Lgraph Error, i try to create a new segnet (not pre trained ). But this is not the same because first encoder and the others do not encompass the mpool/size and mpool/indices. Please help me!

filterSize = 3; numFilters = 32;

layers = [ imageInputLayer([512 512 1],'Name','input')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder1_conv1')
    batchNormalizationLayer('Name','encoder1_BN_1')
    reluLayer('Name','encoder1_relu_1')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder1_conv2')
    batchNormalizationLayer('Name','encoder1_BN_2')
    reluLayer('Name','encoder1_relu_2')

    maxPooling2dLayer(2,'Stride',2,'Name','mpool1','HasUnpoolingOutputs',true)

     convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder2_conv1')
    batchNormalizationLayer('Name','encoder2_BN_3')
    reluLayer('Name','encoder2_relu_3')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder2_conv2')
    batchNormalizationLayer('Name','encoder2_BN_4')
    reluLayer('Name','encoder2_relu_4')

     maxPooling2dLayer(2,'Stride',2,'Name','mpool2','HasUnpoolingOutputs',true)

      convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder3_conv1')
    batchNormalizationLayer('Name','encoder3_BN_5')
    reluLayer('Name','encoder3_relu_5')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder3_conv2')
    batchNormalizationLayer('Name','encoder3_BN_6')
    reluLayer('Name','encoder3_relu_6')

    maxPooling2dLayer(2,'Stride',2,'Name','mpool3','HasUnpoolingOutputs',true)

         convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder4_conv1')
    batchNormalizationLayer('Name','encoder4_BN_7')
    reluLayer('Name','encoder4_relu_7')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder4_conv2')
    batchNormalizationLayer('Name','encoder4_BN_8')
    reluLayer('Name','encoder4_relu_8')

     maxPooling2dLayer(2,'Stride',2,'Name','mpool4','HasUnpoolingOutputs',true)

      convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder5_conv1')
    batchNormalizationLayer('Name','encoder5_BN_9')
    reluLayer('Name','encoder5_relu_9')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','encoder5_conv2')
    batchNormalizationLayer('Name','encoder5_BN_10')
    reluLayer('Name','encoder5_relu_10')

     maxPooling2dLayer(2,'Stride',2,'Name','mpool5','HasUnpoolingOutputs',true)
     maxUnpooling2dLayer('Name','unpool5') 

           convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder5_conv1')
    batchNormalizationLayer('Name','decoder5_BN_19')
    reluLayer('Name','decoder5_relu_19')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder5_conv2')
    batchNormalizationLayer('Name','decoder5_BN_20')
    reluLayer('Name','decoder5_relu_20')

     maxUnpooling2dLayer('Name','unpool4') 
     convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder4_conv1')
    batchNormalizationLayer('Name','decoder4_BN_17')
    reluLayer('Name','decoder4_relu_17')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder4_conv2')
    batchNormalizationLayer('Name','decoder4_BN_18')
    reluLayer('Name','decoder4_relu_18')

    maxUnpooling2dLayer('Name','unpool3') 
       convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder3_conv1')
    batchNormalizationLayer('Name','decoder3_BN_15')
    reluLayer('Name','decoder3_relu_15')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder3_conv2')
    batchNormalizationLayer('Name','decoder3_BN_16')
    reluLayer('Name','decoder3_relu_16')
    maxUnpooling2dLayer('Name','unpool2') 

          convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder2_conv1')
    batchNormalizationLayer('Name','decoder2_BN_13')
    reluLayer('Name','decoder2_relu_13')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder2_conv2')
    batchNormalizationLayer('Name','decoder2_BN_14')
    reluLayer('Name','decoder2_relu_14')

    maxUnpooling2dLayer('Name','unpool1') 
          convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder1_conv1')
    batchNormalizationLayer('Name','decoder1_BN_11')
    reluLayer('Name','decoder1_relu_11')

    convolution2dLayer(filterSize,numFilters,'Padding',1,'Stride',1,'Name','decoder1_conv2')
    batchNormalizationLayer('Name','decoder1_BN_12')
    reluLayer('Name','decoder1_relu_12')

    softmaxLayer('Name','softmax')
    pixelClassificationLayer('Name','classOutput')]

lgraph1 = layerGraph(layers) %

lgraph_1 = connectLayers(lgraph1,'mpool1/indices','unpool1/indices'); lgraph_1 = connectLayers(lgraph1,'mpool1/size','unpool1/size'); %

lgraph_1 = connectLayers(lgraph1,'mpool2/indices','unpool2/indices'); lgraph_1 = connectLayers(lgraph1,'mpool2/size','unpool2/size');

lgraph_1 = connectLayers(lgraph1,'mpool3/indices','unpool3/indices'); lgraph_1 = connectLayers(lgraph1,'mpool3/size','unpool3/size');

lgraph_1 = connectLayers(lgraph1,'mpool4/indices','unpool4/indices'); lgraph_1 = connectLayers(lgraph1,'mpool4/size','unpool4/size');

lgraph_1 = connectLayers(lgraph1,'mpool5/indices','unpool5/indices'); lgraph_1 = connectLayers(lgraph1,'mpool5/size','unpool5/size');

% lgraph=connectLayers(layers,lgraph1);