【pytorch】pytorch查阅文档的使用方法

查阅文档

对于pytorch，一些函数可能不是很熟悉，这里给出查阅函数文档的方法

查找模块中的所有函数和类

为了知道模块中可以调用哪些函数和类，我们调用dir函数。例如，我们可以(查询随机数生成模块中的所有属性：)

import torch

print(dir(torch.distributions))

['AbsTransform', 'AffineTransform', 'Bernoulli', 'Beta', 'Binomial', 'CatTransform', 'Categorical', 'Cauchy', 'Chi2', 'ComposeTransform', 'ContinuousBernoulli', 'CorrCholeskyTransform', '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', 'StackTransform', 'StickBreakingTransform', 'StudentT', 'TanhTransform', 'Transform', 'TransformedDistribution', 'Uniform', 'VonMises', 'Weibull', '__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']

通常，我们可以忽略以__开始和结束的函数（Python中的特殊对象）或以单个_开始的函数（通常是内部函数）。根据剩余的函数名或属性名，我们可能会猜测这个模块提供了各种生成随机数的方法，包括从均匀分布（uniform）、正态分布（normal）和多项分布（multinomial）中采样。

查找特定函数和类的用法

有关如何使用给定函数或类的更具体说明，我们可以调用help函数。例如，我们来[查看张量ones函数的用法。]

# help(torch.ones)，我们可以调用help函数。例如，我们来[查看张量ones函数的用法。]
help(torch.ones)

Help on built-in function ones:

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.])