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