from sklearn import model_selection from sklearn import neural_network from sklearn import datasets from sklearn.model_selection import train_test_split import cv2 from fractions import Fraction import numpy import scipy from sklearn.neural_network import MLPClassifier from sklearn.neural_network import MLPRegressor from sklearn import preprocessing import imageio reg = MLPRegressor(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) def image_to_data(image): im_resized = scipy.misc.imresize(image, (8, 8)) im_gray = cv2.cvtColor(imresized, cv2.COLOR_BGR2GRAY) im_hex = Fraction(16,255) * im_gray im_reverse = 16 - im_hex return imreverse.astype(numpy.int) def data_split(Data): x_train, x_test, y_train, y_test = train_test_split(Data.data, Data.target) return x_train, x_test, y_train, y_test def data_train(x_train, x_test, y_train, y_test): clf = neural_network.MLPClassifier() clf.fit(x_train, y_train) return clf def image_predict(image_path, clf): image = scipy.misc.imread(image_path) image_data = image_to_data(image) image_data_reshaped = image_data.reshape(1, 64) predict_result = clf.predict(image_data_reshaped) print("手写体数字识别结果为:",predict_result,'\n') if __name__=='__main__': print("若要退出，请按q退出!"'\n') str_get = input("请输入识别的手写数字序号:" +'\n') while str_get != 'q': print("识别第{}个手写数字：".format(str_get)+'\n') image_path = r"C: // Users // 33212 // Desktop // "+str_get+".png" Data = datasets.load_digits() x_train, x_test, y_train, y_test = data_split(Data) clf = data_train(x_train, x_test, y_train, y_test) image_predict(image_path, clf) str_get = input("请输入识别的手写数字序号:" +'\n')

这段代码似乎是用来进行手写数字识别的，其中使用了sklearn库中的MLPClassifier和MLPRegressor来进行分类和回归，同时使用了datasets和model_selection等模块来进行数据处理和模型选择，还有一些图像处理相关的函数。具体实现的过程就是输入一个手写数字的图片，通过对图片进行处理和预测，输出该数字的识别结果。

相关问题

写一个python代码基于sklearn的多层感知机用网格搜索交叉验证调整参数

好的，以下是一个基于sklearn的多层感知机用网格搜索交叉验证调整参数的Python代码示例：

from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义参数范围
param_grid = {
    'hidden_layer_sizes': [(10,), (50,), (100,)],
    'activation': ['relu', 'tanh', 'logistic'],
    'solver': ['sgd', 'adam'],
    'learning_rate': ['constant', 'adaptive'],
}

# 定义模型
mlp = MLPClassifier(max_iter=1000)

# 定义网格搜索对象
grid = GridSearchCV(mlp, param_grid, cv=5, scoring='accuracy')

# 训练模型
grid.fit(X_train, y_train)

# 输出最佳参数和最佳得分
print("Best parameters: ", grid.best_params_)
print("Best cross-validation score: {:.2f}".format(grid.best_score_))
print("Test set score: {:.2f}".format(grid.score(X_test, y_test)))

在上述代码中，首先加载了一个经典的鸢尾花数据集，并将其划分为训练集和测试集。然后，定义了一个参数范围字典`param_grid`，其中包括了隐藏层大小、激活函数、优化器和学习率等参数的不同组合。接着，定义了一个多层感知机模型`mlp`，以及一个网格搜索对象`grid`。最后，通过调用`grid.fit(X_train, y_train)`训练模型并进行交叉验证和网格搜索，得到最佳参数和最佳得分，并输出到控制台中。

希望这个示例代码能够帮助您更好地理解基于sklearn的多层感知机用网格搜索交叉验证调整参数的方法。