title: 'Cannot interpret feed_dict key as Tensor: ' + e.args[0])
body: <p>I have retrained the model using this <a href="https://github.com/tensorflow/hub/blob/r0.1/examples/image_retraining/retrain.py" rel="nofollow noreferrer">code</a>. Then followed instruction from this <a href="https://github.com/ccd97/image-classify-server" rel="nofollow noreferrer" title="repo">repo</a> after cloning. Replaced newly generated <strong>labels.txt</strong> and <strong>graph.pb</strong> file. When posting an image to classify using the following code ,</p>
  
  <pre><code>MAX_K = 10
  
  TF_GRAPH = "{base_path}/inception_model/graph.pb".format(
      base_path=os.path.abspath(os.path.dirname(__file__)))
  TF_LABELS = "{base_path}/inception_model/labels.txt".format(
      base_path=os.path.abspath(os.path.dirname(__file__)))
  
  
  def load_graph():
      sess = tf.Session()
      with tf.gfile.FastGFile(TF_GRAPH, 'rb') as tf_graph:
          graph_def = tf.GraphDef()
          graph_def.ParseFromString(tf_graph.read())
          tf.import_graph_def(graph_def, name='')
      label_lines = [line.rstrip() for line in tf.gfile.GFile(TF_LABELS)]
      softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
      return sess, softmax_tensor, label_lines
  
  
  SESS, GRAPH_TENSOR, LABELS = load_graph()
  
  
  @csrf_exempt
  def classify_api(request):
      data = {"success": False}
  
      if request.method == "POST":
          tmp_f = NamedTemporaryFile()
  
      if request.FILES.get("image", None) is not None:
          image_request = request.FILES["image"]
          image_bytes = image_request.read()
          image = Image.open(io.BytesIO(image_bytes))
          image.save(tmp_f, image.format)
      elif request.POST.get("image64", None) is not None:
          base64_data = request.POST.get("image64", None).split(',', 1)[1]
          plain_data = b64decode(base64_data)
          tmp_f.write(plain_data)
  
      classify_result = tf_classify(tmp_f, int(request.POST.get('k', MAX_K)))
      tmp_f.close()
  
      if classify_result:
          data["success"] = True
          data["confidence"] = {}
          for res in classify_result:
              data["confidence"][res[0]] = float(res[1])
  
  return JsonResponse(data)
  
  
  def tf_classify(image_file, k=MAX_K):
      result = list()
  
      image_data = tf.gfile.FastGFile(image_file.name, 'rb').read()
  
      predictions = SESS.run(GRAPH_TENSOR, {'DecodeJpeg/contents:0': image_data})
      predictions = predictions[0][:len(LABELS)]
      top_k = predictions.argsort()[-k:][::-1]
      for node_id in top_k:
          label_string = LABELS[node_id]
          score = predictions[node_id]
          result.append([label_string, score])
  
      return result
  </code></pre>
  
  <p>then it shows the following error.</p>
  
  <p><strong>TypeError: Cannot interpret feed_dict key as Tensor: The name 'DecodeJpeg/contents:0' refers to a Tensor which does not exist. The operation, 'DecodeJpeg/contents', does not exist in the graph.</strong></p>