Define the estimator specification, used as part of the model_fn defined with
custom estimators created by estimator(). See estimator() for more details.
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, ...)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.
The prediction tensor(s).
The training loss tensor. Must be either scalar, or with shape c(1).
The training operation -- typically, a call to optimizer$minimize(...),
depending on the type of optimizer used during training.
A list of metrics to be computed as part of evaluation.
This should be a named list, mapping metric names (e.g. "rmse") to the operation
that computes the associated metric (e.g. tf$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.
(Available since TensorFlow v1.4) A list of session run hooks to run on all workers during training.
(Available since TensorFlow v1.4) A list of session run hooks to run during evaluation.
(Available since TensorFlow v1.7) A list of session run hooks to run during prediciton.
(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.
Other custom estimator methods: estimator,
evaluate.tf_estimator,
export_savedmodel.tf_estimator,
predict.tf_estimator,
train.tf_estimator