def backward()

in reconstruction_model.py [0:0]

• 10 lines of code
• 6 McCabe index (conditional complexity)

	def backward(self,is_train = True):
		if is_train:
			update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
			var_list = tf.trainable_variables()
			update_var_list = [v for v in var_list if 'resnet_v1_50' in v.name or 'fc-' in v.name]
			grads = tf.gradients(self.loss,update_var_list)
			# get train_op with update_ops to ensure updating for bn parameters
			with tf.control_dependencies(update_ops):
				self.train_op = self.Optimizer.apply_gradients(zip(grads,update_var_list),global_step = self.opt.global_step)

		# if not training stage, avoid updating variables 
		else:
			pass