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以下是关于**微调 (fine-tuning)和迁移学习 (Transfer learning)**的区别,涉及到机器学习和深度学习的上下文:
from tensorflow.keras.applications import VGG16 from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.models import Model # 加载预训练的VGG16模型 base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # 冻结预训练层的权重 for layer in base_model.layers: layer.trainable = False # 添加新的分类层 x = Flatten()(base_model.output) x = Dense(256, activation='relu')(x) output = Dense(num_classes, activation='softmax')(x) # 创建新模型 model = Model(inputs=base_model.input, outputs=output) # 编译并在新数据集上训练模型 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=10, validation_data=(val_images, val_labels))
# 解冻一些层以进行微调
for layer in base_model.layers[-5:]:
layer.trainable = True
# 编译并继续训练
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, validation_data=(val_images, val_labels))
总之,迁移学习利用现有知识,而微调通过更新模型的层进一步适应任务。这两种技术在深度学习中都是强大的工具!
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