title: Model training seems to get stuck?
body: <p>I am using tensorflow version 1.5 on Windows 10. I am using the Tensorflow slim model of Inception V4 network which has been picked up from the Github page, using their pretrained weights and adding my own layers at the end to classify 120 different objects.The size of my training dataset is around 10,000 images each of 299*299*3. This is the complete code except the lines containing the import modules and dataset paths.</p>
  
  <pre><code>tf.logging.set_verbosity(tf.logging.INFO)
  with slim.arg_scope(inception_blocks_v4.inception_v4_arg_scope()):
      X_input = tf.placeholder(tf.float32, shape = (None, image_size, image_size, 3))
      Y_label = tf.placeholder(tf.float32, shape = (None, num_classes))
  
      targets = convert_to_onehot(labels_dir, no_of_features = num_classes)
      targets = tf.convert_to_tensor(targets, dtype = tf.float32)
  
      Images = [] #TO STORE THE RESIZED IMAGES IN THE FORM OF LIST TO PASS IT TO tf.train.batch()
      images = glob.glob(images_file_path)
      i = 0
      for my_img in images:
          image = mpimg.imread(my_img)[:, :, :3]
          image = tf.convert_to_tensor(image, dtype = tf.float32)
          Images.append(image)
  
      logits, end_points = inception_blocks_v4.inception_v4(inputs = X_input, num_classes = pre_num_classes, is_training = True, create_aux_logits= False)
      pretrained_weights = slim.assign_from_checkpoint_fn(ckpt_dir, slim.get_model_variables('InceptionV4'))
      with tf.Session() as sess:
          pretrained_weights(sess)
  
      #MY LAYERS, add bias as well
      my_layer = slim.fully_connected(logits, 560, activation_fn=tf.nn.relu, scope='myLayer1', weights_initializer = tf.truncated_normal_initializer(stddev = 0.001), weights_regularizer=slim.l2_regularizer(0.00005),biases_initializer = tf.truncated_normal_initializer(stddev=0.001), biases_regularizer=slim.l2_regularizer(0.00005))
      my_layer = slim.dropout(my_layer, keep_prob = 0.6, scope = 'myLayer2')
      my_layer = slim.fully_connected(my_layer, num_classes,activation_fn = tf.nn.relu,scope= 'myLayer3', weights_initializer = tf.truncated_normal_initializer(stddev=0.001), weights_regularizer=slim.l2_regularizer(0.00005), biases_initializer = tf.truncated_normal_initializer(stddev=0.001), biases_regularizer=slim.l2_regularizer(0.00005))
      my_layer_logits = slim.fully_connected(my_layer, num_classes, activation_fn=None,scope='myLayer4')
  
      loss = tf.losses.softmax_cross_entropy(onehot_labels = Y_label, logits = my_layer_logits)  
  
      optimizer = tf.train.AdamOptimizer(learning_rate=0.0001) 
      train_op = optimizer.minimize(loss)
      batch_size = 8
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          for i in range(100):
              images, labels = tf.train.batch([Images, targets], batch_size = batch_size, num_threads = 1, capacity = (4*batch_size), enqueue_many=True)
              print (images)   #To check their shape
              print (labels)
              train_op.run(feed_dict = {X_input:images.eval(session = sess) ,Y_label:labels.eval(session = sess)})
              print (i)
  </code></pre>
  
  <p>I used the <code>print(i)</code> statement to keep track of how many epochs are done. After running the script for more than 3 hours, not even a single epoch of training is completed. It seems that it gets stuck at <code>train_op.run()</code> step. I don't know what is the problem.</p>