運行MXNetR時與數據形狀相關的錯誤

Question

我正在嘗試使用MXNetR構建前饋神經網絡。我的輸入是一個6380行和180列的數據框。我的訓練和測試輸出是一維向量，每個元素有319個元素。

我運行模型的批量大小設置爲1，輸出層的神經元數量設置爲319.因此，對於每個批次，我預計會得到一個包含319個元素的向量。我的目標是最大限度地減少我的損失函數，這是我的預測輸出向量和實際輸出向量之間的相關性。

下面是我的代碼：

# Define the input data 
    data <- mx.symbol.Variable("data") 

    # Define the first fully connected layer 
    fc1 <- mx.symbol.FullyConnected(data, num_hidden = 100) 
    act.fun <- mx.symbol.Activation(fc1, act_type = "relu") # create a hidden layer with Rectified Linear Unit as its activation function. 
    output <<- mx.symbol.FullyConnected(act.fun, num_hidden = 319) 

    # Customize loss function 
    label <- mx.symbol.Variable("label") 
    lro <- 
     mx.symbol.MakeLoss(mx.symbol.Correlation(mx.symbol.reshape(output 
    ,shape = (1,319)),label)) 

    model <- mx.model.FeedForward.create(symbol=lro, X=train.x, 
             y=train.y, 
             eval.data = list(data = test.x, 
                 label = test.y), 
             num.round=5000, 
             array.batch.size=1, 
             optimizer = "adam", 
             learning.rate = 0.0003, 
             eval.metric = mx.metric.rmse, 
             epoch.end.callback = 
             mx.callback.log.train.metric(20, logger)) 

這裏是錯誤，當我運行上面的代碼：

[15:49:28] /home/cgagnon/src/q5/mxnet/dmlc-core/include/dmlc/./logging.h:304: [15:49:28] src/operator/./correlation-inl.h:176: Check failed: dshape1.ndim() == 4U (2 vs. 4) data should be a 4D tensor 

Stack trace returned 10 entries: 
[bt] (0) /usr/lib64/R/library/mxnet/libs/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x29) [0x7f725a8528b9] 
[bt] (1) /usr/lib64/R/library/mxnet/libs/libmxnet.so(_ZNK5mxnet2op15CorrelationProp10InferShapeEPSt6vectorIN4nnvm6TShapeESaIS4_EES7_S7_+0x2a2) [0x7f725b4a8222] 
[bt] (2) /usr/lib64/R/library/mxnet/libs/libmxnet.so(+0xd461f9) [0x7f725b3241f9] 
[bt] (3) /usr/lib64/R/library/mxnet/libs/libmxnet.so(+0x116630f) [0x7f725b74430f] 
[bt] (4) /usr/lib64/R/library/mxnet/libs/libmxnet.so(+0x1167bb2) [0x7f725b745bb2] 
[bt] (5) /usr/lib64/R/library/mxnet/libs/libmxnet.so(_ZN4nnvm11ApplyPassesENS_5GraphERKSt6vectorISsSaISsEE+0x501) [0x7f725b761481] 
[bt] (6) /usr/lib64/R/library/mxnet/libs/libmxnet.so(_ZN4nnvm9ApplyPassENS_5GraphERKSs+0x8e) [0x7f725b699f2e] 
[bt] (7) /usr/lib64/R/library/mxnet/libs/libmxnet.so(_ZN4nnvm4pass10InferShapeENS_5GraphESt6vectorINS_6TShapeESaIS3_EESs+0x240) [0x7f725b69c520] 
[bt] (8) /usr/lib64/R/library/mxnet/libs/libmxnet.so(MXSymbolInferShape+0x281) [0x7f725b6959a1] 
[bt] (9) /usr/lib64/R/library/mxnet/libs/mxnet.so(_ZNK5mxnet1R6Symbol10InferShapeERKN4Rcpp6VectorILi19ENS2_15PreserveStorageEEE+0x6b9) [0x7f724cef6739] 

此刻，我無能，我應該如何解決這個錯誤。我一直在尋找一種方法來重塑我的數據集，使它們成爲四維張量但找不到任何。我不想爲我的問題找到明確的解決方案，但對於如何解決此錯誤的任何建議將不勝感激。

Answer 1

我無法重現沒有數據的問題，但我認爲如果您正在尋找只是將您的數據集重塑爲4維張量，您應該可以通過 「symbol.reshape（output，shape = c（ 1,1,1,319））」。 不知道它是否可以幫助你。