How to get reproducible results in keras

问题:

I get different results (test accuracy) every time I run the imdb_lstm.py example from Keras framework (https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py) The code contains np.random.seed(1337) in the top, before any keras imports. It should prevent it from generating different numbers for every run. What am I missing?

UPDATE: How to repro:

1. Install Keras (http://keras.io/)
2. Execute https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py a few times. It will train the model and output test accuracy.
   Expected result: Test accuracy is the same on every run.
   Actual result: Test accuracy is different on every run.

UPDATE2: I'm running it on Windows 8.1 with MinGW/msys, module versions:
theano 0.7.0
numpy 1.8.1
scipy 0.14.0c1

UPDATE3: I narrowed the problem down a bit. If I run the example with GPU (set theano flag device=gpu0) then I get different test accuracy every time, but if I run it on CPU then everything works as expected. My graphics card: NVIDIA GeForce GT 635)

回答1:

You can find the answer at the Keras docs: https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development.

In short, to be absolutely sure that you will get reproducible results with your python script on one computer's/laptop's CPU then you will have to do the following:

1. Set the PYTHONHASHSEED environment variable at a fixed value
2. Set the python built-in pseudo-random generator at a fixed value
3. Set the numpy pseudo-random generator at a fixed value
4. Set the tensorflow pseudo-random generator at a fixed value
5. Configure a new global tensorflow session

Following the Keras link at the top, the source code I am using is the following:

# 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)

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

# 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)

It is needless to say that you do not have to to specify any seed or random_state at the numpy, scikit-learn or tensorflow/keras functions that you are using in your python script exactly because with the source code above we set globally their pseudo-random generators at a fixed value.

回答2:

Theano's documentation talks about the difficulties of seeding random variables and why they seed each graph instance with its own random number generator.

Sharing a random number generator between different {{{RandomOp}}} instances makes it difficult to producing the same stream regardless of other ops in graph, and to keep {{{RandomOps}}} isolated. Therefore, each {{{RandomOp}}} instance in a graph will have its very own random number generator. That random number generator is an input to the function. In typical usage, we will use the new features of function inputs ({{{value}}}, {{{update}}}) to pass and update the rng for each {{{RandomOp}}}. By passing RNGs as inputs, it is possible to use the normal methods of accessing function inputs to access each {{{RandomOp}}}’s rng. In this approach it there is no pre-existing mechanism to work with the combined random number state of an entire graph. So the proposal is to provide the missing functionality (the last three requirements) via auxiliary functions: {{{seed, getstate, setstate}}}.

They also provide examples on how to seed all the random number generators.

You can also seed all of the random variables allocated by a RandomStreams object by that object’s seed method. This seed will be used to seed a temporary random number generator, that will in turn generate seeds for each of the random variables.

>>> srng.seed(902340)  # seeds rv_u and rv_n with different seeds each

回答3:

I finally got reproducible results with my code. It's a combination of answers I saw around the web. The first thing is doing what @alex says:

1. Set numpy.random.seed;
2. Use PYTHONHASHSEED=0 for Python 3.

Then you have to solve the issue noted by @user2805751 regarding cuDNN by calling your Keras code with the following additional THEANO_FLAGS:

3. dnn.conv.algo_bwd_filter=deterministic,dnn.conv.algo_bwd_data=deterministic

And finally, you have to patch your Theano installation as per this comment, which basically consists in:

4. replacing all calls to *_dev20 operator by its regular version in theano/sandbox/cuda/opt.py.

This should get you the same results for the same seed.

Note that there might be a slowdown. I saw a running time increase of about 10%.

回答4:

I would like to add something to the previous answers. If you use python 3 and you want to get reproducible results for every run, you have to

1. set numpy.random.seed in the beginning of your code
2. give PYTHONHASHSEED=0 as a parameter to the python interpreter

回答5:

I have trained and tested Sequential() kind of neural networks using Keras. I performed non linear regression on noisy speech data. I used the following code to generate random seed :

import numpy as np
seed = 7
np.random.seed(seed)

I get the exact same results of val_loss each time I train and test on the same data.

回答6:

This works for me:

SEED = 123456
import os
import random as rn
import numpy as np
from tensorflow import set_random_seed

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

回答7:

The problem is now solved in Tensorflow 2.0 ! I had the same issue with TF 1.x (see If Keras results are not reproducible, what's the best practice for comparing models and choosing hyper parameters? ) but

import os
####*IMPORANT*: Have to do this line *before* importing tensorflow
os.environ['PYTHONHASHSEED']=str(1)

import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers 
import random
import pandas as pd
import numpy as np

def reset_random_seeds():
   os.environ['PYTHONHASHSEED']=str(1)
   tf.random.set_seed(1)
   np.random.seed(1)
   random.seed(1)

#make some random data
reset_random_seeds()
NUM_ROWS = 1000
NUM_FEATURES = 10
random_data = np.random.normal(size=(NUM_ROWS, NUM_FEATURES))
df = pd.DataFrame(data=random_data, columns=['x_' + str(ii) for ii in range(NUM_FEATURES)])
y = df.sum(axis=1) + np.random.normal(size=(NUM_ROWS))

def run(x, y):
    reset_random_seeds()

    model = keras.Sequential([
            keras.layers.Dense(40, input_dim=df.shape[1], activation='relu'),
            keras.layers.Dense(20, activation='relu'),
            keras.layers.Dense(10, activation='relu'),
            keras.layers.Dense(1, activation='linear')
        ])
    NUM_EPOCHS = 500
    model.compile(optimizer='adam', loss='mean_squared_error')
    model.fit(x, y, epochs=NUM_EPOCHS, verbose=0)
    predictions = model.predict(x).flatten()
    loss = model.evaluate(x,  y) #This prints out the loss by side-effect

#With Tensorflow 2.0 this is now reproducible! 
run(df, y)
run(df, y)
run(df, y)

回答8:

I agree with the previous comment, but reproducible results sometimes needs the same environment(e.g. installed packages, machine characteristics and so on). So that, I recommend to copy your environment to other place in case to have reproducible results. Try to use one of the next technologies:

1. Docker. If you have a Linux this very easy to move your environment to other place. Also you can try to use DockerHub.
2. Binder. This is a cloud platform for reproducing scientific experiments.
3. Everware. This is yet another cloud platform for "reusable science". See the project repository on Github.

回答9:

The Conference Paper: Non-Random Weight Initialisation in Deep Learning Networks for Repeatable Determinism, publication date Jun 5, 2019 presented at 10th IEEE International Conference Dependable Systems, Services and Technologies (DESSERT-19) at Leeds Beckett University (LBU), United Kingdom, UK, Ireland and the Ukrainian section of IEEE June 5-7, 2019

https://ieeexplore.ieee.org/document/8770007

shows how to get repeatable results by enforcing critical regions of code.

it has been extended to a Journal Paper: Repeatable Determinism using Non-Random Weight Initialisations in Smart City Applications of Deep Learning publication in The Journal of Reliable Intelligent Environments in a Smart Cities special edition, and uses glorot xavier limts and achieve the same accuracy with perceptron layers but grow the weight in to a linear order which may have an advantage for rule extraction in perceptron layers.