https://github.com/lukasturcani/dnn
Implementations of deep network architectures in PyTorch.
https://github.com/lukasturcani/dnn
cnn cnn-architecture cnn-classification dcgan deep-learning deep-neural-networks deeplearning gan gans image-inpainting neural-network neural-networks progressively-growing-gan
Last synced: about 2 months ago
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Implementations of deep network architectures in PyTorch.
- Host: GitHub
- URL: https://github.com/lukasturcani/dnn
- Owner: lukasturcani
- Created: 2018-03-04T20:05:21.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-07-27T21:08:33.000Z (about 6 years ago)
- Last Synced: 2025-01-10T17:17:46.166Z (9 months ago)
- Topics: cnn, cnn-architecture, cnn-classification, dcgan, deep-learning, deep-neural-networks, deeplearning, gan, gans, image-inpainting, neural-network, neural-networks, progressively-growing-gan
- Language: Python
- Homepage:
- Size: 819 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.rst
Awesome Lists containing this project
README
:author: Lukas Turcani
.. image::
This repo provides a number of network architectures, implemented in
PyTorch, as well as scripts used to run them.
To train a network, run the appropriate run script. For example::
$ python -m dnn.train_scripts.mnist.progressive_growing_gan
Notice that the scripts are run from the module level using the
``python -m`` flag.
Networks can be modified using command line arguments, for example::
$ python -m dnn.train_scripts.mnist.simple_cnn --conv_in_channels 1 20 50 --conv_out_channels 20 50 60 --conv_kernel_size 5 5 5 --conv_strides 1 1 1 --conv_paddings 0 0 0 --conv_dilations 1 1 1 --pool_kernel_sizes 2 2 2 --pool_strides 2 2 2 --pool_paddings 0 0 0 --pool-dilations 1 1 1 --train_batch_size 100 --label_smoothing 0.5 --epochs 10
changes the default values for training batch size, label smoothing and
the number of training epochs and adds a new convolution layer and pooling
layer vs the default network. Any number of layers can be added / removed
via the command line in this fashion.
Each script can have options viewed by::
$ python -m path.to.train.script --help
Results
=======
This is a short summary of some of the nice results from this repo.
Not all implemented architectures are listed here.
* `Progressive Growing GAN (PGGAN)`_
* `Image Inpainting`_
* `DCGAN`_
* `FCGAN`_
Progressive Growing GAN (PGGAN)
-------------------------------
This is a somewhat complex GAN, largely due to a unique training
procedure where the resolution of the GAN is slowly increased during
training.
Run with::
$ python -m dnn.train_scripts.mnist.progressive_growing_gan
Results:
.. image:: images/pggan.gif
Image Inpainting
----------------
This is a task where the generator is provided with an image that
is missing some pixels and it is asked to fill them in. In this
example, I cover up the bottom half of MNIST images and get the
generator to fill them in. The generator used an autoencoder
architecture.
Run with::
$ python -m dnn.train_scripts.mnist.image_inpainting
Results:
.. image:: images/mnist_inpainting.jpg
DCGAN
-----
A more advanced GAN architecture, which is fully convolutional.
Run with::
$ python -m dnn.train_scripts.mnist.dcgan
Results:
.. image:: images/mnist_dcgan.jpg
FCGAN
-----
This is a vanilla GAN using feed-forward networks as both the
generator and discriminator.
Run with::
$ python -m dnn.train_scripts.mnist.fcgan
Results:
.. image:: images/mnist_fcgan.jpg