  Neural networks have achieved state of the art results in many areas,
supposedly due to parameter sharing, locality, and depth. Tensor networks (TNs)
are linear algebraic representations of quantum many-body states based on their
entanglement structure. TNs have found use in machine learning. We devise a
novel TN based model called Deep convolutional tensor network (DCTN) for image
classification, which has parameter sharing, locality, and depth. It is based
on the Entangled plaquette states (EPS) TN. We show how EPS can be implemented
as a backpropagatable layer. We test DCTN on MNIST, FashionMNIST, and CIFAR10
datasets. A shallow DCTN performs well on MNIST and FashionMNIST and has a
small parameter count. Unfortunately, depth increases overfitting and thus
decreases test accuracy. Also, DCTN of any depth performs badly on CIFAR10 due
to overfitting. It is to be determined why. We discuss how the hyperparameters
of DCTN affect its training and overfitting.
