Skip to content

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

License

Notifications You must be signed in to change notification settings

huangruomu/tf-estimator-tutorials

 
 

Repository files navigation

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

About

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

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.2%
  • Other 0.8%