Skip to main content

Keras Models Hub

Project description

Keras Models Hub

PyPI - Downloads

This repo aims at providing both reusable Keras Models and pre-trained models, which could easily integrated into your projects.

Install

pip install keras-models

If you will using the NLP models, you need run one more command:

python -m spacy download xx_ent_wiki_sm

Usage Guide

Import

import kearasmodels

Examples

Reusable Models

LinearModel

DNN

CNN

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

Pre-trained Models

VGG16_Places365

This model is forked from GKalliatakis/Keras-VGG16-places365 and CSAILVision/places365

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

keras-models-0.0.7.tar.gz (12.5 kB view details)

Uploaded Source

Built Distribution

keras_models-0.0.7-py3-none-any.whl (18.6 kB view details)

Uploaded Python 3

File details

Details for the file keras-models-0.0.7.tar.gz.

File metadata

  • Download URL: keras-models-0.0.7.tar.gz
  • Upload date:
  • Size: 12.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.4

File hashes

Hashes for keras-models-0.0.7.tar.gz
Algorithm Hash digest
SHA256 6aad06ffc82dc1adf0d319c557e0963c2e9f7932536a1addbca4a539bc33c189
MD5 86d9269547c8a13a8c668e33c5ec3ca9
BLAKE2b-256 d4a54d1dd4a1d31c56a28e32441404c01694faa13d384f7d679987eb16a0456e

See more details on using hashes here.

File details

Details for the file keras_models-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: keras_models-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 18.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.4

File hashes

Hashes for keras_models-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 5983ea2caf98611b079b3bb8d1cc43382ba6d8b337f326246d600286299a1e67
MD5 ac39992b21418e1cebaabbb268c81fee
BLAKE2b-256 25b9b97f7202081346923af692be7c926faf7b10c4ed432f99dca416786fec1e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page