Merge pull request #1024 from lilleswing/move-checkpoints

        if submodel.loss is not None:

          loss = submodel.loss

      if checkpoint_interval > 0:

        saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)

        saver = tf.train.Saver(

            max_to_keep=max_checkpoints_to_keep, save_relative_paths=True)

      if restore:

        self.restore()

      avg_loss, n_averaged_batches = 0.0, 0.0

      var_names = set([x for x in reader.get_variable_to_shape_map()])

      var_map = {

          x.op.name: x

          for x in tf.global_variables() if x.op.name in var_names

          for x in tf.global_variables()

          if x.op.name in var_names

      }

      saver = tf.train.Saver(var_list=var_map)

      saver.restore(self.session, checkpoint)

import os

import tempfile

import unittest

import numpy as np

import os

from nose.tools import assert_true, nottest

from flaky import flaky

import tensorflow as tf

from flaky import flaky

from nose.tools import assert_true

import shutil

import deepchem as dc

from deepchem.data import NumpyDataset

    prediction = np.squeeze(tg.predict_on_batch(X))

    tg.save()

    tg1 = TensorGraph.load_from_dir(tg.model_dir)

    dirpath = tempfile.mkdtemp()

    shutil.rmtree(dirpath)

    shutil.move(tg.model_dir, dirpath)

    tg1 = TensorGraph.load_from_dir(dirpath)

    prediction2 = np.squeeze(tg1.predict_on_batch(X))

    assert_true(np.all(np.isclose(prediction, prediction2, atol=0.01)))