Comments (2)
Doesn't the Titan X have 12 GB memory? Also, this is the smallest DenseNet that is in the paper so it shoudn't run out of memory. However, this DenseNet model is used for CIFAR 10/100 models.
Perhaps try with subsample_initial_block=True, bottleneck=True
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I was able to get it working by changing the depth. The "default" is depth=40. I changed it to depth=7, and fit_generator is now working. That is, I don't get the memory failure. It's still on the first epoch but the accuracy is going up, so this looks very promising!
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Related Issues (20)
- Cifar10 weights HOT 1
- no longer works with newest keras HOT 8
- About implementation of __dense_block HOT 2
- Running Densnet in CPU HOT 1
- Why reshape in fc-densenet in top layer before applying softmax? HOT 2
- AttributeError: 'NoneType' object has no attribute 'get_file' HOT 2
- Where is connection to 12 layers of each dense block? HOT 1
- About DenseNet HOT 1
- About Densenet architecture HOT 1
- Plans for memory efficient implementation in Keras? HOT 1
- How to upload my own dataset instead of the Cifar10 dataset HOT 1
- How to use 'DENSENET_121_WEIGHTS_PATH_NO_TOP'? HOT 1
- Mistake in L2 regularization HOT 1
- creat model HOT 2
- creat model HOT 1
- imagenet_inference
- inter_channel HOT 1
- Poor CIFAR100 accuracy HOT 1
- Error about normalize_data_format
- Unable to convert to frozen graph or checkpoint
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