def evaluate()

in supervised_reptile/reptile.py [0:0]

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

    def evaluate(self,
                 dataset,
                 input_ph,
                 label_ph,
                 minimize_op,
                 predictions,
                 num_classes,
                 num_shots,
                 inner_batch_size,
                 inner_iters,
                 replacement):
        """
        Run a single evaluation of the model.

        Samples a few-shot learning task and measures
        performance.

        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.
          predictions: a Tensor of integer label predictions.
          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.

        Returns:
          The number of correctly predicted samples.
            This always ranges from 0 to num_classes.
        """
        train_set, test_set = _split_train_test(
            _sample_mini_dataset(dataset, num_classes, num_shots+1))
        old_vars = self._full_state.export_variables()
        for batch in _mini_batches(train_set, 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})
        test_preds = self._test_predictions(train_set, test_set, input_ph, predictions)
        num_correct = sum([pred == sample[1] for pred, sample in zip(test_preds, test_set)])
        self._full_state.import_variables(old_vars)
        return num_correct