keras resnet

以下是使用Keras训练自己的数据的ResNet模型的示例代码：

from keras.applications.resnet50 import ResNet50
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator

# 创建ResNet模型
base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

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

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

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

# 冻结ResNet的权重
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
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode='categorical')

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

# 保存模型
model.save('resnet_model.h5')

请注意，上述代码中的一些变量（如`train_data_dir`，`validation_data_dir`，`num_classes`等）需要根据您的数据集进行相应的设置。