Description
Context
Developers may need to change the value of the array in the loops in TensorFlow.
Problem
If the developer initializes an array using tf.constant() and tries to assign a new value to it in the loop to keep it growing, the code will run into an error. The developer can fix this error by the low-level tf.while\_loop() API. However, it is inefficient coding in this way. A lot of intermediate tensors are built in this process.
Solution
Using tf.TensorArray() for growing array in the loop is a better solution for this kind of problem in TensorFlow 2.
Type
API-Specific
Existing Stage
Model Training
Effect
Efficiency & Error-prone
Example
### TensorFlow
import tensorflow as tf
@tf.function
def fibonacci(n):
a = tf.constant(1)
b = tf.constant(1)
- c = tf.constant([1, 1])
+ c = tf.TensorArray(tf.int32, n)
+ c = c.write(0, a)
+ c = c.write(1, b)
for i in range(2, n):
a, b = b, a + b
- c = tf.concat([c, [b]], 0)
+ c = c.write(i, b)
- return c
+ return c.stack()
n = tf.constant(5)
d = fibonacci(n)