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}")@浙大疏锦行
