Beispiel #1
0
#!/usr/bin/env python3

import sys
import deeplearn

retval = deeplearn.load("result.nn")
if retval != 0:
    print("Unable to load network. Error code " + str(retval))
    sys.quit()

print("zero,zero"),
if deeplearn.test(["zero","zero"])[0] > 0.5:
    print("1")
else:
    print("0")

print("one,zero"),
if deeplearn.test(["one","zero"])[0] > 0.5:
    print("1")
else:
    print("0")

print("zero,one"),
if deeplearn.test(["zero","one"])[0] > 0.5:
    print("1")
else:
    print("0")

print("one,one"),
if deeplearn.test(["one","one"])[0] > 0.5:
    print("1")
Beispiel #2
0
#!/usr/bin/env python2

import deeplearn

# Rather than showing numbers show the species names

species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]

# Load the neural network

deeplearn.load("result.nn")

# These dimensions are similar to those which exist in the
# data set, but are adjusted slightly so that the network
# has never seen these exact values before

print "Expected: " + species[0]
deeplearn.test([5.44, 3.436, 1.667, 0.214])
print "Returned: " + species[deeplearn.getClass()]

print "\nExpected: " + species[1]
deeplearn.test([6.14, 2.75, 4.04, 1.32])
print "Returned: " + species[deeplearn.getClass()]

print "\nExpected: " + species[2]
deeplearn.test([6.71, 3.14, 5.92, 2.29])
print "Returned: " + species[deeplearn.getClass()]
Beispiel #3
0
#!/usr/bin/python

import sys
import deeplearn

retval = deeplearn.load("result.nn")
if retval != 0:
    print "Unable to load network. Error code " + str(retval)
    sys.quit()

print("0,0"),
if deeplearn.test([0.0,0.0])[0] > 0.5:
    print "1"
else:
    print "0"

print("1,0"),
if deeplearn.test([1.0,0.0])[0] > 0.5:
    print "1"
else:
    print "0"

print("0,1"),
if deeplearn.test([0.0,1.0])[0] > 0.5:
    print "1"
else:
    print "0"

print("1,1"),
if deeplearn.test([1.0,1.0])[0] > 0.5:
    print "1"
Beispiel #4
0
#!/usr/bin/python

import deeplearn

# Rather than showing numbers show the species names

species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]

# Load the neural network

deeplearn.load("result.nn")

# These dimensions are similar to those which exist in the
# data set, but are adjusted slightly so that the network
# has never seen these exact values before

print "Expected: " + species[0]
deeplearn.test([5.44, 3.436, 1.667, 0.214])
print "Returned: " + species[deeplearn.getClass()]

print "\nExpected: " + species[1]
deeplearn.test([6.14, 2.75, 4.04, 1.32])
print "Returned: " + species[deeplearn.getClass()]

print "\nExpected: " + species[2]
deeplearn.test([6.71, 3.14, 5.92, 2.29])
print "Returned: " + species[deeplearn.getClass()]