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_two_stage_model2():
##########################
# 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 = 128
batch_reps = 1
###############################################
# Setup some parameters for the TwoStageModel #
###############################################
x_dim = Xtr.shape[1]
z_dim = 50
h_dim = 100
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
xin_sym = T.matrix('xin_sym')
xout_sym = T.matrix('xout_sym')
###############
# p_h_given_z #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 100,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_h_given_z = HydraNet(rng=rng, Xd=xin_sym,
params=params, shared_param_dicts=None)
p_h_given_z.init_biases(0.0)
###############
# p_x_given_h #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': h_dim,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 200,
'out_chans': x_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': x_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_x_given_h = HydraNet(rng=rng, Xd=xin_sym,
params=params, shared_param_dicts=None)
p_x_given_h.init_biases(0.0)
###############
# q_h_given_x #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': x_dim,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 200,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
q_h_given_x = HydraNet(rng=rng, Xd=xin_sym,
params=params, shared_param_dicts=None)
q_h_given_x.init_biases(0.0)
###############
# q_z_given_h #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': h_dim,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 100,
'out_chans': z_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': z_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
q_z_given_h = HydraNet(rng=rng, Xd=xin_sym,
params=params, shared_param_dicts=None)
q_z_given_h.init_biases(0.0)
##############################################################
# Define parameters for the TwoStageModel, and initialize it #
##############################################################
print("Building the TwoStageModel...")
tsm_params = {}
tsm_params['x_type'] = x_type
tsm_params['obs_transform'] = 'sigmoid'
TSM = TwoStageModel2(rng=rng, x_in=xin_sym, x_out=xout_sym,
x_dim=x_dim, z_dim=z_dim, h_dim=h_dim,
q_h_given_x=q_h_given_x,
q_z_given_h=q_z_given_h,
p_h_given_z=p_h_given_z,
p_x_given_h=p_x_given_h,
params=tsm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format("TSM2A_TEST")
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.001
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(500000):
scale = min(1.0, ((i+1) / 5000.0))
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)
Xb = to_fX( Xtr.take(batch_idx, axis=0) )
#Xb = binarize_data(Xtr.take(batch_idx, axis=0))
# set sgd and objective function hyperparams for this update
TSM.set_sgd_params(lr=scale*learn_rate,
mom_1=(scale*momentum), mom_2=0.98)
TSM.set_train_switch(1.0)
TSM.set_lam_nll(lam_nll=1.0)
TSM.set_lam_kld(lam_kld_q2p=1.0, lam_kld_p2q=0.0)
TSM.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
result = TSM.train_joint(Xb, Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
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])
str6 = " nll : {0:.4f}".format(np.mean(costs[4]))
str7 = " kld_z : {0:.4f}".format(np.mean(costs[5]))
str8 = " kld_h : {0:.4f}".format(np.mean(costs[6]))
joint_str = "\n".join([str1, str2, str3, str4, str5, str6, str7, str8])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 5000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# draw some independent random samples from the model
samp_count = 300
model_samps = TSM.sample_from_prior(samp_count)
file_name = "TSM2A_SAMPLES_b{0:d}.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=15)
# compute free energy estimate for validation samples
Xva = row_shuffle(Xva)
fe_terms = TSM.compute_fe_terms(Xva[0:5000], Xva[0:5000], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
out_str = " nll_bound : {0:.4f}".format(fe_mean)
print(out_str)
out_file.write(out_str+"\n")
out_file.flush()
return
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
Xtr, Xva, Xte = load_binarized_mnist(data_path='./data/')
del Xte
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 200
batch_reps = 1
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
obs_dim = Xtr.shape[1]
z_dim = 20
h_dim = 200
ir_steps = 6
init_scale = 1.0
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
x_in_sym = T.matrix('x_in_sym')
x_out_sym = T.matrix('x_out_sym')
#################
# p_hi_given_si #
#################
params = {}
shared_config = [obs_dim, 300, 300]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_hi_given_si = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_hi_given_si.init_biases(0.2)
######################
# p_sip1_given_si_hi #
######################
params = {}
shared_config = [h_dim, 300, 300]
output_config = [obs_dim, obs_dim, obs_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_sip1_given_si_hi = HydraNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_si_hi.init_biases(0.2)
################
# p_s0_given_z #
################
params = {}
shared_config = [z_dim, 250, 250]
top_config = [shared_config[-1], obs_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s0_given_z = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
p_s0_given_z.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [obs_dim, 250, 250]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
###################
# q_hi_given_x_si #
###################
params = {}
shared_config = [(obs_dim + obs_dim), 500, 500]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_hi_given_x_si = InfNet(rng=rng, Xd=x_in_sym, \
params=params, shared_param_dicts=None)
q_hi_given_x_si.init_biases(0.2)
################################################################
# Define parameters for the MultiStageModel, and initialize it #
################################################################
print("Building the MultiStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
MSM = MultiStageModel(rng=rng, x_in=x_in_sym, x_out=x_out_sym, \
p_s0_given_z=p_s0_given_z, \
p_hi_given_si=p_hi_given_si, \
p_sip1_given_si_hi=p_sip1_given_si_hi, \
q_z_given_x=q_z_given_x, \
q_hi_given_x_si=q_hi_given_x_si, \
obs_dim=obs_dim, z_dim=z_dim, h_dim=h_dim, \
ir_steps=ir_steps, params=msm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("MSM_A_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0003
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 3000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr = row_shuffle(Xtr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
MSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.99)
MSM.set_train_switch(1.0)
MSM.set_lam_nll(lam_nll=1.0)
MSM.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.8, lam_kld_p2q=0.2)
MSM.set_lam_kld_l1l2(lam_kld_l1l2=1.0)
MSM.set_lam_l2w(1e-4)
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
# perform a minibatch update and record the cost for this batch
Xb_tr = to_fX( Xtr.take(batch_idx, axis=0) )
result = MSM.train_joint(Xb_tr, Xb_tr, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost: {0:.4f}".format(costs[0])
str3 = " nll_cost : {0:.4f}".format(costs[1])
str4 = " kld_cost : {0:.4f}".format(costs[2])
str5 = " reg_cost : {0:.4f}".format(costs[3])
joint_str = "\n".join([str1, str2, str3, str4, str5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
# Get some validation samples for computing diagnostics
Xva = row_shuffle(Xva)
Xb_va = to_fX( Xva[0:2000] )
# draw some independent random samples from the model
samp_count = 200
model_samps = MSM.sample_from_prior(samp_count)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSM_A_SAMPLES_IND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
samp_count = 200
Xs = np.vstack((Xb_tr[0:(samp_count/4)], Xb_va[0:(samp_count/4)]))
Xs = np.repeat(Xs, 2, axis=0)
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(Xs, guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSM_A_SAMPLES_CND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# compute information about posterior KLds on validation set
raw_klds = MSM.compute_raw_klds(Xb_va, Xb_va)
init_kld, q2p_kld, p2q_kld = raw_klds
file_name = "MSM_A_H0_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(init_kld.shape[1]), \
np.mean(init_kld, axis=0), file_name)
file_name = "MSM_A_HI_Q2P_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(q2p_kld.shape[1]), \
np.mean(q2p_kld, axis=0), file_name)
file_name = "MSM_A_HI_P2Q_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(p2q_kld.shape[1]), \
np.mean(p2q_kld, axis=0), file_name)
Xb_tr = to_fX( Xtr[0:2000] )
fe_terms = MSM.compute_fe_terms(Xb_tr, Xb_tr, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
fe_terms = MSM.compute_fe_terms(Xb_va, Xb_va, 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
def test_with_model_init():
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = to_fX(datasets[0][0])
Xva = to_fX(datasets[1][0])
Ytr = datasets[0][1]
Yva = datasets[1][1]
Xtr_class_groups = make_class_groups(Xtr, Ytr)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 300
BD = lambda ary: binarize_data(ary)
############################################################
# Setup some parameters for the Iterative Refinement Model #
############################################################
x_dim = Xtr.shape[1]
z_dim = 50
s_dim = 300
h_dim = 100
ir_steps = 3
init_scale = 1.0
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
x_in = T.matrix('x_in')
x_out = T.matrix('x_out')
y_in = T.lvector('y_in')
#################
# p_hi_given_si #
#################
params = {}
shared_config = [s_dim, 800]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_hi_given_si = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_hi_given_si.init_biases(0.2)
######################
# p_sip1_given_si_hi #
######################
params = {}
shared_config = [(h_dim + s_dim), 800]
output_config = [s_dim, s_dim, s_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_sip1_given_si_hi = HydraNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_sip1_given_si_hi.init_biases(0.2)
################
# p_s0_given_z #
################
params = {}
shared_config = [z_dim, 500, 500]
top_config = [shared_config[-1], s_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
p_s0_given_z = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
p_s0_given_z.init_biases(0.2)
###############
# q_z_given_x #
###############
params = {}
shared_config = [x_dim, 1000, 1000]
top_config = [shared_config[-1], z_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.2
params['hid_drop'] = 0.5
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_z_given_x = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.2)
###################
# q_hi_given_x_si #
###################
params = {}
shared_config = [(x_dim + s_dim + s_dim), 800]
top_config = [shared_config[-1], h_dim]
params['shared_config'] = shared_config
params['mu_config'] = top_config
params['sigma_config'] = top_config
params['activation'] = relu_actfun
params['init_scale'] = init_scale
params['lam_l2a'] = 0.0
params['vis_drop'] = 0.0
params['hid_drop'] = 0.0
params['bias_noise'] = 0.0
params['input_noise'] = 0.0
params['build_theano_funcs'] = False
q_hi_given_x_si = InfNet(rng=rng, Xd=x_in, \
params=params, shared_param_dicts=None)
q_hi_given_x_si.init_biases(0.2)
################################################################
# Define parameters for the MultiStageModel, and initialize it #
################################################################
print("Building the MultiStageModel...")
msm_params = {}
msm_params['x_type'] = x_type
msm_params['obs_transform'] = 'sigmoid'
MSM = MultiStageModelSS(rng=rng, \
x_in=x_in, x_out=x_out, y_in=y_in, \
p_s0_given_z=p_s0_given_z, \
p_hi_given_si=p_hi_given_si, \
p_sip1_given_si_hi=p_sip1_given_si_hi, \
q_z_given_x=q_z_given_x, \
q_hi_given_x_si=q_hi_given_x_si, \
class_count=10, \
x_dim=x_dim, s_dim=s_dim, \
z_dim=z_dim, h_dim=h_dim, \
ir_steps=ir_steps, params=msm_params)
MSM.set_lam_class(lam_class=10.0)
MSM.set_lam_nll(lam_nll=1.0)
MSM.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.9, \
lam_kld_p2q=0.1)
MSM.set_lam_l2w(1e-4)
MSM.set_drop_rate(0.0)
MSM.q_hi_given_x_si.set_bias_noise(0.0)
MSM.p_hi_given_si.set_bias_noise(0.0)
MSM.p_sip1_given_si_hi.set_bias_noise(0.0)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
out_file = open("MSS_B_RESULTS.txt", 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0002
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(250000):
scale = min(1.0, ((i+1) / 2000.0))
if (((i + 1) % 10000) == 0):
learn_rate = learn_rate * 0.95
# get the indices of training samples for this batch update
batch_idx += batch_size
if (np.max(batch_idx) >= tr_samples):
# we finished an "epoch", so we rejumble the training set
Xtr, Ytr = row_shuffle(Xtr, Ytr)
batch_idx = np.arange(batch_size)
# set sgd and objective function hyperparams for this update
MSM.set_sgd_params(lr_1=scale*learn_rate, lr_2=scale*learn_rate, \
mom_1=0.9, mom_2=0.99)
MSM.set_train_switch(1.0)
# perform a minibatch update and record the cost for this batch
Xi_tr = Xtr.take(batch_idx, axis=0)
Yi_tr = Ytr.take(batch_idx, axis=0)
Xp_tr, Xn_tr = sample_class_groups(Yi_tr, Xtr_class_groups)
result = MSM.train_joint(BD(Xi_tr), BD(Xi_tr), Yi_tr)
costs = [(costs[j] + result[j]) for j in range(len(result)-1)]
# output useful information about training progress
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
str1 = "-- batch {0:d} --".format(i)
str2 = " joint_cost : {0:.4f}".format(costs[0])
str3 = " class_cost : {0:.4f}".format(costs[1])
str4 = " nll_cost : {0:.4f}".format(costs[2])
str5 = " kld_cost : {0:.4f}".format(costs[3])
str6 = " reg_cost : {0:.4f}".format(costs[4])
joint_str = "\n".join([str1, str2, str3, str4, str5, str6])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 2000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# Get some validation samples for computing diagnostics
Xva, Yva = row_shuffle(Xva, Yva)
Xb_va = Xva[0:2500]
Yb_va = Yva[0:2500]
# draw some independent random samples from the model
samp_count = 200
model_samps = MSM.sample_from_prior(samp_count)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_B_SAMPLES_IND_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
samp_count = 200
Xs = Xb_va[0:(samp_count/4)] # only use validation set samples
Xs = np.repeat(Xs, 4, axis=0)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(BD(Xs), guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_B_SAMPLES_CND_UD_b{0:d}.png".format(i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
# compute information about posterior KLds on validation set
raw_costs = MSM.compute_raw_costs(BD(Xb_va), BD(Xb_va))
init_nll, init_kld, q2p_kld, p2q_kld, step_nll, step_kld = raw_costs
step_nll[0] = step_nll[1] # scale of first NLL is overwhemling
file_name = "MSS_B_H0_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(init_kld.shape[1]), \
np.mean(init_kld, axis=0), file_name)
file_name = "MSS_B_HI_Q2P_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(q2p_kld.shape[1]), \
np.mean(q2p_kld, axis=0), file_name)
file_name = "MSS_B_HI_P2Q_KLDS_b{0:d}.png".format(i)
utils.plot_stem(np.arange(p2q_kld.shape[1]), \
np.mean(p2q_kld, axis=0), file_name)
# draw weights for the initial encoder/classifier
file_name = "MSS_B_QZX_WEIGHTS_b{0:d}.png".format(i)
W = q_z_given_x.shared_layers[0].W.get_value(borrow=False).T
utils.visualize_samples(W, file_name, num_rows=20)
# compute free-energy terms on training samples
fe_terms = MSM.compute_fe_terms(BD(Xtr[0:2500]), BD(Xtr[0:2500]), 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-tr: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# compute free-energy terms on validation samples
fe_terms = MSM.compute_fe_terms(BD(Xb_va), BD(Xb_va), 30)
fe_nll = np.mean(fe_terms[0])
fe_kld = np.mean(fe_terms[1])
fe_joint = fe_nll + fe_kld
joint_str = " vfe-va: {0:.4f}, nll: ({1:.4f}, {2:.4f}, {3:.4f}), kld: ({4:.4f}, {5:.4f}, {6:.4f})".format( \
fe_joint, fe_nll, np.min(fe_terms[0]), np.max(fe_terms[0]), fe_kld, np.min(fe_terms[1]), np.max(fe_terms[1]))
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# compute multi-sample estimate of classification error
err_rate, err_idx, y_preds = MSM.class_error(Xb_va, Yb_va, \
samples=30, prep_func=BD)
joint_str = " va-class-error: {0:.4f}".format(err_rate)
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# draw some conditional random samples from the model
Xs = Xb_va[err_idx] # use validation samples with class errors
if (Xs.shape[0] > 50):
Xs = Xs[:50]
Xs = np.repeat(Xs, 4, axis=0)
if ((Xs.shape[0] % 20) != 0):
# round-off the number of error examples, for nice display
remainder = Xs.shape[0] % 20
Xs = Xs[:-remainder]
samp_count = Xs.shape[0]
# draw some conditional random samples from the model
model_samps = MSM.sample_from_input(BD(Xs), guided_decoding=False)
model_samps.append(Xs)
seq_len = len(model_samps)
seq_samps = np.zeros((seq_len*samp_count, model_samps[0].shape[1]))
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = model_samps[s2][s1]
idx += 1
file_name = "MSS_B_SAMPLES_CND_ERR_b{0:d}.png".format(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_one_stage_model():
##########################
# 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 = 128
batch_reps = 1
###############################################
# Setup some parameters for the OneStageModel #
###############################################
x_dim = Xtr.shape[1]
z_dim = 64
x_type = 'bernoulli'
xin_sym = T.matrix('xin_sym')
###############
# p_x_given_z #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': 7*7*128,
'activation': relu_actfun,
'apply_bn': True,
'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': True} ]
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)} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_x_given_z = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
p_x_given_z.init_biases(0.0)
###############
# q_z_given_x #
###############
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': True,
'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': True,
'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': True} ]
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'] = 1.0
params['build_theano_funcs'] = False
q_z_given_x = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.0)
##############################################################
# Define parameters for the TwoStageModel, and initialize it #
##############################################################
print("Building the OneStageModel...")
osm_params = {}
osm_params['x_type'] = x_type
osm_params['obs_transform'] = 'sigmoid'
OSM = OneStageModel(rng=rng, x_in=xin_sym,
x_dim=x_dim, z_dim=z_dim,
p_x_given_z=p_x_given_z,
q_z_given_x=q_z_given_x,
params=osm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format("OSM_TEST")
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0005
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(500000):
scale = min(0.5, ((i+1) / 5000.0))
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)
Xb = to_fX( Xtr.take(batch_idx, axis=0) )
#Xb = binarize_data(Xtr.take(batch_idx, axis=0))
# set sgd and objective function hyperparams for this update
OSM.set_sgd_params(lr=scale*learn_rate, \
mom_1=(scale*momentum), mom_2=0.98)
OSM.set_lam_nll(lam_nll=1.0)
OSM.set_lam_kld(lam_kld=1.0)
OSM.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
result = OSM.train_joint(Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
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 % 5000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# draw some independent random samples from the model
samp_count = 300
model_samps = OSM.sample_from_prior(samp_count)
file_name = "OSM_SAMPLES_b{0:d}.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=15)
# compute free energy estimate for validation samples
Xva = row_shuffle(Xva)
fe_terms = OSM.compute_fe_terms(Xva[0:5000], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
out_str = " nll_bound : {0:.4f}".format(fe_mean)
print(out_str)
out_file.write(out_str+"\n")
out_file.flush()
return
def test_two_stage_model2():
##########################
# 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 = 128
batch_reps = 1
###############################################
# Setup some parameters for the TwoStageModel #
###############################################
x_dim = Xtr.shape[1]
z_dim = 50
h_dim = 100
x_type = 'bernoulli'
# some InfNet instances to build the TwoStageModel from
xin_sym = T.matrix('xin_sym')
xout_sym = T.matrix('xout_sym')
###############
# p_h_given_z #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 100,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_h_given_z = HydraNet(rng=rng,
Xd=xin_sym,
params=params,
shared_param_dicts=None)
p_h_given_z.init_biases(0.0)
###############
# p_x_given_h #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': h_dim,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 200,
'out_chans': x_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': x_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_x_given_h = HydraNet(rng=rng,
Xd=xin_sym,
params=params,
shared_param_dicts=None)
p_x_given_h.init_biases(0.0)
###############
# q_h_given_x #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': x_dim,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': 200,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 200,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 200,
'out_chans': h_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
q_h_given_x = HydraNet(rng=rng,
Xd=xin_sym,
params=params,
shared_param_dicts=None)
q_h_given_x.init_biases(0.0)
###############
# q_z_given_h #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': h_dim,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': 100,
'activation': tanh_actfun,
'apply_bn': True} ]
output_config = \
[ {'layer_type': 'fc',
'in_chans': 100,
'out_chans': z_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 100,
'out_chans': z_dim,
'activation': tanh_actfun,
'apply_bn': False} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
q_z_given_h = HydraNet(rng=rng,
Xd=xin_sym,
params=params,
shared_param_dicts=None)
q_z_given_h.init_biases(0.0)
##############################################################
# Define parameters for the TwoStageModel, and initialize it #
##############################################################
print("Building the TwoStageModel...")
tsm_params = {}
tsm_params['x_type'] = x_type
tsm_params['obs_transform'] = 'sigmoid'
TSM = TwoStageModel2(rng=rng,
x_in=xin_sym,
x_out=xout_sym,
x_dim=x_dim,
z_dim=z_dim,
h_dim=h_dim,
q_h_given_x=q_h_given_x,
q_z_given_h=q_z_given_h,
p_h_given_z=p_h_given_z,
p_x_given_h=p_x_given_h,
params=tsm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format("TSM2A_TEST")
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.001
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(500000):
scale = min(1.0, ((i + 1) / 5000.0))
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)
Xb = to_fX(Xtr.take(batch_idx, axis=0))
#Xb = binarize_data(Xtr.take(batch_idx, axis=0))
# set sgd and objective function hyperparams for this update
TSM.set_sgd_params(lr=scale * learn_rate,
mom_1=(scale * momentum),
mom_2=0.98)
TSM.set_train_switch(1.0)
TSM.set_lam_nll(lam_nll=1.0)
TSM.set_lam_kld(lam_kld_q2p=1.0, lam_kld_p2q=0.0)
TSM.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
result = TSM.train_joint(Xb, Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
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])
str6 = " nll : {0:.4f}".format(np.mean(costs[4]))
str7 = " kld_z : {0:.4f}".format(np.mean(costs[5]))
str8 = " kld_h : {0:.4f}".format(np.mean(costs[6]))
joint_str = "\n".join(
[str1, str2, str3, str4, str5, str6, str7, str8])
print(joint_str)
out_file.write(joint_str + "\n")
out_file.flush()
costs = [0.0 for v in costs]
if (((i % 5000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# draw some independent random samples from the model
samp_count = 300
model_samps = TSM.sample_from_prior(samp_count)
file_name = "TSM2A_SAMPLES_b{0:d}.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=15)
# compute free energy estimate for validation samples
Xva = row_shuffle(Xva)
fe_terms = TSM.compute_fe_terms(Xva[0:5000], Xva[0:5000], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
out_str = " nll_bound : {0:.4f}".format(fe_mean)
print(out_str)
out_file.write(out_str + "\n")
out_file.flush()
return
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(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(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 pretrain_osm(lam_kld=0.0):
# Initialize a source of randomness
rng = np.random.RandomState(1234)
# Load some data to train/validate/test with
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, zero_mean=False)
Xtr = datasets[0][0]
Xtr = Xtr.get_value(borrow=False)
Xva = datasets[2][0]
Xva = Xva.get_value(borrow=False)
print("Xtr.shape: {0:s}, Xva.shape: {1:s}".format(str(Xtr.shape),str(Xva.shape)))
# get and set some basic dataset information
Xtr_mean = np.mean(Xtr, axis=0)
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
# setup some symbolic variables and stuff
x_in_sym = T.matrix('x_in_sym')
data_dim = Xtr.shape[1]
##########################
# NETWORK CONFIGURATIONS #
##########################
gn_params = {}
shared_config = [PRIOR_DIM, 1000, 1000]
output_config = [data_dim, data_dim]
gn_params['shared_config'] = shared_config
gn_params['output_config'] = output_config
gn_params['activation'] = relu_actfun
gn_params['init_scale'] = 1.2
gn_params['lam_l2a'] = 0.0
gn_params['vis_drop'] = 0.0
gn_params['hid_drop'] = 0.0
gn_params['bias_noise'] = 0.0
gn_params['input_noise'] = 0.0
# choose some parameters for the continuous inferencer
in_params = {}
shared_config = [data_dim, 1000, 1000]
top_config = [shared_config[-1], PRIOR_DIM]
in_params['shared_config'] = shared_config
in_params['mu_config'] = top_config
in_params['sigma_config'] = top_config
in_params['activation'] = relu_actfun
in_params['init_scale'] = 1.2
in_params['lam_l2a'] = 0.0
in_params['vis_drop'] = 0.0
in_params['hid_drop'] = 0.0
in_params['bias_noise'] = 0.0
in_params['input_noise'] = 0.0
# Initialize the base networks for this OneStageModel
IN = InfNet(rng=rng, Xd=x_in_sym, params=in_params, \
shared_param_dicts=None)
GN = HydraNet(rng=rng, Xd=x_in_sym, params=gn_params, \
shared_param_dicts=None)
# Initialize biases in IN and GN
IN.init_biases(0.2)
GN.init_biases(0.2)
######################################
# LOAD AND RESTART FROM SAVED PARAMS #
######################################
# gn_fname = RESULT_PATH+"pt_osm_params_b110000_GN.pkl"
# in_fname = RESULT_PATH+"pt_osm_params_b110000_IN.pkl"
# IN = load_infnet_from_file(f_name=in_fname, rng=rng, Xd=Xd, \
# new_params=None)
# GN = load_hydranet_from_file(f_name=gn_fname, rng=rng, Xd=Xd, \
# new_params=None)
# in_params = IN.params
# gn_params = GN.params
#########################
# INITIALIZE THE GIPAIR #
#########################
osm_params = {}
osm_params['x_type'] = 'gaussian'
osm_params['xt_transform'] = 'sigmoid'
osm_params['logvar_bound'] = LOGVAR_BOUND
OSM = OneStageModel(rng=rng, x_in=x_in_sym, \
p_x_given_z=GN, q_z_given_x=IN, \
x_dim=data_dim, z_dim=PRIOR_DIM, params=osm_params)
OSM.set_lam_l2w(1e-5)
######################
# BASIC VAE TRAINING #
######################
out_file = open(RESULT_PATH+"pt_osm_results.txt", 'wb')
# Set initial learning rate and basic SGD hyper parameters
obs_costs = np.zeros((batch_size,))
costs = [0. for i in range(10)]
learn_rate = 0.0002
momentum = 0.0
for i in range(200000):
scale = min(1.0, float(i) / 5000.0)
kld_scale = min(1.0, float(i) / 50000.0)
if ((i > 1) and ((i % 10000) == 0)):
learn_rate = learn_rate * 0.9
# do a minibatch update of the model, and compute some costs
tr_idx = npr.randint(low=0,high=tr_samples,size=(batch_size,))
Xb = Xtr.take(tr_idx, axis=0)
# do a minibatch update of the model, and compute some costs
OSM.set_sgd_params(lr=(scale*learn_rate), \
mom_1=(scale*momentum), mom_2=0.98)
OSM.set_lam_nll(1.0)
OSM.set_lam_kld(lam_kld_1=(1.0 + (kld_scale * (lam_kld - 1.0))), \
lam_kld_2=0.0)
result = OSM.train_joint(Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 1000) == 0):
# record and then reset the cost trackers
costs = [(v / 1000.0) for v in costs]
str_1 = "-- batch {0:d} --".format(i)
str_2 = " joint_cost: {0:.4f}".format(costs[0])
str_3 = " nll_cost : {0:.4f}".format(costs[1])
str_4 = " kld_cost : {0:.4f}".format(costs[2])
str_5 = " reg_cost : {0:.4f}".format(costs[3])
costs = [0.0 for v in costs]
# print out some diagnostic information
joint_str = "\n".join([str_1, str_2, str_3, str_4, str_5])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if ((i % 2000) == 0):
Xva = row_shuffle(Xva)
model_samps = OSM.sample_from_prior(500)
file_name = RESULT_PATH+"pt_osm_samples_b{0:d}_XG.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=20)
file_name = RESULT_PATH+"pt_osm_inf_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.inf_weights.get_value(borrow=False).T, \
file_name, num_rows=30)
file_name = RESULT_PATH+"pt_osm_gen_weights_b{0:d}.png".format(i)
utils.visualize_samples(OSM.gen_weights.get_value(borrow=False), \
file_name, num_rows=30)
# compute information about free-energy on validation set
file_name = RESULT_PATH+"pt_osm_free_energy_b{0:d}.png".format(i)
fe_terms = OSM.compute_fe_terms(Xva[0:2500], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
fe_str = " nll_bound : {0:.4f}".format(fe_mean)
print(fe_str)
out_file.write(fe_str+"\n")
utils.plot_scatter(fe_terms[1], fe_terms[0], file_name, \
x_label='Posterior KLd', y_label='Negative Log-likelihood')
# compute information about posterior KLds on validation set
file_name = RESULT_PATH+"pt_osm_post_klds_b{0:d}.png".format(i)
post_klds = OSM.compute_post_klds(Xva[0:2500])
post_dim_klds = np.mean(post_klds, axis=0)
utils.plot_stem(np.arange(post_dim_klds.shape[0]), post_dim_klds, \
file_name)
if ((i % 5000) == 0):
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_IN.pkl".format(i))
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_b{0:d}_GN.pkl".format(i))
IN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_IN.pkl")
GN.save_to_file(f_name=RESULT_PATH+"pt_osm_params_GN.pkl")
return
def test_mnist(step_type='add', \
rev_sched=None):
#########################################
# Format the result tag more thoroughly #
#########################################
result_tag = "{}AAA_SRRM_ST{}".format(RESULT_PATH, 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]
batch_size = 200
############################################################
# 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 = 0.66
x_out_sym = T.matrix('x_out_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'] = tanh_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_out_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
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_out_sym, \
params=params, shared_param_dicts=None)
p_sip1_given_zi.init_biases(0.0)
################
# p_x_given_si #
################
params = {}
shared_config = [s_dim, 500]
output_config = [x_dim, x_dim]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['activation'] = tanh_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_out_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'] = tanh_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_out_sym, \
params=params, shared_param_dicts=None)
q_zi_given_xi.init_biases(0.0)
#################################################
# Setup a revelation schedule if none was given #
#################################################
# if rev_sched is None:
# rev_sched = [(10, 1.0)]
# rev_masks = None
p_masks = np.zeros((16,x_dim))
p_masks[7] = npr.uniform(size=(1,x_dim)) < 0.25
p_masks[-1] = np.ones((1,x_dim))
p_masks = p_masks.astype(theano.config.floatX)
q_masks = np.ones(p_masks.shape).astype(theano.config.floatX)
rev_masks = [p_masks, q_masks]
#########################################################
# Define parameters for the SRRModel, and initialize it #
#########################################################
print("Building the SRRModel...")
srrm_params = {}
srrm_params['x_dim'] = x_dim
srrm_params['z_dim'] = z_dim
srrm_params['s_dim'] = s_dim
srrm_params['use_p_x_given_si'] = False
srrm_params['rev_sched'] = rev_sched
srrm_params['rev_masks'] = rev_masks
srrm_params['step_type'] = step_type
srrm_params['x_type'] = 'bernoulli'
srrm_params['obs_transform'] = 'sigmoid'
SRRM = SRRModel(rng=rng,
x_out=x_out_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=srrm_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 = 0.00015
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) / 50000.0)) # decays from 1.0->0.0
if (((i + 1) % 15000) == 0):
learn_rate = learn_rate * 0.93
if (i > 10000):
momentum = 0.95
else:
momentum = 0.80
# 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
SRRM.set_sgd_params(lr=scale*learn_rate, \
mom_1=scale*momentum, mom_2=0.98)
SRRM.set_train_switch(1.0)
SRRM.set_lam_kld(lam_kld_p=0.0, lam_kld_q=1.0, \
lam_kld_g=0.0, lam_kld_s=0.0)
SRRM.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) )
result = SRRM.train_joint(xb)
# 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
xb = Xva[0:5000]
nll, kld = SRRM.compute_fe_terms(xb, 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()
# draw some sample imputations from the model
xo = Xva[0:100]
samp_count = xo.shape[0]
xm_seq, xi_seq, mi_seq = SRRM.sequence_sampler(xo, use_guide_policy=True)
seq_len = len(xm_seq)
seq_samps = np.zeros((seq_len*samp_count, xm_seq[0].shape[1]))
######
# xm #
######
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = xm_seq[s2,s1,:]
idx += 1
file_name = "{0:s}_xm_samples_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
######
# xi #
######
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = xi_seq[s2,s1,:]
idx += 1
file_name = "{0:s}_xi_samples_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
######
# mi #
######
idx = 0
for s1 in range(samp_count):
for s2 in range(seq_len):
seq_samps[idx] = mi_seq[s2,s1,:]
idx += 1
file_name = "{0:s}_mi_samples_b{1:d}.png".format(result_tag, i)
utils.visualize_samples(seq_samps, file_name, num_rows=20)
def test_one_stage_model():
##########################
# 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 = 128
batch_reps = 1
###############################################
# Setup some parameters for the OneStageModel #
###############################################
x_dim = Xtr.shape[1]
z_dim = 64
x_type = 'bernoulli'
xin_sym = T.matrix('xin_sym')
###############
# p_x_given_z #
###############
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': z_dim,
'out_chans': 256,
'activation': relu_actfun,
'apply_bn': True}, \
{'layer_type': 'fc',
'in_chans': 256,
'out_chans': 7*7*128,
'activation': relu_actfun,
'apply_bn': True,
'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': True} ]
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)} ]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
params['build_theano_funcs'] = False
p_x_given_z = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
p_x_given_z.init_biases(0.0)
###############
# q_z_given_x #
###############
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': True,
'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': True,
'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': True} ]
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'] = 1.0
params['build_theano_funcs'] = False
q_z_given_x = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.0)
##############################################################
# Define parameters for the TwoStageModel, and initialize it #
##############################################################
print("Building the OneStageModel...")
osm_params = {}
osm_params['x_type'] = x_type
osm_params['obs_transform'] = 'sigmoid'
OSM = OneStageModel(rng=rng,
x_in=xin_sym,
x_dim=x_dim,
z_dim=z_dim,
p_x_given_z=p_x_given_z,
q_z_given_x=q_z_given_x,
params=osm_params)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_RESULTS.txt".format("OSM_TEST")
out_file = open(log_name, 'wb')
costs = [0. for i in range(10)]
learn_rate = 0.0005
momentum = 0.9
batch_idx = np.arange(batch_size) + tr_samples
for i in range(500000):
scale = min(0.5, ((i + 1) / 5000.0))
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)
Xb = to_fX(Xtr.take(batch_idx, axis=0))
#Xb = binarize_data(Xtr.take(batch_idx, axis=0))
# set sgd and objective function hyperparams for this update
OSM.set_sgd_params(lr=scale*learn_rate, \
mom_1=(scale*momentum), mom_2=0.98)
OSM.set_lam_nll(lam_nll=1.0)
OSM.set_lam_kld(lam_kld=1.0)
OSM.set_lam_l2w(1e-5)
# perform a minibatch update and record the cost for this batch
result = OSM.train_joint(Xb, batch_reps)
costs = [(costs[j] + result[j]) for j in range(len(result))]
if ((i % 500) == 0):
costs = [(v / 500.0) for v in costs]
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 % 5000) == 0) or ((i < 10000) and ((i % 1000) == 0))):
# draw some independent random samples from the model
samp_count = 300
model_samps = OSM.sample_from_prior(samp_count)
file_name = "OSM_SAMPLES_b{0:d}.png".format(i)
utils.visualize_samples(model_samps, file_name, num_rows=15)
# compute free energy estimate for validation samples
Xva = row_shuffle(Xva)
fe_terms = OSM.compute_fe_terms(Xva[0:5000], 20)
fe_mean = np.mean(fe_terms[0]) + np.mean(fe_terms[1])
out_str = " nll_bound : {0:.4f}".format(fe_mean)
print(out_str)
out_file.write(out_str + "\n")
out_file.flush()
return
