554d91ccf01cba9ae4952340323c585fbc17ea5b,nn_wtf/mnist_graph.py,MNISTGraph,train,#MNISTGraph#Any#Any#Any#Any#,39

Before Change

        // And then after everything is built, start the training loop.
        for self.step in range(max_steps):
            start_time = time.time()

            feed_dict, loss_value = self.run_training_steps(data_sets)

            duration = time.time() - start_time

            // Write the summaries and print an overview fairly often.
            if self.step % steps_between_checks == 0:
                self.write_summary(duration, feed_dict, loss_value, self.step)

After Change

    def train(self, data_sets, max_steps, precision=None, steps_between_checks=100):

        assert precision is None or 0. < precision < 1.

        self.session = self.initialize_session()

        // And then after everything is built, start the training loop.

Instances