mindspore/model_zoo/official/cv/lenet

• Contents
• LeNet Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
• • Script and Sample Code
  • Script Parameters
  • Training Process
  • • Training
  • Evaluation Process
  • • Evaluation
• Model Description
• • Performance
  • • Evaluation Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• LeNet Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Evaluation Performance
• ModelZoo Homepage

LeNet Description

LeNet was proposed in 1998, a typical convolutional neural network. It was used for digit recognition and got big success.

Paper: Y.Lecun, L.Bottou, Y.Bengio, P.Haffner. Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE. 1998.

Model Architecture

LeNet is very simple, which contains 5 layers. The layer composition consists of 2 convolutional layers and 3 fully connected layers.

Dataset

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

• Hardware（Ascend/GPU/CPU）
  • Prepare hardware environment with Ascend, GPU, or CPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

# 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

Script and Sample Code

├── 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

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

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

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

Performance

Evaluation Performance

Parameters	LeNet
Resource	Ascend 910 ；CPU 2.60GHz，192cores；Memory，755G
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

In dataset.py, we set the seed inside create_dataset function.

ModelZoo Homepage

Please check the official homepage.