def test_hopfield2(): import hopfield inputs = 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] ]) h = hopfield.hopfield(inputs) #h = hopfield.hopfield(inputs,synchronous=True,random=False) h.print_net() print "Updating neurons" h.update_neurons() h.print_out() print "Updating weights" h.set_weights(inputs) h.update_neurons() h.print_net() h.print_out() print "------" test_in = 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]) h.set_neurons(test_in) h.print_out() h.update_neurons() h.print_out()
def show_reverse():
import hopfield
inputs = 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],
]
)
h = hopfield.hopfield(inputs)
h.set_weights(inputs)
import pylab as pl
pl.ion()
a = np.array([1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1])
pl.figure(), pl.imshow(a.reshape(4, 4), cmap=pl.cm.gray, interpolation="nearest"), pl.axis("off")
print(a.reshape(4, 4))
h.set_neurons(a)
print(h.compute_energy())
y = h.update_neurons()
pl.figure(), pl.imshow(y.reshape(4, 4), cmap=pl.cm.gray, interpolation="nearest"), pl.axis("off")
print(h.compute_energy())
h.update_neurons()
print(h.compute_energy())
out = h.update_neurons()
print(h.compute_energy())
# pl.figure(), pl.imshow(out.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
a = np.array([-1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, 1, 1, 1])
pl.figure(), pl.imshow(a.reshape(4, 4), cmap=pl.cm.gray, interpolation="nearest"), pl.axis("off")
print(a.reshape(4, 4))
h.set_neurons(a)
print(h.compute_energy())
x = h.activations.copy()
print(x.reshape(4, 4))
pl.figure(), pl.imshow(x.reshape(4, 4), cmap=pl.cm.gray, interpolation="nearest"), pl.axis("off")
print(h.compute_energy())
set = h.update_neurons()
print(set.reshape(4, 4))
print(h.compute_energy())
pl.figure(), pl.imshow(set.reshape(4, 4), cmap=pl.cm.gray, interpolation="nearest"), pl.axis("off")
# next = h.update_neurons()
# print h.compute_energy()
# pl.figure(), pl.imshow(next.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
# new = h.update_neurons()
# print h.compute_energy()
# pl.figure(), pl.imshow(new.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
pl.show()
def test_hopfield(): import hopfield inputs = np.array([[1,1,1,1, -1,-1,-1,-1], [1,-1,1,-1, 1,-1,1,-1]]) print inputs #h = hopfield.hopfield(inputs) h = hopfield.hopfield(inputs,synchronous=True) #h = hopfield.hopfield(inputs,random=False) print "Setting weights" h.set_weights(inputs) h.print_net() print "Input 0" print inputs[0,:] h.set_neurons(inputs[0,:]) h.update_neurons() h.print_out() print "------" print "Input 1" print inputs[1,:] h.set_neurons(inputs[1,:]) h.update_neurons() h.print_out() test_in = np.array([1,1,1,1, 1,-1,-1,-1]) #test_in = np.array([1,1,1,-1, 1,-1,-1,-1]) h.set_neurons(test_in) print h.compute_energy() h.print_out() h.update_neurons() print h.compute_energy() h.update_neurons() print h.compute_energy() h.update_neurons() print h.compute_energy() h.print_out()
def test_hopfield2():
import hopfield
inputs = 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
]])
h = hopfield.hopfield(inputs)
#h = hopfield.hopfield(inputs,synchronous=True,random=False)
h.print_net()
print "Updating neurons"
h.update_neurons()
h.print_out()
print "Updating weights"
h.set_weights(inputs)
h.update_neurons()
h.print_net()
h.print_out()
print "------"
test_in = 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
])
h.set_neurons(test_in)
h.print_out()
h.update_neurons()
h.print_out()
def test_hopfield():
import hopfield
inputs = np.array([[1, 1, 1, 1, -1, -1, -1, -1],
[1, -1, 1, -1, 1, -1, 1, -1]])
print inputs
#h = hopfield.hopfield(inputs)
h = hopfield.hopfield(inputs, synchronous=True)
#h = hopfield.hopfield(inputs,random=False)
print "Setting weights"
h.set_weights(inputs)
h.print_net()
print "Input 0"
print inputs[0, :]
h.set_neurons(inputs[0, :])
h.update_neurons()
h.print_out()
print "------"
print "Input 1"
print inputs[1, :]
h.set_neurons(inputs[1, :])
h.update_neurons()
h.print_out()
test_in = np.array([1, 1, 1, 1, 1, -1, -1, -1])
#test_in = np.array([1,1,1,-1, 1,-1,-1,-1])
h.set_neurons(test_in)
print h.compute_energy()
h.print_out()
h.update_neurons()
print h.compute_energy()
h.update_neurons()
print h.compute_energy()
h.update_neurons()
print h.compute_energy()
h.print_out()
def learn_letters():
import scipy.io as sio
import pylab as pl
pl.ion()
nperclass = 39
classes = np.arange(20)
#classes = [0, 11, 17] # A, C, S
#classes = [10, 13, 28] # A, C, S
nclasses = len(classes)
# Read in the data and prepare it
data = sio.loadmat('binaryalphadigs.mat')
inputs = np.ones((nclasses, nperclass, 20*16))
labels = np.zeros((nclasses, nperclass, nclasses))
for k in range(nclasses):
for m in range(nperclass):
inputs[k,m,:] = (data['dat'][classes[k],m].ravel()).astype('float')
labels[k,m,k] = 1.
inputs = np.where(inputs==0,-1,1)
v = inputs[:,0,:].reshape(nclasses, 20*16)
l = labels[:,0,:].reshape(nclasses, nclasses)
# Train a Hopfield network
import hopfield
h = hopfield.hopfield(v[:10,:])
h.set_weights(v[:10,:])
# This is the training set
pl.figure(),
#pl.title('Training Data')
pl.suptitle('Training Data', fontsize=14)
for i in range(10):
pl.subplot(2,5,i), pl.imshow(v[i,:].reshape(20,16),cmap=pl.cm.gray), pl.axis('off')
#which = 2
#mask = np.ones(20*16)
#x = np.random.randint(320,size=20)
#mask[x] = -1
#h.set_neurons(v[which,:]*mask)
#print h.compute_energy()
#nrec = 3
#new = np.zeros((320,nrec))
#for i in range(1,nrec):
#new[:,i] = h.update_neurons()
#print h.compute_energy()
#pl.figure(), pl.imshow(new[:,i].reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Noisy Image. Reconstruction Step %s'%i), pl.axis('off')
#pl.figure(), pl.imshow((v[which,:]).reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Original Image.'), pl.axis('off')
#pl.figure(), pl.imshow((v[which,:]*mask).reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Noisy Image.'), pl.axis('off')
which = 12
h.set_neurons(v[which,:])
print h.compute_energy()
pl.figure(), pl.imshow((v[which,:]).reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Novel Image.'), pl.axis('off')
nrec = 5
new2 = np.zeros((320,nrec))
for i in range(nrec):
new2[:,i] = h.update_neurons()
print h.compute_energy()
pl.figure(), pl.imshow(new2[:,i].reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Novel Image. Reconstruction Step %s'%i), pl.axis('off')
# make_perfect_data(32)
# make_perfect_data(64)
# make_noisy_data(16, 1)
# make_noisy_data(32, 3)
# make_noisy_data(64, 6)
train_paths = []
path = "train/64/"
for i in "A":
print(i)
train_paths.append(path+i+".bmp")
test_paths = []
path = "test/64/"
for i in "A":
print(i)
test_paths.append(path+i+".bmp")
hopfield(train_files=train_paths, test_files=test_paths, theta=0.5,
time=20000, size=(100, 100), threshold=60)
'''
10% for all of them 100
30% is different and between 10 to 50
60% for all of them 0
def learn_letters():
import scipy.io as sio
import pylab as pl
pl.ion()
nperclass = 39
classes = np.arange(20)
#classes = [0, 11, 17] # A, C, S
#classes = [10, 13, 28] # A, C, S
nclasses = len(classes)
# Read in the data and prepare it
data = sio.loadmat('binaryalphadigs.mat')
inputs = np.ones((nclasses, nperclass, 20 * 16))
labels = np.zeros((nclasses, nperclass, nclasses))
for k in range(nclasses):
for m in range(nperclass):
inputs[k, m, :] = (data['dat'][classes[k],
m].ravel()).astype('float')
labels[k, m, k] = 1.
inputs = np.where(inputs == 0, -1, 1)
v = inputs[:, 0, :].reshape(nclasses, 20 * 16)
l = labels[:, 0, :].reshape(nclasses, nclasses)
# Train a Hopfield network
import hopfield
h = hopfield.hopfield(v[:10, :])
h.set_weights(v[:10, :])
# This is the training set
pl.figure(),
#pl.title('Training Data')
pl.suptitle('Training Data', fontsize=14)
for i in range(10):
pl.subplot(2, 5, i), pl.imshow(v[i, :].reshape(20, 16),
cmap=pl.cm.gray), pl.axis('off')
#which = 2
#mask = np.ones(20*16)
#x = np.random.randint(320,size=20)
#mask[x] = -1
#h.set_neurons(v[which,:]*mask)
#print h.compute_energy()
#nrec = 3
#new = np.zeros((320,nrec))
#for i in range(1,nrec):
#new[:,i] = h.update_neurons()
#print h.compute_energy()
#pl.figure(), pl.imshow(new[:,i].reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Noisy Image. Reconstruction Step %s'%i), pl.axis('off')
#pl.figure(), pl.imshow((v[which,:]).reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Original Image.'), pl.axis('off')
#pl.figure(), pl.imshow((v[which,:]*mask).reshape(20,16),cmap=pl.cm.gray,interpolation='nearest'), pl.title('Noisy Image.'), pl.axis('off')
which = 12
h.set_neurons(v[which, :])
print h.compute_energy()
pl.figure(), pl.imshow(
(v[which, :]).reshape(20, 16),
cmap=pl.cm.gray,
interpolation='nearest'), pl.title('Novel Image.'), pl.axis('off')
nrec = 5
new2 = np.zeros((320, nrec))
for i in range(nrec):
new2[:, i] = h.update_neurons()
print h.compute_energy()
pl.figure(), pl.imshow(
new2[:, i].reshape(20, 16),
cmap=pl.cm.gray,
interpolation='nearest'), pl.title(
'Novel Image. Reconstruction Step %s' % i), pl.axis('off')
def show_reverse():
import hopfield
inputs = 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,
]])
h = hopfield.hopfield(inputs)
h.set_weights(inputs)
import pylab as pl
pl.ion()
a = np.array([1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1])
pl.figure(), pl.imshow(a.reshape(4, 4),
cmap=pl.cm.gray,
interpolation='nearest'), pl.axis('off')
print a.reshape(4, 4)
h.set_neurons(a)
print h.compute_energy()
y = h.update_neurons()
pl.figure(), pl.imshow(y.reshape(4, 4),
cmap=pl.cm.gray,
interpolation='nearest'), pl.axis('off')
print h.compute_energy()
h.update_neurons()
print h.compute_energy()
out = h.update_neurons()
print h.compute_energy()
#pl.figure(), pl.imshow(out.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
a = np.array([-1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, 1, 1, 1])
pl.figure(), pl.imshow(a.reshape(4, 4),
cmap=pl.cm.gray,
interpolation='nearest'), pl.axis('off')
print a.reshape(4, 4)
h.set_neurons(a)
print h.compute_energy()
x = h.activations.copy()
print x.reshape(4, 4)
pl.figure(), pl.imshow(x.reshape(4, 4),
cmap=pl.cm.gray,
interpolation='nearest'), pl.axis('off')
print h.compute_energy()
set = h.update_neurons()
print set.reshape(4, 4)
print h.compute_energy()
pl.figure(), pl.imshow(set.reshape(4, 4),
cmap=pl.cm.gray,
interpolation='nearest'), pl.axis('off')
#next = h.update_neurons()
#print h.compute_energy()
#pl.figure(), pl.imshow(next.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
#new = h.update_neurons()
#print h.compute_energy()
#pl.figure(), pl.imshow(new.reshape(4,4),cmap=pl.cm.gray,interpolation='nearest'),pl.axis('off')
pl.show()
