feature-visualization | tensorflow example of visualizing features | Machine Learning library
kandi X-RAY | feature-visualization Summary
kandi X-RAY | feature-visualization Summary
#Feature Visualization for Convnets in Tensorflow. This is the code to accompany a post on Visualizing Features from a Convolutional Network. Download the cifar10 binary format, place into /cifar-10-batches-py directory. run conv.py to compile many image files into the group ones shown in the post.
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Top functions reviewed by kandi - BETA
- Displays the image .
- Train the model .
- Unpool a tensor .
- Save an image .
- Unpickle a file .
- Initialize weights .
- Initialize bias .
- 2D convolutional layer .
- Max pooling op .
- The main entry point .
feature-visualization Key Features
feature-visualization Examples and Code Snippets
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Trending Discussions on feature-visualization
QUESTION
I need to calculate the covariance matrix for RGB values across an image dataset, and then apply Cholesky decomposition to the final result.
The covariance matrix for RGB values is a 3x3 matrix M, where M_(i, i) is the variance of channel i and M_(i, j) is the covariance between channels i and j.
The end result should be something like this:
...ANSWER
Answered 2020-Sep-22 at 20:34Here is a function for computing the (unbiased) sample covariance matrix on a 3 channel image, named rgb_cov. Cholesky decomposition is straightforward with torch.cholesky:
QUESTION
I'm trying to implement this distill article on feature visualization for VGGFace model. I was able to find a tutorial but it didn't go in detail about optimization and regularization, which the distill article emphasized are crucial in feature visualization. So my question is how to (1) optimize and (2) regularize (using a learned prior like distill article)? My code here used very simple techniques and achieved results that are far from those generated by OpenAI Microscope on VGG16. Can someone help me please?
...ANSWER
Answered 2020-Jun-12 at 02:37So upon closer look at the distill article, in footnote[9]
Images were optimized for 2560 steps in a color-decorrelated fourier-transformed space, using Adam at a learning rate of 0.05. We used each of following transformations in the given order at each step of the optimization: • Padding the input by 16 pixels to avoid edge artifacts • Jittering by up to 16 pixels • Scaling by a factor randomly selected from this list: 1, 0.975, 1.025, 0.95, 1.05 • Rotating by an angle randomly selected from this list; in degrees: -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 • Jittering a second time by up to 8 pixels • Cropping the padding
QUESTION
In a recent article at Distill (link) about visualizing internal representation of convolutional neural networks, there is the following passage (bold is mine):
If neurons are not the right way to understand neural nets, what is? In real life, combinations of neurons work together to represent images in neural networks. Individual neurons are the basis directions of activation space, and it is not clear that these should be any more special than any other direction.
Szegedy et al.[11] found that random directions seem just as meaningful as the basis directions. More recently Bau, Zhou et al.[12] found basis directions to be interpretable more often than random directions. Our experience is broadly consistent with both results; we find that random directions often seem interpretable, but at a lower rate than basis directions.
I feel like they are talking about linear algebra representations, but struggle to understand how one neuron can represent a basis vector.
So at this point I have 2 main questions:
- A neuron has only a scalar output, so how that can be a basic direction?
- What is an activation space and how to intuitively think about it?
I feel like understanding these can really broaden my intuition about internal geometry of neural nets. Can someone please help by explaining or point me in the direction of understanding internal processes of neural nets from the linear algebra point of view?
...ANSWER
Answered 2017-Nov-14 at 10:10My intuition would be: If you have a hidden layer with e.g. 10 neurons, then the activations of these 10 neurons span a 10-dimensional space. "Individual neurons are the basis directions of activation space" then means something like "the 10 states where exactly one neuron is 1 and the others are 0 are unit vectors that span this 'activation space'". But obviously, any independent set of 10 vectors spans the same space. And since a fully-connected layer is basically just a matrix product with the output of the previous layer, there's no obvious reason why these unit vectors should be special in any way.
This is important if you try to visualize what this hidden layer represents: Who says that "neuron 3" or the state "neuron 3 is active and the other neurons are 0" even does represent anything? It's equally possible that "neurons 2,3 and 5 are 1, neuron 7 is -2 and the others are 0" has a visual representation, but the unit vectors do not.
Ideally, you would hope that random vectors represent distinct concepts, because that way a hidden layer with n neurons can represent O(p^n) concepts (for some p > 1), instead of n concepts for n unit vectors
QUESTION
In order to generate Google-Dream like images, I am trying to modify input images optimizing an inceptionV3 network with gradient ascent`.
Desired effect: https://github.com/google/deepdream/blob/master/dream.ipynb
(for more info on this, refer to [https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html.)
For that matter, I have fine-tuned an inception network using the transfer learning method, and have generated the model:inceptionv3-ft.model
model.summary() prints the following architecture (shortened here due to space limitations):
ANSWER
Answered 2018-Mar-04 at 23:25What worked for me was the following:
To avoid installing all dependencies and caffe on my machine, I've pulled this Docker Image with all Deep Learning frameworks in it.
Within minutes I had caffe (as well as keras, tensorflow, CUDA, theano, lasagne, torch, openCV) installed in a container with a shared folder in my host machine.
I then ran this caffe script -->
Deep Dream, and voilá.
models generated by caffe are more resourceful and allow classes as stated above to be 'printed' on input images or from noise.
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Install feature-visualization
You can use feature-visualization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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