我已经使用 Keras 训练了我的 CNN 模型(二元分类),现在我想使用 SVM 分类器而不是使用全连接层进行分类。
我使用 VGG16 预训练网络进行特征提取,还使用了数据增强。
添加 SVM 作为最后一层进行分类的可能方法是什么?
#Parametres
import keras
from keras.applications import VGG16
import sys
from PIL import Image
#Using VGG16 Pre-trained Model
conv_base = VGG16(weights = 'imagenet',
include_top = False,
input_shape=(224, 224, 3))
conv_base.summary()
import numpy as np
import os
from keras.preprocessing.image import ImageDataGenerator
base_dir = 'C:Covid Detection/Code/Dataset-created')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
from keras import models
from keras import layers
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
conv_base.trainable = False
from keras import optimizers
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(224, 224),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(224, 224),
batch_size=20,
class_mode='binary')
# Compile the model
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
# Train the model
history = model.fit_generator(
train_generator,
steps_per_epoch=50,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
# Save the model
model.save('vgg16_aug.h5')