def train_step()

in supervised_reptile/reptile.py [0:0]

• 25 lines of code
• 4 McCabe index (conditional complexity)

    def train_step(self,
                   dataset,
                   input_ph,
                   label_ph,
                   minimize_op,
                   num_classes,
                   num_shots,
                   inner_batch_size,
                   inner_iters,
                   replacement,
                   meta_step_size,
                   meta_batch_size):
        """
        Perform a Reptile training step.

        Args:
          dataset: a sequence of data classes, where each data
            class has a sample(n) method.
          input_ph: placeholder for a batch of samples.
          label_ph: placeholder for a batch of labels.
          minimize_op: TensorFlow Op to minimize a loss on the
            batch specified by input_ph and label_ph.
          num_classes: number of data classes to sample.
          num_shots: number of examples per data class.
          inner_batch_size: batch size for every inner-loop
            training iteration.
          inner_iters: number of inner-loop iterations.
          replacement: sample with replacement.
          meta_step_size: interpolation coefficient.
          meta_batch_size: how many inner-loops to run.
        """
        old_vars = self._model_state.export_variables()
        new_vars = []
        for _ in range(meta_batch_size):
            mini_dataset = _sample_mini_dataset(dataset, num_classes, num_shots)
            for batch in _mini_batches(mini_dataset, inner_batch_size, inner_iters, replacement):
                inputs, labels = zip(*batch)
                if self._pre_step_op:
                    self.session.run(self._pre_step_op)
                self.session.run(minimize_op, feed_dict={input_ph: inputs, label_ph: labels})
            new_vars.append(self._model_state.export_variables())
            self._model_state.import_variables(old_vars)
        new_vars = average_vars(new_vars)
        self._model_state.import_variables(interpolate_vars(old_vars, new_vars, meta_step_size))