# VGG16 Example
## Description
This example is for VGG16 model training and evaluation.
## Requirements
- Install [MindSpore ](https://www.mindspore.cn/install/en ).
- Download the CIFAR-10 binary version dataset.
> 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}
```
### Distribute Training
```
sh run_distribute_train.sh rank_table.json your_data_path
```
The above shell script will run distribute training in the background, you can view the results through the file `train_parallel[X]/log` .
You will get the loss value as following:
```
# grep "result: " train_parallel*/log
train_parallel0/log:epoch: 1 step: 97, loss is 1.9060308
train_parallel0/log:epcoh: 2 step: 97, loss is 1.6003821
...
train_parallel1/log:epoch: 1 step: 97, loss is 1.7095519
train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579
...
...
```
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
## Usage:
### Training
```
usage: train.py [--device_target TARGET][--data_path DATA_PATH]
[--device_id DEVICE_ID][--pre_trained PRE_TRAINED]
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.
--pre_trained the pretrained checkpoint file path.
```
### 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.
```
### Distribute Training
```
Usage: sh script/run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATA_PATH]
parameters/options:
MINDSPORE_HCCL_CONFIG_PATH HCCL configuration file path.
DATA_PATH the storage path of dataset.
```
