abstract
convolutional neural networks (cnns) are similar to “ordinary”
neural networks in the sense that they are made up of hidden layers
consisting of neurons with “learnable” parameters. these neurons
receive inputs, performs a dot product, and then follows it with a
non-linearity. the whole network expresses the mapping between
raw image pixels and their class scores. conventionally, the softmax
function is the classifier used at the last layer of this network.
however, there have been studies [2, 3, 11] conducted to challenge
this norm. the cited studies introduce the usage of linear support
vector machine (svm) in an artificial neural network architecture.
this project is yet another take on the subject, and is inspired by
[11]. empirical data has shown that the cnn-svm model was able
to achieve a test accuracy of ≈99.04% using the mnist dataset[10].
on the other hand, the cnn-softmax was able to achieve a test
accuracy of ≈99.23% using the same dataset. both models were
also tested on the recently-published fashion-mnist dataset[13],
which is suppose to be a more difficult image classification dataset
than mnist[15]. this proved to be the case as cnn-svm reached
a test accuracy of ≈90.72%, while the cnn-softmax reached a test
accuracy of ≈91.86%. the said results may be improved if data
preprocessing techniques were employed on the datasets, and if
the base cnn model was a relatively more sophisticated than the
one used in this study.