sklearn 使用 VGG 模型代码

你可以使用 Keras 中的 VGG 模型，然后将其转换为 sklearn 的模型。代码示例如下：

from keras.applications.vgg16 import VGG16
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Dropout

model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
for layer in model.layers:
    layer.trainable = False
x = model.output
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)

model = Model(inputs=model.input, outputs=predictions)

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = datagen.flow_from_directory(
    'data/train',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical')

validation_generator = datagen.flow_from_directory(
    'data/validation',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical')

history = model.fit_generator(
    train_generator,
    steps_per_epoch=100,
    epochs=100,
    validation_data=validation_generator,
    validation_steps=50)

其中，VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) 为使用VGG16模型的语句，你可以使用VGG19替换VGG16,来使用不同的VGG模型。

注意：上面的代码仅供参考，可能需要根据实际需求进行修改。