def gaussian_kl_divergence(self, mu1, ln_var1, mu2, ln_var2):
# D_KL [ N(z ; mu1, var1) || N(z; mu2, var2) ]
var1 = exponential.exp(ln_var1)
inv_var2 = exponential.exp(-ln_var2)
mu_diff = mu2 - mu1
term1 = (var1 + mu_diff * mu_diff) * inv_var2
loss = (term1 - ln_var1 + ln_var2 - 1.) * 0.5
return sum.sum(loss)
def gaussian_kl_divergence(mean, ln_var):
"""Computes the KL-divergence of Gaussian variables from the standard one.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function returns a variable
representing the KL-divergence between the given multi-dimensional Gaussian
:math:`N(\\mu, S)` and the standard Gaussian :math:`N(0, I)`
.. math::
D_{\\mathbf{KL}}(N(\\mu, S) \\| N(0, I)),
where :math:`S` is a diagonal matrix such that :math:`S_{ii} = \\sigma_i^2`
and :math:`I` is an identity matrix.
Args:
mean (~chainer.Variable): A variable representing mean of given
gaussian distribution, :math:`\\mu`.
ln_var (~chainer.Variable): A variable representing logarithm of
variance of given gaussian distribution, :math:`\\log(\\sigma^2)`.
Returns:
~chainer.Variable: A variable representing KL-divergence between
given gaussian distribution and the standard gaussian.
"""
assert isinstance(mean, variable.Variable)
assert isinstance(ln_var, variable.Variable)
J = mean.size
var = exponential.exp(ln_var)
return (sum.sum(mean * mean) + sum.sum(var) - sum.sum(ln_var) - J) * 0.5
def gaussian_kl_divergence(mean, ln_var):
"""Computes the KL-divergence of Gaussian variables from the standard one.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function returns a variable
representing the KL-divergence between the given multi-dimensional Gaussian
:math:`N(\\mu, S)` and the standard Gaussian :math:`N(0, I)`
.. math::
D_{\\mathbf{KL}}(N(\\mu, S) \\| N(0, I)),
where :math:`S` is a diagonal matrix such that :math:`S_{ii} = \\sigma_i^2`
and :math:`I` is an identity matrix.
Args:
mean (~chainer.Variable): A variable representing mean of given
gaussian distribution, :math:`\\mu`.
ln_var (~chainer.Variable): A variable representing logarithm of
variance of given gaussian distribution, :math:`\\log(\\sigma^2)`.
Returns:
~chainer.Variable: A variable representing KL-divergence between
given gaussian distribution and the standard gaussian.
"""
assert isinstance(mean, variable.Variable)
assert isinstance(ln_var, variable.Variable)
J = mean.size
var = exponential.exp(ln_var)
return (sum.sum(mean * mean) + sum.sum(var) - sum.sum(ln_var) - J) * 0.5
def _kl_gumbel_gumbel(dist1, dist2):
scale_1d2 = dist1.scale / dist2.scale
return dist2._log_scale - dist1._log_scale \
+ EULER * (scale_1d2 - 1.) \
+ exponential.exp((dist2.loc - dist1.loc) / dist2.scale
+ lgamma.lgamma(scale_1d2 + 1.)) \
- 1 + (dist1.loc - dist2.loc) / dist2.scale
def _kl_gumbel_gumbel(dist1, dist2):
scale_1d2 = dist1.scale / dist2.scale
return exponential.log(dist2.scale) - exponential.log(dist1.scale) \
+ EULER * (scale_1d2 - 1.) \
+ exponential.exp((dist2.loc - dist1.loc) / dist2.scale
+ lgamma.lgamma(scale_1d2 + 1.)) \
- 1 + (dist1.loc - dist2.loc) / dist2.scale
def gaussian_nll(x, mean, ln_var, reduce='sum'):
"""Computes the negative log-likelihood of a Gaussian distribution.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function computes in
elementwise manner the negative log-likelihood of :math:`x` on a
Gaussian distribution :math:`N(\\mu, S)`,
.. math::
-\\log N(x; \\mu, \\sigma^2) =
\\log\\left(\\sqrt{(2\\pi)^D |S|}\\right) +
\\frac{1}{2}(x - \\mu)^\\top S^{-1}(x - \\mu),
where :math:`D` is a dimension of :math:`x` and :math:`S` is a diagonal
matrix where :math:`S_{ii} = \\sigma_i^2`.
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds the elementwise
loss values. If it is ``'sum'`` or ``'mean'``, loss values are summed up
or averaged respectively.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): Input variable.
mean (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): A variable representing mean of a Gaussian
distribution, :math:`\\mu`.
ln_var (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`): A variable representing logarithm of
variance of a Gaussian distribution, :math:`\\log(\\sigma^2)`.
reduce (str): Reduction option. Its value must be either
``'sum'``, ``'mean'`` or ``'no'``. Otherwise, :class:`ValueError`
is raised.
Returns:
~chainer.Variable:
A variable representing the negative log-likelihood.
If ``reduce`` is ``'no'``, the output variable holds array
whose shape is same as one of (hence both of) input variables.
If it is ``'sum'`` or ``'mean'``, the output variable holds a
scalar value.
"""
if reduce not in ('sum', 'mean', 'no'):
raise ValueError(
"only 'sum', 'mean' and 'no' are valid for 'reduce', but '%s'"
' is given' % reduce)
x_prec = exponential.exp(-ln_var)
x_diff = x - mean
x_power = (x_diff * x_diff) * x_prec * -0.5
loss = (ln_var + math.log(2 * math.pi)) / 2 - x_power
if reduce == 'sum':
return sum.sum(loss)
elif reduce == 'mean':
return average.average(loss)
else:
return loss
def _kl_laplace_laplace(dist1, dist2):
diff = abs(dist1.loc - dist2.loc)
return (
exponential.log(dist2.scale)
- exponential.log(dist1.scale)
+ diff / dist2.scale
+ dist1.scale / dist2.scale * exponential.exp(-diff / dist1.scale)
- 1)
def gaussian_nll(x, mean, ln_var, reduce='sum'):
"""Computes the negative log-likelihood of a Gaussian distribution.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function computes in
elementwise manner the negative log-likelihood of :math:`x` on a
Gaussian distribution :math:`N(\\mu, S)`,
.. math::
-\\log N(x; \\mu, \\sigma^2) =
\\log\\left(\\sqrt{(2\\pi)^D |S|}\\right) +
\\frac{1}{2}(x - \\mu)^\\top S^{-1}(x - \\mu),
where :math:`D` is a dimension of :math:`x` and :math:`S` is a diagonal
matrix where :math:`S_{ii} = \\sigma_i^2`.
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds the elementwise
loss values. If it is ``'sum'`` or ``'mean'``, loss values are summed up
or averaged respectively.
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
mean (:class:`~chainer.Variable` or :ref:`ndarray`): A variable
representing mean of a Gaussian distribution, :math:`\\mu`.
ln_var (:class:`~chainer.Variable` or :ref:`ndarray`): A variable
representing logarithm of variance of a Gaussian distribution,
:math:`\\log(\\sigma^2)`.
reduce (str): Reduction option. Its value must be either
``'sum'``, ``'mean'`` or ``'no'``. Otherwise, :class:`ValueError`
is raised.
Returns:
~chainer.Variable:
A variable representing the negative log-likelihood.
If ``reduce`` is ``'no'``, the output variable holds array
whose shape is same as one of (hence both of) input variables.
If it is ``'sum'`` or ``'mean'``, the output variable holds a
scalar value.
"""
if reduce not in ('sum', 'mean', 'no'):
raise ValueError(
'only \'sum\', \'mean\' and \'no\' are valid for \'reduce\', but '
'\'%s\' is given' % reduce)
x_prec = exponential.exp(-ln_var)
x_diff = x - mean
x_power = (x_diff * x_diff) * x_prec * -0.5
loss = (ln_var + math.log(2 * math.pi)) / 2 - x_power
if reduce == 'sum':
return sum.sum(loss)
elif reduce == 'mean':
return average.average(loss)
else:
return loss
def gaussian_nll(x, mean, ln_var):
assert isinstance(x, variable.Variable)
assert isinstance(mean, variable.Variable)
assert isinstance(ln_var, variable.Variable)
x_prec = exponential.exp(-ln_var)
x_diff = x - mean
x_power = (x_diff * x_diff) * x_prec * -0.5
return (ln_var + math.log(2 * math.pi)) / 2 - x_power
def _e_chainer(a, b, c, beta=1.0):
b = (a - b) * beta
c = (a - c) * beta
if b.data > 30 or c.data > 30:
return 0
if b.data < -20:
exp_b = 0
else:
exp_b = exp(b)
if c.data < -20:
exp_c = 0
else:
exp_c = exp(c)
return 1. / (1 + exp_b + exp_c)
def gaussian_kl_divergence(mean, ln_var, reduce='sum'):
"""Computes the KL-divergence of Gaussian variables from the standard one.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function calculates
the KL-divergence in elementwise manner between the given multi-dimensional
Gaussian :math:`N(\\mu, S)` and the standard Gaussian :math:`N(0, I)`
.. math::
D_{\\mathbf{KL}}(N(\\mu, S) \\| N(0, I)),
where :math:`S` is a diagonal matrix such that :math:`S_{ii} = \\sigma_i^2`
and :math:`I` is an identity matrix.
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds the elementwise
loss values. If it is ``'sum'`` or ``'mean'``, loss values are summed up
or averaged respectively.
Args:
mean (:class:`~chainer.Variable` or :ref:`ndarray`):
A variable representing mean of given
gaussian distribution, :math:`\\mu`.
ln_var (:class:`~chainer.Variable` or :ref:`ndarray`):
A variable representing logarithm of
variance of given gaussian distribution, :math:`\\log(\\sigma^2)`.
reduce (str): Reduction option. Its value must be either
``'sum'``, ``'mean'`` or ``'no'``. Otherwise, :class:`ValueError`
is raised.
Returns:
~chainer.Variable:
A variable representing KL-divergence between
given gaussian distribution and the standard gaussian.
If ``reduce`` is ``'no'``, the output variable holds array
whose shape is same as one of (hence both of) input variables.
If it is ``'sum'`` or ``'mean'``, the output variable holds a
scalar value.
"""
if reduce not in ('sum', 'mean', 'no'):
raise ValueError(
"only 'sum', 'mean' and 'no' are valid for 'reduce', but '%s'"
' is given' % reduce)
var = exponential.exp(ln_var)
mean_square = mean * mean
loss = (mean_square + var - ln_var - 1) * 0.5
if reduce == 'sum':
return sum.sum(loss)
elif reduce == 'mean':
return average.average(loss)
else:
return loss
def gaussian_kl_divergence(mean, ln_var, reduce='sum'):
"""Computes the KL-divergence of Gaussian variables from the standard one.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function calculates
the KL-divergence in elementwise manner between the given multi-dimensional
Gaussian :math:`N(\\mu, S)` and the standard Gaussian :math:`N(0, I)`
.. math::
D_{\\mathbf{KL}}(N(\\mu, S) \\| N(0, I)),
where :math:`S` is a diagonal matrix such that :math:`S_{ii} = \\sigma_i^2`
and :math:`I` is an identity matrix.
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds the elementwise
loss values. If it is ``'sum'`` or ``'mean'``, loss values are summed up
or averaged respectively.
Args:
mean (:class:`~chainer.Variable` or :ref:`ndarray`):
A variable representing mean of given
gaussian distribution, :math:`\\mu`.
ln_var (:class:`~chainer.Variable` or :ref:`ndarray`):
A variable representing logarithm of
variance of given gaussian distribution, :math:`\\log(\\sigma^2)`.
reduce (str): Reduction option. Its value must be either
``'sum'``, ``'mean'`` or ``'no'``. Otherwise, :class:`ValueError`
is raised.
Returns:
~chainer.Variable:
A variable representing KL-divergence between
given gaussian distribution and the standard gaussian.
If ``reduce`` is ``'no'``, the output variable holds array
whose shape is same as one of (hence both of) input variables.
If it is ``'sum'`` or ``'mean'``, the output variable holds a
scalar value.
"""
if reduce not in ('sum', 'mean', 'no'):
raise ValueError(
'only \'sum\', \'mean\' and \'no\' are valid for \'reduce\', but '
'\'%s\' is given' % reduce)
var = exponential.exp(ln_var)
mean_square = mean * mean
loss = (mean_square + var - ln_var - 1) * 0.5
if reduce == 'sum':
return sum.sum(loss)
elif reduce == 'mean':
return average.average(loss)
else:
return loss
def sample_n(self, n):
xp = cuda.get_array_module(self.mu)
if xp is cuda.cupy:
eps = xp.random.standard_normal(
(n,)+self.mu.shape, dtype=self.mu.dtype)
else:
eps = xp.random.standard_normal(
(n,)+self.mu.shape).astype(self.mu.dtype)
noise = broadcast.broadcast_to(self.sigma, eps.shape) * eps
noise += broadcast.broadcast_to(self.mu, eps.shape)
return exponential.exp(noise)
def sample_n(self, n):
xp = backend.get_array_module(self.mu)
if xp is cuda.cupy:
eps = xp.random.standard_normal((n, ) + self.mu.shape,
dtype=self.mu.dtype)
else:
eps = xp.random.standard_normal((n, ) + self.mu.shape).astype(
self.mu.dtype)
noise = self.sigma * eps
noise += self.mu
return exponential.exp(noise)
def sample_n(self, n):
xp = cuda.get_array_module(self.mu)
if xp is cuda.cupy:
eps = xp.random.standard_normal(
(n,)+self.mu.shape, dtype=self.mu.dtype)
else:
eps = xp.random.standard_normal(
(n,)+self.mu.shape).astype(self.mu.dtype)
noise = self.sigma * eps
noise += self.mu
return exponential.exp(noise)
def black_out(x, t, W, samples):
"""BlackOut loss function.
BlackOut loss function is defined as
.. math::
-\\log(p(t)) - \\sum_{s \\in S} \\log(1 - p(s)),
where :math:`t` is the correct label, :math:`S` is a set of negative
examples and :math:`p(\cdot)` is likelihood of a given label.
And, :math:`p` is defined as
.. math::
p(y) = \\frac{\\exp(W_y^\\top x)}{
\\sum_{s \\in samples} \\exp(W_s^\\top x)}.
Args:
x (~chainer.Variable): Batch of input vectors.
t (~chainer.Variable): Vector of ground truth labels.
W (~chainer.Variable): Weight matrix.
samples (~chainer.Variable): Negative samples.
Returns:
~chainer.Variable: Loss value.
See: `BlackOut: Speeding up Recurrent Neural Network Language Models With \
Very Large Vocabularies <https://arxiv.org/abs/1511.06909>`_
.. seealso:: :class:`~chainer.links.BlackOut`.
"""
batch_size = x.shape[0]
neg_emb = embed_id.embed_id(samples, W)
neg_y = matmul.batch_matmul(neg_emb, x)
neg_y = reshape.reshape(neg_y, neg_y.shape[:-1])
pos_emb = expand_dims.expand_dims(embed_id.embed_id(t, W), 1)
pos_y = matmul.batch_matmul(pos_emb, x)
pos_y = reshape.reshape(pos_y, pos_y.shape[:-1])
logz = logsumexp.logsumexp(concat.concat([pos_y, neg_y]), axis=1)
blogz, bneg_y = broadcast.broadcast(
reshape.reshape(logz, (batch_size, 1)), neg_y)
ny = exponential.log(1 - exponential.exp(bneg_y - blogz))
py = reshape.reshape(pos_y, (batch_size,))
loss = py - logz + _sum.sum(ny, axis=1)
return -_sum.sum(loss) / batch_size
def black_out(x, t, W, samples):
"""BlackOut loss function.
BlackOut loss function is defined as
.. math::
-\\log(p(t)) - \\sum_{s \\in S} \\log(1 - p(s)),
where :math:`t` is the correct label, :math:`S` is a set of negative
examples and :math:`p(\cdot)` is likelihood of a given label.
And, :math:`p` is defined as
.. math::
p(y) = \\frac{\\exp(W_y^\\top x)}{
\\sum_{s \\in samples} \\exp(W_s^\\top x)}.
Args:
x (~chainer.Variable): Batch of input vectors.
t (~chainer.Variable): Vector of ground truth labels.
W (~chainer.Variable): Weight matrix.
samples (~chainer.Variable): Negative samples.
Returns:
~chainer.Variable: Loss value.
See: `BlackOut: Speeding up Recurrent Neural Network Language Models With \
Very Large Vocabularies <https://arxiv.org/abs/1511.06909>`_
.. seealso:: :class:`~chainer.links.BlackOut`.
"""
batch_size = x.shape[0]
neg_emb = embed_id.embed_id(samples, W)
neg_y = matmul.batch_matmul(neg_emb, x)
neg_y = reshape.reshape(neg_y, neg_y.shape[:-1])
pos_emb = expand_dims.expand_dims(embed_id.embed_id(t, W), 1)
pos_y = matmul.batch_matmul(pos_emb, x)
pos_y = reshape.reshape(pos_y, pos_y.shape[:-1])
logz = logsumexp.logsumexp(concat.concat([pos_y, neg_y]), axis=1)
blogz, bneg_y = broadcast.broadcast(reshape.reshape(logz, (batch_size, 1)),
neg_y)
ny = exponential.log(1 - exponential.exp(bneg_y - blogz))
py = reshape.reshape(pos_y, (batch_size, ))
loss = py - logz + _sum.sum(ny, axis=1)
return -_sum.sum(loss) / batch_size
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
logit, x = self.get_retained_inputs()
xp = backend.get_array_module(x)
dlogit = x - 1. / (1. + exponential.exp(-logit))
# extreme logit
nan = xp.array(xp.nan).astype(dlogit.dtype)
logit_isinf = xp.bitwise_or(self.logit_ispinf, self.logit_isminf)
dlogit = where.where(logit_isinf, nan, dlogit)
if self.binary_check:
dlogit = where.where(self.invalid, nan, dlogit)
return sum.sum_to(gy * dlogit, logit.shape), None
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
logit, x = self.get_retained_inputs()
xp = cuda.get_array_module(x)
dlogit = x - 1. / (1. + exponential.exp(-logit))
# extreme logit
nan_dlogit = xp.zeros_like(dlogit.array)
nan_dlogit[self.invalid] = xp.nan
nan_dlogit[self.to_zero] = xp.nan
nan_dlogit[self.to_m_inf] = xp.nan
dlogit += nan_dlogit
return gy * dlogit, None
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
logit, x = self.get_retained_inputs()
xp = backend.get_array_module(x)
dlogit = x - 1. / (1. + exponential.exp(-logit))
# extreme logit
nan_dlogit = xp.zeros_like(dlogit.array)
if self.binary_check:
nan_dlogit[self.invalid] = xp.nan
nan_dlogit[self.logit_ispinf] = xp.nan
nan_dlogit[self.logit_isminf] = xp.nan
dlogit += nan_dlogit
return gy * dlogit, None
def backward(self, indexes, grad_outputs):
gy, = grad_outputs
logit, x = self.get_retained_inputs()
xp = backend.get_array_module(x)
dlogit = x - 1. / (1. + exponential.exp(-logit))
# extreme logit
nan_dlogit = xp.zeros_like(dlogit.array)
if self.binary_check:
nan_dlogit[self.invalid] = xp.nan
nan_dlogit[self.logit_ispinf] = xp.nan
nan_dlogit[self.logit_isminf] = xp.nan
dlogit += nan_dlogit
return gy * dlogit, None
def __call__(self, a_list, state, batch_size, xp):
e_list = []
sum_e = xp.zeros((batch_size, 1), dtype=xp.float32)
for a in a_list:
v = tanh(self.av(array.concat.concat((a, state['h2']), axis=1)))
w = self.vw(v)
e = exp(w)
e_list.append(e)
sum_e = sum_e + e
context = xp.zeros((batch_size, self.hidden_size), dtype=xp.float32)
for a, e in zip(a_list, e_list):
e /= sum_e
context = context + reshape(batch_matmul(a, e),
(batch_size, self.hidden_size))
return context, e_list, sum_e
def __init__(self, p=None, **kwargs):
logit = None
if kwargs:
logit, = argument.parse_kwargs(kwargs, ('logit', logit))
if not (p is None) ^ (logit is None):
raise ValueError(
"Either `p` or `logit` (not both) must have a value.")
with chainer.using_config('enable_backprop', True):
if p is None:
logit = chainer.as_variable(logit)
self.__log_p = log_softmax.log_softmax(logit, axis=-1)
self.__p = exponential.exp(self.__log_p)
else:
self.__p = chainer.as_variable(p)
self.__log_p = exponential.log(self.__p)
def __call__(self, a_list, state, batch_size, xp):
e_list = []
sum_e = xp.zeros((batch_size, 1), dtype=xp.float32)
for a in a_list:
w = self.aw(a, state['h2'])
w.data = xp.clip(w.data, -20, 20)
e = exp(w)
e_list.append(e)
sum_e = sum_e + e
context = xp.zeros((batch_size, self.hidden_size), dtype=xp.float32)
for a, e in zip(a_list, e_list):
e /= sum_e
context = context + reshape(batch_matmul(a, e),
(batch_size, self.hidden_size))
return context, e_list, sum_e
def __init__(self, p=None, **kwargs):
logit = None
if kwargs:
logit, = argument.parse_kwargs(
kwargs, ('logit', logit))
if not (p is None) ^ (logit is None):
raise ValueError(
"Either `p` or `logit` (not both) must have a value.")
with chainer.using_config('enable_backprop', True):
if p is None:
logit = chainer.as_variable(logit)
self.__log_p = log_softmax.log_softmax(logit, axis=-1)
self.__p = exponential.exp(self.__log_p)
else:
self.__p = chainer.as_variable(p)
self.__log_p = exponential.log(self.__p)
def __init__(self, loc, scale=None, **kwargs):
super(Normal, self).__init__()
log_scale = None
if kwargs:
log_scale, = argument.parse_kwargs(
kwargs, ('log_scale', log_scale))
if not (scale is None) ^ (log_scale is None):
raise ValueError(
"Either `scale` or `log_scale` (not both) must have a value.")
self.loc = chainer.as_variable(loc)
with chainer.using_config('enable_backprop', True):
if scale is None:
self.__log_scale = chainer.as_variable(log_scale)
self.__scale = exponential.exp(self.log_scale)
else:
self.__scale = chainer.as_variable(scale)
self.__log_scale = exponential.log(self.scale)
def __init__(self, loc, scale=None, **kwargs):
super(Normal, self).__init__()
log_scale = None
if kwargs:
log_scale, = argument.parse_kwargs(
kwargs, ('log_scale', log_scale))
if not (scale is None) ^ (log_scale is None):
raise ValueError(
"Either `scale` or `log_scale` (not both) must have a value.")
self.loc = chainer.as_variable(loc)
with chainer.using_config('enable_backprop', True):
if scale is None:
self.__log_scale = chainer.as_variable(log_scale)
self.__scale = exponential.exp(self.log_scale)
else:
self.__scale = chainer.as_variable(scale)
self.__log_scale = exponential.log(self.scale)
def softmin_chainer(a, b, c, beta=1.0):
xp = cuda.get_array_module(a)
s = Variable(xp.zeros(1))[0]
if a.data < b.data:
if a.data < c.data:
s += exp(-(b - a) * beta)
s += exp(-(c - a) * beta)
return -1 / beta * log1p(s) + a
else:
s += exp(-(a - c) * beta)
s += exp(-(b - c) * beta)
return -1 / beta * log1p(s) + c
else:
if b.data < c.data:
s += exp(-(a - b) * beta)
s += exp(-(c - b) * beta)
return -1 / beta * log1p(s) + b
else:
s += exp(-(a - c) * beta)
s += exp(-(b - c) * beta)
return -1 / beta * log1p(s) + c
def gaussian_nll(x, mean, ln_var):
"""Computes the negative log-likelihood of a Gaussian distribution.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function returns the negative
log-likelihood of :math:`x` on a Gaussian distribution :math:`N(\\mu, S)`,
.. math::
-\\log N(x; \\mu, \\sigma^2) =
\\log\\left(\\sqrt{(2\\pi)^D |S|}\\right) +
\\frac{1}{2}(x - \\mu)^\\top S^{-1}(x - \\mu),
where :math:`D` is a dimension of :math:`x` and :math:`S` is a diagonal
matrix where :math:`S_{ii} = \\sigma_i^2`.
Args:
x (~chainer.Variable): Input variable.
mean (~chainer.Variable): A variable representing mean of a Gaussian
distribution, :math:`\\mu`.
ln_var (~chainer.Variable): A variable representing logarithm of
variance of a Gaussian distribution, :math:`\\log(\\sigma^2)`.
Returns:
~chainer.Variable: A variable representing the negative log-likelihood.
"""
assert isinstance(x, variable.Variable)
assert isinstance(mean, variable.Variable)
assert isinstance(ln_var, variable.Variable)
D = x.size
x_prec = exponential.exp(-ln_var)
x_diff = x - mean
x_power = (x_diff * x_diff) * x_prec * -0.5
return (sum.sum(ln_var) + D * math.log(2 * math.pi)) / 2 - sum.sum(x_power)
def gaussian_nll(x, mean, ln_var):
"""Computes the negative log-likelihood of a Gaussian distribution.
Given two variable ``mean`` representing :math:`\\mu` and ``ln_var``
representing :math:`\\log(\\sigma^2)`, this function returns the negative
log-likelihood of :math:`x` on a Gaussian distribution :math:`N(\\mu, S)`,
.. math::
-\\log N(x; \\mu, \\sigma^2) =
\\log\\left(\\sqrt{(2\\pi)^D |S|}\\right) +
\\frac{1}{2}(x - \\mu)^\\top S^{-1}(x - \\mu),
where :math:`D` is a dimension of :math:`x` and :math:`S` is a diagonal
matrix where :math:`S_{ii} = \\sigma_i^2`.
Args:
x (~chainer.Variable): Input variable.
mean (~chainer.Variable): A variable representing mean of a Gaussian
distribution, :math:`\\mu`.
ln_var (~chainer.Variable): A variable representing logarithm of
variance of a Gaussian distribution, :math:`\\log(\\sigma^2)`.
Returns:
~chainer.Variable: A variable representing the negative log-likelihood.
"""
assert isinstance(x, variable.Variable)
assert isinstance(mean, variable.Variable)
assert isinstance(ln_var, variable.Variable)
D = x.size
x_prec = exponential.exp(-ln_var)
x_diff = x - mean
x_power = (x_diff * x_diff) * x_prec * -0.5
return (sum.sum(ln_var) + D * math.log(2 * math.pi)) / 2 - sum.sum(x_power)
def prob(self, x):
scale = self.scale
return 0.5 / scale * exponential.exp(- abs(x - self.loc) / scale)
def log_prob(self, x):
y = (x - self.loc) / self.scale
return - self._log_scale - y - exponential.exp(-y)
def variance(self):
return (exponential.exp(2 * self.mu + self.sigma**2) *
(exponential.exp(self.sigma**2) - 1))
def p(self):
if self.__p is not None:
return chainer.as_variable(self.__p)
else:
return exponential.exp(self.log_p)
def mean(self):
return exponential.exp(self.mu + 0.5 * self.sigma**2)
def _kl_laplace_laplace(dist1, dist2):
diff = abs(dist1.loc - dist2.loc)
return exponential.log(dist2.scale) - exponential.log(dist1.scale) \
+ diff / dist2.scale \
+ dist1.scale / dist2.scale * exponential.exp(- diff / dist1.scale) - 1
def prob(self, x):
return PROBC / broadcast.broadcast_to(self.scale, x.shape) * \
exponential.exp(
- 0.5 * (x - broadcast.broadcast_to(self.loc, x.shape)) ** 2
/ broadcast.broadcast_to(self.scale, x.shape) ** 2)
def _kl_bernoulli_bernoulli(dist1, dist2):
return (dist1.logit - dist2.logit) * (dist1.p - 1.) \
- exponential.log(exponential.exp(-dist1.logit) + 1) \
+ exponential.log(exponential.exp(-dist2.logit) + 1)
def log_prob(self, x):
y = (x - self.loc) / self.scale
return -exponential.log(self.scale) - y - exponential.exp(-y)
def scale(self):
if self.__scale is not None:
return chainer.as_variable(self.__scale)
else:
return exponential.exp(self.log_scale)
def _kl_bernoulli_bernoulli(dist1, dist2):
return (dist1.logit - dist2.logit) * (dist1.p - 1.) \
- exponential.log(exponential.exp(-dist1.logit) + 1) \
+ exponential.log(exponential.exp(-dist2.logit) + 1)
def prob(self, x):
return PROBC / broadcast.broadcast_to(self.scale, x.shape) * \
exponential.exp(
- 0.5 * (x - broadcast.broadcast_to(self.loc, x.shape)) ** 2
/ broadcast.broadcast_to(self.scale, x.shape) ** 2)
def variance(self):
return exponential.exp(2 * self.mu + self.sigma ** 2) \
* (exponential.exp(self.sigma ** 2) - 1)
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return (2 * numpy.pi) ** -0.5 * exponential.exp(-0.5 * x ** 2) * gy[0],
def mean(self):
return exponential.exp(self.mu + 0.5 * self.sigma ** 2)
def prob(self, x):
return (PROBC / self.scale) * exponential.exp(
- 0.5 * (x - self.loc) ** 2 / self.scale ** 2)
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return (2 * numpy.pi)**-0.5 * exponential.exp(-0.5 * x**2) * gy[0],
def black_out(x, t, W, samples, reduce='mean'):
"""BlackOut loss function.
BlackOut loss function is defined as
.. math::
-\\log(p(t)) - \\sum_{s \\in S} \\log(1 - p(s)),
where :math:`t` is the correct label, :math:`S` is a set of negative
examples and :math:`p(\\cdot)` is likelihood of a given label.
And, :math:`p` is defined as
.. math::
p(y) = \\frac{\\exp(W_y^\\top x)}{
\\sum_{s \\in samples} \\exp(W_s^\\top x)}.
The output is a variable whose value depends on the value of
the option ``reduce``. If it is ``'no'``, it holds the
no loss values. If it is ``'mean'``, this function takes
a mean of loss values.
Args:
x (~chainer.Variable): Batch of input vectors.
Its shape should be :math:`(N, D)`.
t (~chainer.Variable): Vector of ground truth labels.
Its shape should be :math:`(N,)`. Each elements :math:`v`
should satisfy :math:`0 \\geq v \\geq V` or :math:`-1`
where :math:`V` is the number of label types.
W (~chainer.Variable): Weight matrix.
Its shape should be :math:`(V, D)`
samples (~chainer.Variable): Negative samples.
Its shape should be :math:`(N, S)` where :math:`S` is
the number of negative samples.
reduce (str): Reduction option. Its value must be either
``'no'`` or ``'mean'``. Otherwise,
:class:`ValueError` is raised.
Returns:
~chainer.Variable:
A variable object holding loss value(s).
If ``reduce`` is ``'no'``, the output variable holds an
array whose shape is :math:`(N,)` .
If it is ``'mean'``, it holds a scalar.
See: `BlackOut: Speeding up Recurrent Neural Network Language Models With \
Very Large Vocabularies <https://arxiv.org/abs/1511.06909>`_
.. seealso:: :class:`~chainer.links.BlackOut`.
"""
batch_size = x.shape[0]
neg_emb = embed_id.embed_id(samples, W)
neg_y = matmul.matmul(neg_emb, x[:, :, None])
neg_y = reshape.reshape(neg_y, neg_y.shape[:-1])
pos_emb = expand_dims.expand_dims(embed_id.embed_id(t, W), 1)
pos_y = matmul.matmul(pos_emb, x[:, :, None])
pos_y = reshape.reshape(pos_y, pos_y.shape[:-1])
logz = logsumexp.logsumexp(concat.concat([pos_y, neg_y]), axis=1)
blogz, bneg_y = broadcast.broadcast(
reshape.reshape(logz, (batch_size, 1)), neg_y)
ny = exponential.log(1 - exponential.exp(bneg_y - blogz))
py = reshape.reshape(pos_y, (batch_size,))
loss = -(py - logz + _sum.sum(ny, axis=1))
if reduce == 'mean':
loss = average.average(loss)
return loss
def prob(self, x):
bl = broadcast.broadcast_to(self.loc, x.shape)
bs = broadcast.broadcast_to(self.scale, x.shape)
return 0.5 / bs * exponential.exp(-abs(x - bl) / bs)
def prob(self, x):
scale = self.scale
return 0.5 / scale * exponential.exp(- abs(x - self.loc) / scale)
def p(self):
if self.__p is not None:
return chainer.as_variable(self.__p)
else:
return exponential.exp(self.log_p)
def _kl_gumbel_gumbel(dist1, dist2):
scale_1d2 = dist1.scale / dist2.scale
return (dist2._log_scale - dist1._log_scale + EULER * (scale_1d2 - 1.) +
exponential.exp((dist2.loc - dist1.loc) / dist2.scale +
lgamma.lgamma(scale_1d2 + 1.)) - 1 +
(dist1.loc - dist2.loc) / dist2.scale)