e0d6a5289e0efcdd5365e49d7303ffb0e366f6ea,nn_wtf/neural_network_graph.py,NeuralNetworkGraph,train,#NeuralNetworkGraph#Any#Any#Any#Any#Any#Any#,80

Before Change

            feed_dict, loss_value = self.run_training_steps(data_sets, steps_between_checks, batch_size)
            self.step += steps_between_checks

            if run_as_check is not None:
                run_as_check(feed_dict, loss_value, self.step)

    def _has_reached_precision(self, data_sets, precision, batch_size):
        if precision is not None and self.step > 0:
            self.do_eval(data_sets.test, batch_size)
            if self.precision > precision:

After Change

            self, data_sets, max_steps, precision=None, steps_between_checks=100, run_as_check=None,
            batch_size=1000
    ):
        self.trainer.train(data_sets, max_steps, precision, steps_between_checks, run_as_check, batch_size)

    def save(self):
        pass

Instances