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.ipynb_checkpoints

.ipynb_checkpoints

 
 

MNIST_data

MNIST_data

 
 

Click_Me_049_Ginput_100_hiddenone128_hiddentwo256_LR_2e-05.png

Click_Me_049_Ginput_100_hiddenone128_hiddentwo256_LR_2e-05.png

 
 

GANSerial.ipynb

GANSerial.ipynb

 
 

GANWithBetterParams.ipynb

GANWithBetterParams.ipynb

 
 

GANWithBetterParams_batches.ipynb

GANWithBetterParams_batches.ipynb

 
 

GANWithBetterParamswithLotofModifs.ipynb

GANWithBetterParamswithLotofModifs.ipynb

 
 

GANWithBetterParamswithLotofModifs_modified.ipynb

GANWithBetterParamswithLotofModifs_modified.ipynb

 
 

GANWithBetterParamswithProfiling.ipynb

GANWithBetterParamswithProfiling.ipynb

 
 

PipelingExample.ipynb

PipelingExample.ipynb

 
 

README.md

README.md

 
 

SerialGANWithMultipleLayers.ipynb

SerialGANWithMultipleLayers.ipynb

 
 

input_data.py

input_data.py

 
 

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PipeGAN - Pipelined GAN for the MNIST dataset.

A pipelined version of a Generative Adversarial Neural Network. Achieved a 30% speedup using the MNIST and Fashion-MNIST datasets.

References

PipeDream - https://arxiv.org/pdf/1806.03377.pdf

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A pipelined version of a Generative Adversarial Neural Network. Achieved a 102% speedup using the MNIST and Fashion-MNIST datasets.

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