keras 7 CNN preprocessing

create test, train, valid folder under data

copy 500 cats images into cats folder, 500 into dogs images into dog folder

copy 100 cats and dogs images into valid, and 50 for each into test

data source https://www.kaggle.com/tongpython/cat-and-dog

#cat_dog.py

import  tensorflow as tf

from tensorflow import keras

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Activation, Dense, Flatten

from tensorflow.keras.optimizers import Adam

from tensorflow.keras.metrics import categorical_crossentropy

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from sklearn.metrics import confusion_matrix

import itertools

import shutil

import random

import os

import glob

import matplotlib.pyplot as plt

#train on GPU

pysical_devices = tf.config.experimental.list_physical_devices('GPU')

#print("Num GPUs Available: ", len(pysical_devices))

tf.config.experimental.set_memory_growth(pysical_devices[0], True)

"""

print(os.path.exists('C://Users//bob//keras//data'))

for c in random.sample(glob.glob('C://Users//bob//keras//data//cats//cat*'), 500):

    shutil.move(c, 'C://Users//bob//keras//data//cats//train')

"""

train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\

    .flow_from_directory(directory='C://Users//bob//keras//data//train', target_size=(224, 224),

                         classes=['cat', 'dog'], batch_size=32)

valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\

    .flow_from_directory(directory='C://Users//bob//keras//data//valid', target_size=(224, 224),

                         classes=['cat', 'dog'], batch_size=32)

test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\

    .flow_from_directory(directory='C://Users//bob//keras//data//test', target_size=(224, 224),

                         classes=['cat', 'dog'], batch_size=32, shuffle=False)

imgs, labels = next(train_batches)

def plotImages(image_arr):

    fig, axes = plt.subplots(1, 10, figsize=(20, 20))

    axes = axes.flatten()

    for img, ax in zip(image_arr, axes):

        ax.imshow(img)

        ax.axis('off')

    plt.tight_layout()

    plt.show()

plotImages(imgs)

print(labels)

image after vgg16 preprocess

#logs

Found 1000 images belonging to 2 classes.

Found 200 images belonging to 2 classes.

Found 100 images belonging to 2 classes.

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

...

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

[[1. 0.]

 [1. 0.]

 [0. 1.]

...

 [0. 1.]

 [1. 0.]]

reference:

https://www.youtube.com/watch?v=qFJeN9V1ZsI&feature=youtu.be