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python - 如何将数据集拆分为特定的数字？

问题描述

我正在使用 keras MNIST 数据集，该数据集带有 60k 图像的训练集和 10k 图像的测试集。在我的作业中，系统提示我将训练集进一步拆分为 50k 用于训练和 10k 用于验证。我有点不确定如何执行此操作和/或处理此操作，因为我不必像以前那样将数据集拆分为特定数字。这是我到目前为止的代码：

import numpy as np
import scipy
import matplotlib.pyplot as plt
from keras.datasets import mnist
from util import func_confusion_matrix

# load (downloaded if needed) the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# transform each image from 28 by28 to a 784 pixel vector
pixel_count = x_train.shape[1] * x_train.shape[2]
x_train = x_train.reshape(x_train.shape[0], pixel_count).astype('float32')
x_test = x_test.reshape(x_test.shape[0], pixel_count).astype('float32')

# normalize inputs from gray scale of 0-255 to values between 0-1
x_train = x_train / 255
x_test = x_test / 255

标签： pythontensorflowmachine-learningkerasdataset

解决方案

你可以直接使用 sklearn 包：

import numpy as np
import scipy
import matplotlib.pyplot as plt
from keras.datasets import mnist
from util import func_confusion_matrix

# load (downloaded if needed) the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()

from sklearn.model_selection import train_test_split
x_train,x_val,y_train,y_val = train_test_split(x_train,y_train,test_size=0.1)

# here test_size=0.1 means you are selecting 10% of data for split.

现在您拥有 x_train,x_test,x_val 和 y_train,y_test,y_val 变量中的所有数据。

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