Tensorflow Value Error : Cannot Evaluate Tensor Using Eval
Solution 1:
From the documentation:
When you launch the graph, variables have to be explicitly initialized before you can run Ops that use their value. You can initialize a variable by running its initializer op, restoring the variable from a save file, or simply running an
assign
Op that assigns a value to the variable. In fact, the variable initializer op is just anassign
Op that assigns the variable's initial value to the variable itself.
# Launch the graph in a session. with tf.Session() as sess: # Run the variable initializer. sess.run(w.initializer) # ...you now can run ops that use the value of 'w'...
The most common initialization pattern is to use the convenience function
global_variables_initializer()
to add an Op to the graph that initializes all the variables. You then run that Op after launching the graph.
# Add an Op to initialize global variables. init_op = tf.global_variables_initializer() # Launch the graph in a session.with tf.Session() as sess: # Run the Op that initializes global variables. sess.run(init_op) # ...you can now run any Op that uses variable values...
as a result you need to use something like:
import tensorflow as tf
W = tf.Variable(10)
print('W: {0}'.format(W))
sess = tf.Session()
with sess.as_default():
sess.run(W.initializer)
print(W.eval())
FYI In TensorFlow, what is the difference between Session.run() and Tensor.eval()?
Solution 2:
You need to explicitly run an initializer operation
sess.run(tf.variables_initializer(W))
before evaluating any nodes which are dependent on W.
Solution 3:
Another example,
import tensorflow as tf
W = tf.Variable(tf.truncated_normal([700,10]))
sess = tf.Session()
with sess.as_default():
sess.run(W.initializer)
print(W.eval())
result:
[[-0.3294761 0.6800459 1.33331 ... 1.42762 -1.3164878
1.4831722 ]
[-1.0402402 0.52254885 -1.344712 ... -0.30849338 0.15020785
1.6682776 ]
[-1.1791034 1.4859517 -1.7137778 ... 0.844212 1.5928217
-0.21043983]
...
[ 0.01982834 -1.1290654 0.33557415 ... 0.0510614 -0.6524679
0.16643837]
[-0.09969945 -0.10285325 -1.1134144 ... 1.2253191 0.13343143
-1.7491579 ]
[-1.9345136 0.63447094 1.1200713 ... 0.5357313 1.8579113
0.8549472 ]]
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