As you are using the following syntax of the trainNetwork function: net = trainNetwork(X,Y,layers,options), you can refer to the corresponding explanation of the format of the X & Y to understand the cause of the error.
Image or feature data, specified as a numeric array. The size of the array depends on the type of input:
Input: Description
2-D images: A h-by-w-by-c-by-N numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively, and N is the number of images.
3-D images: A h-by-w-by-d-by-c-by-N numeric array, where h, w, d, and c are the height, width, depth, and number of channels of the images, respectively, and N is the number of images.
Features: A N-by-numFeatures numeric array, where N is the number of observations and numFeatures is the number of features of the input data.
Y — Responses (categorical vector of labels | numeric array | cell array of categorical sequences | cell array of numeric sequences)
Responses, specified as a categorical vector of labels, a numeric array, a cell array of categorical sequences, or cell array of numeric sequences. The format of Y depends on the type of task. Responses must not contain NaNs.
Task: Format
Image or feature classification or Sequence-to-label classification: N-by-1 categorical vector of labels, where N is the number of observations.
Sequence-to-sequence classification: N-by-1 cell array of categorical sequences of labels, where N is the number of observations. Each sequence must have the same number of time steps as the corresponding predictor sequence.
For sequence-to-sequence classification tasks with one observation, sequences can also be a vector. In this case, Y must be a categorical sequence of labels.
