Category: pybrain

PyBrain – Importing Data For Datasets In this chapter, we will learn how to get data to work with Pybrain datasets. The most commonly used are datasets are − Using sklearn From CSV file Using sklearn Using sklearn Here is the link that has details of datasets from sklearn: Here are a few examples of how to use datasets from sklearn − Example 1: load_digits() from sklearn import datasets from pybrain.datasets import ClassificationDataSet digits = datasets.load_digits() X, y = digits.data, digits.target ds = ClassificationDataSet(64, 1, nb_classes=10) for i in range(len(X)): ds.addSample(ravel(X[i]), y[i]) Example 2: load_iris() from sklearn import datasets from pybrain.datasets import ClassificationDataSet digits = datasets.load_iris() X, y = digits.data, digits.target ds = ClassificationDataSet(4, 1, nb_classes=3) for i in range(len(X)): ds.addSample(X[i], y[i]) From CSV file We can also use data from csv file as follows − Here is sample data for xor truth table: datasettest.csv Here is the working example to read the data from .csv file for dataset. Example from pybrain.tools.shortcuts import buildNetwork from pybrain.structure import TanhLayer from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer import pandas as pd print(”Read data…”) df = pd.read_csv(”data/datasettest.csv”,header=0).head(1000) data = df.values train_output = data[:,0] train_data = data[:,1:] print(train_output) print(train_data) # Create a network with two inputs, three hidden, and one output nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer) # Create a dataset that matches network input and output sizes: _gate = SupervisedDataSet(2, 1) # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table for i in range(0, len(train_output)) : _gate.addSample(train_data[i], train_output[i]) #Training the network with dataset norgate. trainer = BackpropTrainer(nn, _gate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() trainer.testOnData(dataset=_gate, verbose = True) Panda is used to read data from csv file as shown in the example. Output C:pybrainpybrainsrc>python testcsv.py Read data… [0 1 1 0] [ [0 0] [0 1] [1 0] [1 1] ] Testing on data: (”out: ”, ”[0.004 ]”) (”correct:”, ”[0 ]”) error: 0.00000795 (”out: ”, ”[0.997 ]”) (”correct:”, ”[1 ]”) error: 0.00000380 (”out: ”, ”[0.996 ]”) (”correct:”, ”[1 ]”) error: 0.00000826 (”out: ”, ”[0.004 ]”) (”correct:”, ”[0 ]”) error: 0.00000829 (”All errors:”, [7.94733477723902e-06, 3.798267582566822e-06, 8.260969076585322e -06, 8.286246525558165e-06]) (”Average error:”, 7.073204490487332e-06) (”Max error:”, 8.286246525558165e-06, ”Median error:”, 8.260969076585322e-06)

PyBrain – API & Tools Now we know how to build a network and train it. In this chapter, we will understand how to create and save the network, and use the network whenever required. Save and Recover Network We are going to make use of NetworkWriter and NetworkReader from Pybrain tool, i.e., pybrain.tools.customxml. Here is a working example of the same − from pybrain.tools.shortcuts import buildNetwork from pybrain.tools.customxml import NetworkWriter from pybrain.tools.customxml import NetworkReader net = buildNetwork(2,1,1) NetworkWriter.writeToFile(net, ”network.xml”) net = NetworkReader.readFrom(”network.xml”) The network is saved inside network.xml. NetworkWriter.writeToFile(net, ”network.xml”) To read the xml when required we can use code as follows − net = NetworkReader.readFrom(”network.xml”) Here is the network.xml file created − <?xml version=”1.0″ ?> <PyBrain> <Network class=”pybrain.structure.networks.feedforward.FeedForwardNetwork” name=”FeedForwardNetwork-8″> <name val=””FeedForwardNetwork-8””/> <Modules> <LinearLayer class=”pybrain.structure.modules.linearlayer.LinearLayer” inmodule=”True” name=”in”> <name val=””in””/> <dim val=”2″/> </LinearLayer> <LinearLayer class=”pybrain.structure.modules.linearlayer.LinearLayer” name=”out” outmodule=”True”> <name val=””out””/> <dim val=”1″/> </LinearLayer> <BiasUnit class=”pybrain.structure.modules.biasunit.BiasUnit” name=”bias”> <name val=””bias””/> </BiasUnit> <SigmoidLayer class=”pybrain.structure.modules.sigmoidlayer.SigmoidLayer” name=”hidden0″> <name val=””hidden0””/> <dim val=”1″/> </SigmoidLayer> </Modules> <Connections> <FullConnection class=”pybrain.structure.connections.full.FullConnection” name=”FullConnection-6″> <inmod val=”bias”/> <outmod val=”out”/> <Parameters>[1.2441093186965146]</Parameters> </FullConnection> <FullConnection class=”pybrain.structure.connections.full.FullConnection” name=”FullConnection-7″> <inmod val=”bias”/> <outmod val=”hidden0″/> <Parameters>[-1.5743530012126412]</Parameters> </FullConnection> <FullConnection class=”pybrain.structure.connections.full.FullConnection” name=”FullConnection-4″> <inmod val=”in”/> <outmod val=”hidden0″/> <Parameters>[-0.9429546042034236, -0.09858196752687162]</Parameters> </FullConnection> <FullConnection class=”pybrain.structure.connections.full.FullConnection” name=”FullConnection-5″> <inmod val=”hidden0″/> <outmod val=”out”/> <Parameters>[-0.29205472354634304]</Parameters> </FullConnection> </Connections> </Network> </PyBrain> API Below is a list of APIs that we have used throughout this tutorial. For Networks activate(input) − It takes parameter, i.e., the value to be tested. It will return back the result based on the input given. activateOnDataset(dataset) − It will iterate over the dataset given and return the output. addConnection(c) − Adds connection to the network. addInputModule(m) − Adds the module given to the network and mark it as an input module. addModule(m) − Adds the given module to the network. addOutputModule(m) − Adds the module to the network and mark it as an output module. reset() − Resets the modules and the network. sortModules() − It prepares the network for activation by sorting internally. It has to be called before activation. For Supervised Datasets addSample(inp, target) − Adds a new sample of input and target. splitWithProportion(proportion=0.5) − Divides dataset into two parts, the first part containing the proportion part data and the next set containing the remaining one. For Trainers trainUntilConvergence(dataset=None, maxEpochs=None, verbose=None, continueEpochs=10, validationProportion=0.25) − It is used to train the module on the dataset until it converges. If dataset is not given, it will try to train on the trained dataset used at the start.

PyBrain – Quick Guide PyBrain – Overview Pybrain is an open-source library for Machine learning implemented using python. The library offers you some easy to use training algorithms for networks, datasets, trainers to train and test the network. Definition of Pybrain as put by its official documentation is as follows − PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive “Backronym”. Features of Pybrain The following are the features of Pybrain − Networks A network is composed of modules and they are connected using connections. Pybrain supports neural networks like Feed-Forward Network, Recurrent Network, etc. feed-forward network is a neural network, where the information between nodes moves in the forward direction and will never travel backward. Feed Forward network is the first and the simplest one among the networks available in the artificial neural network. The information is passed from the input nodes, next to the hidden nodes and later to the output node. Recurrent Networks are similar to Feed Forward Network; the only difference is that it has to remember the data at each step. The history of each step has to be saved. Datasets Datasets is the data to be given to test, validate and train on networks. The type of dataset to be used depends on the tasks that we are going to do with Machine Learning. The most commonly used datasets that Pybrain supports are SupervisedDataSet and ClassificationDataSet. SupervisedDataSet − It consists of fields of input and target. It is the simplest form of a dataset and mainly used for supervised learning tasks. ClassificationDataSet − It is mainly used to deal with classification problems. It takes in input, target field and also an extra field called “class” which is an automated backup of the targets given. For example, the output will be either 1 or 0 or the output will be grouped together with values based on input given, i.e., either it will fall in one particular class. Trainer When we create a network, i.e., neural network, it will get trained based on the training data given to it. Now whether the network is trained properly or not will depend on the prediction of test data tested on that network. The most important concept in Pybrain Training is the use of BackpropTrainer and TrainUntilConvergence. BackpropTrainer − It is a trainer that trains the parameters of a module according to a supervised or ClassificationDataSet dataset (potentially sequential) by backpropagating the errors (through time). TrainUntilConvergence −It is used to train the module on the dataset until it converges. Tools Pybrain offers tools modules which can help to build a network by importing package: pybrain.tools.shortcuts.buildNetwork Visualization The testing data cannot be visualized using pybrain. But Pybrain can work with other frameworks like Mathplotlib, pyplot to visualize the data. Advantages of Pybrain The advantages of Pybrain are − Pybrain is an open-source free library to learn Machine Learning. It is a good start for any newcomer interested in Machine Learning. Pybrain uses python to implement it and that makes it fast in development in comparison to languages like Java/C++. Pybrain works easily with other libraries of python to visualize data. Pybrain offers support for popular networks like Feed-Forward Network, Recurrent Networks, Neural Networks, etc. Working with .csv to load datasets is very easy in Pybrain. It also allows using datasets from another library. Training and testing of data are easy using Pybrain trainers. Limitations of Pybrain Pybrain offers less help for any issues faced. There are some queries unanswered on stackoverflow and on Google Group. Workflow of Pybrain As per Pybrain documentation the flow of machine learning is shown in the following figure − At the start, we have raw data which after preprocessing can be used with Pybrain. The flow of Pybrain starts with datasets which are divided into trained and test data. the network is created, and the dataset and the network are given to the trainer. the trainer trains the data on the network and classifies the outputs as trained error and validation error which can be visualized. the tested data can be validated to see if the output matches the trained data. Terminology There are important terms to be considered while working with Pybrain for machine learning. They are as follows − Total Error − It refers to the error shown after the network is trained. If the error keeps changing on every iteration, it means it still needs time to settle, until it starts showing a constant error between iteration. Once it starts showing the constant error numbers, it means that the network has converged and will remain the same irrespective of any additional training is applied. Trained data − It is the data used to train the Pybrain network. Testing data − It is the data used to test the trained Pybrain network. Trainer − When we create a network, i.e., neural network, it will get trained based on the training data given to it. Now whether the network is trained properly or not will depend on the prediction of test data tested on that network. The most important concept in Pybrain Training is the use of BackpropTrainer and TrainUntilConvergence. BackpropTrainer − It is a trainer that trains the parameters of a module according to a supervised or ClassificationDataSet dataset (potentially sequential) by backpropagating the errors (through time). TrainUntilConvergence − It is used to train the module on the dataset until it converges. Layers − Layers are basically a set of functions that are used on hidden layers of a network. Connections − A connection works similar to a layer; an only difference is that it shifts the data from one node to the other in a network.

PyBrain – Layers Layers are basically a set of functions that are used on hidden layers of a network. We will go through the following details about layers in this chapter − Understanding layer Creating Layer using Pybrain Understanding layers We have seen examples earlier where we have used layers as follows − TanhLayer SoftmaxLayer Example using TanhLayer Below is one example where we have used TanhLayer for building a network − testnetwork.py 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, three hidden, and one output nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer) # Create a dataset that matches network input and output sizes: norgate = SupervisedDataSet(2, 1) # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) # Add input and target values to dataset # Values for NOR truth table nortrain.addSample((0, 0), (1,)) nortrain.addSample((0, 1), (0,)) nortrain.addSample((1, 0), (0,)) nortrain.addSample((1, 1), (0,)) #Training the network with dataset norgate. trainer = BackpropTrainer(nn, norgate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() trainer.testOnData(dataset=nortrain, verbose = True) Output The output for the above code is as follows − python testnetwork.py C:pybrainpybrainsrc>python testnetwork.py Testing on data: (”out: ”, ”[0.887 ]”) (”correct:”, ”[1 ]”) error: 0.00637334 (”out: ”, ”[0.149 ]”) (”correct:”, ”[0 ]”) error: 0.01110338 (”out: ”, ”[0.102 ]”) (”correct:”, ”[0 ]”) error: 0.00522736 (”out: ”, ”[-0.163]”) (”correct:”, ”[0 ]”) error: 0.01328650 (”All errors:”, [0.006373344564625953, 0.01110338071737218, 0.005227359234093431, 0.01328649974219942]) (”Average error:”, 0.008997646064572746) (”Max error:”, 0.01328649974219942, ”Median error:”, 0.01110338071737218) Example using SoftMaxLayer Below is one example where we have used SoftmaxLayer for building a network − from pybrain.tools.shortcuts import buildNetwork from pybrain.structure.modules import SoftmaxLayer from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer # Create a network with two inputs, three hidden, and one output nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=SoftmaxLayer) # Create a dataset that matches network input and output sizes: norgate = SupervisedDataSet(2, 1) # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) # Add input and target values to dataset # Values for NOR truth table nortrain.addSample((0, 0), (1,)) nortrain.addSample((0, 1), (0,)) nortrain.addSample((1, 0), (0,)) nortrain.addSample((1, 1), (0,)) #Training the network with dataset norgate. trainer = BackpropTrainer(nn, norgate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() trainer.testOnData(dataset=nortrain, verbose = True) Output The output is as follows − C:pybrainpybrainsrc>python example16.py Testing on data: (”out: ”, ”[0.918 ]”) (”correct:”, ”[1 ]”) error: 0.00333524 (”out: ”, ”[0.082 ]”) (”correct:”, ”[0 ]”) error: 0.00333484 (”out: ”, ”[0.078 ]”) (”correct:”, ”[0 ]”) error: 0.00303433 (”out: ”, ”[-0.082]”) (”correct:”, ”[0 ]”) error: 0.00340005 (”All errors:”, [0.0033352368788838365, 0.003334842961037291, 0.003034328685718761, 0.0034000458892589056]) (”Average error:”, 0.0032761136037246985) (”Max error:”, 0.0034000458892589056, ”Median error:”, 0.0033352368788838365) Creating Layer in Pybrain In Pybrain, you can create your own layer as follows − To create a layer, you need to use NeuronLayer class as the base class to create all type of layers. Example from pybrain.structure.modules.neuronlayer import NeuronLayer class LinearLayer(NeuronLayer): def _forwardImplementation(self, inbuf, outbuf): outbuf[:] = inbuf def _backwardImplementation(self, outerr, inerr, outbuf, inbuf): inerr[:] = outer To create a Layer, we need to implement two methods: _forwardImplementation() and _backwardImplementation(). The _forwardImplementation() takes in 2 arguments inbuf and outbuf, which are Scipy arrays. Its size is dependent on the layers’ input and output dimensions. The _backwardImplementation() is used to calculate the derivative of the output with respect to the input given. So to implement a layer in Pybrain, this is the skeleton of the layer class − from pybrain.structure.modules.neuronlayer import NeuronLayer class NewLayer(NeuronLayer): def _forwardImplementation(self, inbuf, outbuf): pass def _backwardImplementation(self, outerr, inerr, outbuf, inbuf): pass In case you want to implement a quadratic polynomial function as a layer, we can do so as follows − Consider we have a polynomial function as − f(x) = 3×2 The derivative of the above polynomial function will be as follows − f(x) = 6 x The final layer class for the above polynomial function will be as follows − testlayer.py from pybrain.structure.modules.neuronlayer import NeuronLayer class PolynomialLayer(NeuronLayer): def _forwardImplementation(self, inbuf, outbuf): outbuf[:] = 3*inbuf**2 def _backwardImplementation(self, outerr, inerr, outbuf, inbuf): inerr[:] = 6*inbuf*outerr Now let us make use of the layer created as shown below − testlayer1.py from testlayer import PolynomialLayer from pybrain.tools.shortcuts import buildNetwork from pybrain.tests.helpers import gradientCheck n = buildNetwork(2, 3, 1, hiddenclass=PolynomialLayer) n.randomize() gradientCheck(n) GradientCheck() will test whether the layer is working fine or not.We need to pass the network where the layer is used to gradientCheck(n).It will give the output as “Perfect Gradient” if the layer is working fine. Output C:pybrainpybrainsrc>python testlayer1.py Perfect gradient

PyBrain – Introduction to PyBrain Networks PyBrain is a library developed for Machine Learning with Python. There are some important concepts in Machine Learning and one among them is Networks. A network is composed of modules and they are connected using connections. A layout of a simple neural network is as follows − Pybrain supports neural networks such as Feed-Forward Network, Recurrent Network, etc. A feed-forward network is a neural network, where the information between nodes moves in the forward direction and will never travel backward. Feed Forward network is the first and the simplest one among the networks available in the artificial neural network. The information is passed from the input nodes, next to the hidden nodes and later to the output node. Here is a simple feed forward network layout. The circles are said to be modules and the lines with arrows are connections to the modules. The nodes A, B, C and D are input nodes H1, H2, H3, H4 are hidden nodes and O is the output. In the above network, we have 4 input nodes, 4 hidden layers and 1 output. The number of lines shown in the diagram indicate the weight parameters in the model that are adjusted during training. Recurrent Networks are similar to Feed Forward Network with the only difference that it has to remember the data at each step. The history of each step has to be saved. Here is a simple Layout of Recurrent Network −

Training Network Using Optimization Algorithms We have seen how to train a network using trainers in pybrain. In this chapter, will use optimization algorithms available with Pybrain to train a network. In the example, we will use the GA optimization algorithm which needs to be imported as shown below − from pybrain.optimization.populationbased.ga import GA Example Below is a working example of a training network using a GA optimization algorithm − from pybrain.datasets.classification import ClassificationDataSet from pybrain.optimization.populationbased.ga import GA from pybrain.tools.shortcuts import buildNetwork # create XOR dataset ds = ClassificationDataSet(2) ds.addSample([0., 0.], [0.]) ds.addSample([0., 1.], [1.]) ds.addSample([1., 0.], [1.]) ds.addSample([1., 1.], [0.]) ds.setField(”class”, [ [0.],[1.],[1.],[0.]]) net = buildNetwork(2, 3, 1) ga = GA(ds.evaluateModuleMSE, net, minimize=True) for i in range(100): net = ga.learn(0)[0] print(net.activate([0,0])) print(net.activate([1,0])) print(net.activate([0,1])) print(net.activate([1,1])) Output The activate method on the network for the inputs almost matches with the output as shown below − C:pybrainpybrainsrc>python example15.py [0.03055398] [0.92094839] [1.12246157] [0.02071285]

PyBrain – Connections A connection works similar to a layer; an only difference is that it shifts the data from one node to the other in a network. In this chapter, we are going to learn about − Understanding Connections Creating Connections Understanding Connections Here is a working example of connections used while creating a network. Example ffy.py from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer from pybrain.structure import FullConnection network = FeedForwardNetwork() #creating layer for input => 2 , hidden=> 3 and output=>1 inputLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outputLayer = LinearLayer(1) #adding the layer to feedforward network network.addInputModule(inputLayer) network.addModule(hiddenLayer) network.addOutputModule(outputLayer) #Create connection between input ,hidden and output input_to_hidden = FullConnection(inputLayer, hiddenLayer) hidden_to_output = FullConnection(hiddenLayer, outputLayer) #add connection to the network network.addConnection(input_to_hidden) network.addConnection(hidden_to_output) network.sortModules() print(network) Output C:pybrainpybrainsrc>python ffn.py FeedForwardNetwork-6 Modules: [<LinearLayer ”LinearLayer-3”>, <SigmoidLayer ”SigmoidLayer-7”>, <LinearLayer ”LinearLayer-8”>] Connections: [<FullConnection ”FullConnection-4”: ”SigmoidLayer-7” -> ”LinearLayer-8”>, <FullConnection ”FullConnection-5”: ”LinearLayer-3” -> ”SigmoidLayer-7”>] Creating Connections In Pybrain, we can create connections by using the connection module as shown below − Example connect.py from pybrain.structure.connections.connection import Connection class YourConnection(Connection): def __init__(self, *args, **kwargs): Connection.__init__(self, *args, **kwargs) def _forwardImplementation(self, inbuf, outbuf): outbuf += inbuf def _backwardImplementation(self, outerr, inerr, inbuf): inerr += outer To create a connection, there are 2 methods — _forwardImplementation() and _backwardImplementation(). The _forwardImplementation() is called with the output buffer of the incoming module which is inbuf, and the input buffer of the outgoing module called outbuf. The inbuf is added to the outgoing module outbuf. The _backwardImplementation() is called with outerr, inerr, and inbuf. The outgoing module error is added to the incoming module error in _backwardImplementation(). Let us now use the YourConnection in a network. testconnection.py from pybrain.structure import FeedForwardNetwork from pybrain.structure import LinearLayer, SigmoidLayer from connect import YourConnection network = FeedForwardNetwork() #creating layer for input => 2 , hidden=> 3 and output=>1 inputLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outputLayer = LinearLayer(1) #adding the layer to feedforward network network.addInputModule(inputLayer) network.addModule(hiddenLayer) network.addOutputModule(outputLayer) #Create connection between input ,hidden and output input_to_hidden = YourConnection(inputLayer, hiddenLayer) hidden_to_output = YourConnection(hiddenLayer, outputLayer) #add connection to the network network.addConnection(input_to_hidden) network.addConnection(hidden_to_output) network.sortModules() print(network) Output C:pybrainpybrainsrc>python testconnection.py FeedForwardNetwork-6 Modules: [<LinearLayer ”LinearLayer-3”>, <SigmoidLayer ”SigmoidLayer-7”>, <LinearLayer ”LinearLayer-8”>] Connections: [<YourConnection ”YourConnection-4”: ”LinearLayer-3” -> ”SigmoidLayer-7”>, <YourConnection ”YourConnection-5”: ”SigmoidLayer-7” -> ”LinearLayer-8”>]

PyBrain – Datasets Types Datasets are data to be given to test, validate and train on networks. The type of dataset to be used depends on the tasks that we are going to do with machine learning. We are going to discuss the various dataset types in this chapter. We can work with the dataset by adding the following package − pybrain.dataset SupervisedDataSet SupervisedDataSet consists of fields of input and target. It is the simplest form of a dataset and mainly used for supervised learning tasks. Below is how you can use it in the code − from pybrain.datasets import SupervisedDataSet The methods available on SupervisedDataSet are as follows − addSample(inp, target) This method will add a new sample of input and target. splitWithProportion(proportion=0.10) This will divide the datasets into two parts. The first part will have the % of the dataset given as input, i.e., if the input is .10, then it is 10% of the dataset and 90% of data. You can decide the proportion as per your choice. The divided datasets can be used for testing and training your network. copy() − Returns a deep copy of the dataset. clear() − Clear the dataset. saveToFile(filename, format=None, **kwargs) Save the object to file given by filename. Example Here is a working example using a SupervisedDataset − testnetwork.py 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, three hidden, and one output nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer) # Create a dataset that matches network input and output sizes: norgate = SupervisedDataSet(2, 1) # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) # Add input and target values to dataset # Values for NOR truth table nortrain.addSample((0, 0), (1,)) nortrain.addSample((0, 1), (0,)) nortrain.addSample((1, 0), (0,)) nortrain.addSample((1, 1), (0,)) #Training the network with dataset norgate. trainer = BackpropTrainer(nn, norgate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() trainer.testOnData(dataset=nortrain, verbose = True) Output The output for the above program is as follows − python testnetwork.py C:pybrainpybrainsrc>python testnetwork.py Testing on data: (”out: ”, ”[0.887 ]”) (”correct:”, ”[1 ]”) error: 0.00637334 (”out: ”, ”[0.149 ]”) (”correct:”, ”[0 ]”) error: 0.01110338 (”out: ”, ”[0.102 ]”) (”correct:”, ”[0 ]”) error: 0.00522736 (”out: ”, ”[-0.163]”) (”correct:”, ”[0 ]”) error: 0.01328650 (”All errors:”, [0.006373344564625953, 0.01110338071737218, 0.005227359234093431 , 0.01328649974219942]) (”Average error:”, 0.008997646064572746) (”Max error:”, 0.01328649974219942, ”Median error:”, 0.01110338071737218) ClassificationDataSet This dataset is mainly used to deal with classification problems. It takes in input, target field and also an extra field called “class” which is an automated backup of the targets given. For example, the output will be either 1 or 0 or the output will be grouped together with values based on input given., i.e., it will fall in one particular class. Here is how you can use it in the code − from pybrain.datasets import ClassificationDataSet Syntax // ClassificationDataSet(inp, target=1, nb_classes=0, class_labels=None) The methods available on ClassificationDataSet are as follows − addSample(inp, target) − This method will add a new sample of input and target. splitByClass() − This method will give two new datasets, the first dataset will have the class selected (0..nClasses-1), the second one will have remaining samples. _convertToOneOfMany() − This method will convert the target classes to a 1-of-k representation, retaining the old targets as a field class Here is a working example of ClassificationDataSet. Example from sklearn import datasets import matplotlib.pyplot as plt from pybrain.datasets import ClassificationDataSet from pybrain.utilities import percentError from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers import BackpropTrainer from pybrain.structure.modules import SoftmaxLayer from numpy import ravel digits = datasets.load_digits() X, y = digits.data, digits.target ds = ClassificationDataSet(64, 1, nb_classes=10) for i in range(len(X)): ds.addSample(ravel(X[i]), y[i]) test_data_temp, training_data_temp = ds.splitWithProportion(0.25) test_data = ClassificationDataSet(64, 1, nb_classes=10) for n in range(0, test_data_temp.getLength()): test_data.addSample( test_data_temp.getSample(n)[0], test_data_temp.getSample(n)[1] ) training_data = ClassificationDataSet(64, 1, nb_classes=10) for n in range(0, training_data_temp.getLength()): training_data.addSample( training_data_temp.getSample(n)[0], training_data_temp.getSample(n)[1] ) test_data._convertToOneOfMany() training_data._convertToOneOfMany() net = buildNetwork(training_data.indim, 64, training_data.outdim, outclass=SoftmaxLayer) trainer = BackpropTrainer( net, dataset=training_data, momentum=0.1,learningrate=0.01,verbose=True,weightdecay=0.01 ) trnerr,valerr = trainer.trainUntilConvergence(dataset=training_data,maxEpochs=10) plt.plot(trnerr,”b”,valerr,”r”) plt.show() trainer.trainEpochs(10) print(”Percent Error on testData:”,percentError(trainer.testOnClassData(dataset=test_data), test_data[”class”])) The dataset used in the above example is a digit dataset and the classes are from 0-9, so there are 10 classes. The input is 64, target is 1 and classes, 10. The code trains the network with the dataset and outputs the graph for training error and validation error. It also gives the percent error on testdata which is as follows − Output Total error: 0.0432857814358 Total error: 0.0222276374185 Total error: 0.0149012052174 Total error: 0.011876985318 Total error: 0.00939854792853 Total error: 0.00782202445183 Total error: 0.00714707652044 Total error: 0.00606068893793 Total error: 0.00544257958975 Total error: 0.00463929281336 Total error: 0.00441275665294 (”train-errors:”, ”[0.043286 , 0.022228 , 0.014901 , 0.011877 , 0.009399 , 0.007 822 , 0.007147 , 0.006061 , 0.005443 , 0.004639 , 0.004413 ]”) (”valid-errors:”, ”[0.074296 , 0.027332 , 0.016461 , 0.014298 , 0.012129 , 0.009 248 , 0.008922 , 0.007917 , 0.006547 , 0.005883 , 0.006572 , 0.005811 ]”) Percent Error on testData: 3.34075723830735

PyBrain – Overview Pybrain is an open-source library for Machine learning implemented using python. The library offers you some easy to use training algorithms for networks, datasets, trainers to train and test the network. Definition of Pybrain as put by its official documentation is as follows − PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive “Backronym”. Features of Pybrain The following are the features of Pybrain − Networks A network is composed of modules and they are connected using connections. Pybrain supports neural networks like Feed-Forward Network, Recurrent Network, etc. feed-forward network is a neural network, where the information between nodes moves in the forward direction and will never travel backward. Feed Forward network is the first and the simplest one among the networks available in the artificial neural network. The information is passed from the input nodes, next to the hidden nodes and later to the output node. Recurrent Networks are similar to Feed Forward Network; the only difference is that it has to remember the data at each step. The history of each step has to be saved. Datasets Datasets is the data to be given to test, validate and train on networks. The type of dataset to be used depends on the tasks that we are going to do with Machine Learning. The most commonly used datasets that Pybrain supports are SupervisedDataSet and ClassificationDataSet. SupervisedDataSet − It consists of fields of input and target. It is the simplest form of a dataset and mainly used for supervised learning tasks. ClassificationDataSet − It is mainly used to deal with classification problems. It takes in input, target field and also an extra field called “class” which is an automated backup of the targets given. For example, the output will be either 1 or 0 or the output will be grouped together with values based on input given, i.e., either it will fall in one particular class. Trainer When we create a network, i.e., neural network, it will get trained based on the training data given to it. Now whether the network is trained properly or not will depend on the prediction of test data tested on that network. The most important concept in Pybrain Training is the use of BackpropTrainer and TrainUntilConvergence. BackpropTrainer − It is a trainer that trains the parameters of a module according to a supervised or ClassificationDataSet dataset (potentially sequential) by backpropagating the errors (through time). TrainUntilConvergence −It is used to train the module on the dataset until it converges. Tools Pybrain offers tools modules which can help to build a network by importing package: pybrain.tools.shortcuts.buildNetwork Visualization The testing data cannot be visualized using pybrain. But Pybrain can work with other frameworks like Mathplotlib, pyplot to visualize the data. Advantages of Pybrain The advantages of Pybrain are − Pybrain is an open-source free library to learn Machine Learning. It is a good start for any newcomer interested in Machine Learning. Pybrain uses python to implement it and that makes it fast in development in comparison to languages like Java/C++. Pybrain works easily with other libraries of python to visualize data. Pybrain offers support for popular networks like Feed-Forward Network, Recurrent Networks, Neural Networks, etc. Working with .csv to load datasets is very easy in Pybrain. It also allows using datasets from another library. Training and testing of data are easy using Pybrain trainers. Limitations of Pybrain Pybrain offers less help for any issues faced. There are some queries unanswered on stackoverflow and on Google Group. Workflow of Pybrain As per Pybrain documentation the flow of machine learning is shown in the following figure − At the start, we have raw data which after preprocessing can be used with Pybrain. The flow of Pybrain starts with datasets which are divided into trained and test data. the network is created, and the dataset and the network are given to the trainer. the trainer trains the data on the network and classifies the outputs as trained error and validation error which can be visualized. the tested data can be validated to see if the output matches the trained data. Terminology There are important terms to be considered while working with Pybrain for machine learning. They are as follows − Total Error − It refers to the error shown after the network is trained. If the error keeps changing on every iteration, it means it still needs time to settle, until it starts showing a constant error between iteration. Once it starts showing the constant error numbers, it means that the network has converged and will remain the same irrespective of any additional training is applied. Trained data − It is the data used to train the Pybrain network. Testing data − It is the data used to test the trained Pybrain network. Trainer − When we create a network, i.e., neural network, it will get trained based on the training data given to it. Now whether the network is trained properly or not will depend on the prediction of test data tested on that network. The most important concept in Pybrain Training is the use of BackpropTrainer and TrainUntilConvergence. BackpropTrainer − It is a trainer that trains the parameters of a module according to a supervised or ClassificationDataSet dataset (potentially sequential) by backpropagating the errors (through time). TrainUntilConvergence − It is used to train the module on the dataset until it converges. Layers − Layers are basically a set of functions that are used on hidden layers of a network. Connections − A connection works similar to a layer; an only difference is that it shifts the data from one node to the other in a network. Modules − Modules are

PyBrain – Training Datasets on Networks So far, we have seen how to create a network and a dataset. To work with datasets and networks together, we have to do it with the help of trainers. Below is a working example to see how to add a dataset to the network created, and later trained and tested using trainers. testnetwork.py 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, three hidden, and one output nn = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer) # Create a dataset that matches network input and output sizes: norgate = SupervisedDataSet(2, 1) # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) # Add input and target values to dataset # Values for NOR truth table nortrain.addSample((0, 0), (1,)) nortrain.addSample((0, 1), (0,)) nortrain.addSample((1, 0), (0,)) nortrain.addSample((1, 1), (0,)) #Training the network with dataset norgate. trainer = BackpropTrainer(nn, norgate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() trainer.testOnData(dataset=nortrain, verbose = True) To test the network and dataset, we need BackpropTrainer. BackpropTrainer is a trainer that trains the parameters of a module according to a supervised dataset (potentially sequential) by backpropagating the errors (through time). We have created 2 datasets of class – SupervisedDataSet. We are making use of NOR data model which is as follows − A B A NOR B 0 0 1 0 1 0 1 0 0 1 1 0 The above data model is used to train the network. norgate = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) Following is the dataset used to test − # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (1,)) norgate.addSample((0, 1), (0,)) norgate.addSample((1, 0), (0,)) norgate.addSample((1, 1), (0,)) The trainer is used as follows − #Training the network with dataset norgate. trainer = BackpropTrainer(nn, norgate) # will run the loop 1000 times to train it. for epoch in range(1000): trainer.train() To test on the dataset, we can use the below code − trainer.testOnData(dataset=nortrain, verbose = True) Output python testnetwork.py C:pybrainpybrainsrc>python testnetwork.py Testing on data: (”out: ”, ”[0.887 ]”) (”correct:”, ”[1 ]”) error: 0.00637334 (”out: ”, ”[0.149 ]”) (”correct:”, ”[0 ]”) error: 0.01110338 (”out: ”, ”[0.102 ]”) (”correct:”, ”[0 ]”) error: 0.00522736 (”out: ”, ”[-0.163]”) (”correct:”, ”[0 ]”) error: 0.01328650 (”All errors:”, [0.006373344564625953, 0.01110338071737218, 0.005227359234093431 , 0.01328649974219942]) (”Average error:”, 0.008997646064572746) (”Max error:”, 0.01328649974219942, ”Median error:”, 0.01110338071737218) If you check the output, the test data almost matches with the dataset we have provided and hence the error is 0.008. Let us now change the test data and see an average error. We have changed the output as shown below − Following is the dataset used to test − # Create a dataset to be used for testing. nortrain = SupervisedDataSet(2, 1) # Add input and target values to dataset # Values for NOR truth table norgate.addSample((0, 0), (0,)) norgate.addSample((0, 1), (1,)) norgate.addSample((1, 0), (1,)) norgate.addSample((1, 1), (0,)) Let us now test it. Output python testnework.py C:pybrainpybrainsrc>python testnetwork.py Testing on data: (”out: ”, ”[0.988 ]”) (”correct:”, ”[0 ]”) error: 0.48842978 (”out: ”, ”[0.027 ]”) (”correct:”, ”[1 ]”) error: 0.47382097 (”out: ”, ”[0.021 ]”) (”correct:”, ”[1 ]”) error: 0.47876379 (”out: ”, ”[-0.04 ]”) (”correct:”, ”[0 ]”) error: 0.00079160 (”All errors:”, [0.4884297811030845, 0.47382096780393873, 0.47876378995939756, 0 .0007915982149002194]) (”Average error:”, 0.3604515342703303) (”Max error:”, 0.4884297811030845, ”Median error:”, 0.47876378995939756) We are getting the error as 0.36, which shows that our test data is not completely matching with the network trained.