In this article, we will be looking at the various uses and functionality of the supervised dataset in Pybrain.
A Dataset is a collection of data where we do give the list of values to each member belonging to the dataset. A supervised dataset following supervised learning has input and output fields. In this example, we will learn how to use a Supervised Dataset with PyBrain.To install Pybrain refer to How to Install PyBrain?
Let’s create a OR table where we have an input in form of a 2-D array and we get one output.
0 or 0 -> 0 0 or 1 -> 1 1 or 0 -> 1 1 or 1 -> 1
Libraries used:
- buildNetwork is a simple way to create networks that are composed of Modules connected with Connections.
- TanhLayer: After building a network we have to use some layer either TanhLayer or SoftmaxLayer. We will be using TanhLayer in our example.
- SupervisedDataSet: We have to set two values to input and target fields.
- BackpropTrainer: For training according to the supervised dataset
Example:
In this example, after building a network we will create two datasets one for training and another for testing.
Python
# importing buildNetwork # from pybrain.tools.shortcuts from pybrain.tools.shortcuts import buildNetwork # importing TanhLayer # from pybrain.structure from pybrain.structure import TanhLayer # importing SupervisedDataSet # from pybrain.datasets from pybrain.datasets import SupervisedDataSet # importing BackpropTrainer # from pybrain.trainers from pybrain.supervised.trainers import BackpropTrainer # creating a network with TanhLayer # two input, two hidden and on output network = buildNetwork(2, 2, 1, bias=True, hiddenclass=TanhLayer) # Creating a dataset for training # 2 output # 1 input or_train = SupervisedDataSet(2, 1) # Creating a dataset for testing. or_test = SupervisedDataSet(2, 1) # Adding sample input # 0 or 0 -> 0 # 0 or 1 -> 1 # 1 or 0 -> 1 # 1 or 1 -> 1 or_train.addSample((0, 0), (0,)) or_train.addSample((0, 1), (1,)) or_train.addSample((1, 0), (1,)) or_train.addSample((1, 1), (1,)) # Similarly adding samples for or_test or_test.addSample((0, 0), (0,)) or_test.addSample((0, 1), (1,)) or_test.addSample((1, 0), (1,)) or_test.addSample((1, 1), (1,)) # Training network with dataset or_train. trainer = BackpropTrainer(network, or_train) # 1000 iteration on training data. for iteration in range(1000): trainer.train() # Testing data trainer.testOnData(dataset=or_test, verbose = True) |
Output:
