資源 | 數十種TensorFlow實現案例匯集:代碼+筆記
這是使用 TensorFlow 實現流行的機器學習算法的教程匯集。本匯集的目標是讓讀者可以輕松通過案例深入 TensorFlow。
這些案例適合那些想要清晰簡明的 TensorFlow 實現案例的初學者。本教程還包含了筆記和帶有註解的代碼。
-
項目地址:https://github.com/aymericdamien/TensorFlow-Examples
教程索引
0 - 先決條件
機器學習入門:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb
-
MNIST 數據集入門
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb
1 - 入門
Hello World:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb
-
代碼https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py
基本操作:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py
2 - 基本模型
最近鄰:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py
線性回歸:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py
Logistic 回歸:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py
3 - 神經網絡
多層感知器:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py
卷積神經網絡:
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py
循環神經網絡(LSTM):
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
雙向循環神經網絡(LSTM):
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py
動態循環神經網絡(LSTM)
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py
自編碼器
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py
4 - 實用技術
保存和恢復模型
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py
圖和損失可視化
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py
Tensorboard——高級可視化
-
代碼: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
-
筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb
-
代碼:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py
數據集
一些案例需要 MNIST 數據集進行訓練和測試。不要擔心,運行這些案例時,該數據集會被自動下載下來(使用 input_data.py)。MNIST 是一個手寫數字的數據庫,查看這個筆記了解關於該數據集的描述:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb
-
官方網站:http://yann.lecun.com/exdb/mnist/
更多案例
接下來的示例來自 TFLearn(https://github.com/tflearn/tflearn),這是一個為 TensorFlow 提供了簡化的接口的庫。你可以看看,這裏有很多示例和預構建的運算和層。
-
示例:https://github.com/tflearn/tflearn/tree/master/examples
-
預構建的運算和層:http://tflearn.org/doc_index/#api
教程
TFLearn 快速入門。通過一個具體的機器學習任務學習 TFLearn 基礎。開發和訓練一個深度神經網絡分類器。
-
筆記:https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md
基礎
-
線性回歸,使用 TFLearn 實現線性回歸:https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py
-
邏輯運算符。使用 TFLearn 實現邏輯運算符:https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py
-
權重保持。保存和還原一個模型:https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py
-
微調。在一個新任務上微調一個預訓練的模型:https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py
-
使用 HDF5。使用 HDF5 處理大型數據集:https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py
-
使用 DASK。使用 DASK 處理大型數據集:https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py
計算機視覺
-
多層感知器。一種用於 MNIST 分類任務的多層感知實現:https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py
-
卷積網絡(MNIST)。用於分類 MNIST 數據集的一種卷積神經網絡實現:https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py
-
卷積網絡(CIFAR-10)。用於分類 CIFAR-10 數據集的一種卷積神經網絡實現:https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py
-
網絡中的網絡。用於分類 CIFAR-10 數據集的 Network in Network 實現:https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py
-
Alexnet。將 Alexnet 應用於 Oxford Flowers 17 分類任務:https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
-
VGGNet。將 VGGNet 應用於 Oxford Flowers 17 分類任務:https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py
-
VGGNet Finetuning (Fast Training)。使用一個預訓練的 VGG 網絡並將其約束到你自己的數據上,以便實現快速訓練: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)分類圖像:https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py
-
Highway Network。用於分類 MNIST 數據集的 Highway Network 實現:https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py
-
Highway Convolutional Network。用於分類 MNIST 數據集的 Highway Convolutional Network 實現: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).。應用於 MNIST 分類任務的一種瓶頸殘差網絡(bottleneck residual network):https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py
-
Residual Network (CIFAR-10)。應用於 CIFAR-10 分類任務的一種殘差網絡:https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py
-
Google Inception(v3)。應用於 Oxford Flowers 17 分類任務的谷歌 Inception v3 網絡:https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py
-
自編碼器。用於 MNIST 手寫數字的自編碼器:https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py
自然語言處理
-
循環神經網絡(LSTM),應用 LSTM 到 IMDB 情感數據集分類任務:https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py
-
雙向 RNN(LSTM),將一個雙向 LSTM 應用到 IMDB 情感數據集分類任務:https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py
-
動態 RNN(LSTM),利用動態 LSTM 從 IMDB 數據集分類可變長度文本:https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py
-
城市名稱生成,使用 LSTM 網絡生成新的美國城市名:https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py
-
莎士比亞手稿生成,使用 LSTM 網絡生成新的莎士比亞手稿:https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py
-
Seq2seq,seq2seq 循環網絡的教學示例:https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py
-
CNN Seq,應用一個 1-D 卷積網絡從 IMDB 情感數據集中分類詞序列:https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py
強化學習
Atari Pacman 1-step Q-Learning,使用 1-step Q-learning 教一臺機器玩 Atari 遊戲:https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py
其他
Recommender-Wide&Deep Network,推薦系統中 wide & deep 網絡的教學示例:https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py
Notebooks
-
Spiral Classification Problem,對斯坦福 CS231n spiral 分類難題的 TFLearn 實現:https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb
可延展的 TensorFlow
-
層,與 TensorFlow 一起使用 TFLearn 層:https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py
-
訓練器,使用 TFLearn 訓練器類訓練任何 TensorFlow 圖:https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py
-
Bulit-in Ops,連同 TensorFlow 使用 TFLearn built-in 操作:https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py
-
Summaries,連同 TensorFlow 使用 TFLearn summarizers:https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py
-
Variables,連同 TensorFlow 使用 TFLearn Variables:https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py
資源 | 數十種TensorFlow實現案例匯集:代碼+筆記