ctc损失函数的崩溃

Question

我正在使用Tensorflow 1.2.0-rc0 (python2.7)中的ctc损失函数获得以下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]

由于正在崩溃的SparseTensor (目标序列)是(我设置的批处理大小为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))

在以前的迭代中，它处理了具有类似值的较长序列，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))

提前谢谢你。

Answer 1

ctc.loss (loss)似乎忽略了选项：ignore_longer_outputs_than_inputs=True。它不是忽略比输入序列更长的输出，而是重新创建这个InvalidArgumentError(似乎我没有正确地检查我的序列的长度)。

因此，解决这一问题的方法是对数据集进行预处理，并确保输入到ctc_loss的所有序列都有比输入更多的目标。

Answer 2

我一直面临着这个问题:这个错误意味着

• 第0维的值应该小于1，即只有0才能工作。
• 第一维的值应该小于7，即数值只能介于0到6之间。

这就是为什么它开始从indices7崩溃，因为它的第一个维度的值是7，大于6。

此外，我认为造成这个问题的原因是帧数(时间步长维)的值小于发送给target_labels函数的ctc_loss数。

尝试使帧数/时间步骤数> target_labels数，您的代码肯定会工作的！

我想进一步帮助您，请您给我一个您的代码链接。