Neural Style Transfer Implementation

Neural Style Transfer is a method of creating artistic style images using Deep Neural Networks (Convolutional Neural Networks). This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). Code is adapted from Andrew Ng’s Course ‘Convolutional Neural Networks”.

Github Code: https://github.com/omerbsezer/NeuralStyleTransfer



To run codes

Transfer Learning

“Following the original NST paper (https://arxiv.org/abs/1508.06576), we will use the VGG network. Specifically, we’ll use VGG-19, a 19-layer version of the VGG network. This model has already been trained on the very large ImageNet database, and thus has learned to recognize a variety of low level features (at the earlier layers) and high level features (at the deeper layers)”

Neural Style Transfer Algorithm

  • Calculate the content cost function
  • Calculate the style cost function
  • Put it together to get $J(G) = \alpha J_{content}(C,G) + \beta J_{style}(S,G)$.
  • Create an Interactive Session (tensorflow)
  • Load the content image
  • Load the style image
  • Randomly initialize the image to be generated
  • Load the VGG16 model
  • Build the TensorFlow graph:
  • Run the content image through the VGG16 model and compute the content cost
  • Run the style image through the VGG16 model and compute the style cost
  • Compute the total cost
  • Define the optimizer and the learning rate
  • Initialize the TensorFlow graph and run it for a large number of iterations, updating the generated image at every step.




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