!557 add README file for vgg16

Merge pull request !557 from caojian05/mindspore_master_dev

example/vgg16_cifar10/README.md

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+# VGG16 Example
+
+## Description
+
+This example is for VGG16 model training and evaluation.
+
+## Requirements
+
+- Install [MindSpore](https://www.mindspore.cn/install/en).
+
+- Download the dataset [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz).
+
+> Unzip the CIFAR-10 dataset to any path you want and the folder structure should be as follows:
+> ```
+> .
+> ├── cifar-10-batches-bin  # train dataset
+> └── cifar-10-verify-bin   # infer dataset
+> ```
+
+## Running the Example
+
+### Training
+
+```
+python train.py --data_path=your_data_path --device_id=6 > out.train.log 2>&1 & 
+```
+The python command above will run in the background, you can view the results through the file `out.train.log`.
+
+After training, you'll get some checkpoint files under the script folder by default.
+
+You will get the loss value as following:
+```
+# grep "loss is " out.train.log
+epoch: 1 step: 781, loss is 2.093086
+epcoh: 2 step: 781, loss is 1.827582
+...
+```
+
+### Evaluation
+
+```
+python eval.py --data_path=your_data_path --device_id=6 --checkpoint_path=./train_vgg_cifar10-70-781.ckpt > out.eval.log 2>&1 & 
+```
+The above python command will run in the background, you can view the results through the file `out.eval.log`.
+
+You will get the accuracy as following:
+```
+# grep "result: " out.eval.log
+result: {'acc': 0.92}
+```
+
+
+## Usage:
+
+### Training
+```
+usage: train.py [--device_target TARGET][--data_path DATA_PATH]
+                [--device_id DEVICE_ID]
+
+parameters/options:
+  --device_target       the training backend type, default is Ascend.
+  --data_path           the storage path of dataset
+  --device_id           the device which used to train model.
+
+```
+
+### Evaluation
+
+```
+usage: eval.py [--device_target TARGET][--data_path DATA_PATH]
+                [--device_id DEVICE_ID][--checkpoint_path CKPT_PATH]
+
+parameters/options:
+  --device_target       the evaluation backend type, default is Ascend.
+  --data_path           the storage path of datasetd 
+  --device_id           the device which used to evaluate model.
+  --checkpoint_path     the checkpoint file path used to evaluate model.
+```