title: ValueError: GraphDef cannot be larger than 2GB
body: <p>I am using tensorflow's imageNet trained model to classify images in multiple categories.</p>
  
  <p>I edited the script classify.py as</p>
  
  <pre><code>import tensorflow as tf
  import sys
  import glob
  import os
  import pandas as pd
  
  # Disable tensorflow compilation warnings
  os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
  import tensorflow as tf
  
  test_path = '/Users/kaustubhmundra/Desktop/Multi-Class Classifier/test'
  
  classes = ['room','reception','washroom','facade']
  
  result = pd.DataFrame(columns = ['facade','washroom','room','reception'])
  
  def predict(image_path):
      #image_path = sys.argv[1]
  
      # Read the image_data
      image_data = tf.gfile.FastGFile(image_path, 'rb').read()
  
      # Loads label file, strips off carriage return
      label_lines = [line.rstrip() for line 
                         in tf.gfile.GFile("tf_files/retrained_labels.txt")]
  
      # Unpersists graph from file
      with tf.gfile.FastGFile("tf_files/retrained_graph.pb", 'rb') as f:
          graph_def = tf.GraphDef()
          graph_def.ParseFromString(f.read())
          _ = tf.import_graph_def(graph_def, name='')
  
      with tf.Session() as sess:
          # Feed the image_data as input to the graph and get first prediction
          softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
  
          predictions = sess.run(softmax_tensor, \
                   {'DecodeJpeg/contents:0': image_data})
  
          # print(predictions)
  
          pred = pd.DataFrame(predictions,columns = ['facade','washroom','room','reception'])
  
          # print(pred)
  
          global result
  
          result = result.append(pred)
  
          # print(result)
  
          # Sort to show labels of first prediction in order of confidence
          top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
  
          for node_id in top_k:
              human_string = label_lines[node_id]
              score = predictions[0][node_id]
              print('%s (score = %.5f)' % (human_string, score))
  
  
  
  path = os.path.join(test_path, '*')
  files = sorted(glob.glob(path))
  
  i=1
  
  for fl in files:
      print(i)
      i = i + 1
      predict(fl)
  
  result.to_csv('predictions.csv')
  </code></pre>
  
  <p>While I use it to predict on images, it works perfectly till 24 images but then shows an error :</p>
  
  <blockquote>
    <p>File
    "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
    line 2154, in _as_graph_def
        raise ValueError("GraphDef cannot be larger than 2GB.") ValueError: GraphDef cannot be larger than 2GB.</p>
  </blockquote>
  
  <p>How do I resolve this issue?</p>