Body: <p>I was doing a task using RNN to predict a time series movement.</p>
  <p>I want to make my results reproducible. So I strictly followed this post:
  <a href="https://stackoverflow.com/questions/32419510/how-to-get-reproducible-results-in-keras">https://stackoverflow.com/questions/32419510/how-to-get-reproducible-results-in-keras</a></p>
  <p>My code are as follows:</p>
  <pre><code># Seed value
  # Apparently you may use different seed values at each stage
  seed_value= 0
  
  # 1. Set the `PYTHONHASHSEED` environment variable at a fixed value
  import os
  os.environ['PYTHONHASHSEED']=str(seed_value)
  
  # 2. Set the `python` built-in pseudo-random generator at a fixed value
  import random
  random.seed(seed_value)
  
  # 3. Set the `numpy` pseudo-random generator at a fixed value
  import numpy as np
  np.random.seed(seed_value)
  
  tf.compat.v1.set_random_seed(seed_value)
  
  tf.random.set_seed(seed_value)
  
  # 5. Configure a new global `tensorflow` session
  
  # for later versions:
  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)
  </code></pre>
  <p>However, every time I ran my codes, I still got a different result, what could the reasons be?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-22T15:19:55.547Z
Comments.Text: This question seems to be off-topic (please read the [rules](https://ai.stackexchange.com/help/on-topic)). Consider asking programming questions on StackOverflow.
Comments.UserDisplayName: Aray Karjauv
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-22T15:24:40.580Z
Comments.Text: @ArayKarjauv I don't think this is a programming question, but OK.
Comments.UserDisplayName: user900476
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-06-22T19:14:44.817Z
Comments.Text: FYI If using GPU, non-random reproducibility is more complicated than that because of random results ordering.
Comments.UserDisplayName: Julian Moore
Tags: <deep-learning><recurrent-neural-networks><tensorflow><keras><time-series>
Title: Why can't I reproduce my results in keras using random seed?
UserDisplayName: user900476
ThreadText: # Why can't I reproduce my results in keras using random seed?
  
  (Asked by: username_0 on 2022-06-22)
  
  I was doing a task using RNN to predict a time series movement.
  
  I want to make my results reproducible. So I strictly followed this post:
  <https://stackoverflow.com/questions/32419510/how-to-get-reproducible-results-in-keras>
  
  My code are as follows:
  
  ```
  # Seed value
  # Apparently you may use different seed values at each stage
  seed_value= 0
  
  # 1. Set the `PYTHONHASHSEED` environment variable at a fixed value
  import os
  os.environ['PYTHONHASHSEED']=str(seed_value)
  
  # 2. Set the `python` built-in pseudo-random generator at a fixed value
  import random
  random.seed(seed_value)
  
  # 3. Set the `numpy` pseudo-random generator at a fixed value
  import numpy as np
  np.random.seed(seed_value)
  
  tf.compat.v1.set_random_seed(seed_value)
  
  tf.random.set_seed(seed_value)
  
  # 5. Configure a new global `tensorflow` session
  
  # for later versions:
  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)
  
  ```
  
  However, every time I ran my codes, I still got a different result, what could the reasons be?
  
  **Tags:** deep-learning, recurrent-neural-networks, tensorflow, keras, time-series
  
  > **Comments:**
  > **username_1**: This question seems to be off-topic (please read the [rules](https://ai.stackexchange.com/help/on-topic)). Consider asking programming questions on StackOverflow.  
  > **username_0**: @username_1 I don't think this is a programming question, but OK.  
  > **username_2**: FYI If using GPU, non-random reproducibility is more complicated than that because of random results ordering.  
  
  ---
  
  ## 0 answers
  
  