深度学习笔记007PictureClassificationDataSet图片分类数据集

跟着李沐老师学的，但是今天有点腐败，没学多少，笔记如下：

（关于这堂课之前十几分钟讲的Softmax回归的笔记放在下一篇博客里）

import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l

import pylab

d2l.use_svg_display()

#下载数据集，跑一次即可(把download设为false）
trans=transforms.ToTensor()
mnist_train=torchvision.datasets.FashionMNIST(root="../data",train=True,transform=trans,download=False)
mnist_test=torchvision.datasets.FashionMNIST(root="../data",train=False,transform=trans,download=False)
print(len(mnist_test),len(mnist_train))

# 将数据集的文本标签返回
def get_fashion_mnist_labels(labels):
    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat','sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
    return [text_labels[int(i)] for i in labels]
'''
这里加一段对return的语法知识补充：
列表理解：
list2 = [int(i) for i in list1.split(' ')]
相当于for循环（就功能而言）：
list2 = []
for i in list1.split(' '):
    list2.append(int(i))
'''
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  #@save
    """绘制图像列表"""
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    axes = axes.flatten()
    for i, (ax, img) in enumerate(zip(axes, imgs)):
        if torch.is_tensor(img):
            ax.imshow(img.numpy())
        else:
            ax.imshow(img)
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)
        if titles:
            ax.set_title(titles[i])
    return axes

X,y=next(iter(data.DataLoader(mnist_train,batch_size=18)))
show_images(X.reshape(18,28,28),2,9,titles=get_fashion_mnist_labels(y))

batch_size=256
def get_dataloader_workers():
    return 4
train_iter=data.DataLoader(mnist_train,batch_size,shuffle=True,num_workers=get_dataloader_workers())
timer=d2l.Timer()
for X,y in train_iter:
    continue
print(f'{timer.stop():.2f} sec') #读取数据要比计算快才行


def load_data_fashion_mnist(batch_size, resize=None):  #@save
    """下载Fashion-MNIST数据集，然后将其加载到内存中"""
    dataset = gluon.data.vision
    trans = [dataset.transforms.ToTensor()]
    if resize:
        trans.insert(0, dataset.transforms.Resize(resize))
    trans = dataset.transforms.Compose(trans)
    mnist_train = dataset.FashionMNIST(train=True).transform_first(trans)
    mnist_test = dataset.FashionMNIST(train=False).transform_first(trans)
    return (gluon.data.DataLoader(mnist_train, batch_size, shuffle=True,
                                  num_workers=get_dataloader_workers()),
            gluon.data.DataLoader(mnist_test, batch_size, shuffle=False,
                                  num_workers=get_dataloader_workers()))


pylab.show()