Unable to use tf.while_loop properly

user_10933724 0
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My code :

def entropy(x):
  return tf.convert_to_tensor(skimage.measure_shannon_entropy(np.array(x)))

def calc_entropy(x, fn):
  i = tf.constant(0)
  while_condition = lambda i: tf.less(i, fn)
  #loop
  r = tf.while_loop(while_condition, entropy, x[0, :, :, i])
  return r

a = tf.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]) # shape=(1, 2, 2, 2)
output = calc_entropy(a, 2)

Output : [1.23, 0.12]

But my code is showing this error : ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

keras tensorflow python
1 Answers

Try something like this:

import tensorflow as tf
from skimage.measure.entropy import shannon_entropy
import numpy as np

def entropy(i, v, x):
  v = tf.tensor_scatter_nd_update(v, [[i]], [tf.convert_to_tensor(shannon_entropy(np.array(x[0, :, :, i])))])
  return tf.add(i, 1), v, x

def calc_entropy(x, fn):
  i = tf.constant(0)
  v = tf.zeros((fn,), dtype=tf.float64)
  while_condition = lambda i, v, x: tf.less(i, fn)
  _, v, _ = tf.while_loop(while_condition, entropy, loop_vars=(i, v, x))
  return v 

a = tf.constant([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]])
output = calc_entropy(a, 2)
print(output)

tf.Tensor([2. 2.], shape=(2,), dtype=float64)

However, I do not know how you expect the output [1.23, 0.12]. Check the calculations manually:

a = np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]])
print(shannon_entropy(a[0, :, :, 0]))
print(shannon_entropy(a[0, :, :, 1]))

2.0
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