Initializing variable with another variable using tensorflow

Question

I am building neural network using denoising stacked autoencoders. I train autoencoder and then I would like to take the matrix of weights W and copy/initialize/clone it's values into new variable which is used in supervised optimization. How can I do such thing?

.initialized_value() doesn't work for me :/

I must have error somewhere else, because this still doesn't work :/ — Snurka Bill
– Snurka Bill, Commented Jan 25, 2016 at 0:45

Yaroslav Bulatov · Accepted Answer · 2016-01-25 00:48:01Z

3

Use var.assign, ie

vara = tf.Variable(0)
varb = tf.Variable(0)
init_op = tf.initialize_all_variables()
sess = tf.InteractiveSession()
sess.run([init_op])
sess.run([vara.assign_add(1)])
print 'variable a', vara.eval()
print 'variable b', varb.eval()
sess.run([varb.assign(vara)])
print 'variable b', varb.eval()

You should see

variable a 1
variable b 0
variable b 1

answered Jan 25, 2016 at 0:48

Yaroslav Bulatov

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5 Comments

Snurka Bill Over a year ago

Consider varA is created from different model (in this case autoencoder), so: varB = tf.Variable([size]). varB.assign(varA) init = tf.initialize_all_variables() session = tf.Session() session.run(init) ... this does nothing. varA is trained as: tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))

Yaroslav Bulatov Over a year ago

sess.run([varB.assign(varA) ]) will copy value of varA to varB. Could it be that you have two different versions of "varA"? You need some way to pass Python variable object from the place where it was created to the place where it's getting used.

Yaroslav Bulatov Over a year ago

As a sanity check, try doing "print varA.name", that gives a unique identifier that id's the node in the graph

Snurka Bill Over a year ago

may I contact you with more details, please?

Snurka Bill Over a year ago

ok this works. My problem was different. Thank you for clarification

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