forked from Philip-Bachman/Sequential-Generation
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TestTSM.py
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TestTSM.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
# theano business
import theano
import theano.tensor as T
# phil's sweetness
from LogPDFs import log_prob_bernoulli, log_prob_gaussian2, gaussian_kld
from NetLayers import relu_actfun, softplus_actfun, tanh_actfun
from HelperFuncs import apply_mask, binarize_data, row_shuffle, to_fX
from HydraNet import HydraNet
from TwoStageModel import TwoStageModel1, TwoStageModel2
from load_data import load_udm, load_binarized_mnist
import utils
#####################################
#####################################
## TEST MODEL THAT INFERS TOP-DOWN ##
#####################################
#####################################
def test_two_stage_model1():
##########################
# 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
#h_det_dim = None
h_det_dim = 50
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_z_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': z_dim,
'activation': tanh_actfun,
'apply_bn': False}, \
{'layer_type': 'fc',
'in_chans': 200,
'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_x = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
q_z_given_x.init_biases(0.0)
#################
# q_h_given_z_x #
#################
params = {}
shared_config = \
[ {'layer_type': 'fc',
'in_chans': (h_dim + 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_z_x = HydraNet(rng=rng, Xd=xin_sym, \
params=params, shared_param_dicts=None)
q_h_given_z_x.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 = TwoStageModel1(rng=rng, x_in=xin_sym, x_out=xout_sym,
x_dim=x_dim, z_dim=z_dim, h_dim=h_dim, h_det_dim=h_det_dim,
q_z_given_x=q_z_given_x,
q_h_given_z_x=q_h_given_z_x,
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("TSM1B_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
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 = "TSM1B_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
######################################
######################################
## TEST MODEL THAT INFERS BOTTOM-UP ##
######################################
######################################
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
if __name__=="__main__":
test_two_stage_model1()
#test_two_stage_model2()