block10.py

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):
    #parameters
    self.inputSize = 2
    self.outputSize = 1
    self.hiddenSize = 3
 
    #weights
    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 = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
    self.z2 = self.sigmoid(self.z) # activation function
    self.z3 = np.dot(self.z2, 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))

NN = Neural_Network()

#defining our output 
o = NN.forward(X)

print "Predicted Output: \n" + str(o) 
print "Actual Output: \n" + str(y)