Welcome to TensorLayer

Documentation Version: 1.10.1

Good News: We won the Best Open Source Software Award @ACM Multimedia (MM) 2017.

TensorLayer is a Deep Learning (DL) and Reinforcement Learning (RL) library extended from Google TensorFlow. It provides popular DL and RL modules that can be easily customized and assembled for tackling real-world machine learning problems. More details can be found here.

Note

If you got problem to read the docs online, you could download the repository on GitHub, then go to /docs/_build/html/index.html to read the docs offline. The _build folder can be generated in docs using make html.

User Guide

The TensorLayer user guide explains how to install TensorFlow, CUDA and cuDNN, how to build and train neural networks using TensorLayer, and how to contribute to the library as a developer.

Starting with TensorLayer

• Installation
  • Step 1 : Install dependencies
  • Step 2 : TensorFlow
  • Step 3 : TensorLayer
  • Step 4 : GPU support
  • Windows User
  • Issue
• Tutorials
  • Before we start
  • TensorLayer is simple
  • Run the MNIST example
  • Understand the MNIST example
  • Run the Pong Game example
  • Understand Reinforcement learning
  • Run the Word2Vec example
  • Understand Word Embedding
  • Run the PTB example
  • Understand LSTM
  • More Tutorials
  • More info
• Examples
  • Basics
  • Vision
  • Adversarial Learning
  • Natural Language Processing
  • Reinforcement Learning
  • Pretrained Models
  • Miscellaneous
• Contributing
  • Project Maintainers
  • What to contribute
  • How to contribute
• Get Involved in Research
  • Data Science Institute, Imperial College London
• FAQ
  • How to effectively learn TensorLayer
  • Exclude some layers from training
  • Logging
  • Visualization
  • Install Master Version
  • Editable Mode
  • Load Model

API Reference

If you are looking for information on a specific function, class or method, this part of the documentation is for you.

Stable Functionalities

• API - Activations
  • Your activation
  • Ramp
  • Leaky ReLU
  • Leaky ReLU6
  • Twice Leaky ReLU6
  • Swish
  • Sign
  • Hard Tanh
  • Pixel-wise softmax
  • Parametric activation
• API - Array Operations
  • Tensorflow Tensor Operations
• API - Cost
  • Your cost function
  • Softmax cross entropy
  • Sigmoid cross entropy
  • Binary cross entropy
  • Mean squared error (L2)
  • Normalized mean square error
  • Absolute difference error (L1)
  • Dice coefficient
  • Hard Dice coefficient
  • IOU coefficient
  • Cross entropy for sequence
  • Cross entropy with mask for sequence
  • Cosine similarity
  • Regularization functions
• API - Distributed Training
  • Distributed training
• API - Files
  • Load dataset functions
  • Load and save network
  • Load and save variables
  • Folder/File functions
  • Sort
  • Visualizing npz file
• API - Iteration
  • Non-time series
  • Time series
• API - Layers
  • Name Scope and Sharing Parameters
  • Understanding the Basic Layer
  • Layer list
  • Customizing Layers
  • Basic Layer
  • Input Layers
  • Activation Layers
  • Convolutional Layers
  • Dense Layers
  • Dropout Layers
  • Extend Layers
  • External Libraries Layers
  • Flow Control Layer
  • Image Resampling Layers
  • Lambda Layers
  • Merge Layers
  • Noise Layer
  • Normalization Layers
  • Object Detection Layer
  • Padding Layers
  • Pooling Layers
  • Quantized Nets
  • Recurrent Layers
  • Shape Layers
  • Spatial Transformer
  • Stack Layer
  • Time Distributed Layer
  • Helper Functions
• API - Models
  • VGG16
  • VGG19
  • SqueezeNetV1
  • MobileNetV1
• API - Natural Language Processing
  • Iteration function for training embedding matrix
  • Sampling functions
  • Vector representations of words
  • Read words from file
  • Read analogy question file
  • Build vocabulary, word dictionary and word tokenization
  • Convert words to IDs and IDs to words
  • Functions for translation
  • Metrics
• API - Optimizers
  • Optimizers List
  • AMSGrad Optimizer
• API - Preprocessing
  • Threading
  • Images
  • Object detection
  • Keypoints
  • Sequence
• API - Reinforcement Learning
  • Reward functions
  • Cost functions
  • Sampling functions
• API - Utility
  • Training, testing and predicting
  • Evaluation functions
  • Class balancing functions
  • Random functions
  • Dictionary and list
  • Close TF session and associated processes
  • Open TensorBoard
  • Clear TensorFlow placeholder
  • Set GPU functions
• API - Visualization
  • Save and read images
  • Visualize model parameters
  • Visualize images
  • Visualize embeddings

Alpha Version Functionalities

• API - Database
  • Why Database
  • Preparation
  • Tutorials
  • TensorHub API

Command-line Reference

TensorLayer provides a handy command-line tool tl to perform some common tasks.

Command Line Interface

• CLI - Command Line Interface
  • tl train

Indices and tables

• Index
• Module Index
• Search Page