当然，这里有一个复杂的Python代码示例，它实现了一个简单的神经网络，用于手写数字识别（基于MNIST数据集）： ```python import numpy as np from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical # 加载数据 (x_train, y_train), (x_test, y_test) = mnist.load_data() # 预处理数据 x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32') / 255 x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32') / 255 y_train = to_categorical(y_train, 10) y_test = to_categorical(y_test, 10) # 创建模型 model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1, validation_data=(x_test, y_test)) # 评估模型 score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) ``` 这段代码首先加载并预处理MNIST数据集，然后构建一个卷积神经网络模型，包括卷积层、池化层和全连接层。接着，它编译模型，并在训练数据上进行训练。最后，它在测试数据上评估模型的性能。