carpedm20 / carpedm20/DCGAN-tensorflow

Can't create checkpoint

Open
#390 0 comments 0 reactions 0 assignees View on GitHub
Dominant language
JavaScript
Stars
7.2k
Forks
2.6k
PR merge metrics
No merged PRs in 30d

Description

I have already checked the previous issues, #338 , #380 , #383 , #369 and I still can't create checkpoint under my **checkpoint_dir**.
The `checkpoint` and the `samples` folders remain empty.

![image](https://user-images.githubusercontent.com/46207148/114422830-94c08280-9bf1-11eb-8b0d-8bd878edf299.png)

Does anyone know how to solve this?
Any advice would be really appreciated.
I'm sharing the command and logs :
### command
`python main.py --dataset testdir --input_height 96 --input_width 96 --output_height 96 --output_width 96 --batch_size=1 --train --epoch=300 --ckpt_freq=100 --generate_test_images=10`
### logs
```
---------
Variables: name (type shape) [size]
---------
generator/g_h0_lin/Matrix:0 (float32_ref 100x18432) [1843200, bytes: 7372800]
generator/g_h0_lin/bias:0 (float32_ref 18432) [18432, bytes: 73728]
generator/g_bn0/beta:0 (float32_ref 512) [512, bytes: 2048]
generator/g_bn0/gamma:0 (float32_ref 512) [512, bytes: 2048]
generator/g_h1/w:0 (float32_ref 5x5x256x512) [3276800, bytes: 13107200]
generator/g_h1/biases:0 (float32_ref 256) [256, bytes: 1024]
generator/g_bn1/beta:0 (float32_ref 256) [256, bytes: 1024]
generator/g_bn1/gamma:0 (float32_ref 256) [256, bytes: 1024]
generator/g_h2/w:0 (float32_ref 5x5x128x256) [819200, bytes: 3276800]
generator/g_h2/biases:0 (float32_ref 128) [128, bytes: 512]
generator/g_bn2/beta:0 (float32_ref 128) [128, bytes: 512]
generator/g_bn2/gamma:0 (float32_ref 128) [128, bytes: 512]
generator/g_h3/w:0 (float32_ref 5x5x64x128) [204800, bytes: 819200]
generator/g_h3/biases:0 (float32_ref 64) [64, bytes: 256]
generator/g_bn3/beta:0 (float32_ref 64) [64, bytes: 256]
generator/g_bn3/gamma:0 (float32_ref 64) [64, bytes: 256]
generator/g_h4/w:0 (float32_ref 5x5x3x64) [4800, bytes: 19200]
generator/g_h4/biases:0 (float32_ref 3) [3, bytes: 12]
discriminator/d_h0_conv/w:0 (float32_ref 5x5x3x64) [4800, bytes: 19200]
discriminator/d_h0_conv/biases:0 (float32_ref 64) [64, bytes: 256]
discriminator/d_h1_conv/w:0 (float32_ref 5x5x64x128) [204800, bytes: 819200]
discriminator/d_h1_conv/biases:0 (float32_ref 128) [128, bytes: 512]
discriminator/d_bn1/beta:0 (float32_ref 128) [128, bytes: 512]
discriminator/d_bn1/gamma:0 (float32_ref 128) [128, bytes: 512]
discriminator/d_h2_conv/w:0 (float32_ref 5x5x128x256) [819200, bytes: 3276800]
discriminator/d_h2_conv/biases:0 (float32_ref 256) [256, bytes: 1024]
discriminator/d_bn2/beta:0 (float32_ref 256) [256, bytes: 1024]
discriminator/d_bn2/gamma:0 (float32_ref 256) [256, bytes: 1024]
discriminator/d_h3_conv/w:0 (float32_ref 5x5x256x512) [3276800, bytes: 13107200]
discriminator/d_h3_conv/biases:0 (float32_ref 512) [512, bytes: 2048]
discriminator/d_bn3/beta:0 (float32_ref 512) [512, bytes: 2048]
discriminator/d_bn3/gamma:0 (float32_ref 512) [512, bytes: 2048]
discriminator/d_h4_lin/Matrix:0 (float32_ref 18432x1) [18432, bytes: 73728]
discriminator/d_h4_lin/bias:0 (float32_ref 1) [1, bytes: 4]
Total size of variables: 10496388
Total bytes of variables: 41985552
checkpoint_dir : ./out/20210412.235725 - data - testdir - x96.z100.uniform_signed.y96.b1/checkpoint
sample_dir : ./out/20210412.235725 - data - testdir - x96.z100.uniform_signed.y96.b1/samples
image_path : ./data/testdir/plane.jpg
[*] Reading checkpoints... ./out/20210412.235725 - data - testdir - x96.z100.uniform_signed.y96.b1/checkpoint
[*] Failed to find a checkpoint
[!] Load failed...
```

Contributor guide

No contributing guide indexed for this repository

Research direction

Start with main.py and reproduce the command using the reported dataset and output settings. Trace the checkpoint_dir and sample_dir paths shown in the logs, then determine why training does not produce files; done means the cause is identified and checkpoint and sample output can be verified.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, tensorflow
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.