Poor performance help- muti-class classification by ANN

Question

I'm implementing a 7-class classification task with normalised features and one-hot encoded labels. However, the training and validation accuracies have been extremely poor.

As shown, I normalised features from with StandardScaler() method and each feature vector turns out a 54-dim numpy array. Also, I one-encoded labels in the following manner.

As illustrated below, the labels are (num_y, 7) numpy arrays.

My network architecture:

It is shown here how I designed my model. And I'm wonder if the poor result has something to do with the selection of loss function (I've been using Categorical Cross-Entropy)

I appreciate any response from you. Thanks a lot!

Answer 1

The use of accuracy is obviously wrong. The code I refer to is not provided in your question, but I can speculate that you are comparing the true labels with your model outputs. Your model probably returns a vector of dimensionality 7 which constitutes a probability density function over the classes (due to the softmax activation in your final layer) like this:

model returns: (0.7 0 0.02 0.02 0.02 0.04 0.2) -- they sum to 1 because they represent probabilities

and then you are comparing these numbers with: (1 0 0 0 0 0 0)

what you have to do is translate the model output to the corresponding predicted label ((0.7 0 0.02 0.02 0.02 0.04 0.2) corresponds to (1 0 0 0 0 0 0) because the first output neuron has the larger value (0.7)). You may do that by applying a max function after your model outputs.

To make sure thats whats wrong with your problem formulation print the vector you are comparing with the true labels to get your accuracy and check if they are 7 numbers that sum up to 1.