无法加载模型检查点以继续训练，TensorSliceReader 构造函数失败：未能找到任何匹配的文件答案

【问题标题】：Unable to load model checkpoint to continue training,Unsuccessful TensorSliceReader constructor: Failed to find any matching files无法加载模型检查点以继续训练，TensorSliceReader 构造函数失败：未能找到任何匹配的文件
【发布时间】：2019-03-31 17:25:19
【问题描述】：

我正在尝试加载模型以继续训练，但我不断出错

NotFoundError: Unsuccessful TensorSliceReader 构造函数: 无法为 ./drive/My Drive/DLSRL/Model/ 找到任何匹配文件

[[节点：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 , 保存/RestoreV2_81/shape_and_slices)]]

[[节点：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"]]]

由操作“save/RestoreV2_81”引起，定义在： _run_module_as_main 中的文件“/usr/lib/python3.6/runpy.py”，第 193 行 "main", mod_spec)

我的文件夹结构模型包含检查点,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

这里是代码

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 {:>6} epoch {:>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

您能否提出修复建议？

【问题讨论】：

标签： tensorflow neural-network deep-learning recurrent-neural-network

【解决方案1】：

您没有指定要恢复的检查点。改为：

saver.restore(sess, tf.train.latest_checkpoint('./drive/My Drive/DLSRL/Model/'))

【讨论】：