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
网友评论
这个里面的x = tf.split(x, n_steps, 0) 报错 是不是得改成x = tf.split(batch_size,n_input,x)呢? 现在在学LSTM 望大哥速速回答