2.7.1 查找模块中的所有函数和类
import torch
可以调用 dir 函数查询函数中有哪些模块和类。
以 “__”（双下划线） 开始和结束的函数是 Python 中的特殊对象，以 “_”（单下划线）开始的函数是内部函数，通常以上两种函数可以忽略。
dir(torch.distributions)
['AbsTransform',
'AffineTransform',
'Bernoulli',
'Beta',
'Binomial',
'CatTransform',
'Categorical',
'Cauchy',
'Chi2',
'ComposeTransform',
'ContinuousBernoulli',
'CorrCholeskyTransform',
'CumulativeDistributionTransform',
'Dirichlet',
'Distribution',
'ExpTransform',
'Exponential',
'ExponentialFamily',
'FisherSnedecor',
'Gamma',
'Geometric',
'Gumbel',
'HalfCauchy',
'HalfNormal',
'Independent',
'IndependentTransform',
'Kumaraswamy',
'LKJCholesky',
'Laplace',
'LogNormal',
'LogisticNormal',
'LowRankMultivariateNormal',
'LowerCholeskyTransform',
'MixtureSameFamily',
'Multinomial',
'MultivariateNormal',
'NegativeBinomial',
'Normal',
'OneHotCategorical',
'OneHotCategoricalStraightThrough',
'Pareto',
'Poisson',
'PowerTransform',
'RelaxedBernoulli',
'RelaxedOneHotCategorical',
'ReshapeTransform',
'SigmoidTransform',
'SoftmaxTransform',
'SoftplusTransform',
'StackTransform',
'StickBreakingTransform',
'StudentT',
'TanhTransform',
'Transform',
'TransformedDistribution',
'Uniform',
'VonMises',
'Weibull',
'Wishart',
'__all__',
'__builtins__',
'__cached__',
'__doc__',
'__file__',
'__loader__',
'__name__',
'__package__',
'__path__',
'__spec__',
'bernoulli',
'beta',
'biject_to',
'binomial',
'categorical',
'cauchy',
'chi2',
'constraint_registry',
'constraints',
'continuous_bernoulli',
'dirichlet',
'distribution',
'exp_family',
'exponential',
'fishersnedecor',
'gamma',
'geometric',
'gumbel',
'half_cauchy',
'half_normal',
'identity_transform',
'independent',
'kl',
'kl_divergence',
'kumaraswamy',
'laplace',
'lkj_cholesky',
'log_normal',
'logistic_normal',
'lowrank_multivariate_normal',
'mixture_same_family',
'multinomial',
'multivariate_normal',
'negative_binomial',
'normal',
'one_hot_categorical',
'pareto',
'poisson',
'register_kl',
'relaxed_bernoulli',
'relaxed_categorical',
'studentT',
'transform_to',
'transformed_distribution',
'transforms',
'uniform',
'utils',
'von_mises',
'weibull',
'wishart']
2.7.2 查找特定的函数和类的用法
可以调用 help 函数查看给定函数或类的更具体的说明。
help(torch.ones)
Help on built-in function ones in module torch:
ones(...)
ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
Returns a tensor filled with the scalar value `1`, with the shape defined
by the variable argument :attr:`size`.
Args:
size (int...): a sequence of integers defining the shape of the output tensor.
Can be a variable number of arguments or a collection like a list or tuple.
Keyword arguments:
out (Tensor, optional): the output tensor.
dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`).
layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
Default: ``torch.strided``.
device (:class:`torch.device`, optional): the desired device of returned tensor.
Default: if ``None``, uses the current device for the default tensor type
(see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default: ``False``.
Example::
>>> torch.ones(2, 3)
tensor([[ 1., 1., 1.],
[ 1., 1., 1.]])
>>> torch.ones(5)
tensor([ 1., 1., 1., 1., 1.])
练习
（1）在深度学习框架中查找任何函数或类的文档。请尝试在这个框架的官方网站上找到文档。
查找 torch.distributions.multinomial.Multinomial 的说明，其官方文档在此。
help(torch.distributions.multinomial.Multinomial)
Help on class Multinomial in module torch.distributions.multinomial:
class Multinomial(torch.distributions.distribution.Distribution)
| Multinomial(total_count=1, probs=None, logits=None, validate_args=None)
|
| Creates a Multinomial distribution parameterized by :attr:`total_count` and
| either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of
| :attr:`probs` indexes over categories. All other dimensions index over batches.
|
| Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is
| called (see example below)
|
| .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
| and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
| will return this normalized value.
| The `logits` argument will be interpreted as unnormalized log probabilities
| and can therefore be any real number. It will likewise be normalized so that
| the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
| will return this normalized value.
|
| - :meth:`sample` requires a single shared `total_count` for all
| parameters and samples.
| - :meth:`log_prob` allows different `total_count` for each parameter and
| sample.
|
| Example::
|
| >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))
| >>> x = m.sample() # equal probability of 0, 1, 2, 3
| tensor([ 21., 24., 30., 25.])
|
| >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)
| tensor([-4.1338])
|
| Args:
| total_count (int): number of trials
| probs (Tensor): event probabilities
| logits (Tensor): event log probabilities (unnormalized)
|
| Method resolution order:
| Multinomial
| torch.distributions.distribution.Distribution
| builtins.object
|
| Methods defined here:
|
| __init__(self, total_count=1, probs=None, logits=None, validate_args=None)
| Initialize self. See help(type(self)) for accurate signature.
|
| entropy(self)
| Returns entropy of distribution, batched over batch_shape.
|
| Returns:
| Tensor of shape batch_shape.
|
| 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`.
|
| log_prob(self, value)
| Returns the log of the probability density/mass function evaluated at
| `value`.
|
| Args:
| value (Tensor):
|
| 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.
|
| ----------------------------------------------------------------------
| Readonly properties defined here:
|
| logits
|
| mean
| Returns the mean of the distribution.
|
| param_shape
|
| probs
|
| support
| Returns a :class:`~torch.distributions.constraints.Constraint` object
| representing this distribution's support.
|
| variance
| Returns the variance of the distribution.
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __annotations__ = {'total_count': <class 'int'>}
|
| arg_constraints = {'logits': IndependentConstraint(Real(), 1), 'probs'...
|
| ----------------------------------------------------------------------
| Methods inherited from torch.distributions.distribution.Distribution:
|
| __repr__(self)
| Return repr(self).
|
| cdf(self, value)
| Returns the cumulative density/mass function evaluated at
| `value`.
|
| Args:
| value (Tensor):
|
| 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.
|
| icdf(self, value)
| Returns the inverse cumulative density/mass function evaluated at
| `value`.
|
| Args:
| value (Tensor):
|
| perplexity(self)
| Returns perplexity of distribution, batched over batch_shape.
|
| Returns:
| Tensor of shape batch_shape.
|
| 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.
|
| sample_n(self, n)
| Generates n samples or n batches of samples if the distribution
| parameters are batched.
|
| ----------------------------------------------------------------------
| Static methods inherited from torch.distributions.distribution.Distribution:
|
| set_default_validate_args(value)
| Sets whether validation is enabled or disabled.
|
| The default behavior mimics Python's ``assert`` statement: validation
| is on by default, but is disabled if Python is run in optimized mode
| (via ``python -O``). Validation may be expensive, so you may want to
| disable it once a model is working.
|
| Args:
| value (bool): Whether to enable validation.
|
| ----------------------------------------------------------------------
| Readonly properties inherited from torch.distributions.distribution.Distribution:
|
| batch_shape
| Returns the shape over which parameters are batched.
|
| event_shape
| Returns the shape of a single sample (without batching).
|
| mode
| Returns the mode of the distribution.
|
| stddev
| Returns the standard deviation of the distribution.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from torch.distributions.distribution.Distribution:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from torch.distributions.distribution.Distribution:
|
| has_enumerate_support = False
|
| has_rsample = False