mindspore/model_zoo/official/cv/lenet_quant

• 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.

This is the quantitative network of LeNet.

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
  • Prepare hardware environment with Ascend
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore API

Quick Start

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

# 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

Script and Sample Code

├── 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

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

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

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

Performance

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

Description of Random Situation

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

ModelZoo Homepage

Please check the official homepage.