class_docstr: Callback to save the Keras model or model weights at some frequency.
  
  `ModelCheckpoint` callback is used in conjunction with training using
  `model.fit()` to save a model or weights (in a checkpoint file) at some
  interval, so the model or weights can be loaded later to continue the
  training from the state saved.
  
  A few options this callback provides include:
  
  - Whether to only keep the model that has achieved the "best performance" so
    far, or whether to save the model at the end of every epoch regardless of
    performance.
  - Definition of "best"; which quantity to monitor and whether it should be
    maximized or minimized.
  - The frequency it should save at. Currently, the callback supports saving
    at the end of every epoch, or after a fixed number of training batches.
  - Whether only weights are saved, or the whole model is saved.
  
  Example:
  
  ```python
  model.compile(loss=..., optimizer=...,
                metrics=['accuracy'])
  
  EPOCHS = 10
  checkpoint_filepath = '/tmp/ckpt/checkpoint.model.keras'
  model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
      filepath=checkpoint_filepath,
      monitor='val_accuracy',
      mode='max',
      save_best_only=True)
  
  # Model is saved at the end of every epoch, if it's the best seen so far.
  model.fit(epochs=EPOCHS, callbacks=[model_checkpoint_callback])
  
  # The model (that are considered the best) can be loaded as -
  keras.models.load_model(checkpoint_filepath)
  
  # Alternatively, one could checkpoint just the model weights as -
  checkpoint_filepath = '/tmp/ckpt/checkpoint.weights.h5'
  model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
      filepath=checkpoint_filepath,
      save_weights_only=True,
      monitor='val_accuracy',
      mode='max',
      save_best_only=True)
  
  # Model weights are saved at the end of every epoch, if it's the best seen
  # so far.
  model.fit(epochs=EPOCHS, callbacks=[model_checkpoint_callback])
  
  # The model weights (that are considered the best) can be loaded as -
  model.load_weights(checkpoint_filepath)
  ```
  
  Args:
      filepath: string or `PathLike`, path to save the model file.
          `filepath` can contain named formatting options,
          which will be filled the value of `epoch` and keys in `logs`
          (passed in `on_epoch_end`).
          The `filepath` name needs to end with `".weights.h5"` when
          `save_weights_only=True` or should end with `".keras"` when
          checkpoint saving the whole model (default).
          For example:
          if `filepath` is `"{epoch:02d}-{val_loss:.2f}.keras"`, then the
          model checkpoints will be saved with the epoch number and the
          validation loss in the filename. The directory of the filepath
          should not be reused by any other callbacks to avoid conflicts.
      monitor: The metric name to monitor. Typically the metrics are set by
          the `Model.compile` method. Note:
          * Prefix the name with `"val_"` to monitor validation metrics.
          * Use `"loss"` or `"val_loss"` to monitor the model's total loss.
          * If you specify metrics as strings, like `"accuracy"`, pass the
              same string (with or without the `"val_"` prefix).
          * If you pass `metrics.Metric` objects, `monitor` should be set to
              `metric.name`
          * If you're not sure about the metric names you can check the
              contents of the `history.history` dictionary returned by
              `history = model.fit()`
          * Multi-output models set additional prefixes on the metric names.
      verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1
          displays messages when the callback takes an action.
      save_best_only: if `save_best_only=True`, it only saves when the model
          is considered the "best" and the latest best model according to the
          quantity monitored will not be overwritten. If `filepath` doesn't
          contain formatting options like `{epoch}` then `filepath` will be
          overwritten by each new better model.
      mode: one of {`"auto"`, `"min"`, `"max"`}. If `save_best_only=True`, the
          decision to overwrite the current save file is made based on either
          the maximization or the minimization of the monitored quantity.
          For `val_acc`, this should be `"max"`, for `val_loss` this should be
          `"min"`, etc. In `"auto"` mode, the mode is set to `"max"` if the
          quantities monitored are `"acc"` or start with `"fmeasure"` and are
          set to `"min"` for the rest of the quantities.
      save_weights_only: if `True`, then only the model's weights will be
          saved (`model.save_weights(filepath)`), else the full model is
          saved (`model.save(filepath)`).
      save_freq: `"epoch"` or integer. When using `"epoch"`, the callback
          saves the model after each epoch. When using integer, the callback
          saves the model at end of this many batches. If the `Model` is
          compiled with `steps_per_execution=N`, then the saving criteria will
          be checked every Nth batch. Note that if the saving isn't aligned to
          epochs, the monitored metric may potentially be less reliable (it
          could reflect as little as 1 batch, since the metrics get reset
          every epoch). Defaults to `"epoch"`.
      initial_value_threshold: Floating point initial "best" value of the
          metric to be monitored. Only applies if `save_best_value=True`. Only
          overwrites the model weights already saved if the performance of
          current model is better than this value.
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docstr: Returns whether the checkpoint `filepath` refers to exists.
docstr_tok: ["Returns","whether","the","checkpoint","`","filepath","`","refers","to","exists","."]
code: def _checkpoint_exists(self, filepath):
      """Returns whether the checkpoint `filepath` refers to exists."""
      return file_utils.exists(filepath)
  
file_path: keras/keras/callbacks/model_checkpoint.py
imports: import os
  import re
  import warnings
  import numpy
  from keras.api_export import keras_export
  from keras.callbacks.callback import Callback
  from keras.utils import file_utils
  from keras.utils import io_utils
label_desc: Use image node_id 8 for calling the ModelCheckpoint obj's underlying member method code with example usage: obj._checkpoint_exists(filepath) and returns: file_utils