使用adult数据集，构造全连接神经网络，完成对Adult的收入分类 要求：1.至少使用两种网络结构（不同的连接方式，不同的参数数量，不同的参数值） 2.比较两种网络的性能（分类准确率，训练耗时，验证耗时），并分析原因 3.给我实现功能对应的完整代码

在使用 Adult 数据集构建全连接神经网络进行收入分类时，我们将首先加载数据，然后设计两个不同架构的网络，分别是基础的全连接网络（例如，只有一个隐藏层）和更复杂的网络（如包含多层隐藏层）。为了对比性能，我们将优化模型的超参数，包括层数、节点数以及学习速率等。

**步骤1：导入所需库**

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from sklearn.metrics import accuracy_score, time_series_helper

**步骤2：加载数据**

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
data = pd.read_csv(url, names=column_names, header=None)
X = data.iloc[:, :-1]
y = data.iloc[:, -1].map({'<=50K': 0, '>50K': 1})

**步骤3：预处理数据**

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

**步骤4：创建网络模型 - 简单全连接网络 (Model A)**

model_a = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])
optimizer_a = Adam(lr=0.001)
model_a.compile(optimizer=optimizer_a, loss='binary_crossentropy', metrics=['accuracy'])

**步骤5：创建网络模型 - 复杂网络 (Model B)**

model_b = Sequential([
    Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])
optimizer_b = Adam(lr=0.0001)
model_b.compile(optimizer=optimizer_b, loss='binary_crossentropy', metrics=['accuracy'])

**步骤6：训练和评估**

early_stopping = EarlyStopping(monitor='val_loss', patience=5)

# 训练 Model A
start_time_a = time.time()
history_a = model_a.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, callbacks=[early_stopping])
train_time_a = time.time() - start_time_a

# 训练 Model B
start_time_b = time.time()
history_b = model_b.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, callbacks=[early_stopping])
train_time_b = time.time() - start_time_b

# 验证
y_pred_a = model_a.predict_classes(X_test)
y_pred_b = model_b.predict_classes(X_test)

accuracy_a = accuracy_score(y_test, y_pred_a)
accuracy_b = accuracy_score(y_test, y_pred_b)
validation_time_a = time_series_helper(history_a.history['val_accuracy'][-1], history_b.history['val_accuracy'][-1])
validation_time_b = time_series_helper(history_a.history['val_loss'][-1], history_b.history['val_loss'][-1])

**步骤7：性能对比**

print(f"Model A - Accuracy: {accuracy_a}, Train Time: {train_time_a:.2f} s, Validation Time: {validation_time_a}")
print(f"Model B - Accuracy: {accuracy_b}, Train Time: {train_time_b:.2f} s, Validation Time: {validation_time_b}")

if accuracy_b > accuracy_a and train_time_b < train_time_a:
    print("Model B在分类精度上优于Model A，并且训练时间较短，说明更深的网络结构对于这个任务有更高的效率。")
else:
    print("Model A的性能可能更好，或者两者的差异取决于数据和随机初始化的影响。")

现在我们有了两个网络的训练结果，可以根据它们的表现来分析网络结构对性能的影响。