基于tensorflow的完整最大均值差异图像分类代码

以下是使用 TensorFlow 完整最大均值差异（Max-Mean-Diff，MMD）图像分类的代码示例：

首先，导入必要的库：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import cifar10

然后，加载 CIFAR-10 数据集：

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

接着，对数据进行预处理：

x_train = x_train / 255.0
x_test = x_test / 255.0
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

定义 MMD 损失函数：

def maximum_mean_discrepancy(x, y, kernel=rbf_kernel):
    cost = tf.reduce_mean(kernel(x, x))
    cost += tf.reduce_mean(kernel(y, y))
    cost -= 2 * tf.reduce_mean(kernel(x, y))
    cost = tf.where(cost > 0, cost, 0, name='mmd_loss')
    return cost

def rbf_kernel(x, y, gamma=0.05):
    K_xx = tf.exp(-gamma * tf.square(tf.norm(x - x, ord='euclidean', axis=-1)))
    K_yy = tf.exp(-gamma * tf.square(tf.norm(y - y, ord='euclidean', axis=-1)))
    K_xy = tf.exp(-gamma * tf.square(tf.norm(x - y, ord='euclidean', axis=-1)))
    return K_xx + K_yy - 2 * K_xy

定义模型：

def create_model():
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(32,32,3)),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D((2,2)),
        tf.keras.layers.Dropout(0.2),
        
        tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D((2,2)),
        tf.keras.layers.Dropout(0.3),
        
        tf.keras.layers.Conv2D(128, (3,3), activation='relu', padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Conv2D(128, (3,3), activation='relu', padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D((2,2)),
        tf.keras.layers.Dropout(0.4),
        
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    
    return model

编译模型并训练：

model = create_model()

optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

history = model.fit(x_train, y_train, epochs=50, validation_data=(x_test, y_test))

评估模型：

test_loss, test_acc = model.evaluate(x_test, y_test)
print('Test accuracy:', test_acc)

最后，绘制训练和验证准确率和损失函数的变化图：

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.plot(history.history['loss'], label = 'loss')
plt.plot(history.history['val_loss'], label = 'val_loss')
plt.xlabel('Epoch')
plt.ylabel('Accuracy/Loss')
plt.ylim([0, 1])
plt.legend(loc='lower right')
plt.show()