restore checkpoint in order to retrain new class
<p>I have a checkpoint which is trained with 11 classes. I added one class to my dataset and trying to restore it in order to retain the CNN but it gave me an error related to shape because the previous one was trained with 11 classes and actually have 12 classes, did i saved the weights and biases variable in a right way ? what should I do ? here is the code:</p>

<pre><code>batch_size = 10
num_hidden = 64
num_channels = 1
depth = 32
....
graph = tf.Graph()

with graph.as_default():

# Input data.
  tf_train_dataset = tf.placeholder(
  tf.float32, shape=(batch_size, IMAGE_SIZE_H, IMAGE_SIZE_W, num_channels))
  tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)

  w_b = {
     'weight_0': tf.Variable(tf.random_normal([patch_size_1, patch_size_1, num_channels, depth],stddev=0.1)),
     'weight_1': tf.Variable(tf.random_normal([patch_size_2, patch_size_2, depth, depth], stddev=0.1)),
     'weight_2': tf.Variable(tf.random_normal([patch_size_3, patch_size_3, depth, depth], stddev=0.1)),
     'weight_3': tf.Variable(tf.random_normal([IMAGE_SIZE_H // 32 * IMAGE_SIZE_W // 32 * depth, num_hidden], stddev=0.1)),
     'weight_4': tf.Variable(tf.random_normal([num_hidden, num_labels], stddev=0.1)),

     'bias_0' : tf.Variable(tf.zeros([depth])), 
     'bias_1' : tf.Variable(tf.constant(1.0, shape=[depth])),
     'bias_2' : tf.Variable(tf.constant(1.0, shape=[depth])),
     'bias_3' : tf.Variable(tf.constant(1.0, shape=[num_hidden])),
     'bias_4' : tf.Variable(tf.constant(1.0, shape=[num_labels]))
        }

 # Model.
   def model(data):

      conv_1 = tf.nn.conv2d(data, w_b['weight_0'] , [1, 2, 2, 1], padding='SAME')  
      hidden_1 = tf.nn.relu(conv_1 + w_b['bias_0'])   
      pool_1 = tf.nn.max_pool(hidden_1,ksize = [1,5,5,1], strides= [1,2,2,1],padding ='SAME' )
      conv_2 = tf.nn.conv2d(pool_1, w_b['weight_1'], [1, 2, 2, 1], padding='SAME')   
      hidden_2 = tf.nn.relu(conv_2 + w_b['bias_1'])
      conv_3 = tf.nn.conv2d(hidden_2, w_b['weight_2'], [1, 2, 2, 1], padding='SAME')
      hidden_3 = tf.nn.relu(conv_3 + w_b['bias_2'])
      pool_2 = tf.nn.max_pool(hidden_3,ksize = [1,3,3,1], strides= [1,2,2,1],padding ='SAME' )
      shape = pool_2.get_shape().as_list()
      reshape = tf.reshape(pool_2, [shape[0], shape[1] * shape[2] * shape[3]]) 
      hidden_4 = tf.nn.relu(tf.matmul(reshape, w_b['weight_3']) + w_b['bias_3'])

      return tf.matmul(hidden_4, w_b['weight_4']) + w_b['bias_4']


  # Training computation.
  logits = model(tf_train_dataset)

  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))


  optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss)



  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(model(tf_valid_dataset))
  test_prediction = tf.nn.softmax(model(tf_test_dataset))

  init = tf.initialize_all_variables()
  w_b_saver = tf.train.Saver(var_list = w_b)

num_steps = 1001


with tf.Session(graph=graph) as sess:
 ckpt = ("/home/..../w_b_models.ckpt")
 if os.path.isfile(ckpt) :

    w_b_saver.restore(sess,ckpt)
    print("restore complete")
    print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval() , test_labels))
 else:
    print("Error while loading model checkpoint.")

    print('Initialized')
    sess.run(init)

    for step in range(num_steps):
      .....

    accuracy(test_prediction.eval(),test_labels, force = False ))   
    save_path_w_b = w_b_saver.save(sess, "/home/...../w_b_models.ckpt")
    print("Model saved in file: %s" % save_path_w_b)
</code></pre>

<p>and here is the error :</p>

<pre><code> InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [64,12] rhs shape= [64,11]
 [[Node: save/Assign_9 = Assign[T=DT_FLOAT, _class=["loc:@Variable_4"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_4, save/restore_slice_9/_12)]]
</code></pre>