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mlp.py
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mlp.py
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import numpy as np
# X = input
# Y = output
X = np.array(([0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1, 3], [2, 0], [2, 1], [2, 2], [2, 3], [3, 0], [3, 1], [3, 2], [3, 3]), dtype=float)
y = np.array(([50], [20], [10], [0], [100], [100], [100], [100], [100], [100], [100], [100], [100], [100], [100], [100]), dtype=float)
xPredicted = np.array(([0, 1]), dtype=float)
y = y/100 # / 100 car pourcentage donc le max = 100
class Neural_Network(object):
def __init__(self):
#nombre de perceptron par couche
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 2
#poids entre couche 1 et 2 puis couche 2 et 3
self.W1 = np.random.randn(self.inputSize, self.hiddenSize)
self.W2 = np.random.randn(self.hiddenSize, self.outputSize)
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 weights (inputSize * hiddenSize)
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 weights (hiddenSize * outputSize)
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 = self.o_delta.dot(self.W2.T) # 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 += X.T.dot(self.z2_delta) # adjusting first set (input --> hidden) weights
self.W2 += self.z2.T.dot(self.o_delta) # adjusting second set (hidden --> output) weights
def train(self, X, y):
o = self.forward(X)
self.backward(X, y, o)
def saveWeights(self):
np.savetxt("w1.txt", self.W1, fmt="%s")
np.savetxt("w2.txt", self.W2, fmt="%s")
def predict(self):
print "Predicted data based on trained weights: ";
print "Input (scaled): \n" + str(xPredicted);
print "Output: \n" + str(self.forward(xPredicted)*100);
NN = Neural_Network()
for i in xrange(1000): # trains the NN 1,000 times
# print "# " + str(i) + "\n"
# print "Input (scaled): \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)
print "# " + str(i) + "\n"
print "Input (scaled): \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.saveWeights()
NN.predict()