def _build_stochastic_x1_cross_entropy(self,
qx1_samples,
batch_indices=None):
diag_px1 = self.px1_cov_chol.shape.ndims == 1 or self.multi_diag_px1_cov
if self.multi_diag_px1_cov or self.px1_cov_chol.shape.ndims == 3:
x1_ce = 0.
for s in range(
self.n_seq if batch_indices is None else self.batch_size):
b_s = s if batch_indices is None else batch_indices[s]
_px1_mu = self.px1_mu if self.px1_mu.shape.ndims == 1 else self.px1_mu[
b_s]
if diag_px1:
_x1_ce = diag_mvn_logp(qx1_samples[s] - _px1_mu,
self.px1_cov_chol[b_s])
else:
_x1_ce = mvn_logp(tf.transpose(qx1_samples[s] - _px1_mu),
self.px1_cov_chol[b_s])
x1_ce += tf.reduce_mean(_x1_ce)
else:
_px1_mu = self.px1_mu if self.px1_mu.shape.ndims == 1 else self.px1_mu[:,
None, :]
if diag_px1:
x1_ce = diag_mvn_logp(qx1_samples - _px1_mu, self.px1_cov_chol)
else:
x1_ce = mvn_logp(
tf.transpose(qx1_samples - _px1_mu, [2, 0, 1]),
self.px1_cov_chol)
x1_ce = tf.reduce_sum(tf.reduce_mean(x1_ce, -1))
if batch_indices is not None:
x1_ce *= float(self.n_seq) / float(self.batch_size)
return -x1_ce
def logp(self, X, inputs=None):
d = X[..., 1:, :] - self.conditional_mean(X[..., :-1, :], inputs=inputs)
if self.Qchol.shape.ndims == 2:
dim_perm = [2, 0, 1] if X.shape.ndims == 3 else [1, 0]
return mvn_logp(tf.transpose(d, dim_perm), self.Qchol)
elif self.Qchol.shape.ndims == 1:
return diag_mvn_logp(d, self.Qchol)
def logp(self, X, Y):
"""
:param X: latent state (T x E) or (n_samples x T x E)
:param Y: observations (T x D)
:return: \log P(Y|X(n)) (T) or (n_samples x T)
"""
d = Y - self.conditional_mean(X)
dim_perm = [2, 0, 1] if X.shape.ndims == 3 else [1, 0]
return mvn_logp(tf.transpose(d, dim_perm), tf.ones([1,1], dtype = tf.float64)*tf.sqrt(self.noise))
def logp(self, X, Y):
"""
:param X: latent state (T x E) or (n_samples x T x E)
:param Y: observations (T x D)
:return: \log P(Y|X(n)) (T) or (n_samples x T)
"""
d = Y - self.conditional_mean(X)
dim_perm = [2, 0, 1] if X.shape.ndims == 3 else [1, 0]
return mvn_logp(tf.transpose(d, dim_perm), self.Rchol)
def _build_stochastic_x1_cross_entropy(self, qx1_samples):
return -tf.reduce_mean(
mvn_logp(tf.transpose(qx1_samples - self.px1_mu),
self.px1_cov_chol))