Ошибка в K.ctc_batch_cost: tenenflow.python.framework.errors_impl.InvalidArgumentError: sequence_length (0) <= 30 - PullRequest
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Ошибка в K.ctc_batch_cost: tenenflow.python.framework.errors_impl.InvalidArgumentError: sequence_length (0) <= 30

1 голос
/ 17 апреля 2019

Я использую модель CRNN в Керасе, чтобы выполнить распознавание рукописного ввода, но я получаю ошибку при вычислении потери CTC.

Проблема возникает только тогда, когда я пытаюсь загрузить предварительно обученную сеть для моего CNN. Это работает нормально, если я создаю свою собственную сеть CNN с нуля.

Это модель, в которой я получаю ошибку: 

def get_DensenetLSTM(training):
    input_shape = (img_w, img_h, 3)
    inputs = Input(name='the_input', shape=input_shape, dtype='float32')
#     out = Conv2D(3, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)
#     out = Reshape(target_shape=((250, 80, 3)), name='reshape_inp')(inner)
    densenet = DenseNet121(include_top=False, weights='imagenet', input_tensor= inputs)
    inner = densenet.output
    # CNN to RNN
    inner = Reshape(target_shape=((32, 1536)), name='reshape')(inner)  # (None, 32, 2048)
    #     print(inner.shape)
    inner = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(inner)  # (None, 32, 64)
    #     print(inner.shape)
    # RNN layer
    lstm_1 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm1')(inner)  # (None, 32, 512)
    lstm_1b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm1_b')(inner)
    lstm1_merged = add([lstm_1, lstm_1b])  # (None, 32, 512)
    lstm1_merged = BatchNormalization()(lstm1_merged)
    lstm_2 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm2')(lstm1_merged)
    lstm_2b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm2_b')(lstm1_merged)
    lstm2_merged = concatenate([lstm_2, lstm_2b])  # (None, 32, 1024)
    lstm_merged = BatchNormalization()(lstm2_merged)

    # transforms RNN output to character activations:
    inner = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(lstm2_merged) #(None, 32, 63)
    y_pred = Activation('softmax', name='softmax')(inner)

    labels = Input(name='the_labels', shape=[max_text_len], dtype='float32') # (None ,8)
    input_length = Input(name='input_length', shape=[1], dtype='int64')     # (None, 1)
    label_length = Input(name='label_length', shape=[1], dtype='int64')     # (None, 1)

    # Keras doesn't currently support loss funcs with extra parameters
    # so CTC loss is implemented in a lambda layer
    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)
    if training:
        return Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
    else:
        return Model(inputs=[inputs], outputs=y_pred)

Это отлично работает: 

def get_Model(training):
    input_shape = (img_w, img_h, 1)
    # Make Network
    inputs = Input(name='the_input', shape=input_shape, dtype='float32') 
    inner = Conv2D(64, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)  # (None, 128, 64, 64)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max1')(inner)
    inner = Conv2D(128, (3, 3), padding='same', name='conv2', kernel_initializer='he_normal')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max2')(inner)
    inner = Conv2D(256, (3, 3), padding='same', name='conv3', kernel_initializer='he_normal')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(256, (3, 3), padding='same', name='conv4', kernel_initializer='he_normal')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max3')(inner)
    inner = Conv2D(512, (3, 3), padding='same', name='conv5', kernel_initializer='he_normal')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(512, (3, 3), padding='same', name='conv6')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max4')(inner)
    inner = Conv2D(512, (2, 2), padding='same', kernel_initializer='he_normal', name='con7')(inner)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)

    # CNN to RNN
    inner = Reshape(target_shape=((62, 2560)), name='reshape')(inner) 
    inner = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(inner) 
    # RNN layer
    lstm_1 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm1')(inner) 
    lstm_1b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm1_b')(inner)
    lstm1_merged = add([lstm_1, lstm_1b]) 
    lstm1_merged = BatchNormalization()(lstm1_merged)
    lstm_2 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm2')(lstm1_merged)
    lstm_2b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm2_b')(lstm1_merged)
    lstm2_merged = concatenate([lstm_2, lstm_2b])
    lstm_merged = BatchNormalization()(lstm2_merged)

    # transforms RNN output to character activations:
    inner = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(lstm2_merged)
    y_pred = Activation('softmax', name='softmax')(inner)

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

    # Keras doesn't currently support loss funcs with extra parameters
    # so CTC loss is implemented in a lambda layer
    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)

    if training:
        return Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
    else:
        return Model(inputs=[inputs], outputs=y_pred)

Это функция ctc_loss: 

def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args
    # the 2 is critical here since the first couple outputs of the RNN
    # tend to be garbage:
    y_pred = y_pred[:, 2:, :]
    print(y_pred.shape)
    print(input_length)
    print(labels.shape)
    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)

Когда я запускаю модель Densenet_LSTM, я получаю следующую ошибку: 

tensorflow.python.framework.errors_impl.InvalidArgumentError: sequence_length(0) <= 30
         [[{{node ctc/CTCLoss}} = CTCLoss[_class=["loc:@training/Adam/gradients/ctc/CTCLoss_grad/mul"], ctc_merge_repeated=true, ignore_longer_outputs_than_inputs=false, preprocess_collapse_repeated=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](ctc/Log/_7309, ctc/ToInt64/_7311, ctc/ToInt32_2/_7313, ctc/ToInt32_1/_7315)]]

Пожалуйста, помогите.

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