编写一个用SVM分类MNIST手写集带有batch的迭代循环的代码，并将准确率画出来

好的，我可以回答这个问题。以下是一个使用SVM分类MNIST手写集的带有batch的迭代循环的代码，并将准确率画出来的示例：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import numpy as np
import matplotlib.pyplot as plt

# 加载MNIST手写数字数据集
digits = datasets.load_digits()
X = digits.data
y = digits.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=42)

# 定义SVM分类器
svm = SVC(kernel='linear')

# 定义batch大小和迭代次数
batch_size = 64
n_iterations = 100

# 计算batch数量
n_batches = len(X_train) // batch_size

# 训练模型
accuracies = []
for i in range(n_iterations):
    # 随机选择一个batch
    batch_indices = np.random.choice(len(X_train), batch_size, replace=False)
    X_batch = X_train[batch_indices]
    y_batch = y_train[batch_indices]
    
    # 训练模型
    svm.fit(X_batch, y_batch)
    
    # 计算准确率
    accuracy = svm.score(X_test, y_test)
    accuracies.append(accuracy)

# 绘制准确率曲线
plt.plot(accuracies)
plt.xlabel('Iteration')
plt.ylabel('Accuracy')
plt.show()

希望这个代码示例能够帮助你。