This repo aims at providing both reusable Keras Models and pre-trained models, which could easily integrated into your projects.
pip install keras-modelsIf you will using the NLP models, you need run one more command:
python -m spacy download xx_ent_wiki_smimport kearas_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
VGG16_Places365 [pre-trained]
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']]- LinearModel
- DNN
- WideDeep
- TextCNN
- TextDNN
- SkipGram
- ResNet
- VGG16_Places365 [pre-trained]
- working on more models
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.
Please submit PR if you want to contribute, or submit issues for new model requirements.