# 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. This is the quantitative network of LeNet. # [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) 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 - Prepare hardware environment with Ascend - Framework - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) # [Quick Start](#contents) After installing MindSpore via the official website, you can start training and evaluation as follows: ```python # enter ../lenet directory and train lenet network,then a '.ckpt' file will be generated. sh run_standalone_train_ascend.sh [DATA_PATH] # enter lenet dir, train LeNet-Quant python train.py --device_target=Ascend --data_path=[DATA_PATH] --ckpt_path=[CKPT_PATH] --dataset_sink_mode=True #evaluate LeNet-Quant python eval.py --device_target=Ascend --data_path=[DATA_PATH] --ckpt_path=[CKPT_PATH] --dataset_sink_mode=True ``` # [Script Description](#contents) ## [Script and Sample Code](#contents) ``` ├── model_zoo ├── README.md // descriptions about all the models ├── lenet_quant ├── README.md // descriptions about LeNet-Quant ├── src │ ├── config.py // parameter configuration │ ├── dataset.py // creating dataset │ ├── lenet_fusion.py // auto constructed quantitative network model of LeNet-Quant │ ├── lenet_quant.py // manual constructed quantitative network model of LeNet-Quant │ ├── loss_monitor.py //monitor of network's loss and other data ├── requirements.txt // package needed ├── train.py // training LeNet-Quant network with device Ascend ├── eval.py // evaluating LeNet-Quant network with device Ascend ``` ## [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".Only "Ascend" is supported now. --ckpt_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 --device_target=Ascend --dataset_path=/home/datasets/MNIST --dataset_sink_mode=True > log.txt 2>&1 & ``` After training, the loss value will be achieved as follows: ``` # grep "Epoch " log.txt Epoch: [ 1/ 10], step: [ 937/ 937], loss: [0.0081], avg loss: [0.0081], time: [11268.6832ms] Epoch time: 11269.352, per step time: 12.027, avg loss: 0.008 Epoch: [ 2/ 10], step: [ 937/ 937], loss: [0.0496], avg loss: [0.0496], time: [3085.2389ms] Epoch time: 3085.641, per step time: 3.293, avg loss: 0.050 Epoch: [ 3/ 10], step: [ 937/ 937], loss: [0.0017], avg loss: [0.0017], time: [3085.3510ms] ... ... ``` 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_937.ckpt > log.txt 2>&1 & ``` 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 56cores Memory 314G | | uploaded Date | 06/09/2020 (month/day/year) | | MindSpore Version | 0.5.0-beta | | Dataset | MNIST | | Training Parameters | epoch=10, steps=937, batch_size = 64, lr=0.01 | | Optimizer | Momentum | | Loss Function | Softmax Cross Entropy | | outputs | probability | | Loss | 0.002 | | Speed |3.29 ms/step | | Total time | 40s | | Checkpoint for Fine tuning | 482k (.ckpt file) | | Scripts | [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).