tensorflow_avoid_using_nondeterministic_api.py

[Tanuj-Koduri]

Here are a few suggestions to improve reproducibility in the non-compliant code example:

1. Set random seeds for TensorFlow, NumPy, and the Python random module at the start of the code:

import tensorflow as tf
import numpy as np
import random

tf.random.set_seed(42) 
np.random.seed(42)
random.seed(42)

2. Avoid using tf.compat.v1.Session() in TensorFlow 2.x. Instead, use the tf.function decorator to make functions deterministic:

@tf.function
def my_func():
  # Function code

3. Use deterministic versions of operations like tf.nn.dropout() and Keras layers like Dropout. For example:

tf.nn.dropout(x, rate=0.2, seed=42) 

tf.keras.layers.Dropout(rate=0.2, seed=42)

4. Enable deterministic ops for the entire TensorFlow runtime:

tf.config.experimental.enable_op_determinism()

5. Use deterministic initializers for layers like tf.keras.layers.Dense():

tf.keras.layers.Dense(10, kernel_initializer='glorot_uniform') 

This will help make the model training more reproducible across multiple runs.