Exercise: XOR problem in QGIS¶
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Tutorial Data Set¶
We will use two images:
- xor_training.tiff for training and
- xor.tiff for predicting.
Goal¶
We will solve the XOR problem (see context) with the MLP Classifier.
In order to do this, we will need a Neural Network with 3 layers: an input layer with 2 input neurons, a hidden layer with 2 neurons and an output layer with one neuron.
We will however not do a normal classification. Instead, we will calculate the probability for each input that the output is class 1! Have another look at the training data, we have two classes: class 1 and class 2.
Run the Neural Network MLPClassifier in QGIS¶
We need the following input for the GUI:
- The image for classification: xor
- The raster for training: xor_training
- The no-data-value is -1
- Number of neurons for each hidden layer: 2 neurons in 1 hidden layer
- Test size: 0
- We want the probability of class 1 (we have two classes, 1 and 2)
- Output file path
After the training, the network error is shown as a plot like:
The resulting image should look something like this:
In theory there are two decision surfaces, the image above only shows one. Can you explain?