RandomVariable
ed.RandomVariable
ed.models.RandomVariable
Defined in edward/models/random_variable.py
.
Base class for random variables.
A random variable is an object parameterized by tensors. It is equipped with methods such as the log-density, mean, and sample.
It also wraps a tensor, where the tensor corresponds to a sample from the random variable. This enables operations on the TensorFlow graph, allowing random variables to be used in conjunction with other TensorFlow ops.
The random variable’s shape is given by
sample_shape + batch_shape + event_shape
,
where sample_shape
is an optional argument representing the dimensions of samples drawn from the distribution (default is a scalar); batch_shape
is the number of independent random variables (determined by the shape of its parameters); and event_shape
is the shape of one draw from the distribution (e.g., Normal
has a scalar event_shape
; Dirichlet
has a vector event_shape
).
RandomVariable
assumes use in a multiple inheritance setting. The child class must first inherit RandomVariable
, then second inherit a class in tf.contrib.distributions
. With Python’s method resolution order, this implies the following during initialization (using distributions.Bernoulli
as an example):
__init__()
of the child class, which passes all *args, **kwargs
to RandomVariable
.*args, **kwargs
to distributions.Bernoulli
, completing the __init__()
of distributions.Bernoulli
.__init__()
of RandomVariable
, which calls self.sample()
, relying on the method from distributions.Bernoulli
.__init__()
of the child class.Methods from both RandomVariable
and distributions.Bernoulli
populate the namespace of the child class. Methods from RandomVariable
will take higher priority if there are conflicts.
p = tf.constant(0.5)
x = Bernoulli(p)
z1 = tf.constant([[1.0, -0.8], [0.3, -1.0]])
z2 = tf.constant([[0.9, 0.2], [2.0, -0.1]])
x = Bernoulli(logits=tf.matmul(z1, z2))
mu = Normal(tf.constant(0.0), tf.constant(1.0))
x = Normal(mu, tf.constant(1.0))
sample_shape
Sample shape of random variable.
shape
Shape of random variable.
init
__init__(
*args,
**kwargs
)
Create a new random variable.
sample_shape
: tf.TensorShape. Shape of samples to draw from the random variable.value
: tf.Tensor. Fixed tensor to associate with random variable. Must have shape sample_shape + batch_shape + event_shape
.collections
: list. Optional list of graph collections (lists). The random variable is added to these collections. Defaults to [ed.random_variables()]
.abs
__abs__(
a,
*args
)
Computes the absolute value of a tensor.
Given a tensor x
of complex numbers, this operation returns a tensor of type float32
or float64
that is the absolute value of each element in x
. All elements in x
must be complex numbers of the form \(a + bj\). The absolute value is computed as \( \). For example:
x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x) # [5.25594902, 6.60492229]
x
: A Tensor
or SparseTensor
of type float32
, float64
, int32
, int64
, complex64
or complex128
.name
: A name for the operation (optional).A Tensor
or SparseTensor
the same size and type as x
with absolute values. Note, for complex64
or complex128
input, the returned Tensor
will be of type float32
or float64
, respectively.
add
__add__(
a,
*args
)
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
, uint8
, int8
, int16
, int32
, int64
, complex64
, complex128
, string
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
and
__and__(
a,
*args
)
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
bool
__bool__()
div
__div__(
a,
*args
)
Divide two values using Python 2 semantics. Used for Tensor.__div__.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
returns the quotient of x and y.
eq
__eq__(other)
floordiv
__floordiv__(
a,
*args
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.div(x,y)
for integers, but uses tf.floor(tf.div(x,y))
for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y
floor division in Python 3 and in Python 2.7 with from __future__ import division
.
Note that for efficiency, floordiv
uses C semantics for negative numbers (unlike Python and Numpy).
x
and y
must have the same type, and the result will have the same type as well.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
TypeError
: If the inputs are complex.ge
__ge__(
a,
*args
)
Returns the truth value of (x >= y) element-wise.
NOTE: GreaterEqual
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
getitem
__getitem__(
a,
*args
)
Overload for Tensor.__getitem__.
This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a non-scalar tensor as input is not currently allowed.
Some useful examples:
# strip leading and trailing 2 elements
foo = tf.constant([1,2,3,4,5,6])
print(foo[2:-2].eval()) # => [3,4]
# skip every row and reverse every column
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[::2,::-1].eval()) # => [[3,2,1], [9,8,7]]
# Use scalar tensors as indices on both dimensions
print(foo[tf.constant(0), tf.constant(2)].eval()) # => 3
# Insert another dimension
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]],
[[7],[8],[9]]]
# Ellipses (3 equivalent operations)
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
Notes: - tf.newaxis
is None
as in NumPy. - An implicit ellipsis is placed at the end of the slice_spec
- NumPy advanced indexing is currently not supported.
tensor
: An ops.Tensor object.slice_spec
: The arguments to Tensor.__getitem__.var
: In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable).The appropriate slice of “tensor”, based on “slice_spec”.
ValueError
: If a slice range is negative size.TypeError
: If the slice indices aren’t int, slice, or Ellipsis.gt
__gt__(
a,
*args
)
Returns the truth value of (x > y) element-wise.
NOTE: Greater
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
invert
__invert__(
a,
*args
)
Returns the truth value of NOT x element-wise.
x
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
iter
__iter__()
le
__le__(
a,
*args
)
Returns the truth value of (x <= y) element-wise.
NOTE: LessEqual
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
lt
__lt__(
a,
*args
)
Returns the truth value of (x < y) element-wise.
NOTE: Less
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
of type bool
.
matmul
__matmul__(
a,
*args
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are: float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse
or b_is_sparse
flag to True
. These are False
by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a`
# [[1, 2, 3],
# [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
# 2-D tensor `b`
# [[ 7, 8],
# [ 9, 10],
# [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
# `a` * `b`
# [[ 58, 64],
# [139, 154]]
c = tf.matmul(a, b)
# 3-D tensor `a`
# [[[ 1, 2, 3],
# [ 4, 5, 6]],
# [[ 7, 8, 9],
# [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])
# 3-D tensor `b`
# [[[13, 14],
# [15, 16],
# [17, 18]],
# [[19, 20],
# [21, 22],
# [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])
# `a` * `b`
# [[[ 94, 100],
# [229, 244]],
# [[508, 532],
# [697, 730]]]
c = tf.matmul(a, b)
# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the `tf.matmul()` function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: Tensor
with same type and rank as a
.transpose_a
: If True
, a
is transposed before multiplication.transpose_b
: If True
, b
is transposed before multiplication.adjoint_a
: If True
, a
is conjugated and transposed before multiplication.adjoint_b
: If True
, b
is conjugated and transposed before multiplication.a_is_sparse
: If True
, a
is treated as a sparse matrix.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A Tensor
of the same type as a
and b
where each inner-most matrix is the product of the corresponding matrices in a
and b
, e.g. if all transpose or adjoint attributes are False
:
output
[…, i, j] = sum_k (a
[…, i, k] * b
[…, k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.ValueError
: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.mod
__mod__(
a,
*args
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
NOTE: FloorMod
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: int32
, int64
, bfloat16
, float32
, float64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
mul
__mul__(
a,
*args
)
Dispatches cwise mul for “Dense*Dense" and “Dense*Sparse“.
neg
__neg__(
a,
*args
)
Computes numerical negative value element-wise.
I.e., \(y = -x\).
x
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
, int32
, int64
, complex64
, complex128
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
nonzero
__nonzero__()
or
__or__(
a,
*args
)
Returns the truth value of x OR y element-wise.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
pow
__pow__(
a,
*args
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
x
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.y
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.name
: A name for the operation (optional).A Tensor
.
radd
__radd__(
a,
*args
)
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
, uint8
, int8
, int16
, int32
, int64
, complex64
, complex128
, string
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
rand
__rand__(
a,
*args
)
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
rdiv
__rdiv__(
a,
*args
)
Divide two values using Python 2 semantics. Used for Tensor.__div__.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
returns the quotient of x and y.
rfloordiv
__rfloordiv__(
a,
*args
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.div(x,y)
for integers, but uses tf.floor(tf.div(x,y))
for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y
floor division in Python 3 and in Python 2.7 with from __future__ import division
.
Note that for efficiency, floordiv
uses C semantics for negative numbers (unlike Python and Numpy).
x
and y
must have the same type, and the result will have the same type as well.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
TypeError
: If the inputs are complex.rmatmul
__rmatmul__(
a,
*args
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are: float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the corresponding a_is_sparse
or b_is_sparse
flag to True
. These are False
by default. This optimization is only available for plain matrices (rank-2 tensors) with datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a`
# [[1, 2, 3],
# [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
# 2-D tensor `b`
# [[ 7, 8],
# [ 9, 10],
# [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
# `a` * `b`
# [[ 58, 64],
# [139, 154]]
c = tf.matmul(a, b)
# 3-D tensor `a`
# [[[ 1, 2, 3],
# [ 4, 5, 6]],
# [[ 7, 8, 9],
# [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])
# 3-D tensor `b`
# [[[13, 14],
# [15, 16],
# [17, 18]],
# [[19, 20],
# [21, 22],
# [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])
# `a` * `b`
# [[[ 94, 100],
# [229, 244]],
# [[508, 532],
# [697, 730]]]
c = tf.matmul(a, b)
# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the `tf.matmul()` function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: Tensor
with same type and rank as a
.transpose_a
: If True
, a
is transposed before multiplication.transpose_b
: If True
, b
is transposed before multiplication.adjoint_a
: If True
, a
is conjugated and transposed before multiplication.adjoint_b
: If True
, b
is conjugated and transposed before multiplication.a_is_sparse
: If True
, a
is treated as a sparse matrix.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A Tensor
of the same type as a
and b
where each inner-most matrix is the product of the corresponding matrices in a
and b
, e.g. if all transpose or adjoint attributes are False
:
output
[…, i, j] = sum_k (a
[…, i, k] * b
[…, k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.ValueError
: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.rmod
__rmod__(
a,
*args
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
NOTE: FloorMod
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: int32
, int64
, bfloat16
, float32
, float64
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
rmul
__rmul__(
a,
*args
)
Dispatches cwise mul for “Dense*Dense" and “Dense*Sparse“.
ror
__ror__(
a,
*args
)
Returns the truth value of x OR y element-wise.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
rpow
__rpow__(
a,
*args
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
x
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.y
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.name
: A name for the operation (optional).A Tensor
.
rsub
__rsub__(
a,
*args
)
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
, uint8
, int8
, uint16
, int16
, int32
, int64
, complex64
, complex128
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
rtruediv
__rtruediv__(
a,
*args
)
rxor
__rxor__(
a,
*args
)
x ^ y = (x | y) & ~(x & y).
sub
__sub__(
a,
*args
)
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
x
: A Tensor
. Must be one of the following types: half
, bfloat16
, float32
, float64
, uint8
, int8
, uint16
, int16
, int32
, int64
, complex64
, complex128
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
truediv
__truediv__(
a,
*args
)
xor
__xor__(
a,
*args
)
x ^ y = (x | y) & ~(x & y).
eval
eval(
session=None,
feed_dict=None
)
In a session, computes and returns the value of this random variable.
This is not a graph construction method, it does not add ops to the graph.
This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used.
session
: tf.BaseSession. The tf.Session
to use to evaluate this random variable. If none, the default session is used.feed_dict
: dict. A dictionary that maps tf.Tensor
objects to feed values. See tf.Session.run()
for a description of the valid feed values.x = Normal(0.0, 1.0)
with tf.Session() as sess:
# Usage passing the session explicitly.
print(x.eval(sess))
# Usage with the default session. The 'with' block
# above makes 'sess' the default session.
print(x.eval())
get_ancestors
get_ancestors(collection=None)
Get ancestor random variables.
get_blanket
get_blanket(collection=None)
Get the random variable’s Markov blanket.
get_children
get_children(collection=None)
Get child random variables.
get_descendants
get_descendants(collection=None)
Get descendant random variables.
get_parents
get_parents(collection=None)
Get parent random variables.
get_shape
get_shape()
Get shape of random variable.
get_siblings
get_siblings(collection=None)
Get sibling random variables.
get_variables
get_variables(collection=None)
Get TensorFlow variables that the random variable depends on.
value
value()
Get tensor that the random variable corresponds to.
array_priority