# predict a binary vector of size 40

I have a dataset of shape (2600, 95) with first 55 columns are features and 40 columns are label.

Label is a binary matrix of size 10x4 that flattened, and features are real valued numbers ranging (0.0009, 0.6). The goal is to predict this vector using DNN.

here is the model:

model = Sequential()
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
history = model.fit(X_train, y_train,epochs=50, batch_size=4,validation_data=(X_test, y_test), verbose=1)


but the results are not satisfying

a few questions:

am i modeling the problem right?

what architecture should i use?

what loss function makes more sense?

what else i should take into account?

• Have you tried changing the activations from tanh to relu? Have you tried increasing the number of layers in your network and the number of neurons per layer? Oct 1 at 14:09
• yes i tried them, tanh is better than relu. but i'm afraid of non appropriate loss function or metric.
– PyRe
Oct 1 at 14:17