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70 changes: 35 additions & 35 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,26 +9,26 @@
import json

def main()

# Initialize the command line parser
parser = argparse.ArgumentParser()

# Read command line arguments
parser.add_argument('command',type=str,help='command to execute')
parser.add_argument('-nV',type=int,default=4,help='number of visible nodes')
parser.add_argument('-nH',type=int,default=4,help='number of hidden nodes')
parser.add_argument('-steps',type=int,default=1000000,help='training steps')
parser.add_argument('-lr',type=float,default=1e-3,help='learning rate')
parser.add_argument('-bs',type=int,default=100,help='batch size')
parser.add_argument('-CD',type=int,default=10,help='steps of contrastive divergence')
parser.add_argument('-nC',type=float,default=10,help='number of chains in PCD')
parser.add_argument('command',type=str,help='command to execute')
parser.add_argument('-nV',type=int,default=4,help='number of visible nodes')
parser.add_argument('-nH',type=int,default=4,help='number of hidden nodes')
parser.add_argument('-steps',type=int,default=1000000,help='training steps')
parser.add_argument('-lr',type=float,default=1e-3,help='learning rate')
parser.add_argument('-bs',type=int,default=100,help='batch size')
parser.add_argument('-CD',type=int,default=10,help='steps of contrastive divergence')
parser.add_argument('-nC',type=float,default=10,help='number of chains in PCD')

# Parse the arguments
args = parser.parse_args()

if args.command == 'train':
train(args)

if args.command == 'sample':
sample(args)

Expand All @@ -43,7 +43,7 @@ class Ops(object):
pass

def train(args):

# Simulation parameters
num_visible = args.nV # number of visible nodes
num_hidden = args.nH # number of hidden nodes
Expand All @@ -57,32 +57,32 @@ def train(args):
hidden_bias=None # hidden bias
bcount=0 # counter
epochs_done=1 # epochs counter

# *************************************************************
# INSERT HERE THE PATH TO THE TRAINING AND TESTING DATASETS
trainName = '*******************'
testName = '*******************'

# Loading the data
xtrain = np.loadtxt(trainName)
xtest = np.loadtxt(testName)

ept=np.random.permutation(xtrain) # random permutation of training data
epv=np.random.permutation(xtest) # random permutation of test data
iterations_per_epoch = xtrain.shape[0] / bsize # gradient iteration per epoch

# Initialize RBM class
rbm = RBM(num_hidden=num_hidden, num_visible=num_visible, weights=weights, visible_bias=visible_bias,hidden_bias=hidden_bias, num_samples=num_samples)
rbm = RBM(num_hidden=num_hidden, num_visible=num_visible, weights=weights, visible_bias=visible_bias,hidden_bias=hidden_bias, num_samples=num_samples)

# Initialize operations and placeholders classes
ops = Ops()
placeholders = Placeholders()

placeholders.visible_samples = tf.placeholder(tf.float32, shape=(None, num_visible), name='v') # placeholder for training data

total_iterations = 0 # starts at zero
total_iterations = 0 # starts at zero
ops.global_step = tf.Variable(total_iterations, name='global_step_count', trainable=False)

# Decaying learning rate
learning_rate = tf.train.exponential_decay(
learning_rate_b,
Expand All @@ -98,10 +98,10 @@ def train(args):
ops.lr=learning_rate
ops.train = optimizer.minimize(cost, global_step=ops.global_step)
ops.init = tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())

with tf.Session() as sess:
sess.run(ops.init)

for ii in range(nsteps):
if bcount*bsize+ bsize>=xtrain.shape[0]:
bcount=0
Expand All @@ -110,7 +110,7 @@ def train(args):
batch=ept[ bcount*bsize: bcount*bsize+ bsize,:]
bcount=bcount+1
feed_dict = {placeholders.visible_samples: batch}

_, num_steps = sess.run([ops.train, ops.global_step], feed_dict=feed_dict)

if num_steps % iterations_per_epoch == 0:
Expand All @@ -119,51 +119,51 @@ def train(args):
epochs_done += 1

def sample(args):

num_visible = args.nV # number of visible nodes
num_hidden = args.nH # number of hidden nodes

# *************************************************************
# INSERT HERE THE PATH TO THE PARAMETERS FILE
path_to_params = '*******************'
# Load the RBM parameters

# Load the RBM parameters
params = np.load(path_to_params)
weights = params['weights']
visible_bias = params['visible_bias']
hidden_bias = params['hidden_bias']
hidden_bias=np.reshape(hidden_bias,(hidden_bias.shape[0],1))
visible_bias=np.reshape(visible_bias,(visible_bias.shape[0],1))

# Sampling parameters
num_samples=1000 # how many independent chains will be sampled
gibb_updates=100 # how many gibbs updates per call to the gibbs sampler
nbins=1000 # number of calls to the RBM sampler
nbins=1000 # number of calls to the RBM sampler

# Initialize RBM class
rbm = RBM(num_hidden=num_hidden, num_visible=num_visible, weights=weights, visible_bias=visible_bias,hidden_bias=hidden_bias, num_samples=num_samples)
hsamples,vsamples=rbm.stochastic_maximum_likelihood(gibb_updates)

# Initialize tensorflow
init = tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())

with tf.Session() as sess:
sess.run(init)

for i in range(nbins):
print ('bin %d\t' %i)

# Gibbs sampling
_,samples=sess.run([hsamples,vsamples])


def save_parameters(sess,rbm,epochs):
weights, visible_bias, hidden_bias = sess.run([rbm.weights, rbm.visible_bias, rbm.hidden_bias])

# *************************************************************
# INSERT HERE THE PATH TO THE PARAMETERS FILE
parameter_file_path = '*******************'

np.savez_compressed(parameter_file_path, weights=weights, visible_bias=visible_bias, hidden_bias=hidden_bias,
epochs=epochs)

Expand Down
12 changes: 6 additions & 6 deletions rbm.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
import numpy as np

class RBM(object):

''' Restricted Boltzmann Machine '''

def __init__(self, num_hidden, num_visible, num_samples=128, weights=None, visible_bias=None, hidden_bias=None):
''' Constructor '''
# number of hidden units
Expand Down Expand Up @@ -101,7 +101,7 @@ class variables.
self.hidden_samples = self.hidden_samples.assign(h_samples)
self.p_of_v = p_of_v
return self.hidden_samples, v_samples

def energy(self, hidden_samples, visible_samples):
# type: (tf.Tensor, tf.Tensor) -> tf.Tensor
"""Compute the energy:
Expand All @@ -123,8 +123,8 @@ def free_energy(self, visible_samples):
free_energy = (tf.matmul(visible_samples, self.visible_bias)
+ tf.reduce_sum(tf.nn.softplus(tf.matmul(visible_samples, self.weights)
+ tf.transpose(self.hidden_bias)), 1, keep_dims=True))
return free_energy
return free_energy

def neg_log_likelihood_grad(self, visible_samples, model_samples=None, num_gibbs=2):
# type: (tf.Tensor, tf.Tensor, int) -> tf.Tensor

Expand All @@ -142,7 +142,7 @@ def neg_log_likelihood(self, visible_samples, log_Z):
+ tf.reduce_sum(tf.nn.softplus(tf.matmul(visible_samples, self.weights)
+ tf.transpose(self.hidden_bias)), 1))
return -tf.reduce_mean(free_energy - log_Z)

def exact_log_partition_function(self):
''' Evaluate the partition function by exact enumerations '''
with tf.name_scope('exact_log_Z'):
Expand Down
60 changes: 30 additions & 30 deletions tutorial/ed_tfim1d.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
Sx = np.array([[0,1.],[1,0]])
Sz = np.array([[1,0.],[0,-1]])

# Pauli X operator
# Pauli X operator
def sigmaX(L,i):
''' Return the many-body operator
I x I x .. x Sx x I x .. x I
Expand Down Expand Up @@ -34,11 +34,11 @@ def sigmaZ(L,i):

return reduce(np.kron,OpList)

# Magnetic interaction term
# Magnetic interaction term
def buildMagneticInteraction(i,B,L):
return B*sigmaX(L,i)

# Ising interaction term
# Ising interaction term
def buildIsingInteraction(i,j,J,L):
''' Return the Ising interaction term
I x .. x Sz x Sz x .. x I
Expand All @@ -56,37 +56,37 @@ def buildIsingInteraction(i,j,J,L):

# Build transverse-field Ising model
def build1dIsingModel(L,J,B,OBC):

D = 1<<L # dimension of Hilbert space
''' Return the full Hamiltonian '''
Ham = np.zeros((D,D))
Ham = np.zeros((D,D))
for i in range(L-1):
Ham = Ham - buildIsingInteraction(i,i+1,J,L)
Ham = Ham - buildMagneticInteraction(i,B,L)
Ham = Ham - buildMagneticInteraction(i,B,L)
Ham = Ham - buildMagneticInteraction(L-1,B,L)

# Periodic Boundary Conditions
if OBC is True:
Ham = Ham - buildIsingInteraction(0,L-1,J,L)

return Ham

# Generate the training dataset
def buildDataset(L,B,psi,Nsamples):

D = 1<<L
config = np.zeros((D,L))
psi2 = np.zeros((D))
psi2 = np.zeros((D))

# Build all spin states and psi^2
for i in range(D):
state = (bin(i)[2:].zfill(L)).split()
for j in range(L):
config[i,j] = int(state[0][j])
psi2[i] = psi[i]**2
psi2[i] = psi[i]**2

config_index = range(D)

# Generate the trainset
index_samples = np.random.choice(config_index,
Nsamples,
Expand All @@ -99,7 +99,7 @@ def buildDataset(L,B,psi,Nsamples):
dataFile.write("%d " % config[index_samples[i]][j])
dataFile.write("\n")
dataFile.close()

# Generate the testset
index_samples = np.random.choice(config_index,
Nsamples/10,
Expand All @@ -114,16 +114,16 @@ def buildDataset(L,B,psi,Nsamples):

# MAIN
def main(pars):

print ('\n--------------------------------\n')
print ('EXACT DIAGONALIZATION OF THE 1d-TFIM\n')

# Parameters
L = pars.L; # number of spins
B = pars.B # magnetic field
B = pars.B # magnetic field
J = pars.J # ising interaction strength
Nsamples = 10000 # train dataset size

print ('Number of spins L = %d' % L)
print ('Ising interaction J = %.1f' % J)
print ('\n\n')
Expand All @@ -133,38 +133,38 @@ def main(pars):
Bmax = 2.0
Bsteps = 10
deltaB = (Bmax-Bmin)/float(Bsteps)

# Observables file
obs_Name = '../data/tfim1d/observables/ed_tfim1d_L' + str(L) + '_observables.txt'
obs_File = open(obs_Name,'w')
obs_File.write('# B E ') # write header
obs_File.write('<|Sz|> <Sx>\n') # write header

# Spin-spin correlation file
corr_Name = '../data/tfim1d/observables/ed_tfim1d_L' + str(L) + '_correlations.txt'
corr_File = open(corr_Name,'w')

# Loop over magnetic field values
for b in range(1,Bsteps+1):

B = Bmin + b*deltaB
print('Magnetic field B = %.2f' % B)
print('Magnetic field B = %.2f' % B)

# Wavefunction file
psiName = '../data/tfim1d/wavefunctions/wavefunction_tfim1d_L' + str(L)
psiName += '_B' + str(B) + '.txt'

# Diagonalize the Hamiltonian
print('diagonalizing...')
H = build1dIsingModel(L,J,B,True)
(e,psi) = np.linalg.eigh(H)
psi0 = np.abs(psi[:,0])
e0 = e[0]

# Save energy and wavefunction
obs_File.write('%.1f %.10f ' % (B,e0/float(L)))
np.savetxt(psiName,psi0)

# Magnetic observables
print('computing observables...')
# Compute <|Sz|>
Expand All @@ -184,21 +184,21 @@ def main(pars):
for j in range(L):
for k in range(1<<L):
config = (bin(k)[2:].zfill(L)).split()
SzSz[i,j] += (psi0[k]**2)*(2*float(config[0][i])-1)*(2*float(config[0][j])-1)
SzSz[i,j] += (psi0[k]**2)*(2*float(config[0][i])-1)*(2*float(config[0][j])-1)
corr_File.write('%.10f ' % SzSz[i,j])
corr_File.write('\n')

# Generate the training datasets
print('generating the dataset...')
buildDataset(L,B,psi0,Nsamples)
buildDataset(L,B,psi0,Nsamples)
print('\n')


#---------------------------------------------------------------


if __name__ == "__main__":

# Read arguments from command line
parser = argparse.ArgumentParser()
parser.add_argument('-L',type=int,default=10)
Expand Down
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