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A Simple Neural Net in Python
import numpy as np
import csv
import matplotlib.pyplot as plt
#fix random seed for reproducibility
#Read Dataset
iris = open('iris.csv','r')
iris = csv.reader(iris,delimiter=',')
iris = np.array(list(iris)).astype(np.float64)
#shuffle dataset
#all columns excluding the last is a feature last column is the label
features = iris[:,:-1]
features = (features - features.mean())/features.std()
#make a row sized vectors of 1
biasPad = np.ones((features.shape[0],1), dtype=features.dtype)
#pad 1s on the right side of each feature vector
features = np.concatenate((features,biasPad), axis=1)
#create a one hot matrix repesentation of the labels
label = np.array(iris[:,-1],dtype=int).reshape(-1)
label = np.eye(3)[label]
#split training and testing set 0.8 split
M = features.shape[0]
splitIdx = int(0.8*M)
XTest = features[splitIdx:,:]
XTrain = features[:splitIdx,:]
YTest = label[splitIdx:,:]
YTrain = label[:splitIdx,:]
#no of input neurons is equal to size of feature vector
inputCount = features.shape[1]
#basic case
hiddenCount = inputCount
#3 class classification thus 3 neurons
outputCount = 3
#activations for each layer of neurons
ai = np.ones((inputCount,1))
ah = np.ones((hiddenCount,1))
ao = np.ones((outputCount,1))
#neuron weights
#for each neuron in hidden layer calculate weights from ith input neuron
wih = np.random.rand(inputCount, hiddenCount)*np.sqrt(2./inputCount)
#for each neuron in output layer calculate weights from ith hidden neuron
who = np.random.rand(hiddenCount, outputCount)*np.sqrt(2./hiddenCount)
#Update Arrays for momentum updates
cih = np.zeros((inputCount, hiddenCount))
cho = np.zeros((hiddenCount, outputCount))
#function for feed forward calc
def feedFwd(featureMat):
global ai,ah,ao,wih,who
#input activations
ai = featureMat
'''hidden activations
vectorized matrix multiply
ai.T * wih'''
ah =,wih)
#vectorized sigmoid
ah = np.tanh(ah)
'''output ativations
ah.T * who'''
ao =, who)
#vectorized sigmoid
ao = np.tanh(ao)
#ao = 1.0/1.0+np.exp(-1*ao)
return ao
#function for backpropagation
def backProp(X,label,output,N,batchSize=1,beta=0.0009):
'''N: learning rate'''
global ai,ah,ao,wih,who,cih,cho
delOut = output - label
dwho =,delOut)/batchSize
delHidden =,who.T)*(1.0 - ah**2)
dwih =,delHidden)/batchSize
'''weight updates'''
who -= N*dwho + beta*cho
cho[:] = dwho
wih -= N*dwih + beta*cih
cih[:] = dwih
def train(X,Y,iteration=1000,learningRate=0.001,batchSize=1,beta=0.099,decayRate=0.0005):
errorTimeline = []
epochList = []
#train it for iteration number of epoch
for epoch in xrange(iteration):
#for each mini batch
for i in xrange(0,X.shape[0],batchSize):
#split the dataset into mini batches
batchSplit = min(i+batchSize,X.shape[0])
XminiBatch = X[i:batchSplit,:]
YminiBatch = Y[i:batchSplit,:]
#calculate a forwasd pass through the network
output = feedFwd(XminiBatch)
#calculate mean squared error
error = 0.5*np.sum((YminiBatch-output)**2)/batchSize
#print error
#backprop and update weights
#after every 50 iteration decrease momentum and learning rate
#decreasing momentum helps reduce the chances of overshooting a convergence point
if epoch%50 == 0 and epoch > 0:
learningRate *= 1./(1. + (decayRate * epoch))
beta *= 1./(1. + (decayRate * epoch))
#Store error for ploting graph
print 'Epoch :',epoch,', Error :',error,', alpha :',learningRate
return errorTimeline,epochList
#Work it, make it, do it,
#Makes us harder, better, faster, stronger!
learningRate = 0.0001
beta = 0.099
errorTimeline,epochList = train(XTrain,YTrain,2000,learningRate,M,beta)
#How tough are ya ?
#get output for test features
predOutput = feedFwd(XTest)
#vectorised count compare the indices of output and labels along rows
#add to count if they are same
count = np.sum(np.argmax(predOutput,axis=1) == np.argmax(YTest,axis=1))
#print accuracy
print 'Accuracy : ',(float(count)/float(YTest.shape[0]))
#plot graph
plt.xlabel('Number of epoch')
plt.ylabel('Training Error')
#mow the lawn, take out garbage, have a good nights sleep
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