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  • 开始使用
  • 特性
  • 文档
    • API
    • 使用指南
  • 工具平台
    • 工具
      • AutoDL
  • develop(Fluid)
  • 1.5(Fluid)
  • 1.4(Fluid)
  • 1.3(Fluid)
  • 1.2(Fluid)
  • 1.1(Fluid)
  • 1.0(Fluid)
  • 0.15.0(Fluid)
  • 0.14.0(Fluid)
  • 0.13.0(Fluid)
  • 0.12.0(v2)
  • 0.11.0(v2)
  • 0.10.0(v2)
  • 中文(简)
  • English(En)
  • Beginner’s Guide
    • Installation Manuals
      • Install on Ubuntu
      • Install on CentOS
      • Install on MacOS
      • Installation on Windows
      • Compile From Source Code
        • Compile on Ubuntu from Source Code
        • Compile on CentOS from Source Code
        • Compile on MacOS from Source Code
        • Compile on Windows from Source Code
      • Appendix
    • Basic Deep Learning Models
      • Linear Regression
      • Recognize Digits
      • Image Classification
      • Word Vector
      • Recommender System
      • Sentiment Analysis
      • Label Semantic Roles
      • Machine Translation
    • Guide to Fluid Programming
  • User Guides
    • Basic Concepts
      • LoD-Tensor User Guide
    • Prepare Data
      • Take Numpy Array as Training Data
      • Python Reader
      • Use PyReader to read training and test data
    • Set up Simple Model
    • Train Neural Networks
      • Single-node training
        • Evaluate model while training
      • Multi-node Training
        • Quick Start with Distributed Training
        • Manual for Distributed Training with Fluid
        • Distributed Training on Baidu Cloud
      • Save, Load Models or Variables & Incremental Learning
    • Model Evaluation and Debugging
      • Model Evaluation
      • VisualDL Tools
        • Introduction to VisualDL Toolset
        • VisualDL user guide
  • Advanced User Guides
    • Design Principles of Fluid
    • Deploy Inference Model
      • Server-side Deployment
        • Install and Compile C++ Inference Library
        • Introduction to C++ Inference API
        • Use Paddle-TensorRT Library for inference
        • Performance Profiling for TensorRT Library
        • Model Inference on Windows
      • Mobile Deployment
    • Write New Operators
      • How to write a new operator
      • Notes on operator development
    • Performance Profiling and Optimization
      • Tune CPU performance
      • Heap Memory Profiling and Optimization
      • How to use timeline tool to do profile
    • How to contribute codes to Paddle
      • Guide of local development
      • Guide of submitting PR to Github
    • How to contribute documentation
    • Best Practice
      • Best practices of distributed training on CPU
  • API Reference
    • API Quick Search
      • Basic Concept
      • Neural Network Layer
        • Convolution
        • Pooling
        • Image Detection
        • Sequence
        • Mathematical operation
        • Activation Function
        • Loss function
        • Data input and output
        • Control Flow
        • Sparse update
        • Feed training/inference data with DataFeeder
        • Learning rate scheduler
        • Tensor
      • Complex Networks
      • Optimizer
      • Back Propagation
      • Metrics
      • Save and Load a Model
      • Inference Engine
      • Video Memory Optimization
      • Executor
      • Parallel Executor
      • CompiledProgram
      • Model Parameters
      • Distributed Training
        • Synchronous Distributed Training
        • Asynchronous Distributed Training
        • Training of Models with Large Scale Sparse Features
        • Preparing Data Reader for Distributed Training
    • fluid
    • fluid.average
    • fluid.backward
    • fluid.clip
    • Data Reader
    • dataset
    • fluid.data_feeder
    • fluid.dataset
    • fluid.dygraph
    • fluid.executor
    • fluid.initializer
    • fluid.io
    • fluid.layers
      • control_flow
      • detection
      • io
      • learning_rate_scheduler
      • metric_op
      • nn
      • ops
      • tensor
    • fluid.metrics
    • fluid.nets
    • fluid.optimizer
    • fluid.profiler
    • fluid.recordio_writer
    • fluid.regularizer
    • fluid.transpiler
    • fluid.unique_name
  • FLAGS
    • cudnn
    • data processing
    • debug
    • device management
    • distributed
    • executor
    • memory management
    • others
  • API Quick Search
  • »
  • API Reference »
  • API Quick Search
  • View page source

API Quick Search¶

This section introduces the Fluid API structure and usage, to help you quickly get the full picture of the PaddlePaddle Fluid API. This section is divided into the following modules:

  • Basic Concept
  • Neural Network Layer
  • Complex Networks
  • Optimizer
  • Back Propagation
  • Metrics
  • Save and Load a Model
  • Inference Engine
  • Video Memory Optimization
  • Executor
  • Parallel Executor
  • CompiledProgram
  • Model Parameters
  • Distributed Training