详细介绍：python第31天打卡

import numpy as npfrom tensorflow import kerasfrom tensorflow.keras import layers, optimizers, utils, datasets # 数据加载和预处理函数def load_and_preprocess_data():    (x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()    # 重塑并归一化图像数据    x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255.0    x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255.0    # 转换标签为one-hot编码    y_train = utils.to_categorical(y_train, 10)    y_test = utils.to_categorical(y_test, 10)    return (x_train, y_train), (x_test, y_test) # 模型定义def create_simple_cnn():    return keras.Sequential([        layers.Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1)),        layers.MaxPooling2D((2, 2)),        layers.Flatten(),        layers.Dense(128, activation='relu'),        layers.Dense(10, activation='softmax')    ]) def create_complex_cnn():    return keras.Sequential([        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),        layers.MaxPooling2D((2, 2)),        layers.Conv2D(64, (3, 3), activation='relu'),        layers.MaxPooling2D((2, 2)),        layers.Flatten(),        layers.Dense(256, activation='relu'),        layers.Dense(128, activation='relu'),        layers.Dense(10, activation='softmax')    ]) # 训练和评估函数def train_and_evaluate(model, optimizer, x_train, y_train, x_test, y_test):    model.compile(        optimizer=optimizer,        loss='categorical_crossentropy',        metrics=['accuracy']    )    history = model.fit(        x_train, y_train,        epochs=5,        batch_size=64,        validation_data=(x_test, y_test)    )    return history.history # 主程序if __name__ == "__main__":    # 加载数据    (x_train, y_train), (x_test, y_test) = load_and_preprocess_data()        # 模型和优化器配置    model_configs = [        ('Simple CNN', create_simple_cnn),        ('Complex CNN', create_complex_cnn)    ]    optimizers_config = {        'SGD': optimizers.SGD(learning_rate=0.01),        'Adam': optimizers.Adam(learning_rate=0.001)    }        # 训练和评估所有组合    results = {}    for model_name, model_fn in model_configs:        for opt_name, optimizer in optimizers_config.items():            print(f"\n{'='*50}")            print(f"Training {model_name} with {opt_name} optimizer:")                        model = model_fn()            history = train_and_evaluate(                model, optimizer,                x_train, y_train,                x_test, y_test            )                        # 记录结果            results[f"{model_name}_{opt_name}"] = history            print(f"\nTraining results for {model_name}/{opt_name}:")            print(f"Final Training Accuracy: {history['accuracy'][-1]:.4f}")            print(f"Final Validation Accuracy: {history['val_accuracy'][-1]:.4f}")            print(f"Final Training Loss: {history['loss'][-1]:.4f}")            print(f"Final Validation Loss: {history['val_loss'][-1]:.4f}")

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