Tensorflow: How to replace a node in a calculation graph?

Question

If you have two disjoint graphs, and want to link them, turning this:

x = tf.placeholder(\'float\')
y = f(x)

y = tf.placeholder(\'float\')
z = f(y)
<         


        

Answer 1

If you want to combine trained models (for example to reuse parts of a pretrained model in a new model), you can use a Saver to save a checkpoint of the first model, then restore that model (entirely or partially) into another model.

For example, say you want to reuse model 1's weights w in model 2, and also convert x from a placeholder to a variable:

with tf.Graph().as_default() as g1:
    x = tf.placeholder('float')
    w = tf.Variable(1., name="w")
    y = x * w
    saver = tf.train.Saver()

with tf.Session(graph=g1) as sess:
    w.initializer.run()
    # train...
    saver.save(sess, "my_model1.ckpt")

with tf.Graph().as_default() as g2:
    x = tf.Variable(2., name="v")
    w = tf.Variable(0., name="w")
    z = x + w
    restorer = tf.train.Saver([w]) # only restore w

with tf.Session(graph=g2) as sess:
    x.initializer.run()  # x now needs to be initialized
    restorer.restore(sess, "my_model1.ckpt") # restores w=1
    print(z.eval())  # prints 3.