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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()
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