(self, batch, generator)
return global_step
Runs a training step using the global loss on each of the scale networks.

        @param batch: An array of shape
                      [BATCH_SIZE x self.height x self.width x (3 * (HIST_LEN + 1))]. The input
                      and output frames, concatenated along the channel axis (index 3).
        @param generator: The generator model.

        @return: The global step.
Runs a training step using the global loss on each of the scale networks.
Runs a training step using the global loss on each of the scale networks .
def train_step(self, batch, generator):
        """
        Runs a training step using the global loss on each of the scale networks.

        @param batch: An array of shape
                      [BATCH_SIZE x self.height x self.width x (3 * (HIST_LEN + 1))]. The input
                      and output frames, concatenated along the channel axis (index 3).
        @param generator: The generator model.

        @return: The global step.
        """
        ##
        # Split into inputs and outputs
        ##

        input_frames = batch[:, :, :, :-3]
        gt_output_frames = batch[:, :, :, -3:]

        ##
        # Train
        ##

        feed_dict = self.build_feed_dict(input_frames, gt_output_frames, generator)

        _, global_loss, global_step, summaries = self.sess.run(
            [self.train_op, self.global_loss, self.global_step, self.summaries],
            feed_dict=feed_dict)

        ##
        # User output
        ##

        if global_step % c.STATS_FREQ == 0:
            print 'DiscriminatorModel: step %d | global loss: %f' % (global_step, global_loss)
        if global_step % c.SUMMARY_FREQ == 0:
            print 'DiscriminatorModel: saved summaries'
            self.summary_writer.add_summary(summaries, global_step)

        return global_step
def train_step ( self , batch , generator ) : ## # Split into inputs and outputs ## input_frames = batch [ : , : , : , : - 3 ] gt_output_frames = batch [ : , : , : , - 3 : ] ## # Train ## feed_dict = self . build_feed_dict ( input_frames , gt_output_frames , generator ) _ , global_loss , global_step , summaries = self . sess . run ( [ self . train_op , self . global_loss , self . global_step , self . summaries ] , feed_dict = feed_dict ) ## # User output ## if global_step % c . STATS_FREQ == 0 : print 'DiscriminatorModel: step %d | global loss: %f' % ( global_step , global_loss ) if global_step % c . SUMMARY_FREQ == 0 : print 'DiscriminatorModel: saved summaries' self . summary_writer . add_summary ( summaries , global_step ) return global_step