Comments (6)
@chenhuiphd the inference result was the output of a sigmoid function:)
from attentive-gan-derainnet.
yes, My TF version is 1.10.The range of discriminate_inference value is between (0-1), so log (1-discriminate_inference) is negative. When I train, I find that generator loss will start to appear negative, and finally Nan will appear. So I think adding negative sign before lgan can make lgan positive, so that loss can be avoided in the case of negative numbers or nan.
from attentive-gan-derainnet.
@chenhuiphd I have met the same problem with tf 1.3 but nothing happened when I upgrade tensorflow from 1.3 into 1.12. Amazing tensorflow:)
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FileNotFoundError: [Errno 2] No such file or directory: './data/vgg16.npy'
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After downloading the file, the following problems occurred again. I hope you can help me when you see the message. Thank you very much
Traceback (most recent call last):
File "tools/train_model.py", line 339, in
train_model(args.dataset_dir, weights_path=args.weights_path)
File "tools/train_model.py", line 272, in train_model
encoding='latin1').item()
File "/home/409/anaconda3/envs/derain/lib/python3.5/site-packages/numpy/lib/npyio.py", line 453, in load
pickle_kwargs=pickle_kwargs)
File "/home/409/anaconda3/envs/derain/lib/python3.5/site-packages/numpy/lib/format.py", line 722, in read_array
raise ValueError("Object arrays cannot be loaded when "
ValueError: Object arrays cannot be loaded when allow_pickle=False
from attentive-gan-derainnet.
是numpy的版本没处理好,参考https://blog.csdn.net/qq_35008279/article/details/89496094已经解决
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Related Issues (20)
- test_model.py中的label_path HOT 8
- 归一化层 HOT 2
- tf_io_pipline_tools模块引用错误
- 训练自己图片集有问题
- batch size HOT 2
- vgg16.npy HOT 1
- the bug HOT 3
- How to run this project at google colab ? HOT 1
- Colab Test HOT 2
- question about dataset
- Performance issues in /data_provider (by P3) HOT 1
- no file named "./data/vgg16.npy" HOT 4
- 训练时Discriminator的loss不收敛问题 HOT 4
- Question HOT 7
- no pretrained model "vgg16.npy"
- 關於訓練數據的問題(About train dataset size problem) HOT 1
- 测试的问题 HOT 1
- complexity
- 关于train_model.py文件无法正常运行 HOT 1
- loss function problem HOT 6
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