ce2ff4b47c40fbb29ed297965a8ec607d59b116d,tf_unet/unet.py,Trainer,train,#Trainer#,374

Before Change

                        avg_gradients[i] = (avg_gradients[i] * (1.0 - (1.0 / (step+1)))) + (gradients[i] / (step+1))
                        
                    norm_gradients = [np.linalg.norm(gradient) for gradient in avg_gradients]
                    self.norm_gradients_node.assign(norm_gradients).eval()
                    
                    if step % display_step == 0:
                        self.output_minibatch_stats(sess, summary_writer, step, batch_x, util.crop_to_shape(batch_y, pred_shape))
                        

After Change

                                                                 self.net.y: util.crop_to_shape(batch_y, pred_shape),
                                                                 self.net.keep_prob: dropout})

                    if self.net.summaries and self.norm_grads:
                        avg_gradients = _update_avg_gradients(avg_gradients, gradients, step)
                        norm_gradients = [np.linalg.norm(gradient) for gradient in avg_gradients]
                        self.norm_gradients_node.assign(norm_gradients).eval()
                    
                    if step % display_step == 0:
                        self.output_minibatch_stats(sess, summary_writer, step, batch_x, util.crop_to_shape(batch_y, pred_shape))
                        
                    total_loss += loss

Instances