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deep-dream

TF-Keras
gan
generative adversarial networks
deep dream
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Update README.md

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@@ -26,4 +26,5 @@ Process:
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  - Resize the original image to the smallest scale.
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  - For every scale, starting with the smallest (i.e. current one): - Run gradient ascent - Upscale image to the next scale - Reinject the detail that was lost at upscaling time
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  - Stop when we are back to the original size. To obtain the detail lost during upscaling, we simply take the original image, shrink it down, upscale it, and compare the result to the (resized) original image.
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- ![deepdream](https://keras.io/img/examples/generative/deep_dream/deep_dream_17_0.png)
 
 
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  - Resize the original image to the smallest scale.
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  - For every scale, starting with the smallest (i.e. current one): - Run gradient ascent - Upscale image to the next scale - Reinject the detail that was lost at upscaling time
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  - Stop when we are back to the original size. To obtain the detail lost during upscaling, we simply take the original image, shrink it down, upscale it, and compare the result to the (resized) original image.
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+ ![deepdream](https://keras.io/img/examples/generative/deep_dream/deep_dream_17_0.png)
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+