xN.setSize(x.width() - 6, x.height() - 6)
xN.setBpp(24)
xN.setPixelAll((0,0,0))
for i in xE:
xN.setPixel(i[0] - 3,i[1] - 3,(255,255,255))
xN.saveImage('bmp4.bmp')
xN = x.edgeDetectSobel()
xN.setBpp(24)
xN.saveImage('bmp5.bmp')
"""
x = feedforwardNeuralNetwork.feedforwardNeuralNetwork(3, [5, 3, 2], "NN1")
c = 0
while True:
print "New epoch %d" % (c)
c += 1
x.learn([1, 1, 0, 0, 1], [1, 0], 0.1)
x.learn([0, 1, 1, 1, 0], [0, 1], 0.1)
x.learn([0, 1, 1, 1, 1], [1, 1], 0.1)
x.learn([0, 1, 0, 0, 0], [0, 0], 0.1)
print x.feed([1, 1, 0, 0, 1])
print x.feed([0, 1, 1, 1, 0])
print x.feed([0, 1, 1, 1, 1])
print i
fpO.write('0.01\n')
for j in range(784):
fpO.write('%lf ' % (float(int(fpI.read(1).encode('hex'),16)) / 256))
fpO.write('\n')
c = int(fpL.read(1).encode('hex'),16)
for j in range(10):
if(j == c):
fpO.write('1 ')
else:
fpO.write('0 ')
fpO.write('\n')
fpI.close()
fpL.close()
fpO.close()
'''
tic = time.clock()
fNN = feedforwardNeuralNetwork.feedforwardNeuralNetwork(3,[784,300,10],'fNN')
fNN.learn('training-set.txt')
fNN.exportWeight('fNN-Weight1.txt')
toc = time.clock()
print toc - tic
tic = time.clock()
fNN = feedforwardNeuralNetwork.feedforwardNeuralNetwork(4,[30,20,5,1],'fNN-30-20-5-1-lim','fNN-30-20-5-1-Weight50-lim.txt')
toc = time.clock()
print toc - tic
fNN.learn('train_short_lim_fine.txt','',50)
toc = time.clock()
print toc - tic
fNN.exportWeight('fNN-30-20-5-1-Weight100-lim.txt')
toc = time.clock()
print toc - tic
'''
import feedforwardNeuralNetwork
import time
tic = time.clock()
fNN = feedforwardNeuralNetwork.feedforwardNeuralNetwork(4,[30,20,5,1],'fNN-30-20-5-1','fNN-30-20-5-1-Weight100-lim.txt')
toc = time.clock()
print toc - tic
fNN.feed('rawtest.txt','testout.txt')
toc = time.clock()
print toc - tic
