What does model.compile() do in keras tensorflow?

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According to keras.io:

Once the model is created, you can config the model with losses and metrics with model.compile().

But this explanation does not provide enough information about what exactly compiling model does.

keras tensorflow python
2 Answers

Configures the model for training. documentation

Personally, I wouldn't call it compile, because what it does has got nothing to do with compilation, in computer science terms, and this is very confusing/ overwhelming to think about machine learning and compilation at the same time.

Its just a method which does configuration:

It just sets the arguments you pass it: optimizer, loss function, metrics, eager execution. You can run it multiple times, it will just overwrite the settings you set previously.

My suggestion to developers of TensorFlow would be to rename it to configure in the short term, and perhaps in the future (not that important), move to having 1 setter (or use the factory/ builder pattern) for each configuration argument.

Heres the code for it:

    base_layer.keras_api_gauge.get_cell('compile').set(True)
    with self.distribute_strategy.scope():
      if 'experimental_steps_per_execution' in kwargs:
        logging.warn('The argument `steps_per_execution` is no longer '
                     'experimental. Pass `steps_per_execution` instead of '
                     '`experimental_steps_per_execution`.')
        if not steps_per_execution:
          steps_per_execution = kwargs.pop('experimental_steps_per_execution')

      self._validate_compile(optimizer, metrics, **kwargs)
      self._run_eagerly = run_eagerly

      self.optimizer = self._get_optimizer(optimizer)
      self.compiled_loss = compile_utils.LossesContainer(
          loss, loss_weights, output_names=self.output_names)
      self.compiled_metrics = compile_utils.MetricsContainer(
          metrics, weighted_metrics, output_names=self.output_names)

      self._configure_steps_per_execution(steps_per_execution or 1)

      # Initializes attrs that are reset each time `compile` is called.
      self._reset_compile_cache()
      self._is_compiled = True

      self.loss = loss or {}  # Backwards compat.
user_73658660

model.compile is related to training your model. Actually, your weights need to optimize and this function can optimize them. In a way that your accuracy make increases. This was just one of the input parameters called 'optimizer'.

model.compile(
optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics='acc'
)

These are the main inputs. Also you can find more details in TensorFlow documentation in link below:

https://www.tensorflow.org/api_docs/python/tf/keras/Model#compile

user_170644800
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