Validation loss and loss stuck at 0

Question

I am trying to train a classification problem with two labels to predict. For some reason, my validation_loss and my loss are always stuck at 0 when training. What could be the reason? Is there something wrong when calling loss functions? are they the appropriated ones for multi-label classification?

  X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=12, shuffle=True)
  X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=12, shuffle=True)

  model = keras.Sequential([
        #keras.layers.Flatten(batch_input_shape=(None,24)),
        keras.layers.Dense(first_neurona, activation='relu'),
        keras.layers.Dense(second_neurona, activation='relu'),
        keras.layers.Dense(third_neurona, activation='relu'),
        keras.layers.Dense(fourth_neurona, activation='relu'),
        keras.layers.BatchNormalization(), #WE NORMALIZE THE INPUT DATA 
        keras.layers.Dropout(0.25),
        keras.layers.Dense(2, activation='softmax'),
        #keras.layers.BatchNormalization() #for multi-class problems we use softmax? 2 clases: Forehand or backhand 
    ])

  model.compile(optimizer=keras.optimizers.Adam(learning_rate=lr),
                loss='categorical_crossentropy',
                metrics=['accuracy'])


  history=model.fit(X_train, y_train, epochs=n_epochs, batch_size=batchSize, validation_data=(X_val, y_val))
  test_loss, test_acc = model.evaluate(X_test, y_test)

EDIT: See the shape of my training data:

X_train shape : (280, 14) X_val shape : (94, 14) y_train shape : (280, 2) y_val shape : (94, 2)

the parameters when calling the function:

first neuron units:4
second neuron units: 8
learning rate= 0.0001
epochs= 1000
batch_size=32

also the metrics plots:

Answer 1

1. softmax activation fx should be the last step in multiclass classification - it converts logits to probabilities with no (non)-linearity transformations
2. pay attention to the output - this & this advices helped me once -- in last layer you should have the same number of classes (output_units, here number of neurons) as the number of target classes in the training dataset -- because getting probabilities of belonging to each one class -- you then select argmax as most probable... and for this reason one-hot encoding apriori model.fit() is used for labels - in order to get at finish the slice of all probabilities per sample, from which you will choose further argmax for this sample (as most probable)... OR use sparse_categorical_crossentropy
3. often on this trouble is suggested to decrease learning_rate - against exploding gradients
4. be carefull with activation='relu' & vanishing gradient - - use better Leaky ReLu or PReLU or use gradient clipping
5. this investigation also can matter

p.s. I can not test your data

Answer 2

from the plot of validation accuracy it is clear it is changing so the loss must be changing as well. I suspect you are plotting the wrong values for loss which is why I wanted to see the actual model.fit printed training data. Make sure you have these assignments for loss'

val_loss=history.history('val_loss')
train_loss=history.history('loss')

also recommend you increase the learning_rate to .001