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Regression
In the next section we will delve deeper into TensorFlow Estimators, a versatile
higher-level API to develop your model with ease.
TensorFlow Estimators
TensorFlow provides Estimators as higher-level APIs, to provide scalable and
production-oriented solutions. They take care of all behind-the-scene activities such
as creating computational graphs, initializing the variables, training the model,
saving checkpoints, and logging TensorBoard files. TensorFlow provides two types
of Estimators:
• Canned Estimators: These are premade Estimators available in the
TensorFlow estimator module. These are models in a box; you just pass
them the input features and they are ready to use. Some examples are
Linear Classifier, Linear Regressor, DNN Classifier, and so on.
• Custom Estimators: Users can also create their own estimators from the
models they build in TensorFlow Keras. These are user-defined Estimators.
Before being able to use TensorFlow Estimator let us understand two important
components of the Estimator pipeline:
Feature columns
The feature_column module of TensorFlow 2.0 acts as a bridge between your input
data and the model. The input parameters to be used by the estimators for training
are passed as feature columns. They are defined in TensorFlow feature_column and
specify how the data is interpreted by the model. To create feature columns we will
need to call functions from tensorflow.feature_columns. There are nine functions
available in feature column:
• categorical_column_with_identity: Here each category is one-hot
encoded, and thus has a unique identity. This can be used for numeric values
only.
• categorical_column_with_vocabulary_file: This is used when the
categorical input is a string and the categories are given in a file. The string is
first converted to a numeric value and then one-hot encoded.
• categorical_column_with_vocabulary_list: This is used when the
categorical input is a string and the categories are explicitly defined in a list.
The string is first converted to a numeric value and then one-hot encoded.
• categorical_column_with_hash_bucket: In case the number of categories
is very large, and it is not possible to one-hot encode, we use hashing.
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