deprecate it some time
@@ -32,9 +31,9 @@ class FeedfreeTrainerBase(Trainer):
         self.config.callbacks.extend(cbs)
 
 
-# deprecated
 class SingleCostFeedfreeTrainer(FeedfreeTrainerBase):
     """ A feedfree Trainer which assumes a single cost. """
+    @deprecated("", "2017-11-21")
     def __init__(self, *args, **kwargs):
         super(SingleCostFeedfreeTrainer, self).__init__(*args, **kwargs)
         logger.warn("SingleCostFeedfreeTrainer was deprecated!")
@@ -45,12 +44,6 @@ class SingleCostFeedfreeTrainer(FeedfreeTrainerBase):
         return self.model.get_cost_and_grad()
 
 
-@deprecated("Use SimpleTrainer with config.data is the same!", "2017-09-13")
-def SimpleFeedfreeTrainer(config):
-    assert isinstance(config.data, FeedfreeInput), config.data
-    return SimpleTrainer(config)
-
-
 def QueueInputTrainer(config, input_queue=None):
     """
     A wrapper trainer which automatically wraps ``config.dataflow`` by a :class:`QueueInput`.