def get_RBM():
print "Loading digits...."
digits, classes = load_digits('digits_train.txt')
# digits = [digit_gray_to_binary(d) for d in digits]
r = GB_RBM(196, 25)
r.bias_visible = np.mean(np.array([digits]), 1).transpose()
print r.bias_visible
raw_input()
# Train at the following learning rates:
print "Training..."
learning_rates = [0.1, 0.05, 0.01, 0.005, 0.001]
for rate in learning_rates:
print "Learning rate =", rate
for i in range(20):
print ' ' + str(i) + '...',
r.train_epoch(digits, rate, 5)
err = np.sum([r.free_energy(digit) for digit in digits])
print 'Energy = ' + str(err) + '...',
PL = r.pseudolikelihood(digits)
print 'Pseudolikelihood = ' + str(PL) + '...',
print 'Done'
return r, digits
def get_RBM():
print "Loading digits...."
digits, classes = load_digits("digits_train.txt")
# digits = [digit_gray_to_binary(d) for d in digits]
r = GB_RBM(196, 25)
r.bias_visible = np.mean(np.array([digits]), 1).transpose()
print r.bias_visible
raw_input()
# Train at the following learning rates:
print "Training..."
learning_rates = [0.1, 0.05, 0.01, 0.005, 0.001]
for rate in learning_rates:
print "Learning rate =", rate
for i in range(20):
print " " + str(i) + "...",
r.train_epoch(digits, rate, 5)
err = np.sum([r.free_energy(digit) for digit in digits])
print "Energy = " + str(err) + "...",
PL = r.pseudolikelihood(digits)
print "Pseudolikelihood = " + str(PL) + "...",
print "Done"
return r, digits
def get_RBM():
r = GB_RBM(6, 3)
k = 9
dataset = [[1.485, 0.537, 0.374, -0.786, -0.775, -0.547],
[1.360, 0.143, 0.689, -0.763, -0.709, -0.672],
[1.385, 0.590, 0.346, -0.694, -0.554, -0.772],
[-0.768, -1.486, 0.631, 1.457, 1.299, -0.572],
[-0.542, -1.355, 0.289, 1.572, 1.432, -0.822],
[-0.668, -1.250, 0.346, 1.183, 1.500, -0.797],
[-0.743, 0.879, -1.653, -0.534, -0.753, 1.327],
[-0.718, 1.168, -1.996, -0.649, -0.664, 1.127],
[-0.793, 0.774, 0.974, -0.786, -0.775, 1.727]]
# Train at the following learning rates:
print "Training..."
learning_rates = [0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001]
# learning_rates = [0.1]
for rate in learning_rates:
print "Learning rate =", rate
for i in range(100):
print ' ' + str(i) + '...',
r.train_epoch(dataset, rate, k)
err = np.sum([r.free_energy(data) for data in dataset])
print 'Energy = ' + str(err) + '...',
PL = r.pseudolikelihood(dataset)
print 'Pseudolikelihood = ' + str(PL) + '...',
L = r.likelihood(dataset)
print 'Likelihood = ' + str(L) + '...',
print 'Done'
return r, dataset
def get_RBM():
r = GB_RBM(6,3)
k = 9
dataset = [[ 1.485, 0.537, 0.374, -0.786, -0.775, -0.547],
[ 1.360, 0.143, 0.689, -0.763, -0.709, -0.672],
[ 1.385, 0.590, 0.346, -0.694, -0.554, -0.772],
[-0.768, -1.486, 0.631, 1.457, 1.299, -0.572],
[-0.542, -1.355, 0.289, 1.572, 1.432, -0.822],
[-0.668, -1.250, 0.346, 1.183, 1.500, -0.797],
[-0.743, 0.879, -1.653, -0.534, -0.753, 1.327],
[-0.718, 1.168, -1.996, -0.649, -0.664, 1.127],
[-0.793, 0.774, 0.974, -0.786, -0.775, 1.727]]
# Train at the following learning rates:
print "Training..."
learning_rates = [0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001]
# learning_rates = [0.1]
for rate in learning_rates:
print "Learning rate =", rate
for i in range(100):
print ' ' + str(i) + '...',
r.train_epoch(dataset, rate, k)
err = np.sum([r.free_energy(data) for data in dataset])
print 'Energy = ' + str(err) + '...',
PL = r.pseudolikelihood(dataset)
print 'Pseudolikelihood = ' + str(PL) + '...',
L = r.likelihood(dataset)
print 'Likelihood = ' + str(L) + '...',
print 'Done'
return r, dataset