Comments (3)
Here is my hypothesis:
Once the dataset is expressive enough for the problem, it is more efficient to train the network over that dataset multiple times (i.e. epochs). In this case, preprocessing the data multiple times is unnecessary.
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Sorry for the delayed response! The initial reason I added a separate pre-processing pipeline was because I was being bottlenecked by i/o and it is much more efficient to read in 32x32 image patches than the full-sized images. I don't remember what the memory specs of my machine were, but I think I was running into issues loading the whole dataset into memory. If you are able to do that, it might make more sense to create the patches at runtime.
edit
One problem with creating the patches at runtime on the Ms. Pac-Man data is that most patches will have no movement in them. To solve this, I randomly sample patches until it finds one with movement. This means that it will take longer to generate some patches than others. It's probably more efficient to get all of this generation cost out of the way once during pre-processing than having to deal with it every epoch during training.
from adversarial_video_generation.
Thanks for your reply? It's very helpful.
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
- 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- Tensorflow and packages are out of date
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