The above code is a simple implementation with TensorFlow of the paper， this paper Image Style Transfer Using Convolutional Neural Networks, which is published in CVPR2016. The idea of one image's style transfer to another image is really cool, and it is very easy to implement with TensorFlow. Meanwhile, training time for one image just cost a few minutes.
Firstly, you need these package of python: tensorflow, numpy, scipy, pillow
just use these commands: pip install tensorflow, pip install numpy, pip install scipy, pip install pillow
Secondly, you need the pre-trained model of vgg-19, you can download the model from this address, whose model had removed the fully connected layer to reduce parameters.After you download the model, you should put it in this folder named "vgg_para".
We can see the image above that is from the paper, it shows a simple way to synthesize an image from other style.In this method, x is the variable which we want to update, and it is also an synthesized image as the final result. The squared error is used to control the content which makes the synthesized image is similar to the original content image, and the Gram matrix is used to control the style which makes the synthesized image has the similar style with original style image.
This result's parameter: alpha 1e-5, beta 1.0, width 512, height 512, optimizer: L-BFGS, iteration of L-BFGS 500, the result of Adam is not very well.
