Tutorial13_DataAugmentation.py

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = "2"
import tensorflow as tf
import keras
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds

(ds_train, ds_test) , ds_info = tfds.load(
    'cifar10',
    split=['train','test'],
    shuffle_files=True,
    as_supervised=True, # will return tuple (img, label) otherwise dict
    with_info=True, # able to get info about dataset
)

def normalize_img(image, label):
    '''normalize images'''
    return tf.cast(image, tf.float32) / 255.0, label

AUTOTUNE = tf.data.experimental.AUTOTUNE
BATCH_SIZE = 32

def augment(image, label):
    new_height = new_width = 32
    image = tf.image.resize(image, (new_height, new_width))

    if tf.random.uniform((), minval=0, maxval=1) < 0.1:
        ### copy 3 times channels must checkout the 'tf.tile' function
        image = tf.tile(tf.image.rgb_to_grayscale(image), [1, 1, 3])

    image = tf.image.random_brightness(image, max_delta=0.1)
    image = tf.image.random_contrast(image, lower=0.1, upper=0.2)

    image = tf.image.random_flip_left_right(image) # 50%
    # image = tf.image.random_flip_up_down(image) # 50%

    return image, label





# Setup for train dataset
ds_train = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.map(augment, num_parallel_calls=AUTOTUNE) ## 1 way to augmentation -> adaption technique , num_parallel_calls -> non inherently for each image
ds_train = ds_train.batch(BATCH_SIZE)
ds_train = ds_train.prefetch(AUTOTUNE)

# Setup for test Dataset
ds_test = ds_train.map(normalize_img, num_parallel_calls=AUTOTUNE)
ds_test = ds_train.batch(BATCH_SIZE)
ds_test = ds_train.prefetch(AUTOTUNE)

## 2 way to augmentation
## TF >= 2.3.0
data_augmentation = keras.Sequential(
    [
        layers.experimental.preprocessing.Resizing(height=32, width=32),
        layers.experimental.preprocessing.RandomFlip(mode='horizontal'),
        layers.experimental.preprocessing.RandomContrast(factor=0.1),
    ]
)


model = keras.Sequential(
    [
        keras.Input((32, 32, 3)),
        data_augmentation, ## 2 way to data augumentation
        layers.Conv2D(4, 3, padding='same', activation='relu'),
        layers.Conv2D(8, 3, padding='same', activation='relu'),
        layers.MaxPooling2D(),
        layers.Conv2D(16, 3, padding='same', activation='relu'),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(10),
    ]
)

model.compile(
    optimizer=keras.optimizers.Adam(3e-4),
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'],
)

model.fit(ds_train, epochs=5, verbose=2)
model.evaluate(ds_test)