# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Callbacks: utilities called at certain points during model training.""" import collections import copy import csv import json import os import re import sys import time import numpy as np import tensorflow.compat.v2 as tf from keras import backend from keras.distribute import distributed_file_utils from keras.distribute import worker_training_state from keras.optimizers.schedules import learning_rate_schedule from keras.utils import generic_utils from keras.utils import io_utils from keras.utils import tf_utils from keras.utils import version_utils from keras.utils.data_utils import Sequence from keras.utils.generic_utils import Progbar from keras.utils.mode_keys import ModeKeys # isort: off from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import keras_export from tensorflow.tools.docs import doc_controls try: import requests except ImportError: requests = None # Note: `configure_callbacks` is only used in TF1. def configure_callbacks( callbacks, model, do_validation=False, batch_size=None, epochs=None, steps_per_epoch=None, samples=None, verbose=1, count_mode="steps", mode=ModeKeys.TRAIN, ): """Configures callbacks for use in various training loops. Args: callbacks: List of Callbacks. model: Model being trained. do_validation: Whether or not validation loop will be run. batch_size: Number of samples per batch. epochs: Number of epoch to train. steps_per_epoch: Number of batches to run per training epoch. samples: Number of training samples. verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger. count_mode: One of 'steps' or 'samples'. Per-batch or per-sample count. mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT. Which loop mode to configure callbacks for. Returns: Instance of CallbackList used to control all Callbacks. """ # Check if callbacks have already been configured. if isinstance(callbacks, CallbackList): return callbacks if not callbacks: callbacks = [] # Add additional callbacks during training. if mode == ModeKeys.TRAIN: model.history = History() callbacks = [BaseLogger()] + (callbacks or []) + [model.history] if verbose: callbacks.append(ProgbarLogger(count_mode)) callback_list = CallbackList(callbacks) # Set callback model callback_model = model._get_callback_model() callback_list.set_model(callback_model) set_callback_parameters( callback_list, model, do_validation=do_validation, batch_size=batch_size, epochs=epochs, steps_per_epoch=steps_per_epoch, samples=samples, verbose=verbose, mode=mode, ) callback_list.model.stop_training = False return callback_list def set_callback_parameters( callback_list, model, do_validation=False, batch_size=None, epochs=None, steps_per_epoch=None, samples=None, verbose=1, mode=ModeKeys.TRAIN, ): """Sets callback parameters. Args: callback_list: CallbackList instance. model: Model being trained. do_validation: Whether or not validation loop will be run. batch_size: Number of samples per batch. epochs: Number of epoch to train. steps_per_epoch: Number of batches to run per training epoch. samples: Number of training samples. verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger. mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT. Which loop mode to configure callbacks for. """ metric_names = model.metrics_names for cbk in callback_list: if isinstance(cbk, (BaseLogger, ProgbarLogger)): cbk.stateful_metrics = metric_names[1:] # Exclude `loss` # Set callback parameters callback_metrics = [] # When we have deferred build scenario with iterator input, we will compile # when we standardize first batch of data. if mode != ModeKeys.PREDICT: callback_metrics = copy.copy(metric_names) if do_validation: callback_metrics += ["val_" + n for n in metric_names] callback_params = { "batch_size": batch_size, "epochs": epochs, "steps": steps_per_epoch, "samples": samples, "verbose": verbose, "do_validation": do_validation, "metrics": callback_metrics, } callback_list.set_params(callback_params) def _is_generator_like(data): """Checks if data is a generator, Sequence, or Iterator.""" return ( hasattr(data, "__next__") or hasattr(data, "next") or isinstance( data, (Sequence, tf.compat.v1.data.Iterator, tf.data.Iterator) ) ) def make_logs(model, logs, outputs, mode, prefix=""): """Computes logs for sending to `on_batch_end` methods.""" metric_names = model.metrics_names if mode in {ModeKeys.TRAIN, ModeKeys.TEST} and metric_names: for label, output in zip(metric_names, outputs): logs[prefix + label] = output else: logs["outputs"] = outputs return logs @keras_export("keras.callbacks.CallbackList") class CallbackList: """Container abstracting a list of callbacks.""" def __init__( self, callbacks=None, add_history=False, add_progbar=False, model=None, **params, ): """Container for `Callback` instances. This object wraps a list of `Callback` instances, making it possible to call them all at once via a single endpoint (e.g. `callback_list.on_epoch_end(...)`). Args: callbacks: List of `Callback` instances. add_history: Whether a `History` callback should be added, if one does not already exist in the `callbacks` list. add_progbar: Whether a `ProgbarLogger` callback should be added, if one does not already exist in the `callbacks` list. model: The `Model` these callbacks are used with. **params: If provided, parameters will be passed to each `Callback` via `Callback.set_params`. """ self.callbacks = tf.nest.flatten(callbacks) if callbacks else [] self._add_default_callbacks(add_history, add_progbar) if model: self.set_model(model) if params: self.set_params(params) # Performance optimization: determines if batch hooks need to be called. self._supports_tf_logs = all( getattr(cb, "_supports_tf_logs", False) for cb in self.callbacks ) self._batch_hooks_support_tf_logs = all( getattr(cb, "_supports_tf_logs", False) for cb in self.callbacks if cb._implements_train_batch_hooks() or cb._implements_test_batch_hooks() or cb._implements_predict_batch_hooks() ) self._should_call_train_batch_hooks = any( cb._implements_train_batch_hooks() for cb in self.callbacks ) self._should_call_test_batch_hooks = any( cb._implements_test_batch_hooks() for cb in self.callbacks ) self._should_call_predict_batch_hooks = any( cb._implements_predict_batch_hooks() for cb in self.callbacks ) self._disallow_batch_hooks_in_ps_strategy() # Performance check: Check batch hooks for slowness compared to batch # time. Only run check for custom callbacks (i.e. not present in this # file). self._check_timing = any( cbk.__class__.__name__ not in globals() for cbk in self.callbacks ) self._num_batches_for_timing_check = 5 self._hook_times = {} self._batch_start_time = None self._batch_times = [] def _add_default_callbacks(self, add_history, add_progbar): """Adds `Callback`s that are always present.""" self._progbar = None self._history = None for cb in self.callbacks: if isinstance(cb, ProgbarLogger): self._progbar = cb elif isinstance(cb, History): self._history = cb if self._history is None and add_history: self._history = History() self.callbacks.append(self._history) if self._progbar is None and add_progbar: self._progbar = ProgbarLogger(count_mode="steps") self.callbacks.append(self._progbar) def _process_logs(self, logs, is_batch_hook=False): """Turns tensors into numpy arrays or Python scalars if necessary.""" if logs is None: return {} if self._supports_tf_logs: return logs if is_batch_hook and self._batch_hooks_support_tf_logs: return logs return tf_utils.sync_to_numpy_or_python_type(logs) def append(self, callback): self.callbacks.append(callback) def set_params(self, params): self.params = params for callback in self.callbacks: callback.set_params(params) def set_model(self, model): self.model = model if self._history: model.history = self._history for callback in self.callbacks: callback.set_model(model) def _call_batch_hook(self, mode, hook, batch, logs=None): """Helper function for all batch_{begin | end} methods.""" if not self.callbacks: return if hook == "begin": self._call_batch_begin_hook(mode, batch, logs) elif hook == "end": self._call_batch_end_hook(mode, batch, logs) else: raise ValueError( f"Unrecognized hook: {hook}. " 'Expected values are ["begin", "end"]' ) def _call_batch_begin_hook(self, mode, batch, logs): """Helper function for `on_*_batch_begin` methods.""" hook_name = "on_{mode}_batch_begin".format(mode=mode) self._call_batch_hook_helper(hook_name, batch, logs) if self._check_timing: self._batch_start_time = time.time() def _call_batch_end_hook(self, mode, batch, logs): """Helper function for `on_*_batch_end` methods.""" hook_name = "on_{mode}_batch_end".format(mode=mode) if self._check_timing and batch >= 1: batch_time = time.time() - self._batch_start_time self._batch_times.append(batch_time) self._call_batch_hook_helper(hook_name, batch, logs) if len(self._batch_times) >= self._num_batches_for_timing_check: end_hook_name = hook_name begin_hook_name = "on_{mode}_batch_begin".format(mode=mode) avg_batch_time = sum(self._batch_times) / len(self._batch_times) avg_end_hook_time = sum(self._hook_times[end_hook_name]) / len( self._hook_times[end_hook_name] ) avg_begin_hook_time = sum(self._hook_times[begin_hook_name]) / len( self._hook_times[begin_hook_name] ) threshold_time = 1.0 * avg_batch_time warning_msg = ( "Callback method `{hook}` is slow compared to " "the batch time (batch time: {batch_time:.4f}s vs " "`{hook}` time: {hook_time:.4f}s). Check your callbacks." ) if avg_begin_hook_time > threshold_time: logging.warning( warning_msg.format( hook=begin_hook_name, batch_time=avg_batch_time, hook_time=avg_begin_hook_time, ) ) if avg_end_hook_time > threshold_time: logging.warning( warning_msg.format( hook=end_hook_name, batch_time=avg_batch_time, hook_time=avg_end_hook_time, ) ) self._check_timing = False self._batch_start_time = None self._batch_times = [] self._hook_times = {} def _call_batch_hook_helper(self, hook_name, batch, logs): """Helper function for `on_*_batch_*` methods.""" if self._check_timing: start_time = time.time() logs = self._process_logs(logs, is_batch_hook=True) for callback in self.callbacks: hook = getattr(callback, hook_name) hook(batch, logs) if self._check_timing: if hook_name not in self._hook_times: self._hook_times[hook_name] = [] self._hook_times[hook_name].append(time.time() - start_time) def _call_begin_hook(self, mode): """Helper function for on_{train|test|predict}_begin methods.""" if mode == ModeKeys.TRAIN: self.on_train_begin() elif mode == ModeKeys.TEST: self.on_test_begin() else: self.on_predict_begin() def _call_end_hook(self, mode): """Helper function for on_{train|test|predict}_end methods.""" if mode == ModeKeys.TRAIN: self.on_train_end() elif mode == ModeKeys.TEST: self.on_test_end() else: self.on_predict_end() def on_batch_begin(self, batch, logs=None): if self._should_call_train_batch_hooks: self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs) def on_batch_end(self, batch, logs=None): if self._should_call_train_batch_hooks: self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs) def on_epoch_begin(self, epoch, logs=None): """Calls the `on_epoch_begin` methods of its callbacks. This function should only be called during TRAIN mode. Args: epoch: Integer, index of epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_epoch_begin(epoch, logs) def on_epoch_end(self, epoch, logs=None): """Calls the `on_epoch_end` methods of its callbacks. This function should only be called during TRAIN mode. Args: epoch: Integer, 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_`. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_epoch_end(epoch, logs) def on_train_batch_begin(self, batch, logs=None): """Calls the `on_train_batch_begin` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.train_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ if self._should_call_train_batch_hooks: self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs) def on_train_batch_end(self, batch, logs=None): """Calls the `on_train_batch_end` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. """ if self._should_call_train_batch_hooks: self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs) def on_test_batch_begin(self, batch, logs=None): """Calls the `on_test_batch_begin` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.test_step`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ if self._should_call_test_batch_hooks: self._call_batch_hook(ModeKeys.TEST, "begin", batch, logs=logs) def on_test_batch_end(self, batch, logs=None): """Calls the `on_test_batch_end` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. """ if self._should_call_test_batch_hooks: self._call_batch_hook(ModeKeys.TEST, "end", batch, logs=logs) def on_predict_batch_begin(self, batch, logs=None): """Calls the `on_predict_batch_begin` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict, contains the return value of `model.predict_step`, it typically returns a dict with a key 'outputs' containing the model's outputs. """ if self._should_call_predict_batch_hooks: self._call_batch_hook(ModeKeys.PREDICT, "begin", batch, logs=logs) def on_predict_batch_end(self, batch, logs=None): """Calls the `on_predict_batch_end` methods of its callbacks. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. """ if self._should_call_predict_batch_hooks: self._call_batch_hook(ModeKeys.PREDICT, "end", batch, logs=logs) def on_train_begin(self, logs=None): """Calls the `on_train_begin` methods of its callbacks. Args: logs: Dict. Currently, no data is passed via this argument for this method, but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_train_begin(logs) def on_train_end(self, logs=None): """Calls the `on_train_end` methods of its callbacks. Args: logs: Dict. Currently, no data is passed via this argument for this method, but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_train_end(logs) def on_test_begin(self, logs=None): """Calls the `on_test_begin` methods of its callbacks. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_test_begin(logs) def on_test_end(self, logs=None): """Calls the `on_test_end` methods of its callbacks. Args: logs: Dict. Currently, no data is passed via this argument for this method, but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_test_end(logs) def on_predict_begin(self, logs=None): """Calls the 'on_predict_begin` methods of its callbacks. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_predict_begin(logs) def on_predict_end(self, logs=None): """Calls the `on_predict_end` methods of its callbacks. Args: logs: Dict. Currently, no data is passed via this argument for this method, but that may change in the future. """ logs = self._process_logs(logs) for callback in self.callbacks: callback.on_predict_end(logs) def __iter__(self): return iter(self.callbacks) def _disallow_batch_hooks_in_ps_strategy(self): """Error out if batch-level callbacks are passed with PSStrategy.""" strategy = tf.distribute.get_strategy() if strategy._should_use_with_coordinator: unsupported_callbacks = [] for cb in self.callbacks: # These Callbacks can accept RemoteValues directly. if getattr(cb, "_supports_tf_logs", False): continue if ( cb._implements_train_batch_hooks() or cb._implements_test_batch_hooks() or cb._implements_predict_batch_hooks() ): unsupported_callbacks.append(cb) if unsupported_callbacks: raise ValueError( "Batch-level `Callback`s are not supported with " "`ParameterServerStrategy`. Found unsupported " f"callbacks: {unsupported_callbacks}" ) @keras_export("keras.callbacks.Callback") class Callback: """Abstract base class used to build new callbacks. Callbacks can be passed to keras methods such as `fit`, `evaluate`, and `predict` in order to hook into the various stages of the model training and inference lifecycle. To create a custom callback, subclass `keras.callbacks.Callback` and override the method associated with the stage of interest. See https://www.tensorflow.org/guide/keras/custom_callback for more information. Example: >>> training_finished = False >>> class MyCallback(tf.keras.callbacks.Callback): ... def on_train_end(self, logs=None): ... global training_finished ... training_finished = True >>> model = tf.keras.Sequential([ ... tf.keras.layers.Dense(1, input_shape=(1,))]) >>> model.compile(loss='mean_squared_error') >>> model.fit(tf.constant([[1.0]]), tf.constant([[1.0]]), ... callbacks=[MyCallback()]) >>> assert training_finished == True 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. Like this: Example: ```python callbacks = tf.keras.callbacks.CallbackList([...]) callbacks.append(...) callbacks.on_train_begin(...) for epoch in range(EPOCHS): callbacks.on_epoch_begin(epoch) for i, data in dataset.enumerate(): callbacks.on_train_batch_begin(i) batch_logs = model.train_step(data) callbacks.on_train_batch_end(i, batch_logs) epoch_logs = ... callbacks.on_epoch_end(epoch, epoch_logs) final_logs=... callbacks.on_train_end(final_logs) ``` Attributes: params: Dict. Training parameters (eg. verbosity, batch size, number of epochs...). model: Instance of `keras.models.Model`. Reference of the model being trained. 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). """ def __init__(self): self.validation_data = None self.model = None # Whether this Callback should only run on the chief worker in a # Multi-Worker setting. # TODO(omalleyt): Make this attr public once solution is stable. self._chief_worker_only = None self._supports_tf_logs = False def set_params(self, params): self.params = params def set_model(self, model): self.model = model @doc_controls.for_subclass_implementers @generic_utils.default def on_batch_begin(self, batch, logs=None): """A backwards compatibility alias for `on_train_batch_begin`.""" @doc_controls.for_subclass_implementers @generic_utils.default def on_batch_end(self, batch, logs=None): """A backwards compatibility alias for `on_train_batch_end`.""" @doc_controls.for_subclass_implementers 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: Integer, index of epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. """ @doc_controls.for_subclass_implementers 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: Integer, 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 `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. """ @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, 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) @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, 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) @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, 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. """ @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. """ @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, 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. """ @doc_controls.for_subclass_implementers @generic_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 `tf.keras.Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. """ @doc_controls.for_subclass_implementers 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. """ @doc_controls.for_subclass_implementers 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. """ @doc_controls.for_subclass_implementers 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. """ @doc_controls.for_subclass_implementers 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. """ @doc_controls.for_subclass_implementers 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. """ @doc_controls.for_subclass_implementers 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. """ def _implements_train_batch_hooks(self): """Determines if this Callback should be called for each train batch.""" return ( not generic_utils.is_default(self.on_batch_begin) or not generic_utils.is_default(self.on_batch_end) or not generic_utils.is_default(self.on_train_batch_begin) or not generic_utils.is_default(self.on_train_batch_end) ) def _implements_test_batch_hooks(self): """Determines if this Callback should be called for each test batch.""" return not generic_utils.is_default( self.on_test_batch_begin ) or not generic_utils.is_default(self.on_test_batch_end) def _implements_predict_batch_hooks(self): """Determines if this Callback should be called for each predict batch.""" return not generic_utils.is_default( self.on_predict_batch_begin ) or not generic_utils.is_default(self.on_predict_batch_end) @keras_export("keras.callbacks.BaseLogger") class BaseLogger(Callback): """Callback that accumulates epoch averages of metrics. This callback is automatically applied to every Keras model. Args: stateful_metrics: Iterable of string names of metrics that should *not* be averaged over an epoch. Metrics in this list will be logged as-is in `on_epoch_end`. All others will be averaged in `on_epoch_end`. """ def __init__(self, stateful_metrics=None): super().__init__() self.stateful_metrics = set(stateful_metrics or []) def on_epoch_begin(self, epoch, logs=None): self.seen = 0 self.totals = {} def on_batch_end(self, batch, logs=None): logs = logs or {} batch_size = logs.get("size", 0) # In case of distribution strategy we can potentially run multiple steps # at the same time, we should account for that in the `seen` # calculation. num_steps = logs.get("num_steps", 1) self.seen += batch_size * num_steps for k, v in logs.items(): if k in self.stateful_metrics: self.totals[k] = v else: if k in self.totals: self.totals[k] += v * batch_size else: self.totals[k] = v * batch_size def on_epoch_end(self, epoch, logs=None): if logs is not None: for k in self.params["metrics"]: if k in self.totals: # Make value available to next callbacks. if k in self.stateful_metrics: logs[k] = self.totals[k] else: logs[k] = self.totals[k] / self.seen @keras_export("keras.callbacks.TerminateOnNaN") class TerminateOnNaN(Callback): """Callback that terminates training when a NaN loss is encountered.""" def __init__(self): super().__init__() self._supports_tf_logs = True def on_batch_end(self, batch, logs=None): logs = logs or {} loss = logs.get("loss") if loss is not None: loss = tf_utils.sync_to_numpy_or_python_type(loss) if np.isnan(loss) or np.isinf(loss): io_utils.print_msg( f"Batch {batch}: Invalid loss, terminating training" ) self.model.stop_training = True @keras_export("keras.callbacks.ProgbarLogger") class ProgbarLogger(Callback): """Callback that prints metrics to stdout. Args: count_mode: One of `"steps"` or `"samples"`. Whether the progress bar should count samples seen or steps (batches) seen. stateful_metrics: Iterable of string names of metrics that should *not* be averaged over an epoch. Metrics in this list will be logged as-is. All others will be averaged over time (e.g. loss, etc). If not provided, defaults to the `Model`'s metrics. Raises: ValueError: In case of invalid `count_mode`. """ def __init__(self, count_mode: str = "samples", stateful_metrics=None): super().__init__() self._supports_tf_logs = True if count_mode == "samples": self.use_steps = False elif count_mode == "steps": self.use_steps = True else: raise ValueError( f"Unknown `count_mode`: {count_mode}. " 'Expected values are ["samples", "steps"]' ) # Defaults to all Model's metrics except for loss. self.stateful_metrics = ( set(stateful_metrics) if stateful_metrics else set() ) self.seen = 0 self.progbar = None self.target = None self.verbose = 1 self.epochs = 1 self._train_step, self._test_step, self._predict_step = None, None, None self._call_batch_hooks = True self._called_in_fit = False def set_params(self, params): self.verbose = params["verbose"] self.epochs = params["epochs"] if self.use_steps and "steps" in params: self.target = params["steps"] elif not self.use_steps and "samples" in params: self.target = params["samples"] else: self.target = ( None # Will be inferred at the end of the first epoch. ) self._call_batch_hooks = self.verbose == 1 if self.target is None: try: self._train_step = self.model._train_counter self._test_step = self.model._test_counter self._predict_step = self.model._predict_counter except AttributeError: self._call_batch_hooks = True def on_train_begin(self, logs=None): # When this logger is called inside `fit`, validation is silent. self._called_in_fit = True def on_test_begin(self, logs=None): if not self._called_in_fit: self._reset_progbar() self._maybe_init_progbar() def on_predict_begin(self, logs=None): self._reset_progbar() self._maybe_init_progbar() def on_epoch_begin(self, epoch, logs=None): self._reset_progbar() self._maybe_init_progbar() if self.verbose and self.epochs > 1: io_utils.print_msg(f"Epoch {epoch + 1}/{self.epochs}") def on_train_batch_end(self, batch, logs=None): self._batch_update_progbar(batch, logs) def on_test_batch_end(self, batch, logs=None): if not self._called_in_fit: self._batch_update_progbar(batch, logs) def on_predict_batch_end(self, batch, logs=None): # Don't pass prediction results. self._batch_update_progbar(batch, None) def on_epoch_end(self, epoch, logs=None): self._finalize_progbar(logs, self._train_step) def on_test_end(self, logs=None): if not self._called_in_fit: self._finalize_progbar(logs, self._test_step) def on_predict_end(self, logs=None): self._finalize_progbar(logs, self._predict_step) def _reset_progbar(self): self.seen = 0 self.progbar = None def _maybe_init_progbar(self): """Instantiate a `Progbar` if not yet, and update the stateful metrics.""" # TODO(rchao): Legacy TF1 code path may use list for # `self.stateful_metrics`. Remove "cast to set" when TF1 support is # dropped. self.stateful_metrics = set(self.stateful_metrics) if self.model: # Update the existing stateful metrics as `self.model.metrics` may # contain updated metrics after `MetricsContainer` is built in the # first train step. self.stateful_metrics = self.stateful_metrics.union( set(m.name for m in self.model.metrics) ) if self.progbar is None: self.progbar = Progbar( target=self.target, verbose=self.verbose, stateful_metrics=self.stateful_metrics, unit_name="step" if self.use_steps else "sample", ) self.progbar._update_stateful_metrics(self.stateful_metrics) def _implements_train_batch_hooks(self): return self._call_batch_hooks def _implements_test_batch_hooks(self): return self._call_batch_hooks def _implements_predict_batch_hooks(self): return self._call_batch_hooks def _batch_update_progbar(self, batch, logs=None): """Updates the progbar.""" logs = logs or {} self._maybe_init_progbar() if self.use_steps: self.seen = batch + 1 # One-indexed. else: # v1 path only. logs = copy.copy(logs) batch_size = logs.pop("size", 0) num_steps = logs.pop("num_steps", 1) logs.pop("batch", None) add_seen = num_steps * batch_size self.seen += add_seen if self.verbose == 1: # Only block async when verbose = 1. logs = tf_utils.sync_to_numpy_or_python_type(logs) self.progbar.update(self.seen, list(logs.items()), finalize=False) def _finalize_progbar(self, logs, counter): logs = tf_utils.sync_to_numpy_or_python_type(logs or {}) if self.target is None: if counter is not None: counter = counter.numpy() if not self.use_steps: counter *= logs.get("size", 1) self.target = counter or self.seen self.progbar.target = self.target self.progbar.update(self.target, list(logs.items()), finalize=True) @keras_export("keras.callbacks.History") class History(Callback): """Callback that records events into a `History` object. This callback is automatically applied to every Keras model. The `History` object gets returned by the `fit` method of models. Example: >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> model.compile(tf.keras.optimizers.SGD(), loss='mse') >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), ... epochs=10, verbose=1) >>> print(history.params) {'verbose': 1, 'epochs': 10, 'steps': 1} >>> # check the keys of history object >>> print(history.history.keys()) dict_keys(['loss']) """ def __init__(self): super().__init__() self.history = {} def on_train_begin(self, logs=None): self.epoch = [] def on_epoch_end(self, epoch, logs=None): logs = logs or {} self.epoch.append(epoch) for k, v in logs.items(): self.history.setdefault(k, []).append(v) # Set the history attribute on the model after the epoch ends. This will # make sure that the state which is set is the latest one. self.model.history = self @keras_export("keras.callbacks.ModelCheckpoint") class ModelCheckpoint(Callback): """Callback to save the Keras model or model weights at some frequency. `ModelCheckpoint` callback is used in conjunction with training using `model.fit()` to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved. A few options this callback provides include: - Whether to only keep the model that has achieved the "best performance" so far, or whether to save the model at the end of every epoch regardless of performance. - Definition of 'best'; which quantity to monitor and whether it should be maximized or minimized. - The frequency it should save at. Currently, the callback supports saving at the end of every epoch, or after a fixed number of training batches. - Whether only weights are saved, or the whole model is saved. Note: If you get `WARNING:tensorflow:Can save best model only with available, skipping` see the description of the `monitor` argument for details on how to get this right. Example: ```python model.compile(loss=..., optimizer=..., metrics=['accuracy']) EPOCHS = 10 checkpoint_filepath = '/tmp/checkpoint' model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=checkpoint_filepath, save_weights_only=True, monitor='val_accuracy', mode='max', save_best_only=True) # Model weights are saved at the end of every epoch, if it's the best seen # so far. model.fit(epochs=EPOCHS, callbacks=[model_checkpoint_callback]) # The model weights (that are considered the best) are loaded into the # model. model.load_weights(checkpoint_filepath) ``` Args: filepath: string or `PathLike`, path to save the model file. e.g. filepath = os.path.join(working_dir, 'ckpt', file_name). `filepath` can contain named formatting options, which will be filled the value of `epoch` and keys in `logs` (passed in `on_epoch_end`). For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the model checkpoints will be saved with the epoch number and the validation loss in the filename. The directory of the filepath should not be reused by any other callbacks to avoid conflicts. monitor: The metric name to monitor. Typically the metrics are set by the `Model.compile` method. Note: * Prefix the name with `"val_`" to monitor validation metrics. * Use `"loss"` or "`val_loss`" to monitor the model's total loss. * If you specify metrics as strings, like `"accuracy"`, pass the same string (with or without the `"val_"` prefix). * If you pass `metrics.Metric` objects, `monitor` should be set to `metric.name` * If you're not sure about the metric names you can check the contents of the `history.history` dictionary returned by `history = model.fit()` * Multi-output models set additional prefixes on the metric names. verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. save_best_only: if `save_best_only=True`, it only saves when the model is considered the "best" and the latest best model according to the quantity monitored will not be overwritten. If `filepath` doesn't contain formatting options like `{epoch}` then `filepath` will be overwritten by each new better model. mode: one of {'auto', 'min', 'max'}. If `save_best_only=True`, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For `val_acc`, this should be `max`, for `val_loss` this should be `min`, etc. In `auto` mode, the mode is set to `max` if the quantities monitored are 'acc' or start with 'fmeasure' and are set to `min` for the rest of the quantities. save_weights_only: if True, then only the model's weights will be saved (`model.save_weights(filepath)`), else the full model is saved (`model.save(filepath)`). save_freq: `'epoch'` or integer. When using `'epoch'`, the callback saves the model after each epoch. When using integer, the callback saves the model at end of this many batches. If the `Model` is compiled with `steps_per_execution=N`, then the saving criteria will be checked every Nth batch. Note that if the saving isn't aligned to epochs, the monitored metric may potentially be less reliable (it could reflect as little as 1 batch, since the metrics get reset every epoch). Defaults to `'epoch'`. options: Optional `tf.train.CheckpointOptions` object if `save_weights_only` is true or optional `tf.saved_model.SaveOptions` object if `save_weights_only` is false. initial_value_threshold: Floating point initial "best" value of the metric to be monitored. Only applies if `save_best_value=True`. Only overwrites the model weights already saved if the performance of current model is better than this value. **kwargs: Additional arguments for backwards compatibility. Possible key is `period`. """ def __init__( self, filepath, monitor: str = "val_loss", verbose: int = 0, save_best_only: bool = False, save_weights_only: bool = False, mode: str = "auto", save_freq="epoch", options=None, initial_value_threshold=None, **kwargs, ): super().__init__() self._supports_tf_logs = True self.monitor = monitor self.verbose = verbose self.filepath = io_utils.path_to_string(filepath) self.save_best_only = save_best_only self.save_weights_only = save_weights_only self.save_freq = save_freq self.epochs_since_last_save = 0 self._batches_seen_since_last_saving = 0 self._last_batch_seen = 0 self.best = initial_value_threshold if save_weights_only: if options is None or isinstance( options, tf.train.CheckpointOptions ): self._options = options or tf.train.CheckpointOptions() else: raise TypeError( "If save_weights_only is True, then `options` must be " f"either None or a tf.train.CheckpointOptions. " f"Got {options}." ) else: if options is None or isinstance( options, tf.saved_model.SaveOptions ): self._options = options or tf.saved_model.SaveOptions() else: raise TypeError( "If save_weights_only is False, then `options` must be " f"either None or a tf.saved_model.SaveOptions. " f"Got {options}." ) # Deprecated field `load_weights_on_restart` is for loading the # checkpoint file from `filepath` at the start of `model.fit()` # TODO(rchao): Remove the arg during next breaking release. if "load_weights_on_restart" in kwargs: self.load_weights_on_restart = kwargs["load_weights_on_restart"] logging.warning( "`load_weights_on_restart` argument is deprecated. " "Please use `model.load_weights()` for loading weights " "before the start of `model.fit()`." ) else: self.load_weights_on_restart = False # Deprecated field `period` is for the number of epochs between which # the model is saved. if "period" in kwargs: self.period = kwargs["period"] logging.warning( "`period` argument is deprecated. Please use `save_freq` " "to specify the frequency in number of batches seen." ) else: self.period = 1 if mode not in ["auto", "min", "max"]: logging.warning( "ModelCheckpoint mode %s is unknown, " "fallback to auto mode.", mode, ) mode = "auto" if mode == "min": self.monitor_op = np.less if self.best is None: self.best = np.Inf elif mode == "max": self.monitor_op = np.greater if self.best is None: self.best = -np.Inf else: if "acc" in self.monitor or self.monitor.startswith("fmeasure"): self.monitor_op = np.greater if self.best is None: self.best = -np.Inf else: self.monitor_op = np.less if self.best is None: self.best = np.Inf if self.save_freq != "epoch" and not isinstance(self.save_freq, int): raise ValueError( f"Unrecognized save_freq: {self.save_freq}. " 'Expected save_freq are "epoch" or integer' ) # Only the chief worker writes model checkpoints, but all workers # restore checkpoint at on_train_begin(). self._chief_worker_only = False def on_train_begin(self, logs=None): if self.load_weights_on_restart: filepath_to_load = ( self._get_most_recently_modified_file_matching_pattern( self.filepath ) ) if filepath_to_load is not None and self._checkpoint_exists( filepath_to_load ): try: # `filepath` may contain placeholders such as `{epoch:02d}`, # and thus it attempts to load the most recently modified # file with file name matching the pattern. self.model.load_weights(filepath_to_load) except (IOError, ValueError) as e: raise ValueError( f"Error loading file from {filepath_to_load}. " f"Reason: {e}" ) def _implements_train_batch_hooks(self): # Only call batch hooks when saving on batch return self.save_freq != "epoch" def on_train_batch_end(self, batch, logs=None): if self._should_save_on_batch(batch): self._save_model(epoch=self._current_epoch, batch=batch, logs=logs) def on_epoch_begin(self, epoch, logs=None): self._current_epoch = epoch def on_epoch_end(self, epoch, logs=None): self.epochs_since_last_save += 1 if self.save_freq == "epoch": self._save_model(epoch=epoch, batch=None, logs=logs) def _should_save_on_batch(self, batch): """Handles batch-level saving logic, supports steps_per_execution.""" if self.save_freq == "epoch": return False if batch <= self._last_batch_seen: # New epoch. add_batches = batch + 1 # batches are zero-indexed. else: add_batches = batch - self._last_batch_seen self._batches_seen_since_last_saving += add_batches self._last_batch_seen = batch if self._batches_seen_since_last_saving >= self.save_freq: self._batches_seen_since_last_saving = 0 return True return False def _save_model(self, epoch, batch, logs): """Saves the model. Args: epoch: the epoch this iteration is in. batch: the batch this iteration is in. `None` if the `save_freq` is set to `epoch`. logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`. """ logs = logs or {} if ( isinstance(self.save_freq, int) or self.epochs_since_last_save >= self.period ): # Block only when saving interval is reached. logs = tf_utils.sync_to_numpy_or_python_type(logs) self.epochs_since_last_save = 0 filepath = self._get_file_path(epoch, batch, logs) try: if self.save_best_only: current = logs.get(self.monitor) if current is None: logging.warning( "Can save best model only with %s available, " "skipping.", self.monitor, ) else: if self.monitor_op(current, self.best): if self.verbose > 0: io_utils.print_msg( f"\nEpoch {epoch + 1}: {self.monitor} " "improved " f"from {self.best:.5f} to {current:.5f}, " f"saving model to {filepath}" ) self.best = current if self.save_weights_only: self.model.save_weights( filepath, overwrite=True, options=self._options, ) else: self.model.save( filepath, overwrite=True, options=self._options, ) else: if self.verbose > 0: io_utils.print_msg( f"\nEpoch {epoch + 1}: " f"{self.monitor} did not improve " f"from {self.best:.5f}" ) else: if self.verbose > 0: io_utils.print_msg( f"\nEpoch {epoch + 1}: saving model to {filepath}" ) if self.save_weights_only: self.model.save_weights( filepath, overwrite=True, options=self._options ) else: self.model.save( filepath, overwrite=True, options=self._options ) self._maybe_remove_file() except IsADirectoryError: # h5py 3.x raise IOError( "Please specify a non-directory filepath for " "ModelCheckpoint. Filepath used is an existing " f"directory: {filepath}" ) except IOError as e: # h5py 2.x # `e.errno` appears to be `None` so checking the content of # `e.args[0]`. if "is a directory" in str(e.args[0]).lower(): raise IOError( "Please specify a non-directory filepath for " "ModelCheckpoint. Filepath used is an existing " f"directory: f{filepath}" ) # Re-throw the error for any other causes. raise e def _get_file_path(self, epoch, batch, logs): """Returns the file path for checkpoint.""" try: # `filepath` may contain placeholders such as # `{epoch:02d}`,`{batch:02d}` and `{mape:.2f}`. A mismatch between # logged metrics and the path's placeholders can cause formatting to # fail. if batch is None or "batch" in logs: file_path = self.filepath.format(epoch=epoch + 1, **logs) else: file_path = self.filepath.format( epoch=epoch + 1, batch=batch + 1, **logs ) except KeyError as e: raise KeyError( f'Failed to format this callback filepath: "{self.filepath}". ' f"Reason: {e}" ) self._write_filepath = distributed_file_utils.write_filepath( file_path, self.model.distribute_strategy ) return self._write_filepath def _maybe_remove_file(self): # Remove the checkpoint directory in multi-worker training where this # worker should not checkpoint. It is a dummy directory previously saved # for sync distributed training. distributed_file_utils.remove_temp_dir_with_filepath( self._write_filepath, self.model.distribute_strategy ) def _checkpoint_exists(self, filepath): """Returns whether the checkpoint `filepath` refers to exists.""" if filepath.endswith(".h5"): return tf.io.gfile.exists(filepath) tf_saved_model_exists = tf.io.gfile.exists(filepath) tf_weights_only_checkpoint_exists = tf.io.gfile.exists( filepath + ".index" ) return tf_saved_model_exists or tf_weights_only_checkpoint_exists def _get_most_recently_modified_file_matching_pattern(self, pattern): """Returns the most recently modified filepath matching pattern. Pattern may contain python formatting placeholder. If `tf.train.latest_checkpoint()` does not return None, use that; otherwise, check for most recently modified one that matches the pattern. In the rare case where there are more than one pattern-matching file having the same modified time that is most recent among all, return the filepath that is largest (by `>` operator, lexicographically using the numeric equivalents). This provides a tie-breaker when multiple files are most recent. Note that a larger `filepath` can sometimes indicate a later time of modification (for instance, when epoch/batch is used as formatting option), but not necessarily (when accuracy or loss is used). The tie-breaker is put in the logic as best effort to return the most recent, and to avoid undeterministic result. Modified time of a file is obtained with `os.path.getmtime()`. This utility function is best demonstrated via an example: ```python file_pattern = 'f.batch{batch:02d}epoch{epoch:02d}.h5' test_dir = self.get_temp_dir() path_pattern = os.path.join(test_dir, file_pattern) file_paths = [ os.path.join(test_dir, file_name) for file_name in ['f.batch03epoch02.h5', 'f.batch02epoch02.h5', 'f.batch01epoch01.h5'] ] for file_path in file_paths: # Write something to each of the files self.assertEqual( _get_most_recently_modified_file_matching_pattern(path_pattern), file_paths[-1]) ``` Args: pattern: The file pattern that may optionally contain python placeholder such as `{epoch:02d}`. Returns: The most recently modified file's full filepath matching `pattern`. If `pattern` does not contain any placeholder, this returns the filepath that exactly matches `pattern`. Returns `None` if no match is found. """ dir_name = os.path.dirname(pattern) base_name = os.path.basename(pattern) base_name_regex = "^" + re.sub(r"{.*}", r".*", base_name) + "$" # If tf.train.latest_checkpoint tells us there exists a latest # checkpoint, use that as it is more robust than `os.path.getmtime()`. latest_tf_checkpoint = tf.train.latest_checkpoint(dir_name) if latest_tf_checkpoint is not None and re.match( base_name_regex, os.path.basename(latest_tf_checkpoint) ): return latest_tf_checkpoint latest_mod_time = 0 file_path_with_latest_mod_time = None n_file_with_latest_mod_time = 0 file_path_with_largest_file_name = None if tf.io.gfile.exists(dir_name): for file_name in os.listdir(dir_name): # Only consider if `file_name` matches the pattern. if re.match(base_name_regex, file_name): file_path = os.path.join(dir_name, file_name) mod_time = os.path.getmtime(file_path) if ( file_path_with_largest_file_name is None or file_path > file_path_with_largest_file_name ): file_path_with_largest_file_name = file_path if mod_time > latest_mod_time: latest_mod_time = mod_time file_path_with_latest_mod_time = file_path # In the case a file with later modified time is found, # reset the counter for the number of files with latest # modified time. n_file_with_latest_mod_time = 1 elif mod_time == latest_mod_time: # In the case a file has modified time tied with the # most recent, increment the counter for the number of # files with latest modified time by 1. n_file_with_latest_mod_time += 1 if n_file_with_latest_mod_time == 1: # Return the sole file that has most recent modified time. return file_path_with_latest_mod_time else: # If there are more than one file having latest modified time, # return the file path with the largest file name. return file_path_with_largest_file_name @keras_export("keras.callbacks.BackupAndRestore", v1=[]) class BackupAndRestore(Callback): """Callback to back up and restore the training state. `BackupAndRestore` callback is intended to recover training from an interruption that has happened in the middle of a `Model.fit` execution, by backing up the training states in a temporary checkpoint file (with the help of a `tf.train.CheckpointManager`), at the end of each epoch. Each backup overwrites the previously written checkpoint file, so at any given time there is at most one such checkpoint file for backup/restoring purpose. If training restarts before completion, the training state (which includes the `Model` weights and epoch number) is restored to the most recently saved state at the beginning of a new `Model.fit` run. At the completion of a `Model.fit` run, the temporary checkpoint file is deleted. Note that the user is responsible to bring jobs back after the interruption. This callback is important for the backup and restore mechanism for fault tolerance purpose, and the model to be restored from an previous checkpoint is expected to be the same as the one used to back up. If user changes arguments passed to compile or fit, the checkpoint saved for fault tolerance can become invalid. Note: 1. This callback is not compatible with eager execution disabled. 2. A checkpoint is saved at the end of each epoch. After restoring, `Model.fit` redoes any partial work during the unfinished epoch in which the training got restarted (so the work done before the interruption doesn't affect the final model state). 3. This works for both single worker and multi-worker modes. When `Model.fit` is used with `tf.distribute`, it supports `tf.distribute.MirroredStrategy`, `tf.distribute.MultiWorkerMirroredStrategy`, `tf.distribute.TPUStrategy`, and `tf.distribute.experimental.ParameterServerStrategy`. Example: >>> class InterruptingCallback(tf.keras.callbacks.Callback): ... def on_epoch_begin(self, epoch, logs=None): ... if epoch == 4: ... raise RuntimeError('Interrupting!') >>> callback = tf.keras.callbacks.BackupAndRestore(backup_dir="/tmp/backup") >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> model.compile(tf.keras.optimizers.SGD(), loss='mse') >>> try: ... model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10, ... batch_size=1, callbacks=[callback, InterruptingCallback()], ... verbose=0) ... except: ... pass >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), ... epochs=10, batch_size=1, callbacks=[callback], ... verbose=0) >>> # Only 6 more epochs are run, since first trainning got interrupted at >>> # zero-indexed epoch 4, second training will continue from 4 to 9. >>> len(history.history['loss']) 6 Args: backup_dir: String, path to store the checkpoint. e.g. backup_dir = os.path.join(working_dir, 'backup') This is the directory in which the system stores temporary files to recover the model from jobs terminated unexpectedly. The directory cannot be reused elsewhere to store other files, e.g. by BackupAndRestore callback of another training, or by another callback (ModelCheckpoint) of the same training. save_freq: `'epoch'` or integer. When set to `'epoch'` the callback saves the checkpoint at the end of each epoch. When set to an integer, the callback saves the checkpoint every `save_freq` batches. delete_checkpoint: Boolean, default to True. This `BackupAndRestore` callback works by saving a checkpoint to back up the training state. If `delete_checkpoint=True`, the checkpoint will be deleted after training is finished. Use `False` if you'd like to keep the checkpoint for future usage. """ def __init__(self, backup_dir, save_freq="epoch", delete_checkpoint=True): super().__init__() self.backup_dir = backup_dir self._supports_tf_logs = True self._supported_strategies = ( tf.distribute.MirroredStrategy, tf.distribute.MultiWorkerMirroredStrategy, tf.distribute.experimental.TPUStrategy, tf.distribute.TPUStrategy, tf.distribute.experimental.ParameterServerStrategy, ) self.save_freq = save_freq self.delete_checkpoint = delete_checkpoint self._batches_count = 0 self._current_epoch = 0 if not tf.executing_eagerly(): if tf.inside_function(): raise ValueError( "This Callback's method contains Python state and " "should be called outside of `tf.function`s." ) else: # Legacy graph mode: raise ValueError( "BackupAndRestore only supports eager mode. In graph " "mode, consider using ModelCheckpoint to manually save " "and restore weights with `model.load_weights()` and by " "providing `initial_epoch` in `model.fit()` for fault " "tolerance." ) # Only the chief worker writes model checkpoints, but all workers # restore checkpoint at on_train_begin(). self._chief_worker_only = False def on_train_begin(self, logs=None): # TrainingState is used to manage the training state needed for # failure-recovery of a worker in training. if self.model._distribution_strategy and not isinstance( self.model.distribute_strategy, self._supported_strategies ): raise NotImplementedError( f"{type(self.model.distribute_strategy)} is not supported yet. " "Currently BackupAndRestore callback " "only supports empty strategy, " "MirroredStrategy, MultiWorkerMirroredStrategy and TPUStrategy." ) self.model._training_state = worker_training_state.WorkerTrainingState( self.model, self.backup_dir, self.save_freq ) self._training_state = self.model._training_state self._training_state.restore() def on_train_batch_end(self, batch, logs=None): if self.save_freq != "epoch": self._batches_count += 1 if self._batches_count >= self.save_freq: self._batches_count = 0 self._training_state.back_up( epoch=self._current_epoch, batch=batch ) def _implements_train_batch_hooks(self): return self.save_freq != "epoch" def on_train_end(self, logs=None): if self.delete_checkpoint: # On exit of training, delete the training state backup file saved # for the purpose of worker recovery unless the user opts out. self._training_state.delete_backup() # Clean up the training state. del self._training_state del self.model._training_state def on_epoch_begin(self, epoch, logs=None): self._current_epoch = epoch def on_epoch_end(self, epoch, logs=None): # Back up the model and current epoch for possible future recovery. if self.save_freq == "epoch": self._training_state.back_up(epoch=epoch) @keras_export("keras.callbacks.experimental.BackupAndRestore", v1=[]) @deprecation.deprecated_endpoints( "keras.callbacks.experimental.BackupAndRestore" ) class BackupAndRestoreExperimental(BackupAndRestore): """Deprecated. Please use `tf.keras.callbacks.BackupAndRestore` instead. Caution: `tf.keras.callbacks.experimental.BackupAndRestore` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.callbacks.BackupAndRestore`. """ def __init__(self, *args, **kwargs): logging.warning( "`tf.keras.callbacks.experimental.BackupAndRestore` endpoint is " "deprecated and will be removed in a future release. Please use " "`tf.keras.callbacks.BackupAndRestore`." ) super().__init__(*args, **kwargs) @keras_export("keras.callbacks.EarlyStopping") class EarlyStopping(Callback): """Stop training when a monitored metric has stopped improving. Assuming the goal of a training is to minimize the loss. With this, the metric to be monitored would be `'loss'`, and mode would be `'min'`. A `model.fit()` training loop will check at end of every epoch whether the loss is no longer decreasing, considering the `min_delta` and `patience` if applicable. Once it's found no longer decreasing, `model.stop_training` is marked True and the training terminates. The quantity to be monitored needs to be available in `logs` dict. To make it so, pass the loss or metrics at `model.compile()`. Args: monitor: Quantity to be monitored. min_delta: Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement. patience: Number of epochs with no improvement after which training will be stopped. verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. mode: One of `{"auto", "min", "max"}`. In `min` mode, training will stop when the quantity monitored has stopped decreasing; in `"max"` mode it will stop when the quantity monitored has stopped increasing; in `"auto"` mode, the direction is automatically inferred from the name of the monitored quantity. baseline: Baseline value for the monitored quantity. Training will stop if the model doesn't show improvement over the baseline. restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used. An epoch will be restored regardless of the performance relative to the `baseline`. If no epoch improves on `baseline`, training will run for `patience` epochs and restore weights from the best epoch in that set. Example: >>> callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3) >>> # This callback will stop the training when there is no improvement in >>> # the loss for three consecutive epochs. >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> model.compile(tf.keras.optimizers.SGD(), loss='mse') >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), ... epochs=10, batch_size=1, callbacks=[callback], ... verbose=0) >>> len(history.history['loss']) # Only 4 epochs are run. 4 """ def __init__( self, monitor="val_loss", min_delta=0, patience=0, verbose=0, mode="auto", baseline=None, restore_best_weights=False, ): super().__init__() self.monitor = monitor self.patience = patience self.verbose = verbose self.baseline = baseline self.min_delta = abs(min_delta) self.wait = 0 self.stopped_epoch = 0 self.restore_best_weights = restore_best_weights self.best_weights = None if mode not in ["auto", "min", "max"]: logging.warning( "EarlyStopping mode %s is unknown, " "fallback to auto mode.", mode, ) mode = "auto" if mode == "min": self.monitor_op = np.less elif mode == "max": self.monitor_op = np.greater else: if ( self.monitor.endswith("acc") or self.monitor.endswith("accuracy") or self.monitor.endswith("auc") ): self.monitor_op = np.greater else: self.monitor_op = np.less if self.monitor_op == np.greater: self.min_delta *= 1 else: self.min_delta *= -1 def on_train_begin(self, logs=None): # Allow instances to be re-used self.wait = 0 self.stopped_epoch = 0 self.best = np.Inf if self.monitor_op == np.less else -np.Inf self.best_weights = None self.best_epoch = 0 def on_epoch_end(self, epoch, logs=None): current = self.get_monitor_value(logs) if current is None: return if self.restore_best_weights and self.best_weights is None: # Restore the weights after first epoch if no progress is ever made. self.best_weights = self.model.get_weights() self.wait += 1 if self._is_improvement(current, self.best): self.best = current self.best_epoch = epoch if self.restore_best_weights: self.best_weights = self.model.get_weights() # Only restart wait if we beat both the baseline and our previous # best. if self.baseline is None or self._is_improvement( current, self.baseline ): self.wait = 0 # Only check after the first epoch. if self.wait >= self.patience and epoch > 0: self.stopped_epoch = epoch self.model.stop_training = True if self.restore_best_weights and self.best_weights is not None: if self.verbose > 0: io_utils.print_msg( "Restoring model weights from " "the end of the best epoch: " f"{self.best_epoch + 1}." ) self.model.set_weights(self.best_weights) def on_train_end(self, logs=None): if self.stopped_epoch > 0 and self.verbose > 0: io_utils.print_msg( f"Epoch {self.stopped_epoch + 1}: early stopping" ) def get_monitor_value(self, logs): logs = logs or {} monitor_value = logs.get(self.monitor) if monitor_value is None: logging.warning( "Early stopping conditioned on metric `%s` " "which is not available. Available metrics are: %s", self.monitor, ",".join(list(logs.keys())), ) return monitor_value def _is_improvement(self, monitor_value, reference_value): return self.monitor_op(monitor_value - self.min_delta, reference_value) @keras_export("keras.callbacks.RemoteMonitor") class RemoteMonitor(Callback): """Callback used to stream events to a server. Requires the `requests` library. Events are sent to `root + '/publish/epoch/end/'` by default. Calls are HTTP POST, with a `data` argument which is a JSON-encoded dictionary of event data. If `send_as_json=True`, the content type of the request will be `"application/json"`. Otherwise the serialized JSON will be sent within a form. Args: root: String; root url of the target server. path: String; path relative to `root` to which the events will be sent. field: String; JSON field under which the data will be stored. The field is used only if the payload is sent within a form (i.e. send_as_json is set to False). headers: Dictionary; optional custom HTTP headers. send_as_json: Boolean; whether the request should be sent as `"application/json"`. """ def __init__( self, root="http://localhost:9000", path="/publish/epoch/end/", field="data", headers=None, send_as_json=False, ): super().__init__() self.root = root self.path = path self.field = field self.headers = headers self.send_as_json = send_as_json def on_epoch_end(self, epoch, logs=None): if requests is None: raise ImportError("RemoteMonitor requires the `requests` library.") logs = logs or {} send = {} send["epoch"] = epoch for k, v in logs.items(): # np.ndarray and np.generic are not scalar types # therefore we must unwrap their scalar values and # pass to the json-serializable dict 'send' if isinstance(v, (np.ndarray, np.generic)): send[k] = v.item() else: send[k] = v try: if self.send_as_json: requests.post( self.root + self.path, json=send, headers=self.headers ) else: requests.post( self.root + self.path, {self.field: json.dumps(send)}, headers=self.headers, ) except requests.exceptions.RequestException: logging.warning( "Warning: could not reach RemoteMonitor " "root server at " + str(self.root) ) @keras_export("keras.callbacks.LearningRateScheduler") class LearningRateScheduler(Callback): """Learning rate scheduler. At the beginning of every epoch, this callback gets the updated learning rate value from `schedule` function provided at `__init__`, with the current epoch and current learning rate, and applies the updated learning rate on the optimizer. Args: schedule: a function that takes an epoch index (integer, indexed from 0) and current learning rate (float) as inputs and returns a new learning rate as output (float). verbose: int. 0: quiet, 1: update messages. Example: >>> # This function keeps the initial learning rate for the first ten epochs >>> # and decreases it exponentially after that. >>> def scheduler(epoch, lr): ... if epoch < 10: ... return lr ... else: ... return lr * tf.math.exp(-0.1) >>> >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> model.compile(tf.keras.optimizers.SGD(), loss='mse') >>> round(model.optimizer.lr.numpy(), 5) 0.01 >>> callback = tf.keras.callbacks.LearningRateScheduler(scheduler) >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5), ... epochs=15, callbacks=[callback], verbose=0) >>> round(model.optimizer.lr.numpy(), 5) 0.00607 """ def __init__(self, schedule, verbose=0): super().__init__() self.schedule = schedule self.verbose = verbose def on_epoch_begin(self, epoch, logs=None): if not hasattr(self.model.optimizer, "lr"): raise ValueError('Optimizer must have a "lr" attribute.') try: # new API lr = float(backend.get_value(self.model.optimizer.lr)) lr = self.schedule(epoch, lr) except TypeError: # Support for old API for backward compatibility lr = self.schedule(epoch) if not isinstance(lr, (tf.Tensor, float, np.float32, np.float64)): raise ValueError( 'The output of the "schedule" function ' f"should be float. Got: {lr}" ) if isinstance(lr, tf.Tensor) and not lr.dtype.is_floating: raise ValueError( f"The dtype of `lr` Tensor should be float. Got: {lr.dtype}" ) backend.set_value(self.model.optimizer.lr, backend.get_value(lr)) if self.verbose > 0: io_utils.print_msg( f"\nEpoch {epoch + 1}: LearningRateScheduler setting learning " f"rate to {lr}." ) def on_epoch_end(self, epoch, logs=None): logs = logs or {} logs["lr"] = backend.get_value(self.model.optimizer.lr) def keras_model_summary(name, data, step=None): """Writes a Keras model as JSON to as a Summary. Writing the Keras model configuration allows the TensorBoard graph plugin to render a conceptual graph, as opposed to graph of ops. In case the model fails to serialize as JSON, it ignores and returns False. Args: name: A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes. data: A Keras Model to write. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. Returns: True on success, or False if no summary was written because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. """ summary_metadata = tf.compat.v1.SummaryMetadata() # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for # the rationale. summary_metadata.plugin_data.plugin_name = "graph_keras_model" # version number = 1 summary_metadata.plugin_data.content = b"1" try: json_string = data.to_json() except Exception as exc: # An exception should not break a model code. logging.warning( "Model failed to serialize as JSON. Ignoring... %s", exc ) return False with tf.summary.experimental.summary_scope( name, "graph_keras_model", [data, step] ) as (tag, _): with tf.device("cpu:0"): tensor = tf.constant(json_string, dtype=tf.string) return tf.summary.write( tag=tag, tensor=tensor, step=step, metadata=summary_metadata ) @keras_export("keras.callbacks.TensorBoard", v1=[]) class TensorBoard(Callback, version_utils.TensorBoardVersionSelector): """Enable visualizations for TensorBoard. TensorBoard is a visualization tool provided with TensorFlow. This callback logs events for TensorBoard, including: * Metrics summary plots * Training graph visualization * Weight histograms * Sampled profiling When used in `Model.evaluate`, in addition to epoch summaries, there will be a summary that records evaluation metrics vs `Model.optimizer.iterations` written. The metric names will be prepended with `evaluation`, with `Model.optimizer.iterations` being the step in the visualized TensorBoard. If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line: ``` tensorboard --logdir=path_to_your_logs ``` You can find more information about TensorBoard [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard). Args: log_dir: the path of the directory where to save the log files to be parsed by TensorBoard. e.g. log_dir = os.path.join(working_dir, 'logs') This directory should not be reused by any other callbacks. histogram_freq: frequency (in epochs) at which to compute weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations. write_graph: whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True. write_images: whether to write model weights to visualize as image in TensorBoard. write_steps_per_second: whether to log the training steps per second into Tensorboard. This supports both epoch and batch frequency logging. update_freq: `'batch'` or `'epoch'` or integer. When using `'batch'`, writes the losses and metrics to TensorBoard after each batch. The same applies for `'epoch'`. If using an integer, let's say `1000`, the callback will write the metrics and losses to TensorBoard every 1000 batches. Note that writing too frequently to TensorBoard can slow down your training. profile_batch: Profile the batch(es) to sample compute characteristics. profile_batch must be a non-negative integer or a tuple of integers. A pair of positive integers signify a range of batches to profile. By default, profiling is disabled. embeddings_freq: frequency (in epochs) at which embedding layers will be visualized. If set to 0, embeddings won't be visualized. embeddings_metadata: Dictionary which maps embedding layer names to the filename of a file in which to save metadata for the embedding layer. In case the same metadata file is to be used for all embedding layers, a single filename can be passed. Examples: Basic usage: ```python tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs") model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) # Then run the tensorboard command to view the visualizations. ``` Custom batch-level summaries in a subclassed Model: ```python class MyModel(tf.keras.Model): def build(self, _): self.dense = tf.keras.layers.Dense(10) def call(self, x): outputs = self.dense(x) tf.summary.histogram('outputs', outputs) return outputs model = MyModel() model.compile('sgd', 'mse') # Make sure to set `update_freq=N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1) model.fit(x_train, y_train, callbacks=[tb_callback]) ``` Custom batch-level summaries in a Functional API Model: ```python def my_summary(x): tf.summary.histogram('x', x) return x inputs = tf.keras.Input(10) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Lambda(my_summary)(x) model = tf.keras.Model(inputs, outputs) model.compile('sgd', 'mse') # Make sure to set `update_freq=N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1) model.fit(x_train, y_train, callbacks=[tb_callback]) ``` Profiling: ```python # Profile a single batch, e.g. the 5th batch. tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir='./logs', profile_batch=5) model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir='./logs', profile_batch=(10,20)) model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback]) ``` """ def __init__( self, log_dir="logs", histogram_freq=0, write_graph=True, write_images=False, write_steps_per_second=False, update_freq="epoch", profile_batch=0, embeddings_freq=0, embeddings_metadata=None, **kwargs, ): super().__init__() self._supports_tf_logs = True self._validate_kwargs(kwargs) self.log_dir = io_utils.path_to_string(log_dir) self.histogram_freq = histogram_freq self.write_graph = write_graph self.write_images = write_images self.write_steps_per_second = write_steps_per_second self.update_freq = 1 if update_freq == "batch" else update_freq self.embeddings_freq = embeddings_freq self.embeddings_metadata = embeddings_metadata self._init_profile_batch(profile_batch) self._global_train_batch = 0 self._previous_epoch_iterations = 0 self._train_accumulated_time = 0 self._batch_start_time = 0 # Lazily initialized in order to avoid creating event files when # not needed. self._writers = {} # Used to restore any existing `SummaryWriter` after training ends. self._prev_summary_state = [] def _validate_kwargs(self, kwargs): """Handle arguments were supported in V1.""" if kwargs.get("write_grads", False): logging.warning( "`write_grads` will be ignored in TensorFlow 2.0 " "for the `TensorBoard` Callback." ) if kwargs.get("batch_size", False): logging.warning( "`batch_size` is no longer needed in the " "`TensorBoard` Callback and will be ignored " "in TensorFlow 2.0." ) if kwargs.get("embeddings_layer_names", False): logging.warning( "`embeddings_layer_names` is not supported in " "TensorFlow 2.0. Instead, all `Embedding` layers " "will be visualized." ) if kwargs.get("embeddings_data", False): logging.warning( "`embeddings_data` is not supported in TensorFlow " "2.0. Instead, all `Embedding` variables will be " "visualized." ) supported_kwargs = { "write_grads", "embeddings_layer_names", "embeddings_data", "batch_size", } unrecognized_kwargs = set(kwargs.keys()) - supported_kwargs # Only allow kwargs that were supported in V1. if unrecognized_kwargs: raise ValueError( "Unrecognized arguments in `TensorBoard` Callback: " f"{unrecognized_kwargs}. " f"Supported kwargs are: {supported_kwargs}" ) def set_model(self, model): """Sets Keras model and writes graph if specified.""" self.model = model self._log_write_dir = self._get_log_write_dir() self._train_dir = os.path.join(self._log_write_dir, "train") self._train_step = self.model._train_counter self._val_dir = os.path.join(self._log_write_dir, "validation") self._val_step = self.model._test_counter self._writers = {} # Resets writers. self._should_write_train_graph = False if self.write_graph: self._write_keras_model_summary() self._should_write_train_graph = True if self.embeddings_freq: self._configure_embeddings() @property def _train_writer(self): if "train" not in self._writers: self._writers["train"] = tf.summary.create_file_writer( self._train_dir ) return self._writers["train"] @property def _val_writer(self): if "val" not in self._writers: self._writers["val"] = tf.summary.create_file_writer(self._val_dir) return self._writers["val"] def _get_log_write_dir(self): """For multi-worker, only chief should write, others write to '/tmp'.""" return distributed_file_utils.write_dirpath( self.log_dir, self.model.distribute_strategy ) def _delete_tmp_write_dir(self): """Deletes tmp write directories for multi-worker.""" distributed_file_utils.remove_temp_dirpath( self.log_dir, self.model.distribute_strategy ) def _write_keras_model_train_graph(self): """Writes Keras model train_function graph to TensorBoard.""" with self._train_writer.as_default(): with tf.summary.record_if(True): train_fn = self.model.train_tf_function # If the train_function is a `tf.function`, we can write out a # graph if hasattr(train_fn, "function_spec"): tf.summary.graph(train_fn._concrete_stateful_fn.graph) def _write_keras_model_summary(self): """Writes Keras graph network summary to TensorBoard.""" with self._train_writer.as_default(): with tf.summary.record_if(True): summary_writable = ( self.model._is_graph_network or self.model.__class__.__name__ == "Sequential" ) if summary_writable: keras_model_summary("keras", self.model, step=0) def _configure_embeddings(self): """Configure the Projector for embeddings.""" # TODO(omalleyt): Add integration tests. from keras.layers import core from keras.protobuf import projector_config_pb2 # isort: off from google.protobuf import text_format config = projector_config_pb2.ProjectorConfig() for layer in self.model.layers: if isinstance(layer, core.Embedding): embedding = config.embeddings.add() # Embeddings are always the first layer, so this naming should # be consistent in any keras models checkpoints. name = ( "layer_with_weights-0/embeddings/.ATTRIBUTES/VARIABLE_VALUE" ) embedding.tensor_name = name if self.embeddings_metadata is not None: if isinstance(self.embeddings_metadata, str): embedding.metadata_path = self.embeddings_metadata else: if layer.name in self.embeddings_metadata.keys(): embedding.metadata_path = ( self.embeddings_metadata.pop(layer.name) ) if self.embeddings_metadata and not isinstance( self.embeddings_metadata, str ): raise ValueError( "Unrecognized `Embedding` layer names passed to " "`keras.callbacks.TensorBoard` `embeddings_metadata` " f"argument: {self.embeddings_metadata.keys()}" ) config_pbtxt = text_format.MessageToString(config) path = os.path.join(self._log_write_dir, "projector_config.pbtxt") with tf.io.gfile.GFile(path, "w") as f: f.write(config_pbtxt) def _push_writer(self, writer, step): """Sets the default writer for custom batch-level summaries.""" if self.update_freq == "epoch": return should_record = lambda: tf.equal(step % self.update_freq, 0) # TODO(b/151339474): Fix deadlock when not using .value() here. summary_context = ( writer.as_default(step.value()), tf.summary.record_if(should_record), ) self._prev_summary_state.append(summary_context) summary_context[0].__enter__() summary_context[1].__enter__() def _pop_writer(self): """Pops the current writer.""" if self.update_freq == "epoch": return # See _push_writer for the content of the previous_context, which is # pair of context. previous_context = self._prev_summary_state.pop() previous_context[1].__exit__(*sys.exc_info()) previous_context[0].__exit__(*sys.exc_info()) def _close_writers(self): for writer in self._writers.values(): writer.close() def _init_profile_batch(self, profile_batch): """Validate profile_batch value and set the range of batches to profile. Sets values of _start_batch and _stop_batch attributes, specifying the start and stop batch to profile. Setting `profile_batch=0` disables profiling. Args: profile_batch: The range of batches to profile. Should be a non-negative integer or a comma separated string of pair of positive integers. A pair of positive integers signify a range of batches to profile. Raises: ValueError: If profile_batch is not an integer or a comma separated pair of positive integers. """ profile_batch_error_message = ( "profile_batch must be a non-negative integer or " "2-tuple of positive " "integers. A pair of positive integers " "signifies a range of batches " f"to profile. Found: {profile_batch}" ) # Support legacy way of specifying "start,stop" or "start" as str. if isinstance(profile_batch, str): profile_batch = str(profile_batch).split(",") profile_batch = tf.nest.map_structure(int, profile_batch) if isinstance(profile_batch, int): self._start_batch = profile_batch self._stop_batch = profile_batch elif ( isinstance(profile_batch, (tuple, list)) and len(profile_batch) == 2 ): self._start_batch, self._stop_batch = profile_batch else: raise ValueError(profile_batch_error_message) if self._start_batch < 0 or self._stop_batch < self._start_batch: raise ValueError(profile_batch_error_message) # True when the profiler was successfully started by this callback. # We track the status here to make sure callbacks do not interfere with # each other. The callback will only stop the profiler it started. self._profiler_started = False if self._start_batch > 0: # Warm up and improve the profiling accuracy. self._start_profiler(logdir="") self._stop_profiler(save=False) # True when a trace is running. self._is_tracing = False # Setting `profile_batch=0` disables profiling. self._should_trace = not ( self._start_batch == 0 and self._stop_batch == 0 ) def on_train_begin(self, logs=None): self._global_train_batch = 0 self._previous_epoch_iterations = 0 self._push_writer(self._train_writer, self._train_step) def on_train_end(self, logs=None): self._pop_writer() if self._is_tracing: self._stop_trace() self._close_writers() self._delete_tmp_write_dir() def on_test_begin(self, logs=None): self._push_writer(self._val_writer, self._val_step) def on_test_end(self, logs=None): if self.model.optimizer and hasattr(self.model.optimizer, "iterations"): with tf.summary.record_if(True), self._val_writer.as_default(): for name, value in logs.items(): tf.summary.scalar( "evaluation_" + name + "_vs_iterations", value, step=self.model.optimizer.iterations.read_value(), ) self._pop_writer() def _implements_train_batch_hooks(self): # Only call batch hooks when tracing or write_steps_per_second are # enabled return self._should_trace or self.write_steps_per_second def on_train_batch_begin(self, batch, logs=None): self._global_train_batch += 1 if self.write_steps_per_second: self._batch_start_time = time.time() if not self._should_trace: return if self._global_train_batch == self._start_batch: self._start_trace() def on_train_batch_end(self, batch, logs=None): if self._should_write_train_graph: self._write_keras_model_train_graph() self._should_write_train_graph = False if self.write_steps_per_second: batch_run_time = time.time() - self._batch_start_time tf.summary.scalar( "batch_steps_per_second", 1.0 / batch_run_time, step=self._train_step, ) if not self._should_trace: return if self._is_tracing and self._global_train_batch >= self._stop_batch: self._stop_trace() def on_epoch_begin(self, epoch, logs=None): # Keeps track of epoch for profiling. if self.write_steps_per_second: self._previous_epoch_iterations = ( self.model.optimizer.iterations.numpy() ) self._epoch_start_time = time.time() def on_epoch_end(self, epoch, logs=None): """Runs metrics and histogram summaries at epoch end.""" self._log_epoch_metrics(epoch, logs) if self.histogram_freq and epoch % self.histogram_freq == 0: self._log_weights(epoch) if self.embeddings_freq and epoch % self.embeddings_freq == 0: self._log_embeddings(epoch) def _start_trace(self): tf.summary.trace_on(graph=True, profiler=False) self._start_profiler(logdir=self.log_dir) self._is_tracing = True def _stop_trace(self, batch=None): """Logs the trace graph to TensorBoard.""" if batch is None: batch = self._stop_batch with self._train_writer.as_default(): with tf.summary.record_if(True): # TODO(b/126388999): Remove step info in the summary name. tf.summary.trace_export(name="batch_%d" % batch, step=batch) self._stop_profiler() self._is_tracing = False def _collect_learning_rate(self, logs): lr_schedule = getattr(self.model.optimizer, "lr", None) if isinstance(lr_schedule, learning_rate_schedule.LearningRateSchedule): logs["learning_rate"] = lr_schedule(self.model.optimizer.iterations) return logs def _compute_steps_per_second(self): current_iteration = self.model.optimizer.iterations.numpy() time_since_epoch_begin = time.time() - self._epoch_start_time steps_per_second = ( current_iteration - self._previous_epoch_iterations ) / time_since_epoch_begin return steps_per_second def _log_epoch_metrics(self, epoch, logs): """Writes epoch metrics out as scalar summaries. Args: epoch: Int. The global step to use for TensorBoard. logs: Dict. Keys are scalar summary names, values are scalars. """ if not logs: return train_logs = {k: v for k, v in logs.items() if not k.startswith("val_")} val_logs = {k: v for k, v in logs.items() if k.startswith("val_")} train_logs = self._collect_learning_rate(train_logs) if self.write_steps_per_second: train_logs["steps_per_second"] = self._compute_steps_per_second() with tf.summary.record_if(True): if train_logs: with self._train_writer.as_default(): for name, value in train_logs.items(): tf.summary.scalar("epoch_" + name, value, step=epoch) if val_logs: with self._val_writer.as_default(): for name, value in val_logs.items(): name = name[4:] # Remove 'val_' prefix. tf.summary.scalar("epoch_" + name, value, step=epoch) def _log_weights(self, epoch): """Logs the weights of the Model to TensorBoard.""" with self._train_writer.as_default(): with tf.summary.record_if(True): for layer in self.model.layers: for weight in layer.weights: weight_name = weight.name.replace(":", "_") # Add a suffix to prevent summary tag name collision. histogram_weight_name = weight_name + "/histogram" tf.summary.histogram( histogram_weight_name, weight, step=epoch ) if self.write_images: # Add a suffix to prevent summary tag name # collision. image_weight_name = weight_name + "/image" self._log_weight_as_image( weight, image_weight_name, epoch ) self._train_writer.flush() def _log_weight_as_image(self, weight, weight_name, epoch): """Logs a weight as a TensorBoard image.""" w_img = tf.squeeze(weight) shape = backend.int_shape(w_img) if len(shape) == 1: # Bias case w_img = tf.reshape(w_img, [1, shape[0], 1, 1]) elif len(shape) == 2: # Dense layer kernel case if shape[0] > shape[1]: w_img = tf.transpose(w_img) shape = backend.int_shape(w_img) w_img = tf.reshape(w_img, [1, shape[0], shape[1], 1]) elif len(shape) == 3: # ConvNet case if backend.image_data_format() == "channels_last": # Switch to channels_first to display every kernel as a separate # image. w_img = tf.transpose(w_img, perm=[2, 0, 1]) shape = backend.int_shape(w_img) w_img = tf.reshape(w_img, [shape[0], shape[1], shape[2], 1]) shape = backend.int_shape(w_img) # Not possible to handle 3D convnets etc. if len(shape) == 4 and shape[-1] in [1, 3, 4]: tf.summary.image(weight_name, w_img, step=epoch) def _log_embeddings(self, epoch): embeddings_ckpt = os.path.join( self._log_write_dir, "train", "keras_embedding.ckpt-{}".format(epoch), ) self.model.save_weights(embeddings_ckpt) def _start_profiler(self, logdir): """Starts the profiler if currently inactive. Args: logdir: Directory where profiler results will be saved. """ if self._profiler_started: return try: tf.profiler.experimental.start(logdir=logdir) self._profiler_started = True except tf.errors.AlreadyExistsError as e: # Profiler errors should not be fatal. logging.error("Failed to start profiler: %s", e.message) def _stop_profiler(self, save=True): """Stops the profiler if currently active. Args: save: Whether to save the profiler results to TensorBoard. """ if not self._profiler_started: return try: tf.profiler.experimental.stop(save=save) except tf.errors.UnavailableError as e: # Profiler errors should not be fatal. logging.error("Failed to stop profiler: %s", e.message) finally: self._profiler_started = False @keras_export("keras.callbacks.ReduceLROnPlateau") class ReduceLROnPlateau(Callback): """Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced. Example: ```python reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) model.fit(X_train, Y_train, callbacks=[reduce_lr]) ``` Args: monitor: quantity to be monitored. factor: factor by which the learning rate will be reduced. `new_lr = lr * factor`. patience: number of epochs with no improvement after which learning rate will be reduced. verbose: int. 0: quiet, 1: update messages. mode: one of `{'auto', 'min', 'max'}`. In `'min'` mode, the learning rate will be reduced when the quantity monitored has stopped decreasing; in `'max'` mode it will be reduced when the quantity monitored has stopped increasing; in `'auto'` mode, the direction is automatically inferred from the name of the monitored quantity. min_delta: threshold for measuring the new optimum, to only focus on significant changes. cooldown: number of epochs to wait before resuming normal operation after lr has been reduced. min_lr: lower bound on the learning rate. """ def __init__( self, monitor="val_loss", factor=0.1, patience=10, verbose=0, mode="auto", min_delta=1e-4, cooldown=0, min_lr=0, **kwargs, ): super().__init__() self.monitor = monitor if factor >= 1.0: raise ValueError( f"ReduceLROnPlateau does not support " f"a factor >= 1.0. Got {factor}" ) if "epsilon" in kwargs: min_delta = kwargs.pop("epsilon") logging.warning( "`epsilon` argument is deprecated and " "will be removed, use `min_delta` instead." ) self.factor = factor self.min_lr = min_lr self.min_delta = min_delta self.patience = patience self.verbose = verbose self.cooldown = cooldown self.cooldown_counter = 0 # Cooldown counter. self.wait = 0 self.best = 0 self.mode = mode self.monitor_op = None self._reset() def _reset(self): """Resets wait counter and cooldown counter.""" if self.mode not in ["auto", "min", "max"]: logging.warning( "Learning rate reduction mode %s is unknown, " "fallback to auto mode.", self.mode, ) self.mode = "auto" if self.mode == "min" or ( self.mode == "auto" and "acc" not in self.monitor ): self.monitor_op = lambda a, b: np.less(a, b - self.min_delta) self.best = np.Inf else: self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta) self.best = -np.Inf self.cooldown_counter = 0 self.wait = 0 def on_train_begin(self, logs=None): self._reset() def on_epoch_end(self, epoch, logs=None): logs = logs or {} logs["lr"] = backend.get_value(self.model.optimizer.lr) current = logs.get(self.monitor) if current is None: logging.warning( "Learning rate reduction is conditioned on metric `%s` " "which is not available. Available metrics are: %s", self.monitor, ",".join(list(logs.keys())), ) else: if self.in_cooldown(): self.cooldown_counter -= 1 self.wait = 0 if self.monitor_op(current, self.best): self.best = current self.wait = 0 elif not self.in_cooldown(): self.wait += 1 if self.wait >= self.patience: old_lr = backend.get_value(self.model.optimizer.lr) if old_lr > np.float32(self.min_lr): new_lr = old_lr * self.factor new_lr = max(new_lr, self.min_lr) backend.set_value(self.model.optimizer.lr, new_lr) if self.verbose > 0: io_utils.print_msg( f"\nEpoch {epoch +1}: " f"ReduceLROnPlateau reducing " f"learning rate to {new_lr}." ) self.cooldown_counter = self.cooldown self.wait = 0 def in_cooldown(self): return self.cooldown_counter > 0 @keras_export("keras.callbacks.CSVLogger") class CSVLogger(Callback): """Callback that streams epoch results to a CSV file. Supports all values that can be represented as a string, including 1D iterables such as `np.ndarray`. Example: ```python csv_logger = CSVLogger('training.log') model.fit(X_train, Y_train, callbacks=[csv_logger]) ``` Args: filename: Filename of the CSV file, e.g. `'run/log.csv'`. separator: String used to separate elements in the CSV file. append: Boolean. True: append if file exists (useful for continuing training). False: overwrite existing file. """ def __init__(self, filename, separator=",", append=False): self.sep = separator self.filename = io_utils.path_to_string(filename) self.append = append self.writer = None self.keys = None self.append_header = True super().__init__() def on_train_begin(self, logs=None): if self.append: if tf.io.gfile.exists(self.filename): with tf.io.gfile.GFile(self.filename, "r") as f: self.append_header = not bool(len(f.readline())) mode = "a" else: mode = "w" self.csv_file = tf.io.gfile.GFile(self.filename, mode) def on_epoch_end(self, epoch, logs=None): logs = logs or {} def handle_value(k): is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0 if isinstance(k, str): return k elif ( isinstance(k, collections.abc.Iterable) and not is_zero_dim_ndarray ): return '"[%s]"' % (", ".join(map(str, k))) else: return k if self.keys is None: self.keys = sorted(logs.keys()) if self.model.stop_training: # We set NA so that csv parsers do not fail for this last epoch. logs = dict( (k, logs[k]) if k in logs else (k, "NA") for k in self.keys ) if not self.writer: class CustomDialect(csv.excel): delimiter = self.sep fieldnames = ["epoch"] + self.keys self.writer = csv.DictWriter( self.csv_file, fieldnames=fieldnames, dialect=CustomDialect ) if self.append_header: self.writer.writeheader() row_dict = collections.OrderedDict({"epoch": epoch}) row_dict.update((key, handle_value(logs[key])) for key in self.keys) self.writer.writerow(row_dict) self.csv_file.flush() def on_train_end(self, logs=None): self.csv_file.close() self.writer = None @keras_export("keras.callbacks.LambdaCallback") class LambdaCallback(Callback): r"""Callback for creating simple, custom callbacks on-the-fly. This callback is constructed with anonymous functions that will be called at the appropriate time (during `Model.{fit | evaluate | predict}`). Note that the callbacks expects positional arguments, as: - `on_epoch_begin` and `on_epoch_end` expect two positional arguments: `epoch`, `logs` - `on_batch_begin` and `on_batch_end` expect two positional arguments: `batch`, `logs` - `on_train_begin` and `on_train_end` expect one positional argument: `logs` Args: on_epoch_begin: called at the beginning of every epoch. on_epoch_end: called at the end of every epoch. on_batch_begin: called at the beginning of every batch. on_batch_end: called at the end of every batch. on_train_begin: called at the beginning of model training. on_train_end: called at the end of model training. Example: ```python # Print the batch number at the beginning of every batch. batch_print_callback = LambdaCallback( on_batch_begin=lambda batch,logs: print(batch)) # Stream the epoch loss to a file in JSON format. The file content # is not well-formed JSON but rather has a JSON object per line. import json json_log = open('loss_log.json', mode='wt', buffering=1) json_logging_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: json_log.write( json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'), on_train_end=lambda logs: json_log.close() ) # Terminate some processes after having finished model training. processes = ... cleanup_callback = LambdaCallback( on_train_end=lambda logs: [ p.terminate() for p in processes if p.is_alive()]) model.fit(..., callbacks=[batch_print_callback, json_logging_callback, cleanup_callback]) ``` """ def __init__( self, on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None, on_train_begin=None, on_train_end=None, **kwargs, ): super().__init__() self.__dict__.update(kwargs) if on_epoch_begin is not None: self.on_epoch_begin = on_epoch_begin if on_epoch_end is not None: self.on_epoch_end = on_epoch_end if on_batch_begin is not None: self.on_batch_begin = on_batch_begin if on_batch_end is not None: self.on_batch_end = on_batch_end if on_train_begin is not None: self.on_train_begin = on_train_begin if on_train_end is not None: self.on_train_end = on_train_end