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Caffe2 - Python API
A deep learning, cross platform ML framework
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Caffe2 - Python API
A deep learning, cross platform ML framework
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Public Member Functions | |
| def | __init__ (self, batch_shape=torch.Size(), event_shape=torch.Size(), validate_args=None) |
| def | expand (self, batch_shape, _instance=None) |
| def | batch_shape (self) |
| def | event_shape (self) |
| def | arg_constraints (self) |
| def | support (self) |
| def | mean (self) |
| def | variance (self) |
| def | stddev (self) |
| def | sample (self, sample_shape=torch.Size()) |
| def | rsample (self, sample_shape=torch.Size()) |
| def | sample_n (self, n) |
| def | log_prob (self, value) |
| def | cdf (self, value) |
| def | icdf (self, value) |
| def | enumerate_support (self, expand=True) |
| def | entropy (self) |
| def | perplexity (self) |
| def | __repr__ (self) |
Static Public Member Functions | |
| def | set_default_validate_args (value) |
Static Public Attributes | |
| has_rsample | |
| has_enumerate_support | |
| support | |
| arg_constraints | |
Definition at line 7 of file distribution.py.
| def torch.distributions.distribution.Distribution.arg_constraints | ( | self | ) |
Returns a dictionary from argument names to :class:`~torch.distributions.constraints.Constraint` objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict.
Definition at line 75 of file distribution.py.
| def torch.distributions.distribution.Distribution.batch_shape | ( | self | ) |
Returns the shape over which parameters are batched.
Definition at line 61 of file distribution.py.
| def torch.distributions.distribution.Distribution.cdf | ( | self, | |
| value | |||
| ) |
Returns the cumulative density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 147 of file distribution.py.
| def torch.distributions.distribution.Distribution.entropy | ( | self | ) |
Returns entropy of distribution, batched over batch_shape.
Returns:
Tensor of shape batch_shape.
Definition at line 191 of file distribution.py.
| def torch.distributions.distribution.Distribution.enumerate_support | ( | self, | |
expand = True |
|||
| ) |
Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be `(cardinality,) + batch_shape + event_shape`
(where `event_shape = ()` for univariate distributions).
Note that this enumerates over all batched tensors in lock-step
`[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens
along dim 0, but with the remaining batch dimensions being
singleton dimensions, `[[0], [1], ..`.
To iterate over the full Cartesian product use
`itertools.product(m.enumerate_support())`.
Args:
expand (bool): whether to expand the support over the
batch dims to match the distribution's `batch_shape`.
Returns:
Tensor iterating over dimension 0.
Definition at line 167 of file distribution.py.
| def torch.distributions.distribution.Distribution.event_shape | ( | self | ) |
Returns the shape of a single sample (without batching).
Definition at line 68 of file distribution.py.
| def torch.distributions.distribution.Distribution.expand | ( | self, | |
| batch_shape, | |||
_instance = None |
|||
| ) |
Returns a new distribution instance (or populates an existing instance
provided by a derived class) with batch dimensions expanded to
`batch_shape`. This method calls :class:`~torch.Tensor.expand` on
the distribution's parameters. As such, this does not allocate new
memory for the expanded distribution instance. Additionally,
this does not repeat any args checking or parameter broadcasting in
`__init__.py`, when an instance is first created.
Args:
batch_shape (torch.Size): the desired expanded size.
_instance: new instance provided by subclasses that
need to override `.expand`.
Returns:
New distribution instance with batch dimensions expanded to
`batch_size`.
Definition at line 39 of file distribution.py.
| def torch.distributions.distribution.Distribution.icdf | ( | self, | |
| value | |||
| ) |
Returns the inverse cumulative density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 157 of file distribution.py.
| def torch.distributions.distribution.Distribution.log_prob | ( | self, | |
| value | |||
| ) |
Returns the log of the probability density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 137 of file distribution.py.
| def torch.distributions.distribution.Distribution.mean | ( | self | ) |
Returns the mean of the distribution.
Definition at line 93 of file distribution.py.
| def torch.distributions.distribution.Distribution.perplexity | ( | self | ) |
Returns perplexity of distribution, batched over batch_shape.
Returns:
Tensor of shape batch_shape.
Definition at line 200 of file distribution.py.
| def torch.distributions.distribution.Distribution.rsample | ( | self, | |
sample_shape = torch.Size() |
|||
| ) |
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
Definition at line 121 of file distribution.py.
| def torch.distributions.distribution.Distribution.sample | ( | self, | |
sample_shape = torch.Size() |
|||
| ) |
Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.
Definition at line 113 of file distribution.py.
| def torch.distributions.distribution.Distribution.sample_n | ( | self, | |
| n | |||
| ) |
Generates n samples or n batches of samples if the distribution parameters are batched.
Definition at line 129 of file distribution.py.
| def torch.distributions.distribution.Distribution.stddev | ( | self | ) |
Returns the standard deviation of the distribution.
Definition at line 107 of file distribution.py.
| def torch.distributions.distribution.Distribution.support | ( | self | ) |
Returns a :class:`~torch.distributions.constraints.Constraint` object representing this distribution's support.
Definition at line 85 of file distribution.py.
| def torch.distributions.distribution.Distribution.variance | ( | self | ) |
Returns the variance of the distribution.
Definition at line 100 of file distribution.py.
1.8.11