Tensorflow 具有CRNN的OCR。如何获得预测分数

我有一个

CRNN

配备了

CTC损耗

输出的模型

我有预测，我使用

keras.backend.ctc\u decode

对其进行解码。如文档（）中所述，函数将返回一个带有解码结果的

元组，以及一个带有预测对数概率的
张量

keras.backend.ctc_decode
可以接受多个预测值，但我需要一次传递一次

代码如下：

    def decode_single_prediction(pred, num_to_char):
       input_len = np.ones(pred.shape[0]) * pred.shape[1]
       # Use greedy search. For complex tasks, you can use beam search
       decoded = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)
       # decoded[0] is supposed to be the decoded result
       # decoded[1] is supposed to be it's log probability
       accuracy = float(decoded[1][0][0])
       # take the resultin encoded char until it gets -1
       result = decoded[0][0][:,: np.argmax(decoded[0][0] == -1)]
       output_text = tf.strings.reduce_join(num_to_char(result)).numpy().decode("utf-8")
       return (output_text, accuracy)

    for image in images:
       pred = prediction_model.predict(image)
       # num_to_char is the mapping from number to char
       pred_texts, acc = decode_single_prediction(pred, num_to_char)
       print("True value: " + <true_result> + " prediction: " + pred_texts + " acc: " + str(acc))

预测总是正确的。然而，我认为它的概率似乎不是我所期望的。它们看起来像完全随机的数字，甚至比1或2还要大！我做错了什么

提前谢谢你

True value: test0, prediction: test0, acc: 1.841524362564087
True value: test1, prediction: test1, acc: 0.9661365151405334
True value: test2, prediction: test2, acc: 1.0634151697158813
True value: test3, prediction: test3, acc: 2.471940755844116
True value: test4, prediction: test4, acc: 1.4866207838058472
True value: test5, prediction: test5, acc: 0.7630811333656311
True value: test6, prediction: test6, acc: 0.35642576217651367
True value: test7, prediction: test7, acc: 1.5693446397781372
True value: test8, prediction: test8, acc: 0.9700028896331787
True value: test9, prediction: test9, acc: 1.4783780574798584