Tensorflow running slow on RTX 2060 - tensorflow

Question

I am following a course on deep learning and I am doing right now the CNN networks the train set is 8000 photos 4000 cats and 4000 dogs the training set is 2000/2000 the size I am using for images is 64x64 with RGB. I am using Keras with 2 conv2d/maxpool layers of 32 filters a flatten layer and two dense layers of 128 and 1 output. My problem is that this setup is performing at 15 minutes per epoch and for 25 epochs that means 6 Hours of training at least plus sometimes on some epochs is freezing for sometimes at 7999/8000 I am running this on windows 10 and anaconda with python 3.7 and TensorFlow 1.13. Is this a good performance or I can improve it? I was expecting from the new Turing architecture better performances.
# -*- coding: utf-8 -*-
# Part 1 - Building the convolutional neural network
import tensorflow as tf
from keras import backend as K
config = tf.ConfigProto(intra_op_parallelism_threads=6,
inter_op_parallelism_threads=6,
allow_soft_placement=True,
device_count = {'CPU' : 1,
'GPU' : 1}
)
session = tf.Session(config=config)
K.set_session(session)
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
# Initialising the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
weights = classifier.get_weights()
#Part 2 - Fiting the CNN to the images
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('dataset/training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('dataset/test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 25,
validation_data = test_set,
validation_steps = 2000)
classifier.save("my first model")
Thank you