title: Unsuccessful TensorSliceReader constructor
body: <p>I am trying to make a chatbot using Python for a school project. I am basing mine off this video: <a href="https://www.youtube.com/watch?v=wypVcNIH6D4&amp;list=PLzMcBGfZo4-ndH9FoC4YWHGXG5RZekt-Q" rel="nofollow noreferrer">https://www.youtube.com/watch?v=wypVcNIH6D4&amp;list=PLzMcBGfZo4-ndH9FoC4YWHGXG5RZekt-Q</a> . I ran into an error while trying to run my code. Here is the error message I got:</p>
  
  <pre><code>Traceback (most recent call last):
    File "main.py", line 88, in &lt;module&gt;
      model.load(".\model.tflearn")
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\models\dnn.py", line 308, in load
      self.trainer.restore(model_file, weights_only, **optargs)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\helpers\trainer.py", line 490, in restore
      self.restorer.restore(self.session, model_file)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1686, in restore
      {self.saver_def.filename_tensor_name: save_path})
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 895, in run
      run_metadata_ptr)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1128, in _run
      feed_dict_tensor, options, run_metadata)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1344, in _do_run
      options, run_metadata)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1363, in _do_call
      raise type(e)(node_def, op, message)
  tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for C:\Users\Sachu\Documents\chatbot\model.tflearn
           [[Node: save_1/RestoreV2_2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_2/tensor_names, save_1/RestoreV2_2/shape_and_slices)]]
  
  Caused by op 'save_1/RestoreV2_2', defined at:
    File "main.py", line 80, in &lt;module&gt;
      model = tflearn.DNN(net)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\models\dnn.py", line 65, in __init__
      best_val_accuracy=best_val_accuracy)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tflearn\helpers\trainer.py", line 147, in __init__
      allow_empty=True)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1239, in __init__
      self.build()
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1248, in build
      self._build(self._filename, build_save=True, build_restore=True)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 1284, in _build
      build_save=build_save, build_restore=build_restore)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 765, in _build_internal
      restore_sequentially, reshape)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 428, in _AddRestoreOps
      tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\saver.py", line 268, in restore_op
      [spec.tensor.dtype])[0])
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_io_ops.py", line 1113, in restore_v2
      shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
      op_def=op_def)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3160, in create_op
      op_def=op_def)
    File "C:\Users\Sachu\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1625, in __init__
      self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access
  
  NotFoundError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to find any matching files for C:\Users\Sachu\Documents\chatbot\model.tflearn
           [[Node: save_1/RestoreV2_2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save_1/Const_0_0, save_1/RestoreV2_2/tensor_names, save_1/RestoreV2_2/shape_and_slices)]]
  </code></pre>
  
  <p><strong>Here is my actual code:</strong></p>
  
  <pre><code>import nltk
  from nltk.stem.lancaster import LancasterStemmer
  stemmer = LancasterStemmer()
  
  import numpy
  import tflearn
  import tensorflow
  import random
  import json
  import pickle
  
  nltk.download('punkt')
  
  with open("intents.json") as file:
      data = json.load(file)
  
  try:
      with open("data.pickle", "wb") as f:
          words, labels, training, output = pickle.load(f)
  except:
      words = []
      labels = []
      docs_x = []
      docs_y = []
  
      for intent in data["intents"]:
          for pattern in intent["patterns"]:
              wrds = nltk.word_tokenize(pattern)
              words.extend(wrds)
              docs_x.append(wrds)
              docs_y.append(intent["tag"])
  
          if intent["tag"] not in labels:
              labels.append(intent["tag"])
  
      words = [stemmer.stem(w.lower()) for w in words if w != "?"]
      words = sorted(list(set(words)))
  
      labels = sorted(labels)
  
      training = []
      output = []
  
      out_empty = [0 for _ in range(len(labels))]
  
      for x, doc in enumerate(docs_x):
          bag = []
  
          wrds = [stemmer.stem(w.lower()) for w in doc]
  
          for w in words:
              if w in wrds:
                  bag.append(1)
              else:
                  bag.append(0)
  
          output_row = out_empty[:]
          output_row[labels.index(docs_y[x])] = 1
  
          training.append(bag)
          output.append(output_row)
  
  
      training = numpy.array(training)
      output = numpy.array(output)
  
      with open("data.pickle", "wb") as f:
          pickle.dump((words, labels, training, output), f)
  
  tensorflow.reset_default_graph()
  
  net = tflearn.input_data(shape=[None, len(training[0])])
  net = tflearn.fully_connected(net, 8)
  net = tflearn.fully_connected(net, 8)
  net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
  net = tflearn.regression(net)
  
  model = tflearn.DNN(net)
  '''
  try:
      model.load("model.tflearn")
  except:
      model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
      model.save("model.tflearn")
  '''
  model.load(".\model.tflearn")
  model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
  model.save("model.tflearn")
  
  def bag_of_words(s, words):
      bag = [0 for _ in range(len(words))]
  
      s_words = nltk.word_tokenize(s)
      s_words = [stemmer.stem(word.lower()) for word in s_words]
  
      for se in s_words:
          for i, w in enumerate(words):
              if w == se:
                  bag[i] = 1
  
      return numpy.array(bag)
  
  
  def chat():
      print("Start talking with the bot (type quit to stop)!")
      while True:
          inp = input("You: ")
          if inp.lower() == "quit":
              break
  
          results = model.predict([bag_of_words(inp, words)])
          results_index = numpy.argmax(results)
          tag = labels[results_index]
  
          for tg in data["intents"]:
              if tg['tag'] == tag:
                  responses = tg['responses']
  
          print(random.choice(responses))
  
  chat()
  </code></pre>
  
  <p>Please help me with this. Thank you in advance!</p>