#!/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"
#!/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")
import os.path
import deeplearn
import subprocess
if len(sys.argv) < 2:
print "Error: Specify an image filename"
sys.exit(1)
image_filename = sys.argv[1];
if not os.path.isfile(image_filename):
print "Error: File does not exist"
sys.exit(2)
retval = deeplearn.load("result.nn")
if retval != 0:
print "Error: Unable to load trained neural network. Error code " + str(retval)
sys.exit(3)
p = subprocess.Popen(['./catmuzzle', '-f', image_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
out, err = p.communicate()
inputs = [float(i) for i in out.split(",")]
print deeplearn.test(inputs)
#if deeplearn.test(inputs)[0] > 0.5:
# print "1"
#else:
# print "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()]
#!/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()]
#!/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("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"
#!/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"
