Comments (4)
from deeplearningimplementations.
Looks like you're short on RAM. In the load image dataset, you may choose to load only a fraction of the dataset in memory.
In the Colorful folder, there is code to load data on the fly. This removes the RAM limitation at the cost of slower training.
from deeplearningimplementations.
@tdeboissiere which code and how to use it?
from deeplearningimplementations.
In the README there : https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful/src/model, you'll find the "training_mode" command line argument. From there you can trace back the code to load on the fly.
from deeplearningimplementations.
Related Issues (20)
- Can you provide test code?
- WGAN-GP update formula wrong? HOT 2
- tf.sqrt in WGAN-GP gradient penalty formula throws NaNs HOT 2
- Densenet: takes at least 3 arguments (4 given) HOT 2
- Densenet: growth_rate
- Discriminator Implementation Error HOT 1
- CelebA dataset is not accessible HOT 2
- Wasserstein distance formula
- concat_axis missing for the transition layer call inside Densenet HOT 2
- KeyError: '__flags'
- InfoGAN: wrong argument descriptions in help
- densenet on cifar10
- Training tends to yellow colors
- main.py error HOT 1
- Colorful - Yellow results
- How to load my local dataset instead of the Cifar10 dataset and feed it to the DenseNet
- deconv forward_pass
- Quantitative evaluations verified?
- Explaning discriminator model
- How to generate images using model.predict just as the validation ones?
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