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WalkoutModel.py
653 lines (603 loc) · 29.2 KB
/
WalkoutModel.py
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#############################################################################
# Code for a variational Walkout model (kind of like a GSN). #
#############################################################################
# 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
##############################################
# IMPLEMENTATION FOR A THING THAT DOES STUFF #
##############################################
# #
# This thing does cool stuff, very deeply! #
##############################################
class WalkoutModel(object):
"""
Controller for training a forwards-backwards chainy model.
Parameters:
rng: numpy.random.RandomState (for reproducibility)
x_out: the goal state for forwards-backwards walking process
p_z_given_x: InfNet for stochastic part of step
p_x_given_z: HydraNet for deterministic part of step
params: REQUIRED PARAMS SHOWN BELOW
x_dim: dimension of observations to construct
z_dim: dimension of latent space for policy wobble
walkout_steps: number of steps to walk out
x_type: can be "bernoulli" or "gaussian"
x_transform: can be 'none' or 'sigmoid'
"""
def __init__(self, rng=None,
x_out=None, \
p_z_given_x=None, \
p_x_given_z=None, \
params=None, \
shared_param_dicts=None):
# setup a rng for this WalkoutModel
self.rng = RandStream(rng.randint(100000))
# grab the user-provided parameters
self.params = params
self.x_dim = self.params['x_dim']
self.z_dim = self.params['z_dim']
self.walkout_steps = self.params['walkout_steps']
self.x_type = self.params['x_type']
self.shared_param_dicts = shared_param_dicts
if 'x_transform' in self.params:
assert((self.params['x_transform'] == 'sigmoid') or \
(self.params['x_transform'] == 'none'))
if self.params['x_transform'] == 'sigmoid':
self.x_transform = lambda x: T.nnet.sigmoid(x)
else:
self.x_transform = lambda x: x
else:
self.x_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.x_transform = lambda x: T.nnet.sigmoid(x)
assert((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
assert((self.step_type == 'add') or (self.step_type == 'jump'))
# grab handles to the relevant networks
self.p_z_given_x = p_z_given_x
self.p_x_given_z = p_x_given_z
# record the symbolic variables that will provide inputs to the
# computation graph created for this WalkoutModel
self.x_out = x_out # target output for generation
self.zi_zmuv = T.tensor3() # ZMUV gauss noise for walk-out wobble
if self.shared_param_dicts is None:
# initialize the parameters "owned" by this model
zero_ary = to_fX( np.zeros((1,)) )
self.obs_logvar = theano.shared(value=zero_ary, name='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['obs_logvar'] = self.obs_logvar
else:
# grab the parameters required by this model from a given dict
self.obs_logvar = self.shared_param_dicts['obs_logvar']
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar[0])
###############################################################
# Setup the forwards (i.e. training) walk-out loop using scan #
###############################################################
def forwards_loop(xi_zmuv, zi_zmuv, xi_fw, zi_fw):
# get samples of next zi, according to the forwards model
zi_fw_mean, zi_fw_logvar = self.p_z_given_x.apply(xi_fw, \
do_samples=False)
zi_fw = zi_fw_mean + (T.exp(0.5 * zi_fw_logvar) * zi_zmuv)
# check reverse direction probability p(xi_fw | zi_fw)
xi_bw_mean, xi_bw_logvar = self.p_x_given_z.apply(zi_fw, \
do_samples=False)
xi_bw_mean = self.x_transform(xi_bw_mean)
nll_xi_bw = log_prob_gaussian2(xi_fw, xi_bw_mean, \
log_vars=xi_bw_logvar, mask=None)
nll_xi_bw = nll_xi_bw.flatten()
# get samples of next xi, according to the forwards model
xi_fw_mean, xi_fw_logvar = self.p_x_given_z.apply(zi_fw, \
do_samples=False)
xi_fw_mean = self.x_transform(xi_fw_mean)
xi_fw = xi_fw_mean + (T.exp(0.5 * xi_fw_logvar) * xi_zmuv)
# check reverse direction probability p(zi_fw | xi_fw)
zi_bw_mean, zi_bw_logvar = self.p_z_given_x.apply(xi_fw, \
do_samples=False)
nll_zi_bw = log_prob_gaussian2(zi_fw, zi_bw_mean, \
log_vars=zi_bw_logvar, mask=None)
nll_zi_bw = nll_zi_bw.flatten()
# each loop iteration produces the following values:
# xi_fw: xi generated fom zi by forwards walk
# zi_fw: zi generated fom xi by forwards walk
# xi_fw_mean: ----
# xi_fw_logvar: ----
# zi_fw_mean: ----
# zi_fw_logvar: ----
# nll_xi_bw: NLL for reverse step zi_fw -> xi_fw
# nll_zi_bw: NLL for reverse step xi_fw -> zi_fw
return xi_fw, zi_fw, xi_fw_mean, xi_fw_logvar, zi_fw_mean, zi_fw_logvar, nll_xi_bw, nll_zi_bw
# initialize states for x/z
self.x0 = self.x_out
self.z0 = T.alloc(0.0, self.x0.shape[0], self.z_dim)
# setup initial values to pass to scan op
outputs_init = [self.x0, self.z0, None, None, None, None, None, None]
sequences_init = [self.xi_zmuv, self.zi_zmuv]
# apply scan op for the sequential imputation loop
self.scan_results, self.scan_updates = theano.scan(forwards_loop, \
outputs_info=outputs_init, \
sequences=sequences_init)
# grab results of the scan op. all values are computed for each step
self.xi = self.scan_results[0]
self.zi = self.scan_results[1]
self.xi_fw_mean = self.scan_results[2]
self.xi_fw_logvar = self.scan_results[3]
self.zi_fw_mean = self.scan_results[4]
self.zi_fw_logvar = self.scan_results[5]
self.nll_xi_bw = self.scan_results[6]
self.nll_zi_bw = self.scan_results[7]
######################################################################
# 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='srr_lr')
# shared var momentum parameters for ADAM optimization
self.mom_1 = theano.shared(value=zero_ary, name='srr_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='srr_mom_2')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared vars for weighting prior kld against reconstruction
self.lam_kld_p = theano.shared(value=zero_ary, name='srr_lam_kld_p')
self.lam_kld_q = theano.shared(value=zero_ary, name='srr_lam_kld_q')
self.lam_kld_g = theano.shared(value=zero_ary, name='srr_lam_kld_g')
self.lam_kld_s = theano.shared(value=zero_ary, name='srr_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='srr_lam_l2w')
self.set_lam_l2w(1e-5)
# grab all of the "optimizable" parameters from the base networks
self.joint_params = [self.s0, self.obs_logvar, self.step_scales]
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 = T.sum(self.nlli, axis=0) # sum the per-step NLLs
self.nll_cost = 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 theano functions for training and diagnostic computations
print("Compiling cost computer...")
self.compute_raw_costs = self._construct_raw_costs()
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 sequence sampler...")
self.sequence_sampler = self._construct_sequence_sampler()
# make easy access points for some interesting parameters
#self.gen_inf_weights = self.p_zi_given_xi.shared_layers[0].W
return
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_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, xo):
"""
Construct the necessary ZMUV gaussian samples for generating
trajectories from this WalkoutModel, for input matrix xo.
"""
zi_zmuv = self.rng.normal( \
size=(self.total_steps, xo.shape[0], self.z_dim), \
avg=0.0, std=1.0, dtype=theano.config.floatX)
return zi_zmuv
def _construct_rev_masks(self, xo):
"""
Compute the sequential revelation masks for the input batch in xo.
-- We need to construct mask sequences for both p and q.
"""
if self.use_rev_masks:
# make batch copies of self.rev_masks_p and self.rev_masks_q
pmasks = self.rev_masks_p.dimshuffle(0,'x',1).repeat(xo.shape[0], axis=1)
qmasks = self.rev_masks_q.dimshuffle(0,'x',1).repeat(xo.shape[0], axis=1)
else:
pm_list = []
qm_list = []
# make a zero mask that does nothing
zero_mask = T.alloc(0.0, 1, xo.shape[0], xo.shape[1])
# generate independently sampled masks for each revelation block
for rb in self.rev_sched:
# make a random binary mask with ones at rate rb[1]
rand_vals = self.rng.uniform( \
size=(1, xo.shape[0], xo.shape[1]), \
low=0.0, high=1.0, dtype=theano.config.floatX)
rand_mask = rand_vals < rb[1]
# append the masks for this revleation block to the mask lists
#
# the guide policy (in q) gets to peek at the values that will be
# revealed to the primary policy (in p) for the entire block. The
# primary policy only gets to see these values at end of the final
# step of the block. Within a given step, values are revealed to q
# at the beginning of the step, and to p at the end.
#
# e.g. in a revelation block with only a single step, the guide
# policy sees the values at the beginning of the step, which allows
# it to guide the step. the primary policy only gets to see the
# values at the end of the step.
#
# i.e. a standard variational auto-encoder is equivalent to a
# sequential revelation and refinement model with only one
# revelation block, which has one step and a reveal rate of 1.0.
#
for refine_step in range(rb[0]-1):
pm_list.append(zero_mask)
qm_list.append(rand_mask)
pm_list.append(rand_mask)
qm_list.append(rand_mask)
# concatenate each mask list into a 3-tensor
pmasks = T.cast(T.concatenate(pm_list, axis=0), 'floatX')
qmasks = T.cast(T.concatenate(qm_list, axis=0), 'floatX')
return [pmasks, qmasks]
def _construct_nll_costs(self, si, xo, nll_mask):
"""
Construct the negative log-likelihood part of free energy.
-- only check NLL where nll_mask == 1
"""
xh = self._from_si_to_x( si )
if self.x_type == 'bernoulli':
ll_costs = log_prob_bernoulli(xo, xh, mask=nll_mask)
else:
ll_costs = log_prob_gaussian2(xo, xh, \
log_vars=self.bounded_logvar, mask=nll_mask)
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 = []
s0 = 0.0*self.si[0] + self.s0
for i in range(self.total_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], 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
xo = T.matrix()
zizmuv = self._construct_zi_zmuv(xo)
pmasks, qmasks = self._construct_rev_masks(xo)
# 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=[ xo ], \
outputs=[nll, kld], \
givens={self.x_out: xo, \
self.zi_zmuv: zizmuv, \
self.p_masks: pmasks, \
self.q_masks: qmasks}, \
updates=self.scan_updates, \
on_unused_input='ignore')
# construct a wrapper function for multi-sample free-energy estimate
def fe_term_estimator(XO, 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 the guide policy
self.set_train_switch(switch_val=1.0)
else:
# take samples from the primary policy
self.set_train_switch(switch_val=0.0)
# compute a multi-sample estimate of variational free-energy
nll_sum = np.zeros((XO.shape[0],))
kld_sum = np.zeros((XO.shape[0],))
for i in range(sample_count):
result = fe_term_sample(XO)
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_raw_costs(self):
"""
Construct all the raw, i.e. not weighted by any lambdas, costs.
"""
# setup some symbolic variables for theano to deal with
xo = T.matrix()
zizmuv = self._construct_zi_zmuv(xo)
pmasks, qmasks = self._construct_rev_masks(xo)
# compile theano function for computing the costs
all_step_costs = [self.nlli, self.kldi_q2p, self.kldi_p2q, self.kldi_p2g]
cost_func = theano.function(inputs=[ xo ], \
outputs=all_step_costs, \
givens={self.x_out: xo, \
self.zi_zmuv: zizmuv, \
self.p_masks: pmasks, \
self.q_masks: qmasks}, \
updates=self.scan_updates, \
on_unused_input='ignore')
# make a function for computing batch-based estimates of costs.
# _step_nlls: the expected NLL cost for each step
# _step_klds: the expected KL(q||p) cost for each step
# _kld_q2p: the expected KL(q||p) cost for each latent dim
# _kld_p2q: the expected KL(p||q) cost for each latent dim
# _kld_p2g: the expected KL(p||N(0,I)) cost for each latent dim
def raw_cost_computer(XO):
_all_costs = cost_func(to_fX(XO))
_kld_q2p = np.sum(np.mean(_all_costs[1], axis=1, keepdims=True), axis=0)
_kld_p2q = np.sum(np.mean(_all_costs[2], axis=1, keepdims=True), axis=0)
_kld_p2g = np.sum(np.mean(_all_costs[3], axis=1, keepdims=True), axis=0)
_step_klds = np.mean(np.sum(_all_costs[1], axis=2, keepdims=True), axis=1)
_step_klds = to_fX( np.asarray([k for k in _step_klds]) )
_step_nlls = np.mean(_all_costs[0], axis=1)
_step_nlls = to_fX( np.asarray([k for k in _step_nlls]) )
results = [_step_nlls, _step_klds, _kld_q2p, _kld_p2q, _kld_p2g]
return results
return raw_cost_computer
def _construct_train_joint(self):
"""
Construct theano function to train all networks jointly.
"""
# setup some symbolic variables for theano to deal with
xo = T.matrix()
zizmuv = self._construct_zi_zmuv(xo)
pmasks, qmasks = self._construct_rev_masks(xo)
# 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=[ xo ], \
outputs=outputs, \
givens={self.x_out: xo, \
self.zi_zmuv: zizmuv, \
self.p_masks: pmasks, \
self.q_masks: qmasks}, \
updates=self.joint_updates, \
on_unused_input='ignore')
return func
def _construct_sequence_sampler(self):
"""
Construct theano function to train all networks jointly.
"""
# setup some symbolic variables for theano to deal with
xo = T.matrix()
zizmuv = self._construct_zi_zmuv(xo)
pmasks, qmasks = self._construct_rev_masks(xo)
# collect the outputs to return from this function
states = [self._from_si_to_x(self.s0_full)] + \
[self._from_si_to_x(self.si[i]) for i in range(self.total_steps)]
masks = [self.m0_full] + [self.mi_p[i] for i in range(self.total_steps)]
outputs = states + masks
# compile the theano function
func = theano.function(inputs=[ xo ], \
outputs=outputs, \
givens={self.x_out: xo, \
self.zi_zmuv: zizmuv, \
self.p_masks: pmasks, \
self.q_masks: qmasks}, \
updates=self.joint_updates, \
on_unused_input='ignore')
# visualize trajectories generated by the model
def sample_func(XO, use_guide_policy=False):
# set model to desired generation mode
old_switch = self.train_switch.get_value(borrow=False)
if use_guide_policy:
# take samples from the guide policy
self.set_train_switch(switch_val=1.0)
else:
# take samples from the primary policy
self.set_train_switch(switch_val=0.0)
# get belief states and masks generated by the scan loop
scan_vals = func(to_fX(XO))
step_count = self.total_steps + 1
seq_shape = (step_count, XO.shape[0], XO.shape[1])
xm_seq = np.zeros(seq_shape).astype(theano.config.floatX)
xi_seq = np.zeros(seq_shape).astype(theano.config.floatX)
mi_seq = np.zeros(seq_shape).astype(theano.config.floatX)
for i in range(step_count):
_xi = scan_vals[i]
_mi = scan_vals[i + step_count]
_xm = (_mi * XO) + ((1.0 - _mi) * _xi)
xm_seq[i,:,:] = _xm
xi_seq[i,:,:] = _xi
mi_seq[i,:,:] = _mi
# set model back to either training or generation mode
self.set_train_switch(switch_val=old_switch)
return [xm_seq, xi_seq, mi_seq]
return sample_func
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_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_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_WalkoutModel_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_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_zi_given_xi = load_infnet_from_dict( \
child_model_dicts['q_zi_given_xi'], rng=rng, Xd=xd)
# now, create a new WalkoutModel based on the loaded data
xo = T.matrix()
clone_net = WalkoutModel(rng=rng, \
x_out=xo, \
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_zi_given_xi=q_zi_given_xi, \
params=self_dot_params, \
shared_param_dicts=self_dot_shared_param_dicts)
# helpful output
print("==================================================")
print("LOADED WalkoutModel 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 #
##############