Name: tf-estimator-tutorials
Owner: Google Cloud Platform
Description: This repository includes tutorials on how to use the TensorFlow estimator APIs to perform various ML tasks, in a systematic and standardised way
Created: 2018-01-15 17:54:01.0
Updated: 2018-05-24 13:14:30.0
Pushed: 2018-05-22 10:02:05.0
Homepage: https://www.tensorflow.org/programmers_guide/estimators
Size: 11614
Language: Jupyter Notebook
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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