ed7782f0f59f1eef6453f47633fe9e15bda00f84,deepchem/models/tensorflow_models/__init__.py,TensorflowGraphModel,fit,#TensorflowGraphModel#Any#Any#Any#Any#Any#,298

Before Change

            loss = fetched_values[-1]
            avg_loss += loss
            y_pred = np.squeeze(np.array(output))
            y_b = y_b.flatten()
            n_batches += 1
          if epoch % checkpoint_interval == checkpoint_interval - 1:
            saver.save(sess, self._save_path, global_step=epoch)
          avg_loss = float(avg_loss) / n_batches

After Change

            avg_loss += loss
            index += 1
            index_in_epoch += 1
            if len(epoch_end_indices) > 0 and index >= epoch_end_indices[0]:
              // We have reached the end of an epoch.
              if epoch % checkpoint_interval == checkpoint_interval - 1:
                saver.save(sess, self._save_path, global_step=epoch)
              avg_loss = float(avg_loss) / index_in_epoch
              log("Ending epoch %d: Average loss %g" % (epoch, avg_loss),
                  self.verbose)
              index_in_epoch = 0
              avg_loss = 0.0
              del epoch_end_indices[0]
        except tf.errors.OutOfRangeError:
          // We have reached the end of the data.
          pass
        // Always save a final checkpoint when complete.

Instances