How Do I Get Reproducible Results With Keras?

I'm trying to get reproducible results with Keras, however every time I run the program I get different results. I've set the python hash seed, the Numpy random seed, the random se

Solution 1:

Because you're using Keras with Tensorflow as backend, you will find it is pretty hard to get reproducible result especially when GPU is enable. However, there is still a method to achieve this.

First, do not use GPU.

import osos.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"os.environ["CUDA_VISIBLE_DEVICES"] = ""

Second, as you've did in code, set seed for Numpy, Random, TensorFlow and so on.

import tensorflow as tf
import numpy as np
import random as rn

sd = 1# Here sd means seed.
np.random.seed(sd)
rn.seed(sd)
os.environ['PYTHONHASHSEED']=str(sd)

from keras import backend as K
config = tf.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)
tf.set_random_seed(sd)
sess = tf.Session(graph=tf.get_default_graph(), config=config)
K.set_session(sess)

One final word, both two pieces of code should be placed at the begining of your code.

Solution 2:

with TENSORFLOW 2

import tensorflow as tf

tf.random.set_seed(33)
os.environ['PYTHONHASHSEED'] = str(33)
np.random.seed(33)
random.seed(33)

session_conf = tf.compat.v1.ConfigProto(
    intra_op_parallelism_threads=1, 
    inter_op_parallelism_threads=1
)
sess = tf.compat.v1.Session(
    graph=tf.compat.v1.get_default_graph(), 
    config=session_conf
)
tf.compat.v1.keras.backend.set_session(sess)

Solution 3:

I created a rule to achieve reproducibility:

• Works for python 3.6, not 3.7
• First install Keras 2.2.4
• After install tensorflow 1.9

And finally in the code:

import numpy as np
import random as rn
import tensorflow as tf
import keras
from keras import backend as K

#-----------------------------Keras reproducible------------------#
SEED = 1234

tf.set_random_seed(SEED)
os.environ['PYTHONHASHSEED'] = str(SEED)
np.random.seed(SEED)
rn.seed(SEED)

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)
#-----------------------------------------------------------------#