MXNet 中的几个数据集
from mxnet import gluon
def transform(data, label):
return data.astype('float32') / 255., label.astype('float32')
mnist_train = gluon.data.vision.MNIST(train= True, transform= transform)
mnist_test = gluon.data.vision.MNIST(train= False, transform= transform)
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:118: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
label = np.fromstring(fin.read(), dtype=np.uint8).astype(np.int32)
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:122: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
data = np.fromstring(fin.read(), dtype=np.uint8)
下载几个数据集到本地磁盘
cifar_100
cifar_100_train = gluon.data.vision.CIFAR100(root= 'E:/Data/MXNet/cifar100')
cifar_100_test = gluon.data.vision.CIFAR100(root= 'E:/Data/MXNet/cifar100', train= False)
def show_images(images):
n = images.shape[0]
_, figs = plt.subplots(1, n, figsize=(15, 15))
for i in range(n):
figs[i].imshow(images[i].asnumpy())
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
data, label = cifar_100_train[1: 9]
print(data.shape, label)
show_images(data)
Downloading E:/Data/MXNet/cifar100\cifar-100-binary.tar.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/cifar100/cifar-100-binary.tar.gz...
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:252: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
data = np.fromstring(fin.read(), dtype=np.uint8).reshape(-1, 3072+2)
(8, 32, 32, 3) [15 4 14 1 5 18 3 10]

cifar-10
cifar_10_train = gluon.data.vision.CIFAR10(root= 'E:/Data/MXNet/cifar10')
cifar_10_test = gluon.data.vision.CIFAR10(root= 'E:/Data/MXNet/cifar10', train= False)
def show_images(images):
n = images.shape[0]
_, figs = plt.subplots(1, n, figsize=(15, 15))
for i in range(n):
figs[i].imshow(images[i].asnumpy())
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
data, label = cifar_10_train[1: 9]
print(data.shape, label)
show_images(data)
Downloading E:/Data/MXNet/cifar10\cifar-10-binary.tar.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/cifar10/cifar-10-binary.tar.gz...
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:193: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
data = np.fromstring(fin.read(), dtype=np.uint8).reshape(-1, 3072+1)
(8, 32, 32, 3) [9 9 4 1 1 2 7 8]

mnist_train
mnist_train = gluon.data.vision.MNIST(root= 'E:/Data/MXNet/mnist')
mnist_test = gluon.data.vision.MNIST(root= 'E:/Data/MXNet/mnist', train= False)
def show_images(images):
n = images.shape[0]
_, figs = plt.subplots(1, n, figsize=(15, 15))
for i in range(n):
figs[i].imshow(images[i].reshape((28, 28)).asnumpy())
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
data, label = mnist_train[1: 9]
print(data.shape, label)
show_images(data)
Downloading E:/Data/MXNet/mnist\train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-images-idx3-ubyte.gz...
Downloading E:/Data/MXNet/mnist\train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/train-labels-idx1-ubyte.gz...
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:118: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
label = np.fromstring(fin.read(), dtype=np.uint8).astype(np.int32)
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:122: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
data = np.fromstring(fin.read(), dtype=np.uint8)
Downloading E:/Data/MXNet/mnist\t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-images-idx3-ubyte.gz...
Downloading E:/Data/MXNet/mnist\t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/mnist/t10k-labels-idx1-ubyte.gz...
(8, 28, 28, 1) [0 4 1 9 2 1 3 1]

Fashion-MNIST
fashion_mnist_train = gluon.data.vision.FashionMNIST(root= 'E:/Data/MXNet/fashion_mnist')
fashion_mnist_test = gluon.data.vision.FashionMNIST(root= 'E:/Data/MXNet/fashion_mnist', train= False)
def show_images(images):
n = images.shape[0]
_, figs = plt.subplots(1, n, figsize=(15, 15))
for i in range(n):
figs[i].imshow(images[i].reshape((28, 28)).asnumpy())
figs[i].axes.get_xaxis().set_visible(False)
figs[i].axes.get_yaxis().set_visible(False)
plt.show()
def get_text_labels(label):
text_labels = [
't-shirt', 'trouser', 'pullover', 'dress,', 'coat',
'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'
]
return [text_labels[int(i)] for i in label]
data, label = fashion_mnist_train[0:9]
show_images(data)
print(get_text_labels(label))
Downloading E:/Data/MXNet/fashion_mnist\train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-images-idx3-ubyte.gz...
Downloading E:/Data/MXNet/fashion_mnist\train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz...
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:118: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
label = np.fromstring(fin.read(), dtype=np.uint8).astype(np.int32)
C:\Anaconda3\lib\site-packages\mxnet\gluon\data\vision.py:122: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead
data = np.fromstring(fin.read(), dtype=np.uint8)
Downloading E:/Data/MXNet/fashion_mnist\t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-images-idx3-ubyte.gz...
Downloading E:/Data/MXNet/fashion_mnist\t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-labels-idx1-ubyte.gz...

['pullover', 'ankle boot', 'shirt', 't-shirt', 'dress,', 'coat', 'coat', 'sandal', 'coat']
MXNet 中的几个数据集的更多相关文章
- PyTorch中的MIT ADE20K数据集的语义分割
PyTorch中的MIT ADE20K数据集的语义分割 代码地址:https://github.com/CSAILVision/semantic-segmentation-pytorch Semant ...
- 将 Book-Crossing Dataset 书籍推荐算法中 CVS 格式测试数据集导入到MySQL数据库
本文内容 最近看<写给程序员的数据挖掘指南>,研究推荐算法,书中的测试数据集是 Book-Crossing Dataset 提供的亚马逊用户对书籍评分的真实数据.推荐大家看本书,写得不错, ...
- birt报表中使用多个数据集。
这个问题困扰了几天,也没搜到答案,由于工作需要,创建了两个数据集和两个表格,第一个数据集和表格之间没有任何问题.但是第二个数据集拖过去就显示不可用,除非拖到表格外面,当然也就没用了.一朋友说拖一个网格 ...
- Delphi中JSon SuperObject 使用:数据集与JSON对象互转
在delphi中,数据集是最常用数据存取方式.因此,必须建立JSON与TDataSet之间的互转关系,实现数据之间通讯与转换.值得注意的是,这只是普通的TDataset与JSON之间转换,由于CDS包 ...
- MXNet 中的 hybird_forward 的一个使用技巧
from mxnet.gluon import nn from mxnet import nd class SliceLike(nn.HybridBlock): def __init__(self, ...
- FineReport中如何制作树数据集来实现组织树报表
1. 问题描述 FineReport,组织树报表中由id与父id来实现组织树报表,若层级数较多时,对每个单元格设置过滤条件和形态会比较繁琐,因此FineReport提供了一种特殊的数据集——树数据集, ...
- 如何在nlp问题中定义自己的数据集
我之前大致写了一篇在pytorch中如何自己定义数据集合,在这里如何自定义数据集 不过这个例子使用的是image,也就是图像.如果我们用到的是文本呢,处理的是NLP问题呢? 在解决这个问题的时候,我在 ...
- 关于无法下载sklearn中的MNIST original数据集的问题
在使用Sklearn进行加载自带的数据集MNIST时,总是报错,代码及相应的错误显示如下: from sklearn.datasets import fetch_mldata mnist = fetc ...
- mxnet卷积神经网络训练MNIST数据集测试
mxnet框架下超全手写字体识别—从数据预处理到网络的训练—模型及日志的保存 import numpy as np import mxnet as mx import logging logging. ...
随机推荐
- Vue 的style绑定显示background-image
data () { return { img: require('你的json资源路径') } } :style="{backgroundImage: 'url(' + img + ')'} ...
- Python核心编程——Chapter15
正则表达式在脚本语言里是最重要的一部分,这部分的题目真的不容怠慢. 开始这部分的题目的解答! 15.1识别下列字符串:bat,bit,but,hat,hit和hut. >>> imp ...
- R爬虫实战1(学习)—基于RVEST包
这里用Hadley Wickham开发的rvest包.再次给这位矜矜业业开发各种好用的R包的大神奉上膝盖. 查阅资料如下: rvest的github rvest自身的帮助文档 rvest + CSS ...
- ETL testing
https://www.tutorialspoint.com/etl_testing/index.htm querysurge-installer-6.0.5-linux-x64 测试ETL的工具.
- nginx配置浅析
一.nginx的介绍 nginx是由俄罗斯人开发的一款高性能的http和反向代理服务器,也可以用来作为邮件代理.相比较于其他的服务器,具有占用内存少,稳定性高等优势 二.nginx的配置 nginx的 ...
- 用jquery的ajax方法获取不到return返回值
如果jquery中,获取不到ajax返回值. 两个错误写法会导致这种情况:1.ajax未用同步 2.在ajax方法中直接return返回值. 下面列举了三种写法,如果想成功获取到返回值,参考第三种写法 ...
- 四、Springboot Debug调试
描述: 在使用maven插件执行spring-boot:run进行启动的时候,如果设置的断点进不去,要进行以下的设置. 1.添加jvm参数配置 在spring-boot的maven插件加上jvmArg ...
- poj1976
dp #include <cstdio> #include <cstring> #include <algorithm> using namespace std; ...
- SQLAlchemy-介绍安装
一:概述 SQLAlchemy的SQL工具包和对象关系映射是一个全面的工具集,用来处理数据库和Python. 它有几个不同的功能领域,可以单独使用或组合使用. 所示的主要组件,组件依赖关系组织成层: ...
- acm专题---KMP模板
KMP的子串长n,模式串长m,复杂度o(m+n),朴素做法的复杂度o((n-m+1)*m) 觉得大话数据结果上面这个讲得特别好 改进版本的KMP leetcode 28. Implement strS ...