I am currently working on the task of eCommerce product name classification, so I have categories and subcategories in product data. I noticed that using subcategories as labels delivers worse results (84% acc) than categories (94% acc). But subcategories are more precise as labels, what's important for the whole task. And then I got an idea to first do category classification and then based on the results continue with subcategories within the predicted category.
The problem here is that I do not know how to approach this problem/define network architecture. Any hints on the neural networks, how to deal with it?
Currently I defined network like this:
model = Sequential() model.add(Dense(400, input_shape=(FEATURE_NUM,))) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes)) model.add(Activation('softmax'))