数十种TensorFlow实现案例汇集：代码+笔记

蔚落 2022-07-16 12:23 123阅读 0赞

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2016-10-17 13:08:31 开源

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这是使用 TensorFlow 实现流行的机器学习算法的教程汇集。本汇集的目标是让读者可以轻松通过案例深入 TensorFlow。

这些案例适合那些想要清晰简明的 TensorFlow 实现案例的初学者。本教程还包含了笔记和带有注解的代码。

*  **项目地址：[https://github.com/aymericdamien/TensorFlow-Examples][https_github.com_aymericdamien_TensorFlow-Examples]**

**教程索引**

**0 - 先决条件**

机器学习入门：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0\_Prerequisite/ml\_introduction.ipynb][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][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_0_Prerequisite_mnist_dataset_intro.ipynb]

**1 - 入门**

Hello World：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1\_Introduction/helloworld.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_1_Introduction_helloworld.ipynb]
 *  代码[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1\_Introduction/helloworld.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_1_Introduction_helloworld.py]

基本操作：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1\_Introduction/basic\_operations.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_1_Introduction_basic_operations.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1\_Introduction/basic\_operations.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_1_Introduction_basic_operations.py]

**2 - 基本模型**

最近邻：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2\_BasicModels/nearest\_neighbor.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_nearest_neighbor.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2\_BasicModels/nearest\_neighbor.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_2_BasicModels_nearest_neighbor.py]

线性回归：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2\_BasicModels/linear\_regression.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_linear_regression.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2\_BasicModels/linear\_regression.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_2_BasicModels_linear_regression.py]

Logistic 回归：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2\_BasicModels/logistic\_regression.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_logistic_regression.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2\_BasicModels/logistic\_regression.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_2_BasicModels_logistic_regression.py]

**3 - 神经网络**

多层感知器：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3\_NeuralNetworks/multilayer\_perceptron.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_multilayer_perceptron.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/multilayer\_perceptron.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_multilayer_perceptron.py]

卷积神经网络：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3\_NeuralNetworks/convolutional\_network.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_convolutional_network.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/convolutional\_network.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_convolutional_network.py]

循环神经网络（LSTM）：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3\_NeuralNetworks/recurrent\_network.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_recurrent_network.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/recurrent\_network.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_recurrent_network.py]

双向循环神经网络（LSTM）：

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3\_NeuralNetworks/bidirectional\_rnn.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_bidirectional_rnn.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/bidirectional\_rnn.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_bidirectional_rnn.py]

动态循环神经网络（LSTM）

*  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/dynamic\_rnn.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_dynamic_rnn.py]

自编码器

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3\_NeuralNetworks/autoencoder.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_autoencoder.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3\_NeuralNetworks/autoencoder.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_3_NeuralNetworks_autoencoder.py]

**4 - 实用技术**

保存和恢复模型

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4\_Utils/save\_restore\_model.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_4_Utils_save_restore_model.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4\_Utils/save\_restore\_model.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_4_Utils_save_restore_model.py]

图和损失可视化

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4\_Utils/tensorboard\_basic.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_4_Utils_tensorboard_basic.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4\_Utils/tensorboard\_basic.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_4_Utils_tensorboard_basic.py]

Tensorboard——高级可视化

*  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4\_Utils/tensorboard\_advanced.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_4_Utils_tensorboard_advanced.py]

**5 - 多 GPU**

多 GPU 上的基本操作

*  笔记：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5\_MultiGPU/multigpu\_basics.ipynb][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_5_MultiGPU_multigpu_basics.ipynb]
 *  代码：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5\_MultiGPU/multigpu\_basics.py][https_github.com_aymericdamien_TensorFlow-Examples_blob_master_examples_5_MultiGPU_multigpu_basics.py]

**数据集**

一些案例需要 MNIST 数据集进行训练和测试。不要担心，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 是一个手写数字的数据库，查看这个笔记了解关于该数据集的描述：[https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0\_Prerequisite/mnist\_dataset\_intro.ipynb][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/][http_yann.lecun.com_exdb_mnist]

### **更多案例** ###

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。你可以看看，这里有很多示例和预构建的运算和层。

*  示例：[https://github.com/tflearn/tflearn/tree/master/examples][https_github.com_tflearn_tflearn_tree_master_examples]
 *  预构建的运算和层：[http://tflearn.org/doc\_index/\#api][https_github.com_tflearn_tflearn_tree_master_examples]

### **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。

*  笔记：[https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md][https_github.com_tflearn_tflearn_blob_master_tutorials_intro_quickstart.md]

### **基础** ###

*  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][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][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][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][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][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][https_github.com_tflearn_tflearn_blob_master_examples_basics_use_dask.py]

### **计算机视觉** ###

*  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][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][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][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][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][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][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][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][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][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][https_github.com_tflearn_tflearn_blob_master_examples_images_convnet_highway_mnist.py]
 *  Residual Network (MNIST) ([https://github.com/tflearn/tflearn/blob/master/examples/images/residual\_network\_mnist.py][https_github.com_tflearn_tflearn_blob_master_examples_images_residual_network_mnist.py]).。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][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][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][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][https_github.com_tflearn_tflearn_blob_master_examples_images_autoencoder.py]

### **自然语言处理** ###

*  循环神经网络（LSTM），应用 LSTM 到 IMDB 情感数据集分类任务：[https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py][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][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][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][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][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][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][https_github.com_tflearn_tflearn_blob_master_examples_nlp_cnn_sentence_classification.py]

### **强化学习** ###

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][https_github.com_tflearn_tflearn_blob_master_examples_reinforcement_learning_atari_1step_qlearning.py]

**其他**

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][https_github.com_tflearn_tflearn_blob_master_examples_others_recommender_wide_and_deep.py]

**Notebooks**

*  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][https_github.com_tflearn_tflearn_blob_master_examples_notebooks_spiral.ipynb]

### **可延展的 TensorFlow** ###

*  层，与 TensorFlow 一起使用 TFLearn 层：[https://github.com/tflearn/tflearn/blob/master/examples/extending\_tensorflow/layers.py][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][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][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][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][https_github.com_tflearn_tflearn_blob_master_examples_extending_tensorflow_variables.py]

声明：本文由机器之心编译出品，原文来自GitHub，转载请查看要求，机器之心对于违规侵权者保有法律追诉权。

[0_]: http://www.jiqizhixin.com/user/favorite/do_favorite/id/12435
[https_github.com_aymericdamien_TensorFlow-Examples]: https://github.com/aymericdamien/TensorFlow-Examples
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_0_Prerequisite_ml_introduction.ipynb]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_0_Prerequisite_mnist_dataset_intro.ipynb]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_1_Introduction_helloworld.ipynb]: 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]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_1_Introduction_basic_operations.ipynb]: 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]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_nearest_neighbor.ipynb]: 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]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_linear_regression.ipynb]: 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]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_2_BasicModels_logistic_regression.ipynb]: 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]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py
[https_github.com_aymericdamien_TensorFlow-Examples_blob_master_notebooks_3_NeuralNetworks_multilayer_perceptron.ipynb]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb
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