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https://github.com/googlecloudplatform/tf-estimator-tutorials

This repository includes tutorials on how to use the TensorFlow estimator APIs to perform various ML tasks, in a systematic and standardised way
https://github.com/googlecloudplatform/tf-estimator-tutorials

machine-learning python tensorflow

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This repository includes tutorials on how to use the TensorFlow estimator APIs to perform various ML tasks, in a systematic and standardised way

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README 

        
# TensorFlow Estimator APIs Tutorials



## Setup

Please follow the directions in INSTALL if you need help setting up your environment.



## Theses tutorials use the TF estimator APIs to cover:



* Various ML tasks, currently covering:

  * Classification

  * Regression

  * Clustering (k-means)

  * Time-series Analysis (AR Models)

  * Dimensionality Reduction (Autoencoding)

  * Sequence Models (RNN and LSTMs)

  * Image Analysis (CNN for Image Classification)

  * Text Analysis (Text Classification with embeddings, CNN, and RNN)

*  How to use **canned estimators**  to train ML models.



* How to use **tf.Transform** for preprocessing and feature engineering (TF v1.7)



* Implement **TensorFlow Model Analysis (TFMA)** to assess the quality of the mode (TF v1.7)



* How to use **tf.Hub** text feature column embeddings (TF v1.7)



* How to implement **custom estimators** (model_fn & EstimatorSpec).



* A standard **metadata-driven** approach to build the model **feature_column**(s) including:

  * **numerical** features

  * **categorical** features with **vocabulary**,

  * **categorical** features **hash bucket**, and

  * **categorical** features with **identity**



* Data **input pipelines** (input_fn) using:

  * tf.estimator.inputs.**pandas_input_fn**,

  * tf.train.**string_input_producer**, and

  * tf.data.**Dataset** APIs to read both **.csv** and **.tfrecords** (tf.example) data files

  * tf.contrib.timeseries.**RandomWindowInputFn** and **WholeDatasetInputFn** for time-series data

  * Feature **preprocessing** and **creation** as part of reading data (input_fn), for example, sin, sqrt, polynomial expansion, fourier transform, log, boolean comparisons, euclidean distance, custom formulas, etc.



* A standard approach to prepare **wide** (sparse) and **deep** (dense) feature_column(s) for Wide and Deep **DNN Liner Combined Models**



* The use of **normalizer_fn** in numeric_column() to **scale** the numeric features using pre-computed statistics (for Min-Max or Standard scaling)



* The use of **weight_column** in the canned estimators, as well as in **loss function** in custom estimators.



* Implicit **Feature Engineering** as part of defining feature_colum(s), including:

  * crossing

  * embedding

  * indicators (encoding categorical features), and

  * bucketization



*  How to use the  tf.contrib.learn.**experiment** APIs to train, evaluate, and export models



* Howe to use the tf.estimator.**train_and_evaluate** function (along with trainSpec & evalSpec) train, evaluate, and export models



* How to use **tf.train.exponential_decay** function as a learning rate scheduler



* How to **serve** exported model (export_savedmodel) using **csv** and **json** inputs



## Coming Soon:

* Early-stopping implementation

* DynamicRnnEstimator and the use of variable-length sequences

* Collaborative Filtering for Recommendation Models

* Text Analysis (Topic Models, etc.)

* Keras examples