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
import pathlib as pt

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 = pt.Path(data_dir)

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

3670

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))

batch_size = 32
img_height = 180
img_width = 180
  
train_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    validation_split=0.4,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

Found 3670 files belonging to 5 classes.
Using 2202 files for training.

# create normalization layer
nrmzln_layer = layers.experimental.preprocessing.Rescaling(1./255)
  
print("The map function is used to apply \
this layer to the dataset. ")
nrmlztn_ds = train_ds.map(lambda x,
                          y: (nrmlztn_layer(x), y))
image_batch, labels_batch = next(iter(nrmlztn_ds))
  
first_image = image_batch[0]
  
# pixel values are in the range of [0,1].
print("minimum pixel value:", np.min(first_image),
      " maximum pixel value:", np.max(first_image))

The map function is used to apply this layer to the dataset. 

minimum pixel value: 0.0

maximum pixel value: 0.87026095