def pretrain_osm(lam_kld=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
data_file = 'data/tfd_data_48x48.pkl'
dataset = load_tfd(tfd_pkl_name=data_file, which_set='unlabeled', fold='all')
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set='train', fold='all')
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set='valid', fold='all')
Xva = dataset[0]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 400
batch_reps = 6
carry_frac = 0.25
carry_size = int(batch_size * carry_frac)
reset_prob = 0.04
# setup some symbolic variables and stuff
Xd = T.matrix('Xd_base')
Xc = T.matrix('Xc_base')
Xm = T.matrix('Xm_base')
data_dim = Xtr.shape[1]
prior_sigma = 1.0
Xtr_mean = np.mean(Xtr, axis=0)
##########################
# NETWORK CONFIGURATIONS #
##########################
gn_params = {}
shared_config = [PRIOR_DIM, 1500, 1500]
top_config = [shared_config[-1], data_dim]
gn_params['shared_config'] = shared_config
gn_params['mu_config'] = top_config
gn_params['sigma_config'] = top_config
gn_params['activation'] = relu_actfun
gn_params['init_scale'] = 1.4
gn_params['lam_l2a'] = 0.0
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.0
gn_params['input_noise'] = 0.0
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 1500, 1500]
top_config = [shared_config[-1], PRIOR_DIM]
in_params['shared_config'] = shared_config
in_params['mu_config'] = top_config
in_params['sigma_config'] = top_config
in_params['activation'] = relu_actfun
in_params['init_scale'] = 1.4
in_params['lam_l2a'] = 0.0
in_params['vis_drop'] = 0.0
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.0
in_params['input_noise'] = 0.0
# Initialize the base networks for this OneStageModel
IN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=in_params, shared_param_dicts=None)
GN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=gn_params, shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.2)
GN.init_biases(0.2)
######################################
# LOAD AND RESTART FROM SAVED PARAMS #
######################################
# gn_fname = RESULT_PATH+"pt_osm_params_b110000_GN.pkl"
# in_fname = RESULT_PATH+"pt_osm_params_b110000_IN.pkl"
# IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd, \
# new_params=None)
# GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd, \
# new_params=None)
# in_params = IN.params
# gn_params = GN.params
#########################
# INITIALIZE THE GIPAIR #
#########################
osm_params = {}
osm_params['x_type'] = 'bernoulli'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=data_dim, z_dim=PRIOR_DIM, params=osm_params)
OSM.set_lam_l2w(1e-5)
safe_mean = (0.9 * Xtr_mean) + 0.05
safe_mean_logit = np.log(safe_mean / (1.0 - safe_mean))
OSM.set_output_bias(safe_mean_logit)
OSM.set_input_bias(-Xtr_mean)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH+"pt_osm_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
obs_costs = np.zeros((batch_size,))
costs = [0. for i in range(10)]
learn_rate = 0.002
for i in range(200000):
scale = min(1.0, float(i) / 5000.0)
if ((i > 1) and ((i % 20000) == 0)):
learn_rate = learn_rate * 0.8
if (i < 50000):
momentum = 0.5
elif (i < 10000):
momentum = 0.7
else:
momentum = 0.9
if ((i == 0) or (npr.rand() < reset_prob)):
# sample a fully random batch
batch_idx = npr.randint(low=0,high=tr_samples,size=(batch_size,))
else:
# sample a partially random batch, which retains some portion of
# the worst scoring examples from the previous batch
fresh_idx = npr.randint(low=0,high=tr_samples,size=(batch_size-carry_size,))
batch_idx = np.concatenate((fresh_idx.ravel(), carry_idx.ravel()))
# do a minibatch update of the model, and compute some costs
tr_idx = npr.randint(low=0,high=tr_samples,size=(batch_size,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xc_batch = 0.0 * Xd_batch
Xm_batch = 0.0 * Xd_batch
# do a minibatch update of the model, and compute some costs
OSM.set_sgd_params(lr_1=(scale*learn_rate), \
mom_1=(scale*momentum), mom_2=0.98)
OSM.set_lam_nll(1.0)
OSM.set_lam_kld(lam_kld_1=scale*lam_kld, lam_kld_2=0.0, lam_kld_c=50.0)
result = OSM.train_joint(Xd_batch, Xc_batch, Xm_batch, batch_reps)
batch_costs = result[4] + result[5]
obs_costs = collect_obs_costs(batch_costs, batch_reps)
carry_idx = batch_idx[np.argsort(-obs_costs)[0:carry_size]]
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 1000) == 0):
# record and then reset the cost trackers
costs = [(v / 1000.0) for v in costs]
str_1 = "-- batch {0:d} --".format(i)
str_2 = " joint_cost: {0:.4f}".format(costs[0])
str_3 = " nll_cost : {0:.4f}".format(costs[1])
str_4 = " kld_cost : {0:.4f}".format(costs[2])
str_5 = " reg_cost : {0:.4f}".format(costs[3])
costs = [0.0 for v in costs]
# print out some diagnostic information
joint_str = "\n".join([str_1, str_2, str_3, str_4, str_5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 2000) == 0):
Xva = row_shuffle(Xva)
model_samps = OSM.sample_from_prior(500)
file_name = RESULT_PATH+"pt_osm_samples_b{0:d}_XG.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
file_name = RESULT_PATH+"pt_osm_inf_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.inf_weights.get_value(borrow=False).T, \
file_name, num_rows=30)
file_name = RESULT_PATH+"pt_osm_gen_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.gen_weights.get_value(borrow=False), \
file_name, num_rows=30)
# compute information about free-energy on validation set
file_name = RESULT_PATH+"pt_osm_free_energy_b{0:d}.png".format(i)
fe_terms = OSM.compute_fe_terms(Xva[0:2500], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
fe_str = " nll_bound : {0:.4f}".format(fe_mean)
print(fe_str)
out_file.write(fe_str+"\n")
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# compute information about posterior KLds on validation set
file_name = RESULT_PATH+"pt_osm_post_klds_b{0:d}.png".format(i)
post_klds = OSM.compute_post_klds(Xva[0:2500])
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
if ((i % 5000) == 0):
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_GN.pkl")
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
#dataset = 'data/mnist.pkl.gz'
#datasets = load_udm(dataset, as_shared=False, zero_mean=False)
#Xtr = datasets[0][0]
#Xva = datasets[1][0]
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 20
h_dim = 50
s_dim = 50
init_scale = 1.0
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
###############
# p_h_given_s #
###############
params = {}
shared_config = [s_dim, 250, 250]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_h_given_s = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_h_given_s.init_biases(0.2)
#################
# p_x_given_s_h #
#################
params = {}
shared_config = [(s_dim + h_dim), 250, 250]
top_config = [shared_config[-1], x_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_x_given_s_h = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_x_given_s_h.init_biases(0.2)
###############
# p_s_given_z #
###############
params = {}
shared_config = [z_dim, 250]
top_config = [shared_config[-1], s_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s_given_z = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_s_given_z.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [x_dim, 250, 250]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
#################
# q_h_given_x_s #
#################
params = {}
shared_config = [(x_dim + s_dim), 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_h_given_x_s = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_h_given_x_s.init_biases(0.2)
##############################################################
# Define parameters for the TwoStageModel, and initialize it #
##############################################################
print("Building the TwoStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
TSM = TwoStageModel(rng=rng, \
x_in=x_in_sym, x_out=x_out_sym, \
p_s_given_z=p_s_given_z, \
p_h_given_s=p_h_given_s, \
p_x_given_s_h=p_x_given_s_h, \
q_z_given_x=q_z_given_x, \
q_h_given_x_s=q_h_given_x_s, \
x_dim=x_dim, \
z_dim=z_dim, s_dim=s_dim, h_dim=h_dim, \
params=msm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("TSM_A_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0003
momentum = 0.5
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 3000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
if (i > 50000):
momentum = 0.90
else:
momentum = 0.50
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr = row_shuffle(Xtr)
batch_idx = np.arange(batch_size)
# train on the training set
lam_kld = 1.0
# set sgd and objective function hyperparams for this update
TSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
TSM.set_train_switch(1.0)
TSM.set_lam_nll(lam_nll=1.0)
TSM.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.8, lam_kld_p2q=0.2)
TSM.set_lam_kld_l1l2(lam_kld_l1l2=scale)
TSM.set_lam_l2w(1e-4)
TSM.set_drop_rate(0.0)
TSM.q_h_given_x_s.set_bias_noise(0.0)
TSM.p_h_given_s.set_bias_noise(0.0)
TSM.p_x_given_s_h.set_bias_noise(0.0)
# perform a minibatch update and record the cost for this batch
Xb_tr = to_fX( Xtr.take(batch_idx, axis=0) )
result = TSM.train_joint(Xb_tr, Xb_tr, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_cost : {0:.4f}".format(costs[1])
str4 = " kld_cost : {0:.4f}".format(costs[2])
str5 = " reg_cost : {0:.4f}".format(costs[3])
joint_str = "\n".join([str1, str2, str3, str4, str5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
TSM.set_drop_rate(0.0)
TSM.q_h_given_x_s.set_bias_noise(0.0)
TSM.p_h_given_s.set_bias_noise(0.0)
TSM.p_x_given_s_h.set_bias_noise(0.0)
# Get some validation samples for computing diagnostics
Xva = row_shuffle(Xva)
Xb_va = to_fX( Xva[0:2000] )
# draw some independent random samples from the model
samp_count = 500
model_samps = TSM.sample_from_prior(samp_count)
file_name = "TSM_A_SAMPLES_IND_b{0:d}.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
Xb_tr = to_fX( Xtr[0:2000] )
fe_terms = TSM.compute_fe_terms(Xb_tr, Xb_tr, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
fe_terms = TSM.compute_fe_terms(Xb_va, Xb_va, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 20
h_dim = 200
ir_steps = 6
init_scale = 1.0
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
#################
# p_hi_given_si #
#################
params = {}
shared_config = [obs_dim, 300, 300]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_hi_given_si = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_hi_given_si.init_biases(0.2)
######################
# p_sip1_given_si_hi #
######################
params = {}
shared_config = [h_dim, 300, 300]
output_config = [obs_dim, obs_dim, obs_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_sip1_given_si_hi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_si_hi.init_biases(0.2)
################
# p_s0_given_z #
################
params = {}
shared_config = [z_dim, 250, 250]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s0_given_z = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_s0_given_z.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [obs_dim, 250, 250]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
###################
# q_hi_given_x_si #
###################
params = {}
shared_config = [(obs_dim + obs_dim), 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_hi_given_x_si = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_hi_given_x_si.init_biases(0.2)
################################################################
# Define parameters for the MultiStageModel, and initialize it #
################################################################
print("Building the MultiStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
MSM = MultiStageModel(rng=rng, x_in=x_in_sym, x_out=x_out_sym, \
p_s0_given_z=p_s0_given_z, \
p_hi_given_si=p_hi_given_si, \
p_sip1_given_si_hi=p_sip1_given_si_hi, \
q_z_given_x=q_z_given_x, \
q_hi_given_x_si=q_hi_given_x_si, \
obs_dim=obs_dim, z_dim=z_dim, h_dim=h_dim, \
ir_steps=ir_steps, params=msm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("MSM_A_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0003
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 3000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr = row_shuffle(Xtr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
MSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
MSM.set_train_switch(1.0)
MSM.set_lam_nll(lam_nll=1.0)
MSM.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.8, lam_kld_p2q=0.2)
MSM.set_lam_kld_l1l2(lam_kld_l1l2=1.0)
MSM.set_lam_l2w(1e-4)
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
# perform a minibatch update and record the cost for this batch
Xb_tr = to_fX( Xtr.take(batch_idx, axis=0) )
result = MSM.train_joint(Xb_tr, Xb_tr, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_cost : {0:.4f}".format(costs[1])
str4 = " kld_cost : {0:.4f}".format(costs[2])
str5 = " reg_cost : {0:.4f}".format(costs[3])
joint_str = "\n".join([str1, str2, str3, str4, str5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
# Get some validation samples for computing diagnostics
Xva = row_shuffle(Xva)
Xb_va = to_fX( Xva[0:2000] )
# draw some independent random samples from the model
samp_count = 200
model_samps = MSM.sample_from_prior(samp_count)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSM_A_SAMPLES_IND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
samp_count = 200
Xs = np.vstack((Xb_tr[0:(samp_count/4)], Xb_va[0:(samp_count/4)]))
Xs = np.repeat(Xs, 2, axis=0)
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(Xs, guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSM_A_SAMPLES_CND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# compute information about posterior KLds on validation set
raw_klds = MSM.compute_raw_klds(Xb_va, Xb_va)
init_kld, q2p_kld, p2q_kld = raw_klds
file_name = "MSM_A_H0_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(init_kld.shape[1]), \
np.mean(init_kld, axis=0), file_name)
file_name = "MSM_A_HI_Q2P_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(q2p_kld.shape[1]), \
np.mean(q2p_kld, axis=0), file_name)
file_name = "MSM_A_HI_P2Q_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(p2q_kld.shape[1]), \
np.mean(p2q_kld, axis=0), file_name)
Xb_tr = to_fX( Xtr[0:2000] )
fe_terms = MSM.compute_fe_terms(Xb_tr, Xb_tr, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
fe_terms = MSM.compute_fe_terms(Xb_va, Xb_va, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def pretrain_osm(lam_kld=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr = datasets[0][0]
Xtr = Xtr.get_value(borrow=False)
Xva = datasets[2][0]
Xva = Xva.get_value(borrow=False)
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),str(Xva.shape)))
# get and set some basic dataset information
Xtr_mean = np.mean(Xtr, axis=0)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 100
batch_reps = 5
# setup some symbolic variables and stuff
Xd = T.matrix('Xd_base')
Xc = T.matrix('Xc_base')
Xm = T.matrix('Xm_base')
data_dim = Xtr.shape[1]
prior_sigma = 1.0
##########################
# NETWORK CONFIGURATIONS #
##########################
gn_params = {}
shared_config = [PRIOR_DIM, 1000, 1000]
top_config = [shared_config[-1], data_dim]
gn_params['shared_config'] = shared_config
gn_params['mu_config'] = top_config
gn_params['sigma_config'] = top_config
gn_params['activation'] = relu_actfun
gn_params['init_scale'] = 1.4
gn_params['lam_l2a'] = 0.0
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.0
gn_params['input_noise'] = 0.0
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 1000, 1000]
top_config = [shared_config[-1], PRIOR_DIM]
in_params['shared_config'] = shared_config
in_params['mu_config'] = top_config
in_params['sigma_config'] = top_config
in_params['activation'] = relu_actfun
in_params['init_scale'] = 1.4
in_params['lam_l2a'] = 0.0
in_params['vis_drop'] = 0.0
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.0
in_params['input_noise'] = 0.0
# Initialize the base networks for this OneStageModel
IN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=in_params, shared_param_dicts=None)
GN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=gn_params, shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.2)
GN.init_biases(0.2)
#########################
# INITIALIZE THE GIPAIR #
#########################
osm_params = {}
osm_params['x_type'] = 'bernoulli'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=data_dim, z_dim=PRIOR_DIM, params=osm_params)
OSM.set_lam_l2w(1e-5)
safe_mean = (0.9 * Xtr_mean) + 0.05
safe_mean_logit = np.log(safe_mean / (1.0 - safe_mean))
OSM.set_output_bias(safe_mean_logit)
OSM.set_input_bias(-Xtr_mean)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH+"pt_osm_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
obs_costs = np.zeros((batch_size,))
costs = [0. for i in range(10)]
learn_rate = 0.0005
for i in range(150000):
scale = min(1.0, float(i) / 10000.0)
if ((i > 1) and ((i % 20000) == 0)):
learn_rate = learn_rate * 0.9
# do a minibatch update of the model, and compute some costs
tr_idx = npr.randint(low=0,high=tr_samples,size=(batch_size,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xc_batch = 0.0 * Xd_batch
Xm_batch = 0.0 * Xd_batch
# do a minibatch update of the model, and compute some costs
OSM.set_sgd_params(lr_1=(scale*learn_rate), mom_1=0.5, mom_2=0.98)
OSM.set_lam_nll(1.0)
OSM.set_lam_kld(lam_kld_1=(1.0 + (scale*(lam_kld-1.0))), lam_kld_2=0.0)
result = OSM.train_joint(Xd_batch, Xc_batch, Xm_batch, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 1000) == 0):
# record and then reset the cost trackers
costs = [(v / 1000.0) for v in costs]
str_1 = "-- batch {0:d} --".format(i)
str_2 = " joint_cost: {0:.4f}".format(costs[0])
str_3 = " nll_cost : {0:.4f}".format(costs[1])
str_4 = " kld_cost : {0:.4f}".format(costs[2])
str_5 = " reg_cost : {0:.4f}".format(costs[3])
costs = [0.0 for v in costs]
# print out some diagnostic information
joint_str = "\n".join([str_1, str_2, str_3, str_4, str_5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 2000) == 0):
Xva = row_shuffle(Xva)
model_samps = OSM.sample_from_prior(500)
file_name = RESULT_PATH+"pt_osm_samples_b{0:d}_XG.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
# compute information about free-energy on validation set
file_name = RESULT_PATH+"pt_osm_free_energy_b{0:d}.png".format(i)
fe_terms = OSM.compute_fe_terms(Xva[0:2500], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
fe_str = " nll_bound : {0:.4f}".format(fe_mean)
print(fe_str)
out_file.write(fe_str+"\n")
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# compute information about posterior KLds on validation set
file_name = RESULT_PATH+"pt_osm_post_klds_b{0:d}.png".format(i)
post_klds = OSM.compute_post_klds(Xva[0:2500])
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
if ((i % 5000) == 0):
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_GN.pkl")
return
def pretrain_osm(lam_kld=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr = datasets[0][0]
Xtr = Xtr.get_value(borrow=False)
Xva = datasets[2][0]
Xva = Xva.get_value(borrow=False)
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),
str(Xva.shape)))
# get and set some basic dataset information
Xtr_mean = np.mean(Xtr, axis=0)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 100
batch_reps = 5
# setup some symbolic variables and stuff
Xd = T.matrix('Xd_base')
Xc = T.matrix('Xc_base')
Xm = T.matrix('Xm_base')
data_dim = Xtr.shape[1]
prior_sigma = 1.0
##########################
# NETWORK CONFIGURATIONS #
##########################
gn_params = {}
shared_config = [PRIOR_DIM, 1000, 1000]
top_config = [shared_config[-1], data_dim]
gn_params['shared_config'] = shared_config
gn_params['mu_config'] = top_config
gn_params['sigma_config'] = top_config
gn_params['activation'] = relu_actfun
gn_params['init_scale'] = 1.4
gn_params['lam_l2a'] = 0.0
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.0
gn_params['input_noise'] = 0.0
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 1000, 1000]
top_config = [shared_config[-1], PRIOR_DIM]
in_params['shared_config'] = shared_config
in_params['mu_config'] = top_config
in_params['sigma_config'] = top_config
in_params['activation'] = relu_actfun
in_params['init_scale'] = 1.4
in_params['lam_l2a'] = 0.0
in_params['vis_drop'] = 0.0
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.0
in_params['input_noise'] = 0.0
# Initialize the base networks for this OneStageModel
IN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=in_params, shared_param_dicts=None)
GN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=gn_params, shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.2)
GN.init_biases(0.2)
#########################
# INITIALIZE THE GIPAIR #
#########################
osm_params = {}
osm_params['x_type'] = 'bernoulli'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=data_dim, z_dim=PRIOR_DIM, params=osm_params)
OSM.set_lam_l2w(1e-5)
safe_mean = (0.9 * Xtr_mean) + 0.05
safe_mean_logit = np.log(safe_mean / (1.0 - safe_mean))
OSM.set_output_bias(safe_mean_logit)
OSM.set_input_bias(-Xtr_mean)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH + "pt_osm_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
obs_costs = np.zeros((batch_size, ))
costs = [0. for i in range(10)]
learn_rate = 0.0005
for i in range(150000):
scale = min(1.0, float(i) / 10000.0)
if ((i > 1) and ((i % 20000) == 0)):
learn_rate = learn_rate * 0.9
# do a minibatch update of the model, and compute some costs
tr_idx = npr.randint(low=0, high=tr_samples, size=(batch_size, ))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xc_batch = 0.0 * Xd_batch
Xm_batch = 0.0 * Xd_batch
# do a minibatch update of the model, and compute some costs
OSM.set_sgd_params(lr_1=(scale * learn_rate), mom_1=0.5, mom_2=0.98)
OSM.set_lam_nll(1.0)
OSM.set_lam_kld(lam_kld_1=(1.0 + (scale * (lam_kld - 1.0))),
lam_kld_2=0.0)
result = OSM.train_joint(Xd_batch, Xc_batch, Xm_batch, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 1000) == 0):
# record and then reset the cost trackers
costs = [(v / 1000.0) for v in costs]
str_1 = "-- batch {0:d} --".format(i)
str_2 = " joint_cost: {0:.4f}".format(costs[0])
str_3 = " nll_cost : {0:.4f}".format(costs[1])
str_4 = " kld_cost : {0:.4f}".format(costs[2])
str_5 = " reg_cost : {0:.4f}".format(costs[3])
costs = [0.0 for v in costs]
# print out some diagnostic information
joint_str = "\n".join([str_1, str_2, str_3, str_4, str_5])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
if ((i % 2000) == 0):
Xva = row_shuffle(Xva)
model_samps = OSM.sample_from_prior(500)
file_name = RESULT_PATH + "pt_osm_samples_b{0:d}_XG.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
# compute information about free-energy on validation set
file_name = RESULT_PATH + "pt_osm_free_energy_b{0:d}.png".format(i)
fe_terms = OSM.compute_fe_terms(Xva[0:2500], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
fe_str = " nll_bound : {0:.4f}".format(fe_mean)
print(fe_str)
out_file.write(fe_str + "\n")
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# compute information about posterior KLds on validation set
file_name = RESULT_PATH + "pt_osm_post_klds_b{0:d}.png".format(i)
post_klds = OSM.compute_post_klds(Xva[0:2500])
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
if ((i % 5000) == 0):
IN.save_to_file(f_name=RESULT_PATH +
"pt_osm_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH +
"pt_osm_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH + "pt_osm_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH + "pt_osm_params_GN.pkl")
return
def pretrain_osm(lam_kld=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
data_file = 'data/tfd_data_48x48.pkl'
dataset = load_tfd(tfd_pkl_name=data_file,
which_set='unlabeled',
fold='all')
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set='train', fold='all')
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set='valid', fold='all')
Xva = dataset[0]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 400
batch_reps = 6
carry_frac = 0.25
carry_size = int(batch_size * carry_frac)
reset_prob = 0.04
# setup some symbolic variables and stuff
Xd = T.matrix('Xd_base')
Xc = T.matrix('Xc_base')
Xm = T.matrix('Xm_base')
data_dim = Xtr.shape[1]
prior_sigma = 1.0
Xtr_mean = np.mean(Xtr, axis=0)
##########################
# NETWORK CONFIGURATIONS #
##########################
gn_params = {}
shared_config = [PRIOR_DIM, 1500, 1500]
top_config = [shared_config[-1], data_dim]
gn_params['shared_config'] = shared_config
gn_params['mu_config'] = top_config
gn_params['sigma_config'] = top_config
gn_params['activation'] = relu_actfun
gn_params['init_scale'] = 1.4
gn_params['lam_l2a'] = 0.0
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.0
gn_params['input_noise'] = 0.0
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 1500, 1500]
top_config = [shared_config[-1], PRIOR_DIM]
in_params['shared_config'] = shared_config
in_params['mu_config'] = top_config
in_params['sigma_config'] = top_config
in_params['activation'] = relu_actfun
in_params['init_scale'] = 1.4
in_params['lam_l2a'] = 0.0
in_params['vis_drop'] = 0.0
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.0
in_params['input_noise'] = 0.0
# Initialize the base networks for this OneStageModel
IN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=in_params, shared_param_dicts=None)
GN = InfNet(rng=rng, Xd=Xd, prior_sigma=prior_sigma, \
params=gn_params, shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.2)
GN.init_biases(0.2)
######################################
# LOAD AND RESTART FROM SAVED PARAMS #
######################################
# gn_fname = RESULT_PATH+"pt_osm_params_b110000_GN.pkl"
# in_fname = RESULT_PATH+"pt_osm_params_b110000_IN.pkl"
# IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd, \
# new_params=None)
# GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd, \
# new_params=None)
# in_params = IN.params
# gn_params = GN.params
#########################
# INITIALIZE THE GIPAIR #
#########################
osm_params = {}
osm_params['x_type'] = 'bernoulli'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=data_dim, z_dim=PRIOR_DIM, params=osm_params)
OSM.set_lam_l2w(1e-5)
safe_mean = (0.9 * Xtr_mean) + 0.05
safe_mean_logit = np.log(safe_mean / (1.0 - safe_mean))
OSM.set_output_bias(safe_mean_logit)
OSM.set_input_bias(-Xtr_mean)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH + "pt_osm_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
obs_costs = np.zeros((batch_size, ))
costs = [0. for i in range(10)]
learn_rate = 0.002
for i in range(200000):
scale = min(1.0, float(i) / 5000.0)
if ((i > 1) and ((i % 20000) == 0)):
learn_rate = learn_rate * 0.8
if (i < 50000):
momentum = 0.5
elif (i < 10000):
momentum = 0.7
else:
momentum = 0.9
if ((i == 0) or (npr.rand() < reset_prob)):
# sample a fully random batch
batch_idx = npr.randint(low=0,
high=tr_samples,
size=(batch_size, ))
else:
# sample a partially random batch, which retains some portion of
# the worst scoring examples from the previous batch
fresh_idx = npr.randint(low=0,
high=tr_samples,
size=(batch_size - carry_size, ))
batch_idx = np.concatenate((fresh_idx.ravel(), carry_idx.ravel()))
# do a minibatch update of the model, and compute some costs
tr_idx = npr.randint(low=0, high=tr_samples, size=(batch_size, ))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xc_batch = 0.0 * Xd_batch
Xm_batch = 0.0 * Xd_batch
# do a minibatch update of the model, and compute some costs
OSM.set_sgd_params(lr_1=(scale*learn_rate), \
mom_1=(scale*momentum), mom_2=0.98)
OSM.set_lam_nll(1.0)
OSM.set_lam_kld(lam_kld_1=scale * lam_kld,
lam_kld_2=0.0,
lam_kld_c=50.0)
result = OSM.train_joint(Xd_batch, Xc_batch, Xm_batch, batch_reps)
batch_costs = result[4] + result[5]
obs_costs = collect_obs_costs(batch_costs, batch_reps)
carry_idx = batch_idx[np.argsort(-obs_costs)[0:carry_size]]
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 1000) == 0):
# record and then reset the cost trackers
costs = [(v / 1000.0) for v in costs]
str_1 = "-- batch {0:d} --".format(i)
str_2 = " joint_cost: {0:.4f}".format(costs[0])
str_3 = " nll_cost : {0:.4f}".format(costs[1])
str_4 = " kld_cost : {0:.4f}".format(costs[2])
str_5 = " reg_cost : {0:.4f}".format(costs[3])
costs = [0.0 for v in costs]
# print out some diagnostic information
joint_str = "\n".join([str_1, str_2, str_3, str_4, str_5])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
if ((i % 2000) == 0):
Xva = row_shuffle(Xva)
model_samps = OSM.sample_from_prior(500)
file_name = RESULT_PATH + "pt_osm_samples_b{0:d}_XG.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
file_name = RESULT_PATH + "pt_osm_inf_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.inf_weights.get_value(borrow=False).T, \
file_name, num_rows=30)
file_name = RESULT_PATH + "pt_osm_gen_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.gen_weights.get_value(borrow=False), \
file_name, num_rows=30)
# compute information about free-energy on validation set
file_name = RESULT_PATH + "pt_osm_free_energy_b{0:d}.png".format(i)
fe_terms = OSM.compute_fe_terms(Xva[0:2500], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
fe_str = " nll_bound : {0:.4f}".format(fe_mean)
print(fe_str)
out_file.write(fe_str + "\n")
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# compute information about posterior KLds on validation set
file_name = RESULT_PATH + "pt_osm_post_klds_b{0:d}.png".format(i)
post_klds = OSM.compute_post_klds(Xva[0:2500])
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
if ((i % 5000) == 0):
IN.save_to_file(f_name=RESULT_PATH +
"pt_osm_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH +
"pt_osm_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH + "pt_osm_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH + "pt_osm_params_GN.pkl")
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = to_fX(datasets[0][0])
Xva = to_fX(datasets[1][0])
Ytr = datasets[0][1]
Yva = datasets[1][1]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
BD = lambda ary: binarize_data(ary)
#######################################
# Setup some parameters for the model #
#######################################
obs_dim = Xtr.shape[1]
z_dim = 64
init_scale = 0.2
# some InfNet instances to build the TwoStageModel from
x_in = T.matrix('x_in')
y_in = T.lvector('y_in')
###############
# q_z_given_x #
###############
print("Building q_z_given_x...")
params = {}
shared_config = [obs_dim, 1000, 1000]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.2
params['hid_drop'] = 0.5
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
###########################################################
# Define parameters for the ClassModel, and initialize it #
###########################################################
print("Building the ClassModel...")
CM = ClassModel(rng=rng, \
x_in=x_in, y_in=y_in, \
q_z_given_x=q_z_given_x, \
class_count=10, \
z_dim=z_dim, \
use_samples=False)
CM.set_drop_rate(0.5)
CM.set_lam_nll(lam_nll=1.0)
CM.set_lam_kld(lam_kld_q2p=1.0, lam_kld_p2q=0.0)
CM.set_lam_l2w(lam_l2w=1e-5)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("CM_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0002
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 1000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr, Ytr = row_shuffle(Xtr, Ytr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
CM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
# perform a minibatch update and record the cost for this batch
Xi_tr = Xtr.take(batch_idx, axis=0)
Yi_tr = Ytr.take(batch_idx, axis=0)
result = CM.train_joint(Xi_tr, Yi_tr)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
# output useful information about training progress
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost : {0:.4f}".format(costs[0])
str3 = " nll_cost : {0:.4f}".format(costs[1])
str4 = " kld_cost : {0:.4f}".format(costs[2])
str5 = " reg_cost : {0:.4f}".format(costs[3])
joint_str = "\n".join([str1, str2, str3, str4, str5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
#####################################################
# compute multi-sample estimates of the free-energy #
#####################################################
# training set...
fe_terms = CM.compute_fe_terms(Xtr[0:2500],Ytr[0:2500], 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# validation set...
Xva, Yva = row_shuffle(Xva, Yva)
fe_terms = CM.compute_fe_terms(Xva[0:2500], Yva[0:2500], 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
##########################################################
# compute multi-sample estimates of classification error #
##########################################################
# training set...
va_error, va_preds = CM.class_error(Xtr[:2500], Ytr[:2500], samples=30)
joint_str = " tr-class-error: {0:.4f}".format(va_error)
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# validation set...
va_error, va_preds = CM.class_error(Xva[:2500], Yva[:2500], samples=30)
joint_str = " va-class-error: {0:.4f}".format(va_error)
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def test_gip_sigma_scale_tfd():
from LogPDFs import cross_validate_sigma
# Simple test code, to check that everything is basically functional.
print("TESTING...")
# Initialize a source of randomness
rng = np.random.RandomState(12345)
# Load some data to train/validate/test with
data_file = "data/tfd_data_48x48.pkl"
dataset = load_tfd(tfd_pkl_name=data_file, which_set="unlabeled", fold="all")
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set="train", fold="all")
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set="test", fold="all")
Xva = dataset[0]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape), str(Xva.shape)))
# get and set some basic dataset information
tr_samples = Xtr.shape[0]
data_dim = Xtr.shape[1]
batch_size = 100
# Symbolic inputs
Xd = T.matrix(name="Xd")
Xc = T.matrix(name="Xc")
Xm = T.matrix(name="Xm")
Xt = T.matrix(name="Xt")
# Load inferencer and generator from saved parameters
gn_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_GN.pkl"
in_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_IN.pkl"
IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd)
GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd)
x_dim = IN.shared_layers[0].in_dim
z_dim = IN.mu_layers[-1].out_dim
# construct a GIPair with the loaded InfNet and GenNet
osm_params = {}
osm_params["x_type"] = "gaussian"
osm_params["xt_transform"] = "sigmoid"
osm_params["logvar_bound"] = LOGVAR_BOUND
OSM = OneStageModel(
rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, p_x_given_z=GN, q_z_given_x=IN, x_dim=x_dim, z_dim=z_dim, params=osm_params
)
# # compute variational likelihood bound and its sub-components
Xva = row_shuffle(Xva)
Xb = Xva[0:5000]
# file_name = "A_TFD_POST_KLDS.png"
# post_klds = OSM.compute_post_klds(Xb)
# post_dim_klds = np.mean(post_klds, axis=0)
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
# file_name)
# compute information about free-energy on validation set
file_name = "A_TFD_KLD_FREE_ENERGY.png"
fe_terms = OSM.compute_fe_terms(Xb, 20)
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, x_label="Posterior KLd", y_label="Negative Log-likelihood")
# bound_results = OSM.compute_ll_bound(Xva)
# ll_bounds = bound_results[0]
# post_klds = bound_results[1]
# log_likelihoods = bound_results[2]
# max_lls = bound_results[3]
# print("mean ll bound: {0:.4f}".format(np.mean(ll_bounds)))
# print("mean posterior KLd: {0:.4f}".format(np.mean(post_klds)))
# print("mean log-likelihood: {0:.4f}".format(np.mean(log_likelihoods)))
# print("mean max log-likelihood: {0:.4f}".format(np.mean(max_lls)))
# print("min ll bound: {0:.4f}".format(np.min(ll_bounds)))
# print("max posterior KLd: {0:.4f}".format(np.max(post_klds)))
# print("min log-likelihood: {0:.4f}".format(np.min(log_likelihoods)))
# print("min max log-likelihood: {0:.4f}".format(np.min(max_lls)))
# # compute some information about the approximate posteriors
# post_stats = OSM.compute_post_stats(Xva, 0.0*Xva, 0.0*Xva)
# all_post_klds = np.sort(post_stats[0].ravel()) # post KLds for each obs and dim
# obs_post_klds = np.sort(post_stats[1]) # summed post KLds for each obs
# post_dim_klds = post_stats[2] # average post KLds for each post dim
# post_dim_vars = post_stats[3] # average squared mean for each post dim
# utils.plot_line(np.arange(all_post_klds.shape[0]), all_post_klds, "AAA_ALL_POST_KLDS.png")
# utils.plot_line(np.arange(obs_post_klds.shape[0]), obs_post_klds, "AAA_OBS_POST_KLDS.png")
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, "AAA_POST_DIM_KLDS.png")
# utils.plot_stem(np.arange(post_dim_vars.shape[0]), post_dim_vars, "AAA_POST_DIM_VARS.png")
# draw many samples from the GIP
for i in range(5):
tr_idx = npr.randint(low=0, high=tr_samples, size=(100,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xs = []
for row in range(3):
Xs.append([])
for col in range(3):
sample_lists = OSM.sample_from_chain(Xd_batch[0:10, :], loop_iters=100, sigma_scale=1.0)
Xs[row].append(group_chains(sample_lists["data samples"]))
Xs, block_im_dim = block_video(Xs, (48, 48), (3, 3))
to_video(Xs, block_im_dim, "A_TFD_KLD_CHAIN_VIDEO_{0:d}.avi".format(i), frame_rate=10)
# sample_lists = GIP.sample_from_chain(Xd_batch[0,:].reshape((1,data_dim)), loop_iters=300, \
# sigma_scale=1.0)
# Xs = np.vstack(sample_lists["data samples"])
# file_name = "TFD_TEST_{0:d}.png".format(i)
# utils.visualize_samples(Xs, file_name, num_rows=15)
file_name = "A_TFD_KLD_PRIOR_SAMPLE.png"
Xs = OSM.sample_from_prior(20 * 20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# test Parzen density estimator built from prior samples
# Xs = OSM.sample_from_prior(10000)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.09, 0.095, 0.1, 0.105, 0.11], 10)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_TFD_BEST_LLS_1.png")
# utils.visualize_samples(Xva[sort_idx], "A_TFD_BAD_FACES_1.png", num_rows=20)
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = to_fX(datasets[0][0])
Xva = to_fX(datasets[1][0])
Ytr = datasets[0][1]
Yva = datasets[1][1]
Xtr_class_groups = make_class_groups(Xtr, Ytr)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 300
BD = lambda ary: binarize_data(ary)
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 32
h_dim = 100
ir_steps = 2
init_scale = 1.0
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
x_in = T.matrix('x_in')
x_pos = T.matrix('x_pos')
y_in = T.lvector('y_in')
#################
# p_hi_given_si #
#################
params = {}
shared_config = [obs_dim, 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_hi_given_si = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_hi_given_si.init_biases(0.2)
######################
# p_sip1_given_si_hi #
######################
params = {}
shared_config = [(h_dim + obs_dim), 500, 500]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_sip1_given_si_hi = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_sip1_given_si_hi.init_biases(0.2)
################
# p_s0_given_z #
################
params = {}
shared_config = [z_dim, 500, 500]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s0_given_z = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_s0_given_z.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [obs_dim, (500, 4), (500, 4)]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.2
params['hid_drop'] = 0.5
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.0)
###################
# q_hi_given_x_si #
###################
params = {}
shared_config = [(obs_dim + obs_dim), 800, 800]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_hi_given_x_si = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
q_hi_given_x_si.init_biases(0.2)
################################################################
# Define parameters for the MultiStageModel, and initialize it #
################################################################
print("Building the MultiStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
MSM = MultiStageModelSS(rng=rng, \
x_in=x_in, x_pos=x_pos, y_in=y_in, \
p_s0_given_z=p_s0_given_z, \
p_hi_given_si=p_hi_given_si, \
p_sip1_given_si_hi=p_sip1_given_si_hi, \
q_z_given_x=q_z_given_x, \
q_hi_given_x_si=q_hi_given_x_si, \
class_count=10, \
obs_dim=obs_dim, z_dim=z_dim, h_dim=h_dim, \
ir_steps=ir_steps, params=msm_params)
MSM.set_lam_class(lam_class=20.0)
MSM.set_lam_nll(lam_nll=1.0)
MSM.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.9, \
lam_kld_p2q=0.1)
MSM.set_lam_l2w(1e-4)
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("MSS_A_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0002
momentum = 0.5
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 2000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
if (i > 20000):
momentum = 0.90
else:
momentum = 0.50
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr, Ytr = row_shuffle(Xtr, Ytr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
MSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
MSM.set_train_switch(1.0)
# perform a minibatch update and record the cost for this batch
Xi_tr = Xtr.take(batch_idx, axis=0)
Yi_tr = Ytr.take(batch_idx, axis=0)
Xp_tr, Xn_tr = sample_class_groups(Yi_tr, Xtr_class_groups)
result = MSM.train_joint(BD(Xi_tr), BD(Xp_tr), Yi_tr)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
# output useful information about training progress
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost : {0:.4f}".format(costs[0])
str3 = " class_cost : {0:.4f}".format(costs[1])
str4 = " nll_cost : {0:.4f}".format(costs[2])
str5 = " kld_cost : {0:.4f}".format(costs[3])
str6 = " reg_cost : {0:.4f}".format(costs[4])
joint_str = "\n".join([str1, str2, str3, str4, str5, str6])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# Get some validation samples for computing diagnostics
Xva, Yva = row_shuffle(Xva, Yva)
Xb_va = Xva[0:2500]
Yb_va = Yva[0:2500]
# draw some independent random samples from the model
samp_count = 200
model_samps = MSM.sample_from_prior(samp_count)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_A_SAMPLES_IND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
Xs = Xb_va[0:50] # only use validation set samples
Xs = np.repeat(Xs, 4, axis=0)
samp_count = Xs.shape[0]
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(BD(Xs), guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_A_SAMPLES_CND_UD_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# compute information about posterior KLds on validation set
raw_costs = MSM.compute_raw_costs(BD(Xb_va), BD(Xb_va))
init_nll, init_kld, q2p_kld, p2q_kld, step_nll, step_kld = raw_costs
file_name = "MSS_A_H0_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(init_kld.shape[1]), \
np.mean(init_kld, axis=0), file_name)
file_name = "MSS_A_HI_Q2P_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(q2p_kld.shape[1]), \
np.mean(q2p_kld, axis=0), file_name)
file_name = "MSS_A_HI_P2Q_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(p2q_kld.shape[1]), \
np.mean(p2q_kld, axis=0), file_name)
# draw weights for the initial encoder/classifier
file_name = "MSS_A_QZX_WEIGHTS_b{0:d}.png".format(i)
W = q_z_given_x.shared_layers[0].W.get_value(borrow=False).T
utils.visualize_samples(W, file_name, num_rows=20)
# compute free-energy terms on training samples
fe_terms = MSM.compute_fe_terms(BD(Xtr[0:2500]), BD(Xtr[0:2500]), 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# compute free-energy terms on validation samples
fe_terms = MSM.compute_fe_terms(BD(Xb_va), BD(Xb_va), 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# compute multi-sample estimate of classification error
err_rate, err_idx, y_preds = MSM.class_error(Xb_va, Yb_va, \
samples=30, prep_func=BD)
joint_str = " va-class-error: {0:.4f}".format(err_rate)
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# draw some conditional random samples from the model
Xs = Xb_va[err_idx] # use validation samples with class errors
if (Xs.shape[0] > 50):
Xs = Xs[:50]
Xs = np.repeat(Xs, 4, axis=0)
if ((Xs.shape[0] % 20) != 0):
# round-off the number of error examples, for nice display
remainder = Xs.shape[0] % 20
Xs = Xs[:-remainder]
samp_count = Xs.shape[0]
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(BD(Xs), guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_A_SAMPLES_CND_ERR_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
def test_gip_sigma_scale_mnist():
from LogPDFs import cross_validate_sigma
# Simple test code, to check that everything is basically functional.
print("TESTING...")
# Initialize a source of randomness
rng = np.random.RandomState(12345)
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr = datasets[0][0]
Xtr = Xtr.get_value(borrow=False)
Xva = datasets[2][0]
Xva = Xva.get_value(borrow=False)
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),str(Xva.shape)))
# get and set some basic dataset information
tr_samples = Xtr.shape[0]
batch_size = 100
Xtr_mean = np.mean(Xtr, axis=0, keepdims=True)
Xtr_mean = (0.0 * Xtr_mean) + np.mean(Xtr)
Xc_mean = np.repeat(Xtr_mean, batch_size, axis=0).astype(theano.config.floatX)
# Symbolic inputs
Xd = T.matrix(name='Xd')
Xc = T.matrix(name='Xc')
Xm = T.matrix(name='Xm')
Xt = T.matrix(name='Xt')
# Load inferencer and generator from saved parameters
gn_fname = "MNIST_WALKOUT_TEST_MAX_KLD/pt_walk_params_b70000_GN.pkl"
in_fname = "MNIST_WALKOUT_TEST_MAX_KLD/pt_walk_params_b70000_IN.pkl"
IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd)
GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd)
x_dim = IN.shared_layers[0].in_dim
z_dim = IN.mu_layers[-1].out_dim
# construct a GIPair with the loaded InfNet and GenNet
osm_params = {}
osm_params['x_type'] = 'gaussian'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=x_dim, z_dim=z_dim, params=osm_params)
# compute variational likelihood bound and its sub-components
Xva = row_shuffle(Xva)
Xb = Xva[0:5000]
file_name = "A_MNIST_POST_KLDS.png"
post_klds = OSM.compute_post_klds(Xb)
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
# compute information about free-energy on validation set
file_name = "A_MNIST_FREE_ENERGY.png"
fe_terms = OSM.compute_fe_terms(Xb, 20)
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# bound_results = OSM.compute_ll_bound(Xva)
# ll_bounds = bound_results[0]
# post_klds = bound_results[1]
# log_likelihoods = bound_results[2]
# max_lls = bound_results[3]
# print("mean ll bound: {0:.4f}".format(np.mean(ll_bounds)))
# print("mean posterior KLd: {0:.4f}".format(np.mean(post_klds)))
# print("mean log-likelihood: {0:.4f}".format(np.mean(log_likelihoods)))
# print("mean max log-likelihood: {0:.4f}".format(np.mean(max_lls)))
# print("min ll bound: {0:.4f}".format(np.min(ll_bounds)))
# print("max posterior KLd: {0:.4f}".format(np.max(post_klds)))
# print("min log-likelihood: {0:.4f}".format(np.min(log_likelihoods)))
# print("min max log-likelihood: {0:.4f}".format(np.min(max_lls)))
# # compute some information about the approximate posteriors
# post_stats = OSM.compute_post_stats(Xva, 0.0*Xva, 0.0*Xva)
# all_post_klds = np.sort(post_stats[0].ravel()) # post KLds for each obs and dim
# obs_post_klds = np.sort(post_stats[1]) # summed post KLds for each obs
# post_dim_klds = post_stats[2] # average post KLds for each post dim
# post_dim_vars = post_stats[3] # average squared mean for each post dim
# utils.plot_line(np.arange(all_post_klds.shape[0]), all_post_klds, "AAA_ALL_POST_KLDS.png")
# utils.plot_line(np.arange(obs_post_klds.shape[0]), obs_post_klds, "AAA_OBS_POST_KLDS.png")
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, "AAA_POST_DIM_KLDS.png")
# utils.plot_stem(np.arange(post_dim_vars.shape[0]), post_dim_vars, "AAA_POST_DIM_VARS.png")
# draw many samples from the GIP
for i in range(5):
tr_idx = npr.randint(low=0,high=tr_samples,size=(100,))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xs = []
for row in range(3):
Xs.append([])
for col in range(3):
sample_lists = OSM.sample_from_chain(Xd_batch[0:10,:], loop_iters=100, \
sigma_scale=1.0)
Xs[row].append(group_chains(sample_lists['data samples']))
Xs, block_im_dim = block_video(Xs, (28,28), (3,3))
to_video(Xs, block_im_dim, "A_MNIST_KLD_CHAIN_VIDEO_{0:d}.avi".format(i), frame_rate=10)
#sample_lists = GIP.sample_from_chain(Xd_batch[0,:].reshape((1,data_dim)), loop_iters=300, \
# sigma_scale=1.0)
#Xs = np.vstack(sample_lists["data samples"])
#file_name = "TFD_TEST_{0:d}.png".format(i)
#utils.visualize_samples(Xs, file_name, num_rows=15)
file_name = "A_MNIST_KLD_PRIOR_SAMPLE.png"
Xs = OSM.sample_from_prior(20*20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# # test Parzen density estimator built from prior samples
# Xs = OSM.sample_from_prior(10000)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.12, 0.14, 0.15, 0.16, 0.18], 20)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_MNIST_BEST_LLS_1.png")
# utils.visualize_samples(Xva[sort_idx], "A_MNIST_BAD_DIGITS_1.png", num_rows=20)
# ##########
# # AGAIN! #
# ##########
# Xs = OSM.sample_from_prior(10000)
# tr_idx = npr.randint(low=0,high=tr_samples,size=(5000,))
# Xva = Xtr.take(tr_idx, axis=0)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.12, 0.14, 0.15, 0.16, 0.18], 20)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_MNIST_BEST_LLS_2.png")
# utils.visualize_samples(Xva[sort_idx], "A_MNIST_BAD_DIGITS_2.png", num_rows=20)
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr_shared = datasets[0][0]
Xva_shared = datasets[1][0]
Xtr = Xtr_shared.get_value(borrow=False).astype(theano.config.floatX)
Xva = Xva_shared.get_value(borrow=False).astype(theano.config.floatX)
tr_samples = Xtr.shape[0]
batch_size = 500
batch_reps = 1
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_rnn_dim = 25
z_obs_dim = 5
jnt_dim = obs_dim + z_rnn_dim
h_dim = 100
x_type = 'bernoulli'
prior_sigma = 1.0
# some InfNet instances to build the TwoStageModel from
X_sym = T.matrix('X_sym')
########################
# p_s0_obs_given_z_obs #
########################
params = {}
shared_config = [z_obs_dim, 250, 250]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = 1.2
params['lam_l2a'] = 1e-3
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s0_obs_given_z_obs = InfNet(rng=rng, Xd=X_sym, prior_sigma=prior_sigma, \
params=params, shared_param_dicts=None)
p_s0_obs_given_z_obs.init_biases(0.2)
#################
# p_hi_given_si #
#################
params = {}
shared_config = [jnt_dim, 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = 1.2
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_hi_given_si = InfNet(rng=rng, Xd=X_sym, prior_sigma=prior_sigma, \
params=params, shared_param_dicts=None)
p_hi_given_si.init_biases(0.2)
######################
# p_sip1_given_si_hi #
######################
params = {}
shared_config = [(h_dim + z_rnn_dim), 500, 500]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = 1.2
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_sip1_given_si_hi = InfNet(rng=rng, Xd=X_sym, prior_sigma=prior_sigma, \
params=params, shared_param_dicts=None)
p_sip1_given_si_hi.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [obs_dim, 250, 250]
top_config = [shared_config[-1], (z_rnn_dim + z_obs_dim)]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = 1.2
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=X_sym, prior_sigma=prior_sigma, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
###################
# q_hi_given_x_si #
###################
params = {}
shared_config = [(obs_dim + jnt_dim), 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = softplus_actfun
params['init_scale'] = 1.2
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_hi_given_x_si = InfNet(rng=rng, Xd=X_sym, prior_sigma=prior_sigma, \
params=params, shared_param_dicts=None)
q_hi_given_x_si.init_biases(0.2)
################################################################
# Define parameters for the MultiStageModel, and initialize it #
################################################################
print("Building the MultiStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
MSM = MultiStageModel(rng=rng, x_in=X_sym, \
p_s0_obs_given_z_obs=p_s0_obs_given_z_obs, \
p_hi_given_si=p_hi_given_si, \
p_sip1_given_si_hi=p_sip1_given_si_hi, \
q_z_given_x=q_z_given_x, \
q_hi_given_x_si=q_hi_given_x_si, \
obs_dim=obs_dim, z_rnn_dim=z_rnn_dim, z_obs_dim=z_obs_dim, \
h_dim=h_dim, model_init_obs=False, model_init_rnn=True, \
ir_steps=3, params=msm_params)
obs_mean = (0.9 * np.mean(Xtr, axis=0)) + 0.05
obs_mean_logit = np.log(obs_mean / (1.0 - obs_mean))
MSM.set_input_bias(-obs_mean)
MSM.set_obs_bias(0.1*obs_mean_logit)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
costs = [0. for i in range(10)]
learn_rate = 0.003
momentum = 0.5
for i in range(300000):
scale = min(1.0, ((i+1) / 5000.0))
l1l2_weight = 1.0 #min(1.0, ((i+1) / 2500.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.92
if (i > 100000):
momentum = 0.80
if (i > 50000):
momentum = 0.65
else:
momentum = 0.50
# randomly sample a minibatch
tr_idx = npr.randint(low=0,high=tr_samples,size=(batch_size,))
Xb = binarize_data(Xtr.take(tr_idx, axis=0))
Xb = Xb.astype(theano.config.floatX)
# set sgd and objective function hyperparams for this update
MSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=(scale*momentum), mom_2=0.99)
MSM.set_train_switch(1.0)
MSM.set_l1l2_weight(l1l2_weight)
MSM.set_lam_nll(lam_nll=1.0)
MSM.set_lam_kld(lam_kld_1=1.0, lam_kld_2=1.0)
MSM.set_lam_l2w(1e-5)
MSM.set_kzg_weight(0.01)
# perform a minibatch update and record the cost for this batch
result = MSM.train_joint(Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
print("-- batch {0:d} --".format(i))
print(" joint_cost: {0:.4f}".format(costs[0]))
print(" nll_cost : {0:.4f}".format(costs[1]))
print(" kld_cost : {0:.4f}".format(costs[2]))
print(" reg_cost : {0:.4f}".format(costs[3]))
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
Xva = row_shuffle(Xva)
# draw some independent random samples from the model
samp_count = 200
model_samps = MSM.sample_from_prior(samp_count)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MZ_SAMPLES_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# visualize some important weights in the model
file_name = "MZ_INF_1_WEIGHTS_b{0:d}.png".format(i)
W = MSM.inf_1_weights.get_value(borrow=False).T
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "MZ_INF_2_WEIGHTS_b{0:d}.png".format(i)
W = MSM.inf_2_weights.get_value(borrow=False).T
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "MZ_GEN_1_WEIGHTS_b{0:d}.png".format(i)
W = MSM.gen_1_weights.get_value(borrow=False)
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "MZ_GEN_2_WEIGHTS_b{0:d}.png".format(i)
W = MSM.gen_2_weights.get_value(borrow=False)
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "MZ_GEN_INF_WEIGHTS_b{0:d}.png".format(i)
W = MSM.gen_inf_weights.get_value(borrow=False).T
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
# compute information about posterior KLds on validation set
post_klds = MSM.compute_post_klds(Xva[0:5000])
file_name = "MZ_H0_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(post_klds[0].shape[1]), \
np.mean(post_klds[0], axis=0), file_name)
file_name = "MZ_HI_COND_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(post_klds[1].shape[1]), \
np.mean(post_klds[1], axis=0), file_name)
file_name = "MZ_HI_GLOB_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(post_klds[2].shape[1]), \
np.mean(post_klds[2], axis=0), file_name)
# compute information about free-energy on validation set
file_name = "MZ_FREE_ENERGY_b{0:d}.png".format(i)
fe_terms = MSM.compute_fe_terms(binarize_data(Xva[0:5000]), 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
print(" nll_bound : {0:.4f}".format(fe_mean))
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
return
def test_gip_sigma_scale_tfd():
from LogPDFs import cross_validate_sigma
# Simple test code, to check that everything is basically functional.
print("TESTING...")
# Initialize a source of randomness
rng = np.random.RandomState(12345)
# Load some data to train/validate/test with
data_file = 'data/tfd_data_48x48.pkl'
dataset = load_tfd(tfd_pkl_name=data_file,
which_set='unlabeled',
fold='all')
Xtr_unlabeled = dataset[0]
dataset = load_tfd(tfd_pkl_name=data_file, which_set='train', fold='all')
Xtr_train = dataset[0]
Xtr = np.vstack([Xtr_unlabeled, Xtr_train])
dataset = load_tfd(tfd_pkl_name=data_file, which_set='test', fold='all')
Xva = dataset[0]
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),
str(Xva.shape)))
# get and set some basic dataset information
tr_samples = Xtr.shape[0]
data_dim = Xtr.shape[1]
batch_size = 100
# Symbolic inputs
Xd = T.matrix(name='Xd')
Xc = T.matrix(name='Xc')
Xm = T.matrix(name='Xm')
Xt = T.matrix(name='Xt')
# Load inferencer and generator from saved parameters
gn_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_GN.pkl"
in_fname = "TFD_WALKOUT_TEST_KLD/pt_walk_params_b25000_IN.pkl"
IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd)
GN = load_infnet_from_file(f_name=gn_fname, rng=rng, Xd=Xd)
x_dim = IN.shared_layers[0].in_dim
z_dim = IN.mu_layers[-1].out_dim
# construct a GIPair with the loaded InfNet and GenNet
osm_params = {}
osm_params['x_type'] = 'gaussian'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=x_dim, z_dim=z_dim, params=osm_params)
# # compute variational likelihood bound and its sub-components
Xva = row_shuffle(Xva)
Xb = Xva[0:5000]
# file_name = "A_TFD_POST_KLDS.png"
# post_klds = OSM.compute_post_klds(Xb)
# post_dim_klds = np.mean(post_klds, axis=0)
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
# file_name)
# compute information about free-energy on validation set
file_name = "A_TFD_KLD_FREE_ENERGY.png"
fe_terms = OSM.compute_fe_terms(Xb, 20)
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# bound_results = OSM.compute_ll_bound(Xva)
# ll_bounds = bound_results[0]
# post_klds = bound_results[1]
# log_likelihoods = bound_results[2]
# max_lls = bound_results[3]
# print("mean ll bound: {0:.4f}".format(np.mean(ll_bounds)))
# print("mean posterior KLd: {0:.4f}".format(np.mean(post_klds)))
# print("mean log-likelihood: {0:.4f}".format(np.mean(log_likelihoods)))
# print("mean max log-likelihood: {0:.4f}".format(np.mean(max_lls)))
# print("min ll bound: {0:.4f}".format(np.min(ll_bounds)))
# print("max posterior KLd: {0:.4f}".format(np.max(post_klds)))
# print("min log-likelihood: {0:.4f}".format(np.min(log_likelihoods)))
# print("min max log-likelihood: {0:.4f}".format(np.min(max_lls)))
# # compute some information about the approximate posteriors
# post_stats = OSM.compute_post_stats(Xva, 0.0*Xva, 0.0*Xva)
# all_post_klds = np.sort(post_stats[0].ravel()) # post KLds for each obs and dim
# obs_post_klds = np.sort(post_stats[1]) # summed post KLds for each obs
# post_dim_klds = post_stats[2] # average post KLds for each post dim
# post_dim_vars = post_stats[3] # average squared mean for each post dim
# utils.plot_line(np.arange(all_post_klds.shape[0]), all_post_klds, "AAA_ALL_POST_KLDS.png")
# utils.plot_line(np.arange(obs_post_klds.shape[0]), obs_post_klds, "AAA_OBS_POST_KLDS.png")
# utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, "AAA_POST_DIM_KLDS.png")
# utils.plot_stem(np.arange(post_dim_vars.shape[0]), post_dim_vars, "AAA_POST_DIM_VARS.png")
# draw many samples from the GIP
for i in range(5):
tr_idx = npr.randint(low=0, high=tr_samples, size=(100, ))
Xd_batch = Xtr.take(tr_idx, axis=0)
Xs = []
for row in range(3):
Xs.append([])
for col in range(3):
sample_lists = OSM.sample_from_chain(Xd_batch[0:10,:], loop_iters=100, \
sigma_scale=1.0)
Xs[row].append(group_chains(sample_lists['data samples']))
Xs, block_im_dim = block_video(Xs, (48, 48), (3, 3))
to_video(Xs,
block_im_dim,
"A_TFD_KLD_CHAIN_VIDEO_{0:d}.avi".format(i),
frame_rate=10)
#sample_lists = GIP.sample_from_chain(Xd_batch[0,:].reshape((1,data_dim)), loop_iters=300, \
# sigma_scale=1.0)
#Xs = np.vstack(sample_lists["data samples"])
#file_name = "TFD_TEST_{0:d}.png".format(i)
#utils.visualize_samples(Xs, file_name, num_rows=15)
file_name = "A_TFD_KLD_PRIOR_SAMPLE.png"
Xs = OSM.sample_from_prior(20 * 20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# test Parzen density estimator built from prior samples
# Xs = OSM.sample_from_prior(10000)
# [best_sigma, best_ll, best_lls] = \
# cross_validate_sigma(Xs, Xva, [0.09, 0.095, 0.1, 0.105, 0.11], 10)
# sort_idx = np.argsort(best_lls)
# sort_idx = sort_idx[0:400]
# utils.plot_line(np.arange(sort_idx.shape[0]), best_lls[sort_idx], "A_TFD_BEST_LLS_1.png")
# utils.visualize_samples(Xva[sort_idx], "A_TFD_BAD_FACES_1.png", num_rows=20)
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
write_dim = 220
enc_dim = 260
dec_dim = 260
mix_dim = 20
z_dim = 100
n_iter = 18
rnninits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
# setup the reader and writer
read_dim = 2*x_dim
reader_mlp = Reader(x_dim=x_dim, dec_dim=dec_dim, **inits)
writer_mlp = MLP([None, None], [dec_dim, write_dim, x_dim], \
name="writer_mlp", **inits)
# setup the mixture weight sampler
mix_enc_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_enc_mlp", **inits)
mix_dec_mlp = MLP([Tanh(), Tanh()], \
[mix_dim, 250, (2*enc_dim + 2*dec_dim)], \
name="mix_dec_mlp", **inits)
# setup the components of the generative DRAW model
enc_mlp_in = MLP([Identity()], [(read_dim + dec_dim), 4*enc_dim], \
name="enc_mlp_in", **inits)
dec_mlp_in = MLP([Identity()], [ z_dim, 4*dec_dim], \
name="dec_mlp_in", **inits)
enc_mlp_out = CondNet([], [enc_dim, z_dim], name="enc_mlp_out", **inits)
dec_mlp_out = CondNet([], [dec_dim, z_dim], name="dec_mlp_out", **inits)
enc_rnn = BiasedLSTM(dim=enc_dim, ig_bias=2.0, fg_bias=2.0, \
name="enc_rnn", **rnninits)
dec_rnn = BiasedLSTM(dim=dec_dim, ig_bias=2.0, fg_bias=2.0, \
name="dec_rnn", **rnninits)
enc_mlp_stop = MLP([Tanh(), None], [(x_dim + dec_dim), 500, 1], \
name="enc_mlp_stop", **inits)
dec_mlp_stop = MLP([Tanh(), None], [dec_dim, 500, 1], \
name="dec_mlp_stop", **inits)
draw = IMoESDrawModels(
n_iter,
step_type='add', # step_type can be 'add' or 'jump'
mix_enc_mlp=mix_enc_mlp,
mix_dec_mlp=mix_dec_mlp,
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
enc_mlp_in=enc_mlp_in,
enc_mlp_out=enc_mlp_out,
enc_rnn=enc_rnn,
enc_mlp_stop=enc_mlp_stop,
dec_mlp_in=dec_mlp_in,
dec_mlp_out=dec_mlp_out,
dec_rnn=dec_rnn,
dec_mlp_stop=dec_mlp_stop)
draw.initialize()
# some symbolic vars to represent various inputs/outputs
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
# collect reconstructions of x produced by the IMoDRAW model
vfe_cost, cost_all = draw.reconstruct(x_in_sym, x_out_sym)
# grab handles for all the optimizable parameters in our cost
cg = ComputationGraph([vfe_cost])
joint_params = VariableFilter(roles=[PARAMETER])(cg.variables)
# apply some l2 regularization to the model parameters
reg_term = (1e-5 * sum([T.sum(p**2.0) for p in joint_params]))
reg_term.name = "reg_term"
# compute the full cost w.r.t. which we will optimize
total_cost = vfe_cost + reg_term
total_cost.name = "total_cost"
# Get the gradient of the joint cost for all optimizable parameters
print("Computing gradients of total_cost...")
joint_grads = OrderedDict()
grad_list = T.grad(total_cost, joint_params)
for i, p in enumerate(joint_params):
joint_grads[p] = grad_list[i]
# shared var learning rate for generator and inferencer
zero_ary = to_fX( np.zeros((1,)) )
lr_shared = theano.shared(value=zero_ary, name='tbm_lr')
# shared var momentum parameters for generator and inferencer
mom_1_shared = theano.shared(value=zero_ary, name='tbm_mom_1')
mom_2_shared = theano.shared(value=zero_ary, name='tbm_mom_2')
# construct the updates for the generator and inferencer networks
joint_updates = get_adam_updates(params=joint_params, \
grads=joint_grads, alpha=lr_shared, \
beta1=mom_1_shared, beta2=mom_2_shared, \
mom2_init=1e-4, smoothing=1e-6, max_grad_norm=10.0)
# collect the outputs to return from this function
outputs = [total_cost, vfe_cost, reg_term]
# compile the theano function
print("Compiling model training/update function...")
train_joint = theano.function(inputs=[ x_in_sym, x_out_sym ], \
outputs=outputs, updates=joint_updates)
print("Compiling NLL bound estimator function...")
compute_nll_bound = theano.function(inputs=[ x_in_sym, x_out_sym], \
outputs=outputs)
print("Compiling model sampler...")
n_samples = T.iscalar("n_samples")
samples = draw.sample(n_samples)
do_sample = theano.function([n_samples], outputs=samples, allow_input_downcast=True)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
print("Beginning to train the model...")
out_file = open("TBM_ES_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0002
momentum = 0.9
fresh_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 2500.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
# get the indices of training samples for this batch update
fresh_idx += batch_size
if (np.max(fresh_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr = row_shuffle(Xtr)
fresh_idx = np.arange(batch_size)
batch_idx = fresh_idx
# set sgd and objective function hyperparams for this update
zero_ary = np.zeros((1,))
lr_shared.set_value(to_fX(zero_ary + scale*learn_rate))
mom_1_shared.set_value(to_fX(zero_ary + scale*momentum))
mom_2_shared.set_value(to_fX(zero_ary + 0.99))
# perform a minibatch update and record the cost for this batch
Xb = to_fX( Xtr.take(batch_idx, axis=0) )
result = train_joint(Xb, Xb)
# aggregate costs over multiple minibatches
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 200) == 0):
# occasionally dump information about the costs
costs = [(v / 200.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " total_cost: {0:.4f}".format(costs[0])
str3 = " nll_bound : {0:.4f}".format(costs[1])
str4 = " reg_term : {0:.4f}".format(costs[2])
joint_str = "\n".join([str1, str2, str3, str4])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if ((i % 1000) == 0):
# compute a small-sample estimate of NLL bound on validation set
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:5000])
va_costs = compute_nll_bound(Xb, Xb)
str1 = " va_nll_bound : {}".format(va_costs[1])
joint_str = "\n".join([str1])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# draw some independent samples from the model
samples = do_sample(16*16)
n_iter, N, D = samples.shape
samples = samples.reshape( (n_iter, N, 28, 28) )
for j in xrange(n_iter):
img = img_grid(samples[j,:,:,:])
img.save("TBM-ES-samples-b%06d-%03d.png" % (i, j))