# Contents - [LeNet Description](#lenet-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) # [LeNet Description](#contents) LeNet was proposed in 1998, a typical convolutional neural network. It was used for digit recognition and got big success. [Paper](https://ieeexplore.ieee.org/document/726791): Y.Lecun, L.Bottou, Y.Bengio, P.Haffner. Gradient-Based Learning Applied to Document Recognition. *Proceedings of the IEEE*. 1998. # [Model Architecture](#contents) LeNet is very simple, which contains 5 layers. The layer composition consists of 2 convolutional layers and 3 fully connected layers. # [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: [MNIST]() - Dataset size：52.4M，60,000 28*28 in 10 classes - Train：60,000 images - Test：10,000 images - Data format：binary files - Note：Data will be processed in dataset.py - The directory structure is as follows: ``` └─Data ├─test │ t10k-images.idx3-ubyte │ t10k-labels.idx1-ubyte │ └─train train-images.idx3-ubyte train-labels.idx1-ubyte ``` # [Environment Requirements](#contents) - Hardware（Ascend/GPU/CPU） - Prepare hardware environment with Ascend, GPU, or CPU 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 LeNet sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH] # enter script dir, evaluate LeNet sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME] ``` # [Script Description](#contents) ## [Script and Sample Code](#contents) ``` ├── cv ├── lenet ├── README.md // descriptions about lenet ├── requirements.txt // package needed ├── scripts │ ├──run_standalone_train_cpu.sh // train in cpu │ ├──run_standalone_train_gpu.sh // train in gpu │ ├──run_standalone_train_ascend.sh // train in ascend │ ├──run_standalone_eval_cpu.sh // evaluate in cpu │ ├──run_standalone_eval_gpu.sh // evaluate in gpu │ ├──run_standalone_eval_ascend.sh // evaluate in ascend ├── src │ ├──dataset.py // creating dataset │ ├──lenet.py // lenet 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", "CPU". --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 ``` python train.py --data_path Data --ckpt_path ckpt > log.txt 2>&1 & # or enter script dir, and run the script sh run_standalone_train_ascend.sh Data ckpt ``` After training, the loss value will be achieved as follows: ``` # grep "loss is " log.txt 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. ## [Evaluation Process](#contents) ### Evaluation Before running the command below, please check the checkpoint path used for evaluation. ``` python eval.py --data_path Data --ckpt_path ckpt/checkpoint_lenet-1_1875.ckpt > log.txt 2>&1 & # or enter script dir, and run the script sh run_standalone_eval_ascend.sh Data ckpt/checkpoint_lenet-1_1875.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': 0.9842 ``` # [Model Description](#contents) ## [Performance](#contents) ### Evaluation Performance | Parameters | LeNet | | -------------------------- | ----------------------------------------------------------- | | Resource | Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G | | uploaded Date | 09/16/2020 (month/day/year) | | MindSpore Version | 1.0.0 | | Dataset | MNIST | | Training Parameters | epoch=10, steps=1875, batch_size = 32, lr=0.01 | | Optimizer | Momentum | | Loss Function | Softmax Cross Entropy | | outputs | probability | | Loss | 0.002 | | Speed | 1.071 ms/step | | Total time | 32.1s | | | Checkpoint for Fine tuning | 482k (.ckpt file) | | Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/lenet | # [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).