def beta_log_prob(self, val): conc0 = self.parameters['concentration0'] conc1 = self.parameters['concentration1'] result = (conc1 - 1.0) * tf.log(val) result += (conc0 - 1.0) * tf.log(1.0 - val) result += -tf.lgamma(conc1) - tf.lgamma(conc0) + tf.lgamma(conc1 + conc0) return result
def logpmf(self, x, n, p):
"""Log of the probability mass function.
Parameters
----------
x : tf.Tensor
A n-D tensor for n > 1, where the inner (right-most)
dimension represents the multivariate dimension. Each
element is the number of outcomes in a bucket and not a
one-hot.
n : tf.Tensor
A tensor of one less dimension than ``x``,
representing the number of outcomes, equal to sum x[i]
along the inner (right-most) dimension.
p : tf.Tensor
A tensor of one less dimension than ``x``, representing
probabilities which sum to 1.
Returns
-------
tf.Tensor
A tensor of one dimension less than the input.
"""
x = tf.cast(x, dtype=tf.float32)
n = tf.cast(n, dtype=tf.float32)
p = tf.cast(p, dtype=tf.float32)
multivariate_idx = len(get_dims(x)) - 1
if multivariate_idx == 0:
return tf.lgamma(n + 1.0) - \
tf.reduce_sum(tf.lgamma(x + 1.0)) + \
tf.reduce_sum(x * tf.log(p))
else:
return tf.lgamma(n + 1.0) - \
tf.reduce_sum(tf.lgamma(x + 1.0), multivariate_idx) + \
tf.reduce_sum(x * tf.log(p), multivariate_idx)
def logpdf(self, x, df, loc=0, scale=1):
"""Log of the probability density function.
Parameters
----------
x : tf.Tensor
A n-D tensor.
df : tf.Tensor
A tensor of same shape as ``x``, and with all elements
constrained to :math:`df > 0`.
loc : tf.Tensor
A tensor of same shape as ``x``.
scale : tf.Tensor
A tensor of same shape as ``x``, and with all elements
constrained to :math:`scale > 0`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
x = tf.cast(x, dtype=tf.float32)
df = tf.cast(df, dtype=tf.float32)
loc = tf.cast(loc, dtype=tf.float32)
scale = tf.cast(scale, dtype=tf.float32)
z = (x - loc) / scale
return tf.lgamma(0.5 * (df + 1.0)) - tf.lgamma(0.5 * df) - \
0.5 * (tf.log(np.pi) + tf.log(df)) - tf.log(scale) - \
0.5 * (df + 1.0) * tf.log(1.0 + (1.0/df) * tf.square(z))
def beta(alpha, beta, y):
# need to clip y, since log of 0 is nan...
y = tf.clip_by_value(y, 1e-6, 1-1e-6)
return (alpha - 1.) * tf.log(y) + (beta - 1.) * tf.log(1. - y) \
+ tf.lgamma(alpha + beta)\
- tf.lgamma(alpha)\
- tf.lgamma(beta)
def beta(x, alpha, beta):
# need to clip x, since log of 0 is nan...
x = tf.clip_by_value(x, 1e-6, 1-1e-6)
return (alpha - 1.) * tf.log(x) + (beta - 1.) * tf.log(1. - x) \
+ tf.lgamma(alpha + beta)\
- tf.lgamma(alpha)\
- tf.lgamma(beta)
def multinomial_log_prob(self, val): n = self.parameters['total_count'] probs = self.parameters['probs'] f_n = tf.cast(n, tf.float32) f_val = tf.cast(val, tf.float32) result = tf.reduce_sum(tf.log(probs) * f_val, -1) result += tf.lgamma(f_n + 1) - tf.reduce_sum(tf.lgamma(f_val + 1), -1) return result
def student_t(x, mean, scale, deg_free):
const = tf.lgamma(tf.cast((deg_free + 1.) * 0.5, tf.float64))\
- tf.lgamma(tf.cast(deg_free * 0.5, tf.float64))\
- 0.5*(tf.log(tf.square(scale)) + tf.cast(tf.log(deg_free), tf.float64)
+ np.log(np.pi))
const = tf.cast(const, tf.float64)
return const - 0.5*(deg_free + 1.) * \
tf.log(1. + (1. / deg_free) * (tf.square((x - mean) / scale)))
def binomial_log_prob(self, val):
n = self.parameters['total_count']
probs = self.parameters['probs']
f_n = tf.cast(n, tf.float32)
f_val = tf.cast(val, tf.float32)
result = f_val * tf.log(probs) + (f_n - f_val) * tf.log(1.0 - probs)
result += tf.lgamma(f_n + 1) - tf.lgamma(f_val + 1) - \
tf.lgamma(f_n - f_val + 1)
return result
def log_nb_positive(x, mu, theta, eps=1e-8):
"""
log likelihood (scalar) of a minibatch according to a nb model.
Variables:
mu: mean of the negative binomial (has to be positive support) (shape: minibatch x genes)
theta: inverse dispersion parameter (has to be positive support) (shape: minibatch x genes)
eps: numerical stability constant
"""
res = tf.lgamma(x + theta) - tf.lgamma(theta) - tf.lgamma(x + 1) + x * tf.log(mu + eps) \
- x * tf.log(theta + mu + eps) + theta * tf.log(theta + eps) \
- theta * tf.log(theta + mu + eps)
return tf.reduce_sum(res, axis=-1)
def chi2_log_prob(self, val): df = self.parameters['df'] eta = 0.5 * df - 1 result = tf.reduce_sum(eta * tf.log(val), -1) result += tf.exp(-0.5 * val) result -= tf.lgamma(eta + 1) + (eta + 1) * tf.log(2.0) return result
def gamma_log_prob(self, val): conc = self.parameters['concentration'] rate = self.parameters['rate'] result = (conc - 1.0) * tf.log(val) result -= rate * val result += -tf.lgamma(conc) + conc * tf.log(rate) return result
def inverse_gamma_log_prob(self, val): conc = self.parameters['concentration'] rate = self.parameters['rate'] result = -(conc + 1) * tf.log(val) result -= rate * tf.reciprocal(val) result += -tf.lgamma(conc) + conc * tf.log(rate) return result
def testPoissonLogPmfContinuousRelaxation(self):
batch_size = 12
lam = tf.constant([3.0] * batch_size)
x = np.array([-3., -0.5, 0., 2., 2.2, 3., 3.1, 4., 5., 5.5, 6., 7.]).astype(
np.float32)
poisson = self._make_poisson(rate=lam,
interpolate_nondiscrete=True)
expected_continuous_log_pmf = (x * poisson.log_rate - tf.lgamma(1. + x)
- poisson.rate)
neg_inf = tf.fill(
tf.shape(expected_continuous_log_pmf),
value=np.array(-np.inf,
dtype=expected_continuous_log_pmf.dtype.as_numpy_dtype))
expected_continuous_log_pmf = tf.where(x >= 0.,
expected_continuous_log_pmf,
neg_inf)
expected_continuous_pmf = tf.exp(expected_continuous_log_pmf)
log_pmf = poisson.log_prob(x)
self.assertEqual(log_pmf.get_shape(), (batch_size,))
self.assertAllClose(self.evaluate(log_pmf),
self.evaluate(expected_continuous_log_pmf))
pmf = poisson.prob(x)
self.assertEqual(pmf.get_shape(), (batch_size,))
self.assertAllClose(self.evaluate(pmf),
self.evaluate(expected_continuous_pmf))
def Poisson(lambda_, name=None):
k = tf.placeholder(config.int_dtype, name=name)
Distribution.logp = k*lambda_ - lambda_ + tf.lgamma(k+1)
# TODO Distribution.integral = ...
return k
def actual_hypersphere_volume(dims, radius):
# https://en.wikipedia.org/wiki/Volume_of_an_n-ball
# Using tf.lgamma because we'd have to otherwise use SciPy which is not
# a required dependency of core.
radius = np.asarray(radius)
dims = tf.cast(dims, dtype=radius.dtype)
return tf.exp((dims / 2.) * np.log(np.pi) - tf.lgamma(1. + dims / 2.) +
dims * tf.log(radius))
def _log_unnormalized_prob(self, x):
# The log-probability at negative points is always -inf.
# Catch such x's and set the output value accordingly.
safe_x = tf.maximum(x if self.interpolate_nondiscrete else tf.floor(x), 0.)
y = safe_x * self.log_rate - tf.lgamma(1. + safe_x)
is_supported = tf.broadcast_to(tf.equal(x, safe_x), tf.shape(y))
neg_inf = tf.fill(tf.shape(y),
value=np.array(-np.inf, dtype=y.dtype.as_numpy_dtype))
return tf.where(is_supported, y, neg_inf)
def variational_expectations(self, Fmu, Fvar, Y):
if self.invlink is tf.exp:
return (
-self.shape * Fmu
- tf.lgamma(self.shape)
+ (self.shape - 1.0) * tf.log(Y)
- Y * tf.exp(-Fmu + Fvar / 2.0)
)
else:
return Likelihood.variational_expectations(self, Fmu, Fvar, Y)
def _log_normalization(self, name='log_normalization'):
"""Returns the log normalization of an LKJ distribution.
Args:
name: Python `str` name prefixed to Ops created by this function.
Returns:
log_z: A Tensor of the same shape and dtype as `concentration`, containing
the corresponding log normalizers.
"""
# The formula is from D. Lewandowski et al [1], p. 1999, from the
# proof that eqs 16 and 17 are equivalent.
with tf.name_scope('log_normalization_lkj', name, [self.concentration]):
logpi = np.log(np.pi)
ans = tf.zeros_like(self.concentration)
for k in range(1, self.dimension):
ans += logpi * (k / 2.)
ans += tf.lgamma(self.concentration + (self.dimension - 1 - k) / 2.)
ans -= tf.lgamma(self.concentration + (self.dimension - 1) / 2.)
return ans
def GumbelSoftmaxLogDensity(y, p, tau):
# EPS = tf.constant(1e-10)
k = tf.shape(y)[-1]
k = tf.cast(k, tf.float32)
# y = y + EPS
# y = tf.divide(y, tf.reduce_sum(y, -1, keep_dims=True))
y = normalize_to_unit_sum(y)
sum_p_over_y = tf.reduce_sum(tf.divide(p, tf.pow(y, tau)), -1)
logp = tf.lgamma(k)
logp = logp + (k - 1) * tf.log(tau)
logp = logp - k * tf.log(sum_p_over_y)
logp = logp + sum_p_over_y
return logp
def __init__(self, train_set, test_set, dictparam):
super(Elosplit, self).__init__(train_set, test_set, dictparam)
k = tf.constant(map(lambda x: float(x), range(10)))
last_vect = tf.expand_dims(ToolBox.last_vector(10),0)
win_vector = ToolBox.win_vector(10)
# Define parameters
self.elo_atk = tf.Variable(tf.zeros([self.nb_teams, self.nb_times]))
self.elo_def = tf.Variable(tf.zeros([self.nb_teams, self.nb_times]))
# Define the model
for key, proxy in [('train', self.train_data), ('test', self.test_data)]:
elo_atk_h = ToolBox.get_elomatch(proxy['team_h'], proxy['time'], self.elo_atk)
elo_def_h = ToolBox.get_elomatch(proxy['team_h'], proxy['time'], self.elo_def)
elo_atk_a = ToolBox.get_elomatch(proxy['team_a'], proxy['time'], self.elo_atk)
elo_def_a = ToolBox.get_elomatch(proxy['team_a'], proxy['time'], self.elo_def)
lambda_h = tf.expand_dims(tf.exp(self.param['goals_bias'] + elo_atk_h - elo_def_a), 1)
lambda_a = tf.expand_dims(tf.exp(self.param['goals_bias'] + elo_atk_a - elo_def_h), 1)
score_h = tf.exp(-lambda_h + tf.log(lambda_h) * k - tf.lgamma(k + 1))
score_a = tf.exp(-lambda_a + tf.log(lambda_a) * k - tf.lgamma(k + 1))
score_h += tf.matmul(tf.expand_dims((1. - tf.reduce_sum(score_h, reduction_indices=[1])), 1), last_vect)
score_a += tf.matmul(tf.expand_dims((1. - tf.reduce_sum(score_a, reduction_indices=[1])), 1), last_vect)
self.score[key] = tf.batch_matmul(tf.expand_dims(score_h, 2), tf.expand_dims(score_a, 1))
self.res[key] = tf.reduce_sum(self.score[key] * win_vector, reduction_indices=[1,2])
# Define the costs
self.init_cost()
for key in ['train', 'test']:
for proxy in [self.elo_atk, self.elo_def]:
cost = ToolBox.get_raw_elo_cost(self.param['metaparam0'], self.param['metaparam1'], proxy, self.nb_times)
self.regulizer[key].append(cost)
cost = ToolBox.get_timediff_elo_cost(self.param['metaparam2'], proxy, self.nb_times)
self.regulizer[key].append(cost)
# Finish the initialization
super(Elosplit, self).finish_init()
def log_zinb_positive(x, mu, theta, pi, eps=1e-8):
"""
log likelihood (scalar) of a minibatch according to a zinb model.
Notes:
We parametrize the bernouilli using the logits, hence the softplus functions appearing
Variables:
mu: mean of the negative binomial (has to be positive support) (shape: minibatch x genes)
theta: inverse dispersion parameter (has to be positive support) (shape: minibatch x genes)
pi: logit of the dropout parameter (real support) (shape: minibatch x genes)
eps: numerical stability constant
"""
case_zero = tf.nn.softplus(- pi + theta * tf.log(theta + eps) - theta * tf.log(theta + mu + eps)) \
- tf.nn.softplus( - pi)
case_non_zero = - pi - tf.nn.softplus(- pi) \
+ theta * tf.log(theta + eps) - theta * tf.log(theta + mu + eps) \
+ x * tf.log(mu + eps) - x * tf.log(theta + mu + eps) \
+ tf.lgamma(x + theta) - tf.lgamma(theta) - tf.lgamma(x + 1)
mask = tf.cast(tf.less(x, eps), tf.float32)
res = tf.multiply(mask, case_zero) + tf.multiply(1 - mask, case_non_zero)
return tf.reduce_sum(res, axis=-1)
def logp(self, bin_counts):
"""Compute the log probability for the counts in the bin, under the model.
Args:
bin_counts: array-like integer counts
Returns:
The log-probability under the Poisson models for each element of
bin_counts.
"""
k = tf.to_float(bin_counts)
# log poisson(k, r) = log(r^k * e^(-r) / k!) = k log(r) - r - log k!
# log poisson(k, r=exp(x)) = k * x - exp(x) - lgamma(k + 1)
return k * self.logr - tf.exp(self.logr) - tf.lgamma(k + 1)
def logpmf(self, x, n, p):
"""Log of the probability mass function.
Parameters
----------
x : tf.Tensor
A n-D tensor.
n : int
A tensor of same shape as ``x``, and with all elements
constrained to :math:`n > 0`.
p : tf.Tensor
A tensor of same shape as ``x``, and with all elements
constrained to :math:`p\in(0,1)`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
x = tf.cast(x, dtype=tf.float32)
n = tf.cast(n, dtype=tf.float32)
p = tf.cast(p, dtype=tf.float32)
return tf.lgamma(x + n) - tf.lgamma(x + 1.0) - tf.lgamma(n) + \
n * tf.log(p) + x * tf.log(1.0 - p)
def Poisson(lambda_, name=None):
k = tf.placeholder(config.int_dtype, name=name)
# FIXME tf.lgamma only supports floats so cast before
Distribution.logp = (
tf.cast(k, config.dtype)*tf.log(lambda_) -
lambda_ -
tf.lgamma(tf.cast(k+1, config.dtype))
)
# TODO Distribution.integral = ...
def integral(l, u):
return tf.constant(1, dtype=config.dtype)
Distribution.integral = integral
return k
def logpdf(self, x, df):
"""Log of the probability density function.
Parameters
----------
x : tf.Tensor
A n-D tensor.
df : tf.Tensor
A tensor of same shape as ``x``, and with all elements
constrained to :math:`df > 0`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
x = tf.cast(x, dtype=tf.float32)
df = tf.cast(df, dtype=tf.float32)
return (0.5 * df - 1) * tf.log(x) - 0.5 * x - \
0.5 * df * tf.log(2.0) - tf.lgamma(0.5 * df)
def logpmf(self, x, mu):
"""Log of the probability mass function.
Parameters
----------
x : tf.Tensor
A n-D tensor.
mu : tf.Tensor
A tensor of same shape as ``x``, and with all elements
constrained to :math:`mu > 0`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
x = tf.cast(x, dtype=tf.float32)
mu = tf.cast(mu, dtype=tf.float32)
return x * tf.log(mu) - mu - tf.lgamma(x + 1.0)
def _log_normalization(self):
"""Computes the log-normalizer of the distribution."""
event_dim = self.event_shape[0].value
if event_dim is None:
raise ValueError('vMF _log_normalizer currently only supports '
'statically known event shape')
safe_conc = tf.where(self.concentration > 0,
self.concentration,
tf.ones_like(self.concentration))
safe_lognorm = ((event_dim / 2 - 1) * tf.log(safe_conc) -
(event_dim / 2) * np.log(2 * np.pi) -
tf.log(_bessel_ive(event_dim / 2 - 1, safe_conc)) -
tf.abs(safe_conc))
log_nsphere_surface_area = (
np.log(2.) + (event_dim / 2) * np.log(np.pi) -
tf.lgamma(tf.cast(event_dim / 2, self.dtype)))
return tf.where(self.concentration > 0,
-safe_lognorm,
log_nsphere_surface_area * tf.ones_like(safe_lognorm))
def entropy(self, a, scale=1):
"""Entropy of probability distribution.
Parameters
----------
a : tf.Tensor
**Shape** parameter. A n-D tensor with all elements
constrained to :math:`a > 0`.
scale : tf.Tensor
**Scale** parameter. A n-D tensor with all elements
constrained to :math:`scale > 0`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
a = tf.cast(a, dtype=tf.float32)
scale = tf.cast(scale, dtype=tf.float32)
return a + tf.log(scale*tf.exp(tf.lgamma(a))) - \
(1.0 + a) * tf.digamma(a)
def logpdf(self, x, a, scale=1):
"""Log of the probability density function.
Parameters
----------
x : tf.Tensor
A n-D tensor.
a : tf.Tensor
**Shape** parameter. A tensor of same shape as ``x``, and with
all elements constrained to :math:`a > 0`.
scale : tf.Tensor
**Scale** parameter. A tensor of same shape as ``x``, and with
all elements constrained to :math:`scale > 0`.
Returns
-------
tf.Tensor
A tensor of same shape as input.
"""
x = tf.cast(x, dtype=tf.float32)
a = tf.cast(a, dtype=tf.float32)
scale = tf.cast(scale, dtype=tf.float32)
return (a - 1.0) * tf.log(x) - x/scale - a * tf.log(scale) - tf.lgamma(a)
def _log_prob(self, x):
x = self._assert_valid_sample(x)
# broadcast logits or x if need be.
logits = self.logits
if (not x.shape.is_fully_defined() or
not logits.shape.is_fully_defined() or
x.shape != logits.shape):
logits = tf.ones_like(x, dtype=logits.dtype) * logits
x = tf.ones_like(logits, dtype=x.dtype) * x
logits_shape = tf.shape(tf.reduce_sum(logits, axis=[-1]))
logits_2d = tf.reshape(logits, [-1, self.event_size])
x_2d = tf.reshape(x, [-1, self.event_size])
# compute the normalization constant
k = tf.cast(self.event_size, x.dtype)
log_norm_const = (tf.lgamma(k) + (k - 1.) * tf.log(self.temperature))
# compute the unnormalized density
log_softmax = tf.nn.log_softmax(logits_2d - x_2d * self._temperature_2d)
log_unnorm_prob = tf.reduce_sum(log_softmax, [-1], keepdims=False)
# combine unnormalized density with normalization constant
log_prob = log_norm_const + log_unnorm_prob
# Reshapes log_prob to be consistent with shape of user-supplied logits
ret = tf.reshape(log_prob, logits_shape)
return ret
def gamma(x, shape, scale):
return -shape * tf.log(scale) - tf.lgamma(shape)\
+ (shape - 1.) * tf.log(x) - x / scale
def _log_normalization(self):
return (tf.log(tf.abs(self.scale)) +
0.5 * tf.log(self.df) +
0.5 * np.log(np.pi) +
tf.lgamma(0.5 * self.df) -
tf.lgamma(0.5 * (self.df + 1.)))
def _entropy(self):
return (self.concentration + tf.log(self.rate) +
tf.lgamma(self.concentration) -
((1. + self.concentration) * tf.digamma(self.concentration)))
def log_q(self, z, alpha, beta):
return (alpha - 1) * tf.log(z) - beta * z + alpha * tf.log(
beta) - tf.lgamma(alpha)
def GO_Gamma_v2(x, alpha):
# x sim Gamma(alpha, 1)
x = tf.cast(x, tf.float64)
alpha = tf.cast(alpha, tf.float64)
logx = tf.log(x)
ex_gamma_xa = tf.exp(x + tf.lgamma(alpha) + (1. - alpha) * logx)
psi_m_log = tf.digamma(alpha + 1.) - logx
igamma_up_reg = tf.igammac(alpha, x)
# Part 1
indx1 = tf.where(x <= 1e-2)
x_indx1 = tf.gather_nd(x, indx1)
alpha_indx1 = tf.gather_nd(alpha, indx1)
GO_Gamma_alpha_value1 = tf.exp(x_indx1) * x_indx1 / alpha_indx1 * (
tf.gather_nd(psi_m_log, indx1) +
x_indx1 / tf.pow(alpha_indx1 + 1., 2) -
tf.pow(x_indx1 / (alpha_indx1 + 2.), 2) +
0.5 * tf.pow(x_indx1, 3) / tf.pow(alpha_indx1 + 3., 2)
)
# Part 2
N_alpha = tf.round(tf.exp(
- 0.488484605941243044124888683654717169702053070068359375 * tf.log(alpha)
+ 1.6948389987594634220613443176262080669403076171875
))
indx2 = tf.where(tf.logical_and(
tf.logical_and(x > 1e-2, alpha <= 3.),
(x <= (alpha + N_alpha * tf.sqrt(alpha)))
))
KK = 15
kk = tf.cast(tf.range(1, KK + 1), tf.float64)
x_indx2 = tf.gather_nd(x, indx2)
alpha_indx2 = tf.gather_nd(alpha, indx2)
GO_Gamma_alpha_value2 = tf.gather_nd(ex_gamma_xa, indx2) * (
-tf.gather_nd(psi_m_log, indx2) * tf.gather_nd(igamma_up_reg, indx2) +
(
tf.digamma(alpha_indx2 + KK + 1.) - tf.gather_nd(logx, indx2) -
tf.reduce_sum(
tf.igammac(tf.expand_dims(alpha_indx2, 1) + tf.expand_dims(kk, 0), tf.expand_dims(x_indx2, 1)) /
(tf.expand_dims(alpha_indx2, 1) + tf.expand_dims(kk, 0))
, 1)
)
)
# Part 2_1
indx2_1 = tf.where(tf.logical_and(
tf.logical_and(x > 1e-2, alpha <= 3.),
(x > (alpha + N_alpha * tf.sqrt(alpha)))
))
KK = 15
kk = tf.cast(tf.range(1, KK + 1), tf.float64)
x_indx2_1 = tf.gather_nd(x, indx2_1)
alpha_indx2_1 = tf.gather_nd(alpha, indx2_1)
GO_Gamma_alpha_value2_1 = tf.gather_nd(ex_gamma_xa, indx2_1) * (
-tf.gather_nd(psi_m_log, indx2_1) * tf.gather_nd(igamma_up_reg, indx2_1) +
(
tf.digamma(alpha_indx2_1 + KK + 1.) - tf.gather_nd(logx, indx2_1) -
tf.reduce_sum(
tf.igammac(tf.expand_dims(alpha_indx2_1, 1) + tf.expand_dims(kk, 0),
tf.expand_dims(x_indx2_1, 1)) /
(tf.expand_dims(alpha_indx2_1, 1) + tf.expand_dims(kk, 0))
, 1)
)
)
GO_Gamma_alpha_value2_1 = tf.maximum(
GO_Gamma_alpha_value2_1,
1. / alpha_indx2_1 - tf.gather_nd(ex_gamma_xa, indx2_1) *
tf.gather_nd(psi_m_log, indx2_1) * tf.gather_nd(igamma_up_reg, indx2_1)
)
# Part 3
indx3 = tf.where(
tf.logical_and(
tf.logical_and(x > 1e-2, alpha > 3.),
alpha <= 500.
)
)
KK = 10
kk = tf.cast(tf.range(1, KK + 1), tf.float64)
x_indx3 = tf.gather_nd(x, indx3)
alpha_indx3 = tf.gather_nd(alpha, indx3)
x_l = alpha_indx3 - tf.log(alpha_indx3) * tf.sqrt(alpha_indx3)
logx_l = tf.log(x_l)
ex_gamma_xa_l = tf.exp(x_l + tf.lgamma(alpha_indx3) + (1. - alpha_indx3) * logx_l)
psi_m_log_l = tf.digamma(alpha_indx3 + 1.) - logx_l
igamma_low_reg_l = tf.igamma(alpha_indx3, x_l)
# igamma_up_reg_l = tf.igammac(alpha_indx3, x_l)
# f_l = ex_gamma_xa_l * (
# -psi_m_log_l * igamma_up_reg_l +
# (tf.digamma(alpha_indx3 + KK + 1.) - logx_l -
# tf.reduce_sum(
# tf.igammac(tf.expand_dims(alpha_indx3, 1) + tf.expand_dims(kk, 0), tf.expand_dims(x_l, 1)) /
# (tf.expand_dims(alpha_indx3, 1) + tf.expand_dims(kk, 0))
# , 1))
# )
f_l = ex_gamma_xa_l * (
psi_m_log_l * igamma_low_reg_l +
tf.reduce_sum(
tf.igamma(tf.expand_dims(alpha_indx3, 1) + tf.expand_dims(kk, 0), tf.expand_dims(x_l, 1)) /
(tf.expand_dims(alpha_indx3, 1) + tf.expand_dims(kk, 0))
, 1)
)
g_l = (1. + (1. - alpha_indx3) / x_l) * f_l + (
-ex_gamma_xa_l / x_l * igamma_low_reg_l + (psi_m_log_l +
tf.reduce_sum(
tf.exp(
tf.expand_dims(kk, 0) * tf.log(tf.expand_dims(x_l, 1)) +
tf.lgamma(tf.expand_dims(alpha_indx3, 1)) -
tf.lgamma(
tf.expand_dims(alpha_indx3, 1) + tf.expand_dims(kk,
0) + 1.)
)
, 1))
)
x_m = alpha_indx3
f_m = 1. + 0.167303227226226980395296095593948848545551300048828125 / \
(
tf.pow(x_m, 1.0008649793164192676186985409003682434558868408203125) -
0.07516433982238841793321881823430885560810565948486328125
)
x_r = 2. * alpha_indx3 - x_l
f_r = 1. / alpha_indx3 - tf.exp(x_r + tf.lgamma(alpha_indx3) + (1. - alpha_indx3) * tf.log(x_r)) * (
(tf.digamma(alpha_indx3 + 1.) - tf.log(x_r)) * tf.igammac(alpha_indx3, x_r)
)
lambda_r = tf.exp(
959.627335718427275423891842365264892578125 / (
tf.pow(alpha_indx3, 1.324768828487964622553363369661383330821990966796875) +
142.427456986662718918523751199245452880859375
)
- 13.01439996187340142341781756840646266937255859375
)
x_mat_i = tf.concat([tf.expand_dims(x_l, 1), tf.expand_dims(x_m, 1), tf.expand_dims(x_r, 1)], 1)
x_mat_bar_i = x_mat_i - tf.expand_dims(alpha_indx3, 1)
x_mat_hat_i = tf.sqrt(x_mat_i) - tf.sqrt(tf.expand_dims(alpha_indx3, 1))
f_mat_i = tf.concat([tf.expand_dims(f_l, 1), tf.expand_dims(f_m, 1), tf.expand_dims(f_r, 1)], 1)
lambda_mat_i = tf.concat([tf.expand_dims(tf.ones_like(alpha_indx3), 1),
tf.expand_dims(tf.ones_like(alpha_indx3), 1),
tf.expand_dims(lambda_r, 1)
], 1)
x_mat_j = tf.expand_dims(x_l, 1)
g_mat_j = tf.expand_dims(g_l, 1)
lambda_mat_j = tf.expand_dims(tf.ones_like(alpha_indx3), 1)
A = tf.reduce_sum(lambda_mat_i * tf.pow(x_mat_bar_i, 2), 1) + tf.reduce_sum(lambda_mat_j, 1)
B = tf.reduce_sum(lambda_mat_i * x_mat_bar_i * x_mat_hat_i, 1) + \
tf.reduce_sum(lambda_mat_j / 2. / tf.sqrt(x_mat_j), 1)
C = tf.reduce_sum(lambda_mat_i * x_mat_bar_i, 1)
D = tf.reduce_sum(lambda_mat_i * tf.pow(x_mat_hat_i, 2), 1) + tf.reduce_sum(lambda_mat_j / 4. / x_mat_j, 1)
E = tf.reduce_sum(lambda_mat_i * x_mat_hat_i, 1)
F = tf.reduce_sum(lambda_mat_i, 1)
G = tf.reduce_sum(lambda_mat_i * x_mat_bar_i * f_mat_i, 1) + tf.reduce_sum(lambda_mat_j * g_mat_j, 1)
H = tf.reduce_sum(lambda_mat_i * x_mat_hat_i * f_mat_i, 1) + \
tf.reduce_sum(lambda_mat_j / 2. / tf.sqrt(x_mat_j) * g_mat_j, 1)
I = tf.reduce_sum(lambda_mat_i * f_mat_i, 1)
Z = F * tf.pow(B, 2) - 2. * B * C * E + D * tf.pow(C, 2) + A * tf.pow(E, 2) - A * D * F
a_cor = 1. / Z * (G * (tf.pow(E, 2) - D * F) + H * (B * F - C * E) - I * (B * E - C * D))
b_cor = 1. / Z * (G * (B * F - C * E) + H * (tf.pow(C, 2) - A * F) - I * (B * C - A * E))
c_cor = 1. / Z * (-G * (B * E - C * D) + I * (tf.pow(B, 2) - A * D) - H * (B * C - A * E))
GO_Gamma_alpha_value3 = a_cor * (x_indx3 - alpha_indx3) + b_cor * (tf.sqrt(x_indx3) - tf.sqrt(alpha_indx3)) + c_cor
GO_Gamma_alpha_value3 = tf.maximum(
GO_Gamma_alpha_value3,
1. / alpha_indx3 - tf.gather_nd(ex_gamma_xa, indx3) *
tf.gather_nd(psi_m_log, indx3) * tf.gather_nd(igamma_up_reg, indx3)
)
# Part 4
# indx4 = tf.where(
# tf.logical_and(
# tf.logical_and(x > 1e-2, alpha > 500.),
# (x <= (alpha + 2. * tf.log(alpha) * tf.sqrt(alpha)))
# )
# )
indx4 = tf.where(
tf.logical_and(x > 1e-2, alpha > 500.)
)
x_indx4 = tf.gather_nd(x, indx4)
alpha_indx4 = tf.gather_nd(alpha, indx4)
f_m_large = 1. + 0.167303227226226980395296095593948848545551300048828125 / \
(
tf.pow(alpha_indx4, 1.0008649793164192676186985409003682434558868408203125) -
0.07516433982238841793321881823430885560810565948486328125
)
g_m_large = 0.54116502161502622048061539317131973803043365478515625 * \
tf.pow(alpha_indx4, -1.010274491769996618728555404231883585453033447265625)
GO_Gamma_alpha_value4 = f_m_large + g_m_large * (x_indx4 - alpha_indx4)
# Part 4_1
# indx4_1 = tf.where(
# tf.logical_and(
# tf.logical_and(x > 1e-2, alpha > 500.),
# (x > (alpha + 2. * tf.log(alpha) * tf.sqrt(alpha)))
# )
# )
# alpha_indx4_1 = tf.gather_nd(alpha, indx4_1)
# GO_Gamma_alpha_value4_1 = 1. / alpha_indx4_1 - tf.gather_nd(ex_gamma_xa, indx4_1) * \
# tf.gather_nd(psi_m_log, indx4_1) * tf.gather_nd(igamma_up_reg, indx4_1)
# Summerize
GO_Gamma_alpha = tf.sparse_to_dense(indx1, x.shape, GO_Gamma_alpha_value1) + \
tf.sparse_to_dense(indx2, x.shape, GO_Gamma_alpha_value2) + \
tf.sparse_to_dense(indx2_1, x.shape, GO_Gamma_alpha_value2_1) + \
tf.sparse_to_dense(indx3, x.shape, GO_Gamma_alpha_value3) + \
tf.sparse_to_dense(indx4, x.shape, GO_Gamma_alpha_value4)
# + \
# tf.sparse_to_dense(indx4_1, x.shape, GO_Gamma_alpha_value4_1)
GO_Gamma_alpha = tf.cast(GO_Gamma_alpha, tf.float32)
return GO_Gamma_alpha # , x_l, x_r, f_l, f_m, f_r, g_l
def _verifySampleAndPdfConsistency(self, vmf, rtol=0.075):
"""Verifies samples are consistent with the PDF using importance sampling.
In particular, we verify an estimate the surface area of the n-dimensional
hypersphere, and the surface areas of the spherical caps demarcated by
a handful of survival rates.
Args:
vmf: A `VonMisesFisher` distribution instance.
rtol: Relative difference tolerable.
"""
dim = tf.dimension_value(vmf.event_shape[-1])
nsamples = 50000
samples = vmf.sample(sample_shape=[nsamples])
samples = tf.check_numerics(samples, 'samples')
log_prob = vmf.log_prob(samples)
log_prob = tf.check_numerics(log_prob, 'log_prob')
log_importance = -log_prob
sphere_surface_area_estimate, samples, importance, conc = self.evaluate(
[
tf.exp(
tf.reduce_logsumexp(log_importance, axis=0) -
tf.log(tf.to_float(nsamples))), samples,
tf.exp(log_importance), vmf.concentration
])
true_sphere_surface_area = 2 * (np.pi)**(dim / 2) * self.evaluate(
tf.exp(-tf.lgamma(dim / 2)))
# Broadcast to correct size
true_sphere_surface_area += np.zeros_like(sphere_surface_area_estimate)
# Highly concentrated distributions do not get enough coverage to provide
# a reasonable full-sphere surface area estimate. These are covered below
# by CDF-based hypersphere cap surface area estimates.
self.assertAllClose(true_sphere_surface_area[np.where(conc < 3)],
sphere_surface_area_estimate[np.where(conc < 3)],
rtol=rtol)
# Assert surface area of hyperspherical cap For some CDFs in [.05,.45],
# (h must be greater than 0 for the hypersphere cap surface area
# calculation to hold).
for survival_rate in 0.95, .9, .75, .6:
cdf = (1 - survival_rate)
mean_dir = self.evaluate(vmf.mean_direction)
dotprods = np.sum(samples * mean_dir, -1)
# Empirical estimate of the effective dot-product of the threshold that
# selects for a given CDF level, that is the cosine of the largest
# passable angle, or the minimum cosine for a within-CDF sample.
dotprod_thresh = np.percentile(dotprods,
100 * survival_rate,
axis=0,
keepdims=True)
dotprod_above_thresh = np.float32(dotprods > dotprod_thresh)
sphere_cap_surface_area_ests = (
cdf * (importance * dotprod_above_thresh).sum(0) /
dotprod_above_thresh.sum(0))
h = (1 - dotprod_thresh)
self.assertGreaterEqual(h.min(),
0) # h must be >= 0 for the eqn below
true_sphere_cap_surface_area = (
0.5 * true_sphere_surface_area *
self.evaluate(tf.betainc((dim - 1) / 2, 0.5, 2 * h - h**2)))
if dim == 3: # For 3-d we have a simpler form we can double-check.
self.assertAllClose(2 * np.pi * h,
true_sphere_cap_surface_area)
self.assertAllClose(true_sphere_cap_surface_area,
sphere_cap_surface_area_ests +
np.zeros_like(true_sphere_cap_surface_area),
rtol=rtol)
def log_cond_prob(self, num_samples, outputs, latent_samples, sample_means,
sample_vars, weights_samples, means_w, covars_weights,
*args):
full_covar = init_list(0.0, [self.num_latent])
for q in range(self.num_latent):
covar_input = covars_weights[q, :, :]
full_covar[q] = tf.matmul(covar_input, tf.transpose(covar_input))
full_covar = tf.stack(full_covar)
### NOTE
### USE WITH TRUNCATION
# Improve identifiability
# full_covar = tf.matrix_band_part(full_covar, -1, 0)
# means_w = tf.matrix_band_part(means_w, -1, 0)
#cov_diagonal = tf.matrix_band_part(tf.transpose(tf.matrix_diag_part(full_covar)), -1, 0)
#var_w = cov_diagonal
means_w = tf.transpose(means_w)
cov_diagonal = tf.transpose(tf.matrix_diag_part(full_covar))
var_w = cov_diagonal
prediction = init_list(0.0, [self.num_tasks])
outputs_no_missing = tf.boolean_mask(outputs,
self.ytrain_non_missing_index)
product_exp = tf.transpose(
tf.matmul(means_w, tf.transpose(sample_means)) + self.offsets)
means_w = tf.expand_dims(means_w, axis=2),
var_w = tf.expand_dims(var_w, axis=2)
sample_means = tf.transpose(tf.tile(
tf.expand_dims(sample_means, axis=0), [self.num_tasks, 1, 1]),
perm=[0, 2, 1])
sample_vars = tf.transpose(tf.tile(tf.expand_dims(sample_vars, axis=0),
[self.num_tasks, 1, 1]),
perm=[0, 2, 1])
first_term = tf.reduce_prod(tf.exp(
(tf.multiply(means_w, sample_means)[0] +
(tf.multiply(tf.square(means_w), sample_vars)[0] +
tf.multiply(var_w, tf.square(sample_means))) / 2) /
((1.0 - tf.multiply(var_w, sample_vars)))),
axis=1)
second_term = tf.reduce_prod(
1.0 / tf.sqrt(1.0 - tf.multiply(var_w, sample_vars)), axis=1)
prediction = tf.transpose(first_term * second_term *
tf.exp(self.offsets))
prediction_no_missing = tf.boolean_mask(prediction,
self.ytrain_non_missing_index)
product_exp_no_missing = tf.boolean_mask(product_exp,
self.ytrain_non_missing_index)
log_lik = product_exp_no_missing * outputs_no_missing - prediction_no_missing - tf.lgamma(
outputs_no_missing + 1.0)
ell = tf.reduce_sum(log_lik)
return (ell, sample_means, sample_vars, means_w, var_w, self.offsets)
def test_Lgamma(self):
t = tf.lgamma(self.random(4, 3))
self.check(t)
def _log_normalization(self):
return (tf.lgamma(self.concentration) + 0.5 * math.log(2. * math.pi) -
self.concentration * tf.log(self.rate) -
0.5 * tf.log(self._lambda))
def autoencoder(x_hat, x, dim_img, dim_z, n_hidden, keep_prob, last_term, dropout_in):
# encoding
mu1, sigma1, mix1 = Create_Encoder_MNIST(x_hat, n_hidden, dim_z, keep_prob, "encoder1")
mu2, sigma2, mix2 = Create_Encoder_MNIST(x_hat, n_hidden, dim_z, keep_prob, "encoder2")
mu3, sigma3, mix3 = Create_Encoder_MNIST(x_hat, n_hidden, dim_z, keep_prob, "encoder3")
mu4, sigma4, mix4 = Create_Encoder_MNIST(x_hat, n_hidden, dim_z, keep_prob, "encoder4")
z1 = distributions.Normal(loc=mu1, scale=sigma1)
z2 = distributions.Normal(loc=mu2, scale=sigma2)
z3 = distributions.Normal(loc=mu3, scale=sigma3)
z4 = distributions.Normal(loc=mu4, scale=sigma4)
init_min = 0.1
init_max = 0.1
init_min = (np.log(init_min) - np.log(1. - init_min))
init_max = (np.log(init_max) - np.log(1. - init_max))
dropout_a = tf.get_variable(name='dropout',
shape=None,
initializer=tf.random_uniform(
(1,),
init_min,
init_max),
dtype=tf.float32,
trainable=True)
dropout_p = tf.nn.sigmoid(dropout_a[0])
eps = 1e-7
temp = 0.1
unif_noise = tf.random_uniform(shape=[batch_size,4])
drop_prob = (
tf.log(dropout_p + eps)
- tf.log(1. - dropout_p + eps)
+ tf.log(unif_noise + eps)
- tf.log(1. - unif_noise + eps)
)
drop_prob = tf.nn.sigmoid(drop_prob / temp)
random_tensor = 1. - drop_prob
retain_prob = 1. - dropout_p
dropout_samples = random_tensor / retain_prob
dropout_regularizer = dropout_p * tf.log(dropout_p)
dropout_regularizer += (1. - dropout_p) * tf.log(1. - dropout_p)
dropout_regularizer *= dropout_regularizer *10 * -1
dropout_regularizer = tf.clip_by_value(dropout_regularizer,-10,0)
sum1 = mix1 + mix2 + mix3 + mix4
mix1 = mix1 / sum1
mix2 = mix2 / sum1
mix3 = mix3 / sum1
mix4 = mix4 / sum1
mix = tf.concat([mix1, mix2, mix3, mix4], 1)
mix_parameters = mix
dist = tf.distributions.Dirichlet(mix)
mix_samples = dist.sample()
mix = mix_samples
mix_dropout1 = dropout_samples[:,0:1]*mix_samples[:,0:1]
mix_dropout2 = dropout_samples[:,1:2]*mix_samples[:,1:2]
mix_dropout3 = dropout_samples[:,2:3]*mix_samples[:,2:3]
mix_dropout4 = dropout_samples[:,3:4]*mix_samples[:,3:4]
sum1 = mix_dropout1+mix_dropout2+mix_dropout3+mix_dropout4
mix_dropout1 = mix_dropout1/sum1
mix_dropout2 = mix_dropout2/sum1
mix_dropout3 = mix_dropout3/sum1
mix_dropout4 = mix_dropout4/sum1
# sampling by re-parameterization technique
# z = mu + sigma * tf.random_normal(tf.shape(mu), 0, 1, dtype=tf.float32)
z1_samples = z1.sample()
z2_samples = z2.sample()
z3_samples = z3.sample()
z4_samples = z4.sample()
ttf = []
ttf.append(z1_samples)
ttf.append(z2_samples)
ttf.append(z3_samples)
ttf.append(z4_samples)
dHSIC_Value = dHSIC(ttf)
# decoding
y1 = Create_SubDecoder(z1_samples, n_hidden, dim_img, keep_prob, "decoder1")
y2 = Create_SubDecoder(z2_samples, n_hidden, dim_img, keep_prob, "decoder2")
y3 = Create_SubDecoder(z3_samples, n_hidden, dim_img, keep_prob, "decoder3")
y4 = Create_SubDecoder(z4_samples, n_hidden, dim_img, keep_prob, "decoder4")
# dropout out
y1 = y1 * mix_dropout1
y2 = y2 * mix_dropout2
y3 = y3 * mix_dropout3
y4 = y4 * mix_dropout4
y = y1 + y2 + y3 + y4
output = Create_FinalDecoder(y, n_hidden, dim_img, keep_prob, "final")
y = output
m1 = np.zeros(dim_z, dtype=np.float32)
m1[:] = 0
v1 = np.zeros(dim_z, dtype=np.float32)
v1[:] = 1
# p_z1 = distributions.Normal(loc=np.zeros(dim_z, dtype=np.float32),
# scale=np.ones(dim_z, dtype=np.float32))
p_z1 = distributions.Normal(loc=m1,
scale=v1)
m2 = np.zeros(dim_z, dtype=np.float32)
m2[:] = 0
v2 = np.zeros(dim_z, dtype=np.float32)
v2[:] = 1
p_z2 = distributions.Normal(loc=m2,
scale=v2)
m3 = np.zeros(dim_z, dtype=np.float32)
m3[:] = 0
v3 = np.zeros(dim_z, dtype=np.float32)
v3[:] = 1
p_z3 = distributions.Normal(loc=m3,
scale=v3)
m4 = np.zeros(dim_z, dtype=np.float32)
m4[:] = 0
v4 = np.zeros(dim_z, dtype=np.float32)
v4[:] = 1
p_z4 = distributions.Normal(loc=m4,
scale=v4)
kl1 = tf.reduce_mean(tf.reduce_sum(distributions.kl_divergence(z1, p_z1), 1))
kl2 = tf.reduce_mean(tf.reduce_sum(distributions.kl_divergence(z2, p_z2), 1))
kl3 = tf.reduce_mean(tf.reduce_sum(distributions.kl_divergence(z3, p_z3), 1))
kl4 = tf.reduce_mean(tf.reduce_sum(distributions.kl_divergence(z4, p_z4), 1))
KL_divergence = (kl1 + kl2 + kl3 + kl4) / 4.0
# loss
marginal_likelihood = tf.reduce_sum(x * tf.log(y) + (1 - x) * tf.log(1 - y), 1)
marginal_likelihood = tf.reduce_mean(marginal_likelihood)
# KL divergence between two Dirichlet distributions
a1 = tf.clip_by_value(mix_parameters, 0.1, 0.8)
a2 = tf.constant((0.25, 0.25, 0.25, 0.25), shape=(batch_size, 4))
r = tf.reduce_sum((a1 - a2) * (tf.polygamma(0.0, a1) - tf.polygamma(0.0, 1)), axis=1)
a = tf.lgamma(tf.reduce_sum(a1, axis=1)) - tf.lgamma(tf.reduce_sum(a2, axis=1)) + tf.reduce_sum(tf.lgamma(a2),
axis=-1) - tf.reduce_sum(
tf.lgamma(a1), axis=1) + r
kl = a
kl = tf.reduce_mean(kl)
p1 = 1
p2 = 1
p4 = 1
ELBO = marginal_likelihood - KL_divergence * p2
loss = -ELBO + kl * p1 + p4 * dHSIC_Value + dropout_regularizer
z = z1_samples
return y, z, loss, -marginal_likelihood, dropout_regularizer,dropout_p,dropout_samples
def negative_binomial(m, Y, alpha):
k = 1 / alpha
return tf.lgamma(k + Y) - tf.lgamma(Y + 1) - tf.lgamma(k) + Y * tf.log(
m / (m + k)) - k * tf.log(1 + m * alpha)
def poisson(lamb, y):
return y * tf.log(lamb) - lamb - tf.lgamma(y + 1.)
def variational_expectations(self, Fmu, Fvar, Y):
if self.invlink is tf.exp:
return -self.shape * Fmu - tf.lgamma(self.shape) \
+ (self.shape - 1.) * tf.log(Y) - Y * tf.exp(-Fmu + Fvar / 2.)
else:
return Likelihood.variational_expectations(self, Fmu, Fvar, Y)
def variational_expectations(self, Fmu, Fvar, Y):
if self.invlink is tf.exp:
return Y * Fmu - tf.exp(Fmu + Fvar / 2) - tf.lgamma(Y + 1)
else:
return Likelihood.variational_expectations(self, Fmu, Fvar, Y)
def _log_normalization(self):
return (tf.lgamma(self.concentration) -
self.concentration * tf.log(self.rate))
def poisson_loss(y_true, y_pred):
# mask = tf.cast(y_true>0,tf.float32)
y_true = y_true + 0.001
NLL = tf.lgamma(y_pred + 1) - y_pred * tf.log(y_true)
return tf.reduce_mean(NLL)
def mean_poisson_log_likelihood(y_true, y_pred):
poisson = y_pred - y_true * K.log(y_pred + K.epsilon()) + lgamma(y_true + 1)
return K.mean(poisson, axis=-1)
def bezier_net(self, inputs, n_points, bezier_degree):
depth_cpw = 32 * 8
dim_cpw = int((bezier_degree + 1) / 8)
kernel_size = (4, 3)
# noise_std = 0.01
cpw = tf.layers.dense(inputs, 1024)
cpw = tf.layers.batch_normalization(cpw, momentum=0.9)
cpw = tf.nn.leaky_relu(cpw, alpha=0.2)
cpw = tf.layers.dense(cpw, dim_cpw * 3 * depth_cpw)
cpw = tf.layers.batch_normalization(cpw, momentum=0.9)
cpw = tf.nn.leaky_relu(cpw, alpha=0.2)
cpw = tf.reshape(cpw, (-1, dim_cpw, 3, depth_cpw))
cpw = tf.layers.conv2d_transpose(cpw,
int(depth_cpw / 2),
kernel_size,
strides=(2, 1),
padding='same')
cpw = tf.layers.batch_normalization(cpw, momentum=0.9)
cpw = tf.nn.leaky_relu(cpw, alpha=0.2)
# cpw += tf.random_normal(shape=tf.shape(cpw), stddev=noise_std)
cpw = tf.layers.conv2d_transpose(cpw,
int(depth_cpw / 4),
kernel_size,
strides=(2, 1),
padding='same')
cpw = tf.layers.batch_normalization(cpw, momentum=0.9)
cpw = tf.nn.leaky_relu(cpw, alpha=0.2)
# cpw += tf.random_normal(shape=tf.shape(cpw), stddev=noise_std)
cpw = tf.layers.conv2d_transpose(cpw,
int(depth_cpw / 8),
kernel_size,
strides=(2, 1),
padding='same')
cpw = tf.layers.batch_normalization(cpw, momentum=0.9)
cpw = tf.nn.leaky_relu(cpw, alpha=0.2)
# cpw += tf.random_normal(shape=tf.shape(cpw), stddev=noise_std)
# Control points
cp = tf.layers.conv2d(
cpw, 1, (1, 2),
padding='valid') # batch_size x (bezier_degree+1) x 2 x 1
cp = tf.nn.tanh(cp)
cp = tf.squeeze(
cp, axis=-1,
name='control_point') # batch_size x (bezier_degree+1) x 2
# Weights
w = tf.layers.conv2d(cpw, 1, (1, 3), padding='valid')
w = tf.nn.sigmoid(w) # batch_size x (bezier_degree+1) x 1 x 1
w = tf.squeeze(w, axis=-1,
name='weight') # batch_size x (bezier_degree+1) x 1
# Parameters at data points
db = tf.layers.dense(inputs, 1024)
db = tf.layers.batch_normalization(db, momentum=0.9)
db = tf.nn.leaky_relu(db, alpha=0.2)
db = tf.layers.dense(db, 256)
db = tf.layers.batch_normalization(db, momentum=0.9)
db = tf.nn.leaky_relu(db, alpha=0.2)
db = tf.layers.dense(db, n_points - 1)
db = tf.nn.softmax(db) # batch_size x (n_data_points-1)
ub = tf.pad(db, [[0, 0], [1, 0]],
constant_values=0) # batch_size x n_data_points
ub = tf.cumsum(ub, axis=1)
ub = tf.minimum(ub, 1)
ub = tf.expand_dims(ub, axis=-1) # 1 x n_data_points x 1
# Bezier layer
# Compute values of basis functions at data points
num_control_points = bezier_degree + 1
lbs = tf.tile(ub, [1, 1, num_control_points
]) # batch_size x n_data_points x n_control_points
pw1 = tf.range(0, num_control_points, dtype=tf.float32)
pw1 = tf.reshape(pw1, [1, 1, -1]) # 1 x 1 x n_control_points
pw2 = tf.reverse(pw1, axis=[-1])
lbs = tf.add(tf.multiply(pw1, tf.log(lbs + EPSILON)),
tf.multiply(pw2, tf.log(1 - lbs + EPSILON))
) # batch_size x n_data_points x n_control_points
lc = tf.add(tf.lgamma(pw1 + 1), tf.lgamma(pw2 + 1))
lc = tf.subtract(
tf.lgamma(tf.cast(num_control_points, dtype=tf.float32)),
lc) # 1 x 1 x n_control_points
lbs = tf.add(lbs, lc) # batch_size x n_data_points x n_control_points
bs = tf.exp(lbs)
# Compute data points
cp_w = tf.multiply(cp, w)
dp = tf.matmul(bs, cp_w) # batch_size x n_data_points x 2
bs_w = tf.matmul(bs, w) # batch_size x n_data_points x 1
dp = tf.div(dp, bs_w) # batch_size x n_data_points x 2
dp = tf.expand_dims(
dp, axis=-1, name='x_fake') # batch_size x n_data_points x 2 x 1
return dp, cp, w
def log_gamma(x, a1, b1):
yout = tf.reduce_sum(
a1 * tf.log(b1) - tf.lgamma(a1)
+ (a1 - 1.) * tf.log(x) - b1 * x
)
return yout
def build_loss(self, seqs_repr, data_ops):
"""Convert per-location real-valued predictions to a loss."""
# targets
tstart = self.batch_buffer // self.target_pool
tend = (self.batch_length - self.batch_buffer) // self.target_pool
targets = data_ops['label']
targets = tf.identity(targets[:, tstart:tend, :], name='targets_op')
# work-around for specifying my own predictions
self.preds_adhoc = tf.placeholder(tf.float32,
shape=seqs_repr.shape,
name='preds-adhoc')
# choose link
if self.link in ['identity', 'linear']:
self.preds_op = tf.identity(seqs_repr, name='preds')
elif self.link == 'relu':
self.preds_op = tf.relu(seqs_repr, name='preds')
elif self.link == 'exp':
self.preds_op = tf.exp(tf.clip_by_value(seqs_repr, -50, 50),
name='preds')
elif self.link == 'exp_linear':
self.preds_op = tf.where(seqs_repr > 0,
seqs_repr + 1,
tf.exp(
tf.clip_by_value(seqs_repr, -50, 50)),
name='preds')
elif self.link == 'softplus':
self.preds_op = tf.nn.softplus(seqs_repr, name='preds')
elif self.link == 'softmax':
# performed in the loss function, but saving probabilities
self.preds_prob = tf.nn.softmax(seqs_repr, name='preds')
else:
print('Unknown link function %s' % self.link, file=sys.stderr)
exit(1)
# clip
if self.target_clip is not None:
self.preds_op = tf.clip_by_value(self.preds_op, 0,
self.target_clip)
targets = tf.clip_by_value(targets, 0, self.target_clip)
# sqrt
if self.target_sqrt:
self.preds_op = tf.sqrt(self.preds_op)
targets = tf.sqrt(targets)
loss_op = None
loss_adhoc = None
# choose loss
if self.loss == 'gaussian':
loss_op = tf.squared_difference(self.preds_op, targets)
loss_adhoc = tf.squared_difference(self.preds_adhoc, targets)
elif self.loss == 'poisson':
loss_op = tf.nn.log_poisson_loss(targets,
tf.log(self.preds_op),
compute_full_loss=True)
loss_adhoc = tf.nn.log_poisson_loss(targets,
tf.log(self.preds_adhoc),
compute_full_loss=True)
elif self.loss == 'negative_binomial':
# define overdispersion alphas
self.alphas = tf.get_variable(
'alphas',
shape=[self.num_targets],
initializer=tf.constant_initializer(-5),
dtype=tf.float32)
self.alphas = tf.nn.softplus(tf.clip_by_value(
self.alphas, -50, 50))
tf.summary.histogram('alphas', self.alphas)
for ti in np.linspace(0, self.num_targets - 1, 10).astype('int'):
tf.summary.scalar('alpha_t%d' % ti, self.alphas[ti])
# compute w/ inverse
k = 1. / self.alphas
# expand k
k_expand = tf.tile(k, [self.batch_size * seq_length])
k_expand = tf.reshape(
k_expand, (self.batch_size, seq_length, self.num_targets))
# expand lgamma(k)
lgk_expand = tf.tile(tf.lgamma(k), [self.batch_size * seq_length])
lgk_expand = tf.reshape(
lgk_expand, (self.batch_size, seq_length, self.num_targets))
# construct loss
loss1 = targets * tf.log(self.preds_op /
(self.preds_op + k_expand))
loss2 = k_expand * tf.log(k_expand / (self.preds_op + k_expand))
loss3 = tf.lgamma(targets + k_expand) - lgk_expand
loss_op = -(loss1 + loss2 + loss3)
# adhoc
loss1 = targets * tf.log(self.preds_adhoc /
(self.preds_adhoc + k_expand))
loss2 = k_expand * tf.log(k_expand / (self.preds_adhoc + k_expand))
loss_adhoc = -(loss1 + loss2 + loss3)
elif self.loss == 'negative_binomial_hilbe':
# define overdispersion alphas
self.alphas = tf.get_variable(
'alphas',
shape=[self.num_targets],
initializer=tf.constant_initializer(-5),
dtype=tf.float32)
self.alphas = tf.exp(tf.clip_by_value(self.alphas, -50, 50))
# expand
alphas_expand = tf.tile(self.alphas,
[self.batch_size * seq_length])
alphas_expand = tf.reshape(
alphas_expand, (self.batch_size, seq_length, self.num_targets))
# construct loss
loss1 = targets * tf.log(self.preds_op)
loss2 = (alphas_expand * targets + 1) / alphas_expand
loss3 = tf.log(alphas_expand * self.preds_op + 1)
loss_op = -loss1 + loss2 * loss3
# adhoc
loss1 = targets * tf.log(self.preds_adhoc)
loss3 = tf.log(alphas_expand * self.preds_adhoc + 1)
loss_adhoc = -loss1 + loss2 * loss3
elif self.loss == 'gamma':
# jchan document
loss_op = targets / self.preds_op + tf.log(self.preds_op)
loss_adhoc = targets / self.preds_adhoc + tf.log(self.preds_adhoc)
elif self.loss == 'cross_entropy':
loss_op = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=(targets - 1), logits=self.preds_op)
loss_adhoc = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=(targets - 1), logits=self.preds_adhoc)
else:
print('Cannot identify loss function %s' % self.loss)
exit(1)
# set NaN's to zero
# loss_op = tf.boolean_mask(loss_op, tf.logical_not(self.targets_na[:,tstart:tend]))
# reduce lossses by batch and position
loss_op = tf.reduce_mean(loss_op, axis=[0, 1], name='target_loss')
loss_op = tf.check_numerics(loss_op, 'Invalid loss', name='loss_check')
loss_adhoc = tf.reduce_mean(loss_adhoc,
axis=[0, 1],
name='target_loss_adhoc')
tf.summary.histogram('target_loss', loss_op)
for ti in np.linspace(0, self.num_targets - 1, 10).astype('int'):
tf.summary.scalar('loss_t%d' % ti, loss_op[ti])
self.target_losses = loss_op
self.target_losses_adhoc = loss_adhoc
# define target sigmas
"""
self.target_sigmas = tf.get_variable('target_sigmas',
shape=[self.num_targets], initializer=tf.constant_initializer(2),
dtype=tf.float32)
self.target_sigmas =
tf.nn.softplus(tf.clip_by_value(self.target_sigmas,-50,50))
tf.summary.histogram('target_sigmas', self.target_sigmas)
for ti in np.linspace(0,self.num_targets-1,10).astype('int'):
tf.summary.scalar('sigma_t%d'%ti, self.target_sigmas[ti])
# self.target_sigmas = tf.ones(self.num_targets) / 2.
"""
# dot losses target sigmas
# loss_op = loss_op / (2*self.target_sigmas)
# loss_adhoc = loss_adhoc / (2*self.target_sigmas)
# fully reduce
loss_op = tf.reduce_mean(loss_op, name='loss')
loss_adhoc = tf.reduce_mean(loss_adhoc, name='loss_adhoc')
# add extraneous terms
loss_op += self.weights_regularizers # + tf.reduce_mean(tf.log(self.target_sigmas))
loss_adhoc += self.weights_regularizers # + tf.reduce_mean(tf.log(self.target_sigmas))
# track
tf.summary.scalar('loss', loss_op)
self.targets_op = targets
return loss_op, loss_adhoc
if Layers >= 3:
theta3_s = tf.stop_gradient(theta3)
theta3T_s = tf.stop_gradient(theta3T)
theta_alpha3_s = tf.stop_gradient(theta_alpha3)
theta_beta3_s = tf.stop_gradient(theta_beta3)
c4_s = tf.stop_gradient(c4)
c_alpha4_s = tf.stop_gradient(c_alpha4)
c_beta4_s = tf.stop_gradient(c_beta4)
phi3_s = tf.stop_gradient(phi3)
phi_alpha3_s = tf.stop_gradient(phi_alpha3)
r_s = tf.stop_gradient(r)
r_alpha_s = tf.stop_gradient(r_alpha)
r_beta_s = tf.stop_gradient(r_beta)
# Calculate ELBO
Loglike = tf.reduce_sum(x * tf.log(phi_theta1) - phi_theta1 - tf.lgamma(x + 1.)) / (K[0] * batch_size)
# Layer 1
if Layers == 1:
pta_phi_theta2 = tf.transpose(r)
pta_phi_theta2T = tf.transpose(r)
else:
pta_phi_theta2 = phi_theta2
pta_phi_theta2T = phi_theta2T
pta_phi_theta2_s = tf.stop_gradient(pta_phi_theta2)
pta_phi_theta2T_s = tf.stop_gradient(pta_phi_theta2T)
ELBO = Loglike + log_gamma_minus(theta1T, pta_phi_theta2T_s, c2_s, theta_alpha1_s, theta_beta1_s) / (K[0] * batch_size)
ELBO = ELBO + log_dirichlet_minus(phi1, eta_l[1], phi_alpha1_s) / (K[0] * rho_mb * batch_size)
ELBO = ELBO + log_gamma_minus(theta1T_s, pta_phi_theta2T_s, c2, theta_alpha1_s, theta_beta1_s) / (K[0] * batch_size) \
+ prior * log_gamma_minus(c2, e0, f0, c_alpha2_s, c_beta2_s) / (K[0] * batch_size)
def __init__(self, conf):
""" Set up remaining layers, objective and loss functions, gradient
compute and apply ops, network parameter synchronization ops, and
summary ops. """
super(PolicyVNetwork, self).__init__(conf)
self.beta = conf['args'].entropy_regularisation_strength
self.use_recurrent = conf['args'].alg_type == 'a3c-lstm'
with tf.name_scope(self.name):
self.critic_target_ph = tf.placeholder(
'float32', [None], name='target')
self.adv_actor_ph = tf.placeholder("float", [None], name='advantage')
if self.use_recurrent:
layer_name = 'lstm_layer'
self.hidden_state_size = 256
with tf.variable_scope(self.name+'/'+layer_name) as vs:
self.lstm_cell = CustomBasicLSTMCell(self.hidden_state_size, forget_bias=1.0)
self.step_size = tf.placeholder(tf.float32, [1], name='step_size')
self.initial_lstm_state = tf.placeholder(
tf.float32, [1, 2*self.hidden_state_size], name='initital_state')
ox_reshaped = tf.reshape(self.ox, [1,-1,256])
lstm_outputs, self.lstm_state = tf.nn.dynamic_rnn(
self.lstm_cell,
ox_reshaped,
initial_state=self.initial_lstm_state,
sequence_length=self.step_size,
time_major=False,
scope=vs)
self.ox = tf.reshape(lstm_outputs, [-1,256])
# Get all LSTM trainable params
self.lstm_trainable_variables = [v for v in
tf.trainable_variables() if v.name.startswith(vs.name)]
# Final actor layer
layer_name = 'softmax_policy4'
self.wpi, self.bpi, self.output_layer_pi, self.log_output_layer_pi = layers.softmax_and_log_softmax(
layer_name, self.ox, self.num_actions)
# Compute Poisson x Discrete Gaussian
self.t = tf.placeholder(tf.float32, num_actions)
self.l = tf.placeholder(tf.float32, num_actions)
k = np.array(list(range(10)))
t_hat = tf.log(1+tf.exp(self.t))
l_hat = tf.log(1+tf.exp(self.l))
self.action_repeat_probs = tf.exp(-(k-l_hat)**2/(2*t_hat) - l_hat - tf.lgamma(k+1)) * l_hat**k
self.log_action_repeat_probs = -(k-l_hat)**2/(2*t_hat) - l_hat - tf.lgamma(k+1) + k*tf.log(l_hat)
self.selected_repeat = self.placeholder(tf.int32)
self.selected_repeat_prob = self.action_repeat_probs[self.selected_repeat]
# Entropy: ∑_a[-p_a ln p_a]
self.output_layer_entropy = tf.reduce_sum(
- 1.0 * tf.multiply(
self.output_layer_pi * self.action_repeat_probs,
self.log_output_layer_pi + self.log_action_repeat_probs
), axis=1)
self.entropy = tf.reduce_mean(self.output_layer_entropy)
# Final critic layer
self.wv, self.bv, self.output_layer_v = layers.fc(
'fc_value4', self.ox, 1, activation='linear')
self.params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
# Advantage critic
self.adv_critic = tf.subtract(self.critic_target_ph, tf.reshape(self.output_layer_v, [-1]))
# Actor objective
# Multiply the output of the network by a one hot vector, 1 for the
# executed action. This will make the non-regularised objective
# term for non-selected actions to be zero.
self.log_output_selected_action = tf.reduce_sum(
(self.log_output_layer_pi + self.selected_repeat_prob) * self.selected_action_ph,
axis=1
)
actor_objective_advantage_term = tf.multiply(
self.log_output_selected_action, self.adv_actor_ph
)
actor_objective_entropy_term = self.beta * self.output_layer_entropy
self.actor_objective = -tf.reduce_mean(
actor_objective_advantage_term
+ actor_objective_entropy_term
)
# Critic loss
if self.clip_loss_delta > 0:
quadratic_part = tf.reduce_mean(tf.pow(
tf.minimum(
tf.abs(self.adv_critic), self.clip_loss_delta
), 2))
linear_part = tf.subtract(tf.abs(self.adv_critic), quadratic_part)
#OBS! For the standard L2 loss, we should multiply by 0.5. However, the authors of the paper
# recommend multiplying the gradients of the V function by 0.5. Thus the 0.5
self.critic_loss = tf.multiply(tf.constant(0.5), tf.nn.l2_loss(quadratic_part) + \
self.clip_loss_delta * linear_part)
else:
self.critic_loss = 0.5 * tf.reduce_mean(tf.pow(self.adv_critic, 2))
self.loss = self.actor_objective + self.critic_loss
# Optimizer
grads = tf.gradients(self.loss, self.params)
if self.clip_norm_type == 'ignore':
# Unclipped gradients
self.get_gradients = grads
elif self.clip_norm_type == 'global':
# Clip network grads by network norm
self.get_gradients = tf.clip_by_global_norm(
grads, self.clip_norm)[0]
elif self.clip_norm_type == 'local':
# Clip layer grads by layer norm
self.get_gradients = [tf.clip_by_norm(
g, self.clip_norm) for g in grads]
# Placeholders for shared memory vars
self.params_ph = []
for p in self.params:
self.params_ph.append(tf.placeholder(tf.float32,
shape=p.get_shape(),
name="shared_memory_for_{}".format(
(p.name.split("/", 1)[1]).replace(":", "_"))))
# Ops to sync net with shared memory vars
self.sync_with_shared_memory = []
for i in range(len(self.params)):
self.sync_with_shared_memory.append(
self.params[i].assign(self.params_ph[i]))
def gammaln(self, x):
return tf.lgamma(x)
def poisson(x, lam):
return x * tf.log(lam) - lam - tf.lgamma(x + 1.)
def main():
argc = len(sys.argv)
if (argc != 2):
print("Usage: [Data.csv]")
exit()
## Read Data from file into a data frame
DATA = sys.argv[1]
data_df = pd.read_csv(DATA)
num_data_columns = len(data_df.columns)
num_dim = num_data_columns
hypergeom_p = 1
hypergeom_q = hypergeom_p - 1
session_string = ''.join(
random.choice(string.ascii_uppercase + string.digits)
for _ in range(10))
print(
"Data appears to have {} columns. Assuming multivariate probability distribution with {} varaibles."
.format(num_data_columns, num_data_columns))
log_file_string = "MELL_log_{}.txt".format(session_string)
## A random string for this session deatils
## Set to floating point data
data = data_df.astype('float64')
num_rows = data.shape[0]
print("There are {} rows".format(num_rows))
##########################
## Inputs and constants ##
##########################
EPOCHS = 1
n_batches = 50
model_weight = 1.0
normalisation_weight = 100.0
#R_squared_weight = 1.0
#intercept_weight = 1.0
#gradient_weight = 100.0
# Create a placeholder to dynamically switch between batch sizes
batch_size = tf.placeholder(tf.int64)
drop_prob = tf.placeholder(tf.float32)
## Training Boolean
training_bool = tf.placeholder(tf.bool)
##################
## Initializers ##
##################
weight_initialiser_mean = 0.0
weight_initialiser_std = 0.000000001
## Question do we need a separate initializer for everything?
#weight_initer = tf.truncated_normal_initializer(mean=weight_initialiser_mean, stddev=weight_initialiser_std)
#weight_initer = tf.constant_initializer(np.eye(num_gamma,num_dim),dtype=tf.float32)
#avec_initer = tf.random_uniform_initializer(minval=0.90,maxval=1.1,dtype=tf.float32)
#alpha_initer = tf.truncated_normal_initializer(mean=0.0, stddev=0.5)
#beta_initer = tf.truncated_normal_initializer(mean=0.0, stddev=0.5)
#q_initer = tf.constant_initializer(np.eye(num_dim),dtype=tf.float32)
#matrix_M_init = tf.constant_initializer(np.eye(num_dim,num_dim),dtype=tf.float32)
matrix_V_init = tf.truncated_normal_initializer(
mean=weight_initialiser_mean, stddev=weight_initialiser_std)
matrix_W_init = tf.truncated_normal_initializer(
mean=weight_initialiser_mean, stddev=weight_initialiser_std)
param_a_init = tf.truncated_normal_initializer(mean=1.0,
stddev=0.000000001)
param_b_init = tf.truncated_normal_initializer(mean=1.0, stddev=0.1)
eta_init = tf.truncated_normal_initializer(mean=1.0, stddev=0.1)
########################
## Input Placeholders ##
########################
## The data objects, x input data, S set of input exponents
x = tf.placeholder(tf.float32, shape=[None, num_dim])
S = tf.placeholder(tf.float32, shape=[None, num_dim])
##########################
## Restart placeholders ## For continuing and restarting if errors occur
##########################
#eta_reset_input = tf.placeholder( tf.float32, shape=[num_dim])
#q_reset_input = tf.placeholder( tf.float32, shape=[num_dim,num_dim])
#w_reset_input = tf.placeholder( tf.float32, shape=[num_gamma, num_dim])
#alpha_reset_input = tf.placeholder( tf.float32, shape=[num_gamma])
#beta_reset_input = tf.placeholder( tf.float32, shape=[num_dim])
#a_reset_input = tf.placeholder( tf.float32, shape=[num_gamma])
############################
## Iterators and features ##
############################
ds_buffer_constant = 1000
dataset = tf.data.Dataset.from_tensor_slices(x).shuffle(
buffer_size=ds_buffer_constant).batch(batch_size).repeat()
iter = tf.data.Iterator.from_structure(dataset.output_types,
dataset.output_shapes)
features = iter.get_next()
dataset_init_op = iter.make_initializer(dataset)
print(
"TO ADD: Scale parameter exponenets must equal variable exponenets in the series expansion via GRMT!"
)
print(
"TO ADD: Write best models to file as we go (periodically to not loose data) : Checkpointing"
)
print("TO ADD: Parameter files : Check it works first!")
print("TO ADD: Add initialiser constants to log")
print("Put a condition on the sign of the moment?")
print("Num gammas must be greater or equal to num dim")
print(
"Treat some of the gammas as a subset which are a function of the scale weights"
)
print(
"How does GMRT det A come into this? will there be an additional scaling factor?"
)
print(
"Complex network and ability to minimise the complex parts in real predictions? Then we can include the (-1)^(q.s+beta) kind of terms"
)
print(
"Start with a product of marginals (which may be quicker/easier to train) then start with an expression that represents that product for the full distribution"
)
print("#####")
print(
"Check with simple function! How close can loss get to zero? Can we model this with a noise distribution?"
)
print(
"Can we find the s which maximises the error for a given function? Can we work on that s only?"
)
print(
"Consider a loss function that is signed for small errors such that they cancel if they are noise but do not if they are large?"
)
print("For syymetry use the sum of the above function flipped!?")
print("Check Autonorm is working correctly")
print("Check Error expectations are working correctly")
print("Check range 0-1 in s is acceptable?")
## Make a log of parameters
with open(log_file_string, "w") as f:
f.write("Session {} created {}\n".format(session_string,
datetime.datetime.now()))
f.write("Data of shape {}\n".format(data.shape))
f.write("Number of dimensions = {}\n".format(num_dim))
f.write("Hypergeometric p = {}\n".format(hypergeom_p))
f.write("Hypergeometric q = {}\n".format(hypergeom_q))
f.write("EPOCHS = {}\n".format(EPOCHS))
f.write("n_batches = {}\n".format(EPOCHS))
f.write("normalisation_weight = {}\n".format(normalisation_weight))
#f.write("intercept_weight = {}\n".format(intercept_weight))
#f.write("gradient_weight = {}\n".format(gradient_weight))
#f.write("R_squared_weight = {}\n".format(R_squared_weight))
f.write("ds_buffer_constant = {}\n".format(ds_buffer_constant))
#####################
## Model Variables ##
#####################
eta = tf.get_variable(name="eta",
dtype=tf.float32,
shape=[num_dim],
initializer=eta_init)
matrix_V = tf.get_variable(name="V",
dtype=tf.float32,
shape=[hypergeom_p, num_dim],
initializer=matrix_V_init)
matrix_W = tf.get_variable(name="W",
dtype=tf.float32,
shape=[hypergeom_q, num_dim],
initializer=matrix_W_init)
param_a = tf.get_variable(name="a",
dtype=tf.float32,
shape=[hypergeom_p],
initializer=param_a_init)
param_b = tf.get_variable(name="b",
dtype=tf.float32,
shape=[hypergeom_q],
initializer=param_b_init)
eta_input = tf.placeholder(dtype=tf.float32, shape=[num_dim])
param_a_input = tf.placeholder(dtype=tf.float32, shape=[hypergeom_p])
param_b_input = tf.placeholder(dtype=tf.float32, shape=[hypergeom_q])
matrix_V_input = tf.placeholder(dtype=tf.float32,
shape=[hypergeom_p, num_dim])
matrix_W_input = tf.placeholder(dtype=tf.float32,
shape=[hypergeom_q, num_dim])
print("CHECK: use locking status and effect?")
eta_assign_op = tf.assign(eta, eta_input, use_locking=False)
param_a_assign_op = tf.assign(param_a, param_a_input, use_locking=False)
param_b_assign_op = tf.assign(param_b, param_b_input, use_locking=False)
matrix_V_assign_op = tf.assign(matrix_V, matrix_V_input, use_locking=False)
matrix_W_assign_op = tf.assign(matrix_W, matrix_W_input, use_locking=False)
#####################
## Model Equations ##
#####################
print("Warning rawmoments are being used!!")
print("Optimisation: lgamma(snorm) = np.zeroes([num_dim])")
print("Optimisation: tf.reduce_sum(np.zeroes([num_dim])) = 0")
print(
"NOTE: param_a elements must be bigger than the row sums of V for positive arguments to loggamma(x)!"
)
print(
"NOTE: param_b elements must be bigger than the row sums of W for positive arguments to loggamma(x)!"
)
## Return the normalised moment prediction for the input S
logXi_a = tf.reduce_sum(
tf.lgamma(param_a)) ## Hypergeometric p normalisation coeffs
logXi_b = tf.reduce_sum(
tf.lgamma(param_b)) ## Hypergeometric q normalisation coeffs
## Things that depend on the exponent sample
logXi_s = tf.map_fn(lambda s: tf.reduce_sum(tf.lgamma(s)),
S,
dtype=tf.float32)
logXi_2s = tf.map_fn(lambda s: tf.reduce_sum(tf.lgamma(s)),
2.0 * S,
dtype=tf.float32)
logXi_a_minus_V_s = tf.map_fn(lambda s: tf.reduce_sum(
tf.lgamma(param_a + tf.tensordot(matrix_V, s, axes=[[1], [0]]))),
S,
dtype=tf.float32)
logXi_b_minus_W_s = tf.map_fn(lambda s: tf.reduce_sum(
tf.lgamma(param_b + tf.tensordot(matrix_W, s, axes=[[1], [0]]))),
S,
dtype=tf.float32)
logXi_a_minus_V_2s = tf.map_fn(lambda s: tf.reduce_sum(
tf.lgamma(param_a + tf.tensordot(matrix_V, s, axes=[[1], [0]]))),
2.0 * S,
dtype=tf.float32)
logXi_b_minus_W_2s = tf.map_fn(lambda s: tf.reduce_sum(
tf.lgamma(param_b + tf.tensordot(matrix_W, s, axes=[[1], [0]]))),
2.0 * S,
dtype=tf.float32)
## Calculate the log of the analytic moments (as a vector over samples in S)
#logM = logXi_b + logXi_a_minus_V_s + logXi_s - logXi_a - logXi_b_minus_W_s
#logM2s = logXi_b + logXi_a_minus_V_2s + logXi_2s - logXi_a - logXi_b_minus_W_2s
s_norm = tf.ones(num_dim)
#logXi_s_norm = tf.reduce_sum(tf.lgamma(s_norm))
logXi_a_minus_V_s_norm = tf.reduce_sum(
tf.lgamma(param_a + tf.tensordot(matrix_V, s_norm, axes=[[1], [0]])))
logXi_b_minus_W_s_norm = tf.reduce_sum(
tf.lgamma(param_b + tf.tensordot(matrix_W, s_norm, axes=[[1], [0]])))
norm_logM = logXi_b + logXi_a_minus_V_s_norm - logXi_a - logXi_b_minus_W_s_norm
norm_M = tf.exp(
norm_logM) ## Get the raw moment for normalisation (dangerous?)
logM_diff = logXi_a_minus_V_s + logXi_s - logXi_b_minus_W_s - logXi_a_minus_V_s_norm + logXi_b_minus_W_s_norm
M = tf.exp(logM_diff) ## Get the raw moment (dangerous?)
logM2s_diff = logXi_a_minus_V_2s + logXi_2s - logXi_b_minus_W_2s - logXi_a_minus_V_s_norm + logXi_b_minus_W_s_norm
M2s = tf.exp(logM2s_diff) ## Get the raw moment (dangerous?)
#std = tf.sqrt(M2s - tf.square(M)) ## Calculate the std
#std_error = std/tf.sqrt(1.0*num_rows) ## Divide by the number of samples to get the expected margin under this estimator
M_var = tf.subtract(M2s, tf.square(M))
#error_term = tf.divide(M2s - tf.square(M), 1.0*num_rows)
print("Warning, messed with normalisation!!")
norm_target = tf.constant(1.0, dtype=tf.float32)
#########################################################################################
## Values Derived From Data (The things we are trying to fit the analytic function to) ##
#########################################################################################
data_exponents = tf.map_fn(lambda s: tf.subtract(s, tf.constant(1.0)), S)
data_moments = tf.map_fn(lambda s: tf.pow(features, s), data_exponents)
E = tf.map_fn(lambda mom: tf.reduce_mean(tf.reduce_prod(mom, axis=1)),
data_moments,
dtype=tf.float32) ## Careful of overflow here
##reduce to variance
def reduce_var(x, axis=None, keepdims=False):
m = tf.reduce_mean(x, axis=axis, keep_dims=True)
devs_squared = tf.square(x - m)
return tf.reduce_mean(devs_squared, axis=axis, keep_dims=keepdims)
#E_var = tf.map_fn( lambda mom : reduce_var(tf.reduce_prod(mom,axis=1)), data_moments, dtype = tf.float32 ) ## Careful of overflow here
#logE = tf.log(E) ## Possibly useless
###################
## Loss function ##
###################
##Caluclate the R^2 coefficient of the set of S datapoints
#mean_log_E = tf.reduce_mean(logEfunc)
#mean_log_M = tf.reduce_mean(logMfunc)
#total_error = tf.reduce_sum(tf.square(tf.subtract(logEfunc,mean_log_E)))
#unexplained_error = tf.reduce_sum(tf.square(tf.subtract(logEfunc, logMfunc)))
#R_squared = tf.subtract(tf.constant(1.0,dtype=tf.float32), tf.divide(unexplained_error, total_error))
#logM_error = tf.reduce_sum(tf.square(tf.subtract(logMfunc,mean_log_M)))
#derived_gradient = tf.divide(tf.reduce_sum(tf.multiply(logM_error,total_error)),tf.reduce_sum(tf.multiply(logM_error,logM_error)))
#derived_intercept = mean_log_E - tf.multiply(derived_gradient, mean_log_M)
## Define the losses here ##
#intercept_loss = intercept_weight * tf.losses.mean_squared_error(derived_intercept,tf.constant(0.0,dtype=tf.float32))
#gradient_loss = gradient_weight * tf.losses.mean_squared_error(derived_gradient,tf.constant(1.0,dtype=tf.float32))
#R_squared_loss = R_squared_weight * tf.losses.mean_squared_error(R_squared,tf.constant(1.0,dtype=tf.float32))
## Get the loss associated with points beyond the standard error
#model_loss = model_weight * tf.reduce_sum(tf.maximum(0.0, tf.square(M-E) - error_term ))
#var_weight = tf.constant(1.0)
model_loss = model_weight * tf.losses.mean_squared_error(M, E)
norm_loss = normalisation_weight * tf.losses.mean_squared_error(
norm_M, norm_target)
#var_loss = tf.multiply(var_weight,tf.losses.mean_squared_error(M_var,E_var))
#gamma_loss = integer_gamma_weight*tf.reduce_sum(tf.map_fn(lambda i : tf.abs(tf.abs(i)*(tf.abs(i)-1)),a))
## These require a definition of s which are allowed (can this be for s in 1 to 2?)
#positive_argument_a_V_loss = tf. tf.max()
#positive_argument_b_W_loss = ...
## Define the total loss
loss = model_loss + norm_loss
#+ R_squared_loss + intercept_loss + gradient_loss
print("Potential issue using one optimizer for different routines")
optimiser = tf.train.AdamOptimizer()
#optimiser = tf.train.GradientDescentOptimizer(0.001)
## Define training operations for independant sub-losses
train_op = optimiser.minimize(loss)
norm_op = optimiser.minimize(norm_loss)
#model_op = optimiser.minimize(model_loss)
#R_squared_op = optimiser.minimize(R_squared_loss)
#gradient_op = optimiser.minimize(gradient_loss)
#intercept_op = optimiser.minimize(intercept_loss)
num_best_models = 1
best_models_list = []
largest_min_loss = 1e90
def queue_compare(current_list, contender):
clip_num = num_best_models
if (len(current_list) < clip_num):
current_list.append(contender)
else:
## Seach for the correct place, add the contender in there and pop the end of the list till clip num elements
index = clip_num
for i in range(len(current_list)):
if (contender[0] < current_list[i][0]):
index = i
break
if (i < clip_num): current_list.insert(index, contender)
while (len(current_list) > clip_num):
current_list.pop(-1)
return sorted(current_list, key=lambda x: x[0])
## TF graph write for chart viewing
writer = tf.summary.FileWriter('./graphs', tf.get_default_graph())
num_train_exponent_samples = 200
def get_exponent_samples():
samps = np.ones([num_dim]) + np.random.uniform(
0.0, 4.0, size=[num_train_exponent_samples, num_dim])
drop_prob = 0.2
for i in range(len(samps)):
for j in range(len(samps[i])):
if (np.random.uniform(0, 1) < drop_prob): samps[i][j] = 1.0
return samps
#############
## Session ##
#############
epoch_min_max_thresholds = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(EPOCHS):
if (epoch % 5 == 0): s_list = get_exponent_samples()
sess.run(dataset_init_op,
feed_dict={
x: data,
S: s_list,
batch_size: num_rows,
drop_prob: 0.5,
training_bool: True
})
tot_loss = 0.0
for _ in range(n_batches):
#_, loss_value, prlogE, prlogM, prnorm_logM, ml, nl, rl, il, grl = sess.run([train_op, loss, logE, logM, norm_logM, model_loss, norm_loss, R_squared_loss, intercept_loss, gradient_loss], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
#_, loss_value, prnorm_M, ml, nl = sess.run([train_op, loss, norm_M, model_loss, norm_loss], feed_dict={ S:s_list, batch_size : num_rows, drop_prob : 0.5, training_bool : True})
loss_value, prnorm_M, ml, nl = sess.run(
[loss, norm_M, model_loss, norm_loss],
feed_dict={
S: s_list,
batch_size: num_rows,
drop_prob: 0.5,
training_bool: True
})
tot_loss += loss_value
print("Iter: {}, Loss: {:.6f}".format(epoch, tot_loss / n_batches))
print("norm M = {}".format(prnorm_M))
print("model, norm = {},{}".format(ml, nl))
#print(test)
#maxl = max(ml,nl,rl,il,grl)
if (not np.isnan(tot_loss)
and tot_loss / n_batches < largest_min_loss):
b_eta, b_W, b_V, b_a, b_b = sess.run(
[eta, matrix_W, matrix_V, param_a, param_b])
best_models_list = queue_compare(
best_models_list,
[tot_loss / n_batches, [b_eta, b_W, b_V, b_a, b_b]])
largest_min_loss = max(
[element[0] for element in best_models_list])
smallest_min_loss = min(
[element[0] for element in best_models_list])
print("BEST VALUE ADDED! Threshold = {} to {}".format(
smallest_min_loss, largest_min_loss))
epoch_min_max_thresholds.append(
(epoch, smallest_min_loss, largest_min_loss))
if (np.isnan(tot_loss)):
print("\n\n RESETTING \n\n")
sess.run(tf.global_variables_initializer())
sess.run(eta_assign_op,
feed_dict={eta_input: best_models_list[0][1][0]})
sess.run(matrix_W_assign_op,
feed_dict={matrix_W_input: best_models_list[0][1][1]})
sess.run(matrix_V_assign_op,
feed_dict={matrix_V_input: best_models_list[0][1][2]})
sess.run(param_a_assign_op,
feed_dict={param_a_input: best_models_list[0][1][3]})
sess.run(param_b_assign_op,
feed_dict={param_b_input: best_models_list[0][1][4]})
#elif(maxl==nl):
# for _ in range(10) : sess.run([norm_op], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
#elif(maxl==rl):
# for _ in range(10) : sess.run([R_squared_op], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
#elif(maxl==ml):
# for _ in range(10) : sess.run([model_op], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
#elif(maxl==il):
# for _ in range(10) : sess.run([intercept_op], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
#elif(maxl==grl):
# for _ in range(10) : sess.run([gradient_op], feed_dict={ S:s_list, drop_prob : 0.5, training_bool : True})
print("Run finished Writing Files...")
with open("Output_Mell_{}.txt".format(session_string), "w") as f:
for i in range(500):
s_list = [[1.0]] + np.random.exponential(scale=2.0,
size=[1, num_dim])
E_obs, M_obs = sess.run([E, M],
feed_dict={
S: s_list,
drop_prob: 0.5,
training_bool: True
})
for sval in s_list[0]:
f.write("{},".format(sval))
f.write("{},{}\n".format(E_obs[0], M_obs[0]))
with open("Output_Mell_{}_Best_100_Models.txt".format(session_string),
"w") as f:
for i in best_models_list:
f.write("{}:{}\n".format(i[0], i[1]))
## Load the best model back in
model = 0
sess.run(eta_assign_op,
feed_dict={eta_input: best_models_list[model][1][0]})
sess.run(matrix_W_assign_op,
feed_dict={matrix_W_input: best_models_list[model][1][1]})
sess.run(matrix_V_assign_op,
feed_dict={matrix_V_input: best_models_list[model][1][2]})
sess.run(param_a_assign_op,
feed_dict={param_a_input: best_models_list[model][1][3]})
sess.run(param_b_assign_op,
feed_dict={param_b_input: best_models_list[model][1][4]})
with open("Output_Best_Mell_{}.txt".format(session_string), "w") as f:
for i in range(500):
s_list = [[1.0]] + np.random.exponential(scale=2.0,
size=[1, num_dim])
E_obs, M_obs = sess.run([E, M],
feed_dict={
S: s_list,
drop_prob: 0.5,
training_bool: True
})
for sval in s_list[0]:
f.write("{},".format(sval))
f.write("{},{}\n".format(E_obs[0], M_obs[0]))
### Make a huge array of predictions using the best 100 models
with open("Output_Best_Models_Mell_{}.txt".format(session_string),
"w") as f:
for i in range(num_dim):
f.write("s{},".format(i))
f.write("E,")
for i in range(num_best_models):
f.write("M{},".format(i))
f.write("\n")
for i in range(500):
s_list = [[1.0]] + np.random.exponential(scale=2.0,
size=[1, num_dim])
for sval in s_list[0]:
f.write("{},".format(sval))
E_obs = sess.run(E,
feed_dict={
S: s_list,
drop_prob: 0.5,
training_bool: True
})
f.write("{},".format(E_obs[0]))
for model in range(num_best_models):
sess.run(
eta_assign_op,
feed_dict={eta_input: best_models_list[model][1][0]})
sess.run(matrix_W_assign_op,
feed_dict={
matrix_W_input: best_models_list[model][1][1]
})
sess.run(matrix_V_assign_op,
feed_dict={
matrix_V_input: best_models_list[model][1][2]
})
sess.run(param_a_assign_op,
feed_dict={
param_a_input: best_models_list[model][1][3]
})
sess.run(param_b_assign_op,
feed_dict={
param_b_input: best_models_list[model][1][4]
})
M_obs = sess.run(M,
feed_dict={
S: s_list,
drop_prob: 0.5,
training_bool: True
})
f.write("{},".format(M_obs[0]))
f.write("\n")
### Print parameters of best model into a file for reading
with open("Output_params_{}.txt".format(session_string), "w") as f:
for param in sess.run([eta, matrix_W, matrix_V, param_a, param_b]):
f.write("{}\n".format(param))
def _ais_gets_correct_log_normalizer(self,
init,
independent_chain_ndims,
sess,
feed_dict=None):
counter = collections.Counter()
def proposal_log_prob(x):
counter['proposal_calls'] += 1
event_dims = tf.range(independent_chain_ndims, tf.rank(x))
return tf.reduce_sum(tfd.Normal(loc=0., scale=1.).log_prob(x),
axis=event_dims)
def target_log_prob(x):
counter['target_calls'] += 1
event_dims = tf.range(independent_chain_ndims, tf.rank(x))
return self._log_gamma_log_prob(x, event_dims)
if feed_dict is None:
feed_dict = {}
num_steps = 200
def make_kernel(tlp_fn):
return tfp.mcmc.HamiltonianMonteCarlo(target_log_prob_fn=tlp_fn,
step_size=0.5,
num_leapfrog_steps=2,
seed=45)
_, ais_weights, _ = tfp.mcmc.sample_annealed_importance_chain(
num_steps=num_steps,
proposal_log_prob_fn=proposal_log_prob,
target_log_prob_fn=target_log_prob,
current_state=init,
make_kernel_fn=make_kernel,
parallel_iterations=1)
# We have three calls because the calculation of `ais_weights` entails
# another call to the `convex_combined_log_prob_fn`. We could refactor
# things to avoid this, if needed (eg, b/72994218).
self.assertAllEqual(dict(target_calls=3, proposal_calls=3), counter)
event_shape = tf.shape(init)[independent_chain_ndims:]
event_size = tf.reduce_prod(event_shape)
log_true_normalizer = (-self._shape_param * tf.log(self._rate_param) +
tf.lgamma(self._shape_param))
log_true_normalizer *= tf.cast(event_size, log_true_normalizer.dtype)
log_estimated_normalizer = (tf.reduce_logsumexp(ais_weights) -
np.log(num_steps))
ratio_estimate_true = tf.exp(ais_weights - log_true_normalizer)
ais_weights_size = tf.size(ais_weights)
standard_error = tf.sqrt(
_compute_sample_variance(ratio_estimate_true) /
tf.cast(ais_weights_size, ratio_estimate_true.dtype))
[
ratio_estimate_true_,
log_true_normalizer_,
log_estimated_normalizer_,
standard_error_,
ais_weights_size_,
event_size_,
] = sess.run([
ratio_estimate_true,
log_true_normalizer,
log_estimated_normalizer,
standard_error,
ais_weights_size,
event_size,
], feed_dict)
tf.logging.vlog(
1, ' log_true_normalizer: {}\n'
' log_estimated_normalizer: {}\n'
' ais_weights_size: {}\n'
' event_size: {}\n'.format(
log_true_normalizer_, log_estimated_normalizer_,
ais_weights_size_, event_size_))
self.assertNear(ratio_estimate_true_.mean(), 1., 4. * standard_error_)
def _log_normalization(self):
return (tf.lgamma(self.concentration1) +
tf.lgamma(self.concentration0) -
tf.lgamma(self.total_concentration))
def _multi_lgamma(self, a, p, name="multi_lgamma"):
"""Computes the log multivariate gamma function; log(Gamma_p(a))."""
with self._name_scope(name, values=[a, p]):
seq = self._multi_gamma_sequence(a, p)
return (0.25 * p * (p - 1.) * math.log(math.pi) +
tf.reduce_sum(tf.lgamma(seq), axis=[-1]))
def variational_expectations(self, Fmu, Fvar, Y):
if self.invlink is tf.exp:
return Y * Fmu - tf.exp(Fmu + Fvar / 2) * self.binsize \
- tf.lgamma(Y + 1) + Y * tf.log(self.binsize)
return super(Poisson, self).variational_expectations(Fmu, Fvar, Y)
def gamma_loss(z_over_a, log_alpha, alpha, log_beta, beta):
loss = -alpha * log_beta + (
alpha + z_over_a) * tf.log(1 + beta) - tf.lgamma(
alpha + z_over_a) + tf.lgamma(alpha) + tf.lgamma(z_over_a + 1)
return K.mean(loss)