How to get reproducible result when running Keras with Tensorflow backend

Ensuring reproducibility of experiments is crucial when using TensorFlow as the backend for Keras, especially in scientific research and debugging. To achieve reproducible results, we need to control several key points, including random seed settings, session configuration, and specific library settings. The following are steps to ensure reproducible results:

1. Setting Random Seeds

To achieve reproducible results, first fix all seeds that may introduce randomness:

python
import numpy as np
import tensorflow as tf
import random
import os

# Set Python's random seed
random.seed(42)

# Set Numpy's random seed
np.random.seed(42)

# Set TensorFlow's random seed
tf.random.set_seed(42)

2. Forcing TensorFlow to Use Single-Threaded Execution

Multithreading can lead to inconsistent results because thread scheduling may vary between runs. You can force TensorFlow to use a single thread by setting its configuration:

python
from tensorflow.keras.backend import set_session

config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,
                                  inter_op_parallelism_threads=1,
                                  allow_soft_placement=True,
                                  device_count = {'CPU': 1})
session = tf.compat.v1.Session(config=config)
set_session(session)

3. Avoiding Algorithmic Non-Determinism

Some TensorFlow operations are non-deterministic, meaning repeated executions under identical conditions may yield different results. Avoid these operations or check your code to replace them with deterministic alternatives where possible.

4. Ensuring Fixed Seeds for All Model and Data Loading

When initializing model weights or loading datasets, ensure the same random seed is used:

python
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential

# Model initialization
model = Sequential([
    Dense(64, activation='relu', kernel_initializer='glorot_uniform', input_shape=(10,)),
    Dense(1, activation='sigmoid')
])

When using data augmentation or data splitting, also specify the random seed:

python
from sklearn.model_selection import train_test_split

# Data splitting
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)

5. Environment Consistency

Ensure all software packages and environment settings are consistent across runs, including TensorFlow version, Keras version, and any dependent libraries.

Example

Consider an image classification task. Following the above steps ensures consistent model training and prediction results. This not only aids debugging but also enhances scientific validity, particularly when writing experimental reports or academic papers.

In summary, achieving reproducibility requires careful preparation and consistent environment configuration. While completely eliminating all non-determinism can be challenging, these measures significantly improve result reproducibility.