image_model

This module contains the high-level architecture design of our 'style model'.

As 'style model' we define a neural network (represented as a mathematical graph) with several convolutional layers with weights extacted from a pretrained image model (ie the vgg19 model trained for the task of image classification on the imagenet dataset) and some average pooling layers with predefined weights.

All weigths of the style model stay constants during optimization of the training objective (aka cost function).

Here we only take the convolution layer weights and define several new AveragePooling. We opt for AveragePooling compared to MaxPooling, since it has been shown to yield better results.