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