# Contents - [AlexNet Description](#alexnet-description) - [Model Architecture](#model-architecture) - [Dataset](#dataset) - [Environment Requirements](#environment-requirements) - [Quick Start](#quick-start) - [Script Description](#script-description) - [Script and Sample Code](#script-and-sample-code) - [Script Parameters](#script-parameters) - [Training Process](#training-process) - [Training](#training) - [Evaluation Process](#evaluation-process) - [Evaluation](#evaluation) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) - [ModelZoo Homepage](#modelzoo-homepage) ## [AlexNet Description](#contents) AlexNet was proposed in 2012, one of the most influential neural networks. It got big success in ImageNet Dataset recognition than other models. [Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf): Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep ConvolutionalNeural Networks. *Advances In Neural Information Processing Systems*. 2012. ## [Model Architecture](#contents) 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](#contents) Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below. Dataset used: [CIFAR-10]() - Dataset size：175M，60,000 32*32 colorful images in 10 classes - Train：146M，50,000 images - Test：29.3M，10,000 images - Data format：binary files - Note：Data will be processed in dataset.py - Download the dataset, the directory structure is as follows: ```bash ├─cifar-10-batches-bin │ └─cifar-10-verify-bin ``` ## [Environment Requirements](#contents) - Hardware（Ascend/GPU） - Prepare hardware environment with Ascend or GPU processor. - Framework - [MindSpore](https://www.mindspore.cn/install/en) - For more information, please check the resources below： - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) ## [Quick Start](#contents) After installing MindSpore via the official website, you can start training and evaluation as follows: ```python # 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] ``` ## [Script Description](#contents) ### [Script and Sample Code](#contents) ```bash ├── 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 ``` ### [Script Parameters](#contents) ```python 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 ``` ### [Training Process](#contents) #### Training - running on Ascend ```bash python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log 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: ```bash # grep "loss is " 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 ```bash python train.py --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log 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: ```bash # grep "loss is " 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 ``` ### [Evaluation Process](#contents) #### Evaluation Before running the command below, please check the checkpoint path used for evaluation. - running on Ascend ```bash python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > eval_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 "eval_log". The accuracy of the test dataset will be as follows: ```bash # grep "Accuracy: " eval_log 'Accuracy': 0.8832 ``` - running on GPU ```bash python eval.py --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > eval_log 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 "eval_log". The accuracy of the test dataset will be as follows: ```bash # grep "Accuracy: " eval_log 'Accuracy': 0.88512 ``` ## [Model Description](#contents) ### [Performance](#contents) #### Evaluation Performance | Parameters | Ascend | GPU | | -------------------------- | ------------------------------------------------------------| -------------------------------------------------| | Resource | Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G | NV SMX2 V100-32G | | uploaded Date | 06/09/2020 (month/day/year) | 17/09/2020 (month/day/year) | | MindSpore Version | 1.0.0 | 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 | 0.08 | 0.01 | | Speed | 7.3 ms/step | 16.8 ms/step | | Total time | 6 mins | 14 mins | | Checkpoint for Fine tuning | 445M (.ckpt file) | 445M (.ckpt file) | | Scripts | [AlexNet Script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet) | [AlexNet Script](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet) | ## [Description of Random Situation](#contents) In dataset.py, we set the seed inside ```create_dataset``` function. ## [ModelZoo Homepage](#contents) Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).