(zhuan) 资源|TensorFlow初学者必须了解的55个经典案例

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(zhuan) 资源|TensorFlow初学者必须了解的55个经典案例

wangxiaocvpr 2017-07-20 09:28:00 浏览867
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资源|TensorFlow初学者必须了解的55个经典案例

2017-05-27 全球人工智能

 

>>>>>>欢迎投稿:news@top25.cn<<<<<<

文章来源:github      采编:lily

 

本文是TensorFlow实现流行机器学习算法的教程汇集,目标是让读者可以轻松通过清晰简明的案例深入了解 TensorFlow。这些案例适合那些想要实现一些 TensorFlow 案例的初学者。本教程包含还包含笔记和带有注解的代码。

 

第一步:给TF新手的教程指南

 

1:tf初学者需要明白的入门准备

 

  • 机器学习入门笔记:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb

  • MNIST 数据集入门笔记

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb

 

2:tf初学者需要了解的入门基础

 

  • Hello World

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py

 

  • 基本操作

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py

 

3:tf初学者需要掌握的基本模型

 

  • 最近邻:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py

 

  • 线性回归:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py

 

  • Logistic 回归:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py

 

4:tf初学者需要尝试的神经网络

 

  • 多层感知器:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py

 

  • 卷积神经网络:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py

 

  • 循环神经网络(LSTM):

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py

 

  • 双向循环神经网络(LSTM):

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py

 

  • 动态循环神经网络(LSTM)

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py

 

  • 自编码器

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py

 

5:tf初学者需要精通的实用技术

 

  • 保存和恢复模型

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py

 

  • 图和损失可视化

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py

 

  • Tensorboard——高级可视化

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py

 

5:tf初学者需要的懂得的多GPU基本操作

 

  • 多 GPU 上的基本操作

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py

 

6:案例需要的数据集

 

有一些案例需要 MNIST 数据集进行训练和测试。运行这些案例时,该数据集会被自动下载下来(使用 input_data.py)。

MNIST数据集笔记:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb

官方网站:http://yann.lecun.com/exdb/mnist/

 

第二步:为TF新手备的各个类型的案例、模型和数据集

 

初步了解:TFLearn TensorFlow

接下来的示例来自TFLearn,这是一个为 TensorFlow 提供了简化的接口的库。里面有很多示例和预构建的运算和层。

使用教程:TFLearn 快速入门。通过一个具体的机器学习任务学习 TFLearn 基础。开发和训练一个深度神经网络分类器。

TFLearn地址:https://github.com/tflearn/tflearn

示例:https://github.com/tflearn/tflearn/tree/master/examples

预构建的运算和层:http://tflearn.org/doc_index/#api

笔记:https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md

 

基础模型以及数据集

 

  • 线性回归,使用 TFLearn 实现线性回归

https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py

  • 逻辑运算符。使用 TFLearn 实现逻辑运算符

https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py

  • 权重保持。保存和还原一个模型

https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py

  • 微调。在一个新任务上微调一个预训练的模型

https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py

  • 使用 HDF5。使用 HDF5 处理大型数据集

https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py

  • 使用 DASK。使用 DASK 处理大型数据集

https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py

 

计算机视觉模型及数据集

 

  • 多层感知器。一种用于 MNIST 分类任务的多层感知实现

https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py

  • 卷积网络(MNIST)。用于分类 MNIST 数据集的一种卷积神经网络实现

https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py

  • 卷积网络(CIFAR-10)。用于分类 CIFAR-10 数据集的一种卷积神经网络实现

https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py

  • 网络中的网络。用于分类 CIFAR-10 数据集的 Network in Network 实现

https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py

  • Alexnet。将 Alexnet 应用于 Oxford Flowers 17 分类任务

https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py

  • VGGNet。将 VGGNet 应用于 Oxford Flowers 17 分类任务

https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py

  • VGGNet Finetuning (Fast Training)。使用一个预训练的 VGG 网络并将其约束到你自己的数据上,以便实现快速训练

https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py

  • RNN Pixels。使用 RNN(在像素的序列上)分类图像

https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py

  • Highway Network。用于分类 MNIST 数据集的 Highway Network 实现

https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py

  • Highway Convolutional Network。用于分类 MNIST 数据集的 Highway Convolutional Network 实现

https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py

  • Residual Network (MNIST) 。应用于 MNIST 分类任务的一种瓶颈残差网络(bottleneck residual network)

https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py

  • Residual Network (CIFAR-10)。应用于 CIFAR-10 分类任务的一种残差网络

https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py

  • Google Inception(v3)。应用于 Oxford Flowers 17 分类任务的谷歌 Inception v3 网络

https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py

  • 自编码器。用于 MNIST 手写数字的自编码器

https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py

 

自然语言处理模型及数据集

 

  • 循环神经网络(LSTM),应用 LSTM 到 IMDB 情感数据集分类任

https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py

  • 双向 RNN(LSTM),将一个双向 LSTM 应用到 IMDB 情感数据集分类任务:

https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py

  • 动态 RNN(LSTM),利用动态 LSTM 从 IMDB 数据集分类可变长度文本:

https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py

  • 城市名称生成,使用 LSTM 网络生成新的美国城市名:

https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py

  • 莎士比亚手稿生成,使用 LSTM 网络生成新的莎士比亚手稿:

https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py

  • Seq2seq,seq2seq 循环网络的教学示例:

https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py

  • CNN Seq,应用一个 1-D 卷积网络从 IMDB 情感数据集中分类词序列

https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py

 

强化学习案例

 

  • Atari Pacman 1-step Q-Learning,使用 1-step Q-learning 教一台机器玩 Atari 游戏:

https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py

 

第三步:为TF新手准备的其他方面内容

 

  • Recommender-Wide&Deep Network,推荐系统中 wide & deep 网络的教学示例:

https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py

  • Spiral Classification Problem,对斯坦福 CS231n spiral 分类难题的 TFLearn 实现:

https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb

  • 层,与 TensorFlow 一起使用  TFLearn 层:

https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py

  • 训练器,使用 TFLearn 训练器类训练任何 TensorFlow 图:

https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py

  • Bulit-in Ops,连同 TensorFlow 使用 TFLearn built-in 操作:

https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py

  • Summaries,连同 TensorFlow 使用 TFLearn summarizers:

https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py

  • Variables,连同 TensorFlow 使用 TFLearn Variables:

https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py

  

 

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