message: Keras api doc fixit: Supplement with more information and testable example in EarlyStopping callback.
diff: @@ -1208,7 +1208,47 @@ class ModelCheckpoint(Callback):
  @keras_export('keras.callbacks.EarlyStopping')
  class EarlyStopping(Callback):
  - """Stop training when a monitored quantity has stopped improving.
  + """Stop training when a monitored metric has stopped improving.
  +
  + Assuming the goal of a training is to minimize the loss. With this, the
  + metric to be monitored would be 'loss', and mode would be 'min'. A
  + `model.fit()` training loop will check at end of every epoch whether
  + the loss is no longer decreasing, considering the `min_delta` and
  + `patience` if applicable. Once it's found no longer decreasing,
  + `model.stop_training` is marked True and the training terminates.
  +
  + The quantity to be monitored needs to be available in `logs` dict.
  + To make it so, pass the loss or metrics at `model.compile()`.
  +
  + Example:
  +
  + >>> callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
  + >>> # This callback will stop the training when there is no improvement in
  + >>> # the validation loss for three consecutive epochs.
  + >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
  + >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
  + >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
  + ... epochs=10, callbacks=[callback])
  + Train on 5 samples
  + Epoch 1/10
  + 5/5 [==============================] - ... loss: 6533.1904
  + Epoch 2/10
  + 5/5 [==============================] - ... loss: 110183360.0000
  + Epoch 3/10
  + 5/5 [==============================] - ... loss: 1862575718400.0000
  + Epoch 4/10
  + 5/5 [==============================] - ... loss: 31485597793124352.0000
  + """
  +
  + def __init__(self,
  + monitor='val_loss',
  + min_delta=0,
  + patience=0,
  + verbose=0,
  + mode='auto',
  + baseline=None,
  + restore_best_weights=False):
  + """Initialize an EarlyStopping callback.
  Arguments:
  monitor: Quantity to be monitored.
  @@ -1233,26 +1273,7 @@ class EarlyStopping(Callback):
  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:
  -
  - ```python
  - callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
  - # This callback will stop the training when there is no improvement in
  - # the validation loss for three consecutive epochs.
  - model.fit(data, labels, epochs=100, callbacks=[callback],
  - validation_data=(val_data, val_labels))
  - ```
  """
  -
  - def __init__(self,
  - monitor='val_loss',
  - min_delta=0,
  - patience=0,
  - verbose=0,
  - mode='auto',
  - baseline=None,
  - restore_best_weights=False):
  super(EarlyStopping, self).__init__()
  self.monitor = monitor
  