Keras LSTM - why different results with "same" model & same weights?

I resolved this issue by adding os.environ['TF_DETERMINISTIC_OPS'] = '1'

Here an example:

import os
os.environ['TF_DETERMINISTIC_OPS'] = '1'
#rest of the code
# tf version 2.3.1

Machine learning algorithms in general are non-deterministic. This means that every time you run them the outcome should vary. This has to do with the random initialization of the weights. If you want to make the results reproducible you have to eliminate the randomness from the table. A simple way to do this is to use a random seed.

import numpy as np
import tensorflow as tf

np.random.seed(1234)
tf.random.set_seed(1234)

# rest of your code

If you want the randomness factor but not so high variance in your output, I would suggest either lowering your learning rate or changing your optimizer (I would suggest an SGD optimizer with a relatively low learning rate). A cool overview of gradient descent optimization is available here!

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A note on TensorFlow's random generators is that besides a global seed (i.e. tf.random.set_seed()), they also use an internal counter, so if you run

tf.random.set_seed(1234)
print(tf.random.uniform([1]).numpy())
print(tf.random.uniform([1]).numpy())

You'll get 0.5380393 and 0.3253647, respectively. However if you re-run that same snippet, you'll get the same two numbers again.

A detailed explanation of how random seeds work in TensorFlow can be found here.

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For newer TF versions take care of this too: TensorFlow 2.2 ships with a os environment variable TF_DETERMINISTIC_OPS which if set to '1', will ensure that only deterministic GPU ops are used.

This code is for keras using tensorflow backend

This is because the weights are initialised using random numbers and hence you will get different results every time. This is expected behaviour. To have reproducible result you need to set the random seed as. Below example set operation-level and graph-level seeds for more information look here

import tensorflow as tf
import random as rn

os.environ['PYTHONHASHSEED'] = '0'

# Setting the seed for numpy-generated random numbers
np.random.seed(37)

# Setting the seed for python random numbers
rn.seed(1254)

# Setting the graph-level random seed.
tf.set_random_seed(89)

from keras import backend as K

session_conf = tf.ConfigProto(
      intra_op_parallelism_threads=1,
      inter_op_parallelism_threads=1)

#Force Tensorflow to use a single thread
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)

K.set_session(sess)

# Rest of the code follows from here on ...