Reproducible results using Keras with TensorFlow backend

Question

I am using Keras to build a deep learning LSTM model, using TensorFlow backend. Each time I run the model, the result is different. Is there a way to fix the seed to create

Answer 1

As @Poete_Maudit said here: How to get reproducible results in keras

To get reproducible results you will have to do the following at the very beginning of your script (that will be forced to use a single CPU):

# Seed value (can actually be different for each attribution step)
seed_value= 0

# 1. Set `PYTHONHASHSEED` environment variable at a fixed value
import os
os.environ['PYTHONHASHSEED']=str(seed_value)

# 2. Set `python` built-in pseudo-random generator at a fixed value
import random
random.seed(seed_value)

# 3. Set `numpy` pseudo-random generator at a fixed value
import numpy as np
np.random.seed(seed_value)

# 4. Set `tensorflow` pseudo-random generator at a fixed value
import tensorflow as tf
tf.random.set_seed(seed_value) # tensorflow 2.x
# tf.set_random_seed(seed_value) # tensorflow 1.x

# 5. Configure a new global `tensorflow` session
from keras import backend as K
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)

Note: You cannot (anymore) get reproducible results using command: PYTHONHASHSEED=0 python3 script.py, as https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development might let you think, and you have to set PYTHONHASHSEED with os.environ within your script as in step #1. Also, this does NOT work for GPU usage.