While building an image classifier, we use a set of convolution layers and then put a final fully connected layer as the final layer.
If building a 10-class classifier, Can’t we directly use a final convolution layer with output matrix’s dimension as batch_size * w * h*n_channels and transpose it to get the results then use softmax activation function to get probabilities of each class?
It would be great if someone can share the reason of using the final FC layer