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@shamdasani shamdasani/ Secret
Created Aug 5, 2017

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import numpy as np
# X = (hours sleeping, hours studying), y = score on test
X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)
y = np.array(([92], [86], [89]), dtype=float)
# scale units
X = X/np.amax(X, axis=0) # maximum of X array
y = y/100 # max test score is 100
class Neural_Network(object):
def __init__(self):
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 3
self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # (3x2) weight matrix from input to hidden layer
self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # (3x1) weight matrix from hidden to output layer
def forward(self, X):
#forward propagation through our network
self.z =, self.W1) # dot product of X (input) and first set of 3x2 weights
self.z2 = self.sigmoid(self.z) # activation function
self.z3 =, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
o = self.sigmoid(self.z3) # final activation function
return o
def sigmoid(self, s):
# activation function
return 1/(1+np.exp(-s))
def sigmoidPrime(self, s):
#derivative of sigmoid
return s * (1 - s)
def backward(self, X, y, o):
# backward propgate through the network
self.o_error = y - o # error in output
self.o_delta = self.o_error*self.sigmoidPrime(o) # applying derivative of sigmoid to error
self.z2_error = # z2 error: how much our hidden layer weights contributed to output error
self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2) # applying derivative of sigmoid to z2 error
self.W1 += # adjusting first set (input --> hidden) weights
self.W2 += # adjusting second set (hidden --> output) weights
def train (self, X, y):
o = self.forward(X)
self.backward(X, y, o)
NN = Neural_Network()
for i in xrange(1000): # trains the NN 1,000 times
print "Input: \n" + str(X)
print "Actual Output: \n" + str(y)
print "Predicted Output: \n" + str(NN.forward(X))
print "Loss: \n" + str(np.mean(np.square(y - NN.forward(X)))) # mean sum squared loss
print "\n"
NN.train(X, y)
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