Machine-Learning-Collection/ML/TensorFlow/Basics/tutorial4-convnet.py

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import cifar10

physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)),
        layers.Conv2D(32, 3, padding="valid", activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(64, 3, activation="relu"),
        layers.MaxPooling2D(),
        layers.Conv2D(128, 3, activation="relu"),
        layers.Flatten(),
        layers.Dense(64, activation="relu"),
        layers.Dense(10),
    ]
)


def my_model():
    inputs = keras.Input(shape=(32, 32, 3))
    x = layers.Conv2D(32, 3)(inputs)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPooling2D()(x)
    x = layers.Conv2D(64, 3)(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.MaxPooling2D()(x)
    x = layers.Conv2D(128, 3)(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(x)
    x = layers.Flatten()(x)
    x = layers.Dense(64, activation="relu")(x)
    outputs = layers.Dense(10)(x)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model


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

model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=2)
model.evaluate(x_test, y_test, batch_size=64, verbose=2)