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TestImpGPSI_MNIST.py
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TestImpGPSI_MNIST.py
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##################################################################
# Code for testing the variational Multi-Stage Generative Model. #
##################################################################
# basic python
import numpy as np
import numpy.random as npr
import cPickle
# theano business
import theano
import theano.tensor as T
# phil's sweetness
import utils
from NetLayers import relu_actfun, softplus_actfun, tanh_actfun
from HydraNet import HydraNet
from GPSImputer import GPSImputer, load_gpsimputer_from_file
from load_data import load_udm, load_tfd, load_svhn_gray, load_binarized_mnist
from HelperFuncs import construct_masked_data, shift_and_scale_into_01, \
row_shuffle, to_fX
RESULT_PATH = "IMP_MNIST_GPSI/"
###############################
###############################
## TEST GPS IMPUTER ON MNIST ##
###############################
###############################
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)
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 = False
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': 'fc',
'in_chans': x_dim,
'out_chans': 800,
'activation': relu_actfun,
'apply_bn': use_bn}, \
{'layer_type': 'fc',
'in_chans': 800,
'out_chans': 800,
'activation': relu_actfun,
'apply_bn': use_bn} ]
out_layer = {
'layer_type': 'fc',
'in_chans': 800,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False
}
output_config = [out_layer, out_layer]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
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': 800,
'activation': relu_actfun,
'apply_bn': use_bn}, \
{'layer_type': 'fc',
'in_chans': 800,
'out_chans': 800,
'activation': relu_actfun,
'apply_bn': use_bn} ]
out_layer = {
'layer_type': 'fc',
'in_chans': 800,
'out_chans': x_dim,
'activation': relu_actfun,
'apply_bn': False
}
output_config = [out_layer, out_layer, out_layer]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
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)
#################
# q_zi_given_xi #
#################
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': (x_dim+x_dim),
'out_chans': 800,
'activation': relu_actfun,
'apply_bn': use_bn}, \
{'layer_type': 'fc',
'in_chans': 800,
'out_chans': 800,
'activation': relu_actfun,
'apply_bn': use_bn} ]
out_layer = {
'layer_type': 'fc',
'in_chans': 800,
'out_chans': z_dim,
'activation': relu_actfun,
'apply_bn': False
}
output_config = [out_layer, out_layer]
params['shared_config'] = shared_config
params['output_config'] = output_config
params['init_scale'] = 1.0
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)
#################################
#################################
## CHECK MNIST IMPUTER RESULTS ##
#################################
#################################
def test_mnist_results(step_type='add',
imp_steps=6,
occ_dim=15,
drop_prob=0.0):
#########################################
# Format the result tag more thoroughly #
#########################################
dp_int = int(100.0 * drop_prob)
result_tag = "{}GPSI_OD{}_DP{}_IS{}_{}_NA".format(RESULT_PATH, occ_dim, dp_int, imp_steps, step_type)
##########################
# Get some training data #
##########################
rng = np.random.RandomState(1234)
dataset = 'data/mnist.pkl.gz'
datasets = load_udm(dataset, as_shared=False, zero_mean=False)
Xtr = datasets[0][0]
Xva = datasets[1][0]
Xte = datasets[2][0]
# Merge validation set and training set, and test on test set.
Xtr = np.concatenate((Xtr, Xva), axis=0)
Xva = Xte
Xtr = to_fX(shift_and_scale_into_01(Xtr))
Xva = to_fX(shift_and_scale_into_01(Xva))
tr_samples = Xtr.shape[0]
va_samples = Xva.shape[0]
batch_size = 250
batch_reps = 1
all_pix_mean = np.mean(np.mean(Xtr, axis=1))
data_mean = to_fX( all_pix_mean * np.ones((Xtr.shape[1],)) )
# Load parameters from a previously trained model
print("Testing model load from file...")
GPSI = load_gpsimputer_from_file(f_name="{}_PARAMS.pkl".format(result_tag), \
rng=rng)
################################################################
# Apply some updates, to check that they aren't totally broken #
################################################################
log_name = "{}_FINAL_RESULTS_NEW.txt".format(result_tag)
out_file = open(log_name, 'wb')
Xva = row_shuffle(Xva)
# record an estimate of performance on the test set
str0 = "GUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=True)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
vfe = np.mean(nll) + np.mean(kld)
str1 = " va_nll_bound : {}".format(vfe)
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
# record an estimate of performance on the test set
str0 = "UNGUIDED SAMPLE BOUND:"
print(str0)
xi, xo, xm = construct_masked_data(Xva[:5000], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_0, kld_0 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
xi, xo, xm = construct_masked_data(Xva[5000:], drop_prob=drop_prob, \
occ_dim=occ_dim, data_mean=data_mean)
nll_1, kld_1 = GPSI.compute_fe_terms(xi, xo, xm, sample_count=10, \
use_guide_policy=False)
nll = np.concatenate((nll_0, nll_1))
kld = np.concatenate((kld_0, kld_1))
str1 = " va_nll_bound : {}".format(np.mean(nll))
str2 = " va_nll_term : {}".format(np.mean(nll))
str3 = " va_kld_q2p : {}".format(np.mean(kld))
joint_str = "\n".join([str0, str1, str2, str3])
print(joint_str)
out_file.write(joint_str+"\n")
out_file.flush()
if __name__=="__main__":
#########
# MNIST #
#########
# TRAINING
# test_mnist(step_type='add', occ_dim=14, drop_prob=0.0)
# test_mnist(step_type='add', occ_dim=16, drop_prob=0.0)
# test_mnist(step_type='add', occ_dim=0, drop_prob=0.6)
# test_mnist(step_type='add', occ_dim=0, drop_prob=0.8)
# test_mnist(step_type='jump', occ_dim=14, drop_prob=0.0)
# test_mnist(step_type='jump', occ_dim=16, drop_prob=0.0)
# test_mnist(step_type='jump', occ_dim=0, drop_prob=0.6)
# test_mnist(step_type='jump', occ_dim=0, drop_prob=0.8)
# test_mnist(step_type='add', imp_steps=5, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='add', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='add', imp_steps=1, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='add', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='add', imp_steps=15, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='jump', imp_steps=5, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='jump', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='jump', imp_steps=1, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='jump', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='jump', imp_steps=15, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='lstm', imp_steps=5, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='lstm', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='lstm', imp_steps=1, occ_dim=0, drop_prob=0.9)
test_mnist(step_type='lstm', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist(step_type='lstm', imp_steps=15, occ_dim=0, drop_prob=0.9)
# RESULTS
# test_mnist_results(step_type='add', occ_dim=14, drop_prob=0.0)
# test_mnist_results(step_type='add', occ_dim=16, drop_prob=0.0)
# test_mnist_results(step_type='add', occ_dim=0, drop_prob=0.6)
# test_mnist_results(step_type='add', occ_dim=0, drop_prob=0.7)
# test_mnist_results(step_type='add', occ_dim=0, drop_prob=0.8)
# test_mnist_results(step_type='add', occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', occ_dim=14, drop_prob=0.0)
# test_mnist_results(step_type='jump', occ_dim=16, drop_prob=0.0)
# test_mnist_results(step_type='jump', occ_dim=0, drop_prob=0.6)
# test_mnist_results(step_type='jump', occ_dim=0, drop_prob=0.7)
# test_mnist_results(step_type='jump', occ_dim=0, drop_prob=0.8)
# test_mnist_results(step_type='jump', occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='add', imp_steps=1, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='add', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='add', imp_steps=5, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='add', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='add', imp_steps=15, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', imp_steps=1, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', imp_steps=5, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='jump', imp_steps=15, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='lstm', imp_steps=1, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='lstm', imp_steps=2, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='lstm', imp_steps=5, occ_dim=0, drop_prob=0.9)
test_mnist_results(step_type='lstm', imp_steps=10, occ_dim=0, drop_prob=0.9)
# test_mnist_results(step_type='lstm', imp_steps=15, occ_dim=0, drop_prob=0.9)