GAN | Resources and Implementations of Generative Adversarial | Machine Learning library
kandi X-RAY | GAN Summary
kandi X-RAY | GAN Summary
The beginning. The first paper.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Train the model
- Generate a numpy array of samples
- Generate a sample of z
GAN Key Features
GAN Examples and Code Snippets
Lines of Code : 28
License : No License
Copy//Training with MS task
python msdistgan_mnist.py --ss_task=2
//Training with MS task
python msdistgan_mnist1k.py --k=2 --ss_task=2 --is_train=1
//Testing with MS task
python msdistgan_mnist1k.py --k=2 --ss_task=2 --is_train=0
//Training with MS t pip install -r requirements.txt
.
├── arch
│ ├── ...
├── data # Follow the way the dataset has been placed here
│ ├── ACDC # Here the ACDC dataset must be placed
│ └── Cityscape # Here th @inproceedings{I2V-GAN2021,
title = {I2V-GAN: Unpaired Infrared-to-Visible Video Translation},
author = {Shuang Li and Bingfeng Han and Zhenjie Yu and Chi Harold Liu and Kai Chen and Shuigen Wang},
booktitle = {ACMMM},
year = {202 #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: CycleGAN.py
# Author: Yuxin Wu
import argparse
import glob
import os
import tensorflow as tf
from six.moves import range
from tensorpack import *
from tensorpack.tfutils.scope_utils import auto_ #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: InfoGAN-mnist.py
# Author: Yuxin Wu
import argparse
import numpy as np
import os
import cv2
import tensorflow as tf
from tensorpack import *
from tensorpack.dataflow import dataset
from tensorpa #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: DiscoGAN-CelebA.py
# Author: Yuxin Wu
import argparse
import numpy as np
import os
import tensorflow as tf
from tensorpack import *
from tensorpack.tfutils.scope_utils import auto_reuse_variable Community Discussions
Trending Discussions on GAN
QUESTION
I am following this Github Repo for the WGAN implementation with Gradient Penalty.
And I am trying to understand the following method, which does the job of unit-testing the gradient-penalty calulations.
...ANSWER
Answered 2022-Apr-02 at 17:11good_gradient = torch.ones(*image_shape) / torch.sqrt(image_size)
First, note the Gradient Penalty term in WGAN is =>
(norm(gradient(interpolated)) - 1)^2
And for the Ideal Gradient (i.e. a Good Gradient), this Penalty term would be 0. i.e. A Good gradient is one which has its gradient_penalty is as close to 0 as possible
This means the following should satisfy, after considering the L2-Norm of the Gradient
(norm(gradient(x')) -1)^2 = 0
i.e norm(gradient(x')) = 1
i.e. sqrt(Sum(gradient_i^2) ) = 1
Now if you just continue simplifying the above (considering how norm is calculated, see my note below) math expression, you will end up with
good_gradient = torch.ones(*image_shape) / torch.sqrt(image_size)
Since you are passing the image_shape as (256, 1, 28, 28) - so torch.sqrt(image_size) in your case is tensor(28.)
Effectively the above line is dividing each element of A 4-D Tensor like [[[[1., 1. ... ]]]] with a scaler tensor(28.)
Separately, note hownorm is calculated
torch.norm without extra arguments performs, what is called a Frobenius norm which is effectively reshaping the matrix into one long vector and returning the 2-norm of that.
Given an M * N matrix, The Frobenius Norm of a matrix is defined as the square root of the sum of the squares of the elements of the matrix.
QUESTION
I have a Flask back end that is functional without using uwsgi and nginx. I'm trying to deploy it on an EC2 instance with its front-end.
No matter what I do, I can't reach the back-end. I opened all the ports for testing purposes but that does not help.
Here's my uwsgi ini file:
...ANSWER
Answered 2022-Mar-05 at 12:14My guess is that url is not in proper form
Try
proxy_pass http://0.0.0.0:5000;
QUESTION
I am trying to develop a GAN, I have created the generator and the discriminator and now I am trying to train it. I am using the Mnist dataset but I plan to use some more. The problem is that when I train it I get this error: Input 0 of layer "conv2d_transpose_4" is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: (None, 100)
I don't really know if the problem is in the networks or in the data used to train the GAN, can someone tell me how should I train it or where the problem is?
imports:
...ANSWER
Answered 2022-Mar-04 at 09:51The problem is coming from the first Flatten layer in the Discriminator model, which is converting your n-dimensional tensor to a 1D tensor. Since a MaxPooling2D layer cannot work with a 1D tensor, you are seeing that error. If you remove it, it should work:
QUESTION
I'm trying to implement a simple GAN in Pytorch. The following training code works:
...ANSWER
Answered 2022-Feb-16 at 13:43Supplying inputs in either the same batch, or separate batches, can make a difference if the model includes dependencies between different elements of the batch. By far the most common source in current deep learning models is batch normalization. As you mentioned, the discriminator does include batchnorm, so this is likely the reason for different behaviors. Here is an example. Using single numbers and a batch size of 4:
QUESTION
My HTML is something like
...ANSWER
Answered 2022-Jan-25 at 17:06If you know they'll be the topmost element, you could use elementFromPoint. For instance, if the elements are at the left-hand edge:
QUESTION
Currently there are a lot of activation functions like sigmoid, tanh, ReLU ( being the preferred choice ), but I have a question that concerns which choices are needed to be considered so that a certain activation function should be selected.
For example : When we want to Upsample a network in GANs, we prefer using LeakyReLU.
I am a newbie in this subject, and have not found a concrete solution as to which activation function to use in different situations.
My knowledge uptil now :
Sigmoid : When you have a binary class to identify
Tanh : ?
ReLU : ?
LeakyReLU : When you want to upsample
Any help or article will be appreciated.
...ANSWER
Answered 2022-Jan-25 at 14:57This is an open research question. The choice of activation is also very intertwined with the architecture of the model and the computation / resources available so it's not something that can be answered in silo. The paper Efficient Backprop, Yann LeCun et. al. has a lot of good insights into what makes a good activation function.
That being said, here are some toy examples that may help get intuition for activation functions. Consider a simple MLP with one hidden layer and a simple classification task:
In the last layer we can use sigmoid in combination with the binary_crossentropy loss in order to use intuition from logistic regression - because we're just doing simple logistic regression on the learned features that the hidden layer gives to the last layer.
What types of features are learned depends on the activation function used in that hidden layer and the number of neurons in that hidden layer.
Here is what ReLU learns when using two hidden neurons:
https://miro.medium.com/max/2000/1*5nK725uTBUeoIA0XjEyA_A.gif
(on the left is what the decision boundary looks like in the feature space)
As you add more neurons you get more pieces with which to approximate the decision boundary. Here is with 3 hidden neurons:
And 10 hidden neurons:
Sigmoid and Tanh produce similar decsion boundaries (this is tanh https://miro.medium.com/max/2000/1*jynT0RkGsZFqt3WSFcez4w.gif - sigmoid is similar) which are more continuous and sinusoidal.
The main difference is that sigmoid is not zero-centered which doesn't make it a good choice for a hidden layer - especially in deep networks.
QUESTION
I'm training GAN with MNIST and I want to visualize Generator output with noise input during training.
Here is the code:
...ANSWER
Answered 2022-Jan-15 at 02:45when you use cmap="gray" in plt.imshow() you must either unscale your output or set vmin and vmax.
From what I see you scaled by dividing 255, so you must multiply your data by 255 or, alternativle set vmin=0, vmax=1
Option1:
QUESTION
I'm working on GANs model, the generator creates a tensor with size (3,128,128) which I dumped with the pseudo-code
ANSWER
Answered 2022-Jan-12 at 06:59It seems like extracting a sub-tensor directly from the original will bring the whole container with it. The function .clone() can solve it. Example:
QUESTION
I'm trying to develop a GAN using FastAi. When converting the Tensor to an Image I get this error.
...ANSWER
Answered 2021-Dec-11 at 17:40I suggest for you to use this code to convert the output of your model from a tensor to a PIL image:
QUESTION
I'm currently building a GAN with Tensorflow 2 and Keras and noticed a lot of the existing Neural Networks for the generator and discriminator use Conv2D and Conv2DTranspose in Keras.
I'm struggling to find something that functionally explains the difference between the two. Can anyone explain what these two different options for making a NN in Keras mean?
...ANSWER
Answered 2021-Dec-09 at 17:16Conv2D applies convolutional operation on the input, but on the contrary, Conv2DTranspose applies Deconvolutional operation on the input.
For example:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GAN
You can use GAN 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.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
