title: Using pre-trained inception_resnet_v2 with Tensorflow
body: <p>I have been trying to use the pre-trained inception_resnet_v2 model released by Google. I am using their model definition(<a href="https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py" rel="nofollow noreferrer">https://github.com/tensorflow/models/blob/master/slim/nets/inception_resnet_v2.py</a>) and given checkpoint(<a href="http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz" rel="nofollow noreferrer">http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz</a>) to load the model in tensorflow as below [Download a extract the checkpoint file and download sample images dog.jpg and panda.jpg to test this code]-</p>
  
  <pre><code>import tensorflow as tf
  slim = tf.contrib.slim
  from PIL import Image
  from inception_resnet_v2 import *
  import numpy as np
  
  checkpoint_file = 'inception_resnet_v2_2016_08_30.ckpt'
  sample_images = ['dog.jpg', 'panda.jpg']
  #Load the model
  sess = tf.Session()
  arg_scope = inception_resnet_v2_arg_scope()
  with slim.arg_scope(arg_scope):
    logits, end_points = inception_resnet_v2(input_tensor, is_training=False)
  saver = tf.train.Saver()
  saver.restore(sess, checkpoint_file)
  for image in sample_images:
    im = Image.open(image).resize((299,299))
    im = np.array(im)
    im = im.reshape(-1,299,299,3)
    predict_values, logit_values = sess.run([end_points['Predictions'], logits], feed_dict={input_tensor: im})
    print (np.max(predict_values), np.max(logit_values))
    print (np.argmax(predict_values), np.argmax(logit_values))
  </code></pre>
  
  <p>However, the results from this model code does not give the expected results (class no 918 is predicted irrespective of the input image). Can someone help me understand where I am going wrong?</p>