title: How to add layers to a pre-trained model related to variables in the checkpoint?
body: <p>I'm not used to using TensorFlow or nn so forgive me if I don't get what you guys say at the first time.</p>
  
  <p>I'm currently trying to add one batch normalization layer after every convolutional layer in yolo v1 code that I got at the Internet.</p>
  
  <p>Code below is the batch normalization function that I used.</p>
  
  <pre><code>def batchnorm(self, inp):
      with tf.variable_scope("batchnorm"):
          channels = inp.get_shape()[3]
          offset = tf.get_variable("offset",
                               channels,
                               dtype=tf.float32,
                               initializer=tf.zeros_initializer())
          scale = tf.get_variable("scale",
                              channels,
                              dtype=tf.float32,
                              initializer=tf.random_normal_initializer(1.0, 0.02))
  
          mean, variance = tf.nn.moments(inp, axes=[0, 1, 2], keep_dims=False)
          variance_epsilon = 1e-5
          normalized = tf.nn.batch_normalization(inp, mean, variance,
                                             offset, scale, variance_epsilon)
      return normalized
  </code></pre>
  
  <p>Code below is the structure of the yolov1 code that I'm using</p>
  
  <pre><code> if self.verbose:
          print('Building Yolo Graph....')
      # Reset default graph
      tf.reset_default_graph()
      # Input placeholder
      self.x = tf.placeholder('float32', [None, 448, 448, 3])
      self.label_batch = tf.placeholder('float32', [None, 73])
  
      # conv1, pool1
      self.conv1 = self.conv_layer(1, self.x, 64, 7, 2)
      self.pool1 = self.maxpool_layer(2, self.conv1, 2, 2)
      # size reduced to 64x112x112
      # conv2, pool2
      self.conv2 = self.conv_layer(3, self.pool1, 192, 3, 1)
      self.pool2 = self.maxpool_layer(4, self.conv2, 2, 2)
      # size reduced to 192x56x56
      # conv3, conv4, conv5, conv6, pool3
      self.conv3 = self.conv_layer(5, self.pool2, 128, 1, 1)
      self.conv4 = self.conv_layer(6, self.conv3, 256, 3, 1)
      self.conv5 = self.conv_layer(7, self.conv4, 256, 1, 1)
      self.conv6 = self.conv_layer(8, self.conv5, 512, 3, 1)
      self.pool3 = self.maxpool_layer(9, self.conv6, 2, 2)
      # size reduced to 512x28x28
      # conv7 - conv16, pool4
      self.conv7 = self.conv_layer(10, self.pool3, 256, 1, 1)
      self.conv8 = self.conv_layer(11, self.conv7, 512, 3, 1)
      self.conv9 = self.conv_layer(12, self.conv8, 256, 1, 1)
      self.conv10 = self.conv_layer(13, self.conv9, 512, 3, 1)
      self.conv11 = self.conv_layer(14, self.conv10, 256, 1, 1)
      self.conv12 = self.conv_layer(15, self.conv11, 512, 3, 1)
      self.conv13 = self.conv_layer(16, self.conv12, 256, 1, 1)
      self.conv14 = self.conv_layer(17, self.conv13, 512, 3, 1)
      self.conv15 = self.conv_layer(18, self.conv14, 512, 1, 1)
      self.conv16 = self.conv_layer(19, self.conv15, 1024, 3, 1)
      self.pool4 = self.maxpool_layer(20, self.conv16, 2, 2)
      # size reduced to 1024x14x14
      # conv17 - conv24
      self.conv17 = self.conv_layer(21, self.pool4, 512, 1, 1)
      self.conv18 = self.conv_layer(22, self.conv17, 1024, 3, 1)
      self.conv19 = self.conv_layer(23, self.conv18, 512, 1, 1)
      self.conv20 = self.conv_layer(24, self.conv19, 1024, 3, 1)
      self.conv21 = self.conv_layer(25, self.conv20, 1024, 3, 1)
      self.conv22 = self.conv_layer(26, self.conv21, 1024, 3, 2)
      self.conv23 = self.conv_layer(27, self.conv22, 1024, 3, 1)
      self.conv24 = self.conv_layer(28, self.conv23, 1024, 3, 1)
  
      # size reduced to 1024x7x7
      # fc1, fc2, fc3
      self.fc1 = self.fc_layer(29, self.conv24, 512,
                               flatten=True, linear=False)
      self.fc2 = self.fc_layer(
          30, self.fc1, 4096, flatten=False, linear=False)
      self.fc3 = self.fc_layer(
          31, self.fc2, 1470, flatten=False, linear=True)
  
      varlist = self.print_tensors_in_checkpoint_file(file_name=self.weightFile, all_tensors=True, tensor_name=None)
      variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
      self.saver = tf.train.Saver(variables[:len(varlist)])
  
      self.loss = self.calculate_loss_function(self.fc3 , self.label_batch)
  
      self.sess = tf.Session()
  
      self.saver.restore(self.sess, self.weightFile)
  
      self.only_restore_conv20 = False
      if self.only_restore_conv20:
          after_20_initializer = [var.initializer for var in tf.global_variables()[3:]]
          self.sess.run(after_20_initializer)
  
      #exerpath = 'C:/Users/dml/PycharmProjects/YOLOv1-master/exer_ckpt/exer.ckpt'
  
      self.training = tf.train.MomentumOptimizer(momentum=0.5, learning_rate=1e-4).minimize(self.loss)
  
      Momentum_initializers = [var.initializer for var in tf.global_variables() if 'Momentum' in var.name]
  
      self.sess.run(Momentum_initializers)
  </code></pre>
  
  <p>And finally the error I'm getting after putting a batchnorm layer right after conv1 layer like</p>
  
  <pre><code>self.conv1 = self.conv_layer(1, self.x, 64, 7, 2)
      self.bn1 = self.batchnorm(self.conv1)
      self.pool1 = self.maxpool_layer(2, self.bn1, 2, 2)
  </code></pre>
  
  <p>Is</p>
  
  <pre><code>NotFoundError: Key batchnorm/offset not found in checkpoint
   [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]
  </code></pre>
  
  <p>After few days of struggle I found out that it's related to restoring weights in the checkpoint file. And because my batchnorm variable is not in the checkpoint file. But I can't find out how to make my code work.</p>