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

Paul Osinowo
Graduate Student
University of Strathclyde 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:

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

Paul Osinowo
Graduate Student
University of Strathclyde deep learning, cnn MATLAB Answers — New Questions

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