def test_tfd_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_TFD_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
out_file.close()
return
def test_tfd_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_TFD_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
def test_mnist_img(occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1],)))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[:500], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
img_match_on_known, img_match_on_unknown = TM.best_match_img(xo, xm)
display_count = 100
# visualize matches on known elements
Xs = np.zeros((2*display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2*idx] = xi[idx]
Xs[(2*idx)+1] = img_match_on_known[idx]
file_name = "{0:s}_SAMPLES_MOK.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
# visualize matches on unknown elements
Xs = np.zeros((2*display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2*idx] = xi[idx]
Xs[(2*idx)+1] = img_match_on_unknown[idx]
file_name = "{0:s}_SAMPLES_MOU.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
return
def test_mnist_img(occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[:500], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
img_match_on_known, img_match_on_unknown = TM.best_match_img(xo, xm)
display_count = 100
# visualize matches on known elements
Xs = np.zeros((2 * display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2 * idx] = xi[idx]
Xs[(2 * idx) + 1] = img_match_on_known[idx]
file_name = "{0:s}_SAMPLES_MOK.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
# visualize matches on unknown elements
Xs = np.zeros((2 * display_count, Xva.shape[1]))
for idx in range(display_count):
Xs[2 * idx] = xi[idx]
Xs[(2 * idx) + 1] = img_match_on_unknown[idx]
file_name = "{0:s}_SAMPLES_MOU.png".format(result_tag)
utils.visualize_samples(Xs, file_name, num_rows=20)
return
def test_svhn_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_SVHN_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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)
Xtr = to_fX( shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])) )
Xva = to_fX( shift_and_scale_into_01(data['Xte']) )
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
def test_svhn_nll(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_SVHN_TM/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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)
Xtr = to_fX(shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])))
Xva = to_fX(shift_and_scale_into_01(data['Xte']))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
out_file.close()
return
def test_mnist_nll(occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = RESULT_PATH + "TM_OD{}_DP{}".format(occ_dim, dp_int)
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1],)))
TM = TemplateMatchImputer(x_train=Xtr, x_type='bernoulli')
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = TM.best_match_nll(xo, xm)
match_on_known = np.mean(result[0])
match_on_unknown = np.mean(result[1])
str0 = "Test 1:"
str1 = " match on known : {}".format(match_on_known)
str2 = " match on unknown : {}".format(match_on_unknown)
joint_str = "\n".join([str0, str1, str2])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
out_file.close()
return
def test_svhn(occ_dim=15, drop_prob=0.0):
RESULT_PATH = "IMP_SVHN_VAE/"
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}VAE_OD{}_DP{}".format(RESULT_PATH, occ_dim, dp_int)
##########################
# Get some training data #
##########################
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)
Xtr = to_fX( shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])) )
Xva = to_fX( shift_and_scale_into_01(data['Xte']) )
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 100
imp_steps = 15 # we'll check for the best step count (found oracularly)
init_scale = 1.0
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
#################
# p_zi_given_xi #
#################
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'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.2)
###################
# p_xip1_given_zi #
###################
params = {}
shared_config = [z_dim, 1000, 1000]
output_config = [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_xip1_given_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_xip1_given_zi.init_biases(0.2)
###################
# q_zi_given_x_xi #
###################
params = {}
shared_config = [(obs_dim + 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'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_zi_given_x_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_x_xi.init_biases(0.2)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['obs_dim'] = obs_dim
gpsi_params['z_dim'] = z_dim
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = 'jump'
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
gpsi_params['use_osm_mode'] = True
GPSI = GPSImputer(rng=rng,
x_in=x_in_sym, x_out=x_out_sym, x_mask=x_mask_sym, \
p_zi_given_xi=p_zi_given_xi, \
p_xip1_given_zi=p_xip1_given_zi, \
q_zi_given_x_xi=q_zi_given_x_xi, \
params=gpsi_params, \
shared_param_dicts=None)
#########################################################################
# Define parameters for the underlying OneStageModel, and initialize it #
#########################################################################
print("Building the OneStageModel...")
osm_params = {}
osm_params['x_type'] = 'bernoulli'
osm_params['xt_transform'] = 'sigmoid'
OSM = OneStageModel(rng=rng, \
x_in=x_in_sym, \
p_x_given_z=p_xip1_given_zi, \
q_z_given_x=p_zi_given_xi, \
x_dim=obs_dim, z_dim=z_dim, \
params=osm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, '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(200005):
scale = min(1.0, ((i+1) / 5000.0))
if (((i + 1) % 15000) == 0):
learn_rate = learn_rate * 0.92
if (i > 10000):
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)
# set sgd and objective function hyperparams for this update
OSM.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
OSM.set_lam_nll(lam_nll=1.0)
OSM.set_lam_kld(lam_kld_1=1.0, lam_kld_2=0.0)
OSM.set_lam_l2w(1e-4)
# perform a minibatch update and record the cost for this batch
xb = to_fX( Xtr.take(batch_idx, axis=0) )
result = OSM.train_joint(xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 250) == 0):
costs = [(v / 250.0) 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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
step_nll, step_kld = GPSI.compute_per_step_cost(xi, xo, xm, sample_count=10)
min_nll = np.min(step_nll)
str1 = " va_nll_bound : {}".format(min_nll)
str2 = " va_nll_min : {}".format(min_nll)
str3 = " va_nll_final : {}".format(step_nll[-1])
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 10000) == 0):
# Get some validation samples for evaluating model performance
xb = to_fX( Xva[0:100] )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi, xo, xm, use_guide_policy=False)
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 = "{}_samples_ng_b{}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# get visualizations of policy parameters
file_name = "{}_gen_gen_weights_b{}.png".format(result_tag, i)
W = GPSI.gen_gen_weights.get_value(borrow=False)
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "{}_gen_inf_weights_b{}.png".format(result_tag, i)
W = GPSI.gen_inf_weights.get_value(borrow=False).T
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
def test_svhn(step_type='add', occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int,
step_type)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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)
Xtr = to_fX(shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])))
Xva = to_fX(shift_and_scale_into_01(data['Xte']))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 200
imp_steps = 6
init_scale = 1.0
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
#################
# p_zi_given_xi #
#################
params = {}
shared_config = [x_dim, 1500, 1500]
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['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_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.2)
###################
# p_xip1_given_zi #
###################
params = {}
shared_config = [z_dim, 1500, 1500]
output_config = [x_dim, x_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
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_xip1_given_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_xip1_given_zi.init_biases(0.2)
###################
# q_zi_given_xi #
###################
params = {}
shared_config = [(x_dim + x_dim), 1500, 1500]
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['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_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_xi.init_biases(0.2)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['x_dim'] = x_dim
gpsi_params['z_dim'] = z_dim
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = step_type
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
GPSI = GPSImputer(rng=rng,
x_in=x_in_sym, x_out=x_out_sym, x_mask=x_mask_sym, \
p_zi_given_xi=p_zi_given_xi, \
p_xip1_given_zi=p_xip1_given_zi, \
q_zi_given_xi=q_zi_given_xi, \
params=gpsi_params, \
shared_param_dicts=None)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, '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(200005):
scale = min(1.0, ((i + 1) / 5000.0))
if (((i + 1) % 15000) == 0):
learn_rate = learn_rate * 0.92
if (i > 10000):
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)
# set sgd and objective function hyperparams for this update
GPSI.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
GPSI.set_train_switch(1.0)
GPSI.set_lam_nll(lam_nll=1.0)
GPSI.set_lam_kld(lam_kld_p=0.1, lam_kld_q=0.9)
GPSI.set_lam_l2w(1e-4)
# perform a minibatch update and record the cost for this batch
xb = to_fX(Xtr.take(batch_idx, axis=0))
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = GPSI.train_joint(xi, xo, xm, batch_reps)
# do diagnostics and general training tracking
costs = [(costs[j] + result[j]) for j in range(len(result) - 1)]
if ((i % 250) == 0):
costs = [(v / 250.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_bound : {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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll, kld = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10)
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
GPSI.save_to_file("{}_PARAMS.pkl".format(result_tag))
if ((i % 20000) == 0):
# Get some validation samples for evaluating model performance
xb = to_fX(Xva[0:100])
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi,
xo,
xm,
use_guide_policy=False)
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 = "{0:s}_samples_ng_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
def test_mnist(step_type='add',
imp_steps=6,
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_conv_bn_OD{}_DP{}_IS{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, imp_steps, step_type)
##########################
# 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]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 100
init_scale = 1.0
use_bn = True
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
#################
# p_zi_given_xi #
#################
params = {}
shared_config = \
[ {'layer_type': 'conv',
'in_chans': 1, # in shape: (batch, 784)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_in': lambda x: T.reshape(x, (-1, 1, 28, 28))}, \
{'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 128, # out shape: (batch, 128, 7, 7)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'fc',
'in_chans': 128*7*7,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
p_zi_given_xi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.0)
###################
# p_sip1_given_zi #
###################
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': 7*7*128,
'activation': relu_actfun,
'apply_bn': use_bn,
'shape_func_out': lambda x: T.reshape(x, (-1, 128, 7, 7))}, \
{'layer_type': 'conv',
'in_chans': 128, # in shape: (batch, 128, 7, 7)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 1, # out shape: (batch, 1, 28, 28)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'conv',
'in_chans': 64,
'out_chans': 1,
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'conv',
'in_chans': 64,
'out_chans': 1,
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
p_sip1_given_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_zi.init_biases(0.0)
#################
# q_zi_given_xi #
#################
params = {}
shared_config = \
[ {'layer_type': 'conv',
'in_chans': 2, # in shape: (batch, 784+784)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_in': lambda x: T.reshape(x, (-1, 2, 28, 28))}, \
{'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 128, # out shape: (batch, 128, 7, 7)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'fc',
'in_chans': 128*7*7,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
q_zi_given_xi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_xi.init_biases(0.0)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['x_dim'] = x_dim
gpsi_params['z_dim'] = z_dim
# switch between direct construction and construction via p_x_given_si
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = step_type
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
GPSI = GPSImputer(rng=rng,
x_in=x_in_sym, x_out=x_out_sym, x_mask=x_mask_sym,
p_zi_given_xi=p_zi_given_xi,
p_sip1_given_zi=p_sip1_given_zi,
q_zi_given_xi=q_zi_given_xi,
params=gpsi_params,
shared_param_dicts=None)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0001
momentum = 0.90
batch_idx = np.arange(batch_size) + tr_samples
for i in range(200000):
scale = min(1.0, ((i+1) / 5000.0))
if (((i + 1) % 15000) == 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
GPSI.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
GPSI.set_train_switch(1.0)
GPSI.set_lam_nll(lam_nll=1.0)
GPSI.set_lam_kld(lam_kld_q=1.0, lam_kld_p=0.1, lam_kld_g=0.0)
GPSI.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
xb = to_fX( Xtr.take(batch_idx, axis=0) )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = GPSI.train_joint(xi, xo, xm, batch_reps)
# do diagnostics and general training tracking
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_bound : {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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll, kld = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10)
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 2000) == 0):
#GPSI.save_to_file("{}_PARAMS.pkl".format(result_tag))
# Get some validation samples for evaluating model performance
xb = to_fX( Xva[0:100] )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi, xo, xm, use_guide_policy=False)
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 = "{0:s}_samples_ng_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
def test_mnist_results(step_type='add',
imp_steps=6,
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_IS{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, imp_steps, step_type)
##########################
# 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]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
# Load parameters from a previously trained model
print("Testing model load from file...")
GPSI = load_gpsimputer_from_file(f_name="{}_PARAMS.pkl".format(result_tag), \
rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_FINAL_RESULTS_NEW.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
str0 = "GUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# record an estimate of performance on the test set
str0 = "UNGUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
str1 = " va_nll_bound : {}".format(np.mean(nll))
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def test_sgm_mnist(step_type='add', occ_dim=14, drop_prob=0.0, attention=False):
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
Xtr = np.vstack((Xtr, Xva))
Xva = Xte
#del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
writer_dim = 250
reader_dim = 250
dyn_dim = 250
primary_dim = 500
guide_dim = 500
z_dim = 100
n_iter = 20
dp_int = int(100.0 * drop_prob)
rnninits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
att_tag = "NA" # attention not implemented yet
# reader MLP provides input to the dynamics LSTM update
reader_mlp = MLP([Rectifier(), Rectifier(), None], \
[(x_dim + z_dim), reader_dim, reader_dim, 4*dyn_dim], \
name="reader_mlp", **inits)
# writer MLP applies changes to the generation workspace
writer_mlp = MLP([Rectifier(), Rectifier(), None], \
[(dyn_dim + z_dim), writer_dim, writer_dim, x_dim], \
name="writer_mlp", **inits)
# MLPs for computing conditionals over z
primary_policy = CondNet([Rectifier(), Rectifier()], \
[(dyn_dim + x_dim), primary_dim, primary_dim, z_dim], \
name="primary_policy", **inits)
guide_policy = CondNet([Rectifier(), Rectifier()], \
[(dyn_dim + 2*x_dim), guide_dim, guide_dim, z_dim], \
name="guide_policy", **inits)
# LSTMs for the actual LSTMs (obviously, perhaps)
shared_dynamics = BiasedLSTM(dim=dyn_dim, ig_bias=2.0, fg_bias=2.0, \
name="shared_dynamics", **rnninits)
model = SeqGenModel(
n_iter,
step_type=step_type, # step_type can be 'add' or 'jump'
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
primary_policy=primary_policy,
guide_policy=guide_policy,
shared_dynamics=shared_dynamics)
model.initialize()
# build the cost gradients, training function, samplers, etc.
model.build_model_funcs()
#model.load_model_params(f_name="TBSGM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
print("Beginning to train the model...")
out_file = open("TBSGM_IMP_MNIST_RESULTS_OD{}_DP{}_{}_{}.txt".format(occ_dim, dp_int, step_type, att_tag), 'wb')
out_file.flush()
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) / 1000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
if (i > 10000):
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)
# set sgd and objective function hyperparams for this update
zero_ary = np.zeros((1,))
model.lr.set_value(to_fX(zero_ary + learn_rate))
model.mom_1.set_value(to_fX(zero_ary + momentum))
model.mom_2.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))
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
result = model.train_joint(Xb, Mb)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 200) == 0):
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 = " nll_term : {0:.4f}".format(costs[2])
str5 = " kld_q2p : {0:.4f}".format(costs[3])
str6 = " kld_p2q : {0:.4f}".format(costs[4])
str7 = " reg_term : {0:.4f}".format(costs[5])
joint_str = "\n".join([str1, str2, str3, str4, str5, str6, str7])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if ((i % 1000) == 0):
model.save_model_params("TBSGM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
# compute a small-sample estimate of NLL bound on validation set
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:5000])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
va_costs = model.compute_nll_bound(Xb, Mb)
str1 = " va_nll_bound : {}".format(va_costs[1])
str2 = " va_nll_term : {}".format(va_costs[2])
str3 = " va_kld_q2p : {}".format(va_costs[3])
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# draw some independent samples from the model
Xb = to_fX(Xva[:100])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
samples, _ = model.do_sample(Xb, Mb)
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("TBSGM-IMP-MNIST-OD{0:d}-DP{1:d}-{2:s}-samples-{3:03d}.png".format(occ_dim, dp_int, step_type, j))
def test_imocld_mnist(step_type="add", attention=False):
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
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 = 300
enc_dim = 300
dec_dim = 300
mix_dim = 20
z_dim = 100
n_iter = 16
rnninits = {"weights_init": IsotropicGaussian(0.01), "biases_init": Constant(0.0)}
inits = {"weights_init": IsotropicGaussian(0.01), "biases_init": Constant(0.0)}
att_tag = "NA" # attention not implemented yet
# setup the reader and writer (shared by primary and guide policies)
read_dim = 2 * x_dim # dimension of output from reader_mlp
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)
# mlps for setting conditionals over z_mix
mix_var_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], name="mix_var_mlp", **inits)
mix_enc_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], name="mix_enc_mlp", **inits)
# mlp for decoding z_mix into a distribution over initial LSTM states
mix_dec_mlp = MLP(
[Tanh(), Tanh()],
[mix_dim, 250, (2 * enc_dim + 2 * dec_dim + 2 * enc_dim + mix_dim)],
name="mix_dec_mlp",
**inits
)
# mlps for processing inputs to LSTMs
var_mlp_in = MLP([Identity()], [(read_dim + dec_dim + mix_dim), 4 * enc_dim], name="var_mlp_in", **inits)
enc_mlp_in = MLP([Identity()], [(read_dim + dec_dim + mix_dim), 4 * enc_dim], name="enc_mlp_in", **inits)
dec_mlp_in = MLP([Identity()], [z_dim, 4 * dec_dim], name="dec_mlp_in", **inits)
# mlps for turning LSTM outputs into conditionals over z_gen
var_mlp_out = CondNet([], [enc_dim, z_dim], name="var_mlp_out", **inits)
enc_mlp_out = CondNet([], [enc_dim, z_dim], name="enc_mlp_out", **inits)
# LSTMs for the actual LSTMs (obviously, perhaps)
var_rnn = BiasedLSTM(dim=enc_dim, ig_bias=2.0, fg_bias=2.0, name="var_rnn", **rnninits)
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)
draw = IMoCLDrawModels(
n_iter,
step_type=step_type, # step_type can be 'add' or 'jump'
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
mix_enc_mlp=mix_enc_mlp,
mix_dec_mlp=mix_dec_mlp,
mix_var_mlp=mix_var_mlp,
enc_mlp_in=enc_mlp_in,
enc_mlp_out=enc_mlp_out,
enc_rnn=enc_rnn,
dec_mlp_in=dec_mlp_in,
dec_rnn=dec_rnn,
var_mlp_in=var_mlp_in,
var_mlp_out=var_mlp_out,
var_rnn=var_rnn,
)
draw.initialize()
# build the cost gradients, training function, samplers, etc.
draw.build_model_funcs()
# sample several interchangeable versions of the model
conditions = [{"occ_dim": 0, "drop_prob": 0.8}, {"occ_dim": 16, "drop_prob": 0.0}]
for cond_dict in conditions:
occ_dim = cond_dict["occ_dim"]
drop_prob = cond_dict["drop_prob"]
dp_int = int(100.0 * drop_prob)
draw.load_model_params(
f_name="TBCLM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag)
)
# draw some independent samples from the model
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:128])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, occ_dim=occ_dim, data_mean=None)
Xb = np.repeat(Xb, 2, axis=0)
Mb = np.repeat(Mb, 2, axis=0)
samples = draw.do_sample(Xb, Mb)
# save the samples to a pkl file, in their numpy array form
sample_pkl_name = "IMP-MNIST-OD{0:d}-DP{1:d}-{2:s}.pkl".format(occ_dim, dp_int, step_type)
f_handle = file(sample_pkl_name, "wb")
cPickle.dump(samples, f_handle, protocol=-1)
f_handle.close()
print("Saved some samples in: {}".format(sample_pkl_name))
return
def test_tfd(step_type='add',
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, step_type)
##########################
# Get some training data #
##########################
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]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 200
imp_steps = 6
init_scale = 1.0
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
#################
# p_zi_given_xi #
#################
params = {}
shared_config = [obs_dim, 1500, 1500]
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['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_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.2)
###################
# p_xip1_given_zi #
###################
params = {}
shared_config = [z_dim, 1500, 1500]
output_config = [obs_dim, obs_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
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_xip1_given_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_xip1_given_zi.init_biases(0.2)
###################
# q_zi_given_x_xi #
###################
params = {}
shared_config = [(obs_dim + obs_dim), 1500, 1500]
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['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_zi_given_x_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_x_xi.init_biases(0.2)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['obs_dim'] = obs_dim
gpsi_params['z_dim'] = z_dim
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = step_type
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
GPSI = GPSImputer(rng=rng,
x_in=x_in_sym, x_out=x_out_sym, x_mask=x_mask_sym, \
p_zi_given_xi=p_zi_given_xi, \
p_xip1_given_zi=p_xip1_given_zi, \
q_zi_given_x_xi=q_zi_given_x_xi, \
params=gpsi_params, \
shared_param_dicts=None)
# # test model saving
# print("Testing model save to file...")
# GPSI.save_to_file("AAA_GPSI_SAVE_TEST.pkl")
# # test model loading
# print("Testing model load from file...")
# GPSI = load_gpsimputer_from_file(f_name="AAA_GPSI_SAVE_TEST.pkl", rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, '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(200005):
scale = min(1.0, ((i+1) / 5000.0))
if (((i + 1) % 15000) == 0):
learn_rate = learn_rate * 0.92
if (i > 10000):
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)
# set sgd and objective function hyperparams for this update
GPSI.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
GPSI.set_train_switch(1.0)
GPSI.set_lam_nll(lam_nll=1.0)
GPSI.set_lam_kld(lam_kld_p=0.1, lam_kld_q=0.9)
GPSI.set_lam_l2w(1e-4)
# perform a minibatch update and record the cost for this batch
xb = to_fX( Xtr.take(batch_idx, axis=0) )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = GPSI.train_joint(xi, xo, xm, batch_reps)
# do diagnostics and general training tracking
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 250) == 0):
costs = [(v / 250.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_bound : {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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll, kld = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10)
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
GPSI.save_to_file("{}_PARAMS.pkl".format(result_tag))
if ((i % 20000) == 0):
# Get some validation samples for evaluating model performance
xb = to_fX( Xva[0:100] )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi, xo, xm, use_guide_policy=False)
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 = "{0:s}_samples_ng_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# get visualizations of policy parameters
file_name = "{0:s}_gen_gen_weights_b{1:d}.png".format(result_tag, i)
W = GPSI.gen_gen_weights.get_value(borrow=False)
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
file_name = "{0:s}_gen_inf_weights_b{1:d}.png".format(result_tag, i)
W = GPSI.gen_inf_weights.get_value(borrow=False).T
utils.visualize_samples(W[:,:obs_dim], file_name, num_rows=20)
def test_imocld_mnist(step_type='add', attention=False):
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
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 = 300
enc_dim = 300
dec_dim = 300
mix_dim = 20
z_dim = 100
n_iter = 16
rnninits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
att_tag = "NA" # attention not implemented yet
# setup the reader and writer (shared by primary and guide policies)
read_dim = 2*x_dim # dimension of output from reader_mlp
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)
# mlps for setting conditionals over z_mix
mix_var_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_var_mlp", **inits)
mix_enc_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_enc_mlp", **inits)
# mlp for decoding z_mix into a distribution over initial LSTM states
mix_dec_mlp = MLP([Tanh(), Tanh()], \
[mix_dim, 250, (2*enc_dim + 2*dec_dim + 2*enc_dim + mix_dim)], \
name="mix_dec_mlp", **inits)
# mlps for processing inputs to LSTMs
var_mlp_in = MLP([Identity()], [(read_dim + dec_dim + mix_dim), 4*enc_dim], \
name="var_mlp_in", **inits)
enc_mlp_in = MLP([Identity()], [(read_dim + dec_dim + mix_dim), 4*enc_dim], \
name="enc_mlp_in", **inits)
dec_mlp_in = MLP([Identity()], [ z_dim, 4*dec_dim], \
name="dec_mlp_in", **inits)
# mlps for turning LSTM outputs into conditionals over z_gen
var_mlp_out = CondNet([], [enc_dim, z_dim], name="var_mlp_out", **inits)
enc_mlp_out = CondNet([], [enc_dim, z_dim], name="enc_mlp_out", **inits)
# LSTMs for the actual LSTMs (obviously, perhaps)
var_rnn = BiasedLSTM(dim=enc_dim, ig_bias=2.0, fg_bias=2.0, \
name="var_rnn", **rnninits)
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)
draw = IMoCLDrawModels(
n_iter,
step_type=step_type, # step_type can be 'add' or 'jump'
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
mix_enc_mlp=mix_enc_mlp,
mix_dec_mlp=mix_dec_mlp,
mix_var_mlp=mix_var_mlp,
enc_mlp_in=enc_mlp_in,
enc_mlp_out=enc_mlp_out,
enc_rnn=enc_rnn,
dec_mlp_in=dec_mlp_in,
dec_rnn=dec_rnn,
var_mlp_in=var_mlp_in,
var_mlp_out=var_mlp_out,
var_rnn=var_rnn)
draw.initialize()
# build the cost gradients, training function, samplers, etc.
draw.build_model_funcs()
# sample several interchangeable versions of the model
conditions = [{'occ_dim': 0, 'drop_prob': 0.8}, \
{'occ_dim': 16, 'drop_prob': 0.0}]
for cond_dict in conditions:
occ_dim = cond_dict['occ_dim']
drop_prob = cond_dict['drop_prob']
dp_int = int(100.0 * drop_prob)
draw.load_model_params(f_name="TBCLM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
# draw some independent samples from the model
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:128])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
Xb = np.repeat(Xb, 2, axis=0)
Mb = np.repeat(Mb, 2, axis=0)
samples, _ = draw.do_sample(Xb, Mb)
# save the samples to a pkl file, in their numpy array form
sample_pkl_name = "IMP-MNIST-OD{0:d}-DP{1:d}-{2:s}.pkl".format(occ_dim, dp_int, step_type)
f_handle = file(sample_pkl_name, 'wb')
cPickle.dump(samples, f_handle, protocol=-1)
f_handle.close()
print("Saved some samples in: {}".format(sample_pkl_name))
return
def test_imocld_imp_mnist(step_type='add',
occ_dim=14,
drop_prob=0.0,
attention=False):
##########################
# 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]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
write_dim = 300
enc_dim = 300
dec_dim = 300
mix_dim = 20
z_dim = 100
n_iter = 25
dp_int = int(100.0 * drop_prob)
rnninits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
att_tag = "NA" # attention not implemented yet
# setup the reader and writer (shared by primary and guide policies)
read_dim = 2 * x_dim # dimension of output from reader_mlp
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)
# mlps for setting conditionals over z_mix
mix_var_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_var_mlp", **inits)
mix_enc_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_enc_mlp", **inits)
# mlp for decoding z_mix into a distribution over initial LSTM states
mix_dec_mlp = MLP([Tanh(), Tanh()], \
[mix_dim, 250, (2*enc_dim + 2*dec_dim + 2*enc_dim)], \
name="mix_dec_mlp", **inits)
# mlps for processing inputs to LSTMs
var_mlp_in = MLP([Identity()], [(read_dim + dec_dim), 4*enc_dim], \
name="var_mlp_in", **inits)
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)
# mlps for turning LSTM outputs into conditionals over z_gen
var_mlp_out = CondNet([], [enc_dim, z_dim], name="var_mlp_out", **inits)
enc_mlp_out = CondNet([], [enc_dim, z_dim], name="enc_mlp_out", **inits)
# LSTMs for the actual LSTMs (obviously, perhaps)
var_rnn = BiasedLSTM(dim=enc_dim, ig_bias=2.0, fg_bias=2.0, \
name="var_rnn", **rnninits)
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)
draw = IMoCLDrawModels(
n_iter,
step_type=step_type, # step_type can be 'add' or 'jump'
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
mix_enc_mlp=mix_enc_mlp,
mix_dec_mlp=mix_dec_mlp,
mix_var_mlp=mix_var_mlp,
enc_mlp_in=enc_mlp_in,
enc_mlp_out=enc_mlp_out,
enc_rnn=enc_rnn,
dec_mlp_in=dec_mlp_in,
dec_rnn=dec_rnn,
var_mlp_in=var_mlp_in,
var_mlp_out=var_mlp_out,
var_rnn=var_rnn)
draw.initialize()
# build the cost gradients, training function, samplers, etc.
draw.build_model_funcs()
#draw.load_model_params(f_name="TBCLM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
print("Beginning to train the model...")
out_file = open(
"TBCLM_IMP_MNIST_RESULTS_OD{}_DP{}_{}_{}.txt".format(
occ_dim, dp_int, step_type, att_tag), 'wb')
out_file.flush()
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 = row_shuffle(Xtr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
zero_ary = np.zeros((1, ))
draw.lr.set_value(to_fX(zero_ary + learn_rate))
draw.mom_1.set_value(to_fX(zero_ary + momentum))
draw.mom_2.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))
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
result = draw.train_joint(Xb, Mb)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 200) == 0):
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 = " nll_term : {0:.4f}".format(costs[2])
str5 = " kld_q2p : {0:.4f}".format(costs[3])
str6 = " kld_p2q : {0:.4f}".format(costs[4])
str7 = " reg_term : {0:.4f}".format(costs[5])
joint_str = "\n".join([str1, str2, str3, str4, str5, str6, str7])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
costs = [0.0 for v in costs]
if ((i % 1000) == 0):
draw.save_model_params(
"TBCLM_IMP_MNIST_PARAMS_OD{}_DP{}_{}_{}.pkl".format(
occ_dim, dp_int, step_type, att_tag))
# compute a small-sample estimate of NLL bound on validation set
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:5000])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
va_costs = draw.compute_nll_bound(Xb, Mb)
str1 = " va_nll_bound : {}".format(va_costs[1])
str2 = " va_nll_term : {}".format(va_costs[2])
str3 = " va_kld_q2p : {}".format(va_costs[3])
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
# draw some independent samples from the model
Xb = to_fX(Xva[:100])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
samples, _ = draw.do_sample(Xb, Mb)
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(
"TBCLM-IMP-MNIST-OD{0:d}-DP{1:d}-{2:s}-samples-{3:03d}.png"
.format(occ_dim, dp_int, step_type, j))
def test_mnist(step_type='add', imp_steps=6, occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_conv_bn_OD{}_DP{}_IS{}_{}_NA".format(
RESULT_PATH, occ_dim, dp_int, imp_steps, step_type)
##########################
# 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]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 100
init_scale = 1.0
use_bn = True
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
#################
# p_zi_given_xi #
#################
params = {}
shared_config = \
[ {'layer_type': 'conv',
'in_chans': 1, # in shape: (batch, 784)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_in': lambda x: T.reshape(x, (-1, 1, 28, 28))}, \
{'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 128, # out shape: (batch, 128, 7, 7)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'fc',
'in_chans': 128*7*7,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
p_zi_given_xi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.0)
###################
# p_sip1_given_zi #
###################
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': 7*7*128,
'activation': relu_actfun,
'apply_bn': use_bn,
'shape_func_out': lambda x: T.reshape(x, (-1, 128, 7, 7))}, \
{'layer_type': 'conv',
'in_chans': 128, # in shape: (batch, 128, 7, 7)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 1, # out shape: (batch, 1, 28, 28)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'conv',
'in_chans': 64,
'out_chans': 1,
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'conv',
'in_chans': 64,
'out_chans': 1,
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'half',
'apply_bn': False,
'shape_func_out': lambda x: T.flatten(x, 2)} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
p_sip1_given_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_zi.init_biases(0.0)
#################
# q_zi_given_xi #
#################
params = {}
shared_config = \
[ {'layer_type': 'conv',
'in_chans': 2, # in shape: (batch, 784+784)
'out_chans': 64, # out shape: (batch, 64, 14, 14)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_in': lambda x: T.reshape(x, (-1, 2, 28, 28))}, \
{'layer_type': 'conv',
'in_chans': 64, # in shape: (batch, 64, 14, 14)
'out_chans': 128, # out shape: (batch, 128, 7, 7)
'activation': relu_actfun,
'filt_dim': 5,
'conv_stride': 'double',
'apply_bn': use_bn,
'shape_func_out': lambda x: T.flatten(x, 2)}, \
{'layer_type': 'fc',
'in_chans': 128*7*7,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': use_bn} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = init_scale
params['build_theano_funcs'] = False
q_zi_given_xi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_xi.init_biases(0.0)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['x_dim'] = x_dim
gpsi_params['z_dim'] = z_dim
# switch between direct construction and construction via p_x_given_si
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = step_type
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
GPSI = GPSImputer(rng=rng,
x_in=x_in_sym,
x_out=x_out_sym,
x_mask=x_mask_sym,
p_zi_given_xi=p_zi_given_xi,
p_sip1_given_zi=p_sip1_given_zi,
q_zi_given_xi=q_zi_given_xi,
params=gpsi_params,
shared_param_dicts=None)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0001
momentum = 0.90
batch_idx = np.arange(batch_size) + tr_samples
for i in range(200000):
scale = min(1.0, ((i + 1) / 5000.0))
if (((i + 1) % 15000) == 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
GPSI.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
GPSI.set_train_switch(1.0)
GPSI.set_lam_nll(lam_nll=1.0)
GPSI.set_lam_kld(lam_kld_q=1.0, lam_kld_p=0.1, lam_kld_g=0.0)
GPSI.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
xb = to_fX(Xtr.take(batch_idx, axis=0))
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = GPSI.train_joint(xi, xo, xm, batch_reps)
# do diagnostics and general training tracking
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_bound : {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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll, kld = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10)
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
if ((i % 2000) == 0):
#GPSI.save_to_file("{}_PARAMS.pkl".format(result_tag))
# Get some validation samples for evaluating model performance
xb = to_fX(Xva[0:100])
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi,
xo,
xm,
use_guide_policy=False)
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 = "{0:s}_samples_ng_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
def test_svhn_results(step_type='add', occ_dim=15, drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int,
step_type)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
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)
Xtr = to_fX(shift_and_scale_into_01(np.vstack([data['Xtr'], data['Xex']])))
Xva = to_fX(shift_and_scale_into_01(data['Xte']))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 200
imp_steps = 6
init_scale = 1.0
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
# Load parameters from a previously trained model
print("Testing model load from file...")
GPSI = load_gpsimputer_from_file(f_name="{}_PARAMS.pkl".format(result_tag), \
rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_FINAL_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
str0 = "GUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
# record an estimate of performance on the test set
str0 = "UNGUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
str1 = " va_nll_bound : {}".format(np.mean(nll))
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
def test_mnist(step_type='add',
imp_steps=6,
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_IS{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, imp_steps, step_type)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
Xtr = np.vstack((Xtr, Xva))
Xva = Xte
#del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
##########################
# 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]
# Xte = datasets[2][0]
# # Merge validation set and training set, and test on test set.
# #Xtr = np.concatenate((Xtr, Xva), axis=0)
# #Xva = Xte
# Xtr = to_fX(shift_and_scale_into_01(Xtr))
# Xva = to_fX(shift_and_scale_into_01(Xva))
# tr_samples = Xtr.shape[0]
# va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
s_dim = x_dim
h_dim = 50
z_dim = 100
init_scale = 0.6
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
###############
# p_h_given_x #
###############
params = {}
shared_config = [x_dim, 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'] = tanh_actfun #relu_actfun
params['init_scale'] = 'xg' #init_scale
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_x = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_h_given_x.init_biases(0.0)
################
# p_s0_given_h #
################
params = {}
shared_config = [h_dim, 250]
output_config = [s_dim, s_dim, s_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = tanh_actfun #relu_actfun
params['init_scale'] = 'xg' #init_scale
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_h = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_s0_given_h.init_biases(0.0)
#################
# p_zi_given_xi #
#################
params = {}
shared_config = [(x_dim + x_dim), 500, 500]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = tanh_actfun #relu_actfun
params['init_scale'] = init_scale
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_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_zi_given_xi.init_biases(0.0)
###################
# p_sip1_given_zi #
###################
params = {}
shared_config = [z_dim, 500, 500]
output_config = [s_dim, s_dim, s_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = tanh_actfun #relu_actfun
params['init_scale'] = init_scale
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_zi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_zi.init_biases(0.0)
################
# p_x_given_si #
################
params = {}
shared_config = [s_dim]
output_config = [x_dim, x_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = tanh_actfun #relu_actfun
params['init_scale'] = init_scale
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_si = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_x_given_si.init_biases(0.0)
###############
# q_h_given_x #
###############
params = {}
shared_config = [x_dim, 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'] = tanh_actfun #relu_actfun
params['init_scale'] = 'xg' #init_scale
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 = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_h_given_x.init_biases(0.0)
#################
# q_zi_given_xi #
#################
params = {}
shared_config = [(x_dim + x_dim), 500, 500]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = tanh_actfun #relu_actfun
params['init_scale'] = init_scale
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_zi_given_xi = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_zi_given_xi.init_biases(0.0)
###########################################################
# Define parameters for the GPSImputer, and initialize it #
###########################################################
print("Building the GPSImputer...")
gpsi_params = {}
gpsi_params['x_dim'] = x_dim
gpsi_params['h_dim'] = h_dim
gpsi_params['z_dim'] = z_dim
gpsi_params['s_dim'] = s_dim
# switch between direct construction and construction via p_x_given_si
gpsi_params['use_p_x_given_si'] = False
gpsi_params['imp_steps'] = imp_steps
gpsi_params['step_type'] = step_type
gpsi_params['x_type'] = 'bernoulli'
gpsi_params['obs_transform'] = 'sigmoid'
GPSI = GPSImputerWI(rng=rng,
x_in=x_in_sym, x_out=x_out_sym, x_mask=x_mask_sym, \
p_h_given_x=p_h_given_x, \
p_s0_given_h=p_s0_given_h, \
p_zi_given_xi=p_zi_given_xi, \
p_sip1_given_zi=p_sip1_given_zi, \
p_x_given_si=p_x_given_si, \
q_h_given_x=q_h_given_x, \
q_zi_given_xi=q_zi_given_xi, \
params=gpsi_params, \
shared_param_dicts=None)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format(result_tag)
out_file = open(log_name, '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) / 5000.0))
lam_scale = 1.0 - min(1.0, ((i+1) / 100000.0)) # decays from 1.0->0.0
if (((i + 1) % 15000) == 0):
learn_rate = learn_rate * 0.93
if (i > 10000):
momentum = 0.90
else:
momentum = 0.75
# 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
GPSI.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
GPSI.set_train_switch(1.0)
GPSI.set_lam_nll(lam_nll=1.0)
GPSI.set_lam_kld(lam_kld_p=0.05, lam_kld_q=0.95, \
lam_kld_g=(0.1 * lam_scale), lam_kld_s=(0.1 * lam_scale))
GPSI.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
xb = to_fX( Xtr.take(batch_idx, axis=0) )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
result = GPSI.train_joint(xi, xo, xm, batch_reps)
# do diagnostics and general training tracking
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 250) == 0):
costs = [(v / 250.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_bound : {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 % 1000) == 0):
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
xi, xo, xm = construct_masked_data(Xva[0:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll, kld = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10)
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 2000) == 0):
GPSI.save_to_file("{}_PARAMS.pkl".format(result_tag))
# Get some validation samples for evaluating model performance
xb = to_fX( Xva[0:100] )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
xi = np.repeat(xi, 2, axis=0)
xo = np.repeat(xo, 2, axis=0)
xm = np.repeat(xm, 2, axis=0)
# draw some sample imputations from the model
samp_count = xi.shape[0]
_, model_samps = GPSI.sample_imputer(xi, xo, xm, use_guide_policy=False)
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 = "{0:s}_samples_ng_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# show KLds and NLLs on a step-by-step basis
xb = to_fX( Xva[0:1000] )
xi, xo, xm = construct_masked_data(xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
step_costs = GPSI.compute_per_step_cost(xi, xo, xm)
step_nlls = step_costs[0]
step_klds = step_costs[1]
step_nums = np.arange(step_nlls.shape[0])
file_name = "{0:s}_NLL_b{1:d}.png".format(result_tag, i)
utils.plot_stem(step_nums, step_nlls, file_name)
file_name = "{0:s}_KLD_b{1:d}.png".format(result_tag, i)
utils.plot_stem(step_nums, step_klds, file_name)
def test_mnist_results(step_type='add',
imp_steps=6,
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_IS{}_{}_NA".format(RESULT_PATH, occ_dim,
dp_int, imp_steps,
step_type)
##########################
# 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]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX(all_pix_mean * np.ones((Xtr.shape[1], )))
# Load parameters from a previously trained model
print("Testing model load from file...")
GPSI = load_gpsimputer_from_file(f_name="{}_PARAMS.pkl".format(result_tag), \
rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_FINAL_RESULTS_NEW.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
str0 = "GUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
# record an estimate of performance on the test set
str0 = "UNGUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
str1 = " va_nll_bound : {}".format(np.mean(nll))
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
def test_imocld_imp_mnist(step_type='add', occ_dim=14, drop_prob=0.0, attention=False):
##########################
# 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 = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
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 = 300
enc_dim = 300
dec_dim = 300
mix_dim = 20
z_dim = 100
n_iter = 16
dp_int = int(100.0 * drop_prob)
rnninits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
inits = {
'weights_init': IsotropicGaussian(0.01),
'biases_init': Constant(0.),
}
att_tag = "NA" # attention not implemented yet
# setup the reader and writer (shared by primary and guide policies)
read_dim = 2*x_dim # dimension of output from reader_mlp
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)
# mlps for setting conditionals over z_mix
mix_var_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_var_mlp", **inits)
mix_enc_mlp = CondNet([Tanh()], [x_dim, 250, mix_dim], \
name="mix_enc_mlp", **inits)
# mlp for decoding z_mix into a distribution over initial LSTM states
mix_dec_mlp = MLP([Tanh(), Tanh()], \
[mix_dim, 250, (2*enc_dim + 2*dec_dim + 2*enc_dim)], \
name="mix_dec_mlp", **inits)
# mlps for processing inputs to LSTMs
var_mlp_in = MLP([Identity()], [(read_dim + dec_dim), 4*enc_dim], \
name="var_mlp_in", **inits)
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)
# mlps for turning LSTM outputs into conditionals over z_gen
var_mlp_out = CondNet([], [enc_dim, z_dim], name="var_mlp_out", **inits)
enc_mlp_out = CondNet([], [enc_dim, z_dim], name="enc_mlp_out", **inits)
# LSTMs for the actual LSTMs (obviously, perhaps)
var_rnn = BiasedLSTM(dim=enc_dim, ig_bias=2.0, fg_bias=2.0, \
name="var_rnn", **rnninits)
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)
draw = IMoCLDrawModels(
n_iter,
step_type=step_type, # step_type can be 'add' or 'jump'
reader_mlp=reader_mlp,
writer_mlp=writer_mlp,
mix_enc_mlp=mix_enc_mlp,
mix_dec_mlp=mix_dec_mlp,
mix_var_mlp=mix_var_mlp,
enc_mlp_in=enc_mlp_in,
enc_mlp_out=enc_mlp_out,
enc_rnn=enc_rnn,
dec_mlp_in=dec_mlp_in,
dec_rnn=dec_rnn,
var_mlp_in=var_mlp_in,
var_mlp_out=var_mlp_out,
var_rnn=var_rnn)
draw.initialize()
# build the cost gradients, training function, samplers, etc.
draw.build_model_funcs()
#draw.load_model_params(f_name="TBCLM_IMP_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
print("Beginning to train the model...")
out_file = open("TBCLM_IMP_RESULTS_OD{}_DP{}_{}_{}.txt".format(occ_dim, dp_int, step_type, att_tag), 'wb')
out_file.flush()
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) / 1000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
if (i > 10000):
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)
# set sgd and objective function hyperparams for this update
zero_ary = np.zeros((1,))
draw.lr.set_value(to_fX(zero_ary + learn_rate))
draw.mom_1.set_value(to_fX(zero_ary + momentum))
draw.mom_2.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))
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
result = draw.train_joint(Xb, Mb)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 200) == 0):
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 = " nll_term : {0:.4f}".format(costs[2])
str5 = " kld_q2p : {0:.4f}".format(costs[3])
str6 = " kld_p2q : {0:.4f}".format(costs[4])
str7 = " reg_term : {0:.4f}".format(costs[5])
joint_str = "\n".join([str1, str2, str3, str4, str5, str6, str7])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if ((i % 1000) == 0):
draw.save_model_params("TBCLM_IMP_PARAMS_OD{}_DP{}_{}_{}.pkl".format(occ_dim, dp_int, step_type, att_tag))
# compute a small-sample estimate of NLL bound on validation set
Xva = row_shuffle(Xva)
Xb = to_fX(Xva[:5000])
_, Xb, Mb = construct_masked_data(Xb, drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=None)
va_costs = draw.compute_nll_bound(Xb, Mb)
str1 = " va_nll_bound : {}".format(va_costs[1])
str2 = " va_nll_term : {}".format(va_costs[2])
str3 = " va_kld_q2p : {}".format(va_costs[3])
joint_str = "\n".join([str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def test_tfd_results(step_type='add',
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, step_type)
##########################
# Get some training data #
##########################
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]
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 200
imp_steps = 6
init_scale = 1.0
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
x_mask_sym = T.matrix('x_mask_sym')
# Load parameters from a previously trained model
print("Testing model load from file...")
GPSI = load_gpsimputer_from_file(f_name="{}_PARAMS.pkl".format(result_tag), \
rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_FINAL_RESULTS.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
str0 = "GUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# record an estimate of performance on the test set
str0 = "UNGUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
str1 = " va_nll_bound : {}".format(np.mean(nll))
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
