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AlexNet was proposed in 2012, one of the most influential neural networks. It got big success in ImageNet Dataset recognition than other models.
Paper: Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep ConvolutionalNeural Networks. Advances In Neural Information Processing Systems. 2012.
AlexNet composition consists of 5 convolutional layers and 3 fully connected layers. Multiple convolutional kernels can extract interesting features in images and get more accurate classification.
Dataset used: CIFAR-10
├─cifar-10-batches-bin
│
└─cifar-10-verify-bin
After installing MindSpore via the official website, you can start training and evaluation as follows:
# enter script dir, train AlexNet
sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
# enter script dir, evaluate AlexNet
sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
├── cv
├── alexnet
├── README.md // descriptions about alexnet
├── requirements.txt // package needed
├── scripts
│ ├──run_standalone_train_gpu.sh // train in gpu
│ ├──run_standalone_train_ascend.sh // train in ascend
│ ├──run_standalone_eval_gpu.sh // evaluate in gpu
│ ├──run_standalone_eval_ascend.sh // evaluate in ascend
├── src
│ ├──dataset.py // creating dataset
│ ├──alexnet.py // alexnet architecture
│ ├──config.py // parameter configuration
├── train.py // training script
├── eval.py // evaluation script
Major parameters in train.py and config.py as follows:
--data_path: The absolute full path to the train and evaluation datasets.
--epoch_size: Total training epochs.
--batch_size: Training batch size.
--image_height: Image height used as input to the model.
--image_width: Image width used as input the model.
--device_target: Device where the code will be implemented. Optional values are "Ascend", "GPU".
--checkpoint_path: The absolute full path to the checkpoint file saved after training.
--data_path: Path where the dataset is saved
running on Ascend
python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &
# or enter script dir, and run the script
sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt
After training, the loss value will be achieved as follows:
# grep "loss is " train.log
epoch: 1 step: 1, loss is 2.2791853
...
epoch: 1 step: 1536, loss is 1.9366643
epoch: 1 step: 1537, loss is 1.6983616
epoch: 1 step: 1538, loss is 1.0221305
...
The model checkpoint will be saved in the current directory.
running on GPU
python train.py --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &
# or enter script dir, and run the script
sh run_standalone_train_for_gpu.sh cifar-10-batches-bin ckpt
After training, the loss value will be achieved as follows:
# grep "loss is " train.log
epoch: 1 step: 1, loss is 2.3125906
...
epoch: 30 step: 1560, loss is 0.6687547
epoch: 30 step: 1561, loss is 0.20055409
epoch: 30 step: 1561, loss is 0.103845775
Before running the command below, please check the checkpoint path used for evaluation.
running on Ascend
python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > log.txt 2>&1 &
or enter script dir, and run the script
sh run_standalone_eval_ascend.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt
You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
# grep "Accuracy: " log.txt
'Accuracy': 0.8832
running on GPU
python eval.py --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > log.txt 2>&1 &
or enter script dir, and run the script
sh run_standalone_eval_for_gpu.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt
You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
# grep "Accuracy: " log.txt
'Accuracy': 0.88512
| Parameters | Ascend | GPU |
|---|---|---|
| Resource | Ascend 910; CPU 2.60GHz, 56cores; Memory, 314G | NV SMX2 V100-32G |
| uploaded Date | 06/09/2020 (month/day/year) | 17/09/2020 (month/day/year) |
| MindSpore Version | 0.5.0-beta | 0.7.0-beta |
| Dataset | CIFAR-10 | CIFAR-10 |
| Training Parameters | epoch=30, steps=1562, batch_size = 32, lr=0.002 | epoch=30, steps=1562, batch_size = 32, lr=0.002 |
| Optimizer | Momentum | Momentum |
| Loss Function | Softmax Cross Entropy | Softmax Cross Entropy |
| outputs | probability | probability |
| Loss | 0.0016 | 0.01 |
| Speed | 21 ms/step | 16.8 ms/step |
| Total time | 17 mins | 14 mins |
| Checkpoint for Fine tuning | 445M (.ckpt file) | 445M (.ckpt file) |
| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet |
In dataset.py, we set the seed inside create_dataset function.
Please check the official homepage.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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