Training CNN with custom miniBatchDatastore thows cryptic error

I am trying to train a CNN model for a classification task. The input to the model is a `1x1024x2` frame and I have the following network:

layers = 
  28×1 Layer array with layers:

     1   'Input Layer'   Image Input             1×1024×2 images
     2   'CNN1'          2-D Convolution         16 1×8 convolutions with stride [1  1] and padding 'same'
     3   'BN1'           Batch Normalization     Batch normalization
     4   'ReLU1'         ReLU                    ReLU
     5   'MaxPool1'      2-D Max Pooling         1×2 max pooling with stride [1  2] and padding [0  0  0  0]
     6   'CNN2'          2-D Convolution         24 1×8 convolutions with stride [1  1] and padding 'same'
     7   'BN2'           Batch Normalization     Batch normalization
     8   'ReLU2'         ReLU                    ReLU
     9   'MaxPool2'      2-D Max Pooling         1×2 max pooling with stride [1  2] and padding [0  0  0  0]
    10   'CNN3'          2-D Convolution         32 1×8 convolutions with stride [1  1] and padding 'same'
    11   'BN3'           Batch Normalization     Batch normalization
    12   'ReLU3'         ReLU                    ReLU
    13   'MaxPool3'      2-D Max Pooling         1×2 max pooling with stride [1  2] and padding [0  0  0  0]
    14   'CNN4'          2-D Convolution         48 1×8 convolutions with stride [1  1] and padding 'same'
    15   'BN4'           Batch Normalization     Batch normalization
    16   'ReLU4'         ReLU                    ReLU
    17   'MaxPool4'      2-D Max Pooling         1×2 max pooling with stride [1  2] and padding [0  0  0  0]
    18   'CNN5'          2-D Convolution         64 1×8 convolutions with stride [1  1] and padding 'same'
    19   'BN5'           Batch Normalization     Batch normalization
    20   'ReLU5'         ReLU                    ReLU
    21   'MaxPool5'      2-D Max Pooling         1×2 max pooling with stride [1  2] and padding [0  0  0  0]
    22   'CNN6'          2-D Convolution         96 1×8 convolutions with stride [1  1] and padding 'same'
    23   'BN6'           Batch Normalization     Batch normalization
    24   'ReLU6'         ReLU                    ReLU
    25   'AP1'           2-D Average Pooling     1×32 average pooling with stride [1  1] and padding [0  0  0  0]
    26   'FC1'           Fully Connected         24 fully connected layer
    27   'SoftMax'       Softmax                 softmax
    28   'Output'        Classification Output   crossentropyex

I'm using a custom minibatch dataset to train the model. The read funtion returns the following table structure:

i'm using these training options:

% Specify training options
options = trainingOptions('sgdm', ...
    'MiniBatchSize', miniBatchSize, ...
    'MaxEpochs', 10, ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% Train the network
net = trainNetwork(mbds, layers, options);

and i get the following error:

Error using trainNetwork
Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.

Caused by:
    Operands to the logical AND (&&) and OR (||) operators must be convertible to logical scalar values. Use the ANY or ALL functions to reduce operands to logical scalar values.

I've been stuck on this for a while now, and theres no way to inspect the trainNetwork code to properly debug. I'd appreciate your suggestions. Thank you.

Paul Osinowo

Graduate Student

University of Strathclyde