how implement cross validation with flower dataset?

Question

I hope you are all doing well ^_^

how implement cross validation with flower dataset? the dataset contains 3670 images in 5 folders ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips'] i want to use vgg16 or any model and Cross-Validation to improve my accuracy, right now it's between 84-86%.

.I'm a noob in this field, i have tested transfer learning models with the flower dataset, i read some article about cross validation and i want to test it with this dataset. but the dataset does not contain csv file.

link to the dataset : https://www.kaggle.com/rajmehra03/flower-recognition-cnn-keras/data

----------------------------------------code------------------------------------------------------

import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Conv2D,MaxPool2D,Dropout,Flatten

import pathlib
dataset_url = 
"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

batch_size = 32
img_height = 224
img_width = 224

train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,


subset="training",



seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)



for image_batch, labels_batch in train_ds:
      print(image_batch.shape)
      print(labels_batch.shape)
      break

AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

normalization_layer = layers.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixel values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image))

num_classes = len(class_names)

vgg16_model = keras.applications.vgg16.VGG16()
model = Sequential()
for layer in vgg16_model.layers:
    model.add(layer)

print(len(model.layers))
model.summary()

#model.pop()
print(len(model.layers)) 

for layer in model.layers:
    layer.trainable = False
model.add(Dense(1000, activation = 'softmax'))    
model.add(Dense(5, activation = 'softmax'))

print(len(model.layers)) 
model.summary()

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

epochs=10
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()