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https://github.com/www0wwwjs1/Matrix-Capsules-EM-Tensorflow
A Tensorflow implementation of CapsNet based on paper Matrix Capsules with EM Routing
https://github.com/www0wwwjs1/Matrix-Capsules-EM-Tensorflow
Last synced: about 2 months ago
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A Tensorflow implementation of CapsNet based on paper Matrix Capsules with EM Routing
- Host: GitHub
- URL: https://github.com/www0wwwjs1/Matrix-Capsules-EM-Tensorflow
- Owner: www0wwwjs1
- License: apache-2.0
- Created: 2017-11-20T01:37:31.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-02T18:22:16.000Z (about 6 years ago)
- Last Synced: 2024-08-01T08:11:41.316Z (4 months ago)
- Language: Python
- Size: 769 KB
- Stars: 219
- Watchers: 27
- Forks: 83
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-CapsNet - www0wwwjs1/Matrix-Capsules-EM-Tensorflow
README
# MATRIX CAPSULES EM-Tensorflow
[](https://opensource.org/licenses/Apache-2.0)
A Tensorflow implementation of CapsNet based on paper [Matrix Capsules with EM Routing](https://openreview.net/pdf?id=HJWLfGWRb)
> **Status:**
> 1. With configuration A=32, B=8, C=16, D=16, batch_size=128, the code can work on a Tesla P40 GPU at a speed of 8s/iteration. The definitions of A-D can be referred to the paper.
> 2. With configuration A=B=C=D=32, batch_size=64, the code can work on a Tesla P40 GPU at a speed of 25s/iteration. More optimization on implementation structure is required.
> 3. Some modification and optimization is implemented to prompt the numerical stability of GMM. Specific explanations can be found in the code.
> 4. With configuration A=32, B=4, D=4, D=4, batch_size=128, each iteration of training takes around 0.6s on a Tesla P40 GPU.
> **Current Results on smallNORB:**
- Configuration: A=32, B=8, C=16, D=16, batch_size=50, iteration number of EM routing: 2, with Coordinate Addition, spread loss, batch normalization
- Training loss. Variation of loss is suppressed by batch normalization. However there still exists a gap between our best results and the reported results in the original paper.
- Test accuracy(current best result is 91.8%)
> **Ablation Study on smallNORB:**
- Configuration: A=32, B=8, C=16, D=16, batch_size=32, iteration number of EM routing: 2, with Coordinate Addition, spread loss, test accuracy is 79.8%.
> **Current Results on MNIST:**
- Configuration: A=32, B=8, C=16, D=16, batch_size=50, iteration number of EM routing: 2, with Coordinate Addition, spread loss, batch normalization, reconstruction loss.
- Training loss.
- Test accuracy(current best result is 99.3%, only 10% samples are used in test)
> **Ablation Study on MNIST:**
- Configuration: A=32, B=4, C=4, D=4, batch_size=128, iteration number of EM routing: 2, no Coordinate Addition, cross entropy loss, test accuracy is 96.4%.
- Configuration: A=32, B=4, C=4, D=4, batch_size=128, iteration number of EM routing: 2, with Coordinate Addition, cross entropy loss, test accuracy is 96.8%.
- Configuration: A=32, B=8, C=16, D=16, batch_size=32, iteration number of EM routing: 2, with Coordinate Addition, spread loss 99.1%.
> **To Do List:**
> 1. Experiments on smallNORB as in paper is about to be casted.
Any questions and comments to the code and the original algorithms are welcomed!!! My email: zhangsuofei at njupt.edu.cn
## Requirements
- Python >= 3.4
- Numpy
- Tensorflow >= 1.2.0
- Keras
```pip install -r requirement.txt```
## Usage
**Step 1.**
Clone this repository with ``git``.
```
$ git clone https://github.com/www0wwwjs1/Matrix-Capsules-EM-Tensorflow.git
$ cd Matrix-Capsules-EM-Tensorflow
```
**Step 2.**
Download the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), ``mv`` and extract it into ``data/mnist`` directory.(Be careful the backslash appeared around the curly braces when you copy the ``wget `` command to your terminal, remove it)
```
$ mkdir -p data/mnist
$ wget -c -P data/mnist http://yann.lecun.com/exdb/mnist/{train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz}
$ gunzip data/mnist/*.gz
```
***To install smallNORB, follow instructions in ```./data/README.md```***
**Step 3.**
Start the training(MNIST):
```
$ python3 train.py "mnist"
```
**Step 4.**
Download the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), ``mv`` and extract it into ``data/fashion_mnist`` directory.(Be careful the backslash appeared around the curly braces when you copy the ``wget `` command to your terminal, remove it)
```
$ mkdir -p data/fashion_mnist
$ wget -c -P data/fashion_mnist http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/{train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz}
$ gunzip data/fashion_mnist/*.gz
```
Start the training(smallNORB):
```
$ python3 train.py "smallNORB"
```
Start the training(CNN baseline):
```
$ python3 train_baseline.py "smallNORB"
```
**Step 4.**
View the status of training:
```
$ tensorboard --logdir=./logdir/{model_name}/{dataset_name}/train_log/
```
Open the url tensorboard has shown.
**Step 5.**
Start the test on MNIST:
```
$ python3 eval.py "mnist" "caps"
```
Start the test on smallNORB:
```
$ python3 eval.py "smallNORB" "caps"
```
**Step 6.**
View the status of test:
```
$ tensorboard --logdir=./test_logdir/{model_name}/{dataset_name}/
```
Open the url tensorboard has shown.
### Reference
- [naturomics/CapsNet-Tensorflow](https://github.com/naturomics/CapsNet-Tensorflow): the implementation of Hinton's paper [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)