238 lines
8.7 KiB
Plaintext
238 lines
8.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01132d59",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 查阅文档\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7f72d17",
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"metadata": {
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"由于篇幅限制，本书不可能介绍每一个PyTorch函数和类。\n",
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"API文档、其他教程和示例提供了本书之外的大量文档。\n",
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"本节提供了一些查看PyTorch API的指导。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "97173144",
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"metadata": {
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"origin_pos": 4
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},
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"source": [
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"## 查找模块中的所有函数和类\n",
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"\n",
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"为了知道模块中可以调用哪些函数和类，可以调用`dir`函数。\n",
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"例如，我们可以(**查询随机数生成模块中的所有属性：**)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "8f7f4d63",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:30.519062Z",
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"iopub.status.busy": "2023-08-18T07:05:30.518501Z",
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"iopub.status.idle": "2023-08-18T07:05:31.469749Z",
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"shell.execute_reply": "2023-08-18T07:05:31.468858Z"
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},
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"origin_pos": 6,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['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']\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"print(dir(torch.distributions))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a6e589e9",
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"metadata": {
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"origin_pos": 9
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},
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"source": [
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"通常可以忽略以“`__`”（双下划线）开始和结束的函数，它们是Python中的特殊对象，\n",
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"或以单个“`_`”（单下划线）开始的函数，它们通常是内部函数。\n",
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"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，\n",
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"包括从均匀分布（`uniform`）、正态分布（`normal`）和多项分布（`multinomial`）中采样。\n",
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"\n",
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"## 查找特定函数和类的用法\n",
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"\n",
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"有关如何使用给定函数或类的更具体说明，可以调用`help`函数。\n",
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"例如，我们来[**查看张量`ones`函数的用法。**]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a16494ed",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:31.473606Z",
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"iopub.status.busy": "2023-08-18T07:05:31.472946Z",
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"iopub.status.idle": "2023-08-18T07:05:31.477780Z",
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"shell.execute_reply": "2023-08-18T07:05:31.476938Z"
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},
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"origin_pos": 11,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Help on built-in function ones in module torch:\n",
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"\n",
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"ones(...)\n",
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" ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor\n",
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" \n",
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" Returns a tensor filled with the scalar value `1`, with the shape defined\n",
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" by the variable argument :attr:`size`.\n",
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" \n",
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" Args:\n",
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" size (int...): a sequence of integers defining the shape of the output tensor.\n",
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" Can be a variable number of arguments or a collection like a list or tuple.\n",
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" \n",
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" Keyword arguments:\n",
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" out (Tensor, optional): the output tensor.\n",
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" dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.\n",
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" Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`).\n",
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" layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.\n",
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" Default: ``torch.strided``.\n",
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" device (:class:`torch.device`, optional): the desired device of returned tensor.\n",
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" Default: if ``None``, uses the current device for the default tensor type\n",
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" (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU\n",
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" for CPU tensor types and the current CUDA device for CUDA tensor types.\n",
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" requires_grad (bool, optional): If autograd should record operations on the\n",
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" returned tensor. Default: ``False``.\n",
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" \n",
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" Example::\n",
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" \n",
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" >>> torch.ones(2, 3)\n",
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" tensor([[ 1., 1., 1.],\n",
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" [ 1., 1., 1.]])\n",
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" \n",
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" >>> torch.ones(5)\n",
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" tensor([ 1., 1., 1., 1., 1.])\n",
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"\n"
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]
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}
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],
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"source": [
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"help(torch.ones)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "903c096e",
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"metadata": {
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"origin_pos": 14
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},
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"source": [
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"从文档中，我们可以看到`ones`函数创建一个具有指定形状的新张量，并将所有元素值设置为1。\n",
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"下面来[**运行一个快速测试**]来确认这一解释：\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "7870b2f5",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:05:31.481310Z",
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"iopub.status.busy": "2023-08-18T07:05:31.480685Z",
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"iopub.status.idle": "2023-08-18T07:05:31.490398Z",
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"shell.execute_reply": "2023-08-18T07:05:31.489581Z"
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},
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"origin_pos": 16,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([1., 1., 1., 1.])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"torch.ones(4)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd4f531d",
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"metadata": {
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"origin_pos": 19
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},
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"source": [
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"在Jupyter记事本中，我们可以使用`?`指令在另一个浏览器窗口中显示文档。\n",
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"例如，`list?`指令将创建与`help(list)`指令几乎相同的内容，并在新的浏览器窗口中显示它。\n",
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"此外，如果我们使用两个问号，如`list??`，将显示实现该函数的Python代码。\n",
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"\n",
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"## 小结\n",
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"\n",
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"* 官方文档提供了本书之外的大量描述和示例。\n",
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"* 可以通过调用`dir`和`help`函数或在Jupyter记事本中使用`?`和`??`查看API的用法文档。\n",
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"\n",
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"## 练习\n",
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"\n",
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"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。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "197b3dc7",
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"metadata": {
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"origin_pos": 21,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"[Discussions](https://discuss.d2l.ai/t/1765)\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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},
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"required_libs": []
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |