TAC-GAN
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dashayushman
A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions (https://arxiv.org/abs/1703.06412)
when I run train.py file it is running on CPU directly. can you show the command how to run train.py on GPU?
I have followed the README file closely, using the same hyperparameters and the provided text descriptions in text.txt, however the results turn out to be as follow: a flower with...
Traceback (most recent call last): File "encode_text.py", line 32, in main() File "encode_text.py", line 16, in main model = skipthoughts.load_model() File "/home/anu/Downloads/TAC-GAN-master/skipthoughts.py", line 60, in load_model utable, btable = load_tables()...
from numpy.testing.nosetester import import_nose ImportError: No module named 'numpy.testing.nosetester' i am getting this error while running the train.py file. can you help me for that
When I run the msssim.py code, it successfully returned the results. But the results only showed class102's msssim score and it was nan. I don't know where is the error....
I hope to know whether you evaluated the generated images with FID . I try it and it shows 91.16 . It is too high compared with stackGAN++ model ....
When I follow your code to generating images for the text in the dataset, the train.py does not have parameter of outputs_dir so that it always reports error. What should...
While using the generate_images script I am facing this issue that the shapes of both the tensors should be same. I am using the Macbook Pro with High Sierra OS...
Hi, Thanks for the well documented and organised code! I have a few doubts. Firstly, on training the GAN on my own dataset, the output doesn't abide by the text....
Hello, great thanks for sharing this code. Have you tried generating images on [birds](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) dataset?
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A Tensorflow implementation of the Text Conditioned Auxiliary Classifier Generative Adversarial Network for Generating Images from text descriptions (https://arxiv.org/abs/1703.06412)