(CRNN OCR) Ошибка во время тренировки! Недопустимый аргумент: sequence_length (0) <= 18 узлов ctc / CTCLoss

Question

Я использую CRNN (CNN + RNN + CT C Loss) для моей модели на OCR. Я использую Tensorflow Keras

вот мой код [из CT C Loss]:

labels = Input(name='the_labels', shape=[max_label_len], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')


def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args

    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)


loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([outputs, labels, input_length, label_length])

#model to be used at training time
model = Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)

model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = 'adam')

filepath="best_model.hdf5"
checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
callbacks_list = [checkpoint]

training_img = np.array(training_img)
train_input_length = np.array(train_input_length)
train_label_length = np.array(train_label_length)

valid_img = np.array(valid_img)
valid_input_length = np.array(valid_input_length)
valid_label_length = np.array(valid_label_length)

Ошибка здесь во время тренировки:

batch_size = 256
epochs = 10
model.fit(x=[training_img, train_padded_txt, train_input_length, train_label_length], y=np.zeros(len(training_img)), 
          batch_size=batch_size, epochs = epochs, 
          validation_data = ([valid_img, valid_padded_txt, valid_input_length, valid_label_length], [np.zeros(len(valid_img))]), 
          verbose = 1, callbacks = callbacks_list)

РЕЗУЛЬТАТ ОШИБКИ :

Train on 448 samples, validate on 49 samples
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-15-1322212af569> in <module>()
      4           batch_size=batch_size, epochs = epochs,
      5           validation_data = ([valid_img, valid_padded_txt, valid_input_length, valid_label_length], [np.zeros(len(valid_img))]),
----> 6           verbose = 1, callbacks = callbacks_list)

7 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

InvalidArgumentError:  sequence_length(0) <= 18
     [[node ctc/CTCLoss (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_12073]

Function call stack:
keras_scratch_graph

Моя архитектура CRNN основана на VGG-16, я использую 13 слоев конвоя и 3 двунаправленных LSTM Слой. Я использую CT C Loss, а затем я получил ошибку. Мои данные 1000 text-image содержат 4-8 слов (700 для обучения и действительные, 300 для тестирования)

, если вы хотите просмотреть мой код: вот мой код с использованием Google Colab. https://colab.research.google.com/drive/1nMRNUsLDNrpgeTxPFQ4mhobnFdpbmwUx

Answer 1

Я исправил эту ошибку. Это из-за этого!

До:

  # split the 700 data into validation and training dataset as 10% and 90% respectively
        if i%10 == 0:     
            valid_orig_txt.append(txt)   
            valid_label_length.append(len(txt))
            valid_input_length.append(31)
            valid_img.append(img)
            valid_txt.append(encode_to_labels(txt))
        else:
            orig_txt.append(txt)   
            train_label_length.append(len(txt))
            train_input_length.append(31)
            training_img.append(img)
            training_txt.append(encode_to_labels(txt)) 

После:

  # split the 700 data into validation and training dataset as 10% and 90% respectively
        if i%10 == 0:     
            valid_orig_txt.append(txt)   
            valid_label_length.append(len(txt))
            valid_input_length.append(18)
            valid_img.append(img)
            valid_txt.append(encode_to_labels(txt))
        else:
            orig_txt.append(txt)   
            train_label_length.append(len(txt))
            train_input_length.append(18)
            training_img.append(img)
            training_txt.append(encode_to_labels(txt))