python-tensorflowHow can I use TensorFlow 2.x to optimize my Python code?
TensorFlow 2.x is a powerful open-source library for numerical computation that can be used to optimize Python code. It provides a suite of tools for optimizing code, including automatic differentiation, just-in-time compilation, and distributed training. Here is an example of how to use TensorFlow 2.x to optimize a simple Python program:
import tensorflow as tf
# Create a TensorFlow 2.x function
@tf.function
def add(a, b):
return a + b
# Call the function
result = add(tf.constant(2), tf.constant(3))
# Print the result
print(result)
Output example
tf.Tensor(5, shape=(), dtype=int32)
This example demonstrates how to use TensorFlow 2.x to optimize a simple Python program by defining a TensorFlow 2.x function and calling it. This approach can be used to optimize more complex programs as well.
Code explanation
- Importing the TensorFlow 2.x library:
import tensorflow as tf
- Defining a TensorFlow 2.x function:
@tf.function
- Calling the function:
result = add(tf.constant(2), tf.constant(3))
- Printing the result:
print(result)
Helpful links
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