2024-06-25 23:19:13 发布
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我试图运行这个HTR模型https://github.com/arthurflor23/handwritten-text-recognition,但它给了我这个错误Invalid argument: Not enough time for target transition sequence。我认为问题出在{}。我的图像尺寸是(137518),文本的最大长度是137。你知道我该如何解决这个问题吗
https://github.com/arthurflor23/handwritten-text-recognition
Invalid argument: Not enough time for target transition sequence
我修复了这个问题,这是由于输入的大小
Layer (type) Output Shape Param # ================================================================= input (InputLayer) [(None, 1024, 128, 1)] 0 _________________________________________________________________ conv2d (Conv2D) (None, 1024, 64, 16) 160 _________________________________________________________________ p_re_lu (PReLU) (None, 1024, 64, 16) 16 _________________________________________________________________ batch_normalization (BatchNo (None, 1024, 64, 16) 112 _________________________________________________________________ full_gated_conv2d (FullGated (None, 1024, 64, 16) 4640 _________________________________________________________________ conv2d_1 (Conv2D) (None, 1024, 64, 32) 4640 _________________________________________________________________ p_re_lu_1 (PReLU) (None, 1024, 64, 32) 32 _________________________________________________________________ batch_normalization_1 (Batch (None, 1024, 64, 32) 224 _________________________________________________________________ full_gated_conv2d_1 (FullGat (None, 1024, 64, 32) 18496 _________________________________________________________________ conv2d_2 (Conv2D) (None, 512, 16, 40) 10280 _________________________________________________________________ p_re_lu_2 (PReLU) (None, 512, 16, 40) 40 _________________________________________________________________ batch_normalization_2 (Batch (None, 512, 16, 40) 280 _________________________________________________________________ full_gated_conv2d_2 (FullGat (None, 512, 16, 40) 28880 _________________________________________________________________ dropout (Dropout) (None, 512, 16, 40) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 512, 16, 48) 17328 _________________________________________________________________ p_re_lu_3 (PReLU) (None, 512, 16, 48) 48 _________________________________________________________________ batch_normalization_3 (Batch (None, 512, 16, 48) 336 _________________________________________________________________ full_gated_conv2d_3 (FullGat (None, 512, 16, 48) 41568 _________________________________________________________________ dropout_1 (Dropout) (None, 512, 16, 48) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 256, 4, 56) 21560 _________________________________________________________________ p_re_lu_4 (PReLU) (None, 256, 4, 56) 56 _________________________________________________________________ batch_normalization_4 (Batch (None, 256, 4, 56) 392 _________________________________________________________________ full_gated_conv2d_4 (FullGat (None, 256, 4, 56) 56560 _________________________________________________________________ dropout_2 (Dropout) (None, 256, 4, 56) 0 _________________________________________________________________ conv2d_5 (Conv2D) (None, 256, 4, 64) 32320 _________________________________________________________________ p_re_lu_5 (PReLU) (None, 256, 4, 64) 64 _________________________________________________________________ batch_normalization_5 (Batch (None, 256, 4, 64) 448 _________________________________________________________________ reshape (Reshape) (None, 256, 256) 0 _________________________________________________________________ bidirectional (Bidirectional (None, 256, 256) 296448 _________________________________________________________________ dense (Dense) (None, 256, 256) 65792 _________________________________________________________________ bidirectional_1 (Bidirection (None, 256, 256) 296448 _________________________________________________________________ dense_1 (Dense) (None, 256, 332) 85324 =================================================================
look at the final layer ( dense_1 ) the second dimension is 256, so your text label should be <=256, not more. The problem comes from here.
我修复了这个问题,这是由于输入的大小
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