基于tensorflow搭建模型对自定义数据集进行图像分类并通过mmd损失做无监督迁移学习的领域对齐完整代码

以下是基于TensorFlow搭建模型对自定义数据集进行图像分类并通过MMD损失做无监督迁移学习的领域对齐的完整代码：

import os
import numpy as np
import tensorflow as tf
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# 加载数据集
def load_dataset(dataset_path):
    images = []
    labels = []
    for dirname in os.listdir(dataset_path):
        label = int(dirname)
        for filename in os.listdir(os.path.join(dataset_path, dirname)):
            image_path = os.path.join(dataset_path, dirname, filename)
            image = tf.io.read_file(image_path)
            image = tf.image.decode_jpeg(image, channels=3)
            image = tf.image.resize(image, (256, 256))
            image = tf.cast(image, tf.float32) / 255.0
            images.append(image)
            labels.append(label)
    images = tf.stack(images, axis=0)
    labels = tf.constant(labels)
    return images, labels

# 定义卷积神经网络模型
class CNNModel(tf.keras.Model):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(filters=32, kernel_size=(5, 5), activation='relu')
        self.pool1 = tf.keras.layers.MaxPool2D(pool_size=(2, 2))
        self.conv2 = tf.keras.layers.Conv2D(filters=64, kernel_size=(5, 5), activation='relu')
        self.pool2 = tf.keras.layers.MaxPool2D(pool_size=(2, 2))
        self.flatten = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(units=1024, activation='relu')
        self.dropout = tf.keras.layers.Dropout(rate=0.5)
        self.dense2 = tf.keras.layers.Dense(units=10)
        
    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dropout(x)
        x = self.dense2(x)
        return x

# 定义MMD损失函数
def mmd_loss(source_features, target_features):
    source_mean = tf.reduce_mean(source_features, axis=0)
    target_mean = tf.reduce_mean(target_features, axis=0)
    source_cov = tfp.stats.covariance(source_features, sample_axis=0, event_axis=-1)
    target_cov = tfp.stats.covariance(target_features, sample_axis=0, event_axis=-1)
    mmd = tf.reduce_sum(tf.square(source_mean - target_mean)) + tf.trace(source_cov + target_cov - 2 * tf.linalg.sqrtm(tf.matmul(source_cov, target_cov)))
    return mmd

# 加载源域和目标域数据集
source_images, source_labels = load_dataset('source_dataset')
target_images, _ = load_dataset('target_dataset')

# 数据标准化
scaler = StandardScaler()
source_images = scaler.fit_transform(np.reshape(source_images, (source_images.shape[0], -1)))
target_images = scaler.transform(np.reshape(target_images, (target_images.shape[0], -1)))

# PCA降维
pca = PCA(n_components=256)
source_images = pca.fit_transform(source_images)
target_images = pca.transform(target_images)

# 构建模型
model = CNNModel()

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
cross_entropy_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

# 训练模型
for epoch in range(10):
    source_indices = tf.range(start=0, limit=source_images.shape[0], dtype=tf.int32)
    target_indices = tf.range(start=0, limit=target_images.shape[0], dtype=tf.int32)
    np.random.shuffle(source_indices)
    np.random.shuffle(target_indices)
    for i in range(0, source_images.shape[0], 32):
        source_batch_indices = source_indices[i:i+32]
        target_batch_indices = target_indices[i:i+32]
        source_batch_images = source_images[source_batch_indices]
        source_batch_labels = source_labels[source_batch_indices]
        target_batch_images = target_images[target_batch_indices]
        with tf.GradientTape() as tape:
            source_batch_logits = model(source_batch_images)
            source_batch_loss = cross_entropy_loss(source_batch_labels, source_batch_logits)
            target_batch_features = model.layers[-3](target_batch_images)
            source_batch_features = model.layers[-3](source_batch_images)
            mmd = mmd_loss(source_batch_features, target_batch_features)
            loss = source_batch_loss + mmd
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    print('Epoch: {}, Source Loss: {}, MMD Loss: {}'.format(epoch+1, source_batch_loss, mmd))

# 保存模型
model.save('model.h5')