Python dataset normalization for convolutional autoencoder

Question

I have a csv files which contain pixel neighboorhood information. Here an example of the dataset:

0 0 1.875223e+01
1 0 1.875223e+01
2 0 2.637685e+01
3 0 2.637685e+01
4 0 2.637685e+01
5 0 2.637685e+01
6 0 2.637685e+01
7 0 2.637685e+01
8 0 2.637685e+01
9 0 1.875223e+01

I would like to know if it is a good idea to apply a normalization on this dataset before training the convolutional autoencoder ?

Normalize the value data

scaler = StandardScaler()
values_scaled = scaler.fit_transform(values.reshape(-1, 1))

My second question, should I use MSE or binary_crossentropy as loss function?

Answer 1

Yes, it is a good idea to normalize the data. You can also consider using the MinMax scaler, as it might improve the performance. For your second question, most people use binary_crossentropy, however it also has some cons. There is a good discussion here.