迁移学习 #

┌─────────────────────────────────────────────────────────────┐
│                    迁移学习原理                              │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  传统训练:                                                  │
│  随机初始化 ──► 从零训练 ──► 大量数据 + 长时间              │
│                                                             │
│  迁移学习:                                                  │
│  预训练模型 ──► 微调 ──► 少量数据 + 短时间                  │
│                                                             │
│  为什么有效？                                               │
│  ├── 浅层学习通用特征（边缘、纹理）                        │
│  ├── 深层学习特定特征（形状、对象）                        │
│  └── 知识可以迁移到相关任务                                │
│                                                             │
└─────────────────────────────────────────────────────────────┘

import keras

base_model = keras.applications.ResNet50(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

base_model.trainable = False

model = keras.Sequential([
    base_model,
    keras.layers.GlobalAveragePooling2D(),
    keras.layers.Dense(256, activation='relu'),
    keras.layers.Dropout(0.5),
    keras.layers.Dense(10, activation='softmax')
])

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

┌─────────────────────────────────────────────────────────────┐
│                    常用预训练模型                            │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  图像分类:                                                  │
│  ├── ResNet50/101/152                                      │
│  ├── VGG16/19                                              │
│  ├── EfficientNet B0-B7                                    │
│  ├── MobileNet V1/V2/V3                                    │
│  ├── DenseNet 121/169/201                                  │
│  └── Vision Transformer (ViT)                              │
│                                                             │
│  选择建议:                                                  │
│  ├── 移动端: MobileNet, EfficientNet-B0                   │
│  ├── 高精度: EfficientNet-B7, ResNet152                   │
│  └── 平衡: ResNet50, EfficientNet-B3                      │
│                                                             │
└─────────────────────────────────────────────────────────────┘

import keras

base_model = keras.applications.EfficientNetB0(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

base_model.trainable = False

inputs = keras.Input(shape=(224, 224, 3))
x = base_model(inputs, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
x = keras.layers.Dense(128, activation='relu')(x)
x = keras.layers.Dropout(0.3)(x)
outputs = keras.layers.Dense(10, activation='softmax')(x)

model = keras.Model(inputs, outputs)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.fit(train_dataset, epochs=10, validation_data=val_dataset)

import keras

base_model = keras.applications.ResNet50(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

base_model.trainable = True

for layer in base_model.layers[:-20]:
    layer.trainable = False

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=1e-5),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.fit(train_dataset, epochs=10, validation_data=val_dataset)

import keras

base_model = keras.applications.EfficientNetB0(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)
base_model.trainable = False

inputs = keras.Input(shape=(224, 224, 3))
x = base_model(inputs, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

print("阶段 1: 训练分类头")
model.fit(train_dataset, epochs=5, validation_data=val_dataset)

print("阶段 2: 微调")
base_model.trainable = True
for layer in base_model.layers[:-20]:
    layer.trainable = False

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=1e-5),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.fit(train_dataset, epochs=10, validation_data=val_dataset)

import keras
import tensorflow as tf

img_size = (224, 224)
batch_size = 32
num_classes = 10

train_datagen = keras.preprocessing.image.ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2
)

train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=img_size,
    batch_size=batch_size,
    class_mode='categorical',
    subset='training'
)

val_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=img_size,
    batch_size=batch_size,
    class_mode='categorical',
    subset='validation'
)

base_model = keras.applications.EfficientNetB3(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)
base_model.trainable = False

inputs = keras.Input(shape=(224, 224, 3))
x = base_model(inputs, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Dense(256, activation='relu')(x)
x = keras.layers.BatchNormalization()(x)
x = keras.layers.Dropout(0.5)(x)
outputs = keras.layers.Dense(num_classes, activation='softmax')(x)

model = keras.Model(inputs, outputs)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

print("阶段 1: 训练分类头")
history1 = model.fit(
    train_generator,
    epochs=5,
    validation_data=val_generator
)

print("阶段 2: 微调")
base_model.trainable = True
for layer in base_model.layers[:-30]:
    layer.trainable = False

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=1e-5),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

history2 = model.fit(
    train_generator,
    epochs=15,
    validation_data=val_generator,
    callbacks=[
        keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True),
        keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=3)
    ]
)