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# LeNet Example
# Contents
## Description
- [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 )
Training LeNet with dataset in MindSpore.
This is the simple and basic tutorial for constructing a network in MindSpore.
# [LeNet Description ](#contents )
## Requirements
LeNet was proposed in 1998, a typical convolutional neural network. It was used for digit recognition and got big success.
- Install [MindSpore ](https://www.mindspore.cn/install/en ).
[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 .
- Download the dataset, the directory structure is as follows:
# [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 ](<http://yann.lecun.com/exdb/mnist/> )
- Dataset size： ，
- Train：
- Test：
- Data format：
- Note：
- The directory structure is as follows:
```
└─Data
@ -23,40 +51,137 @@ This is the simple and basic tutorial for constructing a network in MindSpore.
train-labels.idx1-ubyte
```
## Running the example
# [Environment Requirements ](#contents )
- Hardware（ ）
- Prepare hardware environment with Ascend, GPU, or CPU processor.
- 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
# train LeNet, hyperparameter setting in config.py
python train.py --data_path Data
# 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]
```
You will get the loss value of each step as following:
# [Script Description ](#contents )
## [Script and Sample Code ](#contents )
```
├── model_zoo
├── README.md // descriptions about all the models
├── 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
```bash
epoch: 1 step: 1, loss is 2.3040335
```
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: 1739, loss is 0.06952668
epoch: 1 step: 1740, loss is 0.05038793
epoch: 1 step: 1741, loss is 0.05018193
epoch: 1 step: 1536, loss is 1.9366643
epoch: 1 step: 1537, loss is 1.6983616
epoch: 1 step: 1538, loss is 1.0221305
...
```
Then, evaluate LeNet according to network model
```python
# evaluate LeNet
python eval.py --data_path Data --ckpt_path checkpoint_lenet-1_1875.ckpt
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
```
## Note
Here are some optional parameters:
You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
```bash
--device_target {Ascend,GPU,CPU}
device where the code will be implemented (default: Ascend)
--data_path DATA_PATH
path where the dataset is saved
--dataset_sink_mode DATASET_SINK_MODE
dataset_sink_mode is False or True
```
# grep "Accuracy: " log.txt
'Accuracy': 0.9842
```
# [Model Description ](#contents )
## [Performance ](#contents )
### Evaluation Performance
| Parameters | LeNet |
| -------------------------- | ----------------------------------------------------------- |
| Resource | Ascend 910 ； ， ； ，
| uploaded Date | 06/09/2020 (month/day/year) |
| MindSpore Version | 0.5.0-beta |
| 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 | 1.70 ms/step |
| Total time | 43.1s | |
| 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.
You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
# [ModelZoo Homepage ](#contents )
Please check the official [homepage ](https://gitee.com/mindspore/mindspore/tree/master/model_zoo ).
wukesong
5 years ago