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import numpy as np
from data_processing import process
#get the data
training_x, training_y, test_x, test_y = process()
X=np.array(training_x)
y=np.array(training_y)
#sigmoid function
def nonlin(x, deriv=False):
if (deriv == True):
return x * (1 - x)
return 1 / (1 + np.exp(-x))
#print(X)
#print(y)
def train(X=X,y=y, descent_rate=0.01, hl1=2):
np.random.seed(1)
# randomly initialize our weights with mean 0
syn0 = 2 * np.random.random((len(X[1]), hl1)) - 1
syn1 = 2 * np.random.random((hl1, len(y[1]))) - 1
for j in range(5000):
# Feed forward through layers 0, 1, and 2
l0 = X
l1 = nonlin(np.dot(l0, syn0))
l2 = nonlin(np.dot(l1, syn1))
#print(l2)
# calculate the error in from the target value
l2_error = y - l2
if (j % 100) == 0:
print ( "Error:" + str(np.mean(np.abs(l2_error))))
# calculate the desired change in weights - bigger if our confidence is higher, use descent rate to control convergence
l2_delta = l2_error * nonlin(l2, deriv=True)*descent_rate
# how much did each l1 value contribute to the l2 error (according to the weights)?
l1_error = l2_delta.dot(syn1.T)
# calculate the desired change in weights - bigger if our confidence is higher, use descent rate to control convergence.
l1_delta = l1_error * nonlin(l1, deriv=True)*descent_rate
#update the weights
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
return syn0, syn1
def calculate_output(syn0, syn1, l0):
l1 = nonlin(np.dot(l0, syn0))
l2 = nonlin(np.dot(l1, syn1))
return l2
#test neural network
l0=test_x
#print(l0)
#train the neural network
syn0, syn1=train(X, y)
print(syn0)
print(syn1)
#get the output values based on the trained network
l2 = calculate_output(syn0, syn1, l0)
#check correctness of nn
correct = np.equal(np.argmax(test_y, 1), np.argmax(l2, 1))
print(correct)
accuracy = np.mean(correct.astype(float))
print(accuracy)
#Print inputs and predicted outputs
#for i in range(len(l0)):
#print(l0[i])
#print(l2[i])
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