tf.estimator.RegressionHead

TensorFlow 1 version

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This class specifies the configurations for an Estimator run.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.estimator.RunConfig

tf.estimator.RunConfig(
    model_dir=None, tf_random_seed=None, save_summary_steps=100,
    save_checkpoints_steps=_USE_DEFAULT, save_checkpoints_secs=_USE_DEFAULT,
    session_config=None, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000,
    log_step_count_steps=100, train_distribute=None, device_fn=None, protocol=None,
    eval_distribute=None, experimental_distribute=None,
    experimental_max_worker_delay_secs=None, session_creation_timeout_secs=7200,
    checkpoint_save_graph_def=True
)

Used in the notebooks

Used in the guide	Used in the tutorials
Estimators	Multi-worker training with Estimator Shape Constraints with Tensorflow Lattice TF Lattice Canned Estimators Preprocessing data with TensorFlow Transform

Args
`model_dir`	directory where model parameters, graph, etc are saved. If `PathLike` object, the path will be resolved. If `None`, will use a default value set by the Estimator.
`tf_random_seed`	Random seed for TensorFlow initializers. Setting this value allows consistency between reruns.
`save_summary_steps`	Save summaries every this many steps.
`save_checkpoints_steps`	Save checkpoints every this many steps. Can not be specified with `save_checkpoints_secs`.
`save_checkpoints_secs`	Save checkpoints every this many seconds. Can not be specified with `save_checkpoints_steps`. Defaults to 600 seconds if both `save_checkpoints_steps` and `save_checkpoints_secs` are not set in constructor. If both `save_checkpoints_steps` and `save_checkpoints_secs` are `None`, then checkpoints are disabled.
`session_config`	a ConfigProto used to set session parameters, or `None`.
`keep_checkpoint_max`	The maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If `None` or 0, all checkpoint files are kept. Defaults to 5 (that is, the 5 most recent checkpoint files are kept). If a saver is passed to the estimator, this argument will be ignored.
`keep_checkpoint_every_n_hours`	Number of hours between each checkpoint to be saved. The default value of 10,000 hours effectively disables the feature.
`log_step_count_steps`	The frequency, in number of global steps, that the global step and the loss will be logged during training. Also controls the frequency that the global steps / s will be logged (and written to summary) during training.
`train_distribute`	An optional instance of `tf.distribute.Strategy`. If specified, then Estimator will distribute the user's model during training, according to the policy specified by that strategy. Setting `experimental_distribute.train_distribute` is preferred.
`device_fn`	A callable invoked for every `Operation` that takes the `Operation` and returns the device string. If `None`, defaults to the device function returned by `tf.train.replica_device_setter` with round-robin strategy.
`protocol`	An optional argument which specifies the protocol used when starting server. `None` means default to grpc.
`eval_distribute`	An optional instance of `tf.distribute.Strategy`. If specified, then Estimator will distribute the user's model during evaluation, according to the policy specified by that strategy. Setting `experimental_distribute.eval_distribute` is preferred.
`experimental_distribute`	An optional `tf.contrib.distribute.DistributeConfig` object specifying DistributionStrategy-related configuration. The `train_distribute` and `eval_distribute` can be passed as parameters to `RunConfig` or set in `experimental_distribute` but not both.
`experimental_max_worker_delay_secs`	An optional integer specifying the maximum time a worker should wait before starting. By default, workers are started at staggered times, with each worker being delayed by up to 60 seconds. This is intended to reduce the risk of divergence, which can occur when many workers simultaneously update the weights of a randomly initialized model. Users who warm-start their models and train them for short durations (a few minutes or less) should consider reducing this default to improve training times.
`session_creation_timeout_secs`	Max time workers should wait for a session to become available (on initialization or when recovering a session) with MonitoredTrainingSession. Defaults to 7200 seconds, but users may want to set a lower value to detect problems with variable / session (re)-initialization more quickly.
`checkpoint_save_graph_def`	Whether to save the GraphDef and MetaGraphDef to `checkpoint_dir`. The GraphDef is saved after the session is created as `graph.pbtxt`. MetaGraphDefs are saved out for every checkpoint as `model.ckpt-*.meta`.

Raises
`ValueError`	If both `save_checkpoints_steps` and `save_checkpoints_secs` are set.

Attributes
`checkpoint_save_graph_def`
`cluster_spec`
`device_fn`	Returns the device_fn. If device_fn is not `None`, it overrides the default device function used in `Estimator`. Otherwise the default one is used.
`eval_distribute`	Optional `tf.distribute.Strategy` for evaluation.
`evaluation_master`
`experimental_max_worker_delay_secs`
`global_id_in_cluster`	The global id in the training cluster. All global ids in the training cluster are assigned from an increasing sequence of consecutive integers. The first id is 0. Note: Task id (the property field `task_id`) is tracking the index of the node among all nodes with the SAME task type. For example, given the cluster definition as follows: `cluster = {'chief': ['host0:2222'], 'ps': ['host1:2222', 'host2:2222'], 'worker': ['host3:2222', 'host4:2222', 'host5:2222']}` Nodes with task type `worker` can have id 0, 1, 2. Nodes with task type `ps` can have id, 0, 1. So, `task_id` is not unique, but the pair (`task_type`, `task_id`) can uniquely determine a node in the cluster. Global id, i.e., this field, is tracking the index of the node among ALL nodes in the cluster. It is uniquely assigned. For example, for the cluster spec given above, the global ids are assigned as: `task_type \| task_id \| global_id -------------------------------- chief \| 0 \| 0 worker \| 0 \| 1 worker \| 1 \| 2 worker \| 2 \| 3 ps \| 0 \| 4 ps \| 1 \| 5`
`is_chief`
`keep_checkpoint_every_n_hours`
`keep_checkpoint_max`
`log_step_count_steps`
`master`
`model_dir`
`num_ps_replicas`
`num_worker_replicas`
`protocol`	Returns the optional protocol value.
`save_checkpoints_secs`
`save_checkpoints_steps`
`save_summary_steps`
`service`	Returns the platform defined (in TF_CONFIG) service dict.
`session_config`
`session_creation_timeout_secs`
`task_id`
`task_type`
`tf_random_seed`
`train_distribute`	Optional `tf.distribute.Strategy` for training.

Methods

`replace`

View source

replace(
    **kwargs
)

Returns a new instance of RunConfig replacing specified properties.

Only the properties in the following list are allowed to be replaced:

model_dir,
tf_random_seed,
save_summary_steps,
save_checkpoints_steps,
save_checkpoints_secs,
session_config,
keep_checkpoint_max,
keep_checkpoint_every_n_hours,
log_step_count_steps,
train_distribute,
device_fn,
protocol.
eval_distribute,
experimental_distribute,
experimental_max_worker_delay_secs,

In addition, either save_checkpoints_steps or save_checkpoints_secs can be set (should not be both).

Args
`**kwargs`	keyword named properties with new values.

Raises
`ValueError`	If any property name in `kwargs` does not exist or is not allowed to be replaced, or both `save_checkpoints_steps` and `save_checkpoints_secs` are set.

Returns
a new instance of `RunConfig`.

TensorFlow

tf

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tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

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tf.lite

tf.lookup

tf.losses

tf.math

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tf.random

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tf.saved_model

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tf.signal

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tf.strings

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tf.sysconfig

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tf.tpu

tf.train

tf.version

tf.xla

tf.estimator / RegressionHead

View aliases

Used in the notebooks

Args

Raises

Attributes

Methods

replace

`replace`