2. 设置随机种子,以获得可复现的结果。 np.random.seed(42) 3. 获取mnist数据集,并将数据集标签 由字符型转换为整数型 1np.random.seed(42)2mnist = fetch_openml("mnist_784", version = 1, as_frame=False)3X, y = mnist['data'], mnist['target']4y = y.astype(np.uint8) 4. 划分训练集...
I tried to run this line from the beginning of Chapter 3: mnist = fetch_openml('mnist_784', version=1) and got this error: ValueError: Dataset mnist_784 with version 1 not found. I tried removing the second parameter or changing the data...
from sklearn.datasets import fetch_openml # X为image数据,y为标签 X, y = fetch_openml('mnist_784', version=1, return_X_y=True) 其中X,y中间既包含了训练集又包含了测试集。也就是说X或者y中有70,000条数据。 那么数据是什么呢? 在X中,每一条数据是一个长为28×28=784的数组,数组的数据是...
一、准备工作:导入MNIST数据集 1 import sys 2 assert sys.version_info >= (3, 5) 3 4 import sklearn 5 assert sklearn.__version__ >= "0.20" 6 7 import numpy as np 8 import os 9 10 from sklearn.datasets import fetch_openml 11 12 mnist = fetch_openml('mnist_784', version=1) ...
X, y = fetch_openml('mnist_784', version=1, return_X_y=True) X = X / 255. 然后划分训练集和测试集,这里就简单地将前6万个样本作为训练集,剩下的作为测试集: # rescale the data, use the traditional train/test split X_train, X_test = X[:60000], X[60000:] ...
from sklearn.datasets import fetch_openml fetch_openml(name="mnist_784") Uses 3GB of RAM during execution and then 1.5 GB. Additional runs make the memory usage go up by 500 MB each time. The whole dataset has 70k values data of dimensio...
from sklearn.datasets import fetch_openml X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame= False) 但是偶尔会遭遇加载缓慢甚至失败。因此建议读者使用keras.datasets.mnist.load_data()方法加载28×28像素版本的MNIST。
1importsys2assertsys.version_info >= (3, 5)34importsklearn5assertsklearn.__version__>="0.20"67importnumpy as np8importos910fromsklearn.datasetsimportfetch_openml1112mnist = fetch_openml('mnist_784', version=1)#加载数据集 fatch_openml用来加载数据集,所加载的数据集是一个key-value的字典结构...
fromsklearn.datasetsimportfetch_openml mnist=fetch_openml('mnist_784',version=1,cache=True) mnist.data.shape 1. 2. 3. 输出结果如下。 一共有70000条数据,每条数据中一共有784个字段。可以数字将字段转化为图片,代码如下。 importmatplotlib.pyplotasplt ...
加载 MNIST 数据集 mnist = fetch_openml('mnist_784', version=1, parser='auto') X, y = mnist.data, mnist.target.astype(int) # 2. 使用 KMeans 进行聚类 kmeans = KMeans(n_clusters=10, random_state=42, n_init="auto") # 因为 MNIST 是 0-9 的数字,所以设置 n_clusters=10 kmeans....