Update error message format and provide expected value for keras/call…

…backs.py

PiperOrigin-RevId: 388983687

keras/callbacks.py

@@ -294,7 +294,8 @@ def _call_batch_hook(self, mode, hook, batch, logs=None):
     elif hook == 'end':
       self._call_batch_end_hook(mode, batch, logs)
     else:
-      raise ValueError('Unrecognized hook: {}'.format(hook))
+      raise ValueError(
+          f'Unrecognized hook: {hook}. Expected values are ["begin", "end"]')
 
   def _call_batch_begin_hook(self, mode, batch, logs):
     """Helper function for `on_*_batch_begin` methods."""
@@ -562,9 +563,10 @@ def _disallow_batch_hooks_in_ps_strategy(self):
             cb._implements_predict_batch_hooks()):
           unsupported_callbacks.append(cb)
       if unsupported_callbacks:
-        raise ValueError('Batch-level `Callback`s are not supported with '
-                         '`ParameterServerStrategy`. Found unsupported '
-                         'callbacks: {}'.format(unsupported_callbacks))
+        raise ValueError(
+            'Batch-level `Callback`s are not supported with '
+            '`ParameterServerStrategy`. Found unsupported '
+            f'callbacks: {unsupported_callbacks}')
     # pylint: enable=protected-access
 
 
@@ -971,7 +973,9 @@ def __init__(self, count_mode='samples', stateful_metrics=None):
     elif count_mode == 'steps':
       self.use_steps = True
     else:
-      raise ValueError('Unknown `count_mode`: ' + str(count_mode))
+      raise ValueError(
+          f'Unknown `count_mode`: {count_mode}. '
+          'Expected values are ["samples", "steps"]')
     # Defaults to all Model's metrics except for loss.
     self.stateful_metrics = set(stateful_metrics) if stateful_metrics else set()
 
@@ -1280,14 +1284,16 @@ def __init__(self,
           options, tf.train.CheckpointOptions):
         self._options = options or tf.train.CheckpointOptions()
       else:
-        raise TypeError('If save_weights_only is True, then `options` must be '
-                        'either None or a tf.train.CheckpointOptions')
+        raise TypeError(
+            'If save_weights_only is True, then `options` must be '
+            f'either None or a tf.train.CheckpointOptions. Got {options}.')
     else:
       if options is None or isinstance(options, tf.saved_model.SaveOptions):
         self._options = options or tf.saved_model.SaveOptions()
       else:
-        raise TypeError('If save_weights_only is False, then `options` must be'
-                        'either None or a tf.saved_model.SaveOptions')
+        raise TypeError(
+            'If save_weights_only is False, then `options` must be '
+            f'either None or a tf.saved_model.SaveOptions. Got {options}.')
 
     # Deprecated field `load_weights_on_restart` is for loading the checkpoint
     # file from `filepath` at the start of `model.fit()`
@@ -1329,7 +1335,9 @@ def __init__(self,
         self.best = np.Inf
 
     if self.save_freq != 'epoch' and not isinstance(self.save_freq, int):
-      raise ValueError('Unrecognized save_freq: {}'.format(self.save_freq))
+      raise ValueError(
+          f'Unrecognized save_freq: {self.save_freq}. '
+          'Expected save_freq are "epoch" or integer')
 
     # Only the chief worker writes model checkpoints, but all workers
     # restore checkpoint at on_train_begin().
@@ -1347,8 +1355,8 @@ def on_train_begin(self, logs=None):
           # name matching the pattern.
           self.model.load_weights(filepath_to_load)
         except (IOError, ValueError) as e:
-          raise ValueError('Error loading file from {}. Reason: {}'.format(
-              filepath_to_load, e))
+          raise ValueError(
+              f'Error loading file from {filepath_to_load}. Reason: {e}')
 
   def _implements_train_batch_hooks(self):
     # Only call batch hooks when saving on batch
@@ -1437,13 +1445,13 @@ def _save_model(self, epoch, batch, logs):
       except IsADirectoryError as e:  # h5py 3.x
         raise IOError('Please specify a non-directory filepath for '
                       'ModelCheckpoint. Filepath used is an existing '
-                      'directory: {}'.format(filepath))
+                      f'directory: {filepath}')
       except IOError as e:  # h5py 2.x
         # `e.errno` appears to be `None` so checking the content of `e.args[0]`.
         if 'is a directory' in str(e.args[0]).lower():
           raise IOError('Please specify a non-directory filepath for '
                         'ModelCheckpoint. Filepath used is an existing '
-                        'directory: {}'.format(filepath))
+                        f'directory: f{filepath}')
         # Re-throw the error for any other causes.
         raise e
 
@@ -1460,8 +1468,9 @@ def _get_file_path(self, epoch, batch, logs):
         file_path = self.filepath.format(
             epoch=epoch + 1, batch=batch + 1, **logs)
     except KeyError as e:
-      raise KeyError('Failed to format this callback filepath: "{}". '
-                     'Reason: {}'.format(self.filepath, e))
+      raise KeyError(
+          f'Failed to format this callback filepath: "{self.filepath}". '
+          f'Reason: {e}')
     self._write_filepath = distributed_file_utils.write_filepath(
         file_path, self.model.distribute_strategy)
     return self._write_filepath
@@ -1665,10 +1674,9 @@ def on_train_begin(self, logs=None):
     if self.model._distribution_strategy and not isinstance(
         self.model.distribute_strategy, self._supported_strategies):
       raise NotImplementedError(
-          '%s is not supported yet. '
+          f'{type(self.model.distribute_strategy)} is not supported yet. '
           'Currently BackupAndRestore callback only supports empty strategy, '
-          'MirroredStrategy, MultiWorkerMirroredStrategy and TPUStrategy.' %
-          type(self.model.distribute_strategy).__name__)
+          'MirroredStrategy, MultiWorkerMirroredStrategy and TPUStrategy.')
     self.model._training_state = (
         worker_training_state.WorkerTrainingState(self.model, self.backup_dir))
     self._training_state = self.model._training_state
@@ -1950,9 +1958,10 @@ def on_epoch_begin(self, epoch, logs=None):
       lr = self.schedule(epoch)
     if not isinstance(lr, (tf.Tensor, float, np.float32, np.float64)):
       raise ValueError('The output of the "schedule" function '
-                       'should be float.')
+                       f'should be float. Got: {lr}')
     if isinstance(lr, tf.Tensor) and not lr.dtype.is_floating:
-      raise ValueError('The dtype of Tensor should be float')
+      raise ValueError(
+          f'The dtype of `lr` Tensor should be float. Got: {lr.dtype}')
     backend.set_value(self.model.optimizer.lr, backend.get_value(lr))
     if self.verbose > 0:
       print('\nEpoch %05d: LearningRateScheduler setting learning '
@@ -2193,14 +2202,15 @@ def _validate_kwargs(self, kwargs):
                       '2.0. Instead, all `Embedding` variables will be '
                       'visualized.')
 
-    unrecognized_kwargs = set(kwargs.keys()) - {
-        'write_grads', 'embeddings_layer_names', 'embeddings_data', 'batch_size'
-    }
+    supported_kwargs = {'write_grads', 'embeddings_layer_names',
+                        'embeddings_data', 'batch_size'}
+    unrecognized_kwargs = set(kwargs.keys()) - supported_kwargs
 
     # Only allow kwargs that were supported in V1.
     if unrecognized_kwargs:
-      raise ValueError('Unrecognized arguments in `TensorBoard` '
-                       'Callback: ' + str(unrecognized_kwargs))
+      raise ValueError(
+          'Unrecognized arguments in `TensorBoard` Callback: '
+          f'{unrecognized_kwargs}. Supported kwargs are: {supported_kwargs}')
 
   def set_model(self, model):
     """Sets Keras model and writes graph if specified."""
@@ -2291,7 +2301,7 @@ def _configure_embeddings(self):
                                                    str):
       raise ValueError('Unrecognized `Embedding` layer names passed to '
                        '`keras.callbacks.TensorBoard` `embeddings_metadata` '
-                       'argument: ' + str(self.embeddings_metadata.keys()))
+                       f'argument: {self.embeddings_metadata.keys()}')
 
     config_pbtxt = text_format.MessageToString(config)
     path = os.path.join(self._log_write_dir, 'projector_config.pbtxt')
@@ -2345,7 +2355,7 @@ def _init_profile_batch(self, profile_batch):
     profile_batch_error_message = (
         'profile_batch must be a non-negative integer or 2-tuple of positive '
         'integers. A pair of positive integers signifies a range of batches '
-        'to profile. Found: {}'.format(profile_batch))
+        f'to profile. Found: {profile_batch}')
 
     # Support legacy way of specifying "start,stop" or "start" as str.
     if isinstance(profile_batch, str):
@@ -2629,7 +2639,8 @@ def __init__(self,
 
     self.monitor = monitor
     if factor >= 1.0:
-      raise ValueError('ReduceLROnPlateau ' 'does not support a factor >= 1.0.')
+      raise ValueError(
+          f'ReduceLROnPlateau does not support a factor >= 1.0. Got {factor}')
     if 'epsilon' in kwargs:
       min_delta = kwargs.pop('epsilon')
       logging.warning('`epsilon` argument is deprecated and '