编写一个用SVM分类MNIST手写集带有batch的迭代循环的代码

可以的，以下是一个用SVM分类MNIST手写集带有batch的迭代循环的代码示例：

from sklearn import svm
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np

# 加载MNIST数据集
mnist = fetch_openml('mnist_784')
X = mnist.data.astype('float32')
y = mnist.target.astype('int32')

# 数据归一化
X /= 255.

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)

# 定义SVM分类器
clf = svm.SVC(kernel='linear', C=1, gamma='auto')

# 定义batch大小和迭代次数
batch_size = 100
n_iterations = 100

# 迭代训练
for iteration in range(n_iterations):
    # 随机选择batch大小的样本
    indices = np.random.randint(, len(X_train), batch_size)
    X_batch = X_train[indices]
    y_batch = y_train[indices]
    # 训练SVM分类器
    clf.fit(X_batch, y_batch)
    # 计算训练集和测试集的准确率
    y_train_pred = clf.predict(X_train)
    y_test_pred = clf.predict(X_test)
    train_accuracy = accuracy_score(y_train, y_train_pred)
    test_accuracy = accuracy_score(y_test, y_test_pred)
    # 打印训练过程中的准确率
    print("Iteration:", iteration+1, "Train accuracy:", train_accuracy, "Test accuracy:", test_accuracy)

希望能对你有所帮助！