Tutorial4_CNNwithSequential_FunctionalAPI.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 # divide by 255 so that the pixel values are between 255
x_test = x_test.astype("float32") / 255

model = keras.Sequential(
    [
        keras.Input(shape=(32, 32, 3)), # we don't flatten it beginning , height 32 , width 32 , channel (RGB 3)
        layers.Conv2D(32, 3, padding='valid', activation='relu'), # 32 out channel , kernel size (3 , 3) , padding argument 'valid' , 'same' hyperparameter
        layers.MaxPooling2D(pool_size=(2,2)), #
        layers.Conv2D(64, 3, activation='relu'),
        layers.MaxPooling2D(),
        layers.Conv2D(128, 3, activation='relu'),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(10),
    ]
)

# just using Batchnormalization technique
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, 5, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = keras.activations.relu(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()

print(model.summary())

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)