FashionMNIST数据集简要分析---深度学习机器学习第五天
图像分类数据集—FashionMNIST数据集
①简介：fashionmnist数据集中共有10种类别的服饰，分别为：
['t-shirt','torur','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boots']
部分服饰为：
②具体介绍：在该数据集中共有7万张图⽚，每张图⽚的形状为：[单通道，长28，宽28]，并且每张图⽚对应⼀种服饰(⼀种标签)。其中训练集和测试集的图⽚是分开的，分别有6万张图⽚和1万张图⽚。
③探索FashionMNIST数据集
导⼊相应的库，并下载数据集
%matplotlib inline
import torch
inaccuracy
from IPython import display
import torchvision # torchvision是关于图像操作的⼀些⽅便⼯具库，对于计算机视觉进⾏实现的⼀个库
from torch.utils import data # ⽤来读取数据
from torchvision import transforms # 为pytorch中图像预处理包，包含了很多种对图像进⾏变化的函数
from d2l import torch as d2l
import matplotlib.pyplot as plt
中国留学生import time
def u_svg_display():
# ⽤⽮量图显⽰图⽚withstand
display.t_matplotlib_formats('svg')# format格式
u_svg_display()# ⽤svg显⽰图⽚，这样图⽚的清晰度会更⾼
# 下载数据集
trans = transforms.ToTensor()# 把shape为(x, y, z)的转换为(z, x, y)，并每个元素除以255
# 得到每个元素的数值均在0到1之间
pwbmnist_train = torchvision.datats.FashionMNIST(root="./data", train=True, transform=trans, download=True)
mnist_test = torchvision.datats.FashionMNIST(root="./data", train=Fal, transform=trans, download=True)
数据集的探索
len(mnist_train),len(mnist_test)
# answer (60000, 10000) 训练集60000张，测试集10000张
mnist_train[0][0].shape
# torch.Size([1, 28, 28]) 单张图⽚的通道数和尺⼨
数据集的可视化，结果为简介中的图⽚
def get_fashion_mnist_labels(labels):
"""返回Fashion-MNIST数据集的⽂本标签。"""
test_labels =['t-shirt','torur','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boots']
return[test_labels[int(i)]for i in labels]
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):# 该函数还未研究
"""Plot a list of images."""
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)):
tin
if torch.is_tensor(img):
ax.imshow(img.numpy())
el:
ax.imshow(img)
_xaxis().t_visible(Fal)
无力回天的意思_yaxis().t_visible(Fal)
if titles:
ax.t_title(titles[i])t mobile
return axes
X, y =next(iter(data.DataLoader(mnist_train, batch_size=18)))
images = show_shape(18,28,28),2,9, titles=get_fashion_mnist_labels(y))
images
plt.savefig('部分服饰.png', facecolor='white', edgecolor='red')# ⽣成图⽚的保存
④导⼊数据集
把数据集通过函数形式导⼊到内存中
def load_data_fashion_mnist(batch_size, resize=None):
"""加载Fashion-MNIST数据集到内存中"""
trans =[transforms.ToTensor()]
if resize:
trans.inrt(0, transforms.Resize(resize))# 把图⽚放⼤成resize * resize⼤⼩
trans = transforms.Compo(trans)# 串联多个图⽚变换的操作
mnist_train = torchvision.datats.FashionMNIST(root="./data", train=True, transform=trans)
mnist_test = torchvision.datats.FashionMNIST(root="./data", train=Fal, transform=trans)
return(data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers()),
data.DataLoader(mnist_test, batch_size, shuffle=Fal, num_workers=get_dataloader_workers()))
解释两个参数的含义：非凡学院
batch_size：我们⼀次读取多少张图⽚
resize：是否要对图⽚进⾏等⽐例的放⼤或缩⼩。eg: resize=66，则图⽚的尺⼨变为66 x 66
⑤加载数据集
train_iter, test_iter = load_data_fashion_mnist(8,12)
for X, y in train_iter:
print(X.shape, X.dtype, y.shape, y.dtype)
break
结果为：torch.Size([8, 1, 12, 12]) torch.float32 torch.Size([8]) torch.int64
说明：我们⼀次读取8张图⽚，每张图⽚为单通道，尺⼨为12 x 12，并且每张图⽚都有对应的标签，⼀共8个标签。
⑥查看单张图⽚
for X, y in test_iter:
print(X[0].tolist(), y[0])
break
结果为：
[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003921568859368563, 0.003921568859368563], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.019607843831181526, 0.1411764770746231, 0.019607843831181526, 0.003921568859368563,
上海高考英语0.10196078568696976, 0.062745101749897], [0.0, 0.0, 0.0, 0.0, 0.0, 0.007843137718737125,
0.18431372940540314, 0.4745098054409027, 0.4745098054409027, 0.43921568989753723,实习医生格蕾第9季
0.47058823704719543, 0.11372549086809158], [0.0, 0.0, 0.0, 0.003921568859368563, 0.003921568859368563, 0.125490203499794, 0.38823530077934265, 0.5333333611488342, 0.6039215922355652,
0.6352941393852234, 0.5803921818733215, 0.1921568661928177], [0.0, 0.003921568859368563,
0.003921568859368563, 0.03529411926865578, 0.14901961386203766, 0.3803921639919281,
0.4588235318660736, 0.5607843399047852, 0.5921568870544434, 0.6117647290229797,
0.5921568870544434, 0.3843137323856354], [0.08235294371843338, 0.1921568661928177,
0.26274511218070984, 0.3607843220233917, 0.4431372582912445, 0.4745098054409027,
0.5254902243614197, 0.5764706134796143, 0.6078431606292725, 0.6078431606292725,
0.6196078658103943, 0.5176470875740051], [0.33725491166114807, 0.47058823704719543,
0.5058823823928833, 0.49803921580314636, 0.5137255191802979, 0.5647059082984924,
0.6078431606292725, 0.6392157077789307, 0.6941176652908325, 0.800000011920929,
0.7686274647712708, 0.5333333611488342], [0.0470588244497776, 0.12156862765550613,
0.24313725531101227, 0.30588236451148987, 0.32156863808631897, 0.3176470696926117,
0.2235294133424759, 0.11764705926179886, 0.20392157137393951, 0.35686275362968445,
0.3176470696926117, 0.20000000298023224], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]]
对应的标签为：tensor(9)，说明为第9种类型的服饰
结束
完整代码链接：
