I am struggling to have the same split of data on every iteration in the following function ?
def data(filename):
X_train = data('train-images.gz')
Y_train = data('train-labels.gz')
X_test = data('t10k-images.gz')
Y_test = data('t10k-labels.gz')
X_train, X_devel = X_train[:, :-devel_size], X_train[:, -devel_size:]
Y_train, Y_devel = Y_train[:-devel_size], Y_train[-devel_size:]
return X_train, Y_train, X_devel, Y_devel, X_test, Y_test
How can I have the same split of data to training & validation for the above function when I call it?
The reason is, I want to re-run the function with several optimization techniques and compare the accuracy.
Set the random seeds.
tf.random.set_seed(1)
np.random.seed(1)