inception V3用迁移学习实现代码

以下是使用Inception V3进行迁移学习实现的Python代码示例。假设你已经安装了TensorFlow和Keras，并且已经准备好了用于训练的数据集。

import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

# 设置数据集路径
train_data_dir = 'path/to/training/dataset'
validation_data_dir = 'path/to/validation/dataset'

# 设置训练参数
batch_size = 32
epochs = 10
num_classes = 2

# 创建Inception V3模型
base_model = InceptionV3(weights='imagenet', include_top=False)

# 添加全局平均池化层
x = base_model.output
x = GlobalAveragePooling2D()(x)

# 添加全连接层和softmax激活层
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

# 构建完整的模型
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结Inception V3模型的所有层
for layer in base_model.layers:
    layer.trainable = False

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

# 数据增强生成器
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(299, 299),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(299, 299),
    batch_size=batch_size,
    class_mode='categorical')

# 训练模型
model.fit_generator(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // batch_size)

# 保存模型
model.save('inceptionv3_model.h5')

以上代码中，我们使用了Keras中的ImageDataGenerator类来进行数据增强，以增加数据集的多样性。我们还通过调用fit_generator()方法来训练模型，并使用了save()方法将训练好的模型保存到本地。