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我試圖使操作者在MXNet將在以下面的方式輸出介紹稀疏性輸入：使用TOPK修剪在MXNet
- 分別做用於每個數據點的修剪（軸0爲對於數據點）
- 滴藥較低的權重爲0
- 保持相同的尺寸輸入
我目前這樣做與下面的代碼段（假定動作是輸入至t他的運營商）：
flat = mx.sym.flatten(act)
mask = mx.sym.topk(flat, k = int(frac * flat.infer_shape(data=shape)[1][0][1]), axis = 1, ret_typ = 'mask').reshape(act.infer_shape(data=shape)[1][0])
custom = mx.sym.where(mask == 1, act, mask)
在這種實現方式下，張量行爲的維度存在限制。一個非常大的張量，夷爲平地，傳遞到在IndexFill錯誤TOPK結果時：
[20:27:53] /home/ubuntu/mxnet/dmlc-core/include/dmlc/logging.h:304: [20:27:53] /home/ubuntu/mxnet/mshadow/mshadow/././././cuda/tensor_gpu-inl.cuh:58: too large launch parameter: IndexFill[100352,1], [32,32,1]
Stack trace returned 10 entries:
[bt] (0) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x3c) [0x7fb593bbc9ac]
[bt] (1) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN7mshadow4cuda9IndexFillIffEEvNS_6TensorINS_3gpuELi2ET0_EERKNS2_IS3_Li1ET_EERKS5_+0x492) [0x7fb59581bf82]
[bt] (2) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2op8TopKImplIN7mshadow3gpuEEEvNS_10RunContextENS_8ResourceERKNS_5TBlobERKSt6vectorIS6_SaIS6_EERKNS0_9TopKParamE+0x3ca1) [0x7fb595841521]
[bt] (3) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet2op4TopKIN7mshadow3gpuEEEvRKN4nnvm9NodeAttrsERKNS_9OpContextERKSt6vectorINS_5TBlobESaISC_EERKSB_INS_9OpReqTypeESaISH_EESG_+0x345) [0x7fb595842cc5]
[bt] (4) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(+0x1318cf9) [0x7fb5947aecf9]
[bt] (5) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet6engine14ThreadedEngine15ExecuteOprBlockENS_10RunContextEPNS0_8OprBlockE+0x8c) [0x7fb5947ef07c]
[bt] (6) /usr/local/lib/python2.7/dist-packages/mxnet-0.10.1-py2.7.egg/mxnet/libmxnet.so(_ZNSt17_Function_handlerIFvvEZZN5mxnet6engine23ThreadedEnginePerDevice13PushToExecuteEPNS2_8OprBlockEbENKUlvE1_clEvEUlvE_E9_M_invokeERKSt9_Any_data+0x60) [0x7fb5947f2190]
[bt] (7) /usr/lib/x86_64-linux-gnu/libstdc++.so.6(+0xb1a60) [0x7fb5a3c45a60]
[bt] (8) /lib/x86_64-linux-gnu/libpthread.so.0(+0x8184) [0x7fb5a9e07184]
[bt] (9) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7fb5a9b34bed]
所以我的問題是：
- 目前，它具有非常小批量的大小的功能。但有沒有辦法增加批量並避免錯誤？
- 是否有更好的實現操作符的方法？
它看起來像一個MXNet的錯誤，我可以問你在這裏提交它：https://github.com/apache/incubator-mxnet/issues/新 –