TensorLayer: A Versatile Library for Efficient Deep Learning Development
arXiv:1707.08551 · doi:10.1145/3123266.3129391
Abstract
Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing data, etc. TensorLayer is a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. It offers rich abstractions for neural networks, model and data management, and parallel workflow mechanism. While boosting efficiency, TensorLayer maintains both performance and scalability. TensorLayer was released in September 2016 on GitHub, and has helped people from academia and industry develop real-world applications of deep learning.
ACM Multimedia 2017
References in corpus (7)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- TensorFlow: A system for large-scale machine learning
- Caffe: Convolutional Architecture for Fast Feature Embedding
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
- Deep Bayesian Active Learning with Image Data
- Unsupervised Image-to-Image Translation with Generative Adversarial Networks
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