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 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 pretrain_gip(extra_lam_kld=0.0, kld2_scale=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
tr_file = 'data/svhn_train_gray.pkl'
te_file = 'data/svhn_test_gray.pkl'
ex_file = 'data/svhn_extra_gray.pkl'
data = load_svhn_gray(tr_file, te_file, ex_file=ex_file, ex_count=200000)
#all_file = 'data/svhn_all_gray_zca.pkl'
#data = load_svhn_all_gray_zca(all_file)
Xtr = np.vstack([data['Xtr'], data['Xex']])
Xtr = Xtr - np.mean(Xtr, axis=1, keepdims=True)
Xtr = Xtr / np.std(Xtr, axis=1, keepdims=True)
Xtr = shift_and_scale_into_01(Xtr)
Xtr, Xva = train_valid_split(Xtr, valid_count=5000)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 100
batch_reps = 5
# setup some symbolic variables and stuff
Xp = T.matrix('Xp_base')
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 = {}
gn_config = [PRIOR_DIM, 2400, 2400, data_dim]
gn_params['mlp_config'] = gn_config
gn_params['activation'] = relu_actfun
gn_params['out_type'] = 'gaussian'
gn_params['mean_transform'] = 'sigmoid'
gn_params['logvar_type'] = 'single_shared'
gn_params['init_scale'] = 1.2
gn_params['lam_l2a'] = 1e-2
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.1
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 2400, 2400]
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'] = 1.2
in_params['lam_l2a'] = 1e-2
in_params['vis_drop'] = 0.2
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.1
in_params['input_noise'] = 0.0
in_params['kld2_scale'] = kld2_scale
# Initialize the base networks for this GIPair
IN = InfNet(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, prior_sigma=prior_sigma, \
params=in_params, shared_param_dicts=None)
GN = GenNet(rng=rng, Xp=Xp, prior_sigma=prior_sigma, \
params=gn_params, shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.1)
GN.init_biases(0.1)
######################################
# LOAD AND RESTART FROM SAVED PARAMS #
######################################
# new_in_params = {'kld2_scale': kld2_scale, 'bias_noise': 0.2}
# new_gn_params = {'bias_noise': 0.2}
# # Load inferencer and generator from saved parameters
# gn_fname = "TMS_RESULTS_DROPLESS/pt_params_b50000_GN.pkl"
# in_fname = "TMS_RESULTS_DROPLESS/pt_params_b50000_IN.pkl"
# IN = INet.load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd, \
# Xc=Xc, Xm=Xm, new_params=new_in_params)
# GN = GNet.load_gennet_from_file(f_name=gn_fname, rng=rng, Xp=Xp, \
# new_params=new_gn_params)
# in_params = IN.params
# gn_params = GN.params
#########################
# INITIALIZE THE GIPAIR #
#########################
GIP = GIPair(rng=rng, Xd=Xd, Xc=Xc, Xm=Xm, g_net=GN, i_net=IN, \
data_dim=data_dim, prior_dim=PRIOR_DIM, params=None)
GIP.set_lam_l2w(1e-4)
####################
# RICA PRETRAINING #
####################
IN.W_rica.set_value(0.05 * IN.W_rica.get_value(borrow=False))
GN.W_rica.set_value(0.05 * GN.W_rica.get_value(borrow=False))
for i in range(6000):
scale = min(1.0, (float(i+1) / 6000.0))
l_rate = 0.0001 * scale
lam_l1 = 0.025
tr_idx = npr.randint(low=0,high=tr_samples,size=(1000,))
Xd_batch = Xtr.take(tr_idx, axis=0)
inr_out = IN.train_rica(Xd_batch, l_rate, lam_l1)
gnr_out = GN.train_rica(Xd_batch, l_rate, lam_l1)
inr_out = [v for v in gnr_out]
if ((i % 1000) == 0):
print("rica batch {0:d}: in_recon={1:.4f}, in_spars={2:.4f}, gn_recon={3:.4f}, gn_spars={4:.4f}".format( \
i, 1.*inr_out[1], 1.*inr_out[2], 1.*gnr_out[1], 1.*gnr_out[2]))
# draw inference net first layer weights
file_name = RESULT_PATH+"pt_rica_inf_weights.png".format(i)
utils.visualize_samples(IN.W_rica.get_value(borrow=False).T, file_name, num_rows=20)
# draw generator net final layer weights
file_name = RESULT_PATH+"pt_rica_gen_weights.png".format(i)
if ('gaussian' in gn_params['out_type']):
lay_num = -2
else:
lay_num = -1
utils.visualize_samples(GN.W_rica.get_value(borrow=False), file_name, num_rows=20)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH+"pt_gip_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
cost_1 = [0. for i in range(10)]
learn_rate = 0.0002
for i in range(300000):
scale = min(1.0, float(i) / 40000.0)
if ((i + 1) % 100000 == 0):
learn_rate = learn_rate * 0.8
# 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)
Xd_batch = np.repeat(Xd_batch, batch_reps, 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
GIP.set_all_sgd_params(lr_gn=(scale*learn_rate), \
lr_in=(scale*learn_rate), mom_1=0.9, mom_2=0.999)
GIP.set_lam_nll(1.0)
GIP.set_lam_kld(1.0 + extra_lam_kld*scale)
outputs = GIP.train_joint(Xd_batch, Xc_batch, Xm_batch)
cost_1 = [(cost_1[k] + 1.*outputs[k]) for k in range(len(outputs))]
if ((i % 1000) == 0):
cost_1 = [(v / 1000.) for v in cost_1]
o_str = "batch: {0:d}, joint_cost: {1:.4f}, data_nll_cost: {2:.4f}, post_kld_cost: {3:.4f}, other_reg_cost: {4:.4f}".format( \
i, cost_1[0], cost_1[1], cost_1[2], cost_1[3])
print(o_str)
out_file.write(o_str+"\n")
out_file.flush()
cost_1 = [0. for v in cost_1]
if ((i % 5000) == 0):
cost_2 = GIP.compute_costs(Xva, 0.*Xva, 0.*Xva)
o_str = "--val: {0:d}, joint_cost: {1:.4f}, data_nll_cost: {2:.4f}, post_kld_cost: {3:.4f}, other_reg_cost: {4:.4f}".format( \
i, 1.*cost_2[0], 1.*cost_2[1], 1.*cost_2[2], 1.*cost_2[3])
print(o_str)
out_file.write(o_str+"\n")
out_file.flush()
if ((i % 5000) == 0):
tr_idx = npr.randint(low=0,high=va_samples,size=(100,))
Xd_batch = Xva.take(tr_idx, axis=0)
file_name = RESULT_PATH+"pt_gip_chain_samples_b{0:d}.png".format(i)
Xd_samps = np.repeat(Xd_batch[0:10,:], 3, axis=0)
sample_lists = GIP.sample_from_chain(Xd_samps, loop_iters=20)
Xs = np.vstack(sample_lists["data samples"])
utils.visualize_samples(Xs, file_name, num_rows=20)
# draw samples freely from the generative model's prior
file_name = RESULT_PATH+"pt_gip_prior_samples_b{0:d}.png".format(i)
Xs = GIP.sample_from_prior(20*20)
utils.visualize_samples(Xs, file_name, num_rows=20)
# draw inference net first layer weights
file_name = RESULT_PATH+"pt_gip_inf_weights_b{0:d}.png".format(i)
utils.visualize_net_layer(GIP.IN.shared_layers[0], file_name)
# draw generator net final layer weights
file_name = RESULT_PATH+"pt_gip_gen_weights_b{0:d}.png".format(i)
if (gn_params['out_type'] == 'gaussian'):
lay_num = -2
else:
lay_num = -1
utils.visualize_net_layer(GIP.GN.mlp_layers[lay_num], file_name, \
colorImg=False, use_transpose=True)
#########################
# Check posterior KLds. #
#########################
post_klds = posterior_klds(IN, Xtr, 5000, 5)
file_name = RESULT_PATH+"pt_gip_post_klds_b{0:d}.png".format(i)
utils.plot_kde_histogram2( \
np.asarray(post_klds), np.asarray(post_klds), file_name, bins=30)
if ((i % 10000) == 0):
IN.save_to_file(f_name=RESULT_PATH+"pt_gip_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH+"pt_gip_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH+"pt_gip_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH+"pt_gip_params_GN.pkl")
return
