title: Unable to load model checkpoint to continue training,Unsuccessful TensorSliceReader constructor: Failed to find any matching files
body: <p>I'am trying to load model to continue training but i kepp getting error </p>
  
  <blockquote>
    <p>NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./drive/My Drive/DLSRL/Model/</p>
    
    <p>[[Node: save/RestoreV2_81 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_81/tensor_names, save/RestoreV2_81/shape_and_slices)]]</p>
    
    <p>[[Node: save/RestoreV2_3/_189 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_396_save/RestoreV2_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]]</p>
    
    <p>Caused by op 'save/RestoreV2_81', defined at:
      File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
        "<strong>main</strong>", mod_spec)</p>
  </blockquote>
  
  <p>my folder structure
  model contain checkpoint,03-27-09-15_epoch_29.ckpt.data-00000-of-00001,03-27-09-15_epoch_29.ckpt.index,03-27-09-15_epoch_29.ckpt.meta</p>
  
  <p>Here is the code </p>
  
  <pre><code>saver = tf.train.import_meta_graph('./drive/My Drive/DLSRL/Model/03-27-09-15_epoch_39.ckpt.meta')
      g = tf.get_default_graph()
      with g.as_default():
  
  
        model = Model(config, embeddings, label_dict.size(), g)
        sess = tf.Session(graph=g, config=tf.ConfigProto(allow_soft_placement=True,
                                                         log_device_placement=False))
        saver.restore(sess,'./drive/My Drive/DLSRL/Model/')
        #sess.run(tf.global_variables_initializer())
        ckpt_saver = tf.train.Saver(max_to_keep=config.max_epochs)
        for epoch in range(39,config.max_epochs):
            # save chckpoint from which to load model
            path = runs_dir / "{}_epoch_{}.ckpt".format(time_of_init, epoch)
            ckpt_saver.save(sess, str(path))
            print('Saved checkpoint.')
            evaluate(dev_data, model, sess, epoch, global_step)
            x1, x2, y = shuffle_stack_pad(train_data, config.train_batch_size)
            epoch_start = time.time()
            for x1_b, x2_b, y_b in get_batches(x1, x2, y, config.train_batch_size):
                feed_dict = make_feed_dict(x1_b, x2_b, y_b, model, config.keep_prob)
                if epoch_step % LOSS_INTERVAL == 0:
                    # tensorboard
                    run_options = tf.RunOptions(trace_level=tf.RunOptions.NO_TRACE)
                    scalar_summaries = sess.run(model.scalar_summaries,
                                       feed_dict=feed_dict,
                                       options=run_options)
                    model.train_writer.add_summary(scalar_summaries, global_step)
                    # print info
                    print("step {:&gt;6} epoch {:&gt;3}: loss={:1.3f}, epoch sec={:3.0f}, total hrs={:.1f}".format(
                        epoch_step,
                        epoch,
                        epoch_loss_sum / max(epoch_step, 1),
                        (time.time() - epoch_start),
                        (time.time() - global_start) / 3600))
                loss, _ = sess.run([model.nonzero_mean_loss, model.update], feed_dict=feed_dict)
  
                epoch_loss_sum+= loss
                epoch_step += 1
                global_step += 1
            epoch_step = 0
            epoch_loss_sum = 0.0
  
  </code></pre>
  
  <p>Can you please suggest fixes?</p>