import tensorflow as tf
from time import perf_counter

def config():
    num_threads = 16
    tf.config.threading.set_inter_op_parallelism_threads(
        num_threads
    )
    tf.config.threading.set_intra_op_parallelism_threads(
        num_threads
    )

def run_benchmark():
    mnist = tf.keras.datasets.mnist

    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

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

    predictions = model(x_train[:1]).numpy()
    print("predictions", predictions)

    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    model.compile(optimizer='adam',
                  loss=loss_fn,
                  metrics=['accuracy'])
    t0 = perf_counter()
    model.fit(x_train, y_train, epochs=5)
    model.evaluate(x_test,  y_test, verbose=2)
    dt = perf_counter() - t0
    print(f"Total time: {dt}")

run_benchmark()