CNN only performs well when split into 2 models I have 2 groups of data of equal shape (the main difference between the 2 are that one has half as many features - and consequently different labels of course)that perform better when they are trained independently (both using the same code). Is there any canonical approach to create a single model that performs well on both from here? Of course, I have some ideas such as increasing the number of layers and neurons but was wondering if there is something I am ignorant of. (p.s. this is for a regression problem)
You could try saving the model after training the one it performs best on. Then restore it and try feeding in the 2nd set.

saver = tf.train.Saver()


Then at the end of training, add the line

save_path = saver.save(sess, "/your/file/path.ckpt")


Then to restore.

with tf.Session() as sess:
      saver.restore(sess, "/your/file/path.ckpt")