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mlp.py
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mlp.py
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# Code from Chapter 4 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by Stephen Marsland (http://stephenmonika.net)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty of any kind.
# Stephen Marsland, 2008, 2014
import numpy as np
class mlp:
""" A Multi-Layer Perceptron"""
def __init__(self,inputs,targets,nhidden,beta=1,momentum=0.9,outtype='logistic'):
""" Constructor """
# Set up network size
self.nin = np.shape(inputs)[1]
self.nout = np.shape(targets)[1]
self.ndata = np.shape(inputs)[0]
self.nhidden = nhidden
self.beta = beta
self.momentum = momentum
self.outtype = outtype
# Initialise network
self.weights1 = (np.random.rand(self.nin+1,self.nhidden)-0.5)*2/np.sqrt(self.nin)
self.weights2 = (np.random.rand(self.nhidden+1,self.nout)-0.5)*2/np.sqrt(self.nhidden)
def earlystopping(self,inputs,targets,valid,validtargets,eta,niterations=100):
valid = np.concatenate((valid,-np.ones((np.shape(valid)[0],1))),axis=1)
old_val_error1 = 100002
old_val_error2 = 100001
new_val_error = 100000
count = 0
while (((old_val_error1 - new_val_error) > 0.001) or ((old_val_error2 - old_val_error1)>0.001)):
count+=1
print (count)
self.mlptrain(inputs,targets,eta,niterations)
old_val_error2 = old_val_error1
old_val_error1 = new_val_error
validout = self.mlpfwd(valid)
new_val_error = 0.5*np.sum((validtargets-validout)**2)
print ("Stopped", new_val_error,old_val_error1, old_val_error2)
return new_val_error
def mlptrain(self,inputs,targets,eta,niterations):
""" Train the thing """
# Add the inputs that match the bias node
inputs = np.concatenate((inputs,-np.ones((self.ndata,1))),axis=1)
change = range(self.ndata)
updatew1 = np.zeros((np.shape(self.weights1)))
updatew2 = np.zeros((np.shape(self.weights2)))
for n in range(niterations):
self.outputs = self.mlpfwd(inputs)
error = 0.5*np.sum((self.outputs-targets)**2)
if (np.mod(n,100)==0):
print ("Iteration: ",n, " Error: ",error )
# Different types of output neurons
if self.outtype == 'linear':
deltao = (self.outputs-targets)/self.ndata
if(n == 5):
print("outputs", self.outputs[100])
print("targets", targets[100])
elif self.outtype == 'logistic':
deltao = self.beta*(self.outputs-targets)*self.outputs*(1.0-self.outputs)
elif self.outtype == 'softmax':
deltao = (self.outputs-targets)*(self.outputs*(-self.outputs)+self.outputs)/self.ndata
else:
print ("error")
deltah = self.hidden*self.beta*(1.0-self.hidden)*(np.dot(deltao,np.transpose(self.weights2)))
updatew1 = eta*(np.dot(np.transpose(inputs),deltah[:,:-1])) + self.momentum*updatew1
updatew2 = eta*(np.dot(np.transpose(self.hidden),deltao)) + self.momentum*updatew2
self.weights1 -= updatew1
self.weights2 -= updatew2
# Randomise order of inputs (not necessary for matrix-based calculation)
#np.random.shuffle(change)
inputs = inputs[change,:]
targets = targets[change,:]
def mlpfwd(self,inputs):
""" Run the network forward """
self.hidden = np.dot(inputs,self.weights1);
self.hidden = 1.0/(1.0+np.exp(-self.beta*self.hidden))
self.hidden = np.concatenate((self.hidden,-np.ones((np.shape(inputs)[0],1))),axis=1)
outputs = np.dot(self.hidden,self.weights2)
#outputs = np.log(outputs)
# Different types of output neurons
if self.outtype == 'linear':
#print(outputs)
return outputs
elif self.outtype == 'logistic':
return 1.0/(1.0+np.exp(-self.beta*outputs))
elif self.outtype == 'softmax':
normalisers = np.sum(np.exp(outputs),axis=1)*np.ones((1,np.shape(outputs)[0]))
return np.transpose(np.transpose(np.exp(outputs))/normalisers)
else:
print ("error")
def confmat(self,inputs,targets):
"""Confusion matrix"""
# Add the inputs that match the bias node
inputs = np.concatenate((inputs,-np.ones((np.shape(inputs)[0],1))),axis=1)
outputs = self.mlpfwd(inputs)
nclasses = np.shape(targets)[1]
if nclasses==1:
nclasses = 2
outputs = np.where(outputs>0.5,1,0)
else:
# 1-of-N encoding
outputs = np.argmax(outputs,1)
targets = np.argmax(targets,1)
cm = np.zeros((nclasses,nclasses))
for i in range(nclasses):
for j in range(nclasses):
cm[i,j] = np.sum(np.where(outputs==i,1,0)*np.where(targets==j,1,0))
print ("Confusion matrix is:")
print (cm)
print ("Percentage Correct: ",np.trace(cm)/np.sum(cm)*100)