1d CNN with classification layer for prediction

19 views (last 30 days)
I'm working on a thesis work in which i have to predict the wind speeds every 30 minutes with a convolutional neural network.
We have used a dataset of one year of wind speeds every 30 mnutes (around 15000 data), divided in training set (70%) and testing set(30%)
Our aim is to use as input 50 wind speeds to forecast the 51th wind speed ( X_train_n is a matrix [50 10500]), and this is done for the entire dataset, by using cnn with classification layer. The number of classes are 13, so Y_train is a categorical matrix [ 13 10500].
where n_input=50, n_output= 13
X_train_n is reshaped in a 4d matrix: the first column contains the wind speed from 1 to 50, the second one the wind speeds from 2 to 51 and so on.
Y_train is a categorical matrix: 13 classes for each column, where the columns represent the 51th, 52th ecc.. wind speed.
The problem is that matlab gives us the error in the figure.
The Y_train can't be a matrix? it has to be only a vector? how can i solve it?

Answers (1)

Asvin Kumar
Asvin Kumar on 8 Jan 2021
A couple of details are unclear to me.
  1. I don’t understand the different classes you have in a wind speed dataset. If the X_train_n is [50 10500] where each row is a 50-length vector containing wind speeds (numbers) and you’re trying to predict the 51st wind speed, I would expect Y_train to be of shape [1 10500].
  2. You say Y_train is a categorical matrix of [13 10500] but it’s unclear to me what the categories are and why they didn’t exist for the preceeding 50 samples.
Nevertheless, you could try the following suggestions:
Option 1:
If you’re working with categorical data, as the error message says, Y_train must be a categorical vector of shape [1 10500]. Each element of this categorical vector could then represent one of 13 categories.
Have a look at this example of a 5x1 categorical vector where each category is 1 of 3 possibilities.
For a deep learning specific usage, have a look at the digit classification example. Run the example and try the following commands:
Notice that it says classificationOutputLayer with 10 outputs. This represents the 10 possible categories it can take. Try this next:
im = read(imdsValidation);
classify(net, im)
Notice that the output is a categorical scalar and not a 10-length array. If what you’re having is a one-hot encoding of the 13 different classes, use the onehotencode function to convert it into a suitable catergorical array. You could adapt your understanding from this example to suit your use case.
Option 2:
If you’re working with numerical data, you could try using a regressionLayer instead.


Find more on Deep Learning Toolbox in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by