Hybrid MonteCarlo SamplingNote This is an advanced tutorial, which shows how one can implemented Hybrid MonteCarlo (HMC) sampling using Theano. We assume the reader is already familiar with Theano and energybased models such as the RBM. Note The code for this section is available for download here. TheoryMaximum likelihood learning of energybased models requires a robust algorithm to sample negative phase particles (see Eq.(4) of the Restricted Boltzmann Machines (RBM) tutorial). When training RBMs with CD or PCD, this is typically done with block Gibbs sampling, where the conditional distributions and are used as the transition operators of the Markov chain. In certain cases however, these conditional distributions might be difficult to sample from (i.e. requiring expensive matrix inversions, as in the case of the “meancovariance RBM”). Also, even if Gibbs sampling can be done efficiently, it nevertheless operates via a random walk which might not be statistically efficient for some distributions. In this context, and when sampling from continuous variables, Hybrid Monte Carlo (HMC) can prove to be a powerful tool [Duane87]. It avoids random walk behavior by simulating a physical system governed by Hamiltonian dynamics, potentially avoiding tricky conditional distributions in the process. In HMC, model samples are obtained by simulating a physical system, where particles move about a highdimensional landscape, subject to potential and kinetic energies. Adapting the notation from [Neal93], particles are characterized by a position vector or state and velocity vector . The combined state of a particle is denoted as . The Hamiltonian is then defined as the sum of potential energy (same energy function defined by energybased models) and kinetic energy , as follows: Instead of sampling directly, HMC operates by sampling from the canonical distribution . Because the two variables are independent, marginalizing over is trivial and recovers the original distribution of interest. Hamiltonian Dynamics State and velocity are modified such that remains constant throughout the simulation. The differential equations are given by: (1) As shown in [Neal93], the above transformation preserves volume and is reversible. The above dynamics can thus be used as transition operators of a Markov chain and will leave invariant. That chain by itself is not ergodic however, since simulating the dynamics maintains a fixed Hamiltonian . HMC thus alternates hamiltonian dynamic steps, with Gibbs sampling of the velocity. Because and are independent, sampling is trivial since , where is often taken to be the univariate Gaussian. The LeapFrog Algorithm In practice, we cannot simulate Hamiltonian dynamics exactly because of the problem of time discretization. There are several ways one can do this. To maintain invariance of the Markov chain however, care must be taken to preserve the properties of volume conservation and time reversibility. The leapfrog algorithm maintains these properties and operates in 3 steps: (2) We thus perform a halfstep update of the velocity at time , which is then used to compute and . Accept / Reject In practice, using finite stepsizes will not preserve exactly and will introduce bias in the simulation. Also, rounding errors due to the use of floating point numbers means that the above transformation will not be perfectly reversible. HMC cancels these effects exactly by adding a Metropolis accept/reject stage, after leapfrog steps. The new state is accepted with probability , defined as: HMC Algorithm In this tutorial, we obtain a new HMC sample as follows:
Implementing HMC Using TheanoIn Theano, update dictionaries and shared variables provide a natural way to implement a sampling algorithm. The current state of the sampler can be represented as a Theano shared variable, with HMC updates being implemented by the updates list of a Theano function. We breakdown the HMC algorithm into the following subcomponents:
simulate_dynamics To perform leapfrog steps, we first need to define a function over which can iterate over. Instead of implementing Eq. (2) verbatim, notice that we can obtain and by performing an initial halfstep update for , followed by fullstep updates for and one last halfstep update for . In loop form, this gives: (3) The innerloop defined above is implemented by the following function, with , and replacing and respectively. def leapfrog(pos, vel, step): """ Inside loop of Scan. Performs one step of leapfrog update, using Hamiltonian dynamics. Parameters  pos: theano matrix in leapfrog update equations, represents pos(t), position at time t vel: theano matrix in leapfrog update equations, represents vel(t  stepsize/2), velocity at time (t  stepsize/2) step: theano scalar scalar value controlling amount by which to move Returns  rval1: [theano matrix, theano matrix] Symbolic theano matrices for new position pos(t + stepsize), and velocity vel(t + stepsize/2) rval2: dictionary Dictionary of updates for the Scan Op """ # from pos(t) and vel(tstepsize/2), compute vel(t+stepsize/2) dE_dpos = TT.grad(energy_fn(pos).sum(), pos) new_vel = vel  step * dE_dpos # from vel(t+stepsize/2) compute pos(t+stepsize) new_pos = pos + step * new_vel return [new_pos, new_vel], {} # compute velocity at timestep: t + stepsize/2 The function performs the full algorithm of Eqs. (3). We start with the initial halfstep update of and fullstep of , and then scan over the method times. def simulate_dynamics(initial_pos, initial_vel, stepsize, n_steps, energy_fn): """ Return final (position, velocity) obtained after an `n_steps` leapfrog updates, using Hamiltonian dynamics. Parameters  initial_pos: shared theano matrix Initial position at which to start the simulation initial_vel: shared theano matrix Initial velocity of particles stepsize: shared theano scalar Scalar value controlling amount by which to move energy_fn: python function Python function, operating on symbolic theano variables, used to compute the potential energy at a given position. Returns  rval1: theano matrix Final positions obtained after simulation rval2: theano matrix Final velocity obtained after simulation """ def leapfrog(pos, vel, step): """ Inside loop of Scan. Performs one step of leapfrog update, using Hamiltonian dynamics. Parameters  pos: theano matrix in leapfrog update equations, represents pos(t), position at time t vel: theano matrix in leapfrog update equations, represents vel(t  stepsize/2), velocity at time (t  stepsize/2) step: theano scalar scalar value controlling amount by which to move Returns  rval1: [theano matrix, theano matrix] Symbolic theano matrices for new position pos(t + stepsize), and velocity vel(t + stepsize/2) rval2: dictionary Dictionary of updates for the Scan Op """ # from pos(t) and vel(tstepsize/2), compute vel(t+stepsize/2) dE_dpos = TT.grad(energy_fn(pos).sum(), pos) new_vel = vel  step * dE_dpos # from vel(t+stepsize/2) compute pos(t+stepsize) new_pos = pos + step * new_vel return [new_pos, new_vel], {} # compute velocity at timestep: t + stepsize/2 initial_energy = energy_fn(initial_pos) dE_dpos = TT.grad(initial_energy.sum(), initial_pos) vel_half_step = initial_vel  0.5 * stepsize * dE_dpos # compute position at timestep: t + stepsize pos_full_step = initial_pos + stepsize * vel_half_step # perform leapfrog updates: the scan op is used to repeatedly compute # vel(t + (m1/2)*stepsize) and pos(t + m*stepsize) for m in [2,n_steps]. (all_pos, all_vel), scan_updates = theano.scan( leapfrog, outputs_info=[ dict(initial=pos_full_step), dict(initial=vel_half_step), ], non_sequences=[stepsize], n_steps=n_steps  1) final_pos = all_pos[1] final_vel = all_vel[1] # NOTE: Scan always returns an updates dictionary, in case the # scanned function draws samples from a RandomStream. These # updates must then be used when compiling the Theano function, to # avoid drawing the same random numbers each time the function is # called. In this case however, we consciously ignore # "scan_updates" because we know it is empty. assert not scan_updates # The last velocity returned by scan is vel(t + # (n_steps  1 / 2) * stepsize) We therefore perform one more halfstep # to return vel(t + n_steps * stepsize) energy = energy_fn(final_pos) final_vel = final_vel  0.5 * stepsize * TT.grad(energy.sum(), final_pos) # return new proposal state return final_pos, final_vel # startsnippet1 A final halfstep is performed to compute , and the final proposed state is returned. hmc_move The function implements the remaining steps (steps 1 and 3) of an HMC move proposal (while wrapping the function). Given a matrix of initial states () and energy function (), it defines the symbolic graph for computing of HMC, using a given . The function prototype is as follows: def hmc_move(s_rng, positions, energy_fn, stepsize, n_steps): """ This function performs onestep of Hybrid MonteCarlo sampling. We start by sampling a random velocity from a univariate Gaussian distribution, perform `n_steps` leapfrog updates using Hamiltonian dynamics and acceptreject using MetropolisHastings. Parameters  s_rng: theano shared random stream Symbolic random number generator used to draw random velocity and perform acceptreject move. positions: shared theano matrix Symbolic matrix whose rows are position vectors. energy_fn: python function Python function, operating on symbolic theano variables, used to compute the potential energy at a given position. stepsize: shared theano scalar Shared variable containing the stepsize to use for `n_steps` of HMC simulation steps. n_steps: integer Number of HMC steps to perform before proposing a new position. Returns  rval1: boolean True if move is accepted, False otherwise rval2: theano matrix Matrix whose rows contain the proposed "new position" """ We start by sampling random velocities, using the provided shared RandomStream object. Velocities are sampled independently for each dimension and for each particle under simulation, yielding a matrix. # sample random velocity initial_vel = s_rng.normal(size=positions.shape) Since we now have an initial position and velocity, we can now call the to obtain the proposal for the new state . # perform simulation of particles subject to Hamiltonian dynamics final_pos, final_vel = simulate_dynamics( initial_pos=positions, initial_vel=initial_vel, stepsize=stepsize, n_steps=n_steps, energy_fn=energy_fn ) We then accept/reject the proposed state based on the Metropolis algorithm. # accept/reject the proposed move based on the joint distribution accept = metropolis_hastings_accept( energy_prev=hamiltonian(positions, initial_vel, energy_fn), energy_next=hamiltonian(final_pos, final_vel, energy_fn), s_rng=s_rng ) where and are helper functions, defined as follows. def metropolis_hastings_accept(energy_prev, energy_next, s_rng): """ Performs a MetropolisHastings acceptreject move. Parameters  energy_prev: theano vector Symbolic theano tensor which contains the energy associated with the configuration at timestep t. energy_next: theano vector Symbolic theano tensor which contains the energy associated with the proposed configuration at timestep t+1. s_rng: theano.tensor.shared_randomstreams.RandomStreams Theano shared random stream object used to generate the random number used in proposal. Returns  return: boolean True if move is accepted, False otherwise """ ediff = energy_prev  energy_next return (TT.exp(ediff)  s_rng.uniform(size=energy_prev.shape)) >= 0 def hamiltonian(pos, vel, energy_fn): """ Returns the Hamiltonian (sum of potential and kinetic energy) for the given velocity and position. Parameters  pos: theano matrix Symbolic matrix whose rows are position vectors. vel: theano matrix Symbolic matrix whose rows are velocity vectors. energy_fn: python function Python function, operating on symbolic theano variables, used tox compute the potential energy at a given position. Returns  return: theano vector Vector whose ith entry is the Hamiltonian at position pos[i] and velocity vel[i]. """ # assuming mass is 1 return energy_fn(pos) + kinetic_energy(vel) def kinetic_energy(vel): """Returns the kinetic energy associated with the given velocity and mass of 1. Parameters  vel: theano matrix Symbolic matrix whose rows are velocity vectors. Returns  return: theano vector Vector whose ith entry is the kinetic entry associated with vel[i]. """ return 0.5 * (vel ** 2).sum(axis=1) finally returns the tuple . is a symbolic boolean variable indicating whether or not the new state should be used or not. hmc_updates The purpose of is to generate the list of updates to perform, whenever our HMC sampling function is called. thus receives as parameters, a series of shared variables to update (, and ), and the parameters required to compute their new state. def hmc_updates(positions, stepsize, avg_acceptance_rate, final_pos, accept, target_acceptance_rate, stepsize_inc, stepsize_dec, stepsize_min, stepsize_max, avg_acceptance_slowness): """This function is executed after `n_steps` of HMC sampling (`hmc_move` function). It creates the updates dictionary used by the `simulate` function. It takes care of updating: the position (if the move is accepted), the stepsize (to track a given target acceptance rate) and the average acceptance rate (computed as a moving average). Parameters  positions: shared variable, theano matrix Shared theano matrix whose rows contain the old position stepsize: shared variable, theano scalar Shared theano scalar containing current step size avg_acceptance_rate: shared variable, theano scalar Shared theano scalar containing the current average acceptance rate final_pos: shared variable, theano matrix Shared theano matrix whose rows contain the new position accept: theano scalar Booleantype variable representing whether or not the proposed HMC move should be accepted or not. target_acceptance_rate: float The stepsize is modified in order to track this target acceptance rate. stepsize_inc: float Amount by which to increment stepsize when acceptance rate is too high. stepsize_dec: float Amount by which to decrement stepsize when acceptance rate is too low. stepsize_min: float Lowerbound on `stepsize`. stepsize_min: float Upperbound on `stepsize`. avg_acceptance_slowness: float Average acceptance rate is computed as an exponential moving average. (1avg_acceptance_slowness) is the weight given to the newest observation. Returns  rval1: dictionarylike A dictionary of updates to be used by the `HMC_Sampler.simulate` function. The updates target the position, stepsize and average acceptance rate. """ ## POSITION UPDATES ## # broadcast `accept` scalar to tensor with the same dimensions as # final_pos. accept_matrix = accept.dimshuffle(0, *(('x',) * (final_pos.ndim  1))) # if accept is True, update to `final_pos` else stay put new_positions = TT.switch(accept_matrix, final_pos, positions) Using the above code, the dictionary can be used to update the state of the sampler with either (1) the new state if is True, or (2) the old state if is False. This conditional assignment is performed by the switch op. expects as its first argument, a boolean mask with the same broadcastable dimensions as the second and third argument. Since is scalarvalued, we must first use dimshuffle to transform it to a tensor with broadcastable dimensions (). additionally implements an adaptive version of HMC, as implemented in the accompanying code to [Ranzato10]. We start by tracking the average acceptance rate of the HMC move proposals (across many simulations), using an exponential moving average with time constant . ## ACCEPT RATE UPDATES ## # perform exponential moving average mean_dtype = theano.scalar.upcast(accept.dtype, avg_acceptance_rate.dtype) new_acceptance_rate = TT.add( avg_acceptance_slowness * avg_acceptance_rate, (1.0  avg_acceptance_slowness) * accept.mean(dtype=mean_dtype)) If the average acceptance rate is larger than the , we increase the by a factor of in order to increase the mixing rate of our chain. If the average acceptance rate is too low however, is decreased by a factor of , yielding a more conservative mixing rate. The clip op allows us to maintain the in the range [, ]. ## STEPSIZE UPDATES ## # if acceptance rate is too low, our sampler is too "noisy" and we reduce # the stepsize. If it is too high, our sampler is too conservative, we can # get away with a larger stepsize (resulting in better mixing). _new_stepsize = TT.switch(avg_acceptance_rate > target_acceptance_rate, stepsize * stepsize_inc, stepsize * stepsize_dec) # maintain stepsize in [stepsize_min, stepsize_max] new_stepsize = TT.clip(_new_stepsize, stepsize_min, stepsize_max) The final updates list is then returned. return [(positions, new_positions), (stepsize, new_stepsize), (avg_acceptance_rate, new_acceptance_rate)] HMC_sampler We finally tie everything together using the class. Its main elements are:
class HMC_sampler(object): """ Convenience wrapper for performing Hybrid Monte Carlo (HMC). It creates the symbolic graph for performing an HMC simulation (using `hmc_move` and `hmc_updates`). The graph is then compiled into the `simulate` function, a theano function which runs the simulation and updates the required shared variables. Users should interface with the sampler thorugh the `draw` function which advances the markov chain and returns the current sample by calling `simulate` and `get_position` in sequence. The hyperparameters are the same as those used by Marc'Aurelio's 'train_mcRBM.py' file (available on his personal home page). """ def __init__(self, **kwargs): self.__dict__.update(kwargs) @classmethod def new_from_shared_positions( cls, shared_positions, energy_fn, initial_stepsize=0.01, target_acceptance_rate=.9, n_steps=20, stepsize_dec=0.98, stepsize_min=0.001, stepsize_max=0.25, stepsize_inc=1.02, # used in geometric avg. 1.0 would be not moving at all avg_acceptance_slowness=0.9, seed=12345 ): """ :param shared_positions: theano ndarray shared var with many particle [initial] positions :param energy_fn: callable such that energy_fn(positions) returns theano vector of energies. The len of this vector is the batchsize. The sum of this energy vector must be differentiable (with theano.tensor.grad) with respect to the positions for HMC sampling to work. """ # allocate shared variables stepsize = sharedX(initial_stepsize, 'hmc_stepsize') avg_acceptance_rate = sharedX(target_acceptance_rate, 'avg_acceptance_rate') s_rng = TT.shared_randomstreams.RandomStreams(seed) # define graph for an `n_steps` HMC simulation accept, final_pos = hmc_move( s_rng, shared_positions, energy_fn, stepsize, n_steps) # define the dictionary of updates, to apply on every `simulate` call simulate_updates = hmc_updates( shared_positions, stepsize, avg_acceptance_rate, final_pos=final_pos, accept=accept, stepsize_min=stepsize_min, stepsize_max=stepsize_max, stepsize_inc=stepsize_inc, stepsize_dec=stepsize_dec, target_acceptance_rate=target_acceptance_rate, avg_acceptance_slowness=avg_acceptance_slowness) # compile theano function simulate = function([], [], updates=simulate_updates) # create HMC_sampler object with the following attributes ... return cls( positions=shared_positions, stepsize=stepsize, stepsize_min=stepsize_min, stepsize_max=stepsize_max, avg_acceptance_rate=avg_acceptance_rate, target_acceptance_rate=target_acceptance_rate, s_rng=s_rng, _updates=simulate_updates, simulate=simulate) def draw(self, **kwargs): """ Returns a new position obtained after `n_steps` of HMC simulation. Parameters  kwargs: dictionary The `kwargs` dictionary is passed to the shared variable (self.positions) `get_value()` function. For example, to avoid copying the shared variable value, consider passing `borrow=True`. Returns  rval: numpy matrix Numpy matrix whose of dimensions similar to `initial_position`. """ self.simulate() return self.positions.get_value(borrow=False) Testing our SamplerWe test our implementation of HMC by sampling from a multivariate Gaussian distribution. We start by generating a random mean vector and covariance matrix , which allows us to define the energy function of the corresponding Gaussian distribution: . We then initialize the state of the sampler by allocating a shared variable. It is passed to the constructor of along with our target energy function. Following a burnin period, we then generate a large number of samples and compare the empirical mean and covariance matrix to their true values. def sampler_on_nd_gaussian(sampler_cls, burnin, n_samples, dim=10): batchsize = 3 rng = numpy.random.RandomState(123) # Define a covariance and mu for a gaussian mu = numpy.array(rng.rand(dim) * 10, dtype=theano.config.floatX) cov = numpy.array(rng.rand(dim, dim), dtype=theano.config.floatX) cov = (cov + cov.T) / 2. cov[numpy.arange(dim), numpy.arange(dim)] = 1.0 cov_inv = linalg.inv(cov) # Define energy function for a multivariate Gaussian def gaussian_energy(x): return 0.5 * (theano.tensor.dot((x  mu), cov_inv) * (x  mu)).sum(axis=1) # Declared shared random variable for positions position = rng.randn(batchsize, dim).astype(theano.config.floatX) position = theano.shared(position) # Create HMC sampler sampler = sampler_cls(position, gaussian_energy, initial_stepsize=1e3, stepsize_max=0.5) # Start with a burnin process garbage = [sampler.draw() for r in xrange(burnin)] # burnin Draw # `n_samples`: result is a 3D tensor of dim [n_samples, batchsize, # dim] _samples = numpy.asarray([sampler.draw() for r in xrange(n_samples)]) # Flatten to [n_samples * batchsize, dim] samples = _samples.T.reshape(dim, 1).T print '****** TARGET VALUES ******' print 'target mean:', mu print 'target cov:\n', cov print '****** EMPIRICAL MEAN/COV USING HMC ******' print 'empirical mean: ', samples.mean(axis=0) print 'empirical_cov:\n', numpy.cov(samples.T) print '****** HMC INTERNALS ******' print 'final stepsize', sampler.stepsize.get_value() print 'final acceptance_rate', sampler.avg_acceptance_rate.get_value() return sampler def test_hmc(): sampler = sampler_on_nd_gaussian(HMC_sampler.new_from_shared_positions, burnin=1000, n_samples=1000, dim=5) assert abs(sampler.avg_acceptance_rate.get_value()  sampler.target_acceptance_rate) < .1 assert sampler.stepsize.get_value() >= sampler.stepsize_min assert sampler.stepsize.get_value() <= sampler.stepsize_max The above code can be run using the command: “nosetests s code/hmc/test_hmc.py”. The output is as follows: [desjagui@atchoum hmc]$ python test_hmc.py ****** TARGET VALUES ****** target mean: [ 6.96469186 2.86139335 2.26851454 5.51314769 7.1946897 ] target cov: [[ 1. 0.66197111 0.71141257 0.55766643 0.35753822] [ 0.66197111 1. 0.31053199 0.45455485 0.37991646] [ 0.71141257 0.31053199 1. 0.62800335 0.38004541] [ 0.55766643 0.45455485 0.62800335 1. 0.50807871] [ 0.35753822 0.37991646 0.38004541 0.50807871 1. ]] ****** EMPIRICAL MEAN/COV USING HMC ****** empirical mean: [ 6.94155164 2.81526039 2.26301715 5.46536853 7.19414496] empirical_cov: [[ 1.05152997 0.68393537 0.76038645 0.59930252 0.37478746] [ 0.68393537 0.97708159 0.37351422 0.48362404 0.3839558 ] [ 0.76038645 0.37351422 1.03797111 0.67342957 0.41529132] [ 0.59930252 0.48362404 0.67342957 1.02865056 0.53613649] [ 0.37478746 0.3839558 0.41529132 0.53613649 0.98721449]] ****** HMC INTERNALS ****** final stepsize 0.460446628091 final acceptance_rate 0.922502043428 As can be seen above, the samples generated by our HMC sampler yield an empirical mean and covariance matrix, which are very close to the true underlying parameters. The adaptive algorithm also seemed to work well as the final acceptance rate is close to our target of . References

Artificial Intelligence + NLP + deep learning > AI > Machine Learning > Neural Networks > Deep Learning > python > MNIST (Theano) >