# Neural Network using numpi

WordPress butchered the tabs and python loves tabs, would need some love before this snipit would run.

```import numpy as np

def nonlin(x,deriv=False):
if(deriv==True):
return x*(1-x)

return 1/(1+np.exp(-x))

#small dataset
X = np.array([[0,0,1],
[0,1,1],
[1,0,1],
[1,1,1],
[0,1,0],
[0,0,0]])

#small result set
y = np.array([,
,
,
,
,
])

np.random.seed(1)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((3,2)) - 1
syn1 = 2*np.random.random((2,1)) - 1

for j in xrange(90000):
if (j == 89999):
print j
X = np.array([[1,0,0]])
# Feed forward through layers 0, 1, and 2
l0 = X
l1 = nonlin(np.dot(l0,syn0))
l2 = nonlin(np.dot(l1,syn1))

if (j == 89999):
print l2

# how much did we miss the target value?
l2_error = y - l2

if (j% 10000) == 0:
print "Error:" + str(np.mean(np.abs(l2_error)))

# in what direction is the target value?
# were we really sure? if so, don't change too much.
l2_delta = l2_error*nonlin(l2,deriv=True)

# how much did each l1 value contribute to the l2 error (according to the weights)?
l1_error = l2_delta.dot(syn1.T)

# in what direction is the target l1?
# were we really sure? if so, don't change too much.
l1_delta = l1_error * nonlin(l1,deriv=True)

syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
```