How to add epoch in sklearn LinearSVC?

Question

I have a model that i need to train multiple times using epochs, i tried adding this code clf_svm.fit(train_features, train_labels, epochs=10, batch_size=64) and it didn't work. how to add epoches in skleran linear svc? training that model several times and save the model here is the code am working with

# empty list to hold feature vectors and train labels
train_features = []
train_labels   = []
# loop over the training dataset
print ("[STATUS] Started extracting haralick textures..")

for train_name in train_names:
    cur_path = train_path + "/" + train_name
    cur_label = train_name
    i = 1
    for file in glob.glob(cur_path + "/*.jpg"):
        
        print ("Processing Image - {} in {}".format(i, cur_label))
        
        # read the training image
        image = cv2.imread(file)
        #resize = ResizeWithAspectRatio(image, width=1250, height=1000) # Resize by width OR
        # convert the image to grayscale
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # extract haralick texture from the image
        features = extract_features(gray)

        # append the feature vector and label
        train_features.append(features)
        train_labels.append(cur_label)
        
        # otherwise create the model, train the model and save the model
if os.path.exists("D:/ai training/aphids/joblib_model.sav"):
    print("Loading Trained Model")
    clf_svm = joblib.load("D:/ai training/aphids/joblib_model.sav")
else:
        # have a look at the size of our feature vector and labels
        print ("Training features: {}".format(np.array(train_features).shape))
        print ("Training labels: {}".format(np.array(train_labels).shape))

        # create the classifier
        print ("[STATUS] Creating the classifier..")
        clf_svm = LinearSVC(random_state=9, dual=False, max_iter=1000)

        # fit the training data and labels
        print ("[STATUS] Fitting data/label to model..")
        clf_svm.fit(train_features, train_labels)

        #savemodel
        joblib_file = 'D:/ai training/aphids/joblib_model.sav'
        joblib.dump(clf_svm, joblib_file)
        

Answer 1

Support vector machine model in sklearn support adding max iterations parameter which you can change to a higher value. But they don't have epochs parameters nor do they support batch sizes.

To go into more depth, support vectors use an exact convex optimization algorithm, not stochastic gradient descent (like Neural nets). They work on the entire dataset at the same time. The algorithm will run for max iterations or till minimum value is achieved and report one single accuracy number.

From the question it is not clear why you want to train multiple times using epochs. Comparing with neural networks, the equivalent here will be to just set high max iterations parameter and training once.

Answer 2

SVM does not learn iteratively unlike Neural Networks that's why they cant possess EPOCH parameters. see: https://scikit-learn.org/stable/modules/svm.html