我在Tensorflow 1.2.0-rc0（python 2.7）中使用ctc-loss函数得到以下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]
由于崩溃的SparseTensor（目标序列）是（我将批量大小设置为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))
在之前的迭代中，它处理了具有相似值的较长序列，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))
提前谢谢。
答案 0 :(得分：0)
我一直面临着这个问题： 错误意味着
这就是为什么它从索引[7]开始崩溃，因为它的第一个维度的值是7，大于6。
此外，我认为问题是由于帧数（时间步长维度）的值小于发送到ctc_loss函数的target_labels的数量而引起的。
尝试制作帧数/时间步数＆gt; target_labels的数量，你的代码绝对应该工作！
我想进一步提供帮助，请您发给我一个代码链接。
答案 1 :(得分：0)
似乎ctc.loss（https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss）忽略了选项：printf。它不是忽略比输入序列更长的输出，而是重新使用这个ignore_longer_outputs_than_inputs=True（似乎我没有正确检查序列的长度）。
因此，解决方法是预处理数据集，并确保输入ctc_loss的所有序列都有比目标更多的目标。