lenet5.py

import tensorflow as tf

def lenet5():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(filters=6, kernel_size=(5, 5), activation='relu', input_shape=(32, 32, 3)),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Conv2D(filters=16, kernel_size=(5, 5), activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(units=120, activation='relu'),
        tf.keras.layers.Dense(units=84, activation='relu'),
        tf.keras.layers.Dense(units=10, activation='softmax')
    ])
    return model

model = lenet5()
model.summary()

model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

x_train = x_train / 255.0
x_test = x_test / 255.0

y_train = tf.keras.utils.to_categorical(y_train)
y_test = tf.keras.utils.to_categorical(y_test)

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))