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资源 | 数十种TensorFlow实现案例汇集:代码+笔记

资源 | 数十种TensorFlow实现案例汇集:代码+笔记

作者: MapleLeaff | 来源:发表于2017-01-12 16:07 被阅读1214次

    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

    教程索引

    0 - 先决条件

    机器学习入门:

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

    2、MNIST 数据集入门

    3、笔记: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

    代码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

    代码: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

    代码: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

    代码: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

    代码: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

    代码: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

    代码: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

    代码: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

    代码: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

    自编码器

    笔记: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

    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

    代码: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

    代码: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

    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

    代码: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

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

    更多案例

    接下来的示例来自 TFLearn(https://github.com/tflearn/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

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

    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

    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

    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

    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) (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

    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

    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

    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

    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

    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

    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

    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

    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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