61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import tensorflow.compat.v2 as tf
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import tensorflow_datasets as tfds
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tf.enable_v2_behavior()
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from tensorflow.python.framework.ops import disable_eager_execution
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disable_eager_execution()
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(ds_train, ds_test), ds_info = tfds.load(
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'mnist',
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split=['train', 'test'],
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shuffle_files=True,
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as_supervised=True,
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with_info=True,
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)
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def normalize_img(image, label):
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"""Normalizes images: `uint8` -> `float32`."""
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return tf.cast(image, tf.float32) / 255., label
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batch_size = 128
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ds_train = ds_train.map(
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normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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ds_train = ds_train.cache()
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ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
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ds_train = ds_train.batch(batch_size)
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ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)
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ds_test = ds_test.map(
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normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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ds_test = ds_test.batch(batch_size)
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ds_test = ds_test.cache()
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ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)
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model = tf.keras.models.Sequential([
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tf.keras.layers.Conv2D(32, kernel_size=(3, 3),
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activation='relu'),
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tf.keras.layers.Conv2D(64, kernel_size=(3, 3),
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activation='relu'),
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tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
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# tf.keras.layers.Dropout(0.25),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(128, activation='relu'),
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# tf.keras.layers.Dropout(0.5),
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tf.keras.layers.Dense(10, activation='softmax')
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])
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model.compile(
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loss='sparse_categorical_crossentropy',
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optimizer=tf.keras.optimizers.Adam(0.001),
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metrics=['accuracy'],
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)
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model.fit(
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ds_train,
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epochs=12,
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validation_data=ds_test,
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) |