Comments (5)
After a few more hours of training, test error continues to drop; default hyperparams seem to be working. It seems that training the networks to produce realistic samples and training to do semi-supervised learning happen at different timescales.
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About how many epochs are required before the test error gets close to the value reported in the paper?
from improved-gan.
Hello!why discriminator dense layer just have 10 units? why not 10+1(real/fake)?
from improved-gan.
Hello!why discriminator dense layer just have 10 units? why not 10+1(real/fake)?
because other(11) class be eliminated,
from improved-gan.
Has anyone managed to reproduce the exact results for semi-supervised learning using
train_cifar_feature_matching.py
? With the default hyperparameters and 4000 labeled examples, I'm overfitting and getting 32% test error after 48 epochs. Getting 0.5% training error. Paper claims to get test error of only 18.6% on this task.Do I need to train longer (for the full 1200 epochs?), or are others having this same problem?
i meet same problem,final your results test erro?
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Related Issues (20)
- Loss Functions: Paper vs Code HOT 1
- Variable does not exist or was not created with tf.get_variable
- loss about l_unl is not clear
- why not zero mean the input HOT 1
- Inception Score calculation HOT 5
- Inception score for conditional GANs HOT 1
- Batch size mismatch with Inception classifier HOT 10
- how to specify num of classes or labels in imagenet code
- can't run it HOT 2
- Tab Error HOT 3
- ValueError: squeeze_dims[1] not in [-2,2). for 'Squeeze_1' (op: 'Squeeze') with input shapes: [?,2048]. HOT 1
- question of implementation of inception score HOT 7
- AttributeError: 'module' object has no attribute 'absolute_import' HOT 1
- how long train_mnist_feature_matching.py run?
- What version of your python
- Will historical averaging from the paper be implemented?
- Segmentation fault
- Implementation of cifar10_match_feature on Pytorch.
- What's the meaning of the parameter splits(default=10)?
- Inference mode vs training mode
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