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GPSImputerWI.py
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GPSImputerWI.py
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#############################################################################
# Code for managing and training a variational Iterative Refinement Model. #
#############################################################################
# basic python
import cPickle
import numpy as np
import numpy.random as npr
from collections import OrderedDict
import numexpr as ne
# theano business
import theano
import theano.tensor as T
#from theano.tensor.shared_randomstreams import RandomStreams as RandStream
from theano.sandbox.cuda.rng_curand import CURAND_RandomStreams as RandStream
# phil's sweetness
from DKCode import get_adam_updates, get_adadelta_updates
from LogPDFs import log_prob_bernoulli, log_prob_gaussian2, gaussian_kld
from HelperFuncs import to_fX
#######################################
# IMPLEMENT THE THING THAT DOES STUFF #
#######################################
class GPSImputerWI(object):
"""
Controller for training a multi-step imputater via guided policy search.
This model adds an "initialization" step, prior to iterative refinement.
The init step requires three additional networks: action selectors for both
the primary and guide policies, and an action->state transformer.
Parameters:
rng: numpy.random.RandomState (for reproducibility)
x_in: the initial state for imputation
x_out: the goal state for imputation
x_mask: mask for state dims to keep fixed during imputation
p_h_given_x: InfNet for stochastic part of init step
p_s0_given_h: HydraNet for deterministic part of init step
p_zi_given_xi: InfNet for stochastic part of refinement steps
p_sip1_given_zi: HydraNet for deterministic part of refinement steps
p_x_given_si: HydraNet for transform from s-space to x-space
q_h_given_x: InfNet for the guide policy (init step)
q_zi_given_xi: InfNet for the guide policy (refinement steps)
params: REQUIRED PARAMS SHOWN BELOW
x_dim: dimension of inputs to reconstruct
h_dim: dimension of latent space for init step
z_dim: dimension of latent space for policy wobble
s_dim: dimension of space for hypothesis construction
use_p_x_given_si: boolean for whether to use ----
imp_steps: number of reconstruction steps to perform
step_type: either "add" or "jump"
x_type: can be "bernoulli" or "gaussian"
obs_transform: can be 'none' or 'sigmoid'
"""
def __init__(self, rng=None,
x_in=None, x_mask=None, x_out=None, \
p_h_given_x=None, \
p_s0_given_h=None, \
p_zi_given_xi=None, \
p_sip1_given_zi=None, \
p_x_given_si=None, \
q_h_given_x=None, \
q_zi_given_xi=None, \
params=None, \
shared_param_dicts=None):
# setup a rng for this GIPair
self.rng = RandStream(rng.randint(100000))
# grab the user-provided parameters
self.params = params
self.x_dim = self.params['x_dim']
self.h_dim = self.params['h_dim']
self.z_dim = self.params['z_dim']
self.s_dim = self.params['s_dim']
self.use_p_x_given_si = self.params['use_p_x_given_si']
self.imp_steps = self.params['imp_steps']
self.step_type = self.params['step_type']
self.x_type = self.params['x_type']
if self.use_p_x_given_si:
print("Constructing hypotheses via p_x_given_si...")
else:
print("Constructing hypotheses directly in x-space...")
assert(self.s_dim == self.x_dim)
assert((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
if 'obs_transform' in self.params:
assert((self.params['obs_transform'] == 'sigmoid') or \
(self.params['obs_transform'] == 'none'))
if self.params['obs_transform'] == 'sigmoid':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
else:
self.obs_transform = lambda x: x
else:
self.obs_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
self.shared_param_dicts = shared_param_dicts
assert((self.step_type == 'add') or (self.step_type == 'jump'))
# grab handles to the relevant InfNets
self.p_h_given_x = p_h_given_x
self.p_s0_given_h = p_s0_given_h
self.p_zi_given_xi = p_zi_given_xi
self.p_sip1_given_zi = p_sip1_given_zi
self.p_x_given_si = p_x_given_si
self.q_h_given_x = q_h_given_x
self.q_zi_given_xi = q_zi_given_xi
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this MultiStageModel
self.x_in = x_in
self.x_out = x_out
self.x_mask = x_mask
self.zi_zmuv = T.tensor3()
# setup switching variable for changing between sampling/training
zero_ary = to_fX( np.zeros((1,)) )
self.train_switch = theano.shared(value=zero_ary, name='gpsi_train_switch')
self.set_train_switch(1.0)
if self.shared_param_dicts is None:
# initialize parameters "owned" by this model
init_ary = to_fX( np.zeros((self.x_dim,)) )
self.s_null = theano.shared(value=init_ary, name='gpis_sn')
self.grad_null = theano.shared(value=init_ary, name='gpsi_gn')
self.obs_logvar = theano.shared(value=zero_ary, name='gpsi_obs_logvar')
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar[0])
self.shared_param_dicts = {}
self.shared_param_dicts['s_null'] = self.s_null
self.shared_param_dicts['grad_null'] = self.grad_null
self.shared_param_dicts['obs_logvar'] = self.obs_logvar
self.x_null = self._from_si_to_x(self.s_null)
else:
# grab the parameters required by this model from a given dict
self.s_null = self.shared_param_dicts['s_null']
self.grad_null = self.shared_param_dicts['grad_null']
self.obs_logvar = self.shared_param_dicts['obs_logvar']
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar[0])
self.x_null = self._from_si_to_x(self.s_null)
##############################################
# Compute results of the initialization step #
##############################################
self.x_init = (self.x_mask * self.x_in) + \
((1.0 - self.x_mask) * self.x_null)
# sample from primary and guide conditionals over h
h_p_mean, h_p_logvar, h_p = \
self.p_h_given_x.apply(self.x_init, do_samples=True)
h_q_mean, h_q_logvar, h_q = \
self.q_h_given_x.apply(self.x_in, do_samples=True)
# make h samples that can be switched between h_p and h_q
self.h = ((self.train_switch[0] * h_q) + \
((1.0 - self.train_switch[0]) * h_p))
# get the emitted initial state s0 (sampled via either p or q)
hydra_out = self.p_s0_given_h.apply(self.h)
self.s0 = hydra_out[0]
# compute NLL reconstruction cost for the initialization step
self.nll0 = self._construct_nll_costs(self.s0, self.x_out, self.x_mask)
# compute KLds for the initialization step
self.kldh_q2p = gaussian_kld(h_q_mean, h_q_logvar, \
h_p_mean, h_p_logvar) # KL(q || p)
self.kldh_p2q = gaussian_kld(h_p_mean, h_p_logvar, \
h_q_mean, h_q_logvar) # KL(p || q)
self.kldh_p2g = gaussian_kld(h_p_mean, h_p_logvar, \
0.0, 0.0) # KL(p || global prior)
##################################################
# Setup the iterative imputation loop using scan #
##################################################
self.ones_mask = T.ones_like(self.x_mask)
def imp_step_func(zi_zmuv, si):
si_as_x = self._from_si_to_x(si)
xi_unmasked = self.x_out
xi_masked = (self.x_mask * xi_unmasked) + \
((1.0 - self.x_mask) * si_as_x)
grad_unmasked = self.x_out - si_as_x
grad_masked = (self.x_mask * grad_unmasked) + \
((1.0 - self.x_mask) * self.grad_null)
# get samples of next zi, according to the global policy
zi_p_mean, zi_p_logvar = self.p_zi_given_xi.apply( \
T.horizontal_stack(xi_masked, grad_masked), \
do_samples=False)
zi_p = zi_p_mean + (T.exp(0.5 * zi_p_logvar) * zi_zmuv)
# get samples of next zi, according to the guide policy
zi_q_mean, zi_q_logvar = self.q_zi_given_xi.apply( \
T.horizontal_stack(xi_masked, grad_unmasked), \
do_samples=False)
zi_q = zi_q_mean + (T.exp(0.5 * zi_q_logvar) * zi_zmuv)
# make zi samples that can be switched between zi_p and zi_q
zi = ((self.train_switch[0] * zi_q) + \
((1.0 - self.train_switch[0]) * zi_p))
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(zi_q_mean, zi_q_logvar, \
zi_p_mean, zi_p_logvar) # KL(q || p)
kldi_p2q = gaussian_kld(zi_p_mean, zi_p_logvar, \
zi_q_mean, zi_q_logvar) # KL(p || q)
kldi_p2g = gaussian_kld(zi_p_mean, zi_p_logvar, \
0.0, 0.0) # KL(p || global prior)
# compute the next si, given the sampled zi
hydra_out = self.p_sip1_given_zi.apply(zi)
si_step = hydra_out[0]
if (self.step_type == 'jump'):
# jump steps always completely overwrite the current guesses
sip1 = si_step
else:
# additive steps update the current guesses like an LSTM
write_gate = T.nnet.sigmoid(3.0 + hydra_out[1])
erase_gate = T.nnet.sigmoid(3.0 + hydra_out[2])
sip1 = (erase_gate * si) + (write_gate * si_step)
# compute NLL for the current imputation
nlli = self._construct_nll_costs(sip1, self.x_out, self.x_mask)
return sip1, nlli, kldi_q2p, kldi_p2q, kldi_p2g
# apply scan op for the sequential imputation loop
init_vals = [self.s0, None, None, None, None]
self.scan_results, self.scan_updates = theano.scan(imp_step_func, \
outputs_info=init_vals, sequences=self.zi_zmuv)
self.si = self.scan_results[0]
self.nlli = self.scan_results[1]
self.kldi_q2p = self.scan_results[2]
self.kldi_p2q = self.scan_results[3]
self.kldi_p2g = self.scan_results[4]
######################################################################
# ALL SYMBOLIC VARS NEEDED FOR THE OBJECTIVE SHOULD NOW BE AVAILABLE #
######################################################################
# shared var learning rate for generator and inferencer
zero_ary = to_fX( np.zeros((1,)) )
self.lr = theano.shared(value=zero_ary, name='gpsi_lr')
# shared var momentum parameters for generator and inferencer
self.mom_1 = theano.shared(value=zero_ary, name='gpsi_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='gpsi_mom_2')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='gpsi_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting prior kld against reconstruction
self.lam_kld_p = theano.shared(value=zero_ary, name='gpsi_lam_kld_p')
self.lam_kld_q = theano.shared(value=zero_ary, name='gpsi_lam_kld_q')
self.lam_kld_g = theano.shared(value=zero_ary, name='gpsi_lam_kld_g')
self.lam_kld_s = theano.shared(value=zero_ary, name='gpsi_lam_kld_s')
self.set_lam_kld(lam_kld_p=0.0, lam_kld_q=1.0, lam_kld_g=0.0, lam_kld_s=0.0)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='msm_lam_l2w')
self.set_lam_l2w(1e-5)
# Grab all of the "optimizable" parameters in the model
self.joint_params = [self.s_null, self.grad_null, self.obs_logvar]
self.joint_params.extend(self.p_zi_given_xi.mlp_params)
self.joint_params.extend(self.p_sip1_given_zi.mlp_params)
self.joint_params.extend(self.p_x_given_si.mlp_params)
self.joint_params.extend(self.q_zi_given_xi.mlp_params)
#################################
# CONSTRUCT THE KLD-BASED COSTS #
#################################
self.kld_p, self.kld_q, self.kld_g, self.kld_s = \
self._construct_kld_costs(p=1.0)
self.kld_costs = (self.lam_kld_p[0] * self.kld_p) + \
(self.lam_kld_q[0] * self.kld_q) + \
(self.lam_kld_g[0] * self.kld_g) + \
(self.lam_kld_s[0] * self.kld_s)
self.kld_cost = T.mean(self.kld_costs)
#################################
# CONSTRUCT THE NLL-BASED COSTS #
#################################
self.nll_costs = self.nlli[-1]
self.nll_cost = self.lam_nll[0] * T.mean(self.nll_costs)
self.nll_bounds = self.nll_costs.ravel() + self.kld_q.ravel()
self.nll_bound = T.mean(self.nll_bounds)
########################################
# CONSTRUCT THE REST OF THE JOINT COST #
########################################
param_reg_cost = self._construct_reg_costs()
self.reg_cost = self.lam_l2w[0] * param_reg_cost
self.joint_cost = self.nll_cost + self.kld_cost + self.reg_cost
##############################
# CONSTRUCT A PER-TRIAL COST #
##############################
self.obs_costs = self.nll_costs + self.kld_costs
# Get the gradient of the joint cost for all optimizable parameters
print("Computing gradients of self.joint_cost...")
self.joint_grads = OrderedDict()
grad_list = T.grad(self.joint_cost, self.joint_params)
for i, p in enumerate(self.joint_params):
self.joint_grads[p] = grad_list[i]
# Construct the updates for the generator and inferencer networks
self.joint_updates = get_adam_updates(params=self.joint_params, \
grads=self.joint_grads, alpha=self.lr, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-5, max_grad_norm=10.0)
for k, v in self.scan_updates.items():
self.joint_updates[k] = v
# Construct a function for jointly training the generator/inferencer
print("Compiling training function...")
self.train_joint = self._construct_train_joint()
print("Compiling free-energy sampler...")
self.compute_fe_terms = self._construct_compute_fe_terms()
print("Compiling best step cost computer...")
self.compute_per_step_cost = self._construct_compute_per_step_cost()
print("Compiling data-guided imputer sampler...")
self.sample_imputer = self._construct_sample_imputer()
# make easy access points for some interesting parameters
#self.gen_inf_weights = self.p_zi_given_xi.shared_layers[0].W
return
def _from_si_to_x(self, si):
"""
Convert the given si from s-space to x-space.
"""
if self.use_p_x_given_si:
x_pre_trans, _ = self.p_x_given_si.apply(si)
else:
x_pre_trans = si
x_post_trans = self.obs_transform(x_pre_trans)
return x_post_trans
def set_sgd_params(self, lr=0.01, mom_1=0.9, mom_2=0.999):
"""
Set learning rate and momentum parameter for all updates.
"""
zero_ary = np.zeros((1,))
# set learning rate
new_lr = zero_ary + lr
self.lr.set_value(to_fX(new_lr))
# set momentums (use first and second order "momentum")
new_mom_1 = zero_ary + mom_1
self.mom_1.set_value(to_fX(new_mom_1))
new_mom_2 = zero_ary + mom_2
self.mom_2.set_value(to_fX(new_mom_2))
return
def set_lam_nll(self, lam_nll=1.0):
"""
Set weight for controlling the influence of the data likelihood.
"""
zero_ary = np.zeros((1,))
new_lam = zero_ary + lam_nll
self.lam_nll.set_value(to_fX(new_lam))
return
def set_lam_kld(self, lam_kld_p=0.0, lam_kld_q=1.0, lam_kld_g=0.0, lam_kld_s=0.0):
"""
Set the relative weight of prior KL-divergence vs. data likelihood.
"""
zero_ary = np.zeros((1,))
new_lam = zero_ary + lam_kld_p
self.lam_kld_p.set_value(to_fX(new_lam))
new_lam = zero_ary + lam_kld_q
self.lam_kld_q.set_value(to_fX(new_lam))
new_lam = zero_ary + lam_kld_g
self.lam_kld_g.set_value(to_fX(new_lam))
new_lam = zero_ary + lam_kld_s
self.lam_kld_s.set_value(to_fX(new_lam))
return
def set_lam_l2w(self, lam_l2w=1e-3):
"""
Set the relative strength of l2 regularization on network params.
"""
zero_ary = np.zeros((1,))
new_lam = zero_ary + lam_l2w
self.lam_l2w.set_value(to_fX(new_lam))
return
def set_train_switch(self, switch_val=0.0):
"""
Set the switch for changing between training and sampling behavior.
"""
if (switch_val < 0.5):
switch_val = 0.0
else:
switch_val = 1.0
zero_ary = np.zeros((1,))
new_val = zero_ary + switch_val
self.train_switch.set_value(to_fX(new_val))
return
def _construct_zi_zmuv(self, xi, br):
"""
Construct the necessary (symbolic) samples for computing through this
GPSImputer for input (sybolic) matrix xi.
"""
zi_zmuv = self.rng.normal( \
size=(self.imp_steps, xi.shape[0]*br, self.z_dim), \
avg=0.0, std=1.0, dtype=theano.config.floatX)
return zi_zmuv
def _construct_nll_costs(self, si, xo, xm):
"""
Construct the negative log-likelihood part of free energy.
"""
# average log-likelihood over the refinement sequence
xh = self._from_si_to_x( si )
xm_inv = 1.0 - xm # we will measure nll only where xm_inv is 1
if self.x_type == 'bernoulli':
ll_costs = log_prob_bernoulli(xo, xh, mask=xm_inv)
else:
ll_costs = log_prob_gaussian2(xo, xh, \
log_vars=self.bounded_logvar, mask=xm_inv)
nll_costs = -ll_costs.flatten()
return nll_costs
def _construct_kld_s(self, s_i, s_j):
"""
Compute KL(s_i || s_j) -- assuming bernoullish outputs
"""
x_i = self._from_si_to_x( s_i )
x_j = self._from_si_to_x( s_j )
kld_s = (x_i * (T.log(x_i) - T.log(x_j))) + \
((1.0 - x_i) * (T.log(1.0-x_i) - T.log(1.0-x_j)))
sum_kld = T.sum(kld_s, axis=1)
return sum_kld
def _construct_kld_costs(self, p=1.0):
"""
Construct the policy KL-divergence part of cost to minimize.
"""
kld_pis = []
kld_qis = []
kld_gis = []
kld_sis = [self._construct_kld_s(self.s0, self.s_null)]
for i in range(self.imp_steps):
kld_pis.append(T.sum(self.kldi_p2q[i]**p, axis=1))
kld_qis.append(T.sum(self.kldi_q2p[i]**p, axis=1))
kld_gis.append(T.sum(self.kldi_p2g[i]**p, axis=1))
if i == 0:
kld_sis.append(self._construct_kld_s(self.si[i], self.s0))
else:
kld_sis.append(self._construct_kld_s(self.si[i], self.si[i-1]))
# compute the batch-wise costs
kld_pi = sum(kld_pis)
kld_qi = sum(kld_qis)
kld_gi = sum(kld_gis)
kld_si = sum(kld_sis)
return [kld_pi, kld_qi, kld_gi, kld_si]
def _construct_reg_costs(self):
"""
Construct the cost for low-level basic regularization. E.g. for
applying l2 regularization to the network activations and parameters.
"""
param_reg_cost = sum([T.sum(p**2.0) for p in self.joint_params])
return param_reg_cost
def _construct_compute_fe_terms(self):
"""
Construct a function for computing terms in variational free energy.
"""
# setup some symbolic variables for theano to deal with
xi = T.matrix()
xo = T.matrix()
xm = T.matrix()
zizmuv = self._construct_zi_zmuv(xi, 1)
# construct values to output
nll = self.nll_costs.flatten()
kld = self.kld_q.flatten()
# compile theano function for a one-sample free-energy estimate
fe_term_sample = theano.function(inputs=[ xi, xo, xm ], \
outputs=[nll, kld], \
givens={self.x_in: xi, \
self.x_out: xo, \
self.x_mask: xm, \
self.zi_zmuv: zizmuv}, \
updates=self.scan_updates, \
on_unused_input='ignore')
# construct a wrapper function for multi-sample free-energy estimate
def fe_term_estimator(XI, XO, XM, sample_count=20, use_guide_policy=True):
# set model to desired generation mode
old_switch = self.train_switch.get_value(borrow=False)
if use_guide_policy:
# take samples from guide policies (i.e. variational q)
self.set_train_switch(switch_val=1.0)
else:
# take samples from model's imputation policy
self.set_train_switch(switch_val=0.0)
# compute a multi-sample estimate of variational free-energy
nll_sum = np.zeros((XI.shape[0],))
kld_sum = np.zeros((XI.shape[0],))
for i in range(sample_count):
result = fe_term_sample(XI, XO, XM)
nll_sum += result[0].ravel()
kld_sum += result[1].ravel()
mean_nll = nll_sum / float(sample_count)
mean_kld = kld_sum / float(sample_count)
# set model back to either training or generation mode
self.set_train_switch(switch_val=old_switch)
if not use_guide_policy:
# no KLd if samples are from the primary policy...
mean_kld = 0.0 * mean_kld
return [mean_nll, mean_kld]
return fe_term_estimator
def _construct_compute_per_step_cost(self):
"""
Construct a theano function for computing the best possible cost
achieved by sequential imputation.
"""
# setup some symbolic variables for theano to deal with
xi = T.matrix()
xo = T.matrix()
xm = T.matrix()
zizmuv = self._construct_zi_zmuv(xi, 1)
# construct symbolic variables for the step-wise cost
init_nll = T.mean(self.nll0)
init_kld = T.mean(T.sum(self.kldh_q2p, axis=1))
step_nll = T.mean(self.nlli, axis=1).flatten()
step_kld = T.mean(T.sum(self.kldi_q2p, axis=2), axis=1).flatten()
# compile theano function for computing the step-wise cost
step_cost_func = theano.function(inputs=[xi, xo, xm], \
outputs=[init_nll, step_nll, init_kld, step_kld], \
givens={ self.x_in: xi, \
self.x_out: xo, \
self.x_mask: xm, \
self.zi_zmuv: zizmuv }, \
updates=self.scan_updates, \
on_unused_input='ignore')
def step_cost_computer(XI, XO, XM, sample_count=20):
# compute a multi-sample estimate of variational free-energy
step_nll_sum = np.zeros((1+self.imp_steps,))
step_kld_sum = np.zeros((1+self.imp_steps,))
for i in range(sample_count):
result = step_cost_func(XI, XO, XM)
step_nll_sum[0] += result[0]
step_nll_sum[1:] += result[1].ravel()
step_kld_sum[0] += result[2]
step_kld_sum[1:] += result[3].ravel()
mean_step_nll = step_nll_sum / float(sample_count)
mean_step_kld = step_kld_sum / float(sample_count)
return [mean_step_nll, mean_step_kld]
return step_cost_computer
def _construct_train_joint(self):
"""
Construct theano function to train all networks jointly.
"""
# setup some symbolic variables for theano to deal with
xi = T.matrix()
xo = T.matrix()
xm = T.matrix()
br = T.lscalar()
zizmuv = self._construct_zi_zmuv(xi, br)
# collect the outputs to return from this function
outputs = [self.joint_cost, self.nll_bound, self.nll_cost, \
self.kld_cost, self.reg_cost, self.obs_costs]
# compile the theano function
func = theano.function(inputs=[ xi, xo, xm, br ], \
outputs=outputs, \
givens={ self.x_in: xi.repeat(br, axis=0), \
self.x_out: xo.repeat(br, axis=0), \
self.x_mask: xm.repeat(br, axis=0), \
self.zi_zmuv: zizmuv }, \
updates=self.joint_updates, \
on_unused_input='ignore')
return func
def _construct_sample_imputer(self):
"""
Construct a function for drawing samples from the distribution
generated by running this imputer.
"""
xi = T.matrix()
xo = T.matrix()
xm = T.matrix()
zizmuv = self._construct_zi_zmuv(xi, 1)
oputs = [self.x_init, self._from_si_to_x(self.s0)] + \
[self._from_si_to_x(self.si[i]) for i in range(self.imp_steps)]
sample_func = theano.function(inputs=[xi, xo, xm], outputs=oputs, \
givens={self.x_in: xi, \
self.x_out: xo, \
self.x_mask: xm, \
self.zi_zmuv: zizmuv}, \
updates=self.scan_updates, \
on_unused_input='ignore')
def imputer_sampler(XI, XO, XM, use_guide_policy=False):
XI = to_fX( XI )
XO = to_fX( XO )
XM = to_fX( XM )
# set model to desired generation mode
old_switch = self.train_switch.get_value(borrow=False)
if use_guide_policy:
# take samples from guide policies (i.e. variational q)
self.set_train_switch(switch_val=1.0)
else:
# take samples from model's imputation policy
self.set_train_switch(switch_val=0.0)
# draw guided/unguided conditional samples
model_samps = sample_func(XI, XO, XM)
# set model back to either training or generation mode
self.set_train_switch(switch_val=old_switch)
# reverse engineer the "masked" samples...
masked_samps = []
for xs in model_samps:
xsm = (XM * XI) + ((1.0 - XM) * xs)
masked_samps.append(xsm)
return model_samps, masked_samps
return imputer_sampler
def save_to_file(self, f_name=None):
"""
Dump important stuff to a Python pickle, so that we can reload this
model later.
"""
assert(not (f_name is None))
f_handle = file(f_name, 'wb')
# dump the dict self.params, which just holds "simple" python values
cPickle.dump(self.params, f_handle, protocol=-1)
# make a copy of self.shared_param_dicts, with numpy arrays in place
# of the theano shared variables
numpy_param_dicts = {}
for key in self.shared_param_dicts:
numpy_ary = self.shared_param_dicts[key].get_value(borrow=False)
numpy_param_dicts[key] = numpy_ary
# dump the numpy version of self.shared_param_dicts to pickle file
cPickle.dump(numpy_param_dicts, f_handle, protocol=-1)
# get numpy dicts for each of the "child" models that we must save
child_model_dicts = {}
child_model_dicts['p_h_given_x'] = self.p_h_given_x.save_to_dict()
child_model_dicts['p_s0_given_h'] = self.p_s0_given_h.save_to_dict()
child_model_dicts['p_zi_given_xi'] = self.p_zi_given_xi.save_to_dict()
child_model_dicts['p_sip1_given_zi'] = self.p_sip1_given_zi.save_to_dict()
child_model_dicts['p_x_given_si'] = self.p_x_given_si.save_to_dict()
child_model_dicts['q_h_given_x'] = self.q_h_given_x.save_to_dict()
child_model_dicts['q_zi_given_xi'] = self.q_zi_given_xi.save_to_dict()
# dump the numpy child model dicts to the pickle file
cPickle.dump(child_model_dicts, f_handle, protocol=-1)
f_handle.close()
return
def load_gpsimputer_from_file(f_name=None, rng=None):
"""
Load a clone of some previously trained model.
"""
from InfNet import load_infnet_from_dict
from HydraNet import load_hydranet_from_dict
assert(not (f_name is None))
pickle_file = open(f_name)
# reload the basic python parameters
self_dot_params = cPickle.load(pickle_file)
# reload the theano shared parameters
self_dot_numpy_param_dicts = cPickle.load(pickle_file)
self_dot_shared_param_dicts = {}
for key in self_dot_numpy_param_dicts:
val = to_fX(self_dot_numpy_param_dicts[key])
self_dot_shared_param_dicts[key] = theano.shared(val)
# reload the child models
child_model_dicts = cPickle.load(pickle_file)
xd = T.matrix()
p_h_given_x = load_infnet_from_dict( \
child_model_dicts['p_h_given_x'], rng=rng, Xd=xd)
p_s0_given_h = load_hydranet_from_dict( \
child_model_dicts['p_s0_given_h'], rng=rng, Xd=xd)
p_zi_given_xi = load_infnet_from_dict( \
child_model_dicts['p_zi_given_xi'], rng=rng, Xd=xd)
p_sip1_given_zi = load_hydranet_from_dict( \
child_model_dicts['p_sip1_given_zi'], rng=rng, Xd=xd)
p_x_given_si = load_hydranet_from_dict( \
child_model_dicts['p_x_given_si'], rng=rng, Xd=xd)
q_h_given_x = load_infnet_from_dict( \
child_model_dicts['q_h_given_x'], rng=rng, Xd=xd)
q_zi_given_xi = load_infnet_from_dict( \
child_model_dicts['q_zi_given_xi'], rng=rng, Xd=xd)
# now, create a new GPSImputerWI based on the loaded data
xi = T.matrix()
xm = T.matrix()
xo = T.matrix()
clone_net = GPSImputerWI(rng=rng, \
x_in=xi, x_mask=xm, x_out=xo, \
p_h_given_x=p_h_given_x, \
p_s0_given_h=p_s0_given_h, \
p_zi_given_xi=p_zi_given_xi, \
p_sip1_given_zi=p_sip1_given_zi, \
p_x_given_si=p_x_given_si, \
q_h_given_x=q_h_given_x, \
q_zi_given_xi=q_zi_given_xi, \
params=self_dot_params, \
shared_param_dicts=self_dot_shared_param_dicts)
# helpful output
print("==================================================")
print("LOADED GPSImputerWI WITH PARAMS:")
for k in self_dot_params:
print(" {0:s}: {1:s}".format(str(k), str(self_dot_params[k])))
print("==================================================")
return clone_net
if __name__=="__main__":
print("Hello world!")
##############
# EYE BUFFER #
##############