adversarial | Code and hyperparameters for the paper | Machine Learning library
kandi X-RAY | adversarial Summary
kandi X-RAY | adversarial Summary
this repository contains the code and hyperparameters for the paper:. "generative adversarial networks." ian j. goodfellow, jean pouget-abadie, mehdi mirza, bing xu, david warde-farley, sherjil ozair, aaron courville, yoshua bengio. arxiv 2014. please cite this paper if you use the code in this repository as part of a published research project. we are an academic lab, not a software company, and have no personnel devoted to documenting and maintaing this research code. therefore this code is offered with absolutely no support. exact reproduction of the numbers in the paper depends on exact reproduction of many factors, including the version of all software dependencies and the choice of underlying hardware (gpu model, etc). we used nvida ge-force gtx-580 graphics cards; other hardware will use different tree structures for summation and incur different rounding error. if you do not reproduce our setup exactly you should expect to need to re-tune your hyperparameters slight for your
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Top functions reviewed by kandi - BETA
- Called when a monitor is received
- Returns the current learning rate
- Update the optimizer
- Get the topological view of a dataset
adversarial Key Features
adversarial Examples and Code Snippets
Lines of Code : 35License : Permissive (Apache-2.0)
Copy@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
import timm
model = timm.create_model('adv_inception_v3', pretrained=True)
model.eval()
import urllib
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
config = resolve_data_co # Implements a Generative Adversarial Network, from
# arxiv.org/abs/1406.2661
# but, it always collapses to generating a single image.
# Let me know if you can get it to work! - David Duvenaud
from __future__ import absolute_import, division
from __ def adversarial_model(self):
if self.AM:
return self.AM
# optimizer = RMSprop(lr=0.001, decay=3e-8)
optimizer = Adam(0.0002, 0.5)
self.AM = Sequential()
self.AM.add(self.generator())
self.AM Community Discussions
Trending Discussions on adversarial
QUESTION
Someone asked me an interview question: write a function match(s, t) to decide if a string s is a generalized substring of another string t. More concretely, match should return True if and only if removing some characters in t can equalize it to s. For example, match("abc", "abbbc") is True, because we can remove the two extra bs in t.
Surely the interviewer is expecting some kind of recursive solution, but I'm feeling adventurous and wrote
...ANSWER
Answered 2021-May-29 at 02:16Lazy quantifiers are generally quite good for performance, but AFAIK they do not prevent the pathological emphasized behaviour.
This is especially true when the beginning of the regexp match with the beginning of a text but the match is early and will fail at the end of the text requiring a lot of backtracks to "fix" the bad early lazy match of the beginning of the regexp.
In your case, here is an example of pathological input requiring an exponential number of steps:
QUESTION
what neural network is used in this generative models code?
...ANSWER
Answered 2021-May-13 at 19:12I think its CNN , If u put your full code it may easy to find
Batch Normalisation maximum used in conventional Neural network only
QUESTION
I want to use pytorch DistributedDataParallel for adversarial training. The loss function is trades.The code can run in DataParallel mode. But in DistributedDataParallel mode, I got this error. When I change the loss to AT, it can run successfully. Why can't run with trades loss? The two loss functions are as follows：
-- Process 1 terminated with the following error:
...ANSWER
Answered 2021-May-06 at 01:29I changed the code of trades and solved this error. But I don't know why this works.
QUESTION
What array formula will return values which don't appear in another list?
Example:
Cells named ShortList contain (one word per cell):
ANSWER
Answered 2021-Apr-19 at 07:52i think i got it. This returns the expected result in my question.
QUESTION
I'm trying to build a basic GAN to familiarise myself with Pytorch. I have some (limited) experience with Keras, but since I'm bound to do a larger project in Pytorch, I wanted to explore first using 'basic' networks.
I'm using Pytorch Lightning. I think I've added all necessary components. I tried passing some noise through the generator and the discriminator separately, and I think the output has the expected shape. Nonetheless, I get a runtime error when I try to train the GAN (full traceback below):
RuntimeError: mat1 and mat2 shapes cannot be multiplied (7x9 and 25x1)
I noticed that 7 is the size of the batch (by printing out the batch dimensions), even though I specified batch_size to be 64. Other than that, quite honestly, I don't know where to begin: the error traceback doesn't help me.
Chances are, I made multiple mistakes. However, I'm hoping some of you will be able to spot the current error from the code, since the multiplication error seems to point towards a dimensionality problem somewhere. Here's the code.
...ANSWER
Answered 2021-Apr-18 at 14:32This multiplication problem comes from the DoppelDiscriminator. There is a linear layer
QUESTION
I am trying to install cleverhans verion 3.1.0 but getting following error
pip install cleverhans==3.1.0
Note: you may need to restart the kernel to use updated packages. ERROR: Could not find a version that satisfies the requirement cleverhans==3.1.0 (from versions: 2.1.0, 3.0.0, 3.0.0.post0, 3.0.1) ERROR: No matching distribution found for cleverhans==3.1.0
I want to access random_lp_vector method in 3.1.0 version which I am unable to access if I try in 3.0.1 also Is there any option available for adversarial training in the latest version which is 4.0.0
Please kindly help
...ANSWER
Answered 2021-Mar-28 at 07:13You were not able to install version 3.1.0 via pip install as that version is not listed in Python package index(PyPI).
You can download the source code of the required version 3.1.0 or 4.0.0 from github directly and install using setup.py
QUESTION
I was reading the code for Generative Adversarial Nets Code by https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/gan/gan.py, I would like to know what the * sign means here, I searched on Google and Stackoverflow but could not find a clear explanation.
...ANSWER
Answered 2021-Mar-24 at 12:10*x is iterable unpacking notation in Python. See this related answer.
def block returns a list of layers, and *block(...) unpacks the returned list into positional arguments to the nn.Sequential call.
Here's a simpler example:
QUESTION
I'm trying to estimate the gradient of a function by the finite difference method : finite difference method for estimating gradient
TLDR:
grad f(x) = [f(x+h)-f(x-h)]/(2h) for sufficiently small h.
this is also used in the gradient check phase to check your backpropagation in AI as you might know.
This is my network :
...ANSWER
Answered 2021-Mar-21 at 20:05I replaced the gradient estimation code with my own solution gradient and the code works now. Calculating errors can be tricky. As on can see on the histogram (note the log-scale) at the bottom, for most pixels the relative error is smaller than 10^-4 but where the gradient is close to zero, the relative error explodes.
The problem with max(rel_err) and mean(rel_err) is, that they are both easily perturbed by outlier pixels. Better measures for if the order of magnitude is most relevant are the geometric mean and median over all non-zero pixels.
QUESTION
I am trying to replicate this experiment presented in this webpage https://adversarial-ml-tutorial.org/adversarial_examples/
I got the jupyter notebook and loaded in my localhost and open it using Jupiter notebook. When I run the following code to get the dataset using the following code:
...ANSWER
Answered 2021-Mar-04 at 13:43Yes it's a known bug: https://github.com/pytorch/vision/issues/3500
The possible solution can be to patch MNIST download method.
But it requires wget to be installed.
For Linux:
QUESTION
We all know the time complexity of inserting something into a hash set is on average O(1). However, I'm focusing on the worst-case behavior. I mean, there must exist a specific sequence of integral keys which can trigger many hash collisions when the elements are inserted successively, and that's the "worst case" I was referring to.
More concretely:
- What's the worst-case complexity of inserting an key-value pair to a dictionary with respect to N, the number of existing items in the dictionary?
- What's the corresponding adversarial input?
Here is my work so far
...ANSWER
Answered 2021-Mar-01 at 16:55Dict insertion takes O(n) element comparisons worst-case, where n is the number of occupied entries in the table. Additionally, an individual insertion may require rebuilding the hash table, which may involve every element colliding with every other element all over again and take O(n^2) element comparisons.
Hash collision isn't what's causing the slowdown in your tests, though - your tests are spending all their time building and hashing absurdly huge integers. 1<<(1<<33) is an entire gigabyte's worth of integer, for example.
It's fairly easy to construct adversarial input. For example,
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Install adversarial
You can use adversarial 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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