Created
May 22, 2022 01:51
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import tensorflow as tf | |
import tensorflow_datasets as tfds | |
(ds_train, ds_test), ds_info = tfds.load( | |
'mnist', | |
split=['train', 'test'], | |
shuffle_files=True, | |
as_supervised=True, | |
with_info=True, | |
) | |
def normalize_img(image, label): | |
return tf.cast(image, tf.float32) / 255., label | |
ds_train = ds_train.map( | |
normalize_img, num_parallel_calls=tf.data.AUTOTUNE | |
) | |
ds_train = ds_train.cache() | |
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples) | |
ds_train = ds_train.batch(128) | |
ds_train = ds_train.prefetch(tf.data.AUTOTUNE) | |
ds_test = ds_test.map( | |
normalize_img, num_parallel_calls=tf.data.AUTOTUNE) | |
ds_test = ds_test.batch(128) | |
ds_test = ds_test.cache() | |
ds_test = ds_test.prefetch(tf.data.AUTOTUNE) | |
model = tf.keras.models.Sequential([ | |
tf.keras.layers.Flatten(input_shape=(28, 28)), | |
tf.keras.layers.Dense(128, activation='relu'), | |
tf.keras.layers.Dense(10) | |
]) | |
model.compile( | |
optimizer=tf.keras.optimizers.Adam(0.001), | |
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), | |
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()], | |
) | |
model.fit( | |
ds_train, | |
epochs=6, | |
validation_data=ds_test, | |
) |
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