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225
main.py
225
main.py
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# -*- coding: utf-8 -*-
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"""
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主程序入口
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使用方式：
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1. 运行单个模型（默认）：
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python main.py
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修改 config.py 中的 MODEL_TYPE 和 VECTORIZER_TYPE 来切换配置
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2. 运行对比实验：
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修改 config.py 中 RUN_COMPARISON = True
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这会依次运行：
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- 实验1: BoW vs TF-IDF (固定LR模型)
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- 实验2: LR vs MLP (固定TF-IDF)
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- 实验3: 不同学习率对比
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- 实验4: 不同隐藏层大小对比
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最后输出汇总报告
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"""
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from train import main
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if __name__ == '__main__':
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print("\n" + "=" * 70)
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print("文本分类实验 - 纯NumPy实现")
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print("数据集: ChnSentiCorp (中文酒店评论)")
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print("模型: Logistic Regression / MLP")
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print("向量化: BoW / TF-IDF")
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print("=" * 70 + "\n")
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main()
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# -*- coding: utf-8 -*-
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"""
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主程序 - 手写数字识别 MLP 纯NumPy实现
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使用方法:
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python main.py # 运行默认配置
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python main.py --compare # 运行对比实验
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依赖:
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pip install numpy requests
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"""
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import numpy as np
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import time
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from datetime import datetime
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from model_numpy import MLP
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from dataset import load_data
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from config import *
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def train_and_evaluate():
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"""
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训练并评估模型
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"""
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print("=" * 60)
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print("手写数字识别 - 纯NumPy MLP实现")
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print("=" * 60)
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# ===== 加载数据 =====
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try:
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X_train, y_train, X_test, y_test = load_data()
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except Exception as e:
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print(f"\n错误: {e}")
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print("\n请手动下载数据文件:")
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print(" 1. 创建 data/ 目录")
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print(" 2. 下载以下文件到 data/:")
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print(" - train-images-idx3-ubyte.gz (9.9 MB)")
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print(" - train-labels-idx1-ubyte.gz (28 KB)")
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print(" - t10k-images-idx3-ubyte.gz (1.6 MB)")
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print(" - t10k-labels-idx1-ubyte.gz (5 KB)")
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print(" 下载地址: https://storage.googleapis.com/tensorflow/tf-keras-datasets/")
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return None, None, None
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# ===== 创建模型 =====
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print("\n[2] 创建MLP模型...")
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model = MLP(
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input_size=INPUT_SIZE,
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hidden_size=HIDDEN_SIZE,
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num_classes=NUM_CLASSES,
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learning_rate=LEARNING_RATE,
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seed=SEED
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)
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# ===== 训练模型 =====
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print("\n[3] 开始训练...")
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start_time = time.time()
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model.fit(
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X_train, y_train,
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X_val=X_test, y_val=y_test,
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epochs=NUM_EPOCHS,
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batch_size=BATCH_SIZE,
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verbose=True
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)
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train_time = time.time() - start_time
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# ===== 最终评估 =====
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print("\n" + "=" * 60)
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print("训练完成!")
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print("=" * 60)
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train_acc = model.accuracy(X_train, y_train)
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test_acc = model.accuracy(X_test, y_test)
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print(f"\n最终结果:")
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print(f" 训练准确率: {train_acc:.4f} ({train_acc*100:.2f}%)")
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print(f" 测试准确率: {test_acc:.4f} ({test_acc*100:.2f}%)")
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print(f" 训练时间: {train_time:.2f} 秒")
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# ===== 保存模型 =====
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timestamp = datetime.now().strftime("%m%d_%H%M%S")
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model_path = f"mnist_mlp_{timestamp}"
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model.save(model_path)
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# ===== 预测示例 =====
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print("\n[4] 预测示例:")
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indices = np.random.choice(len(X_test), 5, replace=False)
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for i, idx in enumerate(indices):
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img = X_test[idx]
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true_label = np.argmax(y_test[idx])
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pred_label = model.predict(img.reshape(1, -1))[0]
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prob = model.predict_proba(img.reshape(1, -1))[0]
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status = '✓' if true_label == pred_label else '✗'
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print(f" 样本{i+1}: 真实={true_label}, 预测={pred_label}, "
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f"置信度={prob[pred_label]:.2f} {status}")
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return model, train_acc, test_acc
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def run_comparison():
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"""
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运行对比实验
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"""
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print("\n" + "=" * 60)
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print("超参数对比实验")
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print("=" * 60)
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# 加载数据
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try:
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X_train, y_train, X_test, y_test = load_data()
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except Exception as e:
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print(f"加载数据失败: {e}")
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return
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# 实验配置
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experiments = [
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{"hidden_size": 32, "lr": 0.1, "name": "小模型(32神经元)"},
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{"hidden_size": 128, "lr": 0.1, "name": "标准(128神经元)"},
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{"hidden_size": 256, "lr": 0.1, "name": "大模型(256神经元)"},
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{"hidden_size": 128, "lr": 0.01, "name": "小学习率(0.01)"},
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{"hidden_size": 128, "lr": 0.5, "name": "大学习率(0.5)"},
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]
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results = []
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for exp in experiments:
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print(f"\n实验: {exp['name']}")
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print("-" * 40)
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model = MLP(
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input_size=INPUT_SIZE,
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hidden_size=exp['hidden_size'],
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num_classes=NUM_CLASSES,
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learning_rate=exp['lr'],
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seed=SEED
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)
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start_time = time.time()
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model.fit(X_train, y_train, epochs=30, batch_size=BATCH_SIZE, verbose=False)
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train_time = time.time() - start_time
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train_acc = model.accuracy(X_train, y_train)
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test_acc = model.accuracy(X_test, y_test)
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results.append({
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'name': exp['name'],
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'hidden_size': exp['hidden_size'],
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'lr': exp['lr'],
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'train_acc': train_acc,
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'test_acc': test_acc,
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'train_time': train_time
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})
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print(f" 训练准确率: {train_acc:.4f} | 测试准确率: {test_acc:.4f} | 时间: {train_time:.1f}s")
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# 汇总
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print("\n" + "=" * 60)
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print("实验结果汇总")
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print("=" * 60)
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print(f"\n{'配置':<25} {'训练准确率':<12} {'测试准确率':<12} {'时间':<8}")
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print("-" * 60)
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for r in results:
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print(f"{r['name']:<25} {r['train_acc']:<12.4f} {r['test_acc']:<12.4f} {r['train_time']:<8.1f}s")
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best = max(results, key=lambda x: x['test_acc'])
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print(f"\n最佳配置: {best['name']}, 测试准确率: {best['test_acc']:.4f}")
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def main():
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"""主函数"""
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if RUN_COMPARISON:
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run_comparison()
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else:
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train_and_evaluate()
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print("\n" + "=" * 60)
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print("程序结束!")
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print("=" * 60)
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if __name__ == '__main__':
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import sys
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if '--compare' in sys.argv:
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RUN_COMPARISON = True
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main()
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