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!4988 modify lenetalexnet README.md
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# AlexNet Example
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## Description
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Training AlexNet with dataset in MindSpore.
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This is the simple tutorial for training AlexNet in MindSpore.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the dataset, the directory structure is as follows:
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```
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├─10-batches-bin
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│
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└─10-verify-bin
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```
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## Running the example
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```python
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# train AlexNet, hyperparameter setting in config.py
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python train.py --data_path 10-batches-bin
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```
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You will get the loss value of each step as following:
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```bash
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epoch: 1 step: 1, loss is 2.2791853
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...
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epoch: 1 step: 1536, loss is 1.9366643
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epoch: 1 step: 1537, loss is 1.6983616
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epoch: 1 step: 1538, loss is 1.0221305
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...
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```
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Then, evaluate AlexNet according to network model
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```python
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# evaluate AlexNet
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python eval.py --data_path 10-verify-bin --ckpt_path checkpoint_alexnet-1_1562.ckpt
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```
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## Note
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Here are some optional parameters:
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```bash
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--device_target {Ascend,GPU}
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device where the code will be implemented (default: Ascend)
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--data_path DATA_PATH
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path where the dataset is saved
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--dataset_sink_mode DATASET_SINK_MODE
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dataset_sink_mode is False or True
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```
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You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
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# Contents
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- [AlexNet Description](#alexnet-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Process](#training-process)
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- [Training](#training)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [AlexNet Description](#contents)
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AlexNet was proposed in 2012, one of the most influential neural networks. It got big success in ImageNet Dataset recognition than other models.
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[Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf): Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep ConvolutionalNeural Networks. *Advances In Neural Information Processing Systems*. 2012.
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# [Model Architecture](#contents)
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AlexNet composition consists of 5 convolutional layers and 3 fully connected layers. Multiple convolutional kernels can extract interesting features in images and get more accurate classification.
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# [Dataset](#contents)
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Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
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- Dataset size：175M，60,000 32*32 colorful images in 10 classes
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- Train：146M，50,000 images
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- Test：29.3M，10,000 images
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- Data format：binary files
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- Note：Data will be processed in dataset.py
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- Download the dataset, the directory structure is as follows:
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```
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├─cifar-10-batches-bin
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│
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└─cifar-10-verify-bin
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```
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# [Environment Requirements](#contents)
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- Hardware（Ascend/GPU）
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- Prepare hardware environment with Ascend or GPU processor.
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- Framework
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- [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
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- For more information, please check the resources below：
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||||
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
|
||||
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
|
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|
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# [Quick Start](#contents)
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After installing MindSpore via the official website, you can start training and evaluation as follows:
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```python
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# enter script dir, train AlexNet
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sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
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# enter script dir, evaluate AlexNet
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sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
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```
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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```
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├── model_zoo
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├── README.md // descriptions about all the models
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├── alexnet
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├── README.md // descriptions about alexnet
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├── requirements.txt // package needed
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├── scripts
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│ ├──run_standalone_train_gpu.sh // train in gpu
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│ ├──run_standalone_train_ascend.sh // train in ascend
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│ ├──run_standalone_eval_gpu.sh // evaluate in gpu
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│ ├──run_standalone_eval_ascend.sh // evaluate in ascend
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├── src
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│ ├──dataset.py // creating dataset
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│ ├──alexnet.py // alexnet architecture
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│ ├──config.py // parameter configuration
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├── train.py // training script
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├── eval.py // evaluation script
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```
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## [Script Parameters](#contents)
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```python
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Major parameters in train.py and config.py as follows:
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--data_path: The absolute full path to the train and evaluation datasets.
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--epoch_size: Total training epochs.
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--batch_size: Training batch size.
|
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--image_height: Image height used as input to the model.
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--image_width: Image width used as input the model.
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--device_target: Device where the code will be implemented. Optional values are "Ascend", "GPU".
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--checkpoint_path: The absolute full path to the checkpoint file saved after training.
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--data_path: Path where the dataset is saved
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```
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## [Training Process](#contents)
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### Training
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```
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python train.py --data_path cifar-10-batches-bin --ckpt_path ckpt > log.txt 2>&1 &
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# or enter script dir, and run the script
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sh run_standalone_train_ascend.sh cifar-10-batches-bin ckpt
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```
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After training, the loss value will be achieved as follows:
|
||||
|
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# grep "loss is " train.log
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```
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||||
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
|
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...
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```
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||||
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The model checkpoint will be saved in the current directory.
|
||||
|
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## [Evaluation Process](#contents)
|
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|
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### Evaluation
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|
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Before running the command below, please check the checkpoint path used for evaluation.
|
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|
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```
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python eval.py --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > log.txt 2>&1 &
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or enter script dir, and run the script
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sh run_standalone_eval_ascend.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt
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```
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|
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You can view the results through the file "log.txt". The accuracy of the test dataset will be as follows:
|
||||
|
||||
```
|
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# grep "Accuracy: " log.txt
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'Accuracy': 0.8832
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```
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|
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# [Model Description](#contents)
|
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|
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## [Performance](#contents)
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### Evaluation Performance
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||||
|
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| Parameters | AlexNet |
|
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| -------------------------- | ----------------------------------------------------------- |
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| Resource | Ascend 910 ；CPU 2.60GHz，56cores；Memory，314G |
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| uploaded Date | 06/09/2020 (month/day/year) |
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| MindSpore Version | 0.5.0-beta |
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| Dataset | CIFAR-10 |
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| Training Parameters | epoch=30, steps=1562, batch_size = 32, lr=0.002 |
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| Optimizer | Momentum |
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| Loss Function | Softmax Cross Entropy |
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| outputs | probability |
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| Loss | 0.0016 |
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| Speed | 21 ms/step |
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| Total time | 17 mins |
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| Checkpoint for Fine tuning | 445M (.ckpt file) |
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| Scripts | https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/alexnet |
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# [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside “create_dataset" function.
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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|
|
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@ -1,62 +1,187 @@
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# LeNet Example
|
||||
|
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## Description
|
||||
|
||||
Training LeNet with dataset in MindSpore.
|
||||
|
||||
This is the simple and basic tutorial for constructing a network in MindSpore.
|
||||
|
||||
## Requirements
|
||||
|
||||
- Install [MindSpore](https://www.mindspore.cn/install/en).
|
||||
|
||||
- Download the dataset, 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
|
||||
```
|
||||
|
||||
## Running the example
|
||||
|
||||
```python
|
||||
# train LeNet, hyperparameter setting in config.py
|
||||
python train.py --data_path Data
|
||||
```
|
||||
|
||||
You will get the loss value of each step as following:
|
||||
|
||||
```bash
|
||||
epoch: 1 step: 1, loss is 2.3040335
|
||||
...
|
||||
epoch: 1 step: 1739, loss is 0.06952668
|
||||
epoch: 1 step: 1740, loss is 0.05038793
|
||||
epoch: 1 step: 1741, loss is 0.05018193
|
||||
...
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||||
```
|
||||
|
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Then, evaluate LeNet according to network model
|
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```python
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# evaluate LeNet
|
||||
python eval.py --data_path Data --ckpt_path checkpoint_lenet-1_1875.ckpt
|
||||
```
|
||||
|
||||
## Note
|
||||
Here are some optional parameters:
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
You can run ```python train.py -h``` or ```python eval.py -h``` to get more information.
|
||||
# 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.
|
||||
|
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[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)
|
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|
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LeNet is very simple, which contains 5 layers. The layer composition consists of 2 convolutional layers and 3 fully connected layers.
|
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|
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# [Dataset](#contents)
|
||||
|
||||
Dataset used: [MNIST](<http://yann.lecun.com/exdb/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](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 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)
|
||||
|
||||
```
|
||||
├── 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
|
||||
|
||||
```
|
||||
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，56cores；Memory，314G |
|
||||
| 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)
|
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In dataset.py, we set the seed inside “create_dataset" function.
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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