/TensorFlow-Examples

TensorFlow Tutorial and Examples for Beginners with Latest APIs

Primary LanguageJupyter NotebookOtherNOASSERTION

TensorFlow 教程示例

这个仓库是一个初级的TensorFlow使用示例，目的是方便初学者能够快速的熟悉TensorFlow，为了方便理解这些示例，这个仓库不经包含了代码，还有对应的jupyter notebook解释。

这个示例适合初学者，除了有原始TensorFlow的实现例子，还有最新的High Level API实现的例子，这些高级的API包含layers, Eager, estimator, dataset等

更新 (03/18/2018): TensorFlow开始支持Eager API (建议使用 TensorFlow v1.5+).

示例索引

0 - 预习

1 - 简介

  • 在TensorFlow中使用示例MNist数据集 (notebook)
  • TensorFlow Hello World (notebook) (code). 使用TensorFlow的最简单操作.
  • 基本操作 (notebook) (code). 使用TensorFlow的基本操作.
  • TensorFlow Eager 基本操作 (notebook) (code).使用TensorFlow的 Eager API最基本操作.

2 - 基本模型

  • 线性回归 (notebook) (code). 使用TensorFlow实现线性回归模型.
  • 线性回归 (eager api) (notebook) (code). 使用TensorFlow的 Eager API实现线性回归.
  • 逻辑回归 (notebook) (code). 使用TensorFlow实现逻辑回归.
  • 逻辑回归(eager api) (notebook) (code). 使用TensorFlow的 Eager API实现逻辑回归.
  • 最近邻算法 (notebook) (code). 使用 TensorFlow实现最近邻算法.
  • K-Means (notebook) (code). 使用 TensorFlow 实现K-Means分类器.
  • 随机森林 (notebook) (code). 使用TensorFlow实现随机森林分类器.

3 - 神经网络

有监督
  • 简单的神经网络 (notebook) (code).使用TensorFlow原始的Low Level API实现简单的BP神经网络用于对MNist数据库进行分类.
  • 简单的神经网络(tf.layers/estimator api) (notebook) (code).使用 TensorFlow 'layers' 和 'estimator' API 实现简单的神经网络实现对MNist数据库进行分类。
  • 简单的神经网络(eager api) (notebook) (code). 使用 TensorFlow Eager API 构建一个多层感知机分类MNist数据库。
  • 卷积神经网络（原始TensorFlow） (notebook) (code). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
  • Convolutional Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
  • Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.
  • Bi-directional Recurrent Neural Network (LSTM) (notebook) (code). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.
  • Dynamic Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.
无监督
  • 自动编码 (notebook) (code). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
  • Variational Auto-Encoder (notebook) (code). Build a variational auto-encoder (VAE), to encode and generate images from noise.
  • GAN (Generative Adversarial Networks) (notebook) (code). Build a Generative Adversarial Network (GAN) to generate images from noise.
  • DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook) (code). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.

4 - 实用工具

  • Save and Restore a model (notebook) (code). Save and Restore a model with TensorFlow.
  • Tensorboard - Graph and loss visualization (notebook) (code). Use Tensorboard to visualize the computation Graph and plot the loss.
  • Tensorboard - Advanced visualization (notebook) (code). Going deeper into Tensorboard; visualize the variables, gradients, and more...

5 - 数据管理

  • Build an image dataset (notebook) (code). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.
  • TensorFlow Dataset API (notebook) (code). Introducing TensorFlow Dataset API for optimizing the input data pipeline.

6 - 多GPU

  • Basic Operations on multi-GPU (notebook) (code). A simple example to introduce multi-GPU in TensorFlow.
  • Train a Neural Network on multi-GPU (notebook) (code). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs.

数据库

Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

Official Website: http://yann.lecun.com/exdb/mnist/

安装TensorFlow

To download all the examples, simply clone this repository:

git clone https://github.com/debugCVML/TensorFlow-Examples

To run them, you also need the latest version of TensorFlow. To install it:

pip install tensorflow

or (if you want GPU support):

pip install tensorflow_gpu

For more details about TensorFlow installation, you can check TensorFlow Installation Guide

更多例程

The following examples are coming from TFLearn, a library that provides a simplified interface for TensorFlow. You can have a look, there are many examples and pre-built operations and layers.

教程

  • TFLearn Quickstart. Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier.

样例