Method compile

tensorflow/python/keras/engine/training.py:185–400

Configures the model for training. Arguments: optimizer: String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`. loss: String (name of objective function), objective function or `tf.losses.Loss` instance. See `tf.losses`. If the model

(self,
              optimizer='rmsprop',
              loss=None,
              metrics=None,
              loss_weights=None,
              sample_weight_mode=None,
              weighted_metrics=None,
              target_tensors=None,
              distribute=None,
              **kwargs)

  @trackable.no_automatic_dependency_tracking
  def compile(self,
              optimizer='rmsprop',
              loss=None,
              metrics=None,
              loss_weights=None,
              sample_weight_mode=None,
              weighted_metrics=None,
              target_tensors=None,
              distribute=None,
              **kwargs):
    """Configures the model for training.

    Arguments:
        optimizer: String (name of optimizer) or optimizer instance.
            See `tf.keras.optimizers`.
        loss: String (name of objective function), objective function or
            `tf.losses.Loss` instance. See `tf.losses`. If the model has
            multiple outputs, you can use a different loss on each output by
            passing a dictionary or a list of losses. The loss value that will
            be minimized by the model will then be the sum of all individual
            losses.
        metrics: List of metrics to be evaluated by the model during training
            and testing. Typically you will use `metrics=['accuracy']`.
            To specify different metrics for different outputs of a
            multi-output model, you could also pass a dictionary, such as
            `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.
            You can also pass a list (len = len(outputs)) of lists of metrics
            such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or
            `metrics=['accuracy', ['accuracy', 'mse']]`.
        loss_weights: Optional list or dictionary specifying scalar
            coefficients (Python floats) to weight the loss contributions
            of different model outputs.
            The loss value that will be minimized by the model
            will then be the *weighted sum* of all individual losses,
            weighted by the `loss_weights` coefficients.
            If a list, it is expected to have a 1:1 mapping
            to the model's outputs. If a tensor, it is expected to map
            output names (strings) to scalar coefficients.
        sample_weight_mode: If you need to do timestep-wise
            sample weighting (2D weights), set this to `"temporal"`.
            `None` defaults to sample-wise weights (1D).
            If the model has multiple outputs, you can use a different
            `sample_weight_mode` on each output by passing a
            dictionary or a list of modes.
        weighted_metrics: List of metrics to be evaluated and weighted
            by sample_weight or class_weight during training and testing.
        target_tensors: By default, Keras will create placeholders for the
            model's target, which will be fed with the target data during
            training. If instead you would like to use your own
            target tensors (in turn, Keras will not expect external
            Numpy data for these targets at training time), you
            can specify them via the `target_tensors` argument. It can be
            a single tensor (for a single-output model), a list of tensors,
            or a dict mapping output names to target tensors.
        distribute: NOT SUPPORTED IN TF 2.0, please create and compile the
            model under distribution strategy scope instead of passing it to
            compile.
        **kwargs: Any additional arguments.