def main():
# Load data
camera = skimage.data.camera()
astronaut = rgb2gray(skimage.data.astronaut())
horse = skimage.data.horse()
coffee = rgb2gray(skimage.data.coffee())
# Marge data
data = [camera, astronaut, horse, coffee]
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
# Generate testset
test = [get_corrupted_input(d, 0.3) for d in data]
predicted = model.predict(test, threshold=0, asyn=False)
print("Show prediction results...")
plot(data, test, predicted)
print("Show network weights matrix...")
print("Number of Epochs %i: ", self.count)
def main():
# Load data
pateta = io.imread('pateta.png')
renato = io.imread('renato.png')
lenna = io.imread('lenna.png')
# Marge data
data = [pateta, renato, lenna]
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
# Generate testset
test = [get_corrupted_input(d, 0.15) for d in data]
predicted = model.predict(test, threshold=0, asyn=True)
print("Show prediction results...")
plot(data, test, predicted)
print("Show network weights matrix...")
model.plot_weights()
def main():
# Load data
import cv2
import glob
img_dir = "train_custom/" # Enter Directory of all images
data_path = os.path.join(img_dir, '*g')
files = glob.glob(data_path)
data = []
for f1 in files:
img = rgb2gray(cv2.imread(f1))
data.append(img)
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
# Generate testset
img_dir = "test_custom/" # Enter Directory of all images
data_path = os.path.join(img_dir, '*g')
files = glob.glob(data_path)
test = []
# Since same name order will be the same
for f1 in files:
img = rgb2gray(cv2.imread(f1))
test.append(img)
test = [preprocessing(d) for d in test]
predicted = model.predict(test, threshold=0, asyn=False)
print("Show prediction results...")
plot(data, test, predicted)
def main():
# Load data
(x_train, y_train), (_, _) = mnist.load_data()
data = []
for i in range(3):
xi = x_train[y_train == i]
data.append(xi[0])
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
# Make test datalist
test = []
for i in range(3):
xi = x_train[y_train == i]
test.append(xi[1])
test = [preprocessing(d) for d in test]
predicted = model.predict(test, threshold=50, asyn=True)
print("Show prediction results...")
plot(data, test, predicted, figsize=(5, 5))
print("Show network weights matrix...")
model.plot_weights()
def main():
#Debug
np.set_printoptions(threshold=np.inf)
print("[LOG] Creando set de caracteres - ", datetime.datetime.now())
train_images = init_train_images()
test_images = init_test_images()
print("=>train_images[0]")
print(train_images[0])
print("=>test_images[0][0]:")
print(test_images[0][0])
print("[LOG] Training - ", datetime.datetime.now())
# No of neurons
n_neurons = get_pattern_length()
print("n neurons:")
print(n_neurons)
#W = train(n_neurons, train_images)
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(train_images)
predicted = model.predict(test_images, threshold=0, asyn=False)
print("Show prediction results...")
plot(train_images, test_images, predicted)
print("Show network weights matrix...")
model.plot_weights()
exit(0)
def main():
# Load data
camera = skimage.data.camera()
astronaut = rgb2gray(skimage.data.astronaut())
horse = skimage.data.horse()
coffee = rgb2gray(skimage.data.coffee())
print(coffee.shape)
man = rgb2gray(mpi.imread("yosuke.jpg"))
print(man.shape)
# Marge data
data = [camera, astronaut, horse, coffee, man]
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
# Generate testset
test = [get_corrupted_input(d, 0.3) for d in data]
predicted = model.predict(test, threshold=0, asyn=False)
print("Show prediction results...")
plot(data, test, predicted)
print("Show network weights matrix...")
model.plot_weights()
def main():
# Load data
shannon = io.imread('shannon.png')
renato = io.imread('renato.png')
boltzmann = io.imread('boltzmann.png')
# Marge data
data = [shannon, renato, boltzmann]
# Preprocessing
print("Start to data preprocessing...")
data = [preprocessing(d) for d in data]
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data)
model.shuffle()
# Generate testset
test = [get_corrupted_input(d, 0.30) for d in data]
predicted = model.predict(test, threshold=0, asyn=True)
print("Show prediction results...")
plot(data, test, predicted)
print("Show network weights matrix...")
model.plot_weights()
if toString(col): #print("not mpty col:", col)
gooddata.append(col)
def toMinusOne(goodDataSample):
tempstr = toString(goodDataSample)
tmp = toBinary(tempstr)
tmp = tmp.replace(" ", "") #max len 408
if len(tmp)>599: print("WARNING")
tmp = "".join("0" for i in range(600-len(tmp)))+tmp
bi = list(tmp)
for j in range(len(bi)):
if bi[j]=="0": bi[j]=-1
if bi[j]=="1": bi[j]=1
return bi
model = network.HopfieldNetwork()
gooddata=[gooddata[0], gooddata[1], gooddata[2], gooddata[3]]
print(gooddata[0])
print(gooddata[1])
print(gooddata[2])
print(gooddata[3])
bingooddata = [toMinusOne(d) for d in gooddata]
cmon = numpy.array(bingooddata)
#print("----")
#print(fromBin(cmon[3]))
#print("----")
model.train_weights(cmon)
def main():
# Load data
animals = dataload.load('data\\animals-14x9.csv') # h = 9, w = 14
large25 = dataload.load('data\\large-25x25.csv') # h = 25, w = 25
large25plus = dataload.load('data\\large-25x25.plus.csv') # h = 25, w = 25
large50 = dataload.load('data\\large-25x50.csv') # h = 50, w = 25
letters = dataload.load('data\\letters-14x20.csv') # h = 20, w = 14
lettersabc = dataload.load('data\\letters-abc-8x12.csv') # h = 12, w = 8
ocra = dataload.load('data\\OCRA-12x30-cut.csv') # h = 30, w = 12
small = dataload.load('data\\small-7x7.csv') # h = 7, w = 7
cats = dataload.load('data\\cats') # h = 60, w = 60
cats = np.rint(cats + 0.2) * 2 - 1
custom_data = np.array([
[
1, 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1,
-1, 1, 1, 1, 1, 1
],
[
1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1,
1, 1, -1, 1, -1, 1
],
[
1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1,
1, 1, 1, 1
],
[
-1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1,
-1, -1, 1, -1, -1
],
[
1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1,
-1, 1, -1, -1, -1, 1
],
[
-1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1,
-1, 1, 1, 1, -1
],
[
-1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1,
-1, -1, -1, -1, 1, 1
],
[
1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1,
1, 1, 1, 1, -1, -1, -1
],
[
-i for i in [
1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1,
-1, 1, 1, -1, 1, -1, 1
]
],
[
-i for i in [
-1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1,
-1, -1, -1, 1, -1, -1
]
],
[
1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1,
-1, 1, -1, 1, -1, 1
], # szachownica
[
-i for i in [
1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1,
1, -1, 1, -1, 1, -1, 1
]
] # szachownica
])
# Marge data
data = custom_data
height = 5
width = 5
# Create Hopfield Network Model
model = network.HopfieldNetwork()
model.train_weights(data, 'Hebb')
# Generate testset
test = [get_corrupted_input(d, 0.) for d in data] # 0.1
predicted = model.predict(test, threshold=0, asyn=True)
print("Show prediction results...")
plot(data, test, predicted, height, width)
print("Show network weights matrix...")
# model.plot_weights()
print(check_stability(data, predicted, 0.1))
