Variable initialization error in Tensorflow

Lovely ... a crash confession, rather than an error message. I suspect that something in your set-up left one of your formal TF variables hanging, probably one of the one-letter names. To debug, I suggest that you insert a simple print statement after each initialization to report the value computed, or at least the variable type descriptor. For instance:

x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]
print (x_data, y_data)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
print (W)
b = tf.Variable(tf.zeros([1]))
print(b)
...

Not only will this track data values, but when the program crashes again, you'll have a trace of where it died -- more information that you didn't get from the stack trace you got above.

Yes, this is dirty and low-tech ... but it will get you to a solution as fast as anything else I know, unless you have your debugger already launched on this program.

You are running inside a ipython notebook, is better to always scope the construction of the graph and the session instantiation like this:

g = tf.Graph()
with g.as_default():
  ... build your graph ..

with tf.Session() as sess:
  sess.run(...)

This will prevent to add the same variables to the default graph, and guarantees that you always have the same graph.

To understand further the problem:

if your run a cell multiple time something simple like

a = tf.Variable()

It will create in the tf.default_graph() each time a NEW variable.

The scoping will prevent that by creating a new graph each time.

The problem is with this line tf.initialize_all_variables(). This method had been deprecated and was removed after 2017-03-02.

Because it has been deprecated, it means your variables are not actually initializing.

Try this instead

init = tf.global_variables_initializer()
sess.run(init)

Initializing variables is only required for TensorFlow v1 (TF1). Consider using TF2 as variables are initialized immediately when they are created.