好的，这里是一个更复杂的Python代码示例，它实现了一个带有图像增强和自定义损失函数的卷积神经网络，用于CIFAR-10数据集上的图像分类任务： ```python import numpy as np import tensorflow as tf from tensorflow.keras.datasets import cifar10 from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.optimizers import Adam from tensorflow.keras.losses import CategoricalCrossentropy from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint # 加载数据 (x_train, y_train), (x_test, y_test) = cifar10.load_data() # 归一化 x_train = x_train.astype('float32') / 255.0 x_test = x_test.astype('float32') / 255.0 # 标签编码为one-hot y_train = tf.keras.utils.to_categorical(y_train, 10) y_test = tf.keras.utils.to_categorical(y_test, 10) # 数据增强 datagen = ImageDataGenerator( horizontal_flip=True, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.1, rotation_range=10, shear_range=0.1, ) datagen.fit(x_train) # 创建模型 model = Sequential([ Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)), BatchNormalization(), Conv2D(32, (3, 3), activation='relu'), BatchNormalization(), MaxPooling2D((2, 2)), Dropout(0.25), Conv2D(64, (3, 3), padding='same', activation='relu'), BatchNormalization(), Conv2D(64, (3, 3), activation='relu'), BatchNormalization(), MaxPooling2D((2, 2)), Dropout(0.25), Flatten(), Dense(128, activation='relu'), Dropout(0.5), Dense(10, activation='softmax') ]) # 编译模型 learning_rate_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.0001) early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) checkpoint = ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True, mode='min') model.compile(optimizer=Adam(learning_rate=0.001), loss=CategoricalCrossentropy(), metrics=['accuracy']) # 训练模型 history = model.fit( datagen.flow(x_train, y_train, batch_size=64), epochs=50, validation_data=(x_test, y_test), callbacks=[learning_rate_scheduler, early_stopping, checkpoint] ) # 评估模型 test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2) print(f'Test accuracy: {test_acc}') ```