Add Callbacks hooks for evaluate and predict.

Adds Callback methods that can be used during validation, evaluation, and
prediction.

PiperOrigin-RevId: 225611013

tensorflow/python/keras/callbacks.py

@@ -45,13 +45,17 @@ from tensorflow.python.summary import summary as tf_summary
 from tensorflow.python.training import saver
 from tensorflow.python.util.tf_export import tf_export
 
-
 try:
   import requests
 except ImportError:
   requests = None
 
 
+_TRAIN = 'train'
+_TEST = 'test'
+_PREDICT = 'predict'
+
+
 # pylint: disable=protected-access
 def configure_callbacks(callbacks,
                         model,
@@ -62,7 +66,7 @@ def configure_callbacks(callbacks,
                         samples=None,
                         verbose=1,
                         count_mode='steps',
-                        mode='train'):
+                        mode=_TRAIN):
   """Configures callbacks for use in various training loops.
 
   Arguments:
@@ -89,7 +93,7 @@ def configure_callbacks(callbacks,
     callbacks = []
 
   # Add additional callbacks during training.
-  if mode == 'train':
+  if mode == _TRAIN:
     model.history = History()
     stateful_metric_names = None
     if hasattr(model, 'metrics_names'):
@@ -109,7 +113,7 @@ def configure_callbacks(callbacks,
   callback_metrics = []
   # When we have deferred build scenario with iterator input, we will compile
   # when we standardize first batch of data.
-  if mode != 'predict' and hasattr(model, 'metrics_names'):
+  if mode != _PREDICT and hasattr(model, 'metrics_names'):
     callback_metrics = copy.copy(model.metrics_names)
     if do_validation:
       callback_metrics += ['val_' + n for n in model.metrics_names]
@@ -142,6 +146,17 @@ def _is_generator_like(data):
       data, (Sequence, iterator_ops.Iterator, iterator_ops.EagerIterator)))
 
 
+def make_logs(model, logs, outputs, mode, prefix=''):
+  """Computes logs for sending to `on_batch_end` methods."""
+  if mode in {_TRAIN, _TEST}:
+    if hasattr(model, 'metrics_names'):
+      for label, output in zip(model.metrics_names, outputs):
+        logs[prefix + label] = output
+  else:
+    logs['outputs'] = outputs
+  return logs
+
+
 class CallbackList(object):
   """Container abstracting a list of callbacks.
 
@@ -179,10 +194,6 @@ class CallbackList(object):
 
   def _call_batch_hook(self, mode, hook, batch, logs=None):
     """Helper function for all batch_{begin | end} methods."""
-    # TODO(omalleyt): add batch hooks for test/predict.
-    if mode != 'train':
-      return
-
     hook_name = 'on_{mode}_batch_{hook}'.format(mode=mode, hook=hook)
     if hook == 'begin':
       self._t_enter_batch = time.time()
@@ -207,87 +218,175 @@ class CallbackList(object):
 
   def _call_begin_hook(self, mode):
     """Helper function for on_{train|test|predict}_begin methods."""
-    # TODO(omalleyt): add test/predict methods.
-    if mode == 'train':
+    if mode == _TRAIN:
       self.on_train_begin()
+    elif mode == _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."""
-    # TODO(omalleyt): add test/predict methods.
-    if mode == 'train':
+    if mode == _TRAIN:
       self.on_train_end()
+    elif mode == _TEST:
+      self.on_test_end()
+    else:
+      self.on_predict_end()
 
   def on_batch_begin(self, batch, logs=None):
-    self._call_batch_hook('train', 'begin', batch, logs=logs)
+    self._call_batch_hook(_TRAIN, 'begin', batch, logs=logs)
 
   def on_batch_end(self, batch, logs=None):
-    self._call_batch_hook('train', 'end', batch, logs=logs)
+    self._call_batch_hook(_TRAIN, 'end', batch, logs=logs)
 
   def on_epoch_begin(self, epoch, logs=None, mode='train'):
-    """Called at the start of an epoch.
+    """Calls the `on_epoch_begin` methods of its callbacks.
 
     Arguments:
         epoch: integer, index of epoch.
-        logs: dictionary of logs.
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
         mode: One of 'train'/'test'/'predict'
     """
-    if mode == 'train':
+    if mode == _TRAIN:
       logs = logs or {}
       for callback in self.callbacks:
         callback.on_epoch_begin(epoch, logs)
     self._reset_batch_timing()
 
   def on_epoch_end(self, epoch, logs=None, mode='train'):
-    """Called at the end of an epoch.
+    """Calls the `on_epoch_end` methods of its callbacks.
 
     Arguments:
         epoch: integer, index of epoch.
-        logs: dictionary of logs.
+        logs: dict, metric results for this training epoch, and for the
+          validation epoch if validation is performed. Validation result keys
+          are prefixed with `val_`.
         mode: One of 'train'/'test'/'predict'
     """
-    if mode == 'train':
+    if mode == _TRAIN:
       logs = logs or {}
       for callback in self.callbacks:
         callback.on_epoch_end(epoch, logs)
 
   def on_train_batch_begin(self, batch, logs=None):
-    """Called at the beginning of a training batch in `fit` methods.
+    """Calls the `on_train_batch_begin` methods of its callbacks.
 
     Arguments:
         batch: integer, index of batch within the current epoch.
-        logs: dictionary of logs.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
     """
-    self._call_batch_hook('train', 'begin', batch, logs=logs)
+    self._call_batch_hook(_TRAIN, 'begin', batch, logs=logs)
 
   def on_train_batch_end(self, batch, logs=None):
-    """Called at the end of a training batch in `fit` methods.
+    """Calls the `on_train_batch_end` methods of its callbacks.
 
     Arguments:
         batch: integer, index of batch within the current epoch.
-        logs: dictionary of logs.
+        logs: dict. Metric results for this batch.
     """
-    self._call_batch_hook('train', 'end', batch, logs=logs)
+    self._call_batch_hook(_TRAIN, 'end', batch, logs=logs)
 
-  def on_train_begin(self, logs=None):
-    """Called at the beginning of training.
+  def on_test_batch_begin(self, batch, logs=None):
+    """Calls the `on_test_batch_begin` methods of its callbacks.
 
     Arguments:
-        logs: dictionary of logs.
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
+    """
+    self._call_batch_hook(_TEST, 'begin', batch, logs=logs)
+
+  def on_test_batch_end(self, batch, logs=None):
+    """Calls the `on_test_batch_end` methods of its callbacks.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Metric results for this batch.
+    """
+    self._call_batch_hook(_TEST, 'end', batch, logs=logs)
+
+  def on_predict_batch_begin(self, batch, logs=None):
+    """Calls the `on_predict_batch_begin` methods of its callbacks.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
+    """
+    self._call_batch_hook(_PREDICT, 'begin', batch, logs=logs)
+
+  def on_predict_batch_end(self, batch, logs=None):
+    """Calls the `on_predict_batch_end` methods of its callbacks.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Metric results for this batch.
+    """
+    self._call_batch_hook(_PREDICT, 'end', batch, logs=logs)
+
+  def on_train_begin(self, logs=None):
+    """Calls the `on_train_begin` methods of its callbacks.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
     """
-    logs = logs or {}
     for callback in self.callbacks:
       callback.on_train_begin(logs)
 
   def on_train_end(self, logs=None):
-    """Called at the end of training.
+    """Calls the `on_train_end` methods of its callbacks.
 
     Arguments:
-        logs: dictionary of logs.
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
     """
-    logs = logs or {}
     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.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+    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.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+    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.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+    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.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+    for callback in self.callbacks:
+      callback.on_predict_end(logs)
+
   def __iter__(self):
     return iter(self.callbacks)
 
@@ -330,31 +429,169 @@ class Callback(object):
   def set_model(self, model):
     self.model = model
 
-  def on_epoch_begin(self, epoch, logs=None):
-    pass
-
-  def on_epoch_end(self, epoch, logs=None):
-    pass
-
   def on_batch_begin(self, batch, logs=None):
-    pass
+    """A backwards compatibility alias for `on_train_batch_begin`."""
 
   def on_batch_end(self, batch, logs=None):
-    pass
+    """A backwards compatibility alias for `on_train_batch_end`."""
+
+  def on_epoch_begin(self, epoch, logs=None, mode='train'):
+    """Called at the start of an epoch.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        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.
+        mode: One of 'train'/'test'/'predict'
+    """
+
+  def on_epoch_end(self, epoch, logs=None, mode='train'):
+    """Called at the end of an epoch.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        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_`.
+        mode: One of 'train'/'test'/'predict'
+    """
 
   def on_train_batch_begin(self, batch, logs=None):
-    # For backwards compatibility
+    """Called at the beginning of a training batch in `fit` methods.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
+    """
+    # For backwards compatibility.
     self.on_batch_begin(batch, logs=logs)
 
   def on_train_batch_end(self, batch, logs=None):
-    # For backwards compatibility
+    """Called at the end of a training batch in `fit` methods.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Metric results for this batch.
+    """
+    # For backwards compatibility.
     self.on_batch_end(batch, logs=logs)
 
+  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.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
+    """
+
+  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.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Metric results for this batch.
+    """
+
+  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.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Has keys `batch` and `size` representing the current batch
+          number and the size of the batch.
+    """
+
+  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.
+
+    Arguments:
+        batch: integer, index of batch within the current epoch.
+        logs: dict. Metric results for this batch.
+    """
+
   def on_train_begin(self, logs=None):
-    pass
+    """Called at the beginning of training.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
 
   def on_train_end(self, logs=None):
-    pass
+    """Called at the end of training.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+
+  def on_test_begin(self, logs=None):
+    """Called at the beginning of evaluation or validation.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+
+  def on_test_end(self, logs=None):
+    """Called at the end of evaluation or validation.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+
+  def on_predict_begin(self, logs=None):
+    """Called at the beginning of prediction.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
+
+  def on_predict_end(self, logs=None):
+    """Called at the end of prediction.
+
+    Subclasses should override for any actions to run.
+
+    Arguments:
+        logs: dict. Currently no data is passed to this argument for this method
+          but that may change in the future.
+    """
 
 
 @tf_export('keras.callbacks.BaseLogger')

tensorflow/python/keras/callbacks_test.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import csv
 import os
 import re
@@ -33,6 +34,7 @@ from tensorflow.python import keras
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import random_seed
 from tensorflow.python.framework import test_util
+from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging as logging
@@ -57,6 +59,142 @@ NUM_HIDDEN = 5
 BATCH_SIZE = 5
 
 
+class Counter(keras.callbacks.Callback):
+  """Counts the number of times each callback method was run.
+
+  Attributes:
+    method_counts: dict. Contains the counts of time  each callback method was
+      run.
+  """
+
+  def __init__(self):
+    self.method_counts = collections.defaultdict(int)
+    methods_to_count = [
+        'on_batch_begin', 'on_batch_end', 'on_epoch_begin', 'on_epoch_end',
+        'on_predict_batch_begin', 'on_predict_batch_end', 'on_predict_begin',
+        'on_predict_end', 'on_test_batch_begin', 'on_test_batch_end',
+        'on_test_begin', 'on_test_end', 'on_train_batch_begin',
+        'on_train_batch_end', 'on_train_begin', 'on_train_end'
+    ]
+    for method_name in methods_to_count:
+      setattr(self, method_name,
+              self.wrap_with_counts(method_name, getattr(self, method_name)))
+
+  def wrap_with_counts(self, method_name, method):
+
+    def _call_and_count(*args, **kwargs):
+      self.method_counts[method_name] += 1
+      return method(*args, **kwargs)
+
+    return _call_and_count
+
+
+@keras_parameterized.run_with_all_model_types
+@keras_parameterized.run_all_keras_modes
+class CallbackCountsTest(keras_parameterized.TestCase):
+
+  def _check_counts(self, counter, expected_counts):
+    """Checks that the counts registered by `counter` are those expected."""
+    for method_name, expected_count in expected_counts.items():
+      self.assertEqual(
+          counter.method_counts[method_name],
+          expected_count,
+          msg='For method {}: expected {}, got: {}'.format(
+              method_name, expected_count, counter.method_counts[method_name]))
+
+  def _get_model(self):
+    layers = [
+        keras.layers.Dense(10, activation='relu'),
+        keras.layers.Dense(1, activation='sigmoid')
+    ]
+    model = testing_utils.get_model_from_layers(layers, input_shape=(10,))
+    model.compile(
+        adam.AdamOptimizer(0.001),
+        'binary_crossentropy',
+        run_eagerly=testing_utils.should_run_eagerly())
+    return model
+
+  def test_callback_hooks_are_called_in_fit(self):
+    x, y = np.ones((10, 10)), np.ones((10, 1))
+    val_x, val_y = np.ones((4, 10)), np.ones((4, 1))
+
+    model = self._get_model()
+    counter = Counter()
+    model.fit(
+        x,
+        y,
+        validation_data=(val_x, val_y),
+        batch_size=2,
+        epochs=5,
+        callbacks=[counter])
+
+    self._check_counts(
+        counter, {
+            'on_batch_begin': 25,
+            'on_batch_end': 25,
+            'on_epoch_begin': 5,
+            'on_epoch_end': 5,
+            'on_predict_batch_begin': 0,
+            'on_predict_batch_end': 0,
+            'on_predict_begin': 0,
+            'on_predict_end': 0,
+            'on_test_batch_begin': 10,
+            'on_test_batch_end': 10,
+            'on_test_begin': 5,
+            'on_test_end': 5,
+            'on_train_batch_begin': 25,
+            'on_train_batch_end': 25,
+            'on_train_begin': 1,
+            'on_train_end': 1
+        })
+
+  def test_callback_hooks_are_called_in_evaluate(self):
+    x, y = np.ones((10, 10)), np.ones((10, 1))
+
+    model = self._get_model()
+    counter = Counter()
+    model.evaluate(x, y, batch_size=2, callbacks=[counter])
+    self._check_counts(
+        counter, {
+            'on_test_batch_begin': 5,
+            'on_test_batch_end': 5,
+            'on_test_begin': 1,
+            'on_test_end': 1
+        })
+
+  def test_callback_hooks_are_called_in_predict(self):
+    x = np.ones((10, 10))
+
+    model = self._get_model()
+    counter = Counter()
+    model.predict(x, batch_size=2, callbacks=[counter])
+    self._check_counts(
+        counter, {
+            'on_predict_batch_begin': 5,
+            'on_predict_batch_end': 5,
+            'on_predict_begin': 1,
+            'on_predict_end': 1
+        })
+
+  def test_callback_list_methods(self):
+    counter = Counter()
+    callback_list = keras.callbacks.CallbackList([counter])
+
+    batch = 0
+    callback_list.on_test_batch_begin(batch)
+    callback_list.on_test_batch_end(batch)
+    callback_list.on_predict_batch_begin(batch)
+    callback_list.on_predict_batch_end(batch)
+
+    self._check_counts(
+        counter, {
+            'on_test_batch_begin': 1,
+            'on_test_batch_end': 1,
+            'on_predict_batch_begin': 1,
+            'on_predict_batch_end': 1
+        })
+
+
 class KerasCallbacksTest(test.TestCase):
 
   def test_ModelCheckpoint(self):

tensorflow/python/keras/engine/training.py

@@ -893,6 +893,7 @@ class Model(Network):
                verbose=1,
                sample_weight=None,
                steps=None,
+               callbacks=None,
                max_queue_size=10,
                workers=1,
                use_multiprocessing=False):
@@ -943,6 +944,9 @@ class Model(Network):
             Total number of steps (batches of samples)
             before declaring the evaluation round finished.
             Ignored with the default value of `None`.
+        callbacks: List of `keras.callbacks.Callback` instances.
+            List of callbacks to apply during evaluation.
+            See [callbacks](/api_docs/python/tf/keras/callbacks).
         max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
             input only. Maximum size for the generator queue.
             If unspecified, `max_queue_size` will default to 10.
@@ -1002,7 +1006,8 @@ class Model(Network):
           steps=steps,
           batch_size=batch_size,
           verbose=verbose,
-          workers=0)
+          workers=0,
+          callbacks=callbacks)
     elif distributed_training_utils.is_tpu_strategy(
         self._distribution_strategy):
       return training_distributed.experimental_test_loop(
@@ -1015,13 +1020,15 @@ class Model(Network):
           sample_weights=sample_weights,
           batch_size=batch_size,
           verbose=verbose,
-          steps=steps)
+          steps=steps,
+          callbacks=callbacks)
 
   def predict(self,
               x,
               batch_size=None,
               verbose=0,
               steps=None,
+              callbacks=None,
               max_queue_size=10,
               workers=1,
               use_multiprocessing=False):
@@ -1048,6 +1055,9 @@ class Model(Network):
         steps: Total number of steps (batches of samples)
             before declaring the prediction round finished.
             Ignored with the default value of `None`.
+        callbacks: List of `keras.callbacks.Callback` instances.
+            List of callbacks to apply during prediction.
+            See [callbacks](/api_docs/python/tf/keras/callbacks).
         max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
             input only. Maximum size for the generator queue.
             If unspecified, `max_queue_size` will default to 10.
@@ -1110,14 +1120,20 @@ class Model(Network):
           steps=steps,
           batch_size=batch_size,
           verbose=verbose,
-          workers=0)
+          workers=0,
+          callbacks=callbacks)
     elif distributed_training_utils.is_tpu_strategy(
         self._distribution_strategy):
       return training_distributed.experimental_predict_loop(
           self, x, verbose=verbose, steps=steps)
     else:
       return training_arrays.predict_loop(
-          self, x, batch_size=batch_size, verbose=verbose, steps=steps)
+          self,
+          x,
+          batch_size=batch_size,
+          verbose=verbose,
+          steps=steps,
+          callbacks=callbacks)
 
   def reset_metrics(self):
     """Resets the state of metrics."""
@@ -1440,6 +1456,7 @@ class Model(Network):
   def evaluate_generator(self,
                          generator,
                          steps=None,
+                         callbacks=None,
                          max_queue_size=10,
                          workers=1,
                          use_multiprocessing=False,
@@ -1459,6 +1476,9 @@ class Model(Network):
             to yield from `generator` before stopping.
             Optional for `Sequence`: if unspecified, will use
             the `len(generator)` as a number of steps.
+        callbacks: List of `keras.callbacks.Callback` instances.
+            List of callbacks to apply during evaluation.
+            See [callbacks](/api_docs/python/tf/keras/callbacks).
         max_queue_size: maximum size for the generator queue
         workers: Integer. Maximum number of processes to spin up
             when using process-based threading.
@@ -1494,11 +1514,13 @@ class Model(Network):
         max_queue_size=max_queue_size,
         workers=workers,
         use_multiprocessing=use_multiprocessing,
-        verbose=verbose)
+        verbose=verbose,
+        callbacks=callbacks)
 
   def predict_generator(self,
                         generator,
                         steps=None,
+                        callbacks=None,
                         max_queue_size=10,
                         workers=1,
                         use_multiprocessing=False,
@@ -1516,6 +1538,9 @@ class Model(Network):
             to yield from `generator` before stopping.
             Optional for `Sequence`: if unspecified, will use
             the `len(generator)` as a number of steps.
+        callbacks: List of `keras.callbacks.Callback` instances.
+            List of callbacks to apply during prediction.
+            See [callbacks](/api_docs/python/tf/keras/callbacks).
         max_queue_size: Maximum size for the generator queue.
         workers: Integer. Maximum number of processes to spin up
             when using process-based threading.
@@ -1545,7 +1570,8 @@ class Model(Network):
         max_queue_size=max_queue_size,
         workers=workers,
         use_multiprocessing=use_multiprocessing,
-        verbose=verbose)
+        verbose=verbose,
+        callbacks=callbacks)
 
   def _get_callback_model(self):
     """Returns the Callback Model for this Model."""

tensorflow/python/keras/engine/training_arrays.py

@@ -285,7 +285,7 @@ def model_iteration(model,
         aggregator.aggregate(batch_outs)
 
         # Callbacks batch end.
-        batch_logs.update(training_utils.make_logs(model, batch_outs, mode))
+        batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
         callbacks._call_batch_hook(mode, 'end', step, batch_logs)
         progbar.on_batch_end(step, batch_logs)
 
@@ -336,7 +336,7 @@ def model_iteration(model,
         aggregator.aggregate(batch_outs, batch_start, batch_end)
 
         # Callbacks batch end.
-        batch_logs.update(training_utils.make_logs(model, batch_outs, mode))
+        batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
         callbacks._call_batch_hook(mode, 'end', batch_index, batch_logs)
         progbar.on_batch_end(batch_index, batch_logs)
 
@@ -345,7 +345,7 @@ def model_iteration(model,
 
     aggregator.finalize()
     results = aggregator.results
-    epoch_logs.update(training_utils.make_logs(model, results, mode))
+    epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
     if len(results) == 1:
       results = results[0]
 
@@ -364,11 +364,14 @@ def model_iteration(model,
           validation_in_fit=True)
       if not isinstance(val_results, list):
         val_results = [val_results]
-      epoch_logs.update(
-          training_utils.make_logs(model, val_results, mode, prefix='val_'))
+      epoch_logs = cbks.make_logs(
+          model, epoch_logs, val_results, mode, prefix='val_')
+
+    if mode == 'train':
+      # Epochs only apply to `fit`.
+      callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
+      progbar.on_epoch_end(epoch, epoch_logs)
 
-    callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
-    progbar.on_epoch_end(epoch, epoch_logs)
   callbacks._call_end_hook(mode)
 
   if model._distribution_strategy:

tensorflow/python/keras/engine/training_generator.py

@@ -198,7 +198,7 @@ def model_iteration(model,
       aggregator.aggregate(batch_outs)
 
       # Callbacks batch end.
-      batch_logs.update(training_utils.make_logs(model, batch_outs, mode))
+      batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
       callbacks._call_batch_hook(mode, 'end', step, batch_logs)
       progbar.on_batch_end(step, batch_logs)
 
@@ -207,7 +207,7 @@ def model_iteration(model,
 
     aggregator.finalize()
     results = aggregator.results
-    epoch_logs.update(training_utils.make_logs(model, results, mode))
+    epoch_logs = cbks.make_logs(model, epoch_logs, results, mode)
     if len(results) == 1:
       results = results[0]
 
@@ -222,15 +222,20 @@ def model_iteration(model,
           workers=workers,
           use_multiprocessing=use_multiprocessing,
           max_queue_size=max_queue_size,
+          callbacks=callbacks,
+          verbose=0,
           mode='test')
 
       if not isinstance(val_results, list):
         val_results = [val_results]
-      epoch_logs.update(
-          training_utils.make_logs(model, val_results, mode, prefix='val_'))
+      epoch_logs = cbks.make_logs(
+          model, epoch_logs, val_results, mode, prefix='val_')
+
+    if mode == 'train':
+      # Epochs only apply to `fit`.
+      callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
+      progbar.on_epoch_end(epoch, epoch_logs)
 
-    callbacks.on_epoch_end(epoch, epoch_logs, mode=mode)
-    progbar.on_epoch_end(epoch, epoch_logs)
   callbacks._call_end_hook(mode)
 
   if enqueuer is not None:

tensorflow/python/keras/engine/training_utils.py

@@ -136,18 +136,6 @@ class OutputsAggregator(Aggregator):
       self.results = [np.concatenate(result, axis=0) for result in self.results]
 
 
-def make_logs(model, outputs, mode, prefix=''):
-  """Computes logs for sending to `on_batch_end` methods."""
-  logs = {}
-  # TODO(omalleyt): handle outputs in prediction when Callback
-  # hooks are ready.
-  if mode in ['train', 'test']:
-    if hasattr(model, 'metrics_names'):
-      for label, output in zip(model.metrics_names, outputs):
-        logs[prefix + label] = output
-  return logs
-
-
 def get_progbar(model, count_mode):
   """Get Progbar."""
   stateful_metric_names = None

tensorflow/tools/api/golden/v1/tensorflow.keras.-model.pbtxt

@@ -167,11 +167,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -235,11 +235,11 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v1/tensorflow.keras.-sequential.pbtxt

@@ -172,11 +172,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -244,7 +244,7 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_classes"
@@ -252,7 +252,7 @@ tf_class {
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-base-logger.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-c-s-v-logger.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-callback.pbtxt

@@ -16,11 +16,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-early-stopping.pbtxt

@@ -21,12 +21,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-history.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-lambda-callback.pbtxt

@@ -17,11 +17,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-model-checkpoint.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-progbar-logger.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt

@@ -21,12 +21,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-remote-monitor.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-tensor-board.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt

@@ -17,11 +17,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v1/tensorflow.keras.models.-model.pbtxt

@@ -167,11 +167,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -235,11 +235,11 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v1/tensorflow.keras.models.-sequential.pbtxt

@@ -172,11 +172,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -244,7 +244,7 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_classes"
@@ -252,7 +252,7 @@ tf_class {
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v2/tensorflow.keras.-model.pbtxt

@@ -167,11 +167,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -235,11 +235,11 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v2/tensorflow.keras.-sequential.pbtxt

@@ -172,11 +172,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -244,7 +244,7 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_classes"
@@ -252,7 +252,7 @@ tf_class {
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-base-logger.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-c-s-v-logger.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-callback.pbtxt

@@ -16,11 +16,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-early-stopping.pbtxt

@@ -21,12 +21,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-history.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-lambda-callback.pbtxt

@@ -17,11 +17,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-learning-rate-scheduler.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-model-checkpoint.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-progbar-logger.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-reduce-l-r-on-plateau.pbtxt

@@ -21,12 +21,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-remote-monitor.pbtxt

@@ -17,12 +17,44 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-tensor-board.pbtxt

@@ -23,6 +23,38 @@ tf_class {
     name: "on_epoch_end"
     argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
   member_method {
     name: "on_train_batch_begin"
     argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-terminate-on-na-n.pbtxt

@@ -17,11 +17,43 @@ tf_class {
   }
   member_method {
     name: "on_epoch_begin"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
   }
   member_method {
     name: "on_epoch_end"
-    argspec: "args=[\'self\', \'epoch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'epoch\', \'logs\', \'mode\'], varargs=None, keywords=None, defaults=[\'None\', \'train\'], "
+  }
+  member_method {
+    name: "on_predict_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_predict_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_begin"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_batch_end"
+    argspec: "args=[\'self\', \'batch\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_begin"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "on_test_end"
+    argspec: "args=[\'self\', \'logs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "on_train_batch_begin"

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-linear-model.pbtxt

@@ -172,11 +172,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -240,11 +240,11 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v2/tensorflow.keras.models.-model.pbtxt

@@ -167,11 +167,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -235,11 +235,11 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"

tensorflow/tools/api/golden/v2/tensorflow.keras.models.-sequential.pbtxt

@@ -172,11 +172,11 @@ tf_class {
   }
   member_method {
     name: "evaluate"
-    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'y\', \'batch_size\', \'verbose\', \'sample_weight\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'1\', \'None\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "evaluate_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "fit"
@@ -244,7 +244,7 @@ tf_class {
   }
   member_method {
     name: "predict"
-    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'10\', \'1\', \'False\'], "
+    argspec: "args=[\'self\', \'x\', \'batch_size\', \'verbose\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\'], varargs=None, keywords=None, defaults=[\'None\', \'0\', \'None\', \'None\', \'10\', \'1\', \'False\'], "
   }
   member_method {
     name: "predict_classes"
@@ -252,7 +252,7 @@ tf_class {
   }
   member_method {
     name: "predict_generator"
-    argspec: "args=[\'self\', \'generator\', \'steps\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'10\', \'1\', \'False\', \'0\'], "
+    argspec: "args=[\'self\', \'generator\', \'steps\', \'callbacks\', \'max_queue_size\', \'workers\', \'use_multiprocessing\', \'verbose\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'10\', \'1\', \'False\', \'0\'], "
   }
   member_method {
     name: "predict_on_batch"