update docs

README.md

@@ -64,7 +64,7 @@ Dependencies:
 
 + Python 2.7 or 3.3+. Python 2.7 is supported until [it retires in 2020](https://pythonclock.org/).
 + Python bindings for OpenCV (Optional, but required by a lot of features)
-+ TensorFlow >= 1.3. (If you only want to use `tensorpack.dataflow` alone as a data processing library, TensorFlow is not needed)
++ TensorFlow ≥ 1.3. (If you only want to use `tensorpack.dataflow` alone as a data processing library, TensorFlow is not needed)
 ```
 pip install --upgrade git+https://github.com/tensorpack/tensorpack.git
 # or add `--user` to install to user's local directories

examples/DoReFa-Net/README.md

@@ -41,7 +41,7 @@ In this implementation, quantized operations are all performed through `tf.float
 
 ## Use
 
-+ Install TensorFlow>=1.7, tensorpack and scipy.
++ Install TensorFlow ≥ 1.7, tensorpack and scipy.
 
 + Look at the docstring in `*-dorefa.py` to see detailed usage and performance.
 

examples/FasterRCNN/README.md

@@ -14,7 +14,7 @@ with the support of:
 
 ## Dependencies
 + Python 3.3+; OpenCV.
-+ TensorFlow >= 1.6 (1.4 or 1.5 can run but may crash due to a TF bug);
++ TensorFlow ≥ 1.6 (1.4 or 1.5 can run but may crash due to a TF bug);
 + pycocotools: `pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
 + Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/FasterRCNN/)
   from tensorpack model zoo.

examples/ResNet/README.md

@@ -9,7 +9,7 @@ __Training__ code of three variants of ResNet on ImageNet:
 
 The training follows the __exact__ recipe used by the [Training ImageNet in 1 Hour paper](https://arxiv.org/abs/1706.02677)
 and gets the same performance.
-Distributed training code & results can be found at [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks/tree/master/ResNet-Horovod).
+__Distributed training__ code & results can be found at [tensorpack/benchmarks](https://github.com/tensorpack/benchmarks/tree/master/ResNet-Horovod).
 
 This recipe has better performance than most open source implementations.
 In fact, many papers that claim to "improve" ResNet by .5% only compete with a lower

tensorpack/callbacks/inference_runner.py

@@ -202,9 +202,10 @@ class DataParallelInferenceRunner(InferenceRunnerBase):
             tower_name (str): the name scope of the tower to build. Need to set a
                 different one if multiple InferenceRunner are used.
             tower_func (tfutils.TowerFuncWrapper or None): the tower function to be used to build the graph.
-                By defaults to call `trainer.tower_func` under a `training=False` TowerContext,
+                The tower function will be called under a `training=False` TowerContext.
+                The default is `trainer.tower_func`,
                 but you can change it to a different tower function
-                if you need to inference with several different graphs.
+                if you need to inference with several different models.
         """
         if isinstance(gpus, int):
             gpus = list(range(gpus))