title: Accuracy loss after transfer keras (.h5) model to tf frozen graph (.pd) format
body: <p>I'm using tensorflow 1.12.0 and keras 2.1.4 (I used 2.2.4 at beginning, the problem is same.)<br />
  I am working on some model transferring task and one of the step is to convert keras model (.h5) to tensorflow frozen graph format (.pb).  The model accuracy was 98% while loading .h5 and test in keras, but only remain 10% while loading frozen graph (.pb) and test in tensorflow.<br />
  I transfer the h5 to frozen graph through following code.</p>
  <pre><code>import tensorflow.keras as keras
  import tensorflow as tf
  import os
  def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
      &quot;&quot;&quot;
      Freezes the state of a session into a pruned computation graph.
      Creates a new computation graph where variable nodes are replaced by
      constants taking their current value in the session. The new graph will be
      pruned so subgraphs that are not necessary to compute the requested
      outputs are removed.
      @param session The TensorFlow session to be frozen.
      @param keep_var_names A list of variable names that should not be frozen,
                            or None to freeze all the variables in the graph.
      @param output_names Names of the relevant graph outputs.
      @param clear_devices Remove the device directives from the graph for better portability.
      @return The frozen graph definition.
      &quot;&quot;&quot;
      session.run(tf.initialize_all_variables())
      graph = session.graph
      with graph.as_default():
          freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
          output_names = output_names or []
          output_names += [v.op.name for v in tf.global_variables()]
          input_graph_def = graph.as_graph_def()
          if clear_devices:
              for node in input_graph_def.node:
                  node.device = ''
          frozen_graph = tf.graph_util.convert_variables_to_constants(
              session, input_graph_def, output_names, freeze_var_names)
          return frozen_graph
   
   
  #keras h5 file
  input_path='./'
  input_file = './CNN_Mnist.h5'
  weight_file_path = os.path.join(input_path, input_file)
  output_graph_name = 'frozen_graph.pb'
  
  #Load Module
  keras.backend.set_learning_phase(0)
  h5_model = keras.models.load_model(weight_file_path)
  
  frozen_graph = freeze_session(keras.backend.get_session(), output_names=[out.op.name for out in h5_model.outputs])
  tf.train.write_graph(frozen_graph, input_path, output_graph_name, as_text=False)
  print('finish!')
  </code></pre>
  <p>Then I'm trying to load the frozen graph in tensorflow and evaluate it again, but I got very worse result. Here is my evaluation code.</p>
  <pre><code>mport os
  import argparse
  import shutil
  import tensorflow as tf
  import numpy as np
  
  from tensorflow.python.platform import gfile
  import tensorflow.contrib.decent_q
  def graph_eval(input_graph_def, input_node, output_node):
  
      # MNIST dataset    
      (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
      x_test = (x_test/255.0).astype(np.float32)
      x_test = np.reshape(x_test, [-1, 28, 28, 1])
      y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
  
      tf.import_graph_def(input_graph_def,name = '')
  
      # Get input placeholders &amp; tensors
      images_in = tf.get_default_graph().get_tensor_by_name(input_node+':0')
      labels = tf.placeholder(tf.int32,shape = [None,10])
  
      # get output tensors
      logits = tf.get_default_graph().get_tensor_by_name(output_node+':0')
  
      # top 5 and top 1 accuracy
      in_top5 = tf.nn.in_top_k(predictions=logits, targets=tf.argmax(labels, 1), k=5)
      in_top1 = tf.nn.in_top_k(predictions=logits, targets=tf.argmax(labels, 1), k=1)
      top5_acc = tf.reduce_mean(tf.cast(in_top5, tf.float32))
      top1_acc = tf.reduce_mean(tf.cast(in_top1, tf.float32))
      
      # Create the Computational graph
      with tf.Session() as sess:
          
          sess.run(tf.initializers.global_variables())
        
          feed_dict={images_in: x_test, labels: y_test}
          t5_acc,t1_acc = sess.run([top5_acc,top1_acc], feed_dict)
      
          print (' Top 1 accuracy with validation set: {:1.4f}'.format(t1_acc))
          print (' Top 5 accuracy with validation set: {:1.4f}'.format(t5_acc))
  
      print ('FINISHED!')
  
  #define arguments
  graph='./frozen_graph_opt.pb'#output_graph #'./frozen_graph.pb'
  input_node='conv2d_1_input_5'
  output_node='dense_2_3/Softmax'
  ####
  input_graph_def = tf.Graph().as_graph_def()
  input_graph_def.ParseFromString(tf.gfile.FastGFile(graph, &quot;rb&quot;).read())
  graph_eval(input_graph_def,input_node,output_node)
  </code></pre>
  <p>The data pre-processing in training and evaluation are both use the same function.
  Could someone please help to point me out what part should I check or something should be noticed while transferring .h5 to .pd file?<br />
  Or is there any problem in my evaluation code?</p>
  <p>Here is my model read in Netron.<br />
  keras .h5 model
  <a href="https://i.stack.imgur.com/8Z1FI.png" rel="nofollow noreferrer">keras .h5 model</a></p>
  <p>Tensorflow .pb model
  <a href="https://i.stack.imgur.com/Kkgdy.png" rel="nofollow noreferrer">Tensorflow .pb model</a></p>