while_loop in tensorflow returns type error - tensorflow

Question

I am confused why the following code returns this error message:
Traceback (most recent call last):
File "/Users/Desktop/TestPython/tftest.py", line 46, in <module>
main(sys.argv[1:])
File "/Users/Desktop/TestPython/tftest.py", line 35, in main
result = tf.while_loop(Cond_f2, Body_f1, loop_vars=loopvars)
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2518, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2356, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2292, in _BuildLoop
c = ops.convert_to_tensor(pred(*packed_vars))
File "/Users/Desktop/TestPython/tftest.py", line 18, in Cond_f2
boln = tf.less(tf.cast(tf.constant(ind), dtype=tf.int32), tf.cast(tf.constant(N), dtype=tf.int32))
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/framework/constant_op.py", line 163, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape))
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 353, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/Users/Desktop/HPC_LIB/TENSORFLOW/lib/python2.7/site-packages/tensorflow/python/framework/tensor_util.py", line 287, in _AssertCompatible
raise TypeError("List of Tensors when single Tensor expected")
TypeError: List of Tensors when single Tensor expected
I would appreciate if someone could help me fix this error. Thanks!
from math import *
import numpy as np
import sys
import tensorflow as tf
def Body_f1(n, ind, N, T):
# Compute trace
a = tf.trace(tf.random_normal(0.0, 1.0, (n, n)))
# Update trace
a = tf.cast(a, dtype=T.dtype)
T = tf.scatter_update(T, ind, a)
# Update index
ind = ind + 1
return n, ind, N, T
def Cond_f2(n, ind, N, T):
boln = tf.less(tf.cast(tf.constant(ind), dtype=tf.int32), tf.cast(tf.constant(N), dtype=tf.int32))
return boln
def main(argv):
# Open tensorflow session
sess = tf.Session()
# Parameters
N = 10
T = tf.zeros((N), dtype=tf.float64)
n = 4
ind = 0
# While loop
loopvars = [n, ind, N, T]
result = tf.while_loop(Cond_f2, Body_f1, loop_vars=loopvars, shape_invariants=None, \
parallel_iterations=1, back_prop=False, swap_memory=False, name=None)
trace = result[3]
trace = sess.run(trace)
print trace
print 'Done!'
# Close tensorflow session
if session==None:
sess.close()
if __name__ == "__main__":
main(sys.argv[1:])
Update: I have added the full error message. I am not sure why I get this error message. Does loop_vars expect a single tensor and not a list of tensors? I hope not.

Answer 1

tf.constant expects a non-Tensor value, like a Python list or a numpy array. You can get the same error by iterating tf.constant, as in tf.constant(tf.constant(5.)). Removing those calls fixes that first error. It's a very poor error message, so I would encourage you to file a bug on Github.
It also looks like the arguments to random_normal are a bit mixed up; keyword arguments are good for avoiding issues like that:
tf.random_normal(mean=0.0, stddev=1.0, shape=(n, n))
Finally scatter_update expects a variable. It looks like a TensorArray may be what you're looking for here (or one of the higher level looping constructs which use a TensorArray implicitly).