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Author Topic: Turning Neural Networks Upside Down  (Read 37118 times)
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Chillheimer
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« Reply #120 on: July 16, 2015, 12:12:51 AM »

best lookup reference yet, 81 layers at 99 iterations 8 octaves:
http://imgur.com/a/or4rZ?gallery
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kram1032
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« Reply #121 on: July 16, 2015, 12:29:31 AM »

Very nice stuff.
I'm assume the numbers in those images mean what layer depth we are talking about?
So inception_3b/output would be in the third layer of 5?
If that's the case that really show just how biased the network is towards dogs. In all the layer 3 pictures, inception_3b/output is the only thing that features something recognizable and that's dogs.
All other more complex objects (like, say, humans) only start becoming recognizable in layer 4 upwards.
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Chillheimer
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« Reply #122 on: July 17, 2015, 07:49:52 PM »

what a nice guy, I asked if he could do this for the google205 places set and he did smiley
https://www.reddit.com/r/deepdream/comments/3dm3ba/exploring_places_81_layer_99_iterations_8_octaves/

I'm assume the numbers in those images mean what layer depth we are talking about?
So inception_3b/output would be in the third layer of 5?
If that's the case that really show just how biased the network is towards dogs. In all the layer 3 pictures, inception_3b/output is the only thing that features something recognizable and that's dogs.
All other more complex objects (like, say, humans) only start becoming recognizable in layer 4 upwards.
I don't think so.. I think that these are different training sets. some were sets with lots of dogs, others with reptiles, or with cars. and those with "pool" are combinations of them. at least that's what I believe. I have no clue what is really going on wink
« Last Edit: July 17, 2015, 07:56:36 PM by Chillheimer » Logged

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kram1032
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« Reply #123 on: July 17, 2015, 08:51:41 PM »

That places-training set is really neat
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Syntopia
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« Reply #124 on: July 18, 2015, 07:23:04 PM »

Very nice stuff.
I'm assume the numbers in those images mean what layer depth we are talking about?
So inception_3b/output would be in the third layer of 5?
If that's the case that really show just how biased the network is towards dogs. In all the layer 3 pictures, inception_3b/output is the only thing that features something recognizable and that's dogs.
All other more complex objects (like, say, humans) only start becoming recognizable in layer 4 upwards.

All of the 9 'inception modules' are at different layers (the modules are applied after each other). The reason for the naming seems to be that e.g. 'inception_3b' and 'inception_3a' operate on the same spatial size of data. See http://arxiv.org/pdf/1409.4842.pdf, in particular page 7 for the NN architecture, and table 1. Each inception module spans 3 layers.

what a nice guy, I asked if he could do this for the google205 places set and he did smiley
https://www.reddit.com/r/deepdream/comments/3dm3ba/exploring_places_81_layer_99_iterations_8_octaves/
I don't think so.. I think that these are different training sets. some were sets with lots of dogs, others with reptiles, or with cars. and those with "pool" are combinations of them. at least that's what I believe. I have no clue what is really going on wink

All of the layers are trained on the same data set.
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« Reply #125 on: July 26, 2015, 01:35:30 AM »

I managed to run the deepdream network with CUDA acceleration on Windows, using this build for the Python wrappers
https://github.com/drakh/caffe-win-python-anaconda/

I used Anaconda 2.7, together with CUDA 7 on Windows 8.1. I needed to install protobuf using:
"conda install -c https://conda.binstar.org/dhirschfeld protobuf"
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schizo
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« Reply #126 on: August 23, 2015, 12:43:41 PM »

Another free online service to create deep dream images is https://dreamdeeply.com/
The results are quite small but still interesting.
Here three examples of deep dreamed fractals created with this service:






Have a good dream  wink
« Last Edit: August 23, 2015, 12:53:13 PM by schizo » Logged

kram1032
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« Reply #127 on: August 23, 2015, 01:22:54 PM »

those are actually amongst the most interesting I've seen thus far. Neat!
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Caleidoscope
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« Reply #128 on: August 23, 2015, 02:41:40 PM »

I just try one, funny!  Thank you for the url. 
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Chillheimer
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« Reply #129 on: August 30, 2015, 12:51:17 PM »

Next Level!  shocked

<a href="https://www.youtube.com/v/-R9bJGNHltQ&rel=1&fs=1&hd=1" target="_blank">https://www.youtube.com/v/-R9bJGNHltQ&rel=1&fs=1&hd=1</a>

« Last Edit: August 31, 2015, 10:22:54 AM by Chillheimer » Logged

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kram1032
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« Reply #130 on: August 30, 2015, 01:03:32 PM »

Really neat! I'm waiting for ones working on movies though. (They also already are in the works)
And I'm curious what DNNs which have attention will do. (Currently the movie-networks mostly "just" have what's called long short term memory)
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Chillheimer
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« Reply #131 on: September 01, 2015, 07:48:41 PM »

and it is open source!
woohoo!
https://github.com/kaishengtai/neuralart

now I'll just have to wait for a guide for dummies.. wink
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Kalles Fraktaler
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« Reply #132 on: September 01, 2015, 09:02:26 PM »

and it is open source!
woohoo!
https://github.com/kaishengtai/neuralart

now I'll just have to wait for a guide for dummies.. wink
I want a mandelbrot fractal as van Gogh!
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Chillheimer
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« Reply #133 on: September 04, 2015, 10:06:40 AM »

wow:
http://imgur.com/a/ujf0c
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kram1032
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« Reply #134 on: September 04, 2015, 09:00:04 PM »

http://gitxiv.com/posts/jG46ukGod8R7Rdtud/a-neural-algorithm-of-artistic-style an implementation
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