I have a very basic code that tries to create a single-layered Dense neural net and predicts the output for a deterministic input. The code is as follows:

import tensorflow as tf
from tensorflow.keras import layers
model = tf.keras.models.Sequential()
model.add(layers.Dense(units = 10))
import numpy as np
inp = np.ones((1,10))
model.predict(inp)

But the output that I am getting isn't being deterministic. I think it is related to initializing the weights and biases. So, how do I fix this without writing the initializing function from scratch?

CodePudding user response：

Set global seed before initializing model tf.random.set_seed(42)

You can also set seed for specific parts of model, e.g. kernel_initializer in Dense layer, but with this approach, you may miss initializers that will still be nondeterministic. In your case setting it globally will be the best solution.