What is TanhLayer in PyBrain

Recipe Objective - What is TanhLayer in PyBrain?

Pybrain provides mainly two layers: TanhLayer and SoftmaxLayer. TanhLayer implements the tanh squashing function.

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Let's try to build a network using TanhLayer -

# Importing libraries
from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import TanhLayer
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer

# Create a network with two inputs, two hidden, and one output
network = buildNetwork(2, 2, 1, bias=True, hiddenclass=TanhLayer)

# Create a dataset that matches network input and output sizes:
and_gate = SupervisedDataSet(2, 1)

# Create a dataset to be used for testing.
and_testing_data = SupervisedDataSet(2, 1)

# Add input and target values to dataset values for AND truth table
and_gate.addSample((0, 0), (0,))
and_gate.addSample((0, 1), (0,))
and_gate.addSample((1, 0), (0,))
and_gate.addSample((1, 1), (1,))

# Add input and target values to dataset values for AND truth table
and_testing_data.addSample((0, 0), (0,))
and_testing_data.addSample((0, 1), (0,))
and_testing_data.addSample((1, 0), (0,))
and_testing_data.addSample((1, 1), (1,))

#Training the network with dataset and_gate.
backprop_trainer = BackpropTrainer(network, and_gate)

# 5000 iteration on training data.
for iteration in range(5000):
   backprop_trainer.train()

# Testing data
backprop_trainer.testOnData(dataset=and_testing_data, verbose = True)


Output -
Testing on data:
('out:    ', '[-0.001]')
('correct:', '[0     ]')
error:  0.00000020
('out:    ', '[0.001 ]')
('correct:', '[0     ]')
error:  0.00000035
('out:    ', '[0     ]')
('correct:', '[0     ]')
error:  0.00000012
('out:    ', '[1     ]')
('correct:', '[1     ]')
error:  0.00000010
('All errors:', [1.954198645657772e-07, 3.4636537042714615e-07, 1.1671764649484939e-07, 1.0268408027397674e-07])
('Average error:', 1.9029674044043737e-07)
('Max error:', 3.4636537042714615e-07, 'Median error:', 1.954198645657772e-07)
1.9029674044043737e-07

In this way, we can use TanhLayer to build a network in pybrain.

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