diff --git a/tensorflow2fluid/vgg_translate_tutorial.ipynb b/tensorflow2fluid/vgg_translate_tutorial.ipynb
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@@ -0,0 +1,369 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial : tensorflow2fluid转换VGG_16模型\n",
+    "\n",
+    "VGG_16是CV领域的一个经典模型，本文档以tensorflow/models下的[VGG_16](https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py)为例，展示如何将TensorFlow训练好的模型转换为PaddlePaddle模型。 \n",
+    "### 下载预训练模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Download percentage 100.00%"
+     ]
+    }
+   ],
+   "source": [
+    "import urllib\n",
+    "import sys\n",
+    "def schedule(a, b, c):\n",
+    "    per = 100.0 * a * b / c\n",
+    "    per = int(per)\n",
+    "    sys.stderr.write(\"\\rDownload percentage %.2f%%\" % per)\n",
+    "    sys.stderr.flush()\n",
+    "\n",
+    "url = \"http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz\"\n",
+    "fetch = urllib.urlretrieve(url, \"./vgg_16.tar.gz\", schedule)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 解压下载的压缩文件"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tarfile\n",
+    "with tarfile.open(\"./vgg_16.tar.gz\", \"r:gz\") as f:\n",
+    "    file_names = f.getnames()\n",
+    "    for file_name in file_names:\n",
+    "        f.extract(file_name, \"./\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 保存模型为checkpoint格式\n",
+    "\n",
+    "tensorflow2fluid目前支持checkpoint格式的模型或者是将网络结构和参数序列化的pb格式模型，上面下载的`vgg_16.ckpt`仅仅存储了模型参数，因此我们需要重新加载参数，并将网络结构和参数一起保存为checkpoint模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from vgg_16.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow.contrib.slim as slim\n",
+    "from tensorflow.contrib.slim.nets import vgg\n",
+    "import tensorflow as tf\n",
+    "import numpy\n",
+    "\n",
+    "with tf.Session() as sess:\n",
+    "    inputs = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224, 3], name=\"inputs\")\n",
+    "    logits, endpoint = vgg.vgg_16(inputs, num_classes=1000, is_training=False)\n",
+    "    load_model = slim.assign_from_checkpoint_fn(\"vgg_16.ckpt\", slim.get_model_variables(\"vgg_16\"))\n",
+    "    load_model(sess)\n",
+    "    \n",
+    "    numpy.random.seed(13)\n",
+    "    data = numpy.random.rand(5, 224, 224, 3)\n",
+    "    input_tensor = sess.graph.get_tensor_by_name(\"inputs:0\")\n",
+    "    output_tensor = sess.graph.get_tensor_by_name(\"vgg_16/fc8/squeezed:0\")\n",
+    "    result = sess.run([output_tensor], {input_tensor:data})\n",
+    "    numpy.save(\"tensorflow.npy\", numpy.array(result))\n",
+    "    \n",
+    "    saver = tf.train.Saver()\n",
+    "    saver.save(sess, \"./checkpoint/model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 将模型转换为PaddlePaddle模型\n",
+    "\n",
+    "注意：部分OP在转换时，需要将参数写入文件；或者是运行tensorflow模型进行infer，获取tensor值。两种情况下均会消耗一定的时间用于IO或计算，对于后一种情况，建议转换模型时将`use_cuda`参数设为`True`，加快infer速度"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Loading tensorflow model...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from checkpoint/model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Restoring parameters from checkpoint/model\n",
+      "INFO:root:Tensorflow model loaded!\n",
+      "INFO:root:TotalNum:86,TraslatedNum:1,CurrentNode:inputs\n",
+      "INFO:root:TotalNum:86,TraslatedNum:2,CurrentNode:vgg_16/conv1/conv1_1/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:3,CurrentNode:vgg_16/conv1/conv1_1/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:4,CurrentNode:vgg_16/conv1/conv1_2/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:5,CurrentNode:vgg_16/conv1/conv1_2/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:6,CurrentNode:vgg_16/conv2/conv2_1/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:7,CurrentNode:vgg_16/conv2/conv2_1/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:8,CurrentNode:vgg_16/conv2/conv2_2/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:9,CurrentNode:vgg_16/conv2/conv2_2/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:10,CurrentNode:vgg_16/conv3/conv3_1/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:11,CurrentNode:vgg_16/conv3/conv3_1/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:12,CurrentNode:vgg_16/conv3/conv3_2/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:13,CurrentNode:vgg_16/conv3/conv3_2/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:14,CurrentNode:vgg_16/conv3/conv3_3/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:15,CurrentNode:vgg_16/conv3/conv3_3/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:16,CurrentNode:vgg_16/conv4/conv4_1/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:17,CurrentNode:vgg_16/conv4/conv4_1/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:18,CurrentNode:vgg_16/conv4/conv4_2/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:19,CurrentNode:vgg_16/conv4/conv4_2/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:20,CurrentNode:vgg_16/conv4/conv4_3/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:21,CurrentNode:vgg_16/conv4/conv4_3/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:22,CurrentNode:vgg_16/conv5/conv5_1/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:23,CurrentNode:vgg_16/conv5/conv5_1/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:24,CurrentNode:vgg_16/conv5/conv5_2/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:25,CurrentNode:vgg_16/conv5/conv5_2/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:26,CurrentNode:vgg_16/conv5/conv5_3/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:27,CurrentNode:vgg_16/conv5/conv5_3/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:28,CurrentNode:vgg_16/fc6/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:29,CurrentNode:vgg_16/fc6/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:30,CurrentNode:vgg_16/fc7/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:31,CurrentNode:vgg_16/fc7/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:32,CurrentNode:vgg_16/fc8/weights\n",
+      "INFO:root:TotalNum:86,TraslatedNum:33,CurrentNode:vgg_16/fc8/biases\n",
+      "INFO:root:TotalNum:86,TraslatedNum:34,CurrentNode:vgg_16/conv1/conv1_1/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:35,CurrentNode:vgg_16/conv1/conv1_1/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:36,CurrentNode:vgg_16/conv1/conv1_1/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:37,CurrentNode:vgg_16/conv1/conv1_2/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:38,CurrentNode:vgg_16/conv1/conv1_2/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:39,CurrentNode:vgg_16/conv1/conv1_2/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:40,CurrentNode:vgg_16/pool1/MaxPool\n",
+      "INFO:root:TotalNum:86,TraslatedNum:41,CurrentNode:vgg_16/conv2/conv2_1/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:42,CurrentNode:vgg_16/conv2/conv2_1/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:43,CurrentNode:vgg_16/conv2/conv2_1/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:44,CurrentNode:vgg_16/conv2/conv2_2/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:45,CurrentNode:vgg_16/conv2/conv2_2/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:46,CurrentNode:vgg_16/conv2/conv2_2/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:47,CurrentNode:vgg_16/pool2/MaxPool\n",
+      "INFO:root:TotalNum:86,TraslatedNum:48,CurrentNode:vgg_16/conv3/conv3_1/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:49,CurrentNode:vgg_16/conv3/conv3_1/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:50,CurrentNode:vgg_16/conv3/conv3_1/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:51,CurrentNode:vgg_16/conv3/conv3_2/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:52,CurrentNode:vgg_16/conv3/conv3_2/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:53,CurrentNode:vgg_16/conv3/conv3_2/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:54,CurrentNode:vgg_16/conv3/conv3_3/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:55,CurrentNode:vgg_16/conv3/conv3_3/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:56,CurrentNode:vgg_16/conv3/conv3_3/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:57,CurrentNode:vgg_16/pool3/MaxPool\n",
+      "INFO:root:TotalNum:86,TraslatedNum:58,CurrentNode:vgg_16/conv4/conv4_1/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:59,CurrentNode:vgg_16/conv4/conv4_1/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:60,CurrentNode:vgg_16/conv4/conv4_1/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:61,CurrentNode:vgg_16/conv4/conv4_2/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:62,CurrentNode:vgg_16/conv4/conv4_2/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:63,CurrentNode:vgg_16/conv4/conv4_2/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:64,CurrentNode:vgg_16/conv4/conv4_3/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:65,CurrentNode:vgg_16/conv4/conv4_3/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:66,CurrentNode:vgg_16/conv4/conv4_3/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:67,CurrentNode:vgg_16/pool4/MaxPool\n",
+      "INFO:root:TotalNum:86,TraslatedNum:68,CurrentNode:vgg_16/conv5/conv5_1/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:69,CurrentNode:vgg_16/conv5/conv5_1/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:70,CurrentNode:vgg_16/conv5/conv5_1/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:71,CurrentNode:vgg_16/conv5/conv5_2/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:72,CurrentNode:vgg_16/conv5/conv5_2/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:73,CurrentNode:vgg_16/conv5/conv5_2/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:74,CurrentNode:vgg_16/conv5/conv5_3/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:75,CurrentNode:vgg_16/conv5/conv5_3/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:76,CurrentNode:vgg_16/conv5/conv5_3/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:77,CurrentNode:vgg_16/pool5/MaxPool\n",
+      "INFO:root:TotalNum:86,TraslatedNum:78,CurrentNode:vgg_16/fc6/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:79,CurrentNode:vgg_16/fc6/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:80,CurrentNode:vgg_16/fc6/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:81,CurrentNode:vgg_16/fc7/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:82,CurrentNode:vgg_16/fc7/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:83,CurrentNode:vgg_16/fc7/Relu\n",
+      "INFO:root:TotalNum:86,TraslatedNum:84,CurrentNode:vgg_16/fc8/Conv2D\n",
+      "INFO:root:TotalNum:86,TraslatedNum:85,CurrentNode:vgg_16/fc8/BiasAdd\n",
+      "INFO:root:TotalNum:86,TraslatedNum:86,CurrentNode:vgg_16/fc8/squeezed\n",
+      "INFO:root:Model translated!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tf2fluid.convert as convert\n",
+    "import argparse\n",
+    "parser = convert._get_parser()\n",
+    "parser.meta_file = \"checkpoint/model.meta\"\n",
+    "parser.ckpt_dir = \"checkpoint\"\n",
+    "parser.in_nodes = [\"inputs\"]\n",
+    "parser.input_shape = [\"None,224,224,3\"]\n",
+    "parser.output_nodes = [\"vgg_16/fc8/squeezed\"]\n",
+    "parser.use_cuda = \"True\"\n",
+    "parser.input_format = \"NHWC\"\n",
+    "parser.save_dir = \"paddle_model\"\n",
+    "\n",
+    "convert.run(parser)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 加载转换后的PaddlePaddle模型，并进行预测\n",
+    "**需要注意，转换后的PaddlePaddle CV模型输入格式为NCHW**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-14T10:18:46.124339Z",
+     "start_time": "2019-03-14T10:18:40.858372Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import tf2fluid.model_loader as ml\n",
+    "\n",
+    "model = ml.ModelLoader(\"paddle_model\", use_cuda=True)\n",
+    "\n",
+    "numpy.random.seed(13)\n",
+    "data = numpy.random.rand(5, 224, 224, 3).astype(\"float32\")\n",
+    "# NHWC -> NCHW\n",
+    "data = numpy.transpose(data, (0, 3, 1, 2))\n",
+    "\n",
+    "results = model.inference(feed_dict={model.inputs[0]:data})\n",
+    "\n",
+    "numpy.save(\"paddle.npy\", numpy.array(results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 对比转换前后模型之前的预测结果diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-14T10:20:13.611132Z",
+     "start_time": "2019-03-14T10:20:13.598874Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.33786e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "paddle_result = numpy.load(\"paddle.npy\")\n",
+    "tensorflow_result = numpy.load(\"tensorflow.npy\")\n",
+    "diff = numpy.fabs(paddle_result - tensorflow_result)\n",
+    "print(numpy.max(diff))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:CPU-Paddle]",
+   "language": "python",
+   "name": "conda-env-CPU-Paddle-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}