tfestimators: estimator_spec – R documentation

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estimator_spec

Define an Estimator Specification

Description

Define the estimator specification, used as part of the model_fn defined with custom estimators created by estimator(). See estimator() for more details.

Usage

estimator_spec(mode, predictions = NULL, loss = NULL,
  train_op = NULL, eval_metric_ops = NULL, training_hooks = NULL,
  evaluation_hooks = NULL, prediction_hooks = NULL,
  training_chief_hooks = NULL, ...)

Arguments

`mode`	A key that specifies whether we are performing training (`"train"`), evaluation (`"eval"`), or prediction (`"infer"`). These values can also be accessed through the `mode_keys()` object.
`predictions`	The prediction tensor(s).
`loss`	The training loss tensor. Must be either scalar, or with shape `c(1)`.
`train_op`	The training operation – typically, a call to `optimizer$minimize(...)`, depending on the type of optimizer used during training.
`eval_metric_ops`	A list of metrics to be computed as part of evaluation. This should be a named list, mapping metric names (e.g. `"rmse"`) to the operation that computes the associated metric (e.g. `tf$metrics$root_mean_squared_error(...)`). These metric operations should be evaluated without any impact on state (typically is a pure computation results based on variables). For example, it should not trigger the update ops or requires any input fetching.
`training_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run on all workers during training.
`evaluation_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run during evaluation.
`prediction_hooks`	(Available since TensorFlow v1.7) A list of session run hooks to run during prediciton.
`training_chief_hooks`	(Available since TensorFlow v1.4) A list of session run hooks to run on chief worker during training.
`...`	Other optional (named) arguments, to be passed to the `EstimatorSpec` constructor.

estimator_spec

Description

Usage

Arguments

See Also

tfestimators

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