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Base Callback class

Callback

Base class used to build new callbacks.

Callbacks can be passed to synalinks methods such as fit(), evaluate(), and predict() in order to hook into the various stages of the program training, evaluation, and inference lifecycle.

To create a custom callback, subclass synalinks.callbacks.Callback and override the method associated with the stage of interest.

If you want to use Callback objects in a custom training loop:

  1. You should pack all your callbacks into a single callbacks.CallbackList so they can all be called together.
  2. You will need to manually call all the on_* methods at the appropriate locations in your loop.

The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings).

Attributes:

Name Type Description
params dict

Training parameters (eg. verbosity, batch size, number of epochs...).
program Program

Instance of Program. Reference of the program being trained.
Source code in synalinks/src/callbacks/callback.py 
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@synalinks_export("synalinks.callbacks.Callback")
class Callback:
    """Base class used to build new callbacks.

    Callbacks can be passed to synalinks methods such as `fit()`, `evaluate()`, and
    `predict()` in order to hook into the various stages of the program training,
    evaluation, and inference lifecycle.

    To create a custom callback, subclass `synalinks.callbacks.Callback` and
    override the method associated with the stage of interest.

    If you want to use `Callback` objects in a custom training loop:

    1. You should pack all your callbacks into a single `callbacks.CallbackList`
       so they can all be called together.
    2. You will need to manually call all the `on_*` methods at the appropriate
       locations in your loop.

    The `logs` dictionary that callback methods
    take as argument will contain keys for quantities relevant to
    the current batch or epoch (see method-specific docstrings).

    Attributes:
        params (dict): Training parameters
            (eg. verbosity, batch size, number of epochs...).
        program (Program): Instance of `Program`.
            Reference of the program being trained.
    """

    def __init__(self):
        self.params = None
        self._program = None

    def set_params(self, params):
        self.params = params

    def set_program(self, program):
        self._program = program

    @property
    def program(self):
        return self._program

    @utils.default
    def on_batch_begin(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_begin`."""

    @utils.default
    def on_batch_end(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_end`."""

    @utils.default
    def on_epoch_begin(self, epoch, logs=None):
        """Called at the start of an epoch.

        Subclasses should override for any actions to run. This function should
        only be called during TRAIN mode.

        Args:
            epoch (int): Index of epoch.
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_epoch_end(self, epoch, logs=None):
        """Called at the end of an epoch.

        Subclasses should override for any actions to run. This function should
        only be called during TRAIN mode.

        Args:
            epoch (int): Index of epoch.
            logs (dict): Metric results for this training epoch, and for the
                validation epoch if validation is performed. Validation result
                keys are prefixed with `val_`. For training epoch, the values of
                the `Program`'s metrics are returned. Example:
                `{'reward': 0.2, 'accuracy': 0.7}`.
        """

    @utils.default
    def on_train_batch_begin(self, batch, logs=None):
        """Called at the beginning of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """
        # For backwards compatibility.
        self.on_batch_begin(batch, logs=logs)

    @utils.default
    def on_train_batch_end(self, batch, logs=None):
        """Called at the end of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Aggregated metric results up until this batch.
        """
        # For backwards compatibility.
        self.on_batch_end(batch, logs=logs)

    @utils.default
    def on_test_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `evaluate` methods.

        Also called at the beginning of a validation batch in the `fit`
        methods, if validation data is provided.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_test_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `evaluate` methods.

        Also called at the end of a validation batch in the `fit`
        methods, if validation data is provided.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Aggregated metric results up until this batch.
        """

    @utils.default
    def on_predict_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_predict_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `Program` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch (int): Index of batch within the current epoch.
            logs (dict): Aggregated metric results up until this batch.
        """

    @utils.default
    def on_train_begin(self, logs=None):
        """Called at the beginning of training.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_train_end(self, logs=None):
        """Called at the end of training.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently the output of the last call to
                `on_epoch_end()` is passed to this argument for this method but
                that may change in the future.
        """

    @utils.default
    def on_test_begin(self, logs=None):
        """Called at the beginning of evaluation or validation.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_test_end(self, logs=None):
        """Called at the end of evaluation or validation.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently the output of the last call to
                `on_test_batch_end()` is passed to this argument for this method
                but that may change in the future.
        """

    @utils.default
    def on_predict_begin(self, logs=None):
        """Called at the beginning of prediction.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

    @utils.default
    def on_predict_end(self, logs=None):
        """Called at the end of prediction.

        Subclasses should override for any actions to run.

        Args:
            logs (dict): Currently no data is passed to this argument for this
                method but that may change in the future.
        """

on_batch_begin(batch, logs=None)

A backwards compatibility alias for on_train_batch_begin.

Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_batch_begin(self, batch, logs=None):
    """A backwards compatibility alias for `on_train_batch_begin`."""

on_batch_end(batch, logs=None)

A backwards compatibility alias for on_train_batch_end.

Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_batch_end(self, batch, logs=None):
    """A backwards compatibility alias for `on_train_batch_end`."""

on_epoch_begin(epoch, logs=None)

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Parameters:

Name Type Description Default
epoch int

Index of epoch.

 required
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_epoch_begin(self, epoch, logs=None):
    """Called at the start of an epoch.

    Subclasses should override for any actions to run. This function should
    only be called during TRAIN mode.

    Args:
        epoch (int): Index of epoch.
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_epoch_end(epoch, logs=None)

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Parameters:

Name Type Description Default
epoch int

Index of epoch.

 required
logs dict

Metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the Program's metrics are returned. Example: {'reward': 0.2, 'accuracy': 0.7}.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_epoch_end(self, epoch, logs=None):
    """Called at the end of an epoch.

    Subclasses should override for any actions to run. This function should
    only be called during TRAIN mode.

    Args:
        epoch (int): Index of epoch.
        logs (dict): Metric results for this training epoch, and for the
            validation epoch if validation is performed. Validation result
            keys are prefixed with `val_`. For training epoch, the values of
            the `Program`'s metrics are returned. Example:
            `{'reward': 0.2, 'accuracy': 0.7}`.
    """

on_predict_batch_begin(batch, logs=None)

Called at the beginning of a batch in predict methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_predict_batch_begin(self, batch, logs=None):
    """Called at the beginning of a batch in `predict` methods.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_predict_batch_end(batch, logs=None)

Called at the end of a batch in predict methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Aggregated metric results up until this batch.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_predict_batch_end(self, batch, logs=None):
    """Called at the end of a batch in `predict` methods.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Aggregated metric results up until this batch.
    """

on_predict_begin(logs=None)

Called at the beginning of prediction.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_predict_begin(self, logs=None):
    """Called at the beginning of prediction.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_predict_end(logs=None)

Called at the end of prediction.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_predict_end(self, logs=None):
    """Called at the end of prediction.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_test_batch_begin(batch, logs=None)

Called at the beginning of a batch in evaluate methods.

Also called at the beginning of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_test_batch_begin(self, batch, logs=None):
    """Called at the beginning of a batch in `evaluate` methods.

    Also called at the beginning of a validation batch in the `fit`
    methods, if validation data is provided.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_test_batch_end(batch, logs=None)

Called at the end of a batch in evaluate methods.

Also called at the end of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Aggregated metric results up until this batch.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_test_batch_end(self, batch, logs=None):
    """Called at the end of a batch in `evaluate` methods.

    Also called at the end of a validation batch in the `fit`
    methods, if validation data is provided.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Aggregated metric results up until this batch.
    """

on_test_begin(logs=None)

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_test_begin(self, logs=None):
    """Called at the beginning of evaluation or validation.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_test_end(logs=None)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently the output of the last call to on_test_batch_end() is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_test_end(self, logs=None):
    """Called at the end of evaluation or validation.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently the output of the last call to
            `on_test_batch_end()` is passed to this argument for this method
            but that may change in the future.
    """

on_train_batch_begin(batch, logs=None)

Called at the beginning of a training batch in fit methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_train_batch_begin(self, batch, logs=None):
    """Called at the beginning of a training batch in `fit` methods.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """
    # For backwards compatibility.
    self.on_batch_begin(batch, logs=logs)

on_train_batch_end(batch, logs=None)

Called at the end of a training batch in fit methods.

Subclasses should override for any actions to run.

Note that if the steps_per_execution argument to compile in Program is set to N, this method will only be called every N batches.

Parameters:

Name Type Description Default
batch int

Index of batch within the current epoch.

 required
logs dict

Aggregated metric results up until this batch.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_train_batch_end(self, batch, logs=None):
    """Called at the end of a training batch in `fit` methods.

    Subclasses should override for any actions to run.

    Note that if the `steps_per_execution` argument to `compile` in
    `Program` is set to `N`, this method will only be called every
    `N` batches.

    Args:
        batch (int): Index of batch within the current epoch.
        logs (dict): Aggregated metric results up until this batch.
    """
    # For backwards compatibility.
    self.on_batch_end(batch, logs=logs)

on_train_begin(logs=None)

Called at the beginning of training.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently no data is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_train_begin(self, logs=None):
    """Called at the beginning of training.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently no data is passed to this argument for this
            method but that may change in the future.
    """

on_train_end(logs=None)

Called at the end of training.

Subclasses should override for any actions to run.

Parameters:

Name Type Description Default
logs dict

Currently the output of the last call to on_epoch_end() is passed to this argument for this method but that may change in the future.

 None
Source code in synalinks/src/callbacks/callback.py 
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@utils.default
def on_train_end(self, logs=None):
    """Called at the end of training.

    Subclasses should override for any actions to run.

    Args:
        logs (dict): Currently the output of the last call to
            `on_epoch_end()` is passed to this argument for this method but
            that may change in the future.
    """