Creating a Neural Network class in Python is easy.

class NeuralNetwork:

def __init__(self, x, y):
    self.input      = x
    self.weights1   = np.random.rand(self.input.shape[1],4) 
    self.weights2   = np.random.rand(4,1)                 
    self.y          = y
    self.output     = np.zeros(y.shape)

https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6

Something to try.

To get a random 80%, 10%, 10% split for training, validation and testing. images is the list of image -> class

{training, testing} = ResourceFunction["TrainTestSplit"][images];
{testing, validation} = ResourceFunction["TrainTestSplit"][testing, "TestSetSize" -> Scaled[0.5]]

classifier = Classify[training, ValidationSet -> validation, PerformanceGoal -> "Quality"]
cm = ClassifierMeasurements[classifier, testing]

cm["ConfusionMatrixPlot"]
cm /@ {"Accuracy", "Precision", "Recall", "F1Score"}

If the results are not satisfactory there are a lot of things to try to improve it. I would do that rather than building a DNN from scratch or even attempting transfer learning.

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Your post is too vague. Please "Edit" your post with all the answers of the replies you had here. Thank you.

Many thanks for your questions. The answers are the following:

a. What is the subject in the images? These are photos of people who belong to different non-state armed groups (guerrillas or militias)

b. Are the images very similar even though they are labeled differently? I frankly do not know. This is the very sense of trying to find a classifier that is able to tell them apart. My hypothesis is that they are different enough to be separated by a good classifier.

c. How large is the training set? I used 400 pics for each group --that means 800 in total. Maybe this is too small?

d. Is there a large class imbalance in the training set? No imbalance

I am attaching two examples (one for each group) of the type of photos I am dealing with (by the way, retrieved with WebImageSearch).

I will give a hard look at the Classification function documentation, though I have already know it (I think) quite well --thanks again

Hi Francisco,

Can you explain what you mean by

but this was obviously too heavy a task for it.