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形状、数据类型与索引

读取张量的形状，并对其进行切片

第 1 / 4 课13 个步骤

形状、数据类型与索引 是 CoddyKit 上的免费 Deep Learning Academy 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Deep Learning Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Deep Learning Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Tensors Are Just Numbers in a Grid

A tensor is a box of numbers arranged in rows, columns, or deeper. Everything in deep learning flows through them.

Shape Describes the Layout

The shape tells you how many values sit along each dimension. Read it like dimensions of a box: rows by columns. 📦

Check Shape with .shape

Ask any tensor for its .shape and PyTorch hands you the size of every dimension in order.

import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(x.shape)  # torch.Size([2, 3])

Dimensions Count with .ndim

The number of dimensions is the tensor's rank. A scalar is 0, a vector is 1, a matrix is 2, and images go higher.

x = torch.tensor([[1, 2], [3, 4]])
print(x.ndim)  # 2

Every Tensor Has a Dtype

The dtype is the kind of number stored, like float32 or int64. It controls precision and which math is allowed.

x = torch.tensor([1.0, 2.0, 3.0])
print(x.dtype)  # torch.float32

Float32 Is the Default for Learning

Most networks train in float32 because gradients need decimals. Integers can't hold the tiny fractional updates.

Cast Between Dtypes

Change a tensor's type with .to() or .float(). This is how you match a model's expected precision.

x = torch.tensor([1, 2, 3])
y = x.to(torch.float32)
print(y.dtype)  # torch.float32

Indexing Grabs One Value

Indexing uses square brackets to pull out a single element by its position, counting from zero.

x = torch.tensor([10, 20, 30])
print(x[0])  # tensor(10)

Two Indices for a Matrix

For a 2D tensor, give a row then a column. The order always follows the shape you saw earlier.

x = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(x[1, 2])  # tensor(6)

Slicing Takes a Range

Use a colon to grab a whole slice. start:stop keeps everything up to but not including stop.

x = torch.tensor([10, 20, 30, 40])
print(x[1:3])  # tensor([20, 30])

Slice Rows and Columns Together

A lone colon means take all of that dimension. This is how you select a whole column across every row.

x = torch.tensor([[1, 2], [3, 4]])
print(x[:, 0])  # tensor([1, 3])

Quick Check

Time to test what shape and indexing really mean.

在交互式课程中回答

Recap: Shapes, Dtypes & Indexing

You can now read a tensor's shape, check its dtype, and slice into it. These three skills unlock every tensor operation ahead. 🎉

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常见问题解答

「形状、数据类型与索引」课时是免费的吗？

是的 — 「形状、数据类型与索引」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Deep Learning Academy 课程的其余内容，请升级到 CoddyKit PRO。 Deep Learning Academy 课程共包含 4 节课。

「形状、数据类型与索引」这节课中我会学到什么？

读取张量的形状，并对其进行切片你通过在浏览器中直接运行的动手代码来练习 Deep Learning Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Deep Learning Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Deep Learning Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「形状、数据类型与索引」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Deep Learning Academy 课中编写并运行代码吗？

能。每节 Deep Learning Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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