我想从keras模型创建PB文件，并使用DnnInvoke.readnetfromTensorflow将其提供给EmguCV（或至少使用opencv，最好使用EmguCV） 我使用代码创建网络：

from keras import backend as K
from keras.callbacks import *
from keras.layers import *
from keras.models import *
from keras.utils import *
from keras.optimizers import Adadelta,RMSprop,Adam,SGD
from keras.callbacks import ModelCheckpoint
from keras.callbacks import TensorBoard

from config import *


def ctc_lambda_func(args):
    iy_pred,ilabels,iinput_length,ilabel_length = args
    # the 2 is critical here since the first couple outputs of the RNN
    # tend to be garbage:
    iy_pred = iy_pred[:,2:,:]  # no such influence
    return K.ctc_batch_cost(ilabels,iy_pred,ilabel_length)


def CRNN_model(is_training=True):
    inputShape = Input((width,height,1),name='input')  # base on         Tensorflow backend
    conv_1 = Conv2D(64,(3,3),activation='relu',padding='same')(inputShape)
    conv_2 = Conv2D(64,padding='same')(conv_1)
    #batchnorm_2 = BatchNormalization()(conv_2)
    pool_2 = MaxPooling2D(pool_size=(2,2))(conv_2)

    conv_3 = Conv2D(64,padding='same')(pool_2)
    conv_4 = Conv2D(128,padding='same')(conv_3)
    #batchnorm_4 = BatchNormalization()(conv_4)
    pool_4 = MaxPooling2D(pool_size=(2,2))(conv_4)

    conv_5 = Conv2D(128,padding='same')(pool_4)
    conv_6 = Conv2D(128,padding='same')(conv_5)
    pool_5 = MaxPool2D(pool_size=(2,2))(conv_6)
    #batchnorm_6 = BatchNormalization()(conv_6)

    #bn_shape = batchnorm_6.get_shape()


    #print(bn_shape)

    #x_reshape = Reshape(target_shape=(int(bn_shape[1]),int(bn_shape[2] * bn_shape[3])))(batchnorm_6)
    #drop_reshape = Dropout(0.25,name='d1')(x_reshape)
    fl_1 = flatten()(pool_5)
    fc_1 = Dense(256,activation='relu')(fl_1)

    #print(x_reshape.get_shape())
    #print(fc_1.get_shape())

    bi_LSTM_1 = Bidirectional(LSTM(256,return_sequences=True,kernel_initializer='he_normal'),merge_mode='sum')(fc_1)
    bi_LSTM_2 = Bidirectional(LSTM(128,merge_mode='concat')(bi_LSTM_1)

    #drop_rnn = Dropout(0.3,name='d2')(bi_LSTM_2)

    fc_2 = Dense(label_classes,kernel_initializer='he_normal',activation='softmax')(bi_LSTM_2)

    base_model = Model(inputs=[inputShape],outputs=fc_2) 

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

    loss_out = Lambda(ctc_lambda_func,output_shape=(1,),name='ctc')([fc_2,labels,input_length,label_length])

    if is_training:
        return Model(inputs=[inputShape,label_length],outputs=[loss_out]),base_model
    else:
        return base_model

并且我使用下面的代码来创建.pb文件：

import tensorflow as tf

mfname = './models/weights.01-0.080-0.007.hdf5'  # FIXME

tf.keras.backend.set_learning_phase(0)
sess = tf.keras.backend.get_session()
sess.as_default()

model = tf.keras.models.load_model(mfname,compile=False)

constant_graph = tf.graph_util.convert_variables_to_constants(
    sess,sess.graph.as_graph_def(),[out.op.name for out in model.outputs],)
tf.train.write_graph(constant_graph,'',mfname[:-4] + '_graph.pb',as_text=False)

但是当我从Tensorflow调用DnnInvoke.readnet时，它显示此错误：

  

Emgu.CV.Util.CvException：'OpenCV：找不到输入层：   density_1 / Tensordot / free'

我该如何解决这个问题？