tutorial13-data-augmentation.py

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
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):
    """Normalizes 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:
        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)

    # a left upside down flipped is still a dog ;)
    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)
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)

# 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,
        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)