Tensorflow "feed" confusion -

i seem misunderstanding way "feeding" supposed work in tensorflow. here simple example of issue:

import tensorflow tf x = tf.variable(0.0,dtype=tf.float32) sess = tf.session() sess.run(tf.global_variables_initializer()) print(sess.run(x)) # prints 0.0 expected sess.run(x,feed_dict={x:1.0}) print(sess.run(x)) # prints 0.0 again, expected see 1.0 

so, how feed value tensor , value "stick"?

thanks in advance!

import tensorflow tf  y = tf.variable(0.0, name='y') init = tf.global_variables_initializer()  tf.session() sess:     sess.run(init)     print("initial value : ", sess.run(y))     print("feeding values using dict :" ,sess.run(y, feed_dict={y:1.0}))     print("final value : ",sess.run(y))     t = tf.assign(y,10)     print("assigned new value variable using assign method: ", t.eval())     print("final value : ", sess.run(y)) 

output:

initial value :  0.0 feeding values using dict : 1.0 final value :  0.0 assigned new value variable using assign method:  10.0 final value :  10.0 

i hope clarifies concept