Method _make_train_function

tensorflow/python/keras/engine/training.py:2091–2136

(self)

          ' without calling `model.compile` after ?', 1)

  def _make_train_function(self):
    has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
    self._check_trainable_weights_consistency()
    if isinstance(self.optimizer, list):
      raise ValueError('The `optimizer` in `compile` should be a single '
                       'optimizer.')
    # If we have re-compiled the loss/weighted metric sub-graphs then create
    # train function even if one exists already. This is because
    # `_feed_sample_weights` list has been updated on re-copmpile.
    if getattr(self, 'train_function', None) is None or has_recompiled:
      # Restore the compiled trainable state.
      current_trainable_state = self._get_trainable_state()
      self._set_trainable_state(self._compiled_trainable_state)

      inputs = (self._feed_inputs +
                self._feed_targets +
                self._feed_sample_weights)
      if not isinstance(K.symbolic_learning_phase(), int):
        inputs += [K.symbolic_learning_phase()]

      with K.get_graph().as_default():
        with K.name_scope('training'):
          # Training updates
          updates = self.optimizer.get_updates(
              params=self._collected_trainable_weights, loss=self.total_loss)
          # Unconditional updates
          updates += self.get_updates_for(None)
          # Conditional updates relevant to this model
          updates += self.get_updates_for(self.inputs)

        metrics = self._get_training_eval_metrics()
        metrics_tensors = [
            m._call_result for m in metrics if hasattr(m, '_call_result')  # pylint: disable=protected-access
        ]

      with K.name_scope('training'):
        # Gets loss and metrics. Updates weights at each call.
        fn = K.function(
            inputs, [self.total_loss] + metrics_tensors,
            updates=updates,
            name='train_function',
            **self._function_kwargs)
        setattr(self, 'train_function', fn)

      # Restore the current trainable state
      self._set_trainable_state(current_trainable_state)

  def _make_test_function(self):
    has_recompiled = self._recompile_weights_loss_and_weighted_metrics()