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

# Load the MNIST dataset (a collection of handwritten digits)
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Normalize the pixel values between 0 and 1
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

# Reshape the input data
x_train = x_train.reshape(-1, 28 * 28)
x_test = x_test.reshape(-1, 28 * 28)
model = keras.Sequential([
    layers.Dense(64, activation="relu", input_shape=(28 * 28,)),
    layers.Dense(10),
])
model.compile(optimizer="adam",
              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=32, epochs=5, verbose=2)
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print("Test accuracy:", test_acc)