Refine the inception-resnet-v2 related

README.cn.md

@@ -98,12 +98,14 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
 
 图像相比文字能够提供更加生动、容易理解及更具艺术感的信息，是人们转递与交换信息的重要来源。图像分类是根据图像的语义信息对不同类别图像进行区分，是计算机视觉中重要的基础问题，也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础，在许多领域都有着广泛的应用。如：安防领域的人脸识别和智能视频分析等，交通领域的交通场景识别，互联网领域基于内容的图像检索和相册自动归类，医学领域的图像识别等。
 
-在图像分类任务中，我们向大家介绍如何训练AlexNet、VGG、GoogLeNet和ResNet模型。同时提供了一个够将Caffe训练好的模型文件转换为PaddlePaddle模型文件的模型转换工具。
+在图像分类任务中，我们向大家介绍如何训练AlexNet、VGG、GoogLeNet、ResNet和Inception-Resnet-V2模型。同时提供了能够将Caffe或TensorFlow训练好的模型文件转换为PaddlePaddle模型文件的模型转换工具。
 
 - 11.1 [将Caffe模型文件转换为PaddlePaddle模型文件](https://github.com/PaddlePaddle/models/tree/develop/image_classification/caffe2paddle)
-- 11.2 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
-- 11.3 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
-- 11.4 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.2 [将TensorFlow模型文件转换为PaddlePaddle模型文件](https://github.com/PaddlePaddle/models/tree/develop/image_classification/tf2paddle)
+- 11.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 11.6 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
 
 ## 12. 目标检测
 

README.md

@@ -72,11 +72,13 @@ As an example for sequence-to-sequence learning, we take the machine translation
 
 ## 9. Image classification
 
-For the example of image classification, we show you how to train AlexNet, VGG, GoogLeNet and ResNet models in PaddlePaddle. It also provides a model conversion tool that converts Caffe trained model files into PaddlePaddle model files.
+For the example of image classification, we show you how to train AlexNet, VGG, GoogLeNet, ResNet and Inception-Resnet-V2 models in PaddlePaddle. It also provides model conversion tools that convert Caffe or TensorFlow trained model files into PaddlePaddle model files.
 
 - 9.1 [convert Caffe model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/image_classification/caffe2paddle)
-- 9.2 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
-- 9.3 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
-- 9.4 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.2 [convert TensorFlow model file to PaddlePaddle model file](https://github.com/PaddlePaddle/models/tree/develop/image_classification/tf2paddle)
+- 9.3 [AlexNet](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.4 [VGG](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.5 [Residual Network](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
+- 9.6 [Inception-Resnet-V2](https://github.com/PaddlePaddle/models/tree/develop/image_classification)
 
 This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](LICENSE).

image_classification/README.md

@@ -32,7 +32,8 @@ paddle.init(use_gpu=False, trainer_count=1)
 设置算法参数（如数据维度、类别数目和batch size等参数），定义数据输入层`image`和类别标签`lbl`。
 
 ```python
-DATA_DIM = 3 * 224 * 224  # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
+# Use 3 * 331 * 331 or 3 * 299 * 299 for DATA_DIM in Inception-ResNet-v2.
+DATA_DIM = 3 * 224 * 224
 CLASS_DIM = 102
 BATCH_SIZE = 128
 
@@ -94,7 +95,8 @@ out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
 提供的Inception-ResNet-v2模型支持`3 * 331 * 331`和`3 * 299 * 299`两种大小的输入，同时可以自行设置dropout概率，可以通过如下的代码使用：
 
 ```python
-out = inception_resnet_v2.inception_resnet_v2(image, class_dim=CLASS_DIM, dropout_rate=0.5, size=DATA_DIM)
+out = inception_resnet_v2.inception_resnet_v2(
+    image, class_dim=CLASS_DIM, dropout_rate=0.5, size=DATA_DIM)
 ```
 
 注意，由于和其他几种模型输入大小不同，若配合提供的`reader.py`使用Inception-ResNet-v2时请先将`reader.py`中`paddle.image.simple_transform`中的参数为修改为相应大小。

image_classification/inception_resnet_v2.py

@@ -8,6 +8,7 @@ def conv_bn_layer(input,
                   padding=0,
                   active_type=paddle.activation.Relu(),
                   ch_in=None):
+    """layer wrapper assembling convolution and batchnorm layer"""
     tmp = paddle.layer.img_conv(
         input=input,
         filter_size=filter_size,
@@ -21,6 +22,7 @@ def conv_bn_layer(input,
 
 
 def sequential_block(input, *layers):
+    """helper function for sequential layers"""
     for layer in layers:
         layer_func, layer_conf = layer
         input = layer_func(input, **layer_conf)