dim=-1)decoder=build_ctcdecoder(chars)# 返回BeamSearchDecoderCTCtext=decoder.decode(probs.numpy()...
一、tf.nn.ctc_beam_search_decoder tf.nn.ctc_beam_search_decoder( inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True ) 1 2 3 4 5 6 7 定义在:tensorflow/python/ops/ctc_ops.py. 对输入中给出的logits执行波束(beam)搜索解码. 注意:这ctc_greedy_decoder是带有top_pa...
NetDecoder[{"CTCBeamSearch", alphabet}] 表示一个解码器,对概率向量序列进行解释,给出最有可能的序列解码. NetDecoder[{"CTCBeamSearch", ..., "BeamSize" -> n}] 表示具有指定柱宽度 (beam size) 的解码器.
This is an example CTC decoder written in Python. The code is intended to be a simple example and is not designed to be especially efficient. The algorithm is a prefix beam search for a model trained with the CTC loss function. For more details checkout either of these references: https:...
当W=1时,beam search 就是 greedy decode。 Tensorflow 函数 tf.nn.ctc_greedy_decoder( inputs, sequence_length, merge_repeated=True ) tf.nn.ctc_beam_search_decoder( inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True ) 参考 Supervised Sequence Labelling with Recurrent Ne...
Beam Search能够找到最可能的解码结果。当Beam Width设置为1时，Beam Search等同于贪心解码。在Tensorflow中，我们可以利用`tf.nn.ctc_greedy_decoder`和`tf.nn.ctc_beam_search_decoder`函数来实现CTC解码。前者采用贪心策略，后者则通过Beam Search进行解码，提供了更准确的标签序列预测。
Environment info Operating System: OS X 10.11.6 TF Version: 0.10.0rc0 (No GPU) Example Ran log_probabilities op created from decoded, log_probabilities = ctc_ops.ctc_beam_search_decoder(logits, \ seq_length, \ beam_width=beam_width, \ to...
I have the same problem with converting model from TF to IR with CTCBeamSearchDecoder openvino-2021.2. Could you help me with this problem? [ ERROR ] Cannot infer shapes or values for node "CTCGreedyDecoder". [ ERROR ] Incorrect rank of sequence length tensor for CTCG...
Input matrix (mat): is expected to have shape TxBx(C+1) with the softmax-function already applied (in contrast to the TF operations ctc_greedy_decoder and ctc_beam_search_decoder!). The CTC-blank must be the last entry in the matrix Beam Width (beamWidth): number of beams which are...
decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, seq_len, merge_repeated=False) saver = tf.train.Saver() with tf.Session() as sess: # 加载模型 saver.restore(sess, tf.train.latest_checkpoint(model_dir)) # 图像预处理