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https://github.com/marcnuth/keras-models
reusable & predefined models built via Keras, which could be easily integrated into your project.
https://github.com/marcnuth/keras-models
artificial-intelligence artificial-neural-networks deep-learning keras keras-models models pretrained-models
Last synced: about 8 hours ago
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reusable & predefined models built via Keras, which could be easily integrated into your project.
- Host: GitHub
- URL: https://github.com/marcnuth/keras-models
- Owner: Marcnuth
- License: apache-2.0
- Created: 2018-12-10T01:32:45.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-14T15:41:38.000Z (almost 5 years ago)
- Last Synced: 2024-11-06T01:04:56.344Z (1 day ago)
- Topics: artificial-intelligence, artificial-neural-networks, deep-learning, keras, keras-models, models, pretrained-models
- Language: Python
- Homepage:
- Size: 85 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Keras Models Hub
This repo aims at providing both **reusable** Keras Models and **pre-trained** models, which could easily integrated into your projects.
## Install
```shell
pip install keras-models
```
If you will using the NLP models, you need run one more command:
```shell
python -m spacy download xx_ent_wiki_sm
```
## Usage Guide
### Import
```
import kearas_models
```
### Examples
__LinearModel__
__DNN__
__CNN__
```python
from keras_models.models import CNN
# fake data
X = np.random.normal(0, 1.0, size=500 * 100 * 100 * 3).reshape(500, 100, 100, 3)
w1 = np.random.normal(0, 1.0, size=100)
w2 = np.random.normal(0, 1.0, size=3)
Y = np.dot(np.dot(np.dot(X, w2), w1), w1) + np.random.randint(1)
# initialize & train model
model = CNN(input_shape=X.shape[1:], filters=[32, 64], kernel_size=(2, 2), pool_size=(3, 3), padding='same', r_dropout=0.25, num_classes=1)
model.compile(optimizer='adam', loss=mean_squared_error, metrics=['mae', 'mse'])
model.summary()
model.fit(X, Y, batch_size=16, epochs=100, validation_split=0.1)
```
__SkipGram__
__WideDeep__
__VGG16_Places365 [pre-trained]__
> This model is forked from [GKalliatakis/Keras-VGG16-places365](https://github.com/GKalliatakis/Keras-VGG16-places365) and [CSAILVision/places365](https://github.com/CSAILVision/places365)
```python
from keras_models.models.pretrained import vgg16_places365
labels = vgg16_places365.predict(['your_image_file_pathname.jpg', 'another.jpg'], n_top=3)
# Example Result: labels = [['cafeteria', 'food_court', 'restaurant_patio'], ['beach', 'sand']]
```
## Models
- LinearModel
- DNN
- WideDeep
- TextCNN
- TextDNN
- SkipGram
- ResNet
- VGG16_Places365 [pre-trained]
- working on more models
## Citation
__WideDeep__
```
Cheng H T, Koc L, Harmsen J, et al.
Wide & deep learning for recommender systems[C]
Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 2016: 7-10.
```
__TextCNN__
```
Kim Y.
Convolutional neural networks for sentence classification[J].
arXiv preprint arXiv:1408.5882, 2014.
```
__SkipGram__
```
Mikolov T, Chen K, Corrado G, et al.
Efficient estimation of word representations in vector space[J].
arXiv preprint arXiv:1301.3781, 2013.
```
__VGG16_Places365__
```
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A.
Places: A 10 million Image Database for Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
```
__ResNet__
```
He K, Zhang X, Ren S, et al.
Deep residual learning for image recognition[C]
Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
```
## Contribution
Please submit PR if you want to contribute, or submit issues for new model requirements.