在ctc損失功能崩潰

Question

我在Tensorflow 1.2.0-rc0（python 2.7）中使用ctc-loss函數得到以下InvalidArgumentError ：

InvalidArgumentError (see above for traceback): label SparseTensor is not valid: indices[7] = [0,7] is out of bounds: need 0 <= index < [1,7]
         [[Node: loss/CTCLoss = CTCLoss[ctc_merge_repeated=true, ignore_longer_outputs_than_inputs=false, preprocess_collapse_repeated=false, _device="/job:localhost/replica:0/task:0/cpu:0"](output_fc/BiasAdd/_91, _arg_labels/indices_0_1, _arg_labels/values_0_3, seq_len/Cast/_93)]]
         [[Node: loss/CTCLoss/_103 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_103_loss/CTCLoss", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

我不明白：

label SparseTensor is not valid: indices[7] = [0,7] is out of bounds: need 0 <= index < [1,7]

由於崩潰的SparseTensor （目標序列）是（我將批量大小設置為1）：

(array([[ 0,  0],
       [ 0,  1],
       [ 0,  2],
       [ 0,  3],
       [ 0,  4],
       [ 0,  5],
       [ 0,  6],
       [ 0,  7],
       [ 0,  8],
       [ 0,  9],
       [ 0, 10],
       [ 0, 11],
       [ 0, 12],
       [ 0, 13],
       [ 0, 14],
       [ 0, 15],
       [ 0, 16],
       [ 0, 17],
       [ 0, 18],
       [ 0, 19],
       [ 0, 20],
       [ 0, 21],
       [ 0, 22]], dtype=int32), array([41,  2,  7,  0, 13, 19,  4, 11, 11,  4, 40, 19,  8,  1,  1, 18, 40,
       24,  4,  0,  7, 40, 41], dtype=int32), array([ 1, 23], dtype=int32))

在之前的迭代中，它處理了具有相似值的較長序列，例如：

(array([[  0,   0],
       [  0,   1],
       [  0,   2],
       [  0,   3],
       [  0,   4],
       [  0,   5],
       [  0,   6],
       [  0,   7],
       [  0,   8],
       [  0,   9],
       [  0,  10],
       [  0,  11],
       [  0,  12],
       [  0,  13],
       [  0,  14],
       [  0,  15],
       [  0,  16],
       [  0,  17],
       [  0,  18],
       [  0,  19],
       [  0,  20],
       [  0,  21],
       [  0,  22],
       [  0,  23],
       [  0,  24],
       [  0,  25],
       [  0,  26],
       [  0,  27],
       [  0,  28],
       [  0,  29],
       [  0,  30],
       [  0,  31],
       [  0,  32],
       [  0,  33],
       [  0,  34],
       [  0,  35],
       [  0,  36],
       [  0,  37],
       [  0,  38],
       [  0,  39],
       [  0,  40],
       [  0,  41],
       [  0,  42],
       [  0,  43],
       [  0,  44],
       [  0,  45],
       [  0,  46],
       [  0,  47],
       [  0,  48],
       [  0,  49],
       [  0,  50],
       [  0,  51],
       [  0,  52],
       [  0,  53],
       [  0,  54],
       [  0,  55],
       [  0,  56],
       [  0,  57],
       [  0,  58],
       [  0,  59],
       [  0,  60],
       [  0,  61],
       [  0,  62],
       [  0,  63],
       [  0,  64],
       [  0,  65],
       [  0,  66],
       [  0,  67],
       [  0,  68],
       [  0,  69],
       [  0,  70],
       [  0,  71],
       [  0,  72],
       [  0,  73],
       [  0,  74],
       [  0,  75],
       [  0,  76],
       [  0,  77],
       [  0,  78],
       [  0,  79],
       [  0,  80],
       [  0,  81],
       [  0,  82],
       [  0,  83],
       [  0,  84],
       [  0,  85],
       [  0,  86],
       [  0,  87],
       [  0,  88],
       [  0,  89],
       [  0,  90],
       [  0,  91],
       [  0,  92],
       [  0,  93],
       [  0,  94],
       [  0,  95],
       [  0,  96],
       [  0,  97],
       [  0,  98],
       [  0,  99],
       [  0, 100],
       [  0, 101],
       [  0, 102],
       [  0, 103],
       [  0, 104],
       [  0, 105],
       [  0, 106],
       [  0, 107],
       [  0, 108],
       [  0, 109],
       [  0, 110]], dtype=int32), array([41, 22,  4, 36, 17,  4, 40,  6, 14,  8, 13,  6, 40, 19, 14, 40,  4,
        0, 19, 40,  8, 19, 40, 22,  4, 36, 17,  4, 40,  6, 14,  8, 13,  6,
       40, 19, 14, 40, 14, 15,  4, 13, 40, 20, 15, 40, 18, 14, 12,  4, 40,
       22,  8, 13,  4, 40,  0, 13,  3, 40, 22,  4, 36, 17,  4, 40,  6, 14,
        8, 13,  6, 40, 19, 14, 40, 19,  4, 11, 11, 40, 24, 14, 20, 40, 18,
       19, 14, 17,  0,  6,  4, 40,  7, 14, 22, 40, 12, 20,  2,  7, 40,  8,
       19, 40,  2, 14, 18, 19, 18, 40, 41], dtype=int32), array([  1, 111], dtype=int32))

先感謝您。

Answer 1

我一直面臨着這個問題：錯誤意味着

• 您的第0維應具有小於1的值，即僅0的值可以起作用。
• 第一維應具有小於7的值，即值可以僅位於0和6之間。

這就是它開始從指數[7]崩潰的原因，因為它的第一維的值是7，大於6。

此外，我認為問題是由於幀數（時間步長維度）的值小於發送到ctc_loss函數的target_labels的數量而引起的。

嘗試使幀數/時間步數> target_labels的數量，你的代碼肯定會工作！

我想進一步幫助，請你給我一個你的代碼鏈接。

Answer 2

似乎ctc.loss（ https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss ）忽略了該選項： ignore_longer_outputs_than_inputs=True 。 它不是忽略比輸入序列更長的輸出，而是重新生成這個InvalidArgumentError （似乎我沒有正確檢查我的序列的長度）。

因此，解決方案是預處理數據集，並確保輸入到ctc_loss的所有序列都具有比輸入更多的目標。