  Image classification based on over-parametrized convolutional neural networks
with a global average-pooling layer is considered. The weights of the network
are learned by gradient descent. A bound on the rate of convergence of the
difference between the misclassification risk of the newly introduced
convolutional neural network estimate and the minimal possible value is
derived.
