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

mnist = keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images / 255.0 test_images = test_images / 255.0

model = keras.Sequential([ layers.Flatten(input_shape=(28, 28)), layers.Dense(128, activation='relu'), layers.Dropout(0.2), layers.Dense(10) ])

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) model.compile(optimizer='adam', loss=loss_fn, metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) print(f'\n: {test_acc*100:.2f}%')