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Traceback (most recent call last):
  File "C:\Uni\Bachelorarbeit\Mustererkennung\Steine_real.py", line 5, in <module>
    conec = mlgraph((17380,2700,2700,2))
  File "C:\Python26\lib\site-packages\ffnet\ffnet.py", line 41, in mlgraph
    conec.append((src, trg))
MemoryError

Code: Alles auswählen

# -*- coding: cp1252 -*-
from ffnet import ffnet, mlgraph, readdata

# Generate standard layered network architecture and create network
conec = mlgraph((17380,2700,2700,2))
net = ffnet(conec)

# Read data file
print "READING DATA..."
data = readdata( 'Trainingsdaten.dat', delimiter = ';' )
print data
input =  data[:, :17380] #first 17380 columns - bitmap definition
target = data[:, 17380:] #the rest - 2 columns for "ja" o. "nein"

# Train network with scipy tnc optimizer - 16 lines used for training
print "TRAINING NETWORK..."
net.train_tnc(input[:15], target[:15], maxfun = 2000, messages=1)

# Test network - remaining lines used for testing
print
print "TESTING NETWORK..."
output, regression = net.test(input[15:], target[15:], iprint = 1)

try:
    from pylab import *
    from random import randint
    
    digitpat = randint(58, 67) #Choose testing pattern to plot
    
    subplot(211)
##    imshow(input[digitpat].reshape(8,8), interpolation = 'nearest')
    imshow(input[16].reshape(8,8), interpolation = 'nearest')
    
    subplot(212)
    N = 2  # number of digits / network
    ind = arange(N)   # the x locations for the groups
    width = 0.10       # the width of the bars
##    bar(ind, net(input[digitpat]), width, color='b') #make a plot
    bar(ind, net(input[16]), width, color='g') #make a plot
    xticks(ind+width/2., ('ja', 'nein',))
    xlim(-width,N-width)
    axhline(linewidth=1, color='black')
    title("Das trainierte Netz schaezt")
    xlabel("Stein")
    ylabel("Network outputs")
   
    show()
except ImportError, e:
    print "Cannot make plots. For plotting install matplotlib.\n%s" % e
    
print \
"""
Note:
Normalization of input/output data is handled automatically in ffnet.
Just use your raw data both at trainig and recalling phase.
"""

Code: Alles auswählen

>>> a = 17382
>>> b = 2700
>>> c = 2
>>> a * b + b * b + c * b
54226800