GitHub-HongweiZhang / Prediction Flow
Licence: mit
Deep-Learning based CTR models implemented by PyTorch
Stars: ✭ 138
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Prediction Flow
Image Caption Generator
A neural network to generate captions for an image using CNN and RNN with BEAM Search.
Stars: ✭ 126 (-8.7%)
Mutual labels: attention-mechanism, attention
Performer Pytorch
An implementation of Performer, a linear attention-based transformer, in Pytorch
Stars: ✭ 546 (+295.65%)
Mutual labels: attention-mechanism, attention
Neural sp
End-to-end ASR/LM implementation with PyTorch
Stars: ✭ 408 (+195.65%)
Mutual labels: attention-mechanism, attention
Seq2seq Summarizer
Pointer-generator reinforced seq2seq summarization in PyTorch
Stars: ✭ 306 (+121.74%)
Mutual labels: attention-mechanism, attention
Global Self Attention Network
A Pytorch implementation of Global Self-Attention Network, a fully-attention backbone for vision tasks
Stars: ✭ 64 (-53.62%)
Mutual labels: attention-mechanism, attention
Php Opencv Examples
Tutorial for computer vision and machine learning in PHP 7/8 by opencv (installation + examples + documentation)
Stars: ✭ 333 (+141.3%)
Mutual labels: torch, dnn
Structured Self Attention
A Structured Self-attentive Sentence Embedding
Stars: ✭ 459 (+232.61%)
Mutual labels: attention-mechanism, attention
ttslearn
ttslearn: Library for Pythonで学ぶ音声合成 (Text-to-speech with Python)
Stars: ✭ 158 (+14.49%)
Mutual labels: dnn, attention-mechanism
Isab Pytorch
An implementation of (Induced) Set Attention Block, from the Set Transformers paper
Stars: ✭ 21 (-84.78%)
Mutual labels: attention-mechanism, attention
Pytorch Gat
My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
Stars: ✭ 908 (+557.97%)
Mutual labels: attention-mechanism, attention
Adaptiveattention
Implementation of "Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning"
Stars: ✭ 303 (+119.57%)
Mutual labels: attention-mechanism, torch
Lambda Networks
Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute
Stars: ✭ 1,497 (+984.78%)
Mutual labels: attention-mechanism, attention
Attentionwalk
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
Stars: ✭ 266 (+92.75%)
Mutual labels: attention, torch
Gocv
Go package for computer vision using OpenCV 4 and beyond.
Stars: ✭ 4,511 (+3168.84%)
Mutual labels: torch, dnn
Pytorch Original Transformer
My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.
Stars: ✭ 411 (+197.83%)
Mutual labels: attention-mechanism, attention
AoA-pytorch
A Pytorch implementation of Attention on Attention module (both self and guided variants), for Visual Question Answering
Stars: ✭ 33 (-76.09%)
Mutual labels: attention, attention-mechanism
NTUA-slp-nlp
💻Speech and Natural Language Processing (SLP & NLP) Lab Assignments for ECE NTUA
Stars: ✭ 19 (-86.23%)
Mutual labels: attention, attention-mechanism
Vad
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.
Stars: ✭ 622 (+350.72%)
Mutual labels: attention, dnn
Attend infer repeat
A Tensorfflow implementation of Attend, Infer, Repeat
Stars: ✭ 82 (-40.58%)
Mutual labels: attention-mechanism, attention
prediction-flow
prediction-flow is a Python package providing modern Deep-Learning based CTR models. Models are implemented by PyTorch.
how to use
- Install using pip.
pip install prediction-flow
feature
how to define feature
There are two parameters for all feature types, name and column_flow. The name parameter is used to index the column raw data from input data frame. The column_flow parameter is a single transformer of a list of transformers. The transformer is used to pre-process the column data before training the model.
- dense number feature
Number('age', StandardScaler())
Number('ctr', None)
- sparse category feature
Category('movieId', CategoryEncoder(min_cnt=1))
- var length sequence feature
Sequence('genres', SequenceEncoder(sep='|', min_cnt=1))
transformer
The following transformers are provided now.
| transformer | supported feature type | detail |
|---|---|---|
| StandardScaler | Number | Wrapper of scikit-learn's StandardScaler. Null value must be filled in advance. |
| LogTransformer | Number | Log scaler. Null value must be filled in advance. |
| CategoryEncoder | Category | Converting str value to int. Null value must be filled in advance using '__UNKNOWN__'. |
| SequenceEncoder | Sequence | Converting sequence str value to int. Null value must be filled in advance using '__UNKNOWN__'. |
model
| model | reference |
|---|---|
| DNN | - |
| Wide & Deep | [DLRS 2016]Wide & Deep Learning for Recommender Systems |
| DeepFM | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction |
| DIN | [KDD 2018]Deep Interest Network for Click-Through Rate Prediction |
| DNN + GRU + GRU + Attention | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| DNN + GRU + AIGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| DNN + GRU + AGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| DNN + GRU + AUGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| DIEN | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction |
| OTHER | TODO |
example
movielens-1M
This dataset is just used to test the code can run, accuracy does not make sense.
- Prepare the dataset. preprocess.ipynb
- Run the model. movielens-1m.ipynb
amazon
- Prepare the dataset. prepare_neg.ipynb
- Run the model. amazon.ipynb
- An example using pytorch-lightning. amazon-lightning.ipynb
accuracy
acknowledge and reference
- Referring the design from DeepCTR, the features are divided into dense (class Number), sparse (class Category), sequence (class Sequence) types.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].