EstimatorSpec fully defines the model to be run by an Estimator.
Args
mode
A ModeKeys. Specifies if this is training, evaluation or prediction.
predictions
Predictions Tensor or dict of Tensor.
loss
Training loss Tensor. Must be either scalar, or with shape [1].
train_op
Op for the training step.
eval_metric_ops
Dict of metric results keyed by name. The values of the dict can be one of the following: (1) instance of Metric class. (2) Results of calling a metric function, namely a (metric_tensor, update_op) tuple. metric_tensor 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_op or requires any input fetching.
export_outputs
Describes the output signatures to be exported to SavedModel and used during serving. A dict {name: output} where:
name: An arbitrary name for this output.
output: an ExportOutput object such as ClassificationOutput, RegressionOutput, or PredictOutput. Single-headed models only need to specify one entry in this dictionary. Multi-headed models should specify one entry for each head, one of which must be named using tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY. If no entry is provided, a default PredictOutput mapping to predictions will be created.
training_chief_hooks
Iterable of tf.train.SessionRunHook objects to run on the chief worker during training.
training_hooks
Iterable of tf.train.SessionRunHook objects to run on all workers during training.
scaffold
A tf.train.Scaffold object that can be used to set initialization, saver, and more to be used in training.
evaluation_hooks
Iterable of tf.train.SessionRunHook objects to run during evaluation.
prediction_hooks
Iterable of tf.train.SessionRunHook objects to run during predictions.
