Quick Start

PyTorch is an open-source deep learning framework that’s known for its flexibility and ease-of-use.

Python Grammer

understand that as a playground with instructure. define a function use def , function in python can return multiable values no need of type attribute

In python list is reinitializable, and turple is fixed and unchangable

import math

def move(x, y, step, angle=0):
    nx = x + step * math.cos(angle)
    ny = y - step * math.sin(angle)
    return nx, ny


>>> x, y = move(100, 100, 60, math.pi / 6)
>>> print(x, y)
151.96152422706632 70.0

A huge improve is Python supports default para. In tons of value need to write value, we can ignore the fixed values and only trans uniqe ones.

WARN: python 的默认参数是在方法外部传入的, 一定注意Python是引用传参, 默认参数必须指向不变对象！

Written in Python, it’s relatively easy for most machine learning developers to learn and use.

For data, we have two primitives in toch

torch.utils.data.DataLoader torch.utils.data.Dataset

Dataset include a bunch of samples and corresponding lables.

Note here In python map called dict

DataLoad add Itrable ability

we mainly use TorchVision package of  FashionMNIST dataset dataset.pytorch offer domain-specific libraryies.

In this Dataset includes two arguments: transform and target_transform to modify the samples and labels respectively.

DataSet & DataLoaders

Tensors

Pytorch use an unique data type called Tensors, similar to a multidimensional array, used to store and caculate input and output of a model. a important feature is

tensors can run on GPUs to accelerate computing.

Graphs