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faradayfedora

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}%')
