def srr_step_func(ss, zi_zmuv, p_masks, q_masks, si, mi_p, mi_q):
# transform the current belief state into an observation
si_as_x = self._from_si_to_x(si)
full_grad = T.log(1.0 + T.exp(ss)) * (self.x_out - si_as_x)
# get the masked belief state and gradient for primary policy
xi_for_p = (mi_p * self.x_out) + ((1.0 - mi_p) * si_as_x)
grad_for_p = mi_p * full_grad
# update the guide policy's revelation mask
new_to_q = (1.0 - mi_q) * q_masks
mip1_q = mi_q + new_to_q
# get the masked belief state and gradient for guide policy
# xi_for_q = (mip1_q * self.x_out) + ((1.0 - mip1_q) * si_as_x)
xi_for_q = xi_for_p
grad_for_q = mip1_q * full_grad
# get samples of next zi, according to the primary policy
zi_p_mean, zi_p_logvar = self.p_zi_given_xi.apply(
T.horizontal_stack(xi_for_p, grad_for_p), do_samples=False
)
zi_p = zi_p_mean + (T.exp(0.5 * zi_p_logvar) * zi_zmuv)
# get samples of next zi, according to the guide policy
zi_q_mean, zi_q_logvar = self.q_zi_given_xi.apply(
T.horizontal_stack(xi_for_q, grad_for_q), do_samples=False
)
zi_q = zi_q_mean + (T.exp(0.5 * zi_q_logvar) * zi_zmuv)
# make zi samples that can be switched between zi_p and zi_q
zi = (self.train_switch[0] * zi_q) + ((1.0 - self.train_switch[0]) * zi_p)
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(zi_q_mean, zi_q_logvar, zi_p_mean, zi_p_logvar) # KL(q || p)
kldi_p2q = gaussian_kld(zi_p_mean, zi_p_logvar, zi_q_mean, zi_q_logvar) # KL(p || q)
kldi_p2g = gaussian_kld(zi_p_mean, zi_p_logvar, 0.0, 0.0) # KL(p || N(0, I))
# compute next si, given sampled zi (i.e. update the belief state)
hydra_out = self.p_sip1_given_zi.apply(zi)
si_step = hydra_out[0]
if self.step_type == "jump":
# jump steps always do a full swap of belief state
sip1 = si_step
else:
# additive steps adjust the belief state like an LSTM
write_gate = T.nnet.sigmoid(2.0 + hydra_out[1])
erase_gate = T.nnet.sigmoid(2.0 + hydra_out[2])
sip1 = (erase_gate * si) + (write_gate * si_step)
# update the primary policy's revelation mask
new_to_p = (1.0 - mi_p) * p_masks
mip1_p = mi_p + new_to_p
# compute NLL only for the newly revealed values
nlli = self._construct_nll_costs(sip1, self.x_out, new_to_p)
# each loop iteration produces the following values:
# sip1: belief state at end of current step
# mip1_p: revealed values mask to use in next step (primary)
# mip1_q: revealed values mask to use in next step (guide)
# nlli: NLL for values revealed at end of current step
# kldi_q2p: KL(q || p) for the current step
# kldi_p2q: KL(p || q) for the current step
# kldi_p2g: KL(p || N(0,I)) for the current step
return sip1, mip1_p, mip1_q, nlli, kldi_q2p, kldi_p2q, kldi_p2g
def __call__(self, v, output_type='h'):
if hasattr(self, 'layer1_model'):
h1 = self.l1model.h_given_v(v)
s1 = self.l1model.s_given_hv(h1, v)
if hasattr(self, 'layer2_model'):
# preprocessor for input data
h2 = self.l2model.h_given_vx(h1, s1)
s2 = self.l2model.s_given_vxh(h1, s1, h2)
h3 = self.h_given_vx(h2, s2)
return T.horizontal_stack(h1, h2, h3)
else:
h2 = self.h_given_vx(h1, s1)
return T.horizontal_stack(h1, h2)
def __call__(self, v, output_type='h'):
if hasattr(self, 'layer1_model'):
h1 = self.l1model.h_given_v(v)
s1 = self.l1model.s_given_hv(h1, v)
if hasattr(self, 'layer2_model'):
# preprocessor for input data
h2 = self.l2model.h_given_vx(h1, s1)
s2 = self.l2model.s_given_vxh(h1, s1, h2)
h3 = self.h_given_vx(h2, s2)
return T.horizontal_stack(h1,h2,h3)
else:
h2 = self.h_given_vx(h1, s1)
return T.horizontal_stack(h1,h2)
def __call__(self, v, output_type='fg+fh'):
print 'Building representation with %s' % output_type
[g, h, s] = self.e_step(v, n_steps=self.pos_mf_steps)
atoms = {
'g_s' : T.dot(g, self.Wg), # g in s-space
'h_s' : T.dot(h, self.Wh), # h in s-space
's_g' : T.sqrt(T.dot(s**2, self.Wg.T)),
's_h' : T.sqrt(T.dot(s**2, self.Wh.T)),
's_g__h' : T.sqrt(T.dot(s**2 * T.dot(h, self.Wh), self.Wg.T)),
's_h__g' : T.sqrt(T.dot(s**2 * T.dot(g, self.Wg), self.Wh.T))
}
output_prods = {
## factored representations
'g' : g,
'h' : h,
'gs': g * atoms['s_g'],
'hs': h * atoms['s_h'],
's_g': atoms['s_g'],
's_h': atoms['s_h'],
## unfactored representations
'sg_s' : atoms['g_s'] * s,
'sh_s' : atoms['h_s'] * s,
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def get_eem_predict_function(metric_name):
W = T.dmatrix('W')
X = T.dmatrix('X')
beta = T.dmatrix('beta')
m_plus = T.dmatrix('m_plus')
m_minus = T.dmatrix('m_minus')
sigma_plus = T.dmatrix('sigma_plus')
sigma_minus = T.dmatrix('sigma_minus')
H = metric_theano[metric_name](X, W)
def gaussian(x, mu, sigma):
return T.exp(T.power((x - mu[0]), 2) / (-2 * sigma)[0]) / (sigma * T.sqrt(2 * np.pi))[0]
x = T.dot(H, beta)
r_plus = gaussian(x, T.dot(beta.T, m_plus),
T.dot(T.dot(beta.T, sigma_plus), beta))
r_minus = gaussian(x, T.dot(beta.T, m_minus),
T.dot(T.dot(beta.T, sigma_minus), beta))
result = T.argmax(T.horizontal_stack(r_minus, r_plus), axis=1)
eem_predict_function = theano.function(
[X, W, beta, m_plus, m_minus, sigma_plus, sigma_minus], result)
return eem_predict_function
def imp_step_func(zi_zmuv, si):
si_as_x = self._from_si_to_x(si)
xi_unmasked = self.x_out
xi_masked = (self.x_mask * xi_unmasked) + \
((1.0 - self.x_mask) * si_as_x)
grad_unmasked = self.x_out - si_as_x
grad_masked = (self.x_mask * grad_unmasked) + \
((1.0 - self.x_mask) * self.grad_null)
# get samples of next zi, according to the global policy
zi_p_mean, zi_p_logvar = self.p_zi_given_xi.apply( \
T.horizontal_stack(xi_masked, grad_masked), \
do_samples=False)
zi_p = zi_p_mean + (T.exp(0.5 * zi_p_logvar) * zi_zmuv)
# get samples of next zi, according to the guide policy
zi_q_mean, zi_q_logvar = self.q_zi_given_xi.apply( \
T.horizontal_stack(xi_masked, grad_unmasked), \
do_samples=False)
zi_q = zi_q_mean + (T.exp(0.5 * zi_q_logvar) * zi_zmuv)
# make zi samples that can be switched between zi_p and zi_q
zi = ((self.train_switch[0] * zi_q) + \
((1.0 - self.train_switch[0]) * zi_p))
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(zi_q_mean, zi_q_logvar, \
zi_p_mean, zi_p_logvar) # KL(q || p)
kldi_p2q = gaussian_kld(zi_p_mean, zi_p_logvar, \
zi_q_mean, zi_q_logvar) # KL(p || q)
kldi_p2g = gaussian_kld(zi_p_mean, zi_p_logvar, \
0.0, 0.0) # KL(p || global prior)
# compute the next si, given the sampled zi
hydra_out = self.p_sip1_given_zi.apply(zi)
si_step = hydra_out[0]
if (self.step_type == 'jump'):
# jump steps always completely overwrite the current guesses
sip1 = si_step
else:
# additive steps update the current guesses like an LSTM
write_gate = T.nnet.sigmoid(3.0 + hydra_out[1])
erase_gate = T.nnet.sigmoid(3.0 + hydra_out[2])
sip1 = (erase_gate * si) + (write_gate * si_step)
# compute NLL for the current imputation
nlli = self._construct_nll_costs(sip1, self.x_out, self.x_mask)
return sip1, nlli, kldi_q2p, kldi_p2q, kldi_p2g
def fit_cross_cov(self, n_exp=2, n_gauss=2, range_mu=None):
"""
Fit an analytical covariance to the experimental data.
Args:
n_exp (int): number of exponential basic functions
n_gauss (int): number of gaussian basic functions
range_mu: prior mean of the range. Default mean of the lags
Returns:
pymc.Model: PyMC3 model to be sampled using MCMC
"""
self.n_exp = n_exp
self.n_gauss = n_gauss
n_var = self.n_properties
df = self.exp_var
lags = self.lags
# Prior standard deviation for the error of the regression
prior_std_reg = df.std(0).max() * 10
# Prior value for the mean of the ranges
if not range_mu:
range_mu = lags.mean()
# pymc3 Model
with pm.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define priors
sigma = pm.HalfCauchy('sigma', beta=prior_std_reg, testval=1., shape=n_var)
psill = pm.Normal('sill', prior_std_reg, sd=.5 * prior_std_reg, shape=(n_exp + n_gauss))
range_ = pm.Normal('range', range_mu, sd=range_mu * .3, shape=(n_exp + n_gauss))
lambda_ = pm.Uniform('weights', 0, 1, shape=(n_var * (n_exp + n_gauss)))
# Exponential covariance
exp = pm.Deterministic('exp',
# (lambda_[:n_exp*n_var]*
psill[:n_exp] *
(1. - T.exp(T.dot(-lags.as_matrix().reshape((len(lags), 1)),
(range_[:n_exp].reshape((1, n_exp)) / 3.) ** -1))))
gauss = pm.Deterministic('gaus',
psill[n_exp:] *
(1. - T.exp(T.dot(-lags.as_matrix().reshape((len(lags), 1)) ** 2,
(range_[n_exp:].reshape((1, n_gauss)) * 4 / 7.) ** -2))))
# We stack the basic functions in the same matrix and tile it to match the number of properties we have
func = pm.Deterministic('func', T.tile(T.horizontal_stack(exp, gauss), (n_var, 1, 1)))
# We weight each basic function and sum them
func_w = pm.Deterministic("func_w", T.sum(func * lambda_.reshape((n_var, 1, (n_exp + n_gauss))), axis=2))
for e, cross in enumerate(df.columns):
# Likelihoods
pm.Normal(cross + "_like", mu=func_w[e], sd=sigma[e], observed=df[cross].as_matrix())
return model
def __init__(self, inpt, in_sz, n_classes, tied=False):
if tied:
b = share(init_wts(n_classes-1))
w = share(init_wts(in_sz, n_classes-1))
w1 = tt.horizontal_stack(w, tt.zeros((in_sz, 1)))
b1 = tt.concatenate((b, tt.zeros(1)))
self.output = tt.dot(inpt, w1) + b1
else:
b = share(init_wts(n_classes))
w = share(init_wts(in_sz, n_classes))
self.output = tt.dot(inpt, w) + b
self.params = [w, b]
def __init__(self, inpt, in_sz, n_classes, tied=False):
if tied:
b = share(init_wts(n_classes-1))
w = share(init_wts(in_sz, n_classes-1))
w1 = TT.horizontal_stack(w, TT.zeros((in_sz, 1)))
b1 = TT.concatenate((b, TT.zeros(1)))
self.output = TT.dot(inpt, w1) + b1
else:
b = share(init_wts(n_classes))
w = share(init_wts(in_sz, n_classes))
self.output = TT.dot(inpt, w) + b
self.params = [w, b]
def __init__(self, theta):
self.x = T.dmatrix('x')
self.y = T.dmatrix('y')
self.n = self.x.shape[0]
self.theta = theano.shared(theta)
self.a = T.horizontal_stack((self.x.dot(self.theta)).reshape([self.n, 1]), T.zeros([self.n, 1]))
self.prob = T.nnet.softmax(self.a)# T.exp(self.log_prob)
self.l = T.dscalar('l')
self.cost = -(T.sum(T.log(T.nnet.softmax(self.a))*self.y) / self.n) + self.l*T.sum(self.theta[2:]**2)/self.n
self.grad = T.grad(self.cost, wrt=self.theta)
self.hessian = theano.gradient.hessian(self.cost, self.theta)
self.pred = self.prob > .5
self.predict = theano.function(inputs=[self.x], outputs=[self.pred])
def get_output_for(self, inputs, **kwargs):
"""Compute diffusion convolutional activation of inputs."""
Apow = T.horizontal_stack(*inputs[:-1])
X = inputs[-1]
Apow_dot_X = T.dot(Apow, X)
Apow_dot_X_times_W = Apow_dot_X * self.W
out = self.nonlinearity(Apow_dot_X_times_W)
return out
def __call__(self, v, output_type='g+h'):
print 'Building representation with %s' % output_type
init_state = OrderedDict()
init_state['g'] = T.ones(
(v.shape[0], self.n_g)) * T.nnet.sigmoid(self.gbias)
init_state['h'] = T.ones(
(v.shape[0], self.n_h)) * T.nnet.sigmoid(self.hbias)
[g, h, s2_1, s2_0, v, pos_counter] = self.pos_phase(
v, init_state, n_steps=self.pos_steps)
s = s2_1
atoms = {
'g_s': self.from_g(g), # g in s-space
'h_s': self.from_h(h), # h in s-space
's_g': T.sqrt(self.to_g(s**2)),
's_h': T.sqrt(self.to_h(s**2)),
's_g__h': T.sqrt(self.to_g(s**2 * self.from_h(h))),
's_h__g': T.sqrt(self.to_h(s**2 * self.from_g(g))),
}
output_prods = {
## factored representations
'g':
g,
'h':
h,
'gh': (g.dimshuffle(0, 1, 'x') * h.dimshuffle(0, 'x', 1)).flatten(
ndim=2),
'gs':
g * atoms['s_g'],
'hs':
h * atoms['s_h'],
's_g':
atoms['s_g'],
's_h':
atoms['s_h'],
## unfactored representations
'sg_s':
atoms['g_s'] * s,
'sh_s':
atoms['h_s'] * s,
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def imp_step_func(zi_zmuv, si):
si_as_x = self.obs_transform(si)
xi_masked = (self.x_mask * self.x_out) + \
((1.0 - self.x_mask) * si_as_x)
#grad_ll = self.x_out - xi_masked
# get samples of next zi, according to the global policy
zi_p_mean, zi_p_logvar = self.p_zi_given_xi.apply( \
xi_masked, do_samples=False)
zi_p = zi_p_mean + (T.exp(0.5 * zi_p_logvar) * zi_zmuv)
# get samples of next zi, according to the guide policy
zi_q_mean, zi_q_logvar = self.q_zi_given_x_xi.apply( \
T.horizontal_stack(xi_masked, self.x_out), \
do_samples=False)
zi_q = zi_q_mean + (T.exp(0.5 * zi_q_logvar) * zi_zmuv)
if self.use_osm_mode:
zi = zi_p
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(zi_p_mean, zi_p_logvar, 0.0, 0.0)
kldi_p2q = gaussian_kld(zi_p_mean, zi_p_logvar, 0.0, 0.0)
else:
# make zi samples that can be switched between zi_p and zi_q
zi = ((self.train_switch[0] * zi_q) + \
((1.0 - self.train_switch[0]) * zi_p))
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(zi_q_mean, zi_q_logvar, \
zi_p_mean, zi_p_logvar)
kldi_p2q = gaussian_kld(zi_p_mean, zi_p_logvar, \
zi_q_mean, zi_q_logvar)
# compute the next si, given the sampled zi
hydra_out = self.p_xip1_given_zi.apply(zi)
si_step = hydra_out[0]
if (self.step_type == 'jump'):
# jump steps always do a full swap (like standard VAE)
sip1 = si_step
else:
# additive steps adjust the current guesses incrementally
write_gate = T.nnet.sigmoid(2.0 + hydra_out[1])
erase_gate = T.nnet.sigmoid(2.0 + hydra_out[2])
# LSTM-style update
sip1 = (erase_gate * si) + (write_gate * si_step)
# normal update (this was used in workshop papers)
#sip1 = si + si_step
# compute NLL for the current imputation
nlli = self._construct_nll_costs(sip1, self.x_out, 0.0*self.x_mask)
return sip1, nlli, kldi_q2p, kldi_p2q
def get_output_for(self, input, **kwargs):
distances = conv_pairwise_distance(input, self.V)
similarities = T.exp(-distances / T.abs_(self.gamma))
norm = T.sum(similarities, 1).reshape((similarities.shape[0], 1, similarities.shape[2], similarities.shape[3]))
membership = similarities / (norm + self.eps)
histogram = T.mean(membership, axis=(2, 3))
if self.spatial_level == 1:
pivot1, pivot2 = membership.shape[2] / 2, membership.shape[3] / 2
h1 = T.mean(membership[:, :, :pivot1, :pivot2], axis=(2, 3))
h2 = T.mean(membership[:, :, :pivot1, pivot2:], axis=(2, 3))
h3 = T.mean(membership[:, :, pivot1:, :pivot2], axis=(2, 3))
h4 = T.mean(membership[:, :, pivot1:, pivot2:], axis=(2, 3))
# Pyramid is not used in the paper
# histogram = T.horizontal_stack(h1, h2, h3, h4)
histogram = T.horizontal_stack(histogram, h1, h2, h3, h4)
return histogram
def get_output_for(self, inputs, deterministic=False, **kwargs):
# extract inputs
H1, H2 = inputs
# running average projection matrix update
if not deterministic:
# compute batch mean
mean1 = T.mean(H1, axis=0)
mean2 = T.mean(H2, axis=0)
# running average updates of means
mean1 = (floatX(1.0 - self.alpha) * self.mean1 +
self.alpha * mean1)
running_mean1 = theano.clone(self.mean1, share_inputs=False)
running_mean1.default_update = mean1
mean1 += 0 * running_mean1
mean2 = (floatX(1.0 - self.alpha) * self.mean2 +
self.alpha * mean2)
running_mean2 = theano.clone(self.mean2, share_inputs=False)
running_mean2.default_update = mean2
mean2 += 0 * running_mean2
# hidden representations
H1bar = H1 - mean1
H2bar = H2 - mean2
# use means of layer
else:
# hidden representations
H1bar = H1 - self.mean1
H2bar = H2 - self.mean2
# re-project data
lv1_cca = H1bar.dot(self.U)
lv2_cca = H2bar.dot(self.V)
output = T.horizontal_stack(lv1_cca, lv2_cca)
return output
def fit_cross_cov(df, lags, n_exp=2, n_gaus=2, range_mu=None):
n_var = df.columns.shape[0]
n_basis_f = n_var * (n_exp + n_gaus)
prior_std_reg = df.std(0).max() * 10
#
if not range_mu:
range_mu = lags.mean()
# Because is a experimental variogram I am not going to have outliers
nugget_max = df.values.max()
# print(n_basis_f, n_var*n_exp, nugget_max, range_mu, prior_std_reg)
# pymc3 Model
with pm.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define priors
sigma = pm.HalfCauchy('sigma', beta=prior_std_reg, testval=1., shape=n_var)
psill = pm.Normal('sill', prior_std_reg, sd=.5 * prior_std_reg, shape=(n_exp + n_gaus))
range_ = pm.Normal('range', range_mu, sd=range_mu * .3, shape=(n_exp + n_gaus))
# nugget = pm.Uniform('nugget', 0, nugget_max, shape=n_var)
lambda_ = pm.Uniform('weights', 0, 1, shape=(n_var * (n_exp + n_gaus)))
# Exponential covariance
exp = pm.Deterministic('exp',
# (lambda_[:n_exp*n_var]*
psill[:n_exp] *
(1. - T.exp(T.dot(-lags.as_matrix().reshape((len(lags), 1)),
(range_[:n_exp].reshape((1, n_exp)) / 3.) ** -1))))
gaus = pm.Deterministic('gaus',
psill[n_exp:] *
(1. - T.exp(T.dot(-lags.as_matrix().reshape((len(lags), 1)) ** 2,
(range_[n_exp:].reshape((1, n_gaus)) * 4 / 7.) ** -2))))
func = pm.Deterministic('func', T.tile(T.horizontal_stack(exp, gaus), (n_var, 1, 1)))
func_w = pm.Deterministic("func_w", T.sum(func * lambda_.reshape((n_var, 1, (n_exp + n_gaus))), axis=2))
# nugget.reshape((n_var,1)))
for e, cross in enumerate(df.columns):
# Likelihoods
pm.Normal(cross + "_like", mu=func_w[e], sd=sigma[e], observed=df[cross].as_matrix())
return model
def __call__(self, v, output_type='g+h', mean_field=True):
print 'Building representation with %s' % output_type
init_state = OrderedDict()
init_state['g'] = T.ones((v.shape[0],self.n_g)) * T.nnet.sigmoid(self.gbias)
init_state['h'] = T.ones((v.shape[0],self.n_h)) * T.nnet.sigmoid(self.hbias)
init_state['l'] = T.ones((v.shape[0],self.n_l)) * T.nnet.softmax(self.lbias)
[g, h, l] = self.pos_phase(v, init_state, n_steps=self.pos_steps, mean_field=mean_field)
s = self.s_given_ghv(g, h, v)
atoms = {
'g_s' : T.dot(g, self.Wg), # g in s-space
'h_s' : T.dot(h, self.Wh), # h in s-space
's_g' : T.sqrt(T.dot(s**2, self.Wg.T)),
's_h' : T.sqrt(T.dot(s**2, self.Wh.T)),
's_g__h' : T.sqrt(T.dot(s**2 * T.dot(h, self.Wh), self.Wg.T)),
's_h__g' : T.sqrt(T.dot(s**2 * T.dot(g, self.Wg), self.Wh.T))
}
output_prods = {
## factored representations
'g' : g,
'h' : h,
'gh' : (g.dimshuffle(0,1,'x') * h.dimshuffle(0,'x',1)).flatten(ndim=2),
'gs': g * atoms['s_g'],
'hs': h * atoms['s_h'],
's_g': atoms['s_g'],
's_h': atoms['s_h'],
## unfactored representations
'sg_s' : atoms['g_s'] * s,
'sh_s' : atoms['h_s'] * s,
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def __call__(self, v, output_type='h'):
print 'Building representation with %s' % output_type
init_state = OrderedDict()
h = self.h_given_v(v)
s = self.s_given_hv(h, v)
atoms = {
'h_s': self.from_h(h), # h in s-space
's_h': T.sqrt(self.to_h(s**2)),
}
output_prods = {
'h': h,
's': s,
'hs': h * atoms['s_h'],
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def __call__(self, v, output_type='h'):
print 'Building representation with %s' % output_type
init_state = OrderedDict()
h = self.h_given_v(v)
s = self.s_given_hv(h, v)
atoms = {
'h_s' : self.from_h(h), # h in s-space
's_h' : T.sqrt(self.to_h(s**2)),
}
output_prods = {
'h' : h,
's' : s,
'hs': h * atoms['s_h'],
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def apply(self, x_t, h_tm1, c_tm1):
"""
Apply propagate the current input x_t and the previous exposed state
and memory state h_tm1/c_tm1 through this LSTM layer.
"""
hd = self.hid_dim
# merge exogenous (i.e. x_t) and endogenous (i.e. h_tm1) inputs
joint_input = T.horizontal_stack(x_t, h_tm1)
joint_output = T.dot(joint_input, self.W_all) + self.b_all
jo_T = joint_output.T
# compute transformed input to the layer
g_t = T.tanh( jo_T[:,0:(1*hd)].T )
# compute input gate
i_t = T.nnet.sigmoid( jo_T[:,(1*hd):(2*hd)].T )
# compute forget gate
f_t = T.nnet.sigmoid( jo_T[:,(2*hd):(3*hd)].T )
# compute output gate
o_t = T.nnet.sigmoid( jo_T[:,(3*hd):(4*hd)].T )
# compute updated memory state
c_t = (f_t * c_tm1) + (i_t * g_t)
# compute updated exposed state
h_t = (o_t * T.tanh(c_t))
return h_t, c_t
def __call__(self, v, output_type='g+h', mean_field=True):
print 'Building representation with %s' % output_type
init_state = OrderedDict()
init_state['g'] = T.ones((v.shape[0],self.n_g)) * T.nnet.sigmoid(self.gbias)
init_state['h'] = T.ones((v.shape[0],self.n_h)) * T.nnet.sigmoid(self.hbias)
[g, h, pos_counter] = self.pos_phase(v, init_state, n_steps=self.pos_steps, mean_field=mean_field)
atoms = {
'g_s' : T.dot(g, self.Wg), # g in s-space
'h_s' : T.dot(h, self.Wh), # h in s-space
}
output_prods = {
'g' : g,
'h' : h,
'gh' : (g.dimshuffle(0,1,'x') * h.dimshuffle(0,'x',1)).flatten(ndim=2),
}
toks = output_type.split('+')
output = output_prods[toks[0]]
for tok in toks[1:]:
output = T.horizontal_stack(output, output_prods[tok])
return output
def ir_step_func(hi_zmuv, sim1):
# get variables used throughout this refinement step
sim1_obs = self.obs_transform(sim1) # transform state -> obs
grad_ll = self.x_out - sim1_obs
# get samples of next hi, conditioned on current si
hi_p_mean, hi_p_logvar = self.p_hi_given_si.apply( \
sim1_obs, do_samples=False)
# now we build the model for variational hi given si
hi_q_mean, hi_q_logvar = self.q_hi_given_x_si.apply( \
T.horizontal_stack(grad_ll, sim1_obs), \
do_samples=False)
hi_q = (T.exp(0.5 * hi_q_logvar) * hi_zmuv) + hi_q_mean
hi_p = (T.exp(0.5 * hi_p_logvar) * hi_zmuv) + hi_p_mean
# make hi samples that can be switched between hi_p and hi_q
hi = ( ((self.train_switch[0] * hi_q) + \
((1.0 - self.train_switch[0]) * hi_p)) )
# p_sip1_given_si_hi is conditioned on si and hi.
ig_vals, fg_vals, in_vals = self.p_sip1_given_si_hi.apply(hi)
# get the transformed values (for an LSTM style update)
i_gate = 1.0 * T.nnet.sigmoid(ig_vals + 2.0)
f_gate = 1.0 * T.nnet.sigmoid(fg_vals + 2.0)
# perform an LSTM-like update of the state sim1 -> si
si = (in_vals * i_gate) + (sim1 * f_gate)
# compute generator NLL for this step
nlli = self.log_prob_func(self.x_out, self.obs_transform(si))
# compute relevant KLds for this step
kldi_q2p = gaussian_kld(hi_q_mean, hi_q_logvar, \
hi_p_mean, hi_p_logvar)
kldi_p2q = gaussian_kld(hi_p_mean, hi_p_logvar, \
hi_q_mean, hi_q_logvar)
return si, nlli, kldi_q2p, kldi_p2q
def __init__(self, rng=None, \
x_in=None, x_out=None, \
p_h_given_z=None, \
p_x_given_h=None, \
q_z_given_x=None, \
q_h_given_z_x=None, \
x_dim=None, \
z_dim=None, \
h_dim=None, \
params=None, \
shared_param_dicts=None):
# setup a rng for this GIPair
self.rng = RandStream(rng.randint(100000))
# grab the user-provided parameters
self.params = params
self.x_type = self.params['x_type']
assert((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
if 'obs_transform' in self.params:
assert((self.params['obs_transform'] == 'sigmoid') or \
(self.params['obs_transform'] == 'none'))
if self.params['obs_transform'] == 'sigmoid':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
else:
self.obs_transform = lambda x: x
else:
self.obs_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
self.shared_param_dicts = shared_param_dicts
# record the dimensions of various spaces relevant to this model
self.x_dim = x_dim
self.z_dim = z_dim
self.h_dim = h_dim
# grab handles to the relevant InfNets
self.q_z_given_x = q_z_given_x
self.q_h_given_z_x = q_h_given_z_x
self.p_h_given_z = p_h_given_z
self.p_x_given_h = p_x_given_h
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this MultiStageModel
self.x_in = x_in
self.x_out = x_out
# setup switching variable for changing between sampling/training
zero_ary = to_fX( np.zeros((1,)) )
self.train_switch = theano.shared(value=zero_ary, name='tsm_train_switch')
self.set_train_switch(1.0)
if self.shared_param_dicts is None:
# initialize "optimizable" parameters specific to this MSM
init_vec = to_fX( np.zeros((1,self.z_dim)) )
self.p_z_mean = theano.shared(value=init_vec, name='tsm_p_z_mean')
self.p_z_logvar = theano.shared(value=init_vec, name='tsm_p_z_logvar')
self.obs_logvar = theano.shared(value=zero_ary, name='tsm_obs_logvar')
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar)
self.shared_param_dicts = {}
self.shared_param_dicts['p_z_mean'] = self.p_z_mean
self.shared_param_dicts['p_z_logvar'] = self.p_z_logvar
self.shared_param_dicts['obs_logvar'] = self.obs_logvar
else:
self.p_z_mean = self.shared_param_dicts['p_z_mean']
self.p_z_logvar = self.shared_param_dicts['p_z_logvar']
self.obs_logvar = self.shared_param_dicts['obs_logvar']
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar)
##############################################
# Setup the TwoStageModels main computation. #
##############################################
print("Building TSM...")
# samples of "hidden" latent state (from both p and q)
z_q_mean, z_q_logvar, z_q = \
self.q_z_given_x.apply(self.x_in, do_samples=True)
z_p_mean = self.p_z_mean.repeat(z_q.shape[0], axis=0)
z_p_logvar = self.p_z_logvar.repeat(z_q.shape[0], axis=0)
zmuv = self.rng.normal(size=z_q.shape, avg=0.0, std=1.0, \
dtype=theano.config.floatX)
z_p = (T.exp(0.5*z_p_logvar) * zmuv) + z_p_mean
self.z = (self.train_switch[0] * z_q) + \
((1.0 - self.train_switch[0]) * z_p)
# compute relevant KLds for this step
self.kld_z_q2p = gaussian_kld(z_q_mean, z_q_logvar, \
z_p_mean, z_p_logvar)
self.kld_z_p2q = gaussian_kld(z_p_mean, z_p_logvar, \
z_q_mean, z_q_logvar)
# samples of "hidden" latent state (from both p and q)
h_p_mean, h_p_logvar, h_p = self.p_h_given_z.apply(self.z)
h_q_mean, h_q_logvar, h_q = self.q_h_given_z_x.apply( \
T.horizontal_stack(h_p_mean, h_p_logvar, self.x_out))
self.h = (self.train_switch[0] * h_q) + \
((1.0 - self.train_switch[0]) * h_p)
# compute relevant KLds for this step
self.kld_h_q2p = gaussian_kld(h_q_mean, h_q_logvar, \
h_p_mean, h_p_logvar)
self.kld_h_p2q = gaussian_kld(h_p_mean, h_p_logvar, \
h_q_mean, h_q_logvar)
# p_x_given_h generates an observation x conditioned on the "hidden"
# latent variables h.
self.x_gen, _ = self.p_x_given_h.apply(self.h, do_samples=False)
######################################################################
# ALL SYMBOLIC VARS NEEDED FOR THE OBJECTIVE SHOULD NOW BE AVAILABLE #
######################################################################
# shared var learning rate for generator and inferencer
zero_ary = to_fX( np.zeros((1,)) )
self.lr = theano.shared(value=zero_ary, name='tsm_lr')
# shared var momentum parameters for generator and inferencer
self.mom_1 = theano.shared(value=zero_ary, name='tsm_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='tsm_mom_2')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='tsm_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting prior kld against reconstruction
self.lam_kld_q2p = theano.shared(value=zero_ary, name='tsm_lam_kld_q2p')
self.lam_kld_p2q = theano.shared(value=zero_ary, name='tsm_lam_kld_p2q')
self.set_lam_kld(lam_kld_q2p=1.0, lam_kld_p2q=0.0)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='tsm_lam_l2w')
self.set_lam_l2w(1e-5)
# get optimizable parameters belonging to the TwoStageModel
self_params = [self.obs_logvar] #+ [self.p_z_mean, self.p_z_logvar]
# get optimizable parameters belonging to the underlying networks
child_params = []
child_params.extend(self.q_z_given_x.mlp_params)
child_params.extend(self.q_h_given_z_x.mlp_params)
child_params.extend(self.p_h_given_z.mlp_params)
child_params.extend(self.p_x_given_h.mlp_params)
# make a joint list of all optimizable parameters
self.joint_params = self_params + child_params
#################################
# CONSTRUCT THE KLD-BASED COSTS #
#################################
self.kld_z = (self.lam_kld_q2p[0] * self.kld_z_q2p) + \
(self.lam_kld_p2q[0] * self.kld_z_p2q)
self.kld_h = (self.lam_kld_q2p[0] * self.kld_h_q2p) + \
(self.lam_kld_p2q[0] * self.kld_h_p2q)
self.kld_costs = T.sum(self.kld_z, axis=1) + \
T.sum(self.kld_h, axis=1)
# compute "mean" (rather than per-input) costs
self.kld_cost = T.mean(self.kld_costs)
#################################
# CONSTRUCT THE NLL-BASED COSTS #
#################################
self.nll_costs = self._construct_nll_costs(self.x_out)
self.nll_cost = self.lam_nll[0] * T.mean(self.nll_costs)
########################################
# CONSTRUCT THE REST OF THE JOINT COST #
########################################
param_reg_cost = self._construct_reg_costs()
self.reg_cost = self.lam_l2w[0] * param_reg_cost
self.joint_cost = self.nll_cost + self.kld_cost + self.reg_cost
##############################
# CONSTRUCT A PER-INPUT COST #
##############################
self.obs_costs = self.nll_costs + self.kld_costs
# get the gradient of the joint cost for all optimizable parameters
print("Computing gradients of self.joint_cost...")
self.joint_grads = OrderedDict()
grad_list = T.grad(self.joint_cost, self.joint_params)
for i, p in enumerate(self.joint_params):
self.joint_grads[p] = grad_list[i]
# construct the updates for the generator and inferencer networks
all_updates = get_adam_updates(params=self.joint_params, \
grads=self.joint_grads, alpha=self.lr, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-4, max_grad_norm=5.0)
self.joint_updates = OrderedDict()
for k in all_updates:
self.joint_updates[k] = all_updates[k]
# Construct a function for jointly training the generator/inferencer
print("Compiling training function...")
self.train_joint = self._construct_train_joint()
print("Compiling free-energy sampler...")
self.compute_fe_terms = self._construct_compute_fe_terms()
print("Compiling open-loop model sampler...")
self.sample_from_prior = self._construct_sample_from_prior()
return
def learnAndPredict(Ti, C, TOList):
rng = np.random.RandomState(SEED)
learning_rate = learning_rate0
print np.mean(Ti[1000,:])
aminW = np.amin(Ti[:1000,:])
amaxW = np.amax(Ti[:1000,:])
Ti[:1000,:] = (Ti[:1000,:] - aminW) / (amaxW - aminW)
astdW = np.std(Ti[:1000,:])
ameanW = np.mean(Ti[:1000,:])
Ti[:1000,:] = (Ti[:1000,:] - ameanW) / astdW
aminacW = np.amin(Ti[1000,:])
amaxacW = np.amax(Ti[1000,:])
print aminW, amaxW, aminacW, amaxacW
Ti[1000,:] = (Ti[1000,:] - aminacW) / (amaxacW - aminacW)
astdacW = np.std(Ti[1000,:])
ameanacW = np.mean(Ti[1000,:])
Ti[1000,:] = (Ti[1000,:] - ameanacW) / astdacW
ile__ = len(TOList)
ileList = np.zeros(ile__)
for titer in range(len(TOList)):
print np.mean(TOList[titer][1000,:])
TOList[titer][:1000,:] = (TOList[titer][:1000,:] - aminW)/(amaxW - aminW)
TOList[titer][:1000,:] = (TOList[titer][:1000,:] - ameanW)/astdW
TOList[titer][1000,:] = (TOList[titer][1000,:] - aminacW)/(amaxacW - aminacW)
TOList[titer][1000,:] = (TOList[titer][1000,:] - ameanacW)/astdacW
_, ileList[titer] = TOList[titer].shape
_, ile = Ti.shape
N = NN
data = []; yyy = []; need = 1; BYL = {}; j= 0; dwa = 0; ONES = []; ZEROS = []
for i in range(NN):
for j in range(NN):
if i!= j:
if C[i][j]==1:
ONES.append((i,j))
else:
ZEROS.append((i,j))
Nones = len(ONES)
rng.shuffle(ONES)
Nzeros = len(ZEROS)
print Nones
print Nzeros
Needed = NUM_TRAIN/2
onesPerPair = Needed / Nones + 1
onesIter = 0
jj = 0
while jj < NUM_TRAIN:
if jj%300000 == 0:
print jj/300000,
need = 1 - need
if need == 1:
pairNo = onesIter % Nones
ppp = onesIter / Nones
s,t = ONES[pairNo]
shift = rng.randint(0, ile - L)
onesIter += 1
if need == 0:
zer = rng.randint(Nzeros)
s,t = ZEROS[zer]
del ZEROS[zer]
Nzeros -= 1
shift = rng.randint(0, ile - L)
x = np.hstack(( Ti[s][shift:shift+L], Ti[t][shift:shift+L], Ti[1000][shift:shift+L]))
y = C[s][t]
data.append(x); yyy.append(y)
jj+=1
data = np.array(data, dtype=theano.config.floatX)
is_train = np.array( ([0]*96 + [1,1,2,2]) * (NUM_TRAIN / 100))
yyy = np.array(yyy)
train_set_x0, train_set_y0 = np.array(data[is_train==0]), yyy[is_train==0]
test_set_x, test_set_y = np.array(data[is_train==1]), yyy[is_train==1]
valid_set_x, valid_set_y = np.array(data[is_train==2]), yyy[is_train==2]
n_train_batches = len(train_set_y0) / batch_size
n_valid_batches = len(valid_set_y) / batch_size
n_test_batches = len(test_set_y) / batch_size
epoch = T.scalar()
index = T.lscalar()
x = T.matrix('x')
inone2 = T.matrix('inone2')
y = T.ivector('y')
print '... building the model'
#-------- my layers -------------------
#---------------------
layer0_input = x.reshape((batch_size, 1, 3, L))
Cx = 5
layer0 = ConvolutionalLayer(rng, input=layer0_input,
image_shape=(batch_size, 1, 3, L),
filter_shape=(nkerns[0], 1, 2, Cx), poolsize=(1, 1), fac = 0)
ONE = (3 - 2 + 1) / 1
L2 = (L - Cx + 1) / 1
#---------------------
Cx2 = 5
layer1 = ConvolutionalLayer(rng, input=layer0.output,
image_shape=(batch_size, nkerns[0], ONE, L2),
filter_shape=(nkerns[1], nkerns[0], 2, Cx2), poolsize=(1, 1), activation=ReLU, fac = 0)
ONE = (ONE - 2 + 1) /1
L3 = (L2 - Cx2 + 1) /1
#---------------------
Cx3 = 1
layer1b = ConvolutionalLayer(rng, input=layer1.output,
image_shape=(batch_size, nkerns[1], ONE, L3),
filter_shape=(nkerns[2], nkerns[1], 1, Cx3), poolsize=(1, POOL), activation=ReLU, fac = 0)
ONE = (ONE - 1 + 1) /1
L4 = (L3 - Cx3 + 1) /POOL
REGx = 100
#---------------------
layer2_input = layer1b.output.flatten(2)
print layer2_input.shape
use_b = False
layer2 = HiddenLayer(rng, input=layer2_input, n_in=nkerns[2]*L4 , n_out=REGx, activation=T.tanh,
use_bias = use_b)
layer3 = LogisticRegression(input=layer2.output, n_in=REGx, n_out=2)
cost = layer3.negative_log_likelihood(y)
out_x2 = theano.shared(np.asarray(np.zeros((N,L)), dtype=theano.config.floatX))
inone2 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
inone3 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
inone4 = theano.shared(np.asarray(np.zeros((1,L)), dtype=theano.config.floatX))
test_set_x = theano.shared(np.asarray(test_set_x, dtype=theano.config.floatX))
train_set_x = theano.shared(np.asarray(train_set_x0, dtype=theano.config.floatX))
train_set_y = T.cast(theano.shared(np.asarray(train_set_y0, dtype=theano.config.floatX)), 'int32')
test_set_y = T.cast(theano.shared(np.asarray(test_set_y, dtype=theano.config.floatX)), 'int32')
valid_set_y = T.cast(theano.shared(np.asarray(valid_set_y, dtype=theano.config.floatX)), 'int32')
valid_set_x = theano.shared(np.asarray(valid_set_x, dtype=theano.config.floatX))
test_model = theano.function([index], layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function([index], layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
mom_start = 0.5; mom_end = 0.98; mom_epoch_interval = n_epochs * 1.0
#### @@@@@@@@@@@
class_params0 = [layer3, layer2, layer1, layer1b, layer0]
class_params = [ param for layer in class_params0 for param in layer.params ]
gparams = []
for param in class_params:
gparam = T.grad(cost, param)
gparams.append(gparam)
gparams_mom = []
for param in class_params:
gparam_mom = theano.shared(np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
mom = ifelse(epoch < mom_epoch_interval,
mom_start*(1.0 - epoch/mom_epoch_interval) + mom_end*(epoch/mom_epoch_interval),
mom_end)
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
updates[gparam_mom] = mom * gparam_mom - (1. - mom) * learning_rate * gparam
for param, gparam_mom in zip(class_params, gparams_mom):
stepped_param = param + updates[gparam_mom]
squared_filter_length_limit = 15.0
if param.get_value(borrow=True).ndim == 2:
col_norms = T.sqrt(T.sum(T.sqr(stepped_param), axis=0))
desired_norms = T.clip(col_norms, 0, T.sqrt(squared_filter_length_limit))
scale = desired_norms / (1e-7 + col_norms)
updates[param] = stepped_param * scale
else:
updates[param] = stepped_param
output = cost
train_model = theano.function(inputs=[epoch, index], outputs=output,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]})
keep = theano.function([index], layer3.errorsFull(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]}, on_unused_input='warn')
timer = time.clock()
print "finished reading", (timer - start_time0) /60. , "minutes "
# TRAIN MODEL #
print '... training'
validation_frequency = n_train_batches; best_params = None; best_validation_loss = np.inf
best_iter = 0; test_score = 0.; epochc = 0;
while (epochc < n_epochs):
epochc = epochc + 1
learning_rate = learning_rate0 * (1.2 - ((1.0 * epochc)/n_epochs))
for minibatch_index in xrange(n_train_batches):
iter = (epochc - 1) * n_train_batches + minibatch_index
cost_ij = train_model(epochc, minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
this_validation_loss = np.mean(validation_losses)
print(' %i) err %.2f ' % (epochc, this_validation_loss/10)), L, nkerns, REGx, "|", Cx, Cx2, Cx3, batch_size
if this_validation_loss < best_validation_loss or epochc % 30 == 0:
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_score = np.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epochc, minibatch_index + 1, n_train_batches, test_score/10))
############
timel = time.clock()
print "finished learning", (timel - timer) /60. , "minutes "
ppm = theano.function([index], layer3.pred_proba_mine(),
givens={
x: T.horizontal_stack(T.tile(inone2, (batch_size ,1)),
out_x2[index * batch_size: (index + 1) * batch_size], T.tile(inone3, (batch_size ,1))),
y: train_set_y[0 * (batch_size): (0 + 1) * (batch_size)]
}, on_unused_input='warn')
NONZERO = (N*N-N)
gc.collect()
RESList = [np.zeros((N,N)) for it in range(ile__)]
for __net in range(ile__):
TO = TOList[__net]
ileO = ileList[__net]
RES = RESList[__net]
shift = 0.1
DELTAshift = (ileO-L) / (Q-1)
print "DELTAshift:", DELTAshift
for q in range (Q):
dataO = []; print (q+1),"/", Q , " ",
out_x2.set_value(np.asarray(np.array(TO[:,shift:shift+L]), dtype=theano.config.floatX))
PARTIAL = np.zeros((N,N))
inone3.set_value(np.asarray(np.array(TO[1000][shift:shift+L]).reshape(1,L), dtype=theano.config.floatX))
for i in range(N):
inone2.set_value(np.asarray(np.array(TO[i][shift:shift+L]).reshape(1,L), dtype=theano.config.floatX))
p = [ppm(ii) for ii in xrange( N / batch_size)]
for pos in range(N):
if pos != i:
PARTIAL[i][pos] += p[pos / batch_size][pos % batch_size][1]
for i in range(N):
for j in range(N):
RES[i][j] += PARTIAL[i][j]
shift += DELTAshift
print "Finished", __net
RESList[__net] = RES/np.max(RES)
gc.collect()
end_time = time.clock()
print "finished predicting", (end_time - timel) /60. , "minutes ", str(nkerns), "using SEED = ", SEED
print('The code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time0) / 60.))
return RESList
def train_conv_net(
datasets,
rel_tr,
rel_te,
rel_de,
hlen,
U, # yluo: embedding matrix
fnres,
img_w=300,
filter_hs=[3, 4, 5],
hidden_units=[100, 2], # hidden_units[1] is number of classes
dropout_rate=[0.5],
shuffle_batch=True,
n_epochs=25,
batch_size=50, # yluo: how many sentences to extract to compute gradient
lr_decay=0.95,
conv_non_linear="relu",
activations=[Iden],
sqr_norm_lim=9,
non_static=True,
relname=None):
"""
Train a simple conv net
img_h = sentence length (padded where necessary)
img_w = word vector length (300 for word2vec)
filter_hs = filter window sizes
hidden_units = [x,y] x is the number of feature maps (per filter window), and y is the penultimate layer
sqr_norm_lim = s^2 in the paper
lr_decay = adadelta decay parameter
"""
hrel_tr = make_rel_hash(rel_tr)
hrel_te = make_rel_hash(rel_te)
hrel_de = make_rel_hash(rel_de)
rng = np.random.RandomState()
img_h_tot = len(
datasets[0][0]
) - 2 # SS: exclude 2 dimensions: (iid, y). compa1 and compa2 are included
pad = max(filter_hs) - 1
filter_w = img_w
# yluo: what does different feature maps correspond to?
feature_maps = hidden_units[0]
filter_shapes = []
for filter_h in filter_hs:
# yluo: what does 1 in the filter shape mean?
# (number of filters, num input feature maps, filter height, filter width)
# how to interpet different filters?
filter_shapes.append((feature_maps, 1, filter_h, filter_w))
parameters = [("image shape", img_h_tot, img_w),
("filter shape", filter_shapes),
("hidden_units", hidden_units),
("dropout", dropout_rate), ("batch_size", batch_size),
("non_static", non_static), ("learn_decay", lr_decay),
("conv_non_linear", conv_non_linear),
("non_static", non_static),
("sqr_norm_lim", sqr_norm_lim),
("shuffle_batch", shuffle_batch)]
print parameters
#define model architecture
index = T.lscalar()
# x = T.matrix('x')
c1 = T.matrix('c1')
c2 = T.matrix('c2')
prec = T.matrix('prec')
mid = T.matrix('mid')
succ = T.matrix('succ')
y = T.ivector('y')
iid = T.vector('iid')
compa1 = T.vector('compa1') # compatibility1 of c1/c2
compa2 = T.vector('compa2') # compatibility2 of c1/c2
semclass1 = T.vector('semclass1') # semclass of a "predicate"
semclass2 = T.vector('semclass2') # semclass of a "predicate"
semclass3 = T.vector('semclass3') # semclass of a "predicate"
semclass4 = T.vector('semclass4') # semclass of a "predicate"
semclass5 = T.vector('semclass5') # semclass of a "predicate"
#pr = theano.printing.Print("COMPA")(compa)
Words = theano.shared(value=U, name="Words")
zero_vec_tensor = T.vector()
zero_vec = np.zeros(img_w)
set_zero = theano.function([zero_vec_tensor],
updates=[
(Words,
T.set_subtensor(Words[0, :],
zero_vec_tensor))
],
allow_input_downcast=True)
c1_input = Words[T.cast(c1.flatten(), dtype="int32")].reshape(
(c1.shape[0], 1, c1.shape[1], Words.shape[1])) # reshape to 3d array
# Words[T.cast(c1.flatten(),dtype="int32")] >>> len c1 flattened*emb_dim
# c1_input >>> n_insts * 1 * n_ws_per_inst * emb_dim
c2_input = Words[T.cast(c2.flatten(), dtype="int32")].reshape(
(c2.shape[0], 1, c2.shape[1], Words.shape[1])) # reshape to 3d array
prec_input = Words[T.cast(prec.flatten(), dtype="int32")].reshape(
(prec.shape[0], 1, prec.shape[1],
Words.shape[1])) # reshape to 3d array
mid_input = Words[T.cast(mid.flatten(), dtype="int32")].reshape(
(mid.shape[0], 1, mid.shape[1], Words.shape[1])) # reshape to 3d array
succ_input = Words[T.cast(succ.flatten(), dtype="int32")].reshape(
(succ.shape[0], 1, succ.shape[1],
Words.shape[1])) # reshape to 3d array
layer0_input = {
'c1': c1_input,
'c2': c2_input,
'prec': prec_input,
'mid': mid_input,
'succ': succ_input
}
conv_layers = []
layer1_inputs = []
for i in xrange(len(filter_hs)):
for seg in hlen.keys(): # used hlen as a global var, to fix
filter_shape = filter_shapes[i]
img_h = hlen[seg] + 2 * pad
pool_size = (img_h - filter_h + 1, img_w - filter_w + 1)
conv_layer = LeNetConvPoolLayer(rng,
input=layer0_input[seg],
image_shape=(batch_size, 1, img_h,
img_w),
filter_shape=filter_shape,
poolsize=pool_size,
non_linear=conv_non_linear)
layer1_input = conv_layer.output.flatten(
2) # yluo: 2 dimensions >>>
conv_layers.append(conv_layer) # yluo: layer 0
layer1_inputs.append(layer1_input) # yluo: 3 dimensions
layer1_input = T.concatenate(
layer1_inputs, 1) # yluo: 2 dimensions >>> n_insts * concat_dim?
layer1_input = T.horizontal_stack(
layer1_input, compa1.reshape((compa1.shape[0], 1)),
compa2.reshape((compa2.shape[0], 1)),
semclass1.reshape((semclass1.shape[0], 1)),
semclass2.reshape((semclass2.shape[0], 1)),
semclass3.reshape((semclass3.shape[0], 1)),
semclass4.reshape((semclass4.shape[0], 1)),
semclass5.reshape((semclass5.shape[0], 1)))
hidden_units[0] = feature_maps * len(filter_hs) * len(
hlen
) + 2 + 5 # compa: plus 2 (we have two compa feats); semclass: plus 5
classifier = MLPDropout(rng,
input=layer1_input,
layer_sizes=hidden_units,
activations=activations,
dropout_rates=dropout_rate)
#define parameters of the model and update functions using adadelta
params = classifier.params
for conv_layer in conv_layers:
params += conv_layer.params
if non_static:
#if word vectors are allowed to change, add them as model parameters
params += [Words]
cost = classifier.negative_log_likelihood(y)
dropout_cost = classifier.dropout_negative_log_likelihood(y)
grad_updates = sgd_updates_adadelta(params, dropout_cost, lr_decay, 1e-6,
sqr_norm_lim)
#shuffle dataset and assign to mini batches. if dataset size is not a multiple of mini batches, replicate , stochastic gradient descent
#extra data (at random)
tr_size = datasets[0].shape[0]
de_size = datasets[2].shape[0]
hi_seg = datasets[3]
print(hi_seg)
c1s, c1e = hi_seg['c1']
c2s, c2e = hi_seg['c2']
mids, mide = hi_seg['mid']
precs, prece = hi_seg['prec']
succs, succe = hi_seg['succ']
yi = hi_seg['y']
idi = hi_seg['iid']
compa1i = hi_seg['compa1']
compa2i = hi_seg['compa2']
semclass1i = hi_seg['semclass1']
semclass2i = hi_seg['semclass2']
semclass3i = hi_seg['semclass3']
semclass4i = hi_seg['semclass4']
semclass5i = hi_seg['semclass5']
if tr_size % batch_size > 0:
extra_data_num = batch_size - tr_size % batch_size
train_set = rng.permutation(datasets[0])
extra_data = train_set[:extra_data_num]
new_data = np.append(datasets[0], extra_data, axis=0)
else:
new_data = datasets[0]
new_data = rng.permutation(new_data)
n_batches = new_data.shape[0] / batch_size
#n_train_batches = int(np.round(n_batches*0.9))
n_train_batches = n_batches
if de_size % batch_size > 0:
extra_data_num = batch_size - de_size % batch_size
dev_set = rng.permutation(datasets[2])
extra_data = dev_set[:extra_data_num]
new_data_de = np.append(datasets[2], extra_data, axis=0)
else:
new_data_de = datasets[2]
new_data_de = rng.permutation(new_data_de)
n_dev_batches = new_data_de.shape[0] / batch_size
#divide train set into train/val sets
c1_te = datasets[1][:, c1s:c1e]
c2_te = datasets[1][:, c2s:c2e]
prec_te = datasets[1][:, precs:prece]
mid_te = datasets[1][:, mids:mide]
succ_te = datasets[1][:, succs:succe]
test_set = datasets[1]
y_te = np.asarray(test_set[:, yi], "int32")
compa1_te = np.asarray(test_set[:, compa1i], "float32")
compa2_te = np.asarray(test_set[:, compa2i], "float32")
semclass1_te = np.asarray(test_set[:, semclass1i], "float32")
semclass2_te = np.asarray(test_set[:, semclass2i], "float32")
semclass3_te = np.asarray(test_set[:, semclass3i], "float32")
semclass4_te = np.asarray(test_set[:, semclass4i], "float32")
semclass5_te = np.asarray(test_set[:, semclass5i], "float32")
train_set = new_data[:n_train_batches * batch_size, :]
dev_set = new_data_de[:n_dev_batches * batch_size:, :]
x_tr, y_tr = shared_dataset((train_set[:, :img_h_tot], train_set[:, -1]))
x_de, y_de = shared_dataset((dev_set[:, :img_h_tot], dev_set[:, -1]))
iid_tr = train_set[:, idi].flatten()
iid_de = dev_set[:, idi].flatten()
iid_te = test_set[:, idi].flatten()
print('len iid_de %d' % (len(iid_de)))
#compile theano functions to get train/val/test errors
dev_model = theano.function(
[index],
classifier.preds(y),
givens={
c1:
x_de[index * batch_size:(index + 1) * batch_size, c1s:c1e],
c2:
x_de[index * batch_size:(index + 1) * batch_size, c2s:c2e],
prec:
x_de[index * batch_size:(index + 1) * batch_size, precs:prece],
mid:
x_de[index * batch_size:(index + 1) * batch_size, mids:mide],
succ:
x_de[index * batch_size:(index + 1) * batch_size, succs:succe],
compa1:
x_de[index * batch_size:(index + 1) * batch_size, compa1i],
compa2:
x_de[index * batch_size:(index + 1) * batch_size, compa2i],
semclass1:
x_de[index * batch_size:(index + 1) * batch_size, semclass1i],
semclass2:
x_de[index * batch_size:(index + 1) * batch_size, semclass2i],
semclass3:
x_de[index * batch_size:(index + 1) * batch_size, semclass3i],
semclass4:
x_de[index * batch_size:(index + 1) * batch_size, semclass4i],
semclass5:
x_de[index * batch_size:(index + 1) * batch_size, semclass5i],
y:
y_de[index * batch_size:(index + 1) * batch_size],
},
allow_input_downcast=True,
on_unused_input='warn')
# this test_model is batch test model for train
test_model = theano.function(
[index],
classifier.errors(y),
givens={
c1:
x_tr[index * batch_size:(index + 1) * batch_size, c1s:c1e],
c2:
x_tr[index * batch_size:(index + 1) * batch_size, c2s:c2e],
prec:
x_tr[index * batch_size:(index + 1) * batch_size, precs:prece],
mid:
x_tr[index * batch_size:(index + 1) * batch_size, mids:mide],
succ:
x_tr[index * batch_size:(index + 1) * batch_size, succs:succe],
compa1:
x_tr[index * batch_size:(index + 1) * batch_size, compa1i],
compa2:
x_tr[index * batch_size:(index + 1) * batch_size, compa2i],
semclass1:
x_tr[index * batch_size:(index + 1) * batch_size, semclass1i],
semclass2:
x_tr[index * batch_size:(index + 1) * batch_size, semclass2i],
semclass3:
x_tr[index * batch_size:(index + 1) * batch_size, semclass3i],
semclass4:
x_tr[index * batch_size:(index + 1) * batch_size, semclass4i],
semclass5:
x_tr[index * batch_size:(index + 1) * batch_size, semclass5i],
y:
y_tr[index * batch_size:(index + 1) * batch_size]
},
allow_input_downcast=True)
train_model = theano.function(
[index],
cost,
updates=grad_updates,
givens={
c1:
x_tr[index * batch_size:(index + 1) * batch_size, c1s:c1e],
c2:
x_tr[index * batch_size:(index + 1) * batch_size, c2s:c2e],
prec:
x_tr[index * batch_size:(index + 1) * batch_size, precs:prece],
mid:
x_tr[index * batch_size:(index + 1) * batch_size, mids:mide],
succ:
x_tr[index * batch_size:(index + 1) * batch_size, succs:succe],
compa1:
x_tr[index * batch_size:(index + 1) * batch_size, compa1i],
compa2:
x_tr[index * batch_size:(index + 1) * batch_size, compa2i],
semclass1:
x_tr[index * batch_size:(index + 1) * batch_size, semclass1i],
semclass2:
x_tr[index * batch_size:(index + 1) * batch_size, semclass2i],
semclass3:
x_tr[index * batch_size:(index + 1) * batch_size, semclass3i],
semclass4:
x_tr[index * batch_size:(index + 1) * batch_size, semclass4i],
semclass5:
x_tr[index * batch_size:(index + 1) * batch_size, semclass5i],
y:
y_tr[index * batch_size:(index + 1) * batch_size]
},
allow_input_downcast=True)
test_pred_layers = []
test_size = len(y_te)
c1_te_input = Words[T.cast(c1.flatten(), dtype="int32")].reshape(
(c1_te.shape[0], 1, c1_te.shape[1], Words.shape[1]))
c2_te_input = Words[T.cast(c2.flatten(), dtype="int32")].reshape(
(c2_te.shape[0], 1, c2_te.shape[1], Words.shape[1]))
prec_te_input = Words[T.cast(prec.flatten(), dtype="int32")].reshape(
(prec_te.shape[0], 1, prec_te.shape[1], Words.shape[1]))
mid_te_input = Words[T.cast(mid.flatten(), dtype="int32")].reshape(
(mid_te.shape[0], 1, mid_te.shape[1], Words.shape[1]))
succ_te_input = Words[T.cast(succ.flatten(), dtype="int32")].reshape(
(succ_te.shape[0], 1, succ_te.shape[1], Words.shape[1]))
test_layer0_input = {
'c1': c1_te_input,
'c2': c2_te_input,
'prec': prec_te_input,
'mid': mid_te_input,
'succ': succ_te_input
}
cl_id = 0 # conv layer id
for i in xrange(len(filter_hs)):
for seg in hlen.keys():
conv_layer = conv_layers[cl_id]
test_layer0_output = conv_layer.predict(
test_layer0_input[seg], test_size
) ## doesn't seeem to matter if just use layer0_input here
test_pred_layers.append(test_layer0_output.flatten(2))
cl_id += 1
test_layer1_input = T.concatenate(test_pred_layers, 1)
#test_layer1_input = T.horizontal_stack(test_layer1_input, compa_te.reshape((compa_te.shape[0], 1)))
test_layer1_input = T.horizontal_stack(
test_layer1_input, compa1.reshape((compa1.shape[0], 1)),
compa2.reshape((compa2.shape[0], 1)),
semclass1.reshape((semclass1.shape[0], 1)),
semclass2.reshape((semclass2.shape[0], 1)),
semclass3.reshape((semclass3.shape[0], 1)),
semclass4.reshape((semclass4.shape[0], 1)),
semclass5.reshape((semclass5.shape[0], 1)))
test_y_pred = classifier.predict(test_layer1_input)
test_error = T.mean(T.neq(test_y_pred, y))
test_model_all = theano.function([
c1, c2, prec, mid, succ, compa1, compa2, semclass1, semclass2,
semclass3, semclass4, semclass5
],
test_y_pred,
allow_input_downcast=True)
#start training over mini-batches
print '... training'
epoch = 0
best_dev_perf = 0
test_perf = 0
cost_epoch = 0
while (epoch < n_epochs):
start_time = time.time()
epoch = epoch + 1
if shuffle_batch:
for minibatch_index in rng.permutation(range(n_train_batches)):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
else:
for minibatch_index in xrange(n_train_batches):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
train_losses = [
np.mean(test_model(i)) for i in xrange(n_train_batches)
]
train_perf = 1 - np.mean(train_losses)
dev_preds = np.asarray([])
for i in xrange(n_dev_batches):
dev_sb_preds = dev_model(i)
y_sb = y_de[i * batch_size:(i + 1) * batch_size].eval()
dev_sb_errors = dev_sb_preds != y_sb
err_ind = [j for j, x in enumerate(dev_sb_errors) if x == 1]
dev_sb = iid_de[i * batch_size:(i + 1) * batch_size]
dev_preds = np.append(dev_preds, dev_sb_preds)
dev_perf = 1 - np.mean(y_de.eval() != dev_preds)
dev_cm = su.confMat(y_de.eval(), dev_preds, hidden_units[1])
(dev_pres, dev_recs, dev_f1s, dev_mipre, dev_mirec,
dev_mif) = su.cmPRF(dev_cm, ncstart=1)
print(
'epoch: %i, training time: %.2f secs, train perf: %.2f %%, dev_mipre: %.2f %%, dev_mirec: %.2f %%, dev_mif: %.2f %%'
% (epoch, time.time() - start_time, train_perf * 100.,
dev_mipre * 100., dev_mirec * 100., dev_mif * 100.))
if dev_mif >= best_dev_perf:
best_dev_perf = dev_mif
test_pred = test_model_all(c1_te, c2_te, prec_te, mid_te, succ_te,
compa1_te, compa2_te, semclass1_te,
semclass2_te, semclass3_te,
semclass4_te, semclass5_te)
test_preds = extract_preds(rel_te, test_pred, relname)
test_errors = test_pred != y_te
err_ind = [j for j, x in enumerate(test_errors) if x == 1]
test_cm = su.confMat(y_te, test_pred, hidden_units[1])
print('\n'.join([
''.join(['{:10}'.format(int(item)) for item in row])
for row in test_cm
]))
(pres, recs, f1s, mipre, mirec, mif) = su.cmPRF(test_cm, ncstart=1)
mipre_de = dev_mipre
mirec_de = dev_mirec
mif_de = dev_mif
print('mipre %s, mirec %s, mif %s' % (mipre, mirec, mif))
cPickle.dump([y_te, test_pred], open(fnres, "wb"))
return (mipre, mirec, mif, mipre_de, mirec_de, mif_de, test_cm, test_preds)
def get_output_for(self, inputs, deterministic=False, **kwargs):
# extract inputs
H1, H2 = inputs
# train set size
m = H1.shape[0].astype(theano.config.floatX)
# running average projection matrix update
if not deterministic:
# compute batch mean
mean1 = T.mean(H1, axis=0)
mean2 = T.mean(H2, axis=0)
# running average updates of means
mean1 = (floatX(1.0 - self.alpha) * self.mean1 +
self.alpha * mean1)
running_mean1 = theano.clone(self.mean1, share_inputs=False)
running_mean1.default_update = mean1
mean1 += 0 * running_mean1
mean2 = (floatX(1.0 - self.alpha) * self.mean2 +
self.alpha * mean2)
running_mean2 = theano.clone(self.mean2, share_inputs=False)
running_mean2.default_update = mean2
mean2 += 0 * running_mean2
# hidden representations
H1bar = H1 - mean1
H2bar = H2 - mean2
# transpose to formulas in paper
H1bar = H1bar.T
H2bar = H2bar.T
# cross-covariance
S12 = (1.0 / (m - 1)) * T.dot(H1bar, H2bar.T)
# covariance 1
S11 = (1.0 / (m - 1)) * T.dot(H1bar, H1bar.T)
S11 = S11 + self.r1 * T.identity_like(S11)
# covariance 2
S22 = (1.0 / (m - 1)) * T.dot(H2bar, H2bar.T)
S22 = S22 + self.r2 * T.identity_like(S22)
# running average updates of statistics
S12 = (floatX(1.0 - self.alpha) * self.S12 + self.alpha * S12)
running_S12 = theano.clone(self.S12, share_inputs=False)
running_S12.default_update = S12
S12 += 0 * running_S12
S11 = (floatX(1.0 - self.alpha) * self.S11 + self.alpha * S11)
running_S11 = theano.clone(self.S11, share_inputs=False)
running_S11.default_update = S11
S11 += 0 * running_S11
S22 = (floatX(1.0 - self.alpha) * self.S22 + self.alpha * S22)
running_S22 = theano.clone(self.S22, share_inputs=False)
running_S22.default_update = S22
S22 += 0 * running_S22
S21 = S12.T
# theano optimized version of paper
S11c = T.slinalg.cholesky(S11)
S11ci = T.nlinalg.matrix_inverse(S11c)
S11_inv = T.nlinalg.matrix_inverse(S11)
S22c = T.slinalg.cholesky(S22)
S22ci = T.nlinalg.matrix_inverse(S22c)
S22_inv = T.nlinalg.matrix_inverse(S22)
# compute correlation (regularized)
M1 = S11ci.dot(S12).dot(S22_inv).dot(S21).dot(S11ci.T)
M2 = S22ci.dot(S21).dot(S11_inv).dot(S12).dot(S22ci.T)
M1 += self.rT * T.identity_like(M1)
M2 += self.rT * T.identity_like(M2)
# compute eigen decomposition
E1, E = T.nlinalg.eigh(M1)
_, F = T.nlinalg.eigh(M2)
# maximize correlation
E1 = T.clip(E1, 1e-7, 1.0)
E1 = T.sqrt(E1)
self.loss = -T.mean(E1) * self.wl
self.corr = E1
# compute projection matrices
U = S11ci.T.dot(E)
V_prime = S22ci.T.dot(F)
# project data
lv1_cca = H1bar.T.dot(U)
lv2_cca = H2bar.T.dot(V_prime)
# workaround to flip axis of projection vector
def compute_corr(d, lv1_cca, lv2_cca):
CX = lv1_cca[:, d].T.dot(lv2_cca[:, d])
C1 = lv1_cca[:, d].T.dot(lv1_cca[:, d])
C2 = lv2_cca[:, d].T.dot(lv2_cca[:, d])
c = CX / (T.sqrt(C1) * T.sqrt(C2))
return T.sgn(c)
dims = T.arange(0, lv1_cca.shape[1])
corrs, _ = theano.scan(fn=compute_corr,
outputs_info=None,
sequences=[dims],
non_sequences=[lv1_cca, lv2_cca])
# fix projection matrix and reproject data
V = V_prime * corrs
# some casting is required here
U = T.cast(U, 'float32')
V = T.cast(V, 'float32')
# update of projection matrices
running_U = theano.clone(self.U, share_inputs=False)
running_U.default_update = U
U += floatX(0) * running_U
running_V = theano.clone(self.V, share_inputs=False)
running_V.default_update = V
V += floatX(0) * running_V
# use projections of layer
else:
# hidden representations
H1bar = H1 - self.mean1
H2bar = H2 - self.mean2
# transpose to formulas in paper
H1bar = H1bar.T
H2bar = H2bar.T
U, V = self.U, self.V
# re-project data
lv1_cca = H1bar.T.dot(U)
lv2_cca_fixed = H2bar.T.dot(V)
output = T.horizontal_stack(lv1_cca, lv2_cca_fixed)
return output
def __call__(self, v):
return T.horizontal_stack(*self.psamples[1:])
def __init__(self, rng=None, x_in=None, \
p_s0_obs_given_z_obs=None, p_hi_given_si=None, p_sip1_given_si_hi=None, \
p_x_given_si_hi=None, q_z_given_x=None, q_hi_given_x_si=None, \
obs_dim=None, z_rnn_dim=None, z_obs_dim=None, h_dim=None, \
model_init_obs=True, model_init_rnn=True, ir_steps=2, \
params=None):
# setup a rng for this GIPair
self.rng = RandStream(rng.randint(100000))
# TODO: implement functionality for working with "latent" si
assert (p_x_given_si_hi is None)
# decide whether to initialize from a model or from a "constant"
self.model_init_obs = model_init_obs
self.model_init_rnn = model_init_rnn
# grab the user-provided parameters
self.params = params
self.x_type = self.params['x_type']
assert ((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
if 'obs_transform' in self.params:
assert((self.params['obs_transform'] == 'sigmoid') or \
(self.params['obs_transform'] == 'none'))
if self.params['obs_transform'] == 'sigmoid':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
else:
self.obs_transform = lambda x: x
else:
self.obs_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
# record the dimensions of various spaces relevant to this model
self.obs_dim = obs_dim
self.rnn_dim = z_rnn_dim
self.z_dim = z_rnn_dim + z_obs_dim
self.z_rnn_dim = z_rnn_dim
self.z_obs_dim = z_obs_dim
self.h_dim = h_dim
self.ir_steps = ir_steps
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this MultiStageModel
self.x = x_in
self.batch_reps = T.lscalar()
# setup switching variable for changing between sampling/training
zero_ary = np.zeros((1, )).astype(theano.config.floatX)
self.train_switch = theano.shared(value=zero_ary,
name='msm_train_switch')
self.set_train_switch(1.0)
# setup a weight for pulling priors over hi given si towards a
# shared global prior -- e.g. zero mean and unit variance.
self.kzg_weight = theano.shared(value=zero_ary, name='msm_kzg_weight')
self.set_kzg_weight(0.1)
# this weight balances l1 vs. l2 penalty on posterior KLds
self.l1l2_weight = theano.shared(value=zero_ary,
name='msm_l1l2_weight')
self.set_l1l2_weight(1.0)
#############################
# Setup self.z and self.s0. #
#############################
print("Building MSM step 0...")
obs_scale = 0.0
rnn_scale = 0.0
if self.model_init_obs: # initialize obs state from generative model
obs_scale = 1.0
if self.model_init_rnn: # initialize rnn state from generative model
rnn_scale = 1.0
self.q_z_given_x = q_z_given_x.shared_param_clone(rng=rng, Xd=self.x)
self.z = self.q_z_given_x.output
self.z_rnn = self.z[:, :self.z_rnn_dim]
self.z_obs = self.z[:, self.z_rnn_dim:]
self.p_s0_obs_given_z_obs = p_s0_obs_given_z_obs.shared_param_clone( \
rng=rng, Xd=self.z_obs)
_s0_obs_model = self.p_s0_obs_given_z_obs.output_mean
_s0_obs_const = self.p_s0_obs_given_z_obs.mu_layers[-1].b
self.s0_obs = (obs_scale * _s0_obs_model) + \
((1.0 - obs_scale) * _s0_obs_const)
_s0_rnn_model = self.z_rnn
_s0_rnn_const = self.q_z_given_x.mu_layers[-1].b[:self.z_rnn_dim]
self.s0_rnn = (rnn_scale * _s0_rnn_model) + \
((1.0 - rnn_scale) * _s0_rnn_const)
self.s0_jnt = T.horizontal_stack(self.s0_obs, self.s0_rnn)
self.output_logvar = self.p_s0_obs_given_z_obs.sigma_layers[-1].b
self.bounded_logvar = 8.0 * T.tanh((1.0 / 8.0) * self.output_logvar)
###############################################################
# Setup the iterative refinement loop, starting from self.s0. #
###############################################################
self.p_hi_given_si = [] # holds p_hi_given_si for each i
self.p_sip1_given_si_hi = [] # holds p_sip1_given_si_hi for each i
self.q_hi_given_x_si = [] # holds q_hi_given_x_si for each i
self.si = [self.s0_jnt] # holds si for each i
self.hi = [] # holds hi for each i
for i in range(self.ir_steps):
print("Building MSM step {0:d}...".format(i + 1))
_si = self.si[i]
si_obs = _si[:, :self.obs_dim]
si_rnn = _si[:, self.obs_dim:]
# get samples of next hi, conditioned on current si
self.p_hi_given_si.append( \
p_hi_given_si.shared_param_clone(rng=rng, \
Xd=T.horizontal_stack( \
self.obs_transform(si_obs), si_rnn)))
hi_p = self.p_hi_given_si[i].output
# now we build the model for variational hi given si
grad_ll = self.x - self.obs_transform(si_obs)
self.q_hi_given_x_si.append(\
q_hi_given_x_si.shared_param_clone(rng=rng, \
Xd=T.horizontal_stack( \
grad_ll, self.obs_transform(si_obs), si_rnn)))
hi_q = self.q_hi_given_x_si[i].output
# make hi samples that can be switched between hi_p and hi_q
self.hi.append( ((self.train_switch[0] * hi_q) + \
((1.0 - self.train_switch[0]) * hi_p)) )
# p_sip1_given_si_hi is conditioned on hi and the "rnn" part of si.
self.p_sip1_given_si_hi.append( \
p_sip1_given_si_hi.shared_param_clone(rng=rng, \
Xd=T.horizontal_stack(self.hi[i], si_rnn)))
# construct the update from si_obs/si_rnn to sip1_obs/sip1_rnn
sip1_obs = si_obs + self.p_sip1_given_si_hi[i].output_mean
sip1_rnn = si_rnn
sip1_jnt = T.horizontal_stack(sip1_obs, sip1_rnn)
# record the updated state of the generative process
self.si.append(sip1_jnt)
# check that input/output dimensions of our models agree
self._check_model_shapes()
######################################################################
# ALL SYMBOLIC VARS NEEDED FOR THE OBJECTIVE SHOULD NOW BE AVAILABLE #
######################################################################
# shared var learning rate for generator and inferencer
zero_ary = np.zeros((1, )).astype(theano.config.floatX)
self.lr_1 = theano.shared(value=zero_ary, name='msm_lr_1')
self.lr_2 = theano.shared(value=zero_ary, name='msm_lr_2')
# shared var momentum parameters for generator and inferencer
self.mom_1 = theano.shared(value=zero_ary, name='msm_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='msm_mom_2')
self.it_count = theano.shared(value=zero_ary, name='msm_it_count')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='msm_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting prior kld against reconstruction
self.lam_kld_1 = theano.shared(value=zero_ary, name='msm_lam_kld_1')
self.lam_kld_2 = theano.shared(value=zero_ary, name='msm_lam_kld_2')
self.set_lam_kld(lam_kld_1=1.0, lam_kld_2=1.0)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='msm_lam_l2w')
self.set_lam_l2w(1e-5)
# Grab all of the "optimizable" parameters in "group 1"
self.group_1_params = []
self.group_1_params.extend(self.q_z_given_x.mlp_params)
self.group_1_params.extend(self.p_s0_obs_given_z_obs.mlp_params)
# Grab all of the "optimizable" parameters in "group 2"
self.group_2_params = []
for i in range(self.ir_steps):
self.group_2_params.extend(self.q_hi_given_x_si[i].mlp_params)
self.group_2_params.extend(self.p_hi_given_si[i].mlp_params)
self.group_2_params.extend(self.p_sip1_given_si_hi[i].mlp_params)
# Make a joint list of parameters group 1/2
self.joint_params = self.group_1_params + self.group_2_params
#################################
# CONSTRUCT THE KLD-BASED COSTS #
#################################
self.kld_z, self.kld_hi_cond, self.kld_hi_glob = \
self._construct_kld_costs()
self.kld_cost = (self.lam_kld_1[0] * T.mean(self.kld_z)) + \
(self.lam_kld_2[0] * (T.mean(self.kld_hi_cond) + \
(self.kzg_weight[0] * T.mean(self.kld_hi_glob))))
#################################
# CONSTRUCT THE NLL-BASED COSTS #
#################################
self.nll_costs = self._construct_nll_costs()
self.nll_cost = self.lam_nll[0] * T.mean(self.nll_costs)
########################################
# CONSTRUCT THE REST OF THE JOINT COST #
########################################
param_reg_cost = self._construct_reg_costs()
self.reg_cost = self.lam_l2w[0] * param_reg_cost
self.joint_cost = self.nll_cost + self.kld_cost + self.reg_cost
# Get the gradient of the joint cost for all optimizable parameters
self.joint_grads = OrderedDict()
for p in self.joint_params:
self.joint_grads[p] = T.grad(self.joint_cost, p)
# Construct the updates for the generator and inferencer networks
self.group_1_updates = get_param_updates(params=self.group_1_params, \
grads=self.joint_grads, alpha=self.lr_1, \
beta1=self.mom_1, beta2=self.mom_2, it_count=self.it_count, \
mom2_init=1e-3, smoothing=1e-8, max_grad_norm=10.0)
self.group_2_updates = get_param_updates(params=self.group_2_params, \
grads=self.joint_grads, alpha=self.lr_2, \
beta1=self.mom_1, beta2=self.mom_2, it_count=self.it_count, \
mom2_init=1e-3, smoothing=1e-8, max_grad_norm=10.0)
self.joint_updates = OrderedDict()
for k in self.group_1_updates:
self.joint_updates[k] = self.group_1_updates[k]
for k in self.group_2_updates:
self.joint_updates[k] = self.group_2_updates[k]
# Construct a function for jointly training the generator/inferencer
print("Compiling training function...")
self.train_joint = self._construct_train_joint()
self.compute_post_klds = self._construct_compute_post_klds()
self.compute_fe_terms = self._construct_compute_fe_terms()
self.sample_from_prior = self._construct_sample_from_prior()
# make easy access points for some interesting parameters
self.inf_1_weights = self.q_z_given_x.shared_layers[0].W
self.gen_1_weights = self.p_s0_obs_given_z_obs.mu_layers[-1].W
self.inf_2_weights = self.q_hi_given_x_si[0].shared_layers[0].W
self.gen_2_weights = self.p_sip1_given_si_hi[0].mu_layers[-1].W
self.gen_inf_weights = self.p_hi_given_si[0].shared_layers[0].W
return
def __call__(self, v):
return T.horizontal_stack(*self.psamples[1:])
def model_mimlcnn(datasets, Wordv, PF1v, PF2v, img_h, WForATData, linearW):
"""
模型建模.
:param datasets: 放进来的数据集.
:param Wordv: "Word/Token - Embedding" 矩阵.
:param PF1v: "PositionFeature1 - Embedding" 矩阵
:param PF2v: "PositionFeature2 - Embedding" 矩阵
:param img_h: Padding之后的句子长度.
:param RForAT: 关系 - embedding矩阵
:param WForAT: attention算每个句子权值时需要用到的矩阵
:return: 建模之后的所有模型
"""
# 1. 确定超参
logging.info('-------------- Model Settings --------------------')
# if word embedding is initialized with 'word2vec', then 'length' is set by the dimension of the word vector automatically;
# if initialized with 'rand', then the specified value of 'length' is used.
# is_static = False
w2v_static = conf.getboolean('word_vector', 'is_static')
# pfv_length = 5 wordv_length = 50 img_W = 50 + 5 * 2 = 60
image_shape = (None, 1, img_h, _IMG_W)
cp1_filter_shapes = []
cp1_pool_sizes = []
cp2_filter_shape = None
cp2_pool_size = None
assert len(_CP1_FILTER_HS) == 1
# use_stacked_cp = False
if not _USE_STACK_CP:
# _CP1_FILTER_HS = [3]
for filter_h in _CP1_FILTER_HS:
# cp1_n_filters = 230
# _IMG_W = _LEN_WORDV + 2 * _LEN_PFV _CP1_FILTER_W = _IMG_W
# 230,1,3,60
# 每次抽取3个句子的特征
cp1_filter_shapes.append(
(_CP1_N_FILTERS, 1, filter_h, _CP1_FILTER_W))
# filter完后的行数，86,1
cp1_pool_sizes.append(
(img_h - filter_h + 1, _IMG_W - _CP1_FILTER_W + 1))
else:
cp1_filter_shapes.append(
(_CP1_N_FILTERS, 1, _CP1_FILTER_HS[0], _CP1_FILTER_W))
cp1_pool_sizes.append(_CP1_POOL_SIZE_4SCP)
cp2_filter_shape = [_CP2_N_FILTERS, _CP1_N_FILTERS, _CP2_FILTER_H, 1]
cp1_fm_img_h = image_shape[2] - _CP1_FILTER_HS[0] + 1
cp2_img_h = int(np.ceil(cp1_fm_img_h / float(cp1_pool_sizes[0][0])))
cp2_pool_size = [cp2_img_h - _CP2_FILTER_H + 1, 1]
logging.info('| - image_shape: {0}'.format(image_shape))
logging.info('| - cp1_filter_shapes: {0}'.format(cp1_filter_shapes))
logging.info('| - cp1_non_linear: {0}'.format(_CP1_NON_LINEAR))
logging.info('| - cp1_pool_sizes: {0}'.format(cp1_pool_sizes))
if _USE_STACK_CP:
logging.info('| - cp2_filter_shape: {0}'.format(cp2_filter_shape))
logging.info('| - cp2_non_linear: {0}'.format(_CP2_NON_LINEAR))
logging.info('| - cp2_pool_sizes: {0}'.format(cp2_pool_size))
logging.info('| - initial mlp_shape: {0}'.format(_MLP_SHAPE))
logging.info('| - dropout_rates: {0}'.format(_DROPOUT_RATES))
logging.info('| - batch_size: {0}'.format(_BATCH_SIZE))
logging.info('| - word_embedding_length: {0}'.format(_LEN_WORDV))
logging.info('| - word_embedding_initialization: {0}'.format(
conf.get('word_vector', 'initialization')))
logging.info('| - word_embedding_static: {0}'.format(w2v_static))
logging.info('| - shuffle_batch: {0}'.format(_SHUFFLE_BATCH))
logging.info('| - lr_decay: {0}'.format(_LR_DECAY))
logging.info('| - sqr_norm_lim: {0}'.format(_SQR_NORM_LIM))
logging.info('| - learning_rate: {0}'.format(_LR))
logging.info('| - cost_type: {0}'.format(conf.get('mode',
'cost_type')))
logging.info('| - pr_margin: {0}'.format(
conf.getfloat('mode', 'pr_margin')))
logging.info('| - score_margin: {0}'.format(
conf.getfloat('mode', 'score_margin')))
logging.info('| - prediction_type: larger than 0.5 for each label')
logging.info('--------------------------------------------------')
# 2. 计算模型的输入
logging.info(' - Defining model variables for one mini-batch')
bch_idx = T.scalar('batch_idx', dtype='int32')
# bch_idx.tag.test_value = 1
xs = T.matrix('xs', dtype='int32')
# 3个句子
# 不要被test_value欺骗，xs是句子数×88维的
xs.tag.test_value = np.asarray(
[[0, 0, 0, 0, 3, 2, 3, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 2, 4, 1, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 3, 1, 6, 5, 3, 0, 0, 0, 0, 0, 0]],
dtype='int32')
pfinfos = T.matrix('pfinfos', dtype='int32')
pfinfos.tag.test_value = np.array([[4, 2, 1], [5, 1, 3], [5, 0, 1]])
# p0_in_PFv = 52
p0_in_PFv = conf.getint("settings", "p0_in_PFv")
# padding位置信息
def cal_padded_sentpf(pfinfo_m):
slen = pfinfo_m[0]
e1i = pfinfo_m[1]
e2i = pfinfo_m[2]
pf1 = T.arange(p0_in_PFv - e1i,
p0_in_PFv + (slen - e1i),
dtype='int32')
pf2 = T.arange(p0_in_PFv - e2i,
p0_in_PFv + (slen - e2i),
dtype='int32')
# 调整到最小1，最大101,长度与句子长度相同
clipped_pf1 = T.clip(pf1, 1, 101)
clipped_pf2 = T.clip(pf2, 1, 101)
# _N_PAD_HEAD = 4
pad_head = T.zeros(shape=(_N_PAD_HEAD, ), dtype='int32')
pad_tail = T.zeros(shape=(img_h - _N_PAD_HEAD - slen, ), dtype='int32')
# 把两端列表拼成一段列表
pf1_padded = T.concatenate([pad_head, clipped_pf1,
pad_tail]) # 三部分相加=pad后长度.
pf2_padded = T.concatenate([pad_head, clipped_pf2, pad_tail])
return pf1_padded, pf2_padded
# 句子长度，实体1实体2位置 三种信息的padding，padding后向量长度为88，头和尾里面数字是0，实体1和实体2相对位置部分的数字最小是1最大是101
# 返回的是[实体1的位置padding]，[实体2的位置padding]，都是88维，前4维都为空，中间长度就是句子长度，剩下的也是0
(pf1s, pf2s), _ = theano.scan(fn=cal_padded_sentpf, sequences=[pfinfos])
e1is = pfinfos[:, 1]
e2is = pfinfos[:, 2]
# 每次传进来的连续x_m片段都是index从0开始的, 而ep2m依旧是按照数据集中所有的x_m来定位的. 所以在这里让ep2m中的所有元素减一个初始值, 让xs的index和ep2m的每个起始位置对应上.
ep2m_raw = T.matrix('ep2m_raw', dtype='int32')
ep2m_raw.tag.test_value = np.asarray([[25, 27], [27, 28]], dtype='int32')
ep2m = ep2m_raw - ep2m_raw[0][0]
ep2m.tag.test_value = np.asarray([[0, 2], [2, 3]], dtype='int32')
ys = T.matrix('ys', dtype='int32')
# _N_RELATIONS = 26
yl1 = [0] * _N_RELATIONS
yl1[2] = 1
yl2 = [0] * _N_RELATIONS
yl2[5] = 1
ys.tag.test_value = np.asarray([yl1, yl2], dtype='int32')
# 3. 定义模型结构, 定义输出, 定义损失
assert pf1s.dtype == 'int32' and pf2s.dtype == 'int32' and xs.dtype == 'int32'
_use_my_input = True
if _use_my_input:
# 1. 我的拼接方法
# 看到这终于明白了，Wordv是用word2Vec初始好的词向量
# 以维度为1连接传入数据，一个句子本来是向量，现在转成了矩阵
# Wordv[xs.flatten()] = [50维的词向量]
# pf1s =
fltn_vec_stk = T.horizontal_stack(Wordv[xs.flatten()],
PF1v[pf1s.flatten()],
PF2v[pf2s.flatten()])
# 句子数×1×88×60
cp_layer_input = fltn_vec_stk.reshape(
(xs.shape[0], 1, xs.shape[1], _IMG_W))
else:
# 2. Zeng的拼接方法
input_words = Wordv[xs.flatten()].reshape(
(xs.shape[0], 1, xs.shape[1], _LEN_WORDV))
input_pf1s = PF1v[pf1s.flatten()].reshape(
(pf1s.shape[0], 1, pf1s.shape[1], _LEN_PFV))
input_pf2s = PF2v[pf2s.flatten()].reshape(
(pf2s.shape[0], 1, pf2s.shape[1], _LEN_PFV))
cp_layer_input = T.concatenate([input_words, input_pf1s, input_pf2s],
axis=3)
logging.info(' - Defining and assembling CP layer')
cp_params = []
# 句子数×1×88×60
# cp_layer_input =
# 从这里出一个60维的向量，放在卷积层后面
# input = 1×88×60
def atData(input, left, right):
sentence = input[0]
min = T.switch(T.lt(left, right), left, right)
max = T.switch(T.lt(left, right), right, left)
sentenceHead = sentence[:(min + _N_PAD_HEAD)]
sentenceMiddle = sentence[(min + _N_PAD_HEAD + 1):(max + _N_PAD_HEAD)]
sentenceTail = sentence[(max + _N_PAD_HEAD + 1):]
# 去掉了两个entityPair
# 86×60
newSentence = T.vertical_stack(sentenceHead, sentenceMiddle,
sentenceTail)
leftEntity = sentence[min + _N_PAD_HEAD]
rightEntity = sentence[max + _N_PAD_HEAD]
LRConnect = T.concatenate([leftEntity, rightEntity])
def AtLayerData(LRConnect, newSentenceCon):
def forEveryWord(word):
temp = T.concatenate([word, LRConnect])
# return T.concatenate(temp, rightEntity)
return temp
# 将两个entitypair加在了每个句子的后面
# 86×180
sentenceAfAdd, _ = theano.scan(forEveryWord,
sequences=newSentenceCon)
eForWord = T.dot(sentenceAfAdd, WForATData)
aForWord = T.nnet.softmax(eForWord)[0]
def mulWeight(word, weight):
return word * weight
# 86×60
newSRep, _ = theano.scan(mulWeight,
sequences=[newSentence, aForWord])
# 1×60
finalSRep = T.sum(newSRep, axis=0)
return T.dot(finalSRep, linearW)
finalSRep, _ = theano.scan(AtLayerData,
outputs_info=LRConnect,
non_sequences=newSentence,
n_steps=NUMBER_DATA)
return finalSRep[-1]
myobser1, _ = theano.scan(atData, sequences=[cp_layer_input, e1is, e2is])
# No CNN
# cp_out = 句子数×690 myobser1 = 句子数×120
# new_cp_out = 句子数×120
new_cp_out = myobser1
# ****************
# *****源代码******
# *****************
#
#
def ep_max_pooling(ep_mr, csmp_input):
# 取出来的照样是句子数×120的矩阵
input_41ep = csmp_input[ep_mr[0]:ep_mr[1]]
# Cross-sentence Max-pooling
max_pooling_out = T.max(input_41ep, axis=0)
# 返回的就是 Entity-pair Representation
return max_pooling_out
logging.info(' - Aassembling second Max-Pooling layer')
# Entity-pair Representation的列表
# 例子数×(690+120)
sec_maxPooling_out, _ = theano.scan(fn=ep_max_pooling,
sequences=ep2m,
non_sequences=new_cp_out)
logging.info(' - Defining MLP layer')
if not _USE_STACK_CP and _USE_PIECEWISE_POOLING_41CP:
_MLP_SHAPE[0] = 2 * _IMG_W
logging.info(
" - MLP shape changes to {0}, because of piecewise max-pooling".
format(_MLP_SHAPE))
mlp_layer = MLPDropout(rng,
layer_sizes=_MLP_SHAPE,
activations=_MLP_ACTIVATIONS,
dropout_rates=_DROPOUT_RATES)
mlp_layer.feed(sec_maxPooling_out,
input_shape=(_BATCH_SIZE, _MLP_SHAPE[0]))
dropout_score_batch = mlp_layer.dropout_layers[-1].score
score_batch = mlp_layer.layers[-1].score
dropout_p_ygx_batch = T.nnet.sigmoid(dropout_score_batch)
p_ygx_batch = T.nnet.sigmoid(score_batch)
obz_lr_masks = mlp_layer.lrmask
predictions = predict_relations(p_ygx_batch)
pred_pscores = p_ygx_batch
# **************************
# *****加入attention机制******
# **************************
# 针对1个关系
# def forEveryExample(ep_mr, csmp_input):
# # 取出来的照样是句子数×690的矩阵
# # 这些句子是对应一个实体对的
#
# input_41ep = csmp_input[ep_mr[0]: ep_mr[1]]
#
# def forEverySentence(item):
# temp = T.dot(item, WForAT)
# # ???? change this
# re = T.dot(temp, RForAT[0])
# return re
#
# slist, noup = theano.scan(forEverySentence, sequences=input_41ep)
#
# aForRj = T.nnet.softmax(slist)[0]
#
# def mulWeight(sentence, weight):
# return sentence * weight
#
# newSRep, noup = theano.scan(mulWeight, sequences=[input_41ep, aForRj])
#
# finalresult = T.sum(newSRep, axis=0)
#
# # return finalresult
# return finalresult
#
# # # Entity-pair Representation的列表
# my_sec_add_out, _ = theano.scan(fn=forEveryExample, sequences=ep2m, non_sequences=cp_out)
#
# logging.info(' - Defining MLP layer')
# # _USE_STACK_CP = False
# # _USE_PIECEWISE_POOLING_41CP = True
# if not _USE_STACK_CP and _USE_PIECEWISE_POOLING_41CP:
# # _MLP_SHAPE = [230, 26]
# _MLP_SHAPE[0] *= 3
# # _MLP_SHAPE = [690, 26]
# logging.info(" - MLP shape changes to {0}, because of piecewise max-pooling".format(_MLP_SHAPE))
#
# # _MLP_SHAPE = [690,26] _MLP_ACTIVATIONS = [Iden] dropout_rates = [0.5]
# mlp_layer = MLPDropout(rng, layer_sizes=_MLP_SHAPE, activations=_MLP_ACTIVATIONS, dropout_rates=_DROPOUT_RATES)
# # input_shape = (batch_size = 50,_MLP_SHAPE[0] = 690)
# mlp_layer.feed(my_sec_add_out, input_shape=(_BATCH_SIZE, _MLP_SHAPE[0]))
#
# # 针对26个关系
#
# logging.info(' - Defining MLP layer')
#
# assert _USE_STACK_CP == False
# assert _USE_PIECEWISE_POOLING_41CP == True
#
# if not _USE_STACK_CP and _USE_PIECEWISE_POOLING_41CP:
# # _MLP_SHAPE = [230, 26]
# _MLP_SHAPE[0] *= 3
# # _MLP_SHAPE = [690, 26]
# logging.info(" - MLP shape changes to {0}, because of piecewise max-pooling".format(_MLP_SHAPE))
#
# # _MLP_SHAPE = [690,26] _MLP_ACTIVATIONS = [Iden] dropout_rates = [0.5]
#
#
# my_mlp_layer = MyMLPDropout(rng, layer_sizes=[[_MLP_SHAPE[0] + _IMG_W * 2, 2]], activations=_MLP_ACTIVATIONS,
# dropout_rates=_DROPOUT_RATES)
#
# def forEveryRelation(idx, ep2m, cp_out):
# def forEveryExample(ep_mr, csmp_input):
# # 取出来的照样是句子数×(690+120)的矩阵
# # 这些句子是对应一个实体对的
#
# input_41ep = csmp_input[ep_mr[0]: ep_mr[1]]
#
# def attentionLayer(R, input_41ep_out):
# def forEverySentence(item):
# temp = T.dot(item, WForAT)
# # ???? change this
# re = T.dot(temp, R)
# return re
#
# # slist就是ei
# slist, noup = theano.scan(forEverySentence, sequences=input_41ep_out)
#
# aForRj = T.nnet.softmax(slist)[0]
#
# def mulWeight(sentence, weight):
# return sentence * weight
#
# # 句子数×(690+120)
# newSRep, noup = theano.scan(mulWeight, sequences=[input_41ep_out, aForRj])
#
# # 1×(690+120)
# finalresult = T.sum(newSRep, axis=0)
#
# return finalresult
#
# # AT层数×1×(690+120)
# newSRepAf, _ = theano.scan(attentionLayer, outputs_info=RForAT[idx],
# non_sequences=input_41ep, n_steps=NUMBER)
#
# # finalresult = T.sum(newSRepAf[-1], axis=0)
#
# # return finalresult
# # 一次做完吧
#
# return newSRepAf[-1]
#
# # (50, (690+120))
# my_sec_add_out, _ = theano.scan(fn=forEveryExample, sequences=ep2m, non_sequences=[cp_out])
#
# return my_sec_add_out
#
# idx = T.ivector()
# # ok = (26,50,(690+120))
# ok, up = theano.scan(forEveryRelation, sequences=[idx],
# non_sequences=[ep2m, new_cp_out])
#
# # (26, 50, (690 + 120))
# normalre, dropoutre = my_mlp_layer.feed(idx, ok,
# input_shape=(_N_RELATIONS, _BATCH_SIZE, (_MLP_SHAPE[0] + 2 * _IMG_W)))
logging.info(' - Cost, params and grads ...')
# 用这个更新权重
# 计算损失的时候没有用到前面的score，score就是乘出来的26维的向量
# 第二个参数是把score用sigmoid做归一化得出来的结果
dropout_cost = compute_cost(dropout_p_ygx_batch, ys)
cost = compute_cost(p_ygx_batch, ys)
op_params = []
params = []
op_params += [Wordv]
# is_static = False
if not w2v_static: # if word vectors are allowed to change, add them as model hyper_parameters
params += [Wordv]
op_params += [PF1v, PF2v]
params += [PF1v, PF2v]
op_params += cp_params
params += cp_params
op_params += mlp_layer.params
params += mlp_layer.params
op_params += [WForATData, linearW]
params += [WForATData, linearW]
# op_params += [WForAT, RForAT]
# params += [WForAT, RForAT]
logging.info('Params to update: ' +
str(', '.join([param.name for param in params])))
logging.info('Params to output: ' +
str(', '.join([op_param.name for op_param in op_params])))
# 5. 权重更新方式.
grad_updates = lasagne.updates.adadelta(dropout_cost, params)
# 6. 定义theano_function
train_x_m, train_PFinfo_m, train_ep2m, train_y, test_x_m, test_PFinfo_m, test_ep2m, test_y = datasets
logging.info('Compiling train_update_model...')
output_list = [cost, dropout_cost]
if conf.getboolean('mode', 'output_details'):
output_list += ([obz_lr_masks, p_ygx_batch] + op_params)
train_update_model = theano.function(
[bch_idx],
output_list,
updates=grad_updates,
name='train_update_model',
givens={
xs: get_1batch_x_m(bch_idx, train_x_m, train_ep2m),
pfinfos: get_1batch_x_m(bch_idx, train_PFinfo_m, train_ep2m),
ep2m_raw:
train_ep2m[bch_idx * _BATCH_SIZE:(bch_idx + 1) * _BATCH_SIZE],
ys: train_y[bch_idx * _BATCH_SIZE:(bch_idx + 1) * _BATCH_SIZE],
},
)
# }, on_unused_input='warn')
logging.info('Compiling set_zero function ...')
Wordv_0sline = T.vector("Wordv_0sline", dtype=theano.config.floatX)
PFv_0sline = T.vector("PFv_0sline", dtype=theano.config.floatX)
set_zero = theano.function(
[Wordv_0sline, PFv_0sline],
updates=[(Wordv, T.set_subtensor(Wordv[0, :], Wordv_0sline)),
(PF1v, T.set_subtensor(PF1v[0, :], PFv_0sline)),
(PF2v, T.set_subtensor(PF2v[0, :], PFv_0sline))])
logging.info('Compiling trainset_error_model ...')
trainset_error_model = theano.function(
[bch_idx], [predictions, pred_pscores],
givens={
xs:
get_1batch_x_m(bch_idx, train_x_m, train_ep2m),
pfinfos:
get_1batch_x_m(bch_idx, train_PFinfo_m, train_ep2m),
ep2m_raw:
train_ep2m[bch_idx * _BATCH_SIZE:(bch_idx + 1) * _BATCH_SIZE],
})
logging.info('Compiling testset_error_model ...')
testset_error_model = theano.function(
[bch_idx], [predictions, pred_pscores],
givens={
xs: get_1batch_x_m(bch_idx, test_x_m, test_ep2m),
pfinfos: get_1batch_x_m(bch_idx, test_PFinfo_m, test_ep2m),
ep2m_raw:
test_ep2m[bch_idx * _BATCH_SIZE:(bch_idx + 1) * _BATCH_SIZE],
})
init_LR_W = mlp_layer.dropout_layers[-1].W.get_value()
init_LR_b = mlp_layer.dropout_layers[-1].b.get_value()
return train_update_model, trainset_error_model, testset_error_model, set_zero, init_LR_W, init_LR_b
import theano import theano.tensor as T import numpy as np a = T.imatrix() b = T.imatrix() ok = T.horizontal_stack(a, b) myfunc = theano.function([a, b], ok) a_init = np.reshape(np.arange(10, dtype='int32'), (2, 5)) b_init = np.reshape(np.arange(10, 20, dtype='int32'), (2, 5)) ok = myfunc(a_init, b_init) print ok
def __init__(self, rng=None, x_in=None, \
p_s0_obs_given_z_obs=None, p_hi_given_si=None, p_sip1_given_si_hi=None, \
p_x_given_si_hi=None, q_z_given_x=None, q_hi_given_x_si=None, \
obs_dim=None, z_dim=None, h_dim=None, \
model_init_obs=True, ir_steps=2, \
params=None):
# setup a rng for this GIPair
self.rng = RandStream(rng.randint(100000))
# TODO: implement functionality for working with "latent" si
assert(p_x_given_si_hi is None)
# decide whether to initialize from a model or from a "constant"
self.model_init_obs = model_init_obs
# grab the user-provided parameters
self.params = params
self.x_type = self.params['x_type']
assert((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
if 'obs_transform' in self.params:
assert((self.params['obs_transform'] == 'sigmoid') or \
(self.params['obs_transform'] == 'none'))
if self.params['obs_transform'] == 'sigmoid':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
else:
self.obs_transform = lambda x: x
else:
self.obs_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
# record the dimensions of various spaces relevant to this model
self.obs_dim = obs_dim
self.z_dim = z_dim
self.h_dim = h_dim
self.ir_steps = ir_steps
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this MultiStageModel
self.x = x_in
self.batch_reps = T.lscalar()
# setup switching variable for changing between sampling/training
zero_ary = np.zeros((1,)).astype(theano.config.floatX)
self.train_switch = theano.shared(value=zero_ary, name='msm_train_switch')
self.set_train_switch(1.0)
# setup a weight for pulling priors over hi given si towards a
# shared global prior -- e.g. zero mean and unit variance.
self.kzg_weight = theano.shared(value=zero_ary, name='msm_kzg_weight')
self.set_kzg_weight(0.1)
# this weight balances l1 vs. l2 penalty on posterior KLds
self.l1l2_weight = theano.shared(value=zero_ary, name='msm_l1l2_weight')
self.set_l1l2_weight(1.0)
# this parameter controls dropout rate in the generator read function
self.drop_rate = theano.shared(value=zero_ary, name='msm_drop_rate')
self.set_drop_rate(0.0)
#############################
# Setup self.z and self.s0. #
#############################
print("Building MSM step 0...")
obs_scale = 0.0
if self.model_init_obs: # initialize obs state from generative model
obs_scale = 1.0
self.q_z_given_x = q_z_given_x.shared_param_clone(rng=rng, Xd=self.x)
self.z = self.q_z_given_x.output
self.p_s0_obs_given_z_obs = p_s0_obs_given_z_obs.shared_param_clone( \
rng=rng, Xd=self.z)
_s0_obs_model = self.p_s0_obs_given_z_obs.output_mean
_s0_obs_const = self.p_s0_obs_given_z_obs.mu_layers[-1].b
self.s0_obs = (obs_scale * _s0_obs_model) + \
((1.0 - obs_scale) * _s0_obs_const)
self.output_logvar = self.p_s0_obs_given_z_obs.sigma_layers[-1].b
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.output_logvar)
###############################################################
# Setup the iterative refinement loop, starting from self.s0. #
###############################################################
self.p_hi_given_si = [] # holds p_hi_given_si for each i
self.p_sip1_given_si_hi = [] # holds p_sip1_given_si_hi for each i
self.q_hi_given_x_si = [] # holds q_hi_given_x_si for each i
self.si = [self.s0_obs] # holds si for each i
self.hi = [] # holds hi for each i
for i in range(self.ir_steps):
print("Building MSM step {0:d}...".format(i+1))
si_obs = self.si[i]
# get samples of next hi, conditioned on current si
self.p_hi_given_si.append( \
p_hi_given_si.shared_param_clone(rng=rng, \
Xd=self.obs_transform(si_obs)))
hi_p = self.p_hi_given_si[i].output
# now we build the model for variational hi given si
grad_ll = self.x - self.obs_transform(si_obs)
self.q_hi_given_x_si.append(\
q_hi_given_x_si.shared_param_clone(rng=rng, \
Xd=T.horizontal_stack( \
grad_ll, self.obs_transform(si_obs))))
hi_q = self.q_hi_given_x_si[i].output
# make hi samples that can be switched between hi_p and hi_q
self.hi.append( ((self.train_switch[0] * hi_q) + \
((1.0 - self.train_switch[0]) * hi_p)) )
# p_sip1_given_si_hi is conditioned on hi.
self.p_sip1_given_si_hi.append( \
p_sip1_given_si_hi.shared_param_clone(rng=rng, \
Xd=self.hi[i]))
# construct the update from si_obs to sip1_obs
sip1_obs = si_obs + self.p_sip1_given_si_hi[i].output_mean
# record the updated state of the generative process
self.si.append(sip1_obs)
######################################################################
# ALL SYMBOLIC VARS NEEDED FOR THE OBJECTIVE SHOULD NOW BE AVAILABLE #
######################################################################
# shared var learning rate for generator and inferencer
zero_ary = np.zeros((1,)).astype(theano.config.floatX)
self.lr_1 = theano.shared(value=zero_ary, name='msm_lr_1')
self.lr_2 = theano.shared(value=zero_ary, name='msm_lr_2')
# shared var momentum parameters for generator and inferencer
self.mom_1 = theano.shared(value=zero_ary, name='msm_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='msm_mom_2')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='msm_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting prior kld against reconstruction
self.lam_kld_1 = theano.shared(value=zero_ary, name='msm_lam_kld_1')
self.lam_kld_2 = theano.shared(value=zero_ary, name='msm_lam_kld_2')
self.set_lam_kld(lam_kld_1=1.0, lam_kld_2=1.0)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='msm_lam_l2w')
self.set_lam_l2w(1e-5)
# Grab all of the "optimizable" parameters in "group 1"
self.group_1_params = []
self.group_1_params.extend(self.q_z_given_x.mlp_params)
self.group_1_params.extend(self.p_s0_obs_given_z_obs.mlp_params)
# Grab all of the "optimizable" parameters in "group 2"
self.group_2_params = []
for i in range(self.ir_steps):
self.group_2_params.extend(self.q_hi_given_x_si[i].mlp_params)
self.group_2_params.extend(self.p_hi_given_si[i].mlp_params)
self.group_2_params.extend(self.p_sip1_given_si_hi[i].mlp_params)
# Make a joint list of parameters group 1/2
self.joint_params = self.group_1_params + self.group_2_params
#################################
# CONSTRUCT THE KLD-BASED COSTS #
#################################
self.kld_z, self.kld_hi_cond, self.kld_hi_glob = \
self._construct_kld_costs()
self.kld_cost = (self.lam_kld_1[0] * T.mean(self.kld_z)) + \
(self.lam_kld_2[0] * (T.mean(self.kld_hi_cond) + \
(self.kzg_weight[0] * T.mean(self.kld_hi_glob))))
#################################
# CONSTRUCT THE NLL-BASED COSTS #
#################################
self.nll_costs = self._construct_nll_costs()
self.nll_cost = self.lam_nll[0] * T.mean(self.nll_costs)
########################################
# CONSTRUCT THE REST OF THE JOINT COST #
########################################
param_reg_cost = self._construct_reg_costs()
self.reg_cost = self.lam_l2w[0] * param_reg_cost
self.joint_cost = self.nll_cost + self.kld_cost + self.reg_cost
# Get the gradient of the joint cost for all optimizable parameters
print("Computing gradients of self.joint_cost...")
self.joint_grads = OrderedDict()
grad_list = T.grad(self.joint_cost, self.joint_params)
for i, p in enumerate(self.joint_params):
self.joint_grads[p] = grad_list[i]
# Construct the updates for the generator and inferencer networks
self.group_1_updates = get_adam_updates(params=self.group_1_params, \
grads=self.joint_grads, alpha=self.lr_1, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-5, max_grad_norm=10.0)
self.group_2_updates = get_adam_updates(params=self.group_2_params, \
grads=self.joint_grads, alpha=self.lr_2, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-5, max_grad_norm=10.0)
self.joint_updates = OrderedDict()
for k in self.group_1_updates:
self.joint_updates[k] = self.group_1_updates[k]
for k in self.group_2_updates:
self.joint_updates[k] = self.group_2_updates[k]
# Construct a function for jointly training the generator/inferencer
print("Compiling training function...")
self.train_joint = self._construct_train_joint()
self.compute_post_klds = self._construct_compute_post_klds()
self.compute_fe_terms = self._construct_compute_fe_terms()
self.sample_from_prior = self._construct_sample_from_prior()
# make easy access points for some interesting parameters
self.inf_1_weights = self.q_z_given_x.shared_layers[0].W
self.gen_1_weights = self.p_s0_obs_given_z_obs.mu_layers[-1].W
self.inf_2_weights = self.q_hi_given_x_si[0].shared_layers[0].W
self.gen_2_weights = self.p_sip1_given_si_hi[0].mu_layers[-1].W
self.gen_inf_weights = self.p_hi_given_si[0].shared_layers[0].W
return
def __init__(self, rng=None, \
x_in=None, x_out=None, \
p_s_given_z=None, \
p_h_given_s=None, \
p_x_given_s_h=None, \
q_z_given_x=None, \
q_h_given_x_s=None, \
x_dim=None, \
z_dim=None, \
s_dim=None, \
h_dim=None, \
params=None, \
shared_param_dicts=None):
# setup a rng for this GIPair
self.rng = RandStream(rng.randint(100000))
# grab the user-provided parameters
self.params = params
self.x_type = self.params['x_type']
assert((self.x_type == 'bernoulli') or (self.x_type == 'gaussian'))
if 'obs_transform' in self.params:
assert((self.params['obs_transform'] == 'sigmoid') or \
(self.params['obs_transform'] == 'none'))
if self.params['obs_transform'] == 'sigmoid':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
else:
self.obs_transform = lambda x: x
else:
self.obs_transform = lambda x: T.nnet.sigmoid(x)
if self.x_type == 'bernoulli':
self.obs_transform = lambda x: T.nnet.sigmoid(x)
self.shared_param_dicts = shared_param_dicts
# record the dimensions of various spaces relevant to this model
self.x_dim = x_dim
self.z_dim = z_dim
self.s_dim = s_dim
self.h_dim = h_dim
# grab handles to the relevant InfNets
self.q_z_given_x = q_z_given_x
self.q_h_given_x_s = q_h_given_x_s
self.p_s_given_z = p_s_given_z
self.p_h_given_s = p_h_given_s
self.p_x_given_s_h = p_x_given_s_h
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this MultiStageModel
self.x_in = x_in
self.x_out = x_out
self.batch_reps = T.lscalar()
# setup switching variable for changing between sampling/training
zero_ary = to_fX( np.zeros((1,)) )
self.train_switch = theano.shared(value=zero_ary, name='msm_train_switch')
self.set_train_switch(1.0)
# setup a variable for controlling dropout noise
self.drop_rate = theano.shared(value=zero_ary, name='msm_drop_rate')
self.set_drop_rate(0.0)
# this weight balances l1 vs. l2 penalty on posterior KLds
self.lam_kld_l1l2 = theano.shared(value=zero_ary, name='msm_lam_kld_l1l2')
self.set_lam_kld_l1l2(1.0)
if self.shared_param_dicts is None:
# initialize "optimizable" parameters specific to this MSM
init_vec = to_fX( np.zeros((self.z_dim,)) )
self.p_z_mean = theano.shared(value=init_vec, name='msm_p_z_mean')
self.p_z_logvar = theano.shared(value=init_vec, name='msm_p_z_logvar')
init_vec = to_fX( np.zeros((self.x_dim,)) )
self.obs_logvar = theano.shared(value=zero_ary, name='msm_obs_logvar')
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar)
self.shared_param_dicts = {}
self.shared_param_dicts['p_z_mean'] = self.p_z_mean
self.shared_param_dicts['p_z_logvar'] = self.p_z_logvar
self.shared_param_dicts['obs_logvar'] = self.obs_logvar
else:
self.p_z_mean = self.shared_param_dicts['p_z_mean']
self.p_z_logvar = self.shared_param_dicts['p_z_logvar']
self.obs_logvar = self.shared_param_dicts['obs_logvar']
self.bounded_logvar = 8.0 * T.tanh((1.0/8.0) * self.obs_logvar)
# get a drop mask that drops things with probability p
drop_scale = 1. / (1. - self.drop_rate[0])
drop_rnd = self.rng.uniform(size=self.x_out.shape, \
low=0.0, high=1.0, dtype=theano.config.floatX)
drop_mask = drop_scale * (drop_rnd > self.drop_rate[0])
##############################################
# Setup the TwoStageModels main computation. #
##############################################
print("Building TSM...")
# samples of "first" latent state
drop_x = drop_mask * self.x_in
z_q_mean, z_q_logvar, self.z = \
self.q_z_given_x.apply(drop_x, do_samples=True)
# compute relevant KLds for this step
self.kld_z_q2ps = gaussian_kld(z_q_mean, z_q_logvar, \
self.p_z_mean, self.p_z_logvar)
self.kld_z_p2qs = gaussian_kld(self.p_z_mean, self.p_z_logvar, \
z_q_mean, z_q_logvar)
# transform "first" latent state into "second" latent state
self.s, _ = self.p_s_given_z.apply(self.z, do_samples=False)
# get samples of h, conditioned on current s
h_p_mean, h_p_logvar, h_p = self.p_h_given_s.apply( \
self.s, do_samples=True)
# get variational samples of h, given s and x_out
h_q_mean, h_q_logvar, h_q = self.q_h_given_x_s.apply( \
T.horizontal_stack(self.x_out, self.s), \
do_samples=True)
# make h samples that can be switched between h_p and h_q
self.h = (self.train_switch[0] * h_q) + \
((1.0 - self.train_switch[0]) * h_p)
# compute relevant KLds for this step
self.kld_h_q2ps = gaussian_kld(h_q_mean, h_q_logvar, \
h_p_mean, h_p_logvar)
self.kld_h_p2qs = gaussian_kld(h_p_mean, h_p_logvar, \
h_q_mean, h_q_logvar)
# p_x_given_s_h is conditioned on s and h.
self.x_gen, _ = self.p_x_given_s_h.apply( \
T.horizontal_stack(self.s, self.h), \
do_samples=False)
######################################################################
# ALL SYMBOLIC VARS NEEDED FOR THE OBJECTIVE SHOULD NOW BE AVAILABLE #
######################################################################
# shared var learning rate for generator and inferencer
zero_ary = to_fX( np.zeros((1,)) )
self.lr_1 = theano.shared(value=zero_ary, name='msm_lr_1')
self.lr_2 = theano.shared(value=zero_ary, name='msm_lr_2')
# shared var momentum parameters for generator and inferencer
self.mom_1 = theano.shared(value=zero_ary, name='msm_mom_1')
self.mom_2 = theano.shared(value=zero_ary, name='msm_mom_2')
# init parameters for controlling learning dynamics
self.set_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='msm_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting prior kld against reconstruction
self.lam_kld_z = theano.shared(value=zero_ary, name='msm_lam_kld_z')
self.lam_kld_q2p = theano.shared(value=zero_ary, name='msm_lam_kld_q2p')
self.lam_kld_p2q = theano.shared(value=zero_ary, name='msm_lam_kld_p2q')
self.set_lam_kld(lam_kld_z=1.0, lam_kld_q2p=0.7, lam_kld_p2q=0.3)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='msm_lam_l2w')
self.set_lam_l2w(1e-5)
# Grab all of the "optimizable" parameters in "group 1"
self.group_1_params = []
self.group_1_params.extend(self.q_z_given_x.mlp_params)
self.group_1_params.extend(self.q_h_given_x_s.mlp_params)
# Grab all of the "optimizable" parameters in "group 2"
self.group_2_params = [self.p_z_mean, self.p_z_logvar]
self.group_2_params.extend(self.p_s_given_z.mlp_params)
self.group_2_params.extend(self.p_h_given_s.mlp_params)
self.group_2_params.extend(self.p_x_given_s_h.mlp_params)
# Make a joint list of parameters group 1/2
self.joint_params = self.group_1_params + self.group_2_params
#################################
# CONSTRUCT THE KLD-BASED COSTS #
#################################
self.kld_z_q2p, self.kld_z_p2q, self.kld_h_q2p, self.kld_h_p2q = \
self._construct_kld_costs(p=1.0)
self.kld_z = (self.lam_kld_q2p[0] * self.kld_z_q2p) + \
(self.lam_kld_p2q[0] * self.kld_z_p2q)
self.kld_h = (self.lam_kld_q2p[0] * self.kld_h_q2p) + \
(self.lam_kld_p2q[0] * self.kld_h_p2q)
self.kld_costs = (self.lam_kld_z[0] * self.kld_z) + self.kld_h
# now do l2 KLd costs
self.kl2_z_q2p, self.kl2_z_p2q, self.kl2_h_q2p, self.kl2_h_p2q = \
self._construct_kld_costs(p=2.0)
self.kl2_z = (self.lam_kld_q2p[0] * self.kl2_z_q2p) + \
(self.lam_kld_p2q[0] * self.kl2_z_p2q)
self.kl2_h = (self.lam_kld_q2p[0] * self.kl2_h_q2p) + \
(self.lam_kld_p2q[0] * self.kl2_h_p2q)
self.kl2_costs = (self.lam_kld_z[0] * self.kl2_z) + self.kl2_h
# compute joint l1/l2 KLd cost
self.kld_l1l2_costs = (self.lam_kld_l1l2[0] * self.kld_costs) + \
((1.0 - self.lam_kld_l1l2[0]) * self.kl2_costs)
# compute "mean" (rather than per-input) costs
self.kld_cost = T.mean(self.kld_costs)
self.kl2_cost = T.mean(self.kl2_costs)
self.kld_l1l2_cost = T.mean(self.kld_l1l2_costs)
#################################
# CONSTRUCT THE NLL-BASED COSTS #
#################################
self.nll_costs = self._construct_nll_costs(self.x_out)
self.nll_cost = self.lam_nll[0] * T.mean(self.nll_costs)
########################################
# CONSTRUCT THE REST OF THE JOINT COST #
########################################
param_reg_cost = self._construct_reg_costs()
self.reg_cost = self.lam_l2w[0] * param_reg_cost
self.joint_cost = self.nll_cost + self.kld_l1l2_cost + self.reg_cost
##############################
# CONSTRUCT A PER-INPUT COST #
##############################
self.obs_costs = self.nll_costs + self.kld_l1l2_costs
# Get the gradient of the joint cost for all optimizable parameters
print("Computing gradients of self.joint_cost...")
self.joint_grads = OrderedDict()
grad_list = T.grad(self.joint_cost, self.joint_params)
for i, p in enumerate(self.joint_params):
self.joint_grads[p] = grad_list[i]
# Construct the updates for the generator and inferencer networks
self.group_1_updates = get_adam_updates(params=self.group_1_params, \
grads=self.joint_grads, alpha=self.lr_1, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-5, max_grad_norm=10.0)
self.group_2_updates = get_adam_updates(params=self.group_2_params, \
grads=self.joint_grads, alpha=self.lr_2, \
beta1=self.mom_1, beta2=self.mom_2, \
mom2_init=1e-3, smoothing=1e-5, max_grad_norm=10.0)
self.joint_updates = OrderedDict()
for k in self.group_1_updates:
self.joint_updates[k] = self.group_1_updates[k]
for k in self.group_2_updates:
self.joint_updates[k] = self.group_2_updates[k]
# Construct a function for jointly training the generator/inferencer
print("Compiling training function...")
self.train_joint = self._construct_train_joint()
print("Compiling free-energy sampler...")
self.compute_fe_terms = self._construct_compute_fe_terms()
print("Compiling open-loop model sampler...")
self.sample_from_prior = self._construct_sample_from_prior()
print("Compiling data-guided model sampler...")
self.sample_from_input = self._construct_sample_from_input()
# make easy access points for some interesting parameters
self.gen_gen_weights = self.p_x_given_s_h.mu_layers[-1].W
return
def predict_symbolic(self, mx, Sx, unroll_scan=False):
idims = self.D
odims = self.E
Ms = self.sr.shape[1]
sf2M = (self.hyp[:, idims]**2)/tt.cast(Ms, floatX)
sn2 = self.hyp[:, idims+1]**2
# TODO this should just fallback to the method from the SSGP class
if Sx is None:
# first check if we received a vector [D] or a matrix [nxD]
if mx.ndim == 1:
mx = mx[None, :]
srdotx = self.sr.dot(self.X.T).transpose(0,2,1)
phi_x = tt.concatenate([tt.sin(srdotx), tt.cos(srdotx)], 2)
M = (phi_x*self.beta_ss[:, None, :]).sum(-1)
phi_x_L = tt.stack([
solve_lower_triangular(self.Lmm[i], phi_x[i].T)
for i in range(odims)])
S = sn2[:, None]*(1 + (sf2M[:, None])*(phi_x_L**2).sum(-2)) + 1e-6
return M, S
# precompute some variables
srdotx = self.sr.dot(mx)
srdotSx = self.sr.dot(Sx)
srdotSxdotsr = tt.sum(srdotSx*self.sr, 2)
e = tt.exp(-0.5*srdotSxdotsr)
cos_srdotx = tt.cos(srdotx)
sin_srdotx = tt.sin(srdotx)
cos_srdotx_e = cos_srdotx*e
sin_srdotx_e = sin_srdotx*e
# compute the mean vector
mphi = tt.horizontal_stack(sin_srdotx_e, cos_srdotx_e) # E x 2*Ms
M = tt.sum(mphi*self.beta_ss, 1)
# input output covariance
mx_c = mx.dimshuffle(0, 'x')
sin_srdotx_e_r = sin_srdotx_e.dimshuffle(0, 'x', 1)
cos_srdotx_e_r = cos_srdotx_e.dimshuffle(0, 'x', 1)
srdotSx_tr = srdotSx.transpose(0, 2, 1)
c = tt.concatenate([mx_c*sin_srdotx_e_r + srdotSx_tr*cos_srdotx_e_r,
mx_c*cos_srdotx_e_r - srdotSx_tr*sin_srdotx_e_r],
axis=2) # E x D x 2*Ms
beta_ss_r = self.beta_ss.dimshuffle(0, 'x', 1)
# input output covariance (notice this is not premultiplied by the
# input covariance inverse)
V = tt.sum(c*beta_ss_r, 2).T - tt.outer(mx, M)
srdotSxdotsr_c = srdotSxdotsr.dimshuffle(0, 1, 'x')
srdotSxdotsr_r = srdotSxdotsr.dimshuffle(0, 'x', 1)
M2 = tt.zeros((odims, odims))
# initialize indices
triu_indices = np.triu_indices(odims)
indices = [tt.as_index_variable(idx) for idx in triu_indices]
def second_moments(i, j, M2, beta, iA, sn2, sf2M, sr, srdotSx,
srdotSxdotsr_c, srdotSxdotsr_r,
sin_srdotx, cos_srdotx, *args):
# compute the second moments of the spectrum feature vectors
siSxsj = srdotSx[i].dot(sr[j].T) # Ms x Ms
sijSxsij = -0.5*(srdotSxdotsr_c[i] + srdotSxdotsr_r[j])
em = tt.exp(sijSxsij+siSxsj) # MsxMs
ep = tt.exp(sijSxsij-siSxsj) # MsxMs
si = sin_srdotx[i] # Msx1
ci = cos_srdotx[i] # Msx1
sj = sin_srdotx[j] # Msx1
cj = cos_srdotx[j] # Msx1
sicj = tt.outer(si, cj) # MsxMs
cisj = tt.outer(ci, sj) # MsxMs
sisj = tt.outer(si, sj) # MsxMs
cicj = tt.outer(ci, cj) # MsxMs
sm = (sicj-cisj)*em
sp = (sicj+cisj)*ep
cm = (sisj+cicj)*em
cp = (cicj-sisj)*ep
# Populate the second moment matrix of the feature vector
Q_up = tt.concatenate([cm-cp, sm+sp], axis=1)
Q_lo = tt.concatenate([sp-sm, cm+cp], axis=1)
Q = tt.concatenate([Q_up, Q_lo], axis=0)
# Compute the second moment of the output
m2 = 0.5*matrix_dot(beta[i], Q, beta[j].T)
m2 = theano.ifelse.ifelse(
tt.eq(i, j),
m2 + sn2[i]*(1.0 + sf2M[i]*tt.sum(self.iA[i]*Q)) + 1e-6,
m2)
M2 = tt.set_subtensor(M2[i, j], m2)
return M2
nseq = [self.beta_ss, self.iA, sn2, sf2M, self.sr, srdotSx,
srdotSxdotsr_c, srdotSxdotsr_r, sin_srdotx, cos_srdotx,
self.Lmm]
if unroll_scan:
from lasagne.utils import unroll_scan
[M2_] = unroll_scan(second_moments, indices,
[M2], nseq, len(triu_indices[0]))
updts = {}
else:
M2_, updts = theano.scan(fn=second_moments,
sequences=indices,
outputs_info=[M2],
non_sequences=nseq,
allow_gc=False,
name="%s>M2_scan" % (self.name))
M2 = M2_[-1]
M2 = M2 + tt.triu(M2, k=1).T
S = M2 - tt.outer(M, M)
return M, S, V
def test_TransMatConjugateStep_subtensors():
# Confirm that Dirichlet/non-Dirichlet mixed rows can be
# parsed
with pm.Model():
d_0_rv = pm.Dirichlet("p_0", np.r_[1, 1])
d_1_rv = pm.Dirichlet("p_1", np.r_[1, 1])
p_0_rv = tt.as_tensor([0, 0, 1])
p_1_rv = tt.zeros(3)
p_1_rv = tt.set_subtensor(p_0_rv[[0, 2]], d_0_rv)
p_2_rv = tt.zeros(3)
p_2_rv = tt.set_subtensor(p_1_rv[[1, 2]], d_1_rv)
P_tt = tt.stack([p_0_rv, p_1_rv, p_2_rv])
P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt))
DiscreteMarkovChain("S_t", P_rv, np.r_[1, 0, 0], shape=(10, ))
transmat = TransMatConjugateStep(P_rv)
assert transmat.row_remaps == {0: 1, 1: 2}
exp_slices = {0: np.r_[0, 2], 1: np.r_[1, 2]}
assert exp_slices.keys() == transmat.row_slices.keys()
assert all(
np.array_equal(transmat.row_slices[i], exp_slices[i])
for i in exp_slices.keys())
# Same thing, just with some manipulations of the transition matrix
with pm.Model():
d_0_rv = pm.Dirichlet("p_0", np.r_[1, 1])
d_1_rv = pm.Dirichlet("p_1", np.r_[1, 1])
p_0_rv = tt.as_tensor([0, 0, 1])
p_1_rv = tt.zeros(3)
p_1_rv = tt.set_subtensor(p_0_rv[[0, 2]], d_0_rv)
p_2_rv = tt.zeros(3)
p_2_rv = tt.set_subtensor(p_1_rv[[1, 2]], d_1_rv)
P_tt = tt.horizontal_stack(p_0_rv[..., None], p_1_rv[..., None],
p_2_rv[..., None])
P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt.T))
DiscreteMarkovChain("S_t", P_rv, np.r_[1, 0, 0], shape=(10, ))
transmat = TransMatConjugateStep(P_rv)
assert transmat.row_remaps == {0: 1, 1: 2}
exp_slices = {0: np.r_[0, 2], 1: np.r_[1, 2]}
assert exp_slices.keys() == transmat.row_slices.keys()
assert all(
np.array_equal(transmat.row_slices[i], exp_slices[i])
for i in exp_slices.keys())
# Use an observed `DiscreteMarkovChain` and check the conjugate results
with pm.Model():
d_0_rv = pm.Dirichlet("p_0", np.r_[1, 1])
d_1_rv = pm.Dirichlet("p_1", np.r_[1, 1])
p_0_rv = tt.as_tensor([0, 0, 1])
p_1_rv = tt.zeros(3)
p_1_rv = tt.set_subtensor(p_0_rv[[0, 2]], d_0_rv)
p_2_rv = tt.zeros(3)
p_2_rv = tt.set_subtensor(p_1_rv[[1, 2]], d_1_rv)
P_tt = tt.horizontal_stack(p_0_rv[..., None], p_1_rv[..., None],
p_2_rv[..., None])
P_rv = pm.Deterministic("P_tt", tt.shape_padleft(P_tt.T))
DiscreteMarkovChain("S_t",
P_rv,
np.r_[1, 0, 0],
shape=(4, ),
observed=np.r_[0, 1, 0, 2])
transmat = TransMatConjugateStep(P_rv)
def learnAndPredict(Ti, C, TOList):
rng = np.random.RandomState(SEED)
learning_rate = learning_rate0
print np.mean(Ti[1000, :])
aminW = np.amin(Ti[:1000, :])
amaxW = np.amax(Ti[:1000, :])
Ti[:1000, :] = (Ti[:1000, :] - aminW) / (amaxW - aminW)
astdW = np.std(Ti[:1000, :])
ameanW = np.mean(Ti[:1000, :])
Ti[:1000, :] = (Ti[:1000, :] - ameanW) / astdW
aminacW = np.amin(Ti[1000, :])
amaxacW = np.amax(Ti[1000, :])
print aminW, amaxW, aminacW, amaxacW
Ti[1000, :] = (Ti[1000, :] - aminacW) / (amaxacW - aminacW)
astdacW = np.std(Ti[1000, :])
ameanacW = np.mean(Ti[1000, :])
Ti[1000, :] = (Ti[1000, :] - ameanacW) / astdacW
ile__ = len(TOList)
ileList = np.zeros(ile__)
for titer in range(len(TOList)):
print np.mean(TOList[titer][1000, :])
TOList[titer][:1000, :] = (TOList[titer][:1000, :] - aminW) / (amaxW -
aminW)
TOList[titer][:1000, :] = (TOList[titer][:1000, :] - ameanW) / astdW
TOList[titer][1000, :] = (TOList[titer][1000, :] -
aminacW) / (amaxacW - aminacW)
TOList[titer][1000, :] = (TOList[titer][1000, :] - ameanacW) / astdacW
_, ileList[titer] = TOList[titer].shape
_, ile = Ti.shape
N = NN
data = []
yyy = []
need = 1
BYL = {}
j = 0
dwa = 0
ONES = []
ZEROS = []
for i in range(NN):
for j in range(NN):
if i != j:
if C[i][j] == 1:
ONES.append((i, j))
else:
ZEROS.append((i, j))
Nones = len(ONES)
rng.shuffle(ONES)
Nzeros = len(ZEROS)
print Nones
print Nzeros
Needed = NUM_TRAIN / 2
onesPerPair = Needed / Nones + 1
onesIter = 0
jj = 0
while jj < NUM_TRAIN:
if jj % 300000 == 0:
print jj / 300000,
need = 1 - need
if need == 1:
pairNo = onesIter % Nones
ppp = onesIter / Nones
s, t = ONES[pairNo]
shift = rng.randint(0, ile - L)
onesIter += 1
if need == 0:
zer = rng.randint(Nzeros)
s, t = ZEROS[zer]
del ZEROS[zer]
Nzeros -= 1
shift = rng.randint(0, ile - L)
x = np.hstack((Ti[s][shift:shift + L], Ti[t][shift:shift + L],
Ti[1000][shift:shift + L]))
y = C[s][t]
data.append(x)
yyy.append(y)
jj += 1
data = np.array(data, dtype=theano.config.floatX)
is_train = np.array(([0] * 96 + [1, 1, 2, 2]) * (NUM_TRAIN / 100))
yyy = np.array(yyy)
train_set_x0, train_set_y0 = np.array(
data[is_train == 0]), yyy[is_train == 0]
test_set_x, test_set_y = np.array(data[is_train == 1]), yyy[is_train == 1]
valid_set_x, valid_set_y = np.array(
data[is_train == 2]), yyy[is_train == 2]
n_train_batches = len(train_set_y0) / batch_size
n_valid_batches = len(valid_set_y) / batch_size
n_test_batches = len(test_set_y) / batch_size
epoch = T.scalar()
index = T.lscalar()
x = T.matrix('x')
inone2 = T.matrix('inone2')
y = T.ivector('y')
print '... building the model'
#-------- my layers -------------------
#---------------------
layer0_input = x.reshape((batch_size, 1, 3, L))
Cx = 5
layer0 = ConvolutionalLayer(rng,
input=layer0_input,
image_shape=(batch_size, 1, 3, L),
filter_shape=(nkerns[0], 1, 2, Cx),
poolsize=(1, 1),
fac=0)
ONE = (3 - 2 + 1) / 1
L2 = (L - Cx + 1) / 1
#---------------------
Cx2 = 5
layer1 = ConvolutionalLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], ONE, L2),
filter_shape=(nkerns[1], nkerns[0], 2, Cx2),
poolsize=(1, 1),
activation=ReLU,
fac=0)
ONE = (ONE - 2 + 1) / 1
L3 = (L2 - Cx2 + 1) / 1
#---------------------
Cx3 = 1
layer1b = ConvolutionalLayer(rng,
input=layer1.output,
image_shape=(batch_size, nkerns[1], ONE, L3),
filter_shape=(nkerns[2], nkerns[1], 1, Cx3),
poolsize=(1, POOL),
activation=ReLU,
fac=0)
ONE = (ONE - 1 + 1) / 1
L4 = (L3 - Cx3 + 1) / POOL
REGx = 100
#---------------------
layer2_input = layer1b.output.flatten(2)
print layer2_input.shape
use_b = False
layer2 = HiddenLayer(rng,
input=layer2_input,
n_in=nkerns[2] * L4,
n_out=REGx,
activation=T.tanh,
use_bias=use_b)
layer3 = LogisticRegression(input=layer2.output, n_in=REGx, n_out=2)
cost = layer3.negative_log_likelihood(y)
out_x2 = theano.shared(
np.asarray(np.zeros((N, L)), dtype=theano.config.floatX))
inone2 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
inone3 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
inone4 = theano.shared(
np.asarray(np.zeros((1, L)), dtype=theano.config.floatX))
test_set_x = theano.shared(
np.asarray(test_set_x, dtype=theano.config.floatX))
train_set_x = theano.shared(
np.asarray(train_set_x0, dtype=theano.config.floatX))
train_set_y = T.cast(
theano.shared(np.asarray(train_set_y0, dtype=theano.config.floatX)),
'int32')
test_set_y = T.cast(
theano.shared(np.asarray(test_set_y, dtype=theano.config.floatX)),
'int32')
valid_set_y = T.cast(
theano.shared(np.asarray(valid_set_y, dtype=theano.config.floatX)),
'int32')
valid_set_x = theano.shared(
np.asarray(valid_set_x, dtype=theano.config.floatX))
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
})
mom_start = 0.5
mom_end = 0.98
mom_epoch_interval = n_epochs * 1.0
#### @@@@@@@@@@@
class_params0 = [layer3, layer2, layer1, layer1b, layer0]
class_params = [param for layer in class_params0 for param in layer.params]
gparams = []
for param in class_params:
gparam = T.grad(cost, param)
gparams.append(gparam)
gparams_mom = []
for param in class_params:
gparam_mom = theano.shared(
np.zeros(param.get_value(borrow=True).shape,
dtype=theano.config.floatX))
gparams_mom.append(gparam_mom)
mom = ifelse(
epoch < mom_epoch_interval,
mom_start * (1.0 - epoch / mom_epoch_interval) + mom_end *
(epoch / mom_epoch_interval), mom_end)
updates = OrderedDict()
for gparam_mom, gparam in zip(gparams_mom, gparams):
updates[gparam_mom] = mom * gparam_mom - (1. -
mom) * learning_rate * gparam
for param, gparam_mom in zip(class_params, gparams_mom):
stepped_param = param + updates[gparam_mom]
squared_filter_length_limit = 15.0
if param.get_value(borrow=True).ndim == 2:
col_norms = T.sqrt(T.sum(T.sqr(stepped_param), axis=0))
desired_norms = T.clip(col_norms, 0,
T.sqrt(squared_filter_length_limit))
scale = desired_norms / (1e-7 + col_norms)
updates[param] = stepped_param * scale
else:
updates[param] = stepped_param
output = cost
train_model = theano.function(
inputs=[epoch, index],
outputs=output,
updates=updates,
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
})
keep = theano.function(
[index],
layer3.errorsFull(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
},
on_unused_input='warn')
timer = time.clock()
print "finished reading", (timer - start_time0) / 60., "minutes "
# TRAIN MODEL #
print '... training'
validation_frequency = n_train_batches
best_params = None
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
epochc = 0
while (epochc < n_epochs):
epochc = epochc + 1
learning_rate = learning_rate0 * (1.2 - ((1.0 * epochc) / n_epochs))
for minibatch_index in xrange(n_train_batches):
iter = (epochc - 1) * n_train_batches + minibatch_index
cost_ij = train_model(epochc, minibatch_index)
if (iter + 1) % validation_frequency == 0:
validation_losses = [
validate_model(i) for i in xrange(n_valid_batches)
]
this_validation_loss = np.mean(validation_losses)
print(' %i) err %.2f ' % (epochc, this_validation_loss / 10)
), L, nkerns, REGx, "|", Cx, Cx2, Cx3, batch_size
if this_validation_loss < best_validation_loss or epochc % 30 == 0:
best_validation_loss = this_validation_loss
best_iter = iter
test_losses = [
test_model(i) for i in xrange(n_test_batches)
]
test_score = np.mean(test_losses)
print(
(' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') % (epochc, minibatch_index + 1,
n_train_batches, test_score / 10))
############
timel = time.clock()
print "finished learning", (timel - timer) / 60., "minutes "
ppm = theano.function(
[index],
layer3.pred_proba_mine(),
givens={
x:
T.horizontal_stack(
T.tile(inone2, (batch_size, 1)),
out_x2[index * batch_size:(index + 1) * batch_size],
T.tile(inone3, (batch_size, 1))),
y:
train_set_y[0 * (batch_size):(0 + 1) * (batch_size)]
},
on_unused_input='warn')
NONZERO = (N * N - N)
gc.collect()
RESList = [np.zeros((N, N)) for it in range(ile__)]
for __net in range(ile__):
TO = TOList[__net]
ileO = ileList[__net]
RES = RESList[__net]
shift = 0.1
DELTAshift = (ileO - L) / (Q - 1)
print "DELTAshift:", DELTAshift
for q in range(Q):
dataO = []
print(q + 1), "/", Q, " ",
out_x2.set_value(
np.asarray(np.array(TO[:, shift:shift + L]),
dtype=theano.config.floatX))
PARTIAL = np.zeros((N, N))
inone3.set_value(
np.asarray(np.array(TO[1000][shift:shift + L]).reshape(1, L),
dtype=theano.config.floatX))
for i in range(N):
inone2.set_value(
np.asarray(np.array(TO[i][shift:shift + L]).reshape(1, L),
dtype=theano.config.floatX))
p = [ppm(ii) for ii in xrange(N / batch_size)]
for pos in range(N):
if pos != i:
PARTIAL[i][pos] += p[pos / batch_size][pos %
batch_size][1]
for i in range(N):
for j in range(N):
RES[i][j] += PARTIAL[i][j]
shift += DELTAshift
print "Finished", __net
RESList[__net] = RES / np.max(RES)
gc.collect()
end_time = time.clock()
print "finished predicting", (end_time - timel) / 60., "minutes ", str(
nkerns), "using SEED = ", SEED
print('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time0) / 60.))
return RESList
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input,
n_in=50 * ((l1ims-4)/2)**2, n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=2)
else:
# Output (14,14) -> (5, 5)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20, 32 * 4 * 4) = (20, 512)
layer2_input = T.horizontal_stack(layer1.output.flatten(2), x_extra)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(rng, input=layer2_input,
n_in=50 * ((l1ims-4)/2)**2 + ExtraColumns, n_out=500,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=2)
model = [layer0, layer1, layer2, layer3]
# the cost we minimize during training is the negative log likelihood of
# the model plus the regularization terms (L1 and L2); cost is expressed
# here symbolically
cost = layer3.negative_log_likelihood(y)
def get_output_for(self, inputs, deterministic=False, **kwargs):
# extract inputs
H1, H2 = inputs
# train set size
m = H1.shape[0].astype(theano.config.floatX)
# running average projection matrix update
if not deterministic:
# compute batch mean
mean1 = T.mean(H1, axis=0)
mean2 = T.mean(H2, axis=0)
# running average updates of means
mean1 = (floatX(1.0 - self.alpha) * self.mean1 +
self.alpha * mean1)
running_mean1 = theano.clone(self.mean1, share_inputs=False)
running_mean1.default_update = mean1
mean1 += 0 * running_mean1
mean2 = (floatX(1.0 - self.alpha) * self.mean2 +
self.alpha * mean2)
running_mean2 = theano.clone(self.mean2, share_inputs=False)
running_mean2.default_update = mean2
mean2 += 0 * running_mean2
# hidden representations
H1bar = H1 - mean1
H2bar = H2 - mean2
# transpose to formulas in paper
H1bar = H1bar.T
H2bar = H2bar.T
# cross-covariance
S12 = (1.0 / (m - 1)) * T.dot(H1bar, H2bar.T)
# covariance 1
S11 = (1.0 / (m - 1)) * T.dot(H1bar, H1bar.T)
S11 = S11 + self.r1 * T.identity_like(S11)
# covariance 2
S22 = (1.0 / (m - 1)) * T.dot(H2bar, H2bar.T)
S22 = S22 + self.r2 * T.identity_like(S22)
# running average updates of statistics
S12 = (floatX(1.0 - self.alpha) * self.S12 + self.alpha * S12)
running_S12 = theano.clone(self.S12, share_inputs=False)
running_S12.default_update = S12
S12 += 0 * running_S12
S11 = (floatX(1.0 - self.alpha) * self.S11 + self.alpha * S11)
running_S11 = theano.clone(self.S11, share_inputs=False)
running_S11.default_update = S11
S11 += 0 * running_S11
S22 = (floatX(1.0 - self.alpha) * self.S22 + self.alpha * S22)
running_S22 = theano.clone(self.S22, share_inputs=False)
running_S22.default_update = S22
S22 += 0 * running_S22
# theano-compatible formulation of paper
d, A = T.nlinalg.eigh(S11)
S11si = (A * np.reciprocal(np.sqrt(d))).dot(A.T) # = S11^-.5
d, A = T.nlinalg.eigh(S22)
S22si = (A * np.reciprocal(np.sqrt(d))).dot(A.T) # = S22^-.5
# compute TT' and T'T (regularized)
Tnp = S11si.dot(S12).dot(S22si)
M1 = Tnp.dot(Tnp.T)
M2 = Tnp.T.dot(Tnp)
M1 += self.rT * T.identity_like(M1)
M2 += self.rT * T.identity_like(M2)
# compute eigen decomposition
E1, E = T.nlinalg.eigh(M1)
_, F = T.nlinalg.eigh(M2)
# maximize correlation
E1 = T.clip(E1, 1e-7, 1.0)
E1 = T.sqrt(E1)
self.loss = -T.mean(E1) * self.wl
self.corr = E1
# compute projection matrices
U = S11si.dot(E)
V = S22si.dot(F)
# flip signs of projections to match
# (needed because we do two decompositions as opposed to a SVD)
s = T.sgn(U.T.dot(S12).dot(V).diagonal())
U *= s
# update of projection matrices
running_U = theano.clone(self.U, share_inputs=False)
running_U.default_update = U
U += floatX(0) * running_U
running_V = theano.clone(self.V, share_inputs=False)
running_V.default_update = V
V += floatX(0) * running_V
# use projections of layer
else:
# hidden representations
H1bar = H1 - self.mean1
H2bar = H2 - self.mean2
# transpose to formulas in paper
H1bar = H1bar.T
H2bar = H2bar.T
U, V = self.U, self.V
# re-project data
lv1_cca = H1bar.T.dot(U)
lv2_cca_fixed = H2bar.T.dot(V)
output = T.horizontal_stack(lv1_cca, lv2_cca_fixed)
return output
def hstack(tensors):
return T.horizontal_stack(*tensors)
def __init__(self, rng=None, \
Xd=None, Yd=None, Xc=None, Xm=None, \
g_net=None, i_net=None, p_net=None, \
data_dim=None, prior_dim=None, label_dim=None, \
batch_size=None, \
params=None, shared_param_dicts=None):
# TODO: refactor for use with "encoded" inferencer/generator
assert(not (i_net.use_encoder or g_net.use_encoder))
# setup a rng for this GITrip
self.rng = RandStream(rng.randint(100000))
# setup the prior distribution over the categorical variable
if params is None:
self.params = {}
else:
self.params = params
# record the dimensionality of the data handled by this GITrip
self.data_dim = data_dim
self.label_dim = label_dim
self.prior_dim = prior_dim
self.batch_size = batch_size
# create a mask for disabling and/or reweighting input dimensions
row_mask = np.ones((self.data_dim,)).astype(theano.config.floatX)
self.input_mask = theano.shared(value=row_mask, name='git_input_mask')
# record the symbolic variables that will provide inputs to the
# computation graph created to describe this GITrip
self.Xd = self.input_mask * Xd
self.Yd = Yd
self.Xc = Xc
self.Xm = Xm
# construct a vertically-repeated identity matrix for marginalizing
# over possible values of the categorical latent variable.
Ic = np.vstack([np.identity(label_dim) for i in range(batch_size)])
self.Ic = theano.shared(value=Ic.astype(theano.config.floatX), name='git_Ic')
# create "shared-parameter" clones of the continuous and categorical
# inferencers that this GITrip will be built on.
self.IN = i_net.shared_param_clone(rng=rng, \
Xd=self.Xd, Xc=self.Xc, Xm=self.Xm)
self.PN = p_net.shared_param_clone(rng=rng, Xd=self.Xd)
# create symbolic variables for the approximate posteriors over the
# continuous and categorical latent variables
self.Xp = self.IN.output
self.Yp = safe_softmax(self.PN.output_spawn[0])
self.Yp_proto = safe_softmax(self.PN.output_proto)
# create a symbolic variable structured to allow easy "marginalization"
# over possible settings of the categorical latent variable. the left
# matrix (i.e. self.Ic) comprises batch_size copies of the label_dim
# dimensional identity matrix stacked on top of each other, and the
# right matrix comprises a single sample from the approximate posterior
# over the continuous latent variables for each of batch_size examples
# with each sample repeated label_dim times.
self.XYp = T.horizontal_stack(self.Ic, T.repeat(self.Xp, \
self.label_dim, axis=0))
# pipe the "convenient marginlization" matrix into a shared parameter
# clone of the generator network
self.GN = g_net.shared_param_clone(rng=rng, Xp=self.XYp)
# capture a handle for sampled reconstructions from the generator
self.Xg = self.GN.output
# we will be assuming one proto-net in the pseudo-ensemble represented
# by self.PN, and either one or two spawn-nets for that proto-net.
assert(len(self.PN.proto_nets) == 1)
assert((len(self.PN.spawn_nets) == 1) or \
(len(self.PN.spawn_nets) == 2))
# output of the generator and input to the inferencer should both be
# equal to self.data_dim
assert(self.data_dim == self.GN.mlp_layers[-1].out_dim)
assert(self.data_dim == self.IN.shared_layers[0].in_dim)
assert(self.data_dim == self.PN.proto_nets[0][0].in_dim)
# mu/sigma outputs of self.IN should be equal to prior_dim, output of
# self.PN should be equal to label_dim, and input of self.GN should be
# equal to prior_dim + label_dim
assert(self.prior_dim == self.IN.mu_layers[-1].out_dim)
assert(self.prior_dim == self.IN.sigma_layers[-1].out_dim)
assert(self.label_dim == self.PN.proto_nets[0][-1].out_dim)
assert((self.prior_dim + self.label_dim) == self.GN.mlp_layers[0].in_dim)
# determine whether this GITrip is a clone or an original
if shared_param_dicts is None:
# This is not a clone, and we will need to make a dict for
# referring to some important shared parameters.
self.shared_param_dicts = {}
self.is_clone = False
else:
# This is a clone, and its layer parameters can be found by
# referring to the given param dict (i.e. shared_param_dicts).
self.shared_param_dicts = shared_param_dicts
self.is_clone = True
if not self.is_clone:
# shared var learning rate for generator and inferencer
zero_ary = np.zeros((1,)).astype(theano.config.floatX)
self.lr_gn = theano.shared(value=zero_ary, name='git_lr_gn')
self.lr_in = theano.shared(value=zero_ary, name='git_lr_in')
self.lr_pn = theano.shared(value=zero_ary, name='git_lr_pn')
# shared var momentum parameters for generator and inferencer
self.mo_gn = theano.shared(value=zero_ary, name='git_mo_gn')
self.mo_in = theano.shared(value=zero_ary, name='git_mo_in')
self.mo_pn = theano.shared(value=zero_ary, name='git_mo_pn')
# init parameters for controlling learning dynamics
self.set_all_sgd_params()
# init shared var for weighting nll of data given posterior sample
self.lam_nll = theano.shared(value=zero_ary, name='git_lam_nll')
self.set_lam_nll(lam_nll=1.0)
# init shared var for weighting posterior KL-div from prior
self.lam_kld = theano.shared(value=zero_ary, name='git_lam_kld')
self.set_lam_kld(lam_kld=1.0)
# init shared var for weighting semi-supervised classification
self.lam_cat = theano.shared(value=zero_ary, name='git_lam_cat')
self.set_lam_cat(lam_cat=0.0)
# init shared var for weighting ensemble agreement regularization
self.lam_pea = theano.shared(value=zero_ary, name='git_lam_pea')
self.set_lam_pea(lam_pea=0.0)
# init shared var for weighting entropy regularization on the
# inferred posteriors over the categorical variable of interest
self.lam_ent = theano.shared(value=zero_ary, name='git_lam_ent')
self.set_lam_ent(lam_ent=0.0)
# init shared var for weighting dirichlet regularization on the
# inferred posteriors over the categorical variable of interest
self.lam_dir = theano.shared(value=zero_ary, name='git_lam_dir')
self.set_lam_dir(lam_dir=0.0)
# init shared var for controlling l2 regularization on params
self.lam_l2w = theano.shared(value=zero_ary, name='git_lam_l2w')
self.set_lam_l2w(lam_l2w=1e-3)
# record shared parameters that are to be shared among clones
self.shared_param_dicts['git_lr_gn'] = self.lr_gn
self.shared_param_dicts['git_lr_in'] = self.lr_in
self.shared_param_dicts['git_lr_pn'] = self.lr_pn
self.shared_param_dicts['git_mo_gn'] = self.mo_gn
self.shared_param_dicts['git_mo_in'] = self.mo_in
self.shared_param_dicts['git_mo_pn'] = self.mo_pn
self.shared_param_dicts['git_lam_nll'] = self.lam_nll
self.shared_param_dicts['git_lam_kld'] = self.lam_kld
self.shared_param_dicts['git_lam_cat'] = self.lam_cat
self.shared_param_dicts['git_lam_pea'] = self.lam_pea
self.shared_param_dicts['git_lam_ent'] = self.lam_ent
self.shared_param_dicts['git_lam_dir'] = self.lam_dir
self.shared_param_dicts['git_lam_l2w'] = self.lam_l2w
self.shared_param_dicts['git_input_mask'] = self.input_mask
else:
# use some shared parameters that are shared among all clones of
# some "base" GITrip
self.lr_gn = self.shared_param_dicts['git_lr_gn']
self.lr_in = self.shared_param_dicts['git_lr_in']
self.lr_pn = self.shared_param_dicts['git_lr_pn']
self.mo_gn = self.shared_param_dicts['git_mo_gn']
self.mo_in = self.shared_param_dicts['git_mo_in']
self.mo_pn = self.shared_param_dicts['git_mo_pn']
self.lam_nll = self.shared_param_dicts['git_lam_nll']
self.lam_kld = self.shared_param_dicts['git_lam_kld']
self.lam_cat = self.shared_param_dicts['git_lam_cat']
self.lam_pea = self.shared_param_dicts['git_lam_pea']
self.lam_ent = self.shared_param_dicts['git_lam_ent']
self.lam_dir = self.shared_param_dicts['git_lam_dir']
self.lam_l2w = self.shared_param_dicts['git_lam_l2w']
self.input_mask = self.shared_param_dicts['git_input_mask']
# Grab the full set of "optimizable" parameters from the generator
# and inferencer networks that we'll be working with.
self.gn_params = [p for p in self.GN.mlp_params]
self.in_params = [p for p in self.IN.mlp_params]
self.pn_params = [p for p in self.PN.proto_params]
###################################
# CONSTRUCT THE COSTS TO OPTIMIZE #
###################################
self.data_nll_cost = self.lam_nll[0] * self._construct_data_nll_cost()
self.post_kld_cost = self.lam_kld[0] * self._construct_post_kld_cost()
self.post_cat_cost = self.lam_cat[0] * self._construct_post_cat_cost()
self.post_pea_cost = self.lam_pea[0] * self._construct_post_pea_cost()
self.post_ent_cost = self.lam_ent[0] * self._construct_post_ent_cost()
self.post_dir_cost = self.lam_dir[0] * self._construct_post_dir_cost()
self.other_reg_costs = self._construct_other_reg_cost()
self.other_reg_cost = self.other_reg_costs[0]
self.joint_cost = self.data_nll_cost + self.post_kld_cost + self.post_cat_cost + \
self.post_pea_cost + self.post_ent_cost + self.post_dir_cost + \
self.other_reg_cost
# Initialize momentums for mini-batch SGD updates. All parameters need
# to be safely nestled in their lists by now.
self.joint_moms = OrderedDict()
self.gn_moms = OrderedDict()
self.in_moms = OrderedDict()
self.pn_moms = OrderedDict()
for p in self.gn_params:
p_mo = np.zeros(p.get_value(borrow=True).shape) + 5.0
self.gn_moms[p] = theano.shared(value=p_mo.astype(theano.config.floatX))
self.joint_moms[p] = self.gn_moms[p]
for p in self.in_params:
p_mo = np.zeros(p.get_value(borrow=True).shape) + 5.0
self.in_moms[p] = theano.shared(value=p_mo.astype(theano.config.floatX))
self.joint_moms[p] = self.in_moms[p]
for p in self.pn_params:
p_mo = np.zeros(p.get_value(borrow=True).shape) + 5.0
self.pn_moms[p] = theano.shared(value=p_mo.astype(theano.config.floatX))
self.joint_moms[p] = self.pn_moms[p]
# Now, we need to construct updates for inferencers and the generator
self.joint_updates = OrderedDict()
self.gn_updates = OrderedDict()
self.in_updates = OrderedDict()
self.pn_updates = OrderedDict()
self.grad_sq_sums = []
#######################################
# Construct updates for the generator #
#######################################
for var in self.gn_params:
# these updates are for trainable params in the generator net...
# first, get gradient of cost w.r.t. var
var_grad = T.grad(self.joint_cost, var, \
consider_constant=[self.GN.dist_mean, self.GN.dist_cov]).clip(-1.0,1.0)
#var_grad = ifelse(T.any(T.isnan(nan_grad)), T.zeros_like(nan_grad), nan_grad)
#self.grad_sq_sums.append(T.sum(var_grad**2.0))
# get the momentum for this var
var_mom = self.gn_moms[var]
# update the momentum for this var using its grad
self.gn_updates[var_mom] = (self.mo_gn[0] * var_mom) + \
((1.0 - self.mo_gn[0]) * (var_grad**2.0))
self.joint_updates[var_mom] = self.gn_updates[var_mom]
# make basic update to the var
var_new = var - (self.lr_gn[0] * (var_grad / T.sqrt(var_mom + 1e-2)))
self.gn_updates[var] = var_new
# add this var's update to the joint updates too
self.joint_updates[var] = self.gn_updates[var]
###################################################
# Construct updates for the continuous inferencer #
###################################################
for var in self.in_params:
# these updates are for trainable params in the inferencer net...
# first, get gradient of cost w.r.t. var
var_grad = T.grad(self.joint_cost, var, \
consider_constant=[self.GN.dist_mean, self.GN.dist_cov]).clip(-1.0,1.0)
#var_grad = ifelse(T.any(T.isnan(nan_grad)), T.zeros_like(nan_grad), nan_grad)
#self.grad_sq_sums.append(T.sum(var_grad**2.0))
# get the momentum for this var
var_mom = self.in_moms[var]
# update the momentum for this var using its grad
self.in_updates[var_mom] = (self.mo_in[0] * var_mom) + \
((1.0 - self.mo_in[0]) * (var_grad**2.0))
self.joint_updates[var_mom] = self.in_updates[var_mom]
# make basic update to the var
var_new = var - (self.lr_in[0] * (var_grad / T.sqrt(var_mom + 1e-2)))
self.in_updates[var] = var_new
# add this var's update to the joint updates too
self.joint_updates[var] = self.in_updates[var]
####################################################
# Construct updates for the categorical inferencer #
####################################################
for var in self.pn_params:
# these updates are for trainable params in the inferencer net...
# first, get gradient of cost w.r.t. var
var_grad = T.grad(self.joint_cost, var, \
consider_constant=[self.GN.dist_mean, self.GN.dist_cov]).clip(-1.0,1.0)
#var_grad = ifelse(T.any(T.isnan(nan_grad)), T.zeros_like(nan_grad), nan_grad)
#self.grad_sq_sums.append(T.sum(var_grad**2.0))
# get the momentum for this var
var_mom = self.pn_moms[var]
# update the momentum for this var using its grad
self.pn_updates[var_mom] = (self.mo_pn[0] * var_mom) + \
((1.0 - self.mo_pn[0]) * (var_grad**2.0))
self.joint_updates[var_mom] = self.pn_updates[var_mom]
# make basic update to the var
var_new = var - (self.lr_pn[0] * (var_grad / T.sqrt(var_mom + 1e-2)))
self.pn_updates[var] = var_new
# add this var's update to the joint updates too
self.joint_updates[var] = self.pn_updates[var]
# Record the sum of squared gradients (for NaN checking)
self.grad_sq_sum = T.sum(self.grad_sq_sums)
# Construct batch-based training functions for the generator and
# inferer networks, as well as a joint training function.
#self.train_gn = self._construct_train_gn()
#self.train_in = self._construct_train_in()
self.train_joint = self._construct_train_joint()
return
def get_output_for(self, inputs, deterministic=False, **kwargs):
# extract inputs
H1, H2 = inputs
# train set size
m = H1.shape[0].astype(theano.config.floatX)
# running average projection matrix update
if not deterministic:
# compute batch mean
mean1 = T.mean(H1, axis=0)
mean2 = T.mean(H2, axis=0)
# running average updates of means
mean1 = (floatX(1.0 - self.alpha) * self.mean1 +
self.alpha * mean1)
running_mean1 = theano.clone(self.mean1, share_inputs=False)
running_mean1.default_update = mean1
mean1 += 0 * running_mean1
mean2 = (floatX(1.0 - self.alpha) * self.mean2 +
self.alpha * mean2)
running_mean2 = theano.clone(self.mean2, share_inputs=False)
running_mean2.default_update = mean2
mean2 += 0 * running_mean2
# hidden representations
H1bar = H1 - mean1
H2bar = H2 - mean2
# transpose to correlation format
H1bar = H1bar.T
H2bar = H2bar.T
# cross-covariance
S12 = (1.0 / (m - 1)) * T.dot(H1bar, H2bar.T)
# covariance 1
S11 = (1.0 / (m - 1)) * T.dot(H1bar, H1bar.T)
S11 = S11 + self.r1 * T.identity_like(S11)
# covariance 2
S22 = (1.0 / (m - 1)) * T.dot(H2bar, H2bar.T)
S22 = S22 + self.r2 * T.identity_like(S22)
# theano-compatible formulation of paper
d, A = T.nlinalg.eigh(S11)
S11si = (A * np.reciprocal(np.sqrt(d))).dot(A.T) # = S11^-.5
d, A = T.nlinalg.eigh(S22)
S22si = (A * np.reciprocal(np.sqrt(d))).dot(A.T) # = S22^-.5
# compute TT' and T'T (regularized)
Tnp = S11si.dot(S12).dot(S22si)
M1 = Tnp.dot(Tnp.T)
M2 = Tnp.T.dot(Tnp)
M1 += self.rT * T.identity_like(M1)
M2 += self.rT * T.identity_like(M2)
# compute eigen decomposition
E1, E = T.nlinalg.eigh(M1)
_, F = T.nlinalg.eigh(M2)
# compute correlation
E1 = T.clip(E1, 1e-7, 1.0)
E1 = T.sqrt(E1)
self.corr = E1
# transpose back to network format
H1bar = H1bar.T
H2bar = H2bar.T
# use means of layer
else:
# hidden representations
H1bar = H1 - self.mean1
H2bar = H2 - self.mean2
# re-project data
lv1_cca = H1bar.dot(self.U)
lv2_cca = H2bar.dot(self.V)
output = T.horizontal_stack(lv1_cca, lv2_cca)
return output