|TensorFlow 1 version||View source on GitHub|
Groups trackable objects, saving and restoring them.
Used in the guide:
Used in the tutorials:
- Deep Convolutional Generative Adversarial Network
- Image captioning with visual attention
- Neural machine translation with attention
- Transformer model for language understanding
Checkpoint's constructor accepts keyword arguments whose values are types
that contain trackable state, such as
tf.keras.Layer implementations, or
tf.keras.Model implementations. It saves these values with a checkpoint, and
save_counter for numbering checkpoints.
import tensorflow as tf import os checkpoint_directory = "/tmp/training_checkpoints" checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model) status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory)) for _ in range(num_training_steps): optimizer.minimize( ... ) # Variables will be restored on creation. status.assert_consumed() # Optional sanity checks. checkpoint.save(file_prefix=checkpoint_prefix)
Checkpoint.restore write and read object-based
checkpoints, in contrast to TensorFlow 1.x's
variable.name based checkpoints. Object-based checkpointing saves a
graph of dependencies between Python objects (
Variables, etc.) with named edges, and this graph is used to match variables
when restoring a checkpoint. It can be more robust to changes in the Python
program, and helps to support restore-on-create for variables.
Checkpoint objects have dependencies on the objects passed as keyword
arguments to their constructors, and each dependency is given a name that is
identical to the name of the keyword argument for which it was created.
TensorFlow classes like
Optimizers will automatically add
dependencies on their variables (e.g. "kernel" and "bias" for
tf.keras.layers.Dense). Inheriting from
tf.keras.Model makes managing
dependencies easy in user-defined classes, since
Model hooks into attribute
assignment. For example:
class Regress(tf.keras.Model): def __init__(self): super(Regress, self).__init__() self.input_transform = tf.keras.layers.Dense(10) # ... def call(self, inputs): x = self.input_transform(inputs) # ...
Model has a dependency named "input_transform" on its
which in turn depends on its variables. As a result, saving an instance of
tf.train.Checkpoint will also save all the variables created
When variables are assigned to multiple workers, each worker writes its own section of the checkpoint. These sections are then merged/re-indexed to behave as a single checkpoint. This avoids copying all variables to one worker, but does require that all workers see a common filesystem.
tf.train.Checkpoint.save save in the
same format, note that the root of the resulting checkpoint is the object the
save method is attached to. This means saving a
save_weights and loading into a
tf.train.Checkpoint with a
attached (or vice versa) will not match the
Model's variables. See the
guide to training
save_counter: Incremented when
save()is called. Used to number checkpoints.
Group objects into a training checkpoint.
**kwargs: Keyword arguments are set as attributes of this object, and are saved with the checkpoint. Values must be trackable objects.
ValueError: If objects in
kwargsare not trackable.
An integer variable which starts at zero and is incremented on save.
Used to number checkpoints.
The save counter variable.
Restore a training checkpoint.
Checkpoint and any objects it depends on.
Either assigns values immediately if variables to restore have been created already, or defers restoration until the variables are created. Dependencies added after this call will be matched if they have a corresponding object in the checkpoint (the restore request will queue in any trackable object waiting for the expected dependency to be added).
To ensure that loading is complete and no more assignments will take place,
assert_consumed() method of the status object returned by
checkpoint = tf.train.Checkpoint( ... ) checkpoint.restore(path).assert_consumed()
An exception will be raised if any Python objects in the dependency graph were not found in the checkpoint, or if any checkpointed values do not have a matching Python object.
tf.compat.v1.train.Saver checkpoints from TensorFlow 1.x can be
using this method. Names are used to match variables. Re-encode name-based
tf.train.Checkpoint.save as soon as possible.
save_path: The path to the checkpoint, as returned by
tf.train.latest_checkpoint. If None (as when there is no latest checkpoint for
tf.train.latest_checkpointto return), returns an object which may run initializers for objects in the dependency graph. If the checkpoint was written by the name-based
tf.compat.v1.train.Saver, names are used to match variables.
A load status object, which can be used to make assertions about the status of a checkpoint restoration.
The returned status object has the following methods:
assert_consumed(): Raises an exception if any variables/objects are unmatched: either checkpointed values which don't have a matching Python object or Python objects in the dependency graph with no values in the checkpoint. This method returns the status object, and so may be chained with other assertions.
assert_existing_objects_matched(): Raises an exception if any existing Python objects in the dependency graph are unmatched. Unlike
assert_consumed, this assertion will pass if values in the checkpoint have no corresponding Python objects. For example a
tf.keras.Layerobject which has not yet been built, and so has not created any variables, will pass this assertion but fail
assert_consumed. Useful when loading part of a larger checkpoint into a new Python program, e.g. a training checkpoint with a
tf.compat.v1.train.Optimizerwas saved but only the state required for inference is being loaded. This method returns the status object, and so may be chained with other assertions.
assert_nontrivial_match(): Asserts that something aside from the root object was matched. This is a very weak assertion, but is useful for sanity checking in library code where objects may exist in the checkpoint which haven't been created in Python and some Python objects may not have a checkpointed value.
expect_partial(): Silence warnings about incomplete checkpoint restores. Warnings are otherwise printed for unused parts of the checkpoint file or object when the
Checkpointobject is deleted (often at program shutdown).
Saves a training checkpoint and provides basic checkpoint management.
The saved checkpoint includes variables created by this object and any
trackable objects it depends on at the time
save is a basic convenience wrapper around the
sequentially numbering checkpoints using
save_counter and updating the
metadata used by
tf.train.latest_checkpoint. More advanced checkpoint
management, for example garbage collection and custom numbering, may be
provided by other utilities which also wrap
tf.contrib.checkpoint.CheckpointManager for example).
file_prefix: A prefix to use for the checkpoint filenames (/path/to/directory/and_a_prefix). Names are generated based on this prefix and
The full path to the checkpoint.
Writes a training checkpoint.
The checkpoint includes variables created by this object and any
trackable objects it depends on at the time
write does not number checkpoints, increment
save_counter, or update the
metadata used by
tf.train.latest_checkpoint. It is primarily intended for
use by higher level checkpoint management utilities.
save provides a very
basic implementation of these features.
file_prefix: A prefix to use for the checkpoint filenames (/path/to/directory/and_a_prefix).
The full path to the checkpoint (i.e.