Comments (4)
Hi Matt,
Thanks for your suggestion. I started with the "gradient bug" branch, I noticed a small error in resize function (It should be tf.image.resize_images(inputs, [scale_height, scale_width]) and not tf.image.resize_images(inputs, scale_height, scale_width) ). I am able to train till 3000 odd steps with the above suggested learning rates, but again getting "NaN" after this stage. Do you have any other practical hacks in training this GAN to converge?
I am currently working only with UCF101 dataset, even I felt it unreasonable to download 1M YouTube videos through a script.
Thanks
Akilesh
from adversarial_video_generation.
Hi Akilesh,
You may want to try the paper's original learning rates: "The learning rate ρG starts at 0.04 and is reduced over time to 0.005... The network is trained by setting the learning rate ρD to 0.02." However, I usually find NaN is a result of the learning rate being too high. I can't imagine you'd want to go any lower than 0.00004, but that may be worth a shot too.
Can I ask how you went about collecting the Sports1m data off of YouTube? I landed on the Ms. Pac-Man data because it seemed unreasonable to try downloading 1M YouTube videos through a script.
from adversarial_video_generation.
Also, make sure you try on the gradient-bug branch. The code on the master branch may be killing the gradient from adversarial network, so I've been trying to fix that on the other branch
from adversarial_video_generation.
Did you try reducing the discriminator learning rate to 0.002 or 0.0002? Seems like it's definitely an issue with the discriminator diverging, so that could help.
You may want to check out OpenAI's paper "Improved Techniques for Training GANs". This Reddit thread may have some helpful tips as well.
from adversarial_video_generation.
Related Issues (20)
- Confusion using the plug-and-play data HOT 5
- Regarding the normalization step HOT 4
- problem about exist model HOT 4
- Some problem about gdl_loss HOT 1
- Question about discriminator input HOT 3
- What do the output images represent? HOT 12
- Normalization of losses
- Why need the data processing step? HOT 3
- Error with np.random.choice HOT 4
- What is PSNR error exactly? HOT 3
- Can you share the code that generates the gif file? HOT 2
- TypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64
- Alternative GDL loss implemetation
- Loss weighting HOT 1
- ValueError: Dimension 3 in both shapes must be equal HOT 1
- Updating Code to New Tensorflow version HOT 2
- GLARING bug with the process data pipeline.
- TypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64 HOT 1
- ValueError: Dimensions must be equal, but are 1 and 3 for 'generator/train/Conv2D' (op: 'Conv2D') with input shapes: [?,4,4,1], [1,2,3,3].
- Tensorflow and packages are out of date
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