def __init__(self):
""" gets a small batch of data
sets up an S3C model
"""
# We also have to change the value of config.floatX in __init__.
self.prev_floatX = config.floatX
config.floatX = 'float64'
try:
self.tol = 1e-5
#dataset = serial.load('${PYLEARN2_DATA_PATH}/stl10/stl10_patches/data.pkl')
#X = dataset.get_batch_design(1000)
#X = X[:,0:5]
X = np.random.RandomState([1, 2, 3]).randn(1000, 5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 5
#don't give the model an e_step or learning rate so it won't spend years compiling a learn_func
self.model = S3C(
nvis=D,
nhid=N,
irange=.1,
init_bias_hid=0.,
init_B=3.,
min_B=1e-8,
max_B=1000.,
init_alpha=1.,
min_alpha=1e-8,
max_alpha=1000.,
init_mu=1.,
e_step=None,
m_step=Grad_M_Step(),
min_bias_hid=-1e30,
max_bias_hid=1e30,
)
self.model.make_pseudoparams()
self.h_new_coeff_schedule = [
.1, .2, .3, .4, .5, .6, .7, .8, .9, 1.
]
self.e_step = E_Step_Scan(
h_new_coeff_schedule=self.h_new_coeff_schedule)
self.e_step.register_model(self.model)
self.X = X
self.N = N
self.m = m
finally:
config.floatX = self.prev_floatX
def __init__(self):
""" gets a small batch of data
sets up an S3C model and learns on the data
creates an expression for the log likelihood of the data
"""
self.tol = 1e-5
#dataset = serial.load('${GOODFELI_TMP}/cifar10_preprocessed_train_1K.pkl')
X = np.random.RandomState([1, 2, 3]).randn(1000, 108)
#dataset.get_batch_design(1000)
#X = X[:,0:2]
#warnings.warn('hack')
#X[0,0] = 1.
#X[0,1] = -1.
m, D = X.shape
N = 300
self.model = S3C(
nvis=D,
#disable_W_update = 1,
nhid=N,
irange=.5,
init_bias_hid=-.1,
init_B=1.,
min_B=1e-8,
max_B=1e8,
tied_B=1,
e_step=E_Step_Scan(
#h_new_coeff_schedule = [ ],
h_new_coeff_schedule=[.01]),
init_alpha=1.,
min_alpha=1e-8,
max_alpha=1e8,
init_mu=1.,
m_step=Grad_M_Step(learning_rate=1.0),
)
#warnings.warn('hack')
#W = self.model.W.get_value()
#W[0,0] = 1.
#W[1,0] = 1.
#self.model.W.set_value(W)
self.orig_params = self.model.get_param_values()
model = self.model
self.mf_obs = model.e_step.infer(X)
self.stats = SufficientStatistics.from_observations(
needed_stats=model.m_step.needed_stats(), V=X, **self.mf_obs)
self.prob = self.model.expected_log_prob_vhs(
self.stats, H_hat=self.mf_obs['H_hat'], S_hat=self.mf_obs['S_hat'])
self.X = X
self.m = m
self.D = D
self.N = N
def __init__(self):
""" gets a small batch of data
sets up an S3C model and learns on the data
creates an expression for the log likelihood of the data
"""
# We also have to change the value of config.floatX in __init__.
self.prev_floatX = config.floatX
config.floatX = 'float64'
try:
self.tol = 1e-5
if config.mode in ["DebugMode", "DEBUG_MODE"]:
X = np.random.RandomState([1, 2, 3]).randn(30, 108)
m, D = X.shape
N = 10
else:
X = np.random.RandomState([1, 2, 3]).randn(1000, 108)
m, D = X.shape
N = 300
self.model = S3C(nvis = D,
nhid = N,
irange = .5,
init_bias_hid = -.1,
init_B = 1.,
min_B = 1e-8,
max_B = 1e8,
tied_B = 1,
e_step = E_Step_Scan(
h_new_coeff_schedule = [ .01 ]
),
init_alpha = 1.,
min_alpha = 1e-8, max_alpha = 1e8,
init_mu = 1.,
m_step = Grad_M_Step( learning_rate = 1.0 ),
)
self.orig_params = self.model.get_param_values()
model = self.model
self.mf_obs = model.e_step.infer(X)
self.stats = SufficientStatistics.from_observations(needed_stats =
model.m_step.needed_stats(), V =X,
** self.mf_obs)
self.prob = self.model.expected_log_prob_vhs( self.stats , H_hat = self.mf_obs['H_hat'], S_hat = self.mf_obs['S_hat'])
self.X = X
self.m = m
self.D = D
self.N = N
finally:
config.floatX = self.prev_floatX
def __init__(self):
""" gets a small batch of data
sets up an S3C model
"""
# We also have to change the value of config.floatX in __init__.
self.prev_floatX = config.floatX
config.floatX = 'float64'
try:
self.tol = 1e-5
#dataset = serial.load('${PYLEARN2_DATA_PATH}/stl10/stl10_patches/data.pkl')
#X = dataset.get_batch_design(1000)
#X = X[:,0:5]
X = np.random.RandomState([1,2,3]).randn(1000,5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 5
#don't give the model an e_step or learning rate so it won't spend years compiling a learn_func
self.model = S3C(nvis = D,
nhid = N,
irange = .1,
init_bias_hid = 0.,
init_B = 3.,
min_B = 1e-8,
max_B = 1000.,
init_alpha = 1., min_alpha = 1e-8, max_alpha = 1000.,
init_mu = 1., e_step = None,
m_step = Grad_M_Step(),
min_bias_hid = -1e30, max_bias_hid = 1e30,
)
self.model.make_pseudoparams()
self.h_new_coeff_schedule = [.1, .2, .3, .4, .5, .6, .7, .8, .9, 1. ]
self.e_step = E_Step_Scan(h_new_coeff_schedule = self.h_new_coeff_schedule)
self.e_step.register_model(self.model)
self.X = X
self.N = N
self.m = m
finally:
config.floatX = self.prev_floatX
init_mu=init_mu,
init_alpha=init_alpha,
init_B=init_B,
irange=None,
min_B=.01,
max_B=1e6,
min_alpha=.01,
max_alpha=1e6,
e_step=None,
m_step=Grad_M_Step())
model.make_pseudoparams()
#this initial E step is just to help out the
#BatchGradientInference object
model.e_step = E_Step_Scan(clip_reflections=True,
rho=0.5,
h_new_coeff_schedule=[.1] * OVERKILL,
s_new_coeff_schedule=[.3] * OVERKILL)
model.e_step.register_model(model)
import theano.tensor as T
from theano import function
def get_needed_steps(ip, X):
V = T.matrix()
history = ip.infer(V, return_history=True)
print 'compiling'
t1 = time.time()
class Test_S3C_Inference:
def setUp(self):
# Temporarily change config.floatX to float64, as s3c inference
# tests currently fail due to numerical issues for float32.
self.prev_floatX = config.floatX
config.floatX = 'float64'
def tearDown(self):
# Restore previous value of floatX
config.floatX = self.prev_floatX
def __init__(self):
""" gets a small batch of data
sets up an S3C model
"""
# We also have to change the value of config.floatX in __init__.
self.prev_floatX = config.floatX
config.floatX = 'float64'
try:
self.tol = 1e-5
#dataset = serial.load('${PYLEARN2_DATA_PATH}/stl10/stl10_patches/data.pkl')
#X = dataset.get_batch_design(1000)
#X = X[:,0:5]
X = np.random.RandomState([1, 2, 3]).randn(1000, 5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 5
#don't give the model an e_step or learning rate so it won't spend years compiling a learn_func
self.model = S3C(
nvis=D,
nhid=N,
irange=.1,
init_bias_hid=0.,
init_B=3.,
min_B=1e-8,
max_B=1000.,
init_alpha=1.,
min_alpha=1e-8,
max_alpha=1000.,
init_mu=1.,
e_step=None,
m_step=Grad_M_Step(),
min_bias_hid=-1e30,
max_bias_hid=1e30,
)
self.model.make_pseudoparams()
self.h_new_coeff_schedule = [
.1, .2, .3, .4, .5, .6, .7, .8, .9, 1.
]
self.e_step = E_Step_Scan(
h_new_coeff_schedule=self.h_new_coeff_schedule)
self.e_step.register_model(self.model)
self.X = X
self.N = N
self.m = m
finally:
config.floatX = self.prev_floatX
def test_match_unrolled(self):
""" tests that inference with scan matches result using unrolled loops """
unrolled_e_step = E_Step(
h_new_coeff_schedule=self.h_new_coeff_schedule)
unrolled_e_step.register_model(self.model)
V = T.matrix()
scan_result = self.e_step.infer(V)
unrolled_result = unrolled_e_step.infer(V)
outputs = []
for key in scan_result:
outputs.append(scan_result[key])
outputs.append(unrolled_result[key])
f = function([V], outputs)
outputs = f(self.X)
assert len(outputs) % 2 == 0
for i in xrange(0, len(outputs), 2):
assert np.allclose(outputs[i], outputs[i + 1])
def test_grad_s(self):
"tests that the gradients with respect to s_i are 0 after doing a mean field update of s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
model.test_batch_size = X.shape[0]
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
s_idx = S[:, idx]
s_i_func = function([H_var, Mu1_var, idx], s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
grad_Mu1 = T.grad(trunc_kl.sum(), Mu1_var)
grad_Mu1_idx = grad_Mu1[:, idx]
grad_func = function([H_var, Mu1_var, idx], grad_Mu1_idx)
for i in xrange(self.N):
Mu1[:, i] = s_i_func(H, Mu1, i)
g = grad_func(H, Mu1, i)
assert not np.any(np.isnan(g))
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
raise Exception(
'after mean field step, gradient of kl divergence wrt mean field parameter should be 0, but here the max magnitude of a gradient element is '
+ str(g_abs_max) + ' after updating s_' + str(i))
def test_value_s(self):
"tests that the value of the kl divergence decreases with each update to s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
s_idx = S[:, idx]
s_i_func = function([H_var, Mu1_var, idx], s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H, Mu1)
Mu1[:, i] = s_i_func(H, Mu1, i)
new_kl = trunc_kl_func(H, Mu1)
increase = new_kl - prev_kl
mx = increase.max()
if mx > 1e-3:
raise Exception(
'after mean field step in s, kl divergence should decrease, but some elements increased by as much as '
+ str(mx) + ' after updating s_' + str(i))
def test_grad_h(self):
"tests that the gradients with respect to h_i are 0 after doing a mean field update of h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
new_H = e_step.infer_H_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
h_idx = new_H[:, idx]
updates_func = function([H_var, Mu1_var, idx], h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0,
var_s1_hat = Sigma1)
grad_H = T.grad(trunc_kl.sum(), H_var)
assert len(grad_H.type.broadcastable) == 2
#from theano.printing import min_informative_str
#print min_informative_str(grad_H)
#grad_H = Print('grad_H')(grad_H)
#grad_H_idx = grad_H[:,idx]
grad_func = function([H_var, Mu1_var], grad_H)
failed = False
for i in xrange(self.N):
rval = updates_func(H, Mu1, i)
H[:, i] = rval
g = grad_func(H, Mu1)[:, i]
assert not np.any(np.isnan(g))
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
#print "new values of H"
#print H[:,i]
#print "gradient on new values of H"
#print g
failed = True
print 'iteration ', i
#print 'max value of new H: ',H[:,i].max()
#print 'H for failing g: '
failing_h = H[np.abs(g) > self.tol, i]
#print failing_h
#from matplotlib import pyplot as plt
#plt.scatter(H[:,i],g)
#plt.show()
#ignore failures extremely close to h=1
high_mask = failing_h > .001
low_mask = failing_h < .999
mask = high_mask * low_mask
print 'masked failures: ', mask.shape[0], ' err ', g_abs_max
if mask.sum() > 0:
print 'failing h passing the range mask'
print failing_h[mask.astype(bool)]
raise Exception(
'after mean field step, gradient of kl divergence'
' wrt freshly updated variational parameter should be 0, '
'but here the max magnitude of a gradient element is '
+ str(g_abs_max) + ' after updating h_' + str(i))
#assert not failed
def test_value_h(self):
"tests that the value of the kl divergence decreases with each update to h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V=X)
init_Mu1 = e_step.init_S_hat(V=X)
prev_setting = config.compute_test_value
config.compute_test_value = 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0., 1., H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(
-5., 5., Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
newH = e_step.infer_H_hat(V=X, H_hat=H_var, S_hat=Mu1_var)
h_idx = newH[:, idx]
h_i_func = function([H_var, Mu1_var, idx], h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H, Mu1)
H[:, i] = h_i_func(H, Mu1, i)
#we don't update mu, the whole point of the split e step is we don't have to
new_kl = trunc_kl_func(H, Mu1)
increase = new_kl - prev_kl
print 'failures after iteration ', i, ': ', (increase >
self.tol).sum()
mx = increase.max()
if mx > 1e-4:
print 'increase amounts of failing examples:'
print increase[increase > self.tol]
print 'failing H:'
print H[increase > self.tol, :]
print 'failing Mu1:'
print Mu1[increase > self.tol, :]
print 'failing V:'
print X[increase > self.tol, :]
raise Exception(
'after mean field step in h, kl divergence should decrease, but some elements increased by as much as '
+ str(mx) + ' after updating h_' + str(i))
class Test_S3C_Inference:
def setUp(self):
# Temporarily change config.floatX to float64, as s3c inference
# tests currently fail due to numerical issues for float32.
self.prev_floatX = config.floatX
config.floatX = 'float64'
def tearDown(self):
# Restore previous value of floatX
config.floatX = self.prev_floatX
def __init__(self):
""" gets a small batch of data
sets up an S3C model
"""
# We also have to change the value of config.floatX in __init__.
self.prev_floatX = config.floatX
config.floatX = 'float64'
try:
self.tol = 1e-5
#dataset = serial.load('${PYLEARN2_DATA_PATH}/stl10/stl10_patches/data.pkl')
#X = dataset.get_batch_design(1000)
#X = X[:,0:5]
X = np.random.RandomState([1,2,3]).randn(1000,5)
X -= X.mean()
X /= X.std()
m, D = X.shape
N = 5
#don't give the model an e_step or learning rate so it won't spend years compiling a learn_func
self.model = S3C(nvis = D,
nhid = N,
irange = .1,
init_bias_hid = 0.,
init_B = 3.,
min_B = 1e-8,
max_B = 1000.,
init_alpha = 1., min_alpha = 1e-8, max_alpha = 1000.,
init_mu = 1., e_step = None,
m_step = Grad_M_Step(),
min_bias_hid = -1e30, max_bias_hid = 1e30,
)
self.model.make_pseudoparams()
self.h_new_coeff_schedule = [.1, .2, .3, .4, .5, .6, .7, .8, .9, 1. ]
self.e_step = E_Step_Scan(h_new_coeff_schedule = self.h_new_coeff_schedule)
self.e_step.register_model(self.model)
self.X = X
self.N = N
self.m = m
finally:
config.floatX = self.prev_floatX
def test_match_unrolled(self):
""" tests that inference with scan matches result using unrolled loops """
unrolled_e_step = E_Step(h_new_coeff_schedule = self.h_new_coeff_schedule)
unrolled_e_step.register_model(self.model)
V = T.matrix()
scan_result = self.e_step.infer(V)
unrolled_result = unrolled_e_step.infer(V)
outputs = []
for key in scan_result:
outputs.append(scan_result[key])
outputs.append(unrolled_result[key])
f = function([V], outputs)
outputs = f(self.X)
assert len(outputs) % 2 == 0
for i in xrange(0,len(outputs),2):
assert np.allclose(outputs[i],outputs[i+1])
def test_grad_s(self):
"tests that the gradients with respect to s_i are 0 after doing a mean field update of s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
model.test_batch_size = X.shape[0]
init_H = e_step.init_H_hat(V = X)
init_Mu1 = e_step.init_S_hat(V = X)
prev_setting = config.compute_test_value
config.compute_test_value= 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat(V = X, H_hat = H_var, S_hat = Mu1_var)
s_idx = S[:,idx]
s_i_func = function([H_var,Mu1_var,idx],s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
grad_Mu1 = T.grad(trunc_kl.sum(), Mu1_var)
grad_Mu1_idx = grad_Mu1[:,idx]
grad_func = function([H_var, Mu1_var, idx], grad_Mu1_idx)
for i in xrange(self.N):
Mu1[:,i] = s_i_func(H, Mu1, i)
g = grad_func(H,Mu1,i)
assert not contains_nan(g)
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
raise Exception('after mean field step, gradient of kl divergence wrt mean field parameter should be 0, but here the max magnitude of a gradient element is '+str(g_abs_max)+' after updating s_'+str(i))
def test_value_s(self):
"tests that the value of the kl divergence decreases with each update to s_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V = X)
init_Mu1 = e_step.init_S_hat(V = X)
prev_setting = config.compute_test_value
config.compute_test_value= 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
S = e_step.infer_S_hat( V = X, H_hat = H_var, S_hat = Mu1_var)
s_idx = S[:,idx]
s_i_func = function([H_var,Mu1_var,idx],s_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H,Mu1)
Mu1[:,i] = s_i_func(H, Mu1, i)
new_kl = trunc_kl_func(H,Mu1)
increase = new_kl - prev_kl
mx = increase.max()
if mx > 1e-3:
raise Exception('after mean field step in s, kl divergence should decrease, but some elements increased by as much as '+str(mx)+' after updating s_'+str(i))
def test_grad_h(self):
"tests that the gradients with respect to h_i are 0 after doing a mean field update of h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V = X)
init_Mu1 = e_step.init_S_hat(V = X)
prev_setting = config.compute_test_value
config.compute_test_value= 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
new_H = e_step.infer_H_hat(V = X, H_hat = H_var, S_hat = Mu1_var)
h_idx = new_H[:,idx]
updates_func = function([H_var,Mu1_var,idx], h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0,
var_s1_hat = Sigma1)
grad_H = T.grad(trunc_kl.sum(), H_var)
assert len(grad_H.type.broadcastable) == 2
#from theano.printing import min_informative_str
#print min_informative_str(grad_H)
#grad_H = Print('grad_H')(grad_H)
#grad_H_idx = grad_H[:,idx]
grad_func = function([H_var, Mu1_var], grad_H)
failed = False
for i in xrange(self.N):
rval = updates_func(H, Mu1, i)
H[:,i] = rval
g = grad_func(H,Mu1)[:,i]
assert not contains_nan(g)
g_abs_max = np.abs(g).max()
if g_abs_max > self.tol:
#print "new values of H"
#print H[:,i]
#print "gradient on new values of H"
#print g
failed = True
print('iteration ',i)
#print 'max value of new H: ',H[:,i].max()
#print 'H for failing g: '
failing_h = H[np.abs(g) > self.tol, i]
#print failing_h
#from matplotlib import pyplot as plt
#plt.scatter(H[:,i],g)
#plt.show()
#ignore failures extremely close to h=1
high_mask = failing_h > .001
low_mask = failing_h < .999
mask = high_mask * low_mask
print('masked failures: ',mask.shape[0],' err ',g_abs_max)
if mask.sum() > 0:
print('failing h passing the range mask')
print(failing_h[ mask.astype(bool) ])
raise Exception('after mean field step, gradient of kl divergence'
' wrt freshly updated variational parameter should be 0, '
'but here the max magnitude of a gradient element is '
+str(g_abs_max)+' after updating h_'+str(i))
#assert not failed
def test_value_h(self):
"tests that the value of the kl divergence decreases with each update to h_i "
model = self.model
e_step = self.e_step
X = self.X
assert X.shape[0] == self.m
init_H = e_step.init_H_hat(V = X)
init_Mu1 = e_step.init_S_hat(V = X)
prev_setting = config.compute_test_value
config.compute_test_value= 'off'
H, Mu1 = function([], outputs=[init_H, init_Mu1])()
config.compute_test_value = prev_setting
H = broadcast(H, self.m)
Mu1 = broadcast(Mu1, self.m)
H = np.cast[config.floatX](self.model.rng.uniform(0.,1.,H.shape))
Mu1 = np.cast[config.floatX](self.model.rng.uniform(-5.,5.,Mu1.shape))
H_var = T.matrix(name='H_var')
H_var.tag.test_value = H
Mu1_var = T.matrix(name='Mu1_var')
Mu1_var.tag.test_value = Mu1
idx = T.iscalar()
idx.tag.test_value = 0
newH = e_step.infer_H_hat(V = X, H_hat = H_var, S_hat = Mu1_var)
h_idx = newH[:,idx]
h_i_func = function([H_var,Mu1_var,idx],h_idx)
sigma0 = 1. / model.alpha
Sigma1 = e_step.infer_var_s1_hat()
mu0 = T.zeros_like(model.mu)
#by truncated KL, I mean that I am dropping terms that don't depend on H and Mu1
# (they don't affect the outcome of this test and some of them are intractable )
trunc_kl = - model.entropy_hs(H_hat = H_var, var_s0_hat = sigma0, var_s1_hat = Sigma1) + \
model.expected_energy_vhs(V = X, H_hat = H_var, S_hat = Mu1_var, var_s0_hat = sigma0, var_s1_hat = Sigma1)
trunc_kl_func = function([H_var, Mu1_var], trunc_kl)
for i in xrange(self.N):
prev_kl = trunc_kl_func(H,Mu1)
H[:,i] = h_i_func(H, Mu1, i)
#we don't update mu, the whole point of the split e step is we don't have to
new_kl = trunc_kl_func(H,Mu1)
increase = new_kl - prev_kl
print('failures after iteration ',i,': ',(increase > self.tol).sum())
mx = increase.max()
if mx > 1e-4:
print('increase amounts of failing examples:')
print(increase[increase > self.tol])
print('failing H:')
print(H[increase > self.tol,:])
print('failing Mu1:')
print(Mu1[increase > self.tol,:])
print('failing V:')
print(X[increase > self.tol,:])
raise Exception('after mean field step in h, kl divergence should decrease, but some elements increased by as much as '+str(mx)+' after updating h_'+str(i))
