Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Tensorflow Examples | 42,312 | 5 months ago | 218 | other | Jupyter Notebook | |||||
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | ||||||||||
Awesome Tensorflow | 16,809 | 3 months ago | 30 | cc0-1.0 | ||||||
TensorFlow - A curated list of dedicated resources http://tensorflow.org | ||||||||||
Sonnet | 9,515 | 41 | 11 | a month ago | 27 | March 27, 2020 | 33 | apache-2.0 | Python | |
TensorFlow-based neural network library | ||||||||||
Dcgan Tensorflow | 6,761 | 2 years ago | 183 | mit | JavaScript | |||||
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks" | ||||||||||
Tensorflow Tutorials | 5,861 | 4 years ago | 17 | Jupyter Notebook | ||||||
Simple tutorials using Google's TensorFlow Framework | ||||||||||
Tensorflow_tutorials | 5,548 | a year ago | 11 | other | Jupyter Notebook | |||||
From the basics to slightly more interesting applications of Tensorflow | ||||||||||
T81_558_deep_learning | 5,261 | a day ago | 2 | other | Jupyter Notebook | |||||
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks | ||||||||||
Cnn Text Classification Tf | 5,256 | 3 years ago | 110 | apache-2.0 | Python | |||||
Convolutional Neural Network for Text Classification in Tensorflow | ||||||||||
Graph_nets | 5,188 | 7 | 4 months ago | 7 | January 29, 2020 | 5 | apache-2.0 | Python | ||
Build Graph Nets in Tensorflow | ||||||||||
Topdeeplearning | 4,901 | 3 years ago | 17 | mit | Python | |||||
A list of popular github projects related to deep learning |
Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.
First, download dataset with:
$ python download.py mnist celebA
To train a model with downloaded dataset:
$ python main.py --dataset mnist --input_height=28 --output_height=28 --train
$ python main.py --dataset celebA --input_height=108 --train --crop
To test with an existing model:
$ python main.py --dataset mnist --input_height=28 --output_height=28
$ python main.py --dataset celebA --input_height=108 --crop
Or, you can use your own dataset (without central crop) by:
$ mkdir data/DATASET_NAME
... add images to data/DATASET_NAME ...
$ python main.py --dataset DATASET_NAME --train
$ python main.py --dataset DATASET_NAME
$ # example
$ python main.py --dataset=eyes --input_fname_pattern="*_cropped.png" --train
If your dataset is located in a different root directory:
$ python main.py --dataset DATASET_NAME --data_dir DATASET_ROOT_DIR --train
$ python main.py --dataset DATASET_NAME --data_dir DATASET_ROOT_DIR
$ # example
$ python main.py --dataset=eyes --data_dir ../datasets/ --input_fname_pattern="*_cropped.png" --train
After 6th epoch:
After 10th epoch:
MNIST codes are written by @PhoenixDai.
More results can be found here and here.
Details of the loss of Discriminator and Generator (with custom dataset not celebA).
Details of the histogram of true and fake result of discriminator (with custom dataset not celebA).
Taehoon Kim / @carpedm20