org.platanios.tensorflow.api.learn.estimators
InMemoryEstimator
Related Docs:
object InMemoryEstimator
| package estimators
org.platanios.tensorflow.api.learn.estimators
Checkpoint configuration used by this estimator.
Checkpoint configuration used by this estimator.
Run configuration used for this estimator.
Run configuration used for this estimator.
Configuration base for this estimator.
Configuration base for this estimator. This allows for setting up distributed training
environments, for example. Note that this is a *base* for a configuration because the
estimator might modify it and set some missing fields to appropriate default values, in
order to obtain its final configuration that can be obtain through its configuration
field.
Device function used by this estimator for managing replica device placement when using distributed training.
Device function used by this estimator for managing replica device placement when using distributed training.
Evaluates the model managed by this estimator given the provided evaluation data, data.
Evaluates the model managed by this estimator given the provided evaluation data, data.
The evaluation process is iterative. In each step, a data batch is obtained from data and internal metric value
accumulators are updated. The number of steps to perform is controlled through the maxSteps argument. If set to
-1, then all batches from data will be processed.
If metrics is provided, it overrides the value provided in the constructor of this estimator.
Evaluation dataset. Each element is a tuple over input and training inputs (i.e., supervision labels).
Evaluation metrics to use.
Maximum number of evaluation steps to perform. If -1, the evaluation process will run
until data is exhausted.
Boolean indicator specifying whether to save the evaluation results as summaries in the working directory of this estimator.
Name for this evaluation. If provided, it will be used to generate an appropriate directory
name for the resulting summaries. If saveSummaries is false, this argument has no
effect. This is useful if the user needs to run multiple evaluations on different data
sets, such as on training data vs test data. Metrics for different evaluations are saved in
separate folders, and appear separately in TensorBoard.
Evaluation metric values at the end of the evaluation process. The return sequence matches the ordering of
metrics.
InvalidArgumentException If saveSummaries is true, but the estimator has no working directory
specified.
Hooks to use while evaluating.
Evaluation metrics to use.
Gets an existing saver from the current graph, or creates a new one if none exists.
Gets an existing saver from the current graph, or creates a new one if none exists.
Infers output (i.e., computes predictions) for input using the model managed by this estimator.
Infers output (i.e., computes predictions) for input using the model managed by this estimator.
input can be of one of the following types:
(input, output) tuples corresponding to
each element in the dataset. Note that the predictions are computed lazily in this case, whenever an element
is requested from the returned iterator.IT, in which case this method returns a prediction of type I.Note that, ModelInferenceOutput refers to the tensor type that corresponds to the symbolic type I. For
example, if I is (Output, Output), then ModelInferenceOutput will be (Tensor, Tensor).
Input for the predictions.
Either an iterator over (IT, ModelInferenceOutput) tuples, or a single element of type I, depending on
the type of input.
Hooks to use while inferring.
Model-generating function that can optionally have a Configuration argument which will be used to pass the estimator's configuration to the model and allows customizing the model based on the execution environment.
Model-generating function that can optionally have a Configuration argument which will be used to pass the estimator's configuration to the model and allows customizing the model based on the execution environment.
Random seed value to be used by the TensorFlow initializers in this estimator.
Random seed value to be used by the TensorFlow initializers in this estimator.
Session configuration used by this estimator.
Session configuration used by this estimator.
TensorBoard configuration to use while training.
TensorBoard configuration to use while training. If provided, a TensorBoard server is launched while training, using the provided configuration. In that case, it is required that TensorBoard is installed for the default Python environment in the system. If training in a distributed setting, the TensorBoard server is launched on the chief node.
Trains the model managed by this estimator.
Trains the model managed by this estimator.
Training dataset. Each element is a tuple over input and training inputs (i.e., supervision labels).
Stop criteria to use for stopping the training iteration. For the default criteria please refer to the documentation of StopCriteria.
Hooks to use while training for the chief node only.
Hooks to use while training for the chief node only. This argument is only useful for a distributed training setting.
Hooks to use while training (e.g., logging for the loss function value, etc.).
Working directory used by this estimator, used to save model parameters, graph, etc.
Working directory used by this estimator, used to save model parameters, graph, etc. It can also be used to load checkpoints for a previously saved model.
In-memory estimator which is used to train, use, and evaluate TensorFlow models, and uses an underlying TensorFlow session that it keeps alive throughout its lifetime. This means that checkpoint files do not need to be written after every call to
train()and do not need to be loaded on every call toinfer()orevaluate(), since the underlying TensorFlow session used for all these calls stays alive in the background.