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_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_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(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 = "{}RELU_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
#s_dim = 300
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_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'] = 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'] = 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'] = 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_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'] = 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['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 = 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, \
p_x_given_si=p_x_given_si, \
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))
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)
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)