Using SVM classifier as the last layer for a Pre-trained Model(VGG16) - keras

Question

I have trained my model(binary classification) of CNN using Keras and now I want to use an SVM classifier instead of using a fully connected layer for classification.
I used a VGG16 pre-trained network for feature extraction and I also used Data augmentation.
What is the possible way to add SVM as the last layer for classification?
#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')

Answer 1

from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
base_model = VGG16(weights='imagenet')
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('flatten').output)
model.summary()
Create a function to extract features using VGG16
def get_features(img_path):
img = load_img(img_path, target_size=(224, 224))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
flatten = model.predict(x)
return list(flatten[0])
features, labels = [], []
# Loop into the directory of images and extract features and labels
for image_path in folder:
features.append(extract_features(image_path))
labels.append("#some label")
Till here we extracted the features and labels, Now we train these features using SVM as follows:-
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
X_train, X_test, y_train, y_test = train_test_split( features,
labels,
test_size=0.30)
clf = LinearSVC(random_state=0, tol=1e-5)
clf.fit(X_train, y_train)
predicted = clf.predict(X_test)
# get the accuracy
print(accuracy_score(y_test, predicted))