How to run CNN on GPU with cuDNN?

Question

Im trying to run CNNs on GPU and I was wondering if you know what I should change to make it happen.

I have already installed cudnn in anaconda, and I see already that my GPU is being recognized successfully(in anaconda prompt and also on Jupyter Notebook)

(base) C:\Users\USER>python
Python 3.9.13 (main, Aug 25 2022, 23:51:50) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'2.10.1'
>>> len(tf.config.list_physical_devices('GPU'))>0
True

So my code on CPU for CNN to classify dogs and cats is this:

#Importing the libraries
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator

##Data Preprocessing
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

#Building the CNN
cnn = tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))


#Training the CNN
cnn.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
cnn.fit(x = training_set, validation_data = test_set, epochs = 25)

#Testing/making predictions
import numpy as np
from tensorflow.keras.preprocessing import image
test_image = image.load_img('dataset/single_predictions/cat_or_dog_2.jpg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = cnn.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
  prediction = 'dog'
else:
  prediction = 'cat'

What should I add or change in my code to execute it on GPU? Im using Jupiter Notebook!