我正在尝试使用Theano后端在Keras训练模型并使用我的GPU . 如果我运行100张图像或20K我从Theano收到错误（见下文）说我没有足够的内存，但我的GPU上有6GB . 我使用了来自Keras docs "THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py"的THEANO_FLAG问题我使用了stackoverflow的this one（echo -e "\n[global]\nfloatX=float32\ndevice=gpu0\n[lib]\ncnmem=0\n" >>〜/ .theanorc）来设置cnmem变量，所以如果你使用来自Keras的标志它将使用你设置的cnmem堆栈溢出一个 .
我已经将cnmem设置为0.83（它最高可以立即出错）和0并且没有任何822 MB它需要但我有6GB的视频内存 . 我确信我做的是一些简单的错误，但我找不到任何可以找到的信息 .
我在Ubuntu 14.04上安装了CUDA . 我刚刚使用"THEANO_FLAGS=device=gpu,floatX=float32 python mnist_transfer_cnn.py "做了Keras MNIST示例
MemoryError: Error allocating 822083584 bytes of device memory (out of memory).
Apply node that caused the error: GpuElemwise{add,no_inplace} (GpuDnnConv{algo='small', inplace=True}.0, GpuReshape{4}.0)
Toposort index: 375
Inputs types: [CudaNdarrayType(float32, 4D), CudaNdarrayType(float32, (True, False, True, True))]
Inputs shapes: [(64, 64, 224, 224), (1, 64, 1, 1)]
Inputs strides: [(3211264, 50176, 224, 1), (0, 1, 0, 0)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[GpuElemwise{Composite{(i0 * (i1 + Abs(i1)))},no_inplace}(CudaNdarrayConstant{[[[[ 0.5]]]]}, GpuElemwise{add,no_inplace}.0), GpuElemwise{Composite{((i0 * i1) + (i0 * i1 * sgn(i2)))}}[(0, 1)](CudaNdarrayConstant{[[[[ 0.5]]]]}, GpuDnnPoolGrad{mode='max'}.0, GpuElemwise{add,no_inplace}.0)]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.