(轉(zhuǎn))TensorFlow的55個經(jīng)典案例

轉(zhuǎn)自https://mp.weixin.qq.com/s/Qdo1vks94tbGkzXEiuQV7w

導(dǎo)語:本文是TensorFlow實現(xiàn)流行機器學(xué)習(xí)算法的教程匯集,目標(biāo)是讓讀者可以輕松通過清晰簡明的案例深入了解 TensorFlow。這些案例適合那些想要實現(xiàn)一些 TensorFlow 案例的初學(xué)者。本教程包含還包含筆記和帶有注解的代碼。

第一步:給TF新手的教程指南

1:tf初學(xué)者需要明白的入門準(zhǔn)備

機器學(xué)習(xí)入門筆記:

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

MNIST 數(shù)據(jù)集入門筆記

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

2:tf初學(xué)者需要了解的入門基礎(chǔ)

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初學(xué)者需要掌握的基本模型

最近鄰:

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

線性回歸:

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 回歸:

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初學(xué)者需要嘗試的神經(jīng)網(wǎng)絡(luò)

多層感知器:

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

卷積神經(jīng)網(wǎng)絡(luò):

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

循環(huán)神經(jīng)網(wǎng)絡(luò)(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

雙向循環(huán)神經(jīng)網(wǎng)絡(luò)(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

動態(tài)循環(huán)神經(jīng)網(wǎng)絡(luò)(LSTM)

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

自編碼器

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初學(xué)者需要精通的實用技術(shù)

保存和恢復(fù)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

圖和損失可視化

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——高級可視化

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

5:tf初學(xué)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:案例需要的數(shù)據(jù)集

有一些案例需要 MNIST 數(shù)據(jù)集進行訓(xùn)練和測試。運行這些案例時,該數(shù)據(jù)集會被自動下載下來(使用 input_data.py)。

MNIST數(shù)據(jù)集筆記:https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb

官方網(wǎng)站:http://yann.lecun.com/exdb/mnist/

第二步:為TF新手準(zhǔn)備的各個類型的案例、模型和數(shù)據(jù)集

初步了解:TFLearn TensorFlow

接下來的示例來自TFLearn,這是一個為 TensorFlow 提供了簡化的接口的庫。里面有很多示例和預(yù)構(gòu)建的運算和層。

使用教程:TFLearn 快速入門。通過一個具體的機器學(xué)習(xí)任務(wù)學(xué)習(xí) TFLearn 基礎(chǔ)。開發(fā)和訓(xùn)練一個深度神經(jīng)網(wǎng)絡(luò)分類器。

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

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

預(yù)構(gòu)建的運算和層:http://tflearn.org/doc_index/#api

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

基礎(chǔ)模型以及數(shù)據(jù)集

線性回歸,使用 TFLearn 實現(xiàn)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(xiàn)邏輯運算符

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

權(quán)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(diào)。在一個新任務(wù)上微調(diào)一個預(yù)訓(xùn)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(shù)據(jù)集

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

使用 DASK。使用 DASK 處理大型數(shù)據(jù)集

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

計算機視覺模型及數(shù)據(jù)集

多層感知器。一種用于 MNIST 分類任務(wù)的多層感知實現(xiàn)

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

卷積網(wǎng)絡(luò)(MNIST)。用于分類 MNIST 數(shù)據(jù)集的一種卷積神經(jīng)網(wǎng)絡(luò)實現(xiàn)

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

卷積網(wǎng)絡(luò)(CIFAR-10)。用于分類 CIFAR-10 數(shù)據(jù)集的一種卷積神經(jīng)網(wǎng)絡(luò)實現(xiàn)

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

網(wǎng)絡(luò)中的網(wǎng)絡(luò)。用于分類 CIFAR-10 數(shù)據(jù)集的 Network in Network 實現(xiàn)

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

Alexnet。將 Alexnet 應(yīng)用于 Oxford Flowers 17 分類任務(wù)

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

VGGNet。將 VGGNet 應(yīng)用于 Oxford Flowers 17 分類任務(wù)

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

VGGNet Finetuning (Fast Training)。使用一個預(yù)訓(xùn)練的 VGG 網(wǎng)絡(luò)并將其約束到你自己的數(shù)據(jù)上,以便實現(xiàn)快速訓(xùn)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。用于分類 MNIST 數(shù)據(jù)集的 Highway Network 實現(xiàn)

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

Highway Convolutional Network。用于分類 MNIST 數(shù)據(jù)集的 Highway Convolutional Network 實現(xiàn)

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

Residual Network (MNIST) 。應(yīng)用于 MNIST 分類任務(wù)的一種瓶頸殘差網(wǎng)絡(luò)(bottleneck residual network)

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

Residual Network (CIFAR-10)。應(yīng)用于 CIFAR-10 分類任務(wù)的一種殘差網(wǎng)絡(luò)

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

Google Inception(v3)。應(yīng)用于 Oxford Flowers 17 分類任務(wù)的谷歌 Inception v3 網(wǎng)絡(luò)

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

自編碼器。用于 MNIST 手寫數(shù)字的自編碼器

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

自然語言處理模型及數(shù)據(jù)集

循環(huán)神經(jīng)網(wǎng)絡(luò)(LSTM),應(yīng)用 LSTM 到 IMDB 情感數(shù)據(jù)集分類任

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

雙向 RNN(LSTM),將一個雙向 LSTM 應(yīng)用到 IMDB 情感數(shù)據(jù)集分類任務(wù):

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

動態(tài) RNN(LSTM),利用動態(tài) LSTM 從 IMDB 數(shù)據(jù)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(wǎng)絡(luò)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(wǎng)絡(luò)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(huán)網(wǎng)絡(luò)的教學(xué)示例:

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

CNN Seq,應(yīng)用一個 1-D 卷積網(wǎng)絡(luò)從 IMDB 情感數(shù)據(jù)集中分類詞序列

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

強化學(xué)習(xí)案例

Atari Pacman 1-step Q-Learning,使用 1-step Q-learning 教一臺機器玩 Atari 游戲:

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

第三步:為TF新手準(zhǔn)備的其他方面內(nèi)容

Recommender-Wide&Deep Network,推薦系統(tǒng)中 wide & deep 網(wǎng)絡(luò)的教學(xué)示例:

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

Spiral Classification Problem,對斯坦福 CS231n spiral 分類難題的 TFLearn 實現(xiàn):

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

層,與 TensorFlow 一起使用 TFLearn 層:

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

訓(xùn)練器,使用 TFLearn 訓(xùn)練器類訓(xùn)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

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