Issue 1:
Hi,

I have a model that classifies RGB images and it works well.

Now, I would like to use the same model on gray level images without re-training the new model. I could duplicate the gray level image in order to give it three identical channels, but it would use a excessive memory.

So I would like to load the RGB model and get only the weights of a single channel (green for example) that I would affect to my gray level model.

Any idea how I can access the weights of a given channel?

Regards,

Issue 2:
Apologies if this has been reported already at some other place, I have been googling for it quite a bit, with my colleague without success.

While running the simple mnist example with keras+tensorflow using P100 GPGPU we encounter an issue at the intersection of keras/tensorflow/cuda

Using TensorFlow backend.

Using TensorFlow backend.

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally

I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:

name: Tesla P100-PCIE-16GB

major: 6 minor: 0 memoryClockRate (GHz) 1.3285

pciBusID 0000:02:00.0

Total memory: 15.89GiB

Free memory: 15.51GiB

I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0

I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y

I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:02:00.0)

I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:

name: Tesla P100-PCIE-16GB

major: 6 minor: 0 memoryClockRate (GHz) 1.3285

pciBusID 0000:02:00.0

Total memory: 15.89GiB

Free memory: 15.51GiB

I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0

I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y

I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:02:00.0)

F tensorflow/core/common_runtime/gpu/gpu_device.cc:121] Check failed: err == cudaSuccess (71 vs. 0)

F tensorflow/core/common_runtime/gpu/gpu_device.cc:121] Check failed: err == cudaSuccess (71 vs. 0)

srun: error: nid05274: task 1: Aborted

srun: Terminating job step 1250383.0

srun: error: nid05271: task 0: Aborted

we are using keras 2.0.2, tensorflow 1.0.0. cuda 8.0.53.

We seem to be having this issue both in python2.7.12 and python3.5.2 (keras 1.2 and 2.0 ...)

Bare tensorflow runtest are going fine, which lead us to think that this is really at the intersection of keras/tensorflow/cuda.

There is no problem using the theano backend.

The same test runs fine on various machine with the same version of the software but with TitanX GPGPU.

I seem to be tracing this back to

https://github.com/tensorflow/tensorflow/blob/r1.0/tensorflow/core/common_runtime/gpu/gpu_device.cc#L121

http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038

"""

cudaErrorNotSupported = 71

This error indicates the attempted operation is not supported on the current system or device.

"""

Would anyone have insights on what keras specific call is percolating to this unsupported cuda call ? It might be a cuda issue in the end, but this originates from using keras, hence this GH issue. thanks in advance for any help on the matter.