From 8a370a00777a24f0be203ca079433da7c36bfb56 Mon Sep 17 00:00:00 2001
From: Francois Chollet <fchollet@google.com>
Date: Thu, 2 Apr 2020 22:14:06 -0700
Subject: [PATCH] Docstring fixes in Keras datasets, callbacks, and utilities

PiperOrigin-RevId: 304544666
Change-Id: I6049cf11ff6fe8090dfadd36cfcebe4b2443bf5f
---
 tensorflow/python/keras/callbacks.py          | 234 +++++++++---------
 .../python/keras/datasets/boston_housing.py   |   9 +-
 tensorflow/python/keras/datasets/cifar10.py   |   6 +-
 tensorflow/python/keras/datasets/cifar100.py  |   6 +-
 tensorflow/python/keras/datasets/imdb.py      |   2 +-
 tensorflow/python/keras/datasets/mnist.py     |   4 +-
 tensorflow/python/keras/datasets/reuters.py   |   8 +-
 tensorflow/python/keras/utils/data_utils.py   |  63 +++--
 .../python/keras/utils/generic_utils.py       |   3 +-
 tensorflow/python/keras/utils/np_utils.py     |  38 +--
 10 files changed, 182 insertions(+), 191 deletions(-)

diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py
index 307bc382d3a..809c527fb84 100644
--- a/tensorflow/python/keras/callbacks.py
+++ b/tensorflow/python/keras/callbacks.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 # pylint: disable=g-import-not-at-top
+# pylint: disable=g-classes-have-attributes
 """Callbacks: utilities called at certain points during model training.
 """
 from __future__ import absolute_import
@@ -336,8 +337,8 @@ class CallbackList(object):
     This function should only be called during TRAIN mode.
 
     Arguments:
-        epoch: integer, index of epoch.
-        logs: dict. Currently no data is passed to this argument for this method
+        epoch: Integer, index of epoch.
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -357,8 +358,8 @@ class CallbackList(object):
     This function should only be called during TRAIN mode.
 
     Arguments:
-        epoch: integer, index of epoch.
-        logs: dict, metric results for this training epoch, and for the
+        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_`.
     """
@@ -376,8 +377,8 @@ class CallbackList(object):
     """Calls the `on_train_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
+        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.
     """
     # TODO(b/150629188): Make ProgBarLogger callback not use batch hooks
@@ -389,8 +390,8 @@ class CallbackList(object):
     """Calls the `on_train_batch_end` methods of its callbacks.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
     if self._should_call_train_batch_hooks:
       self._call_batch_hook(ModeKeys.TRAIN, 'end', batch, logs=logs)
@@ -399,8 +400,8 @@ class CallbackList(object):
     """Calls the `on_test_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
+        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.
     """
     if self._should_call_test_batch_hooks:
@@ -410,8 +411,8 @@ class CallbackList(object):
     """Calls the `on_test_batch_end` methods of its callbacks.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
     if self._should_call_test_batch_hooks:
       self._call_batch_hook(ModeKeys.TEST, 'end', batch, logs=logs)
@@ -420,8 +421,8 @@ class CallbackList(object):
     """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
+        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.
     """
     if self._should_call_predict_batch_hooks:
@@ -431,8 +432,8 @@ class CallbackList(object):
     """Calls the `on_predict_batch_end` methods of its callbacks.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
     if self._should_call_predict_batch_hooks:
       self._call_batch_hook(ModeKeys.PREDICT, 'end', batch, logs=logs)
@@ -441,7 +442,7 @@ class CallbackList(object):
     """Calls the `on_train_begin` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -458,7 +459,7 @@ class CallbackList(object):
     """Calls the `on_train_end` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -475,7 +476,7 @@ class CallbackList(object):
     """Calls the `on_test_begin` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -492,7 +493,7 @@ class CallbackList(object):
     """Calls the `on_test_end` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -509,7 +510,7 @@ class CallbackList(object):
     """Calls the 'on_predict_begin` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -526,7 +527,7 @@ class CallbackList(object):
     """Calls the `on_predict_end` methods of its callbacks.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
     logs = logs or {}
@@ -548,9 +549,9 @@ class Callback(object):
   """Abstract base class used to build new callbacks.
 
   Attributes:
-      params: dict. Training parameters
+      params: Dict. Training parameters
           (eg. verbosity, batch size, number of epochs...).
-      model: instance of `keras.models.Model`.
+      model: Instance of `keras.models.Model`.
           Reference of the model being trained.
 
   The `logs` dictionary that callback methods
@@ -591,8 +592,8 @@ class Callback(object):
     be called during TRAIN mode.
 
     Arguments:
-        epoch: integer, index of epoch.
-        logs: dict. Currently no data is passed to this argument for this method
+        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.
     """
 
@@ -604,8 +605,8 @@ class Callback(object):
     be called during TRAIN mode.
 
     Arguments:
-        epoch: integer, index of epoch.
-        logs: dict, metric results for this training epoch, and for the
+        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_`.
     """
@@ -618,8 +619,8 @@ class Callback(object):
     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
+        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.
@@ -633,8 +634,8 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
     # For backwards compatibility.
     self.on_batch_end(batch, logs=logs)
@@ -650,8 +651,8 @@ class Callback(object):
     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
+        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.
     """
 
@@ -666,8 +667,8 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
 
   @doc_controls.for_subclass_implementers
@@ -678,8 +679,8 @@ class Callback(object):
     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
+        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.
     """
 
@@ -691,8 +692,8 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        batch: integer, index of batch within the current epoch.
-        logs: dict. Aggregated metric results up until this batch.
+        batch: Integer, index of batch within the current epoch.
+        logs: Dict. Aggregated metric results up until this batch.
     """
 
   @doc_controls.for_subclass_implementers
@@ -702,7 +703,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -713,7 +714,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -724,7 +725,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -735,7 +736,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -746,7 +747,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -757,7 +758,7 @@ class Callback(object):
     Subclasses should override for any actions to run.
 
     Arguments:
-        logs: dict. Currently no data is passed to this argument for this method
+        logs: Dict. Currently no data is passed to this argument for this method
           but that may change in the future.
     """
 
@@ -847,7 +848,7 @@ class ProgbarLogger(Callback):
   """Callback that prints metrics to stdout.
 
   Arguments:
-      count_mode: One of "steps" or "samples".
+      count_mode: One of `"steps"` or `"samples"`.
           Whether the progress bar should
           count samples seen or steps (batches) seen.
       stateful_metrics: Iterable of string names of metrics that
@@ -1411,7 +1412,7 @@ class EarlyStopping(Callback):
   """Stop training when a monitored metric has stopped improving.
 
   Assuming the goal of a training is to minimize the loss. With this, the
-  metric to be monitored would be 'loss', and mode would be 'min'. A
+  metric to be monitored would be `'loss'`, and mode would be `'min'`. A
   `model.fit()` training loop will check at end of every epoch whether
   the loss is no longer decreasing, considering the `min_delta` and
   `patience` if applicable. Once it's found no longer decreasing,
@@ -1420,6 +1421,30 @@ class EarlyStopping(Callback):
   The quantity to be monitored needs to be available in `logs` dict.
   To make it so, pass the loss or metrics at `model.compile()`.
 
+  Arguments:
+    monitor: Quantity to be monitored.
+    min_delta: Minimum change in the monitored quantity
+        to qualify as an improvement, i.e. an absolute
+        change of less than min_delta, will count as no
+        improvement.
+    patience: Number of epochs with no improvement
+        after which training will be stopped.
+    verbose: verbosity mode.
+    mode: One of `{"auto", "min", "max"}`. In `min` mode,
+        training will stop when the quantity
+        monitored has stopped decreasing; in `"max"`
+        mode it will stop when the quantity
+        monitored has stopped increasing; in `"auto"`
+        mode, the direction is automatically inferred
+        from the name of the monitored quantity.
+    baseline: Baseline value for the monitored quantity.
+        Training will stop if the model doesn't show improvement over the
+        baseline.
+    restore_best_weights: Whether to restore model weights from
+        the epoch with the best value of the monitored quantity.
+        If False, the model weights obtained at the last step of
+        training are used.
+
   Example:
 
   >>> callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
@@ -1442,32 +1467,6 @@ class EarlyStopping(Callback):
                mode='auto',
                baseline=None,
                restore_best_weights=False):
-    """Initialize an EarlyStopping callback.
-
-    Arguments:
-        monitor: Quantity to be monitored.
-        min_delta: Minimum change in the monitored quantity
-            to qualify as an improvement, i.e. an absolute
-            change of less than min_delta, will count as no
-            improvement.
-        patience: Number of epochs with no improvement
-            after which training will be stopped.
-        verbose: verbosity mode.
-        mode: One of `{"auto", "min", "max"}`. In `min` mode,
-            training will stop when the quantity
-            monitored has stopped decreasing; in `max`
-            mode it will stop when the quantity
-            monitored has stopped increasing; in `auto`
-            mode, the direction is automatically inferred
-            from the name of the monitored quantity.
-        baseline: Baseline value for the monitored quantity.
-            Training will stop if the model doesn't show improvement over the
-            baseline.
-        restore_best_weights: Whether to restore model weights from
-            the epoch with the best value of the monitored quantity.
-            If False, the model weights obtained at the last step of
-            training are used.
-    """
     super(EarlyStopping, self).__init__()
 
     self.monitor = monitor
@@ -1550,18 +1549,19 @@ class RemoteMonitor(Callback):
   Events are sent to `root + '/publish/epoch/end/'` by default. Calls are
   HTTP POST, with a `data` argument which is a
   JSON-encoded dictionary of event data.
-  If send_as_json is set to True, the content type of the request will be
-  application/json. Otherwise the serialized JSON will be sent within a form.
+  If `send_as_json=True`, the content type of the request will be
+  `"application/json"`.
+  Otherwise the serialized JSON will be sent within a form.
 
   Arguments:
-      root: String; root url of the target server.
-      path: String; path relative to `root` to which the events will be sent.
-      field: String; JSON field under which the data will be stored.
-          The field is used only if the payload is sent within a form
-          (i.e. send_as_json is set to False).
-      headers: Dictionary; optional custom HTTP headers.
-      send_as_json: Boolean; whether the request should be
-          sent as application/json.
+    root: String; root url of the target server.
+    path: String; path relative to `root` to which the events will be sent.
+    field: String; JSON field under which the data will be stored.
+        The field is used only if the payload is sent within a form
+        (i.e. send_as_json is set to False).
+    headers: Dictionary; optional custom HTTP headers.
+    send_as_json: Boolean; whether the request should be
+        sent as `"application/json"`.
   """
 
   def __init__(self,
@@ -1613,6 +1613,12 @@ class LearningRateScheduler(Callback):
   and current learning rate, and applies the updated learning rate
   on the optimizer.
 
+  Arguments:
+    schedule: a function that takes an epoch index (integer, indexed from 0)
+        and current learning rate (float) as inputs and returns a new
+        learning rate as output (float).
+    verbose: int. 0: quiet, 1: update messages.
+
   Example:
 
   >>> # This function keeps the initial learning rate for the first ten epochs
@@ -1637,14 +1643,6 @@ class LearningRateScheduler(Callback):
   """
 
   def __init__(self, schedule, verbose=0):
-    """Initialize a `keras.callbacks.LearningRateScheduler` callback.
-
-    Arguments:
-      schedule: a function that takes an epoch index (integer, indexed from 0)
-          and current learning rate (float) as inputs and returns a new
-          learning rate as output (float).
-      verbose: int. 0: quiet, 1: update messages.
-    """
     super(LearningRateScheduler, self).__init__()
     self.schedule = schedule
     self.verbose = verbose
@@ -1689,7 +1687,7 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
   If you have installed TensorFlow with pip, you should be able
   to launch TensorBoard from the command line:
 
-  ```sh
+  ```
   tensorboard --logdir=path_to_your_logs
   ```
 
@@ -1697,24 +1695,27 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
   [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard).
 
   Example (Basic):
+
   ```python
   tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
   model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
   # run the tensorboard command to view the visualizations.
   ```
+
   Example (Profile):
+
   ```python
   # profile a single batch, e.g. the 5th batch.
-  tensorboard_callback =
-      tf.keras.callbacks.TensorBoard(log_dir='./logs', profile_batch=5)
+  tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs',
+                                                        profile_batch=5)
   model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
-  # run the tensorboard command to view the visualizations in profile plugin.
+  # Now run the tensorboard command to view the visualizations (profile plugin).
 
   # profile a range of batches, e.g. from 10 to 20.
-  tensorboard_callback =
-      tf.keras.callbacks.TensorBoard(log_dir='./logs', profile_batch='10,20')
+  tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir='./logs',
+                                                        profile_batch='10,20')
   model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
-  # run the tensorboard command to view the visualizations in profile plugin.
+  # Now run the tensorboard command to view the visualizations (profile plugin).
   ```
 
   Arguments:
@@ -2143,14 +2144,15 @@ class ReduceLROnPlateau(Callback):
 
   Arguments:
       monitor: quantity to be monitored.
-      factor: factor by which the learning rate will be reduced. new_lr = lr *
-        factor
+      factor: factor by which the learning rate will be reduced.
+        `new_lr = lr * factor`.
       patience: number of epochs with no improvement after which learning rate
         will be reduced.
       verbose: int. 0: quiet, 1: update messages.
-      mode: one of {auto, min, max}. In `min` mode, lr will be reduced when the
-        quantity monitored has stopped decreasing; in `max` mode it will be
-        reduced when the quantity monitored has stopped increasing; in `auto`
+      mode: one of `{'auto', 'min', 'max'}`. In `'min'` mode,
+        the learning rate will be reduced when the
+        quantity monitored has stopped decreasing; in `'max'` mode it will be
+        reduced when the quantity monitored has stopped increasing; in `'auto'`
         mode, the direction is automatically inferred from the name of the
         monitored quantity.
       min_delta: threshold for measuring the new optimum, to only focus on
@@ -2249,10 +2251,10 @@ class ReduceLROnPlateau(Callback):
 
 @keras_export('keras.callbacks.CSVLogger')
 class CSVLogger(Callback):
-  """Callback that streams epoch results to a csv file.
+  """Callback that streams epoch results to a CSV file.
 
   Supports all values that can be represented as a string,
-  including 1D iterables such as np.ndarray.
+  including 1D iterables such as `np.ndarray`.
 
   Example:
 
@@ -2262,10 +2264,10 @@ class CSVLogger(Callback):
   ```
 
   Arguments:
-      filename: filename of the csv file, e.g. 'run/log.csv'.
-      separator: string used to separate elements in the csv file.
-      append: True: append if file exists (useful for continuing
-          training). False: overwrite existing file,
+      filename: Filename of the CSV file, e.g. `'run/log.csv'`.
+      separator: String used to separate elements in the CSV file.
+      append: Boolean. True: append if file exists (useful for continuing
+          training). False: overwrite existing file.
   """
 
   def __init__(self, filename, separator=',', append=False):
@@ -2348,12 +2350,12 @@ class LambdaCallback(Callback):
   at the appropriate time. Note that the callbacks expects positional
   arguments, as:
 
-   - `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
-      `epoch`, `logs`
-   - `on_batch_begin` and `on_batch_end` expect two positional arguments:
-      `batch`, `logs`
-   - `on_train_begin` and `on_train_end` expect one positional argument:
-      `logs`
+  - `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
+    `epoch`, `logs`
+  - `on_batch_begin` and `on_batch_end` expect two positional arguments:
+    `batch`, `logs`
+  - `on_train_begin` and `on_train_end` expect one positional argument:
+    `logs`
 
   Arguments:
       on_epoch_begin: called at the beginning of every epoch.
diff --git a/tensorflow/python/keras/datasets/boston_housing.py b/tensorflow/python/keras/datasets/boston_housing.py
index f3900cc075a..2c0badfefba 100644
--- a/tensorflow/python/keras/datasets/boston_housing.py
+++ b/tensorflow/python/keras/datasets/boston_housing.py
@@ -40,7 +40,7 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
 
   Arguments:
       path: path where to cache the dataset locally
-          (relative to ~/.keras/datasets).
+          (relative to `~/.keras/datasets`).
       test_split: fraction of the data to reserve as test set.
       seed: Random seed for shuffling the data
           before computing the test split.
@@ -48,10 +48,11 @@ def load_data(path='boston_housing.npz', test_split=0.2, seed=113):
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
-      **x_train, x_test**: numpy arrays with shape (num_samples, 13) containing
-        either the training samples (for x_train), or test samples (for y_train)
+      **x_train, x_test**: numpy arrays with shape `(num_samples, 13)`
+        containing either the training samples (for x_train),
+        or test samples (for y_train).
 
-      **y_train, y_test**: numpy arrays of shape (num_samples, ) containing the
+      **y_train, y_test**: numpy arrays of shape `(num_samples,)` containing the
         target scalars. The targets are float scalars typically between 10 and
         50 that represent the home prices in k$.
   """
diff --git a/tensorflow/python/keras/datasets/cifar10.py b/tensorflow/python/keras/datasets/cifar10.py
index 60afd2c5b78..7b74feb4726 100644
--- a/tensorflow/python/keras/datasets/cifar10.py
+++ b/tensorflow/python/keras/datasets/cifar10.py
@@ -40,9 +40,9 @@ def load_data():
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
       **x_train, x_test**: uint8 arrays of RGB image data with shape
-        (num_samples, 3, 32, 32) if the `tf.keras.backend.image_data_format` is
-        'channels_first', or (num_samples, 32, 32, 3) if the data format
-        is 'channels_last'.
+        `(num_samples, 3, 32, 32)` if `tf.keras.backend.image_data_format()` is
+        `'channels_first'`, or `(num_samples, 32, 32, 3)` if the data format
+        is `'channels_last'`.
 
       **y_train, y_test**: uint8 arrays of category labels
         (integers in range 0-9) each with shape (num_samples, 1).
diff --git a/tensorflow/python/keras/datasets/cifar100.py b/tensorflow/python/keras/datasets/cifar100.py
index 0c835b40d5d..5596f6ebb9b 100644
--- a/tensorflow/python/keras/datasets/cifar100.py
+++ b/tensorflow/python/keras/datasets/cifar100.py
@@ -46,9 +46,9 @@ def load_data(label_mode='fine'):
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
 
       **x_train, x_test**: uint8 arrays of RGB image data with shape
-        (num_samples, 3, 32, 32) if the `tf.keras.backend.image_data_format` is
-        'channels_first', or (num_samples, 32, 32, 3) if the data format
-        is 'channels_last'.
+        `(num_samples, 3, 32, 32)` if `tf.keras.backend.image_data_format()` is
+        `'channels_first'`, or `(num_samples, 32, 32, 3)` if the data format
+        is `'channels_last'`.
 
       **y_train, y_test**: uint8 arrays of category labels with shape
         (num_samples, 1).
diff --git a/tensorflow/python/keras/datasets/imdb.py b/tensorflow/python/keras/datasets/imdb.py
index d6f7cf6ae3d..61fbf92eaef 100644
--- a/tensorflow/python/keras/datasets/imdb.py
+++ b/tensorflow/python/keras/datasets/imdb.py
@@ -80,7 +80,7 @@ def load_data(path='imdb.npz',
 
       **x_train, x_test**: lists of sequences, which are lists of indexes
         (integers). If the num_words argument was specific, the maximum
-        possible index value is num_words-1. If the `maxlen` argument was
+        possible index value is `num_words - 1`. If the `maxlen` argument was
         specified, the largest possible sequence length is `maxlen`.
 
       **y_train, y_test**: lists of integer labels (1 or 0).
diff --git a/tensorflow/python/keras/datasets/mnist.py b/tensorflow/python/keras/datasets/mnist.py
index 1d41de197b3..f371ad4ece5 100644
--- a/tensorflow/python/keras/datasets/mnist.py
+++ b/tensorflow/python/keras/datasets/mnist.py
@@ -31,12 +31,12 @@ def load_data(path='mnist.npz'):
   This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
   along with a test set of 10,000 images.
   More info can be found at the
-  (MNIST homepage)[http://yann.lecun.com/exdb/mnist/].
+  [MNIST homepage](http://yann.lecun.com/exdb/mnist/).
 
 
   Arguments:
       path: path where to cache the dataset locally
-          (relative to ~/.keras/datasets).
+          (relative to `~/.keras/datasets`).
 
   Returns:
       Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
diff --git a/tensorflow/python/keras/datasets/reuters.py b/tensorflow/python/keras/datasets/reuters.py
index ceec8d3bfd5..46ac9249637 100644
--- a/tensorflow/python/keras/datasets/reuters.py
+++ b/tensorflow/python/keras/datasets/reuters.py
@@ -42,11 +42,11 @@ def load_data(path='reuters.npz',
   """Loads the Reuters newswire classification dataset.
 
   This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics.
+
   This was originally generated by parsing and preprocessing the classic
   Reuters-21578 dataset, but the preprocessing code is no longer packaged
-  with Keras.
-
-  See this [github discussion](https://github.com/keras-team/keras/issues/12072)
+  with Keras. See this
+  [github discussion](https://github.com/keras-team/keras/issues/12072)
   for more info.
 
   Each newswire is encoded as a list of word indexes (integers).
@@ -91,7 +91,7 @@ def load_data(path='reuters.npz',
 
       **x_train, x_test**: lists of sequences, which are lists of indexes
         (integers). If the num_words argument was specific, the maximum
-        possible index value is num_words-1. If the `maxlen` argument was
+        possible index value is `num_words - 1`. If the `maxlen` argument was
         specified, the largest possible sequence length is `maxlen`.
 
       **y_train, y_test**: lists of integer labels (1 or 0).
diff --git a/tensorflow/python/keras/utils/data_utils.py b/tensorflow/python/keras/utils/data_utils.py
index 179d9b6020c..00df6b59739 100644
--- a/tensorflow/python/keras/utils/data_utils.py
+++ b/tensorflow/python/keras/utils/data_utils.py
@@ -195,9 +195,9 @@ def get_file(fname,
       fname: Name of the file. If an absolute path `/path/to/file.txt` is
           specified the file will be saved at that location.
       origin: Original URL of the file.
-      untar: Deprecated in favor of 'extract'.
+      untar: Deprecated in favor of `extract` argument.
           boolean, whether the file should be decompressed
-      md5_hash: Deprecated in favor of 'file_hash'.
+      md5_hash: Deprecated in favor of `file_hash` argument.
           md5 hash of the file for verification
       file_hash: The expected hash string of the file after download.
           The sha256 and md5 hash algorithms are both supported.
@@ -205,17 +205,16 @@ def get_file(fname,
           saved. If an absolute path `/path/to/folder` is
           specified the file will be saved at that location.
       hash_algorithm: Select the hash algorithm to verify the file.
-          options are 'md5', 'sha256', and 'auto'.
+          options are `'md5'`, `'sha256'`, and `'auto'`.
           The default 'auto' detects the hash algorithm in use.
       extract: True tries extracting the file as an Archive, like tar or zip.
       archive_format: Archive format to try for extracting the file.
-          Options are 'auto', 'tar', 'zip', and None.
-          'tar' includes tar, tar.gz, and tar.bz files.
-          The default 'auto' is ['tar', 'zip'].
+          Options are `'auto'`, `'tar'`, `'zip'`, and `None`.
+          `'tar'` includes tar, tar.gz, and tar.bz files.
+          The default `'auto'` corresponds to `['tar', 'zip']`.
           None or an empty list will return no matches found.
       cache_dir: Location to store cached files, when None it
-          defaults to the [Keras
-            Directory](/faq/#where-is-the-keras-configuration-filed-stored).
+          defaults to the default directory `~/.keras/`.
 
   Returns:
       Path to the downloaded file
@@ -315,8 +314,8 @@ def _hash_file(fpath, algorithm='sha256', chunk_size=65535):
 
   Arguments:
       fpath: path to the file being validated
-      algorithm: hash algorithm, one of 'auto', 'sha256', or 'md5'.
-          The default 'auto' detects the hash algorithm in use.
+      algorithm: hash algorithm, one of `'auto'`, `'sha256'`, or `'md5'`.
+          The default `'auto'` detects the hash algorithm in use.
       chunk_size: Bytes to read at a time, important for large files.
 
   Returns:
@@ -420,32 +419,32 @@ class Sequence(object):
   Examples:
 
   ```python
-      from skimage.io import imread
-      from skimage.transform import resize
-      import numpy as np
-      import math
+  from skimage.io import imread
+  from skimage.transform import resize
+  import numpy as np
+  import math
 
-      # Here, `x_set` is list of path to the images
-      # and `y_set` are the associated classes.
+  # Here, `x_set` is list of path to the images
+  # and `y_set` are the associated classes.
 
-      class CIFAR10Sequence(Sequence):
+  class CIFAR10Sequence(Sequence):
 
-          def __init__(self, x_set, y_set, batch_size):
-              self.x, self.y = x_set, y_set
-              self.batch_size = batch_size
+      def __init__(self, x_set, y_set, batch_size):
+          self.x, self.y = x_set, y_set
+          self.batch_size = batch_size
 
-          def __len__(self):
-              return math.ceil(len(self.x) / self.batch_size)
+      def __len__(self):
+          return math.ceil(len(self.x) / self.batch_size)
 
-          def __getitem__(self, idx):
-              batch_x = self.x[idx * self.batch_size:(idx + 1) *
-              self.batch_size]
-              batch_y = self.y[idx * self.batch_size:(idx + 1) *
-              self.batch_size]
+      def __getitem__(self, idx):
+          batch_x = self.x[idx * self.batch_size:(idx + 1) *
+          self.batch_size]
+          batch_y = self.y[idx * self.batch_size:(idx + 1) *
+          self.batch_size]
 
-              return np.array([
-                  resize(imread(file_name), (200, 200))
-                     for file_name in batch_x]), np.array(batch_y)
+          return np.array([
+              resize(imread(file_name), (200, 200))
+                 for file_name in batch_x]), np.array(batch_y)
   ```
   """
 
@@ -485,10 +484,10 @@ def iter_sequence_infinite(seq):
   """Iterates indefinitely over a Sequence.
 
   Arguments:
-    seq: Sequence instance.
+    seq: `Sequence` instance.
 
   Yields:
-    Batches of data from the Sequence.
+    Batches of data from the `Sequence`.
   """
   while True:
     for item in seq:
diff --git a/tensorflow/python/keras/utils/generic_utils.py b/tensorflow/python/keras/utils/generic_utils.py
index 897f97c793b..27015cbc8f2 100644
--- a/tensorflow/python/keras/utils/generic_utils.py
+++ b/tensorflow/python/keras/utils/generic_utils.py
@@ -234,7 +234,7 @@ def get_registered_object(name, custom_objects=None, module_objects=None):
 
 @keras_export('keras.utils.serialize_keras_object')
 def serialize_keras_object(instance):
-  """Serialize Keras object into JSON."""
+  """Serialize a Keras object into a JSON-compatible representation."""
   _, instance = tf_decorator.unwrap(instance)
   if instance is None:
     return None
@@ -327,6 +327,7 @@ def deserialize_keras_object(identifier,
                              module_objects=None,
                              custom_objects=None,
                              printable_module_name='object'):
+  """Turns the serialized form of a Keras object back into an actual object."""
   if identifier is None:
     return None
 
diff --git a/tensorflow/python/keras/utils/np_utils.py b/tensorflow/python/keras/utils/np_utils.py
index f2ca5d213e8..1e8fcf34693 100644
--- a/tensorflow/python/keras/utils/np_utils.py
+++ b/tensorflow/python/keras/utils/np_utils.py
@@ -27,7 +27,18 @@ def to_categorical(y, num_classes=None, dtype='float32'):
 
   E.g. for use with categorical_crossentropy.
 
-  Usage Example:
+  Arguments:
+      y: class vector to be converted into a matrix
+          (integers from 0 to num_classes).
+      num_classes: total number of classes. If `None`, this would be inferred
+        as the (largest number in `y`) + 1.
+      dtype: The data type expected by the input. Default: `'float32'`.
+
+  Returns:
+      A binary matrix representation of the input. The classes axis is placed
+      last.
+
+  Example:
 
   >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
   >>> a = tf.constant(a, shape=[4, 4])
@@ -51,29 +62,6 @@ def to_categorical(y, num_classes=None, dtype='float32'):
   >>> print(np.around(loss, 5))
   [0. 0. 0. 0.]
 
-  Arguments:
-      y: class vector to be converted into a matrix
-          (integers from 0 to num_classes).
-      num_classes: total number of classes. If `None`, this would be inferred
-        as the (largest number in `y`) + 1.
-      dtype: The data type expected by the input. Default: `'float32'`.
-
-  Returns:
-      A binary matrix representation of the input. The classes axis is placed
-      last.
-
-  Usage example:
-
-  >>> y = [0, 1, 2, 3, 3, 1, 0]
-  >>> tf.keras.utils.to_categorical(y, 4)
-  array([[1., 0., 0., 0.],
-         [0., 1., 0., 0.],
-         [0., 0., 1., 0.],
-         [0., 0., 0., 1.],
-         [0., 0., 0., 1.],
-         [0., 1., 0., 0.],
-         [1., 0., 0., 0.]], dtype=float32)
-
   Raises:
       Value Error: If input contains string value
 
@@ -100,7 +88,7 @@ def normalize(x, axis=-1, order=2):
   Arguments:
       x: Numpy array to normalize.
       axis: axis along which to normalize.
-      order: Normalization order (e.g. 2 for L2 norm).
+      order: Normalization order (e.g. `order=2` for L2 norm).
 
   Returns:
       A normalized copy of the array.