ValueError: Could not interpret optimizer identifier: [] - python-3.x

Question

Hi i'm working on Image classification using XGBoost and VGG16 imagenet as feature extractor here's my code
I tried to implement feature extractor on image dataset from kaggle CK+48 with tensorflow 2.11.0. I'm facing this error,
ValueError: Could not interpret optimizer identifier: []
have tried alot to solve it, will appreciate the help
import numpy as np
import matplotlib.pyplot as plt
import glob
import cv2
import keras
from tensorflow.keras import Model
#from tensorflow.python.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Activatation
from keras.models import Model, Sequential
from keras.models import load_model
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
import os
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from keras.callbacks import TensorBoard
from keras.callbacks import ModelCheckpoint
#from keras.optimizers import adam
import seaborn as sns
from keras.applications.vgg16 import VGG16
# Read input images and assign labels based on folder names
print(os.listdir("C:/Users/Tanzeel ur Rehman/Desktop/CK+48"))
SIZE = 256 #Resize
#Capture training data and labels into respective lists
train_images = []
train_labels = []
for directory_path in glob.glob("C:/Users/Tanzeel ur Rehman/Desktop/CK+48/train"):
label = directory_path.split("\\")[-1]
print(label)
for img_path in glob.glob(os.path.join(directory_path, "*.jpg")):
print(img_path)
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (SIZE, SIZE))
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
train_images.append(img)
train_labels.append(label)
#Convert lists to arrays
train_images = np.array(train_images)
train_labels = np.array(train_labels)
# Capture test/validation data and labels into respective lists
test_images = []
test_labels = []
for directory_path in glob.glob("C:/Users/Tanzeel ur Rehman/Desktop/CK+48/test"):
fruit_label = directory_path.split("\\")[-1]
for img_path in glob.glob(os.path.join(directory_path, "*.jpg")):
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.resize(img, (SIZE, SIZE))
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
test_images.append(img)
test_labels.append(fruit_label)
#Convert lists to arrays
test_images = np.array(test_images)
test_labels = np.array(test_labels)
#Encode labels from text to integers.
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(test_labels)
test_labels_encoded = le.transform(test_labels)
le.fit(train_labels)
train_labels_encoded = le.transform(train_labels)
#Split data into test and train datasets (already split but assigning to meaningful convention)
x_train, y_train, x_test, y_test = train_images, train_labels_encoded, test_images, test_labels_encoded
###################################################################
# Normalize pixel values to between 0 and 1
x_train, x_test = x_train / 255.0, x_test / 255.0
#One hot encode y values for neural network.
#from keras.utils import to_categorical
#y_train_one_hot = to_categorical(y_train)
#y_test_one_hot = to_categorical(y_test)
#############################
#Load model wothout classifier/fully connected layers
VGG_model = VGG16(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))
#Make loaded layers as non-trainable. This is important as we want to work with pre-trained weights
for layer in VGG_model.layers:
layer.trainable = True
VGG_model.summary() #Trainable parameters will be 0
#Now, let us use features from convolutional network for RF
feature_extractor=VGG_model.predict(x_train)
The error is in the last line of the code, when I try to use features from convolutional network for random Forests on training dataset of images.