train_data = pd.read_csv('mnist_dataset/mnist_train.csv') test_data = pd.read_csv('mnist_dataset/mnist_test.csv')对上述mnist数据集进行深度神经网络搭建，标准化和归一化，模型搭建，训练，评估，调参优化的代码请给出

好的，以下是对MNIST数据集进行深度神经网络搭建，标准化和归一化，模型搭建，训练，评估，调参优化的完整代码：

import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier

# 加载数据集
train_data = pd.read_csv('mnist_dataset/mnist_train.csv')
test_data = pd.read_csv('mnist_dataset/mnist_test.csv')

# 提取特征和标签
X_train, y_train = train_data.iloc[:, 1:].values, train_data.iloc[:, 0].values
X_test, y_test = test_data.iloc[:, 1:].values, test_data.iloc[:, 0].values

# 标准化
scaler = StandardScaler()
X_train_std = scaler.fit_transform(X_train)
X_test_std = scaler.transform(X_test)

# 归一化
normalizer = MinMaxScaler()
X_train_norm = normalizer.fit_transform(X_train)
X_test_norm = normalizer.transform(X_test)

# 定义模型
def create_model(input_dim=784, hidden_units=64, dropout_rate=0.2, optimizer='adam'):
    model = Sequential([
        Dense(hidden_units, input_dim=input_dim, activation='relu'),
        Dropout(dropout_rate),
        Dense(10, activation='softmax')
    ])
    model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    return model

# 创建Keras分类器
model = KerasClassifier(build_fn=create_model, verbose=0)

# 定义参数网格
param_grid = {
    'input_dim': [784],
    'hidden_units': [32, 64, 128],
    'dropout_rate': [0.2, 0.4],
    'optimizer': ['adam', 'rmsprop']
}

# 使用网格搜索调参
grid_search = GridSearchCV(model, param_grid=param_grid, cv=3)
grid_search.fit(X_train_norm, y_train)

# 输出最优参数
print('Best parameters:', grid_search.best_params_)

# 使用最优参数重新训练模型
best_model = create_model(
    input_dim=784,
    hidden_units=grid_search.best_params_['hidden_units'],
    dropout_rate=grid_search.best_params_['dropout_rate'],
    optimizer=grid_search.best_params_['optimizer']
)
best_model.fit(X_train_norm, y_train, epochs=5, validation_data=(X_test_norm, y_test))

# 评估模型
loss, acc = best_model.evaluate(X_test_norm, y_test, verbose=0)
print('Test loss:', loss)
print('Test accuracy:', acc)

首先通过`pd.read_csv`加载MNIST数据集，然后使用`iloc`方法提取特征和标签。接着使用`StandardScaler`对数据进行标准化处理，或者使用`MinMaxScaler`对数据进行归一化处理。然后定义了一个包含一个隐层和一个输出层的神经网络模型，并使用网格搜索调参，找到最优超参数。最后使用最优参数重新训练模型，并对模型进行评估。