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38 lines
1.2 KiB
Python
38 lines
1.2 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import tensorflow as tf
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from tensorflow.keras import datasets, layers, models
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(train_images, train_labels), (test_images,
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test_labels) = datasets.mnist.load_data()
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train_images = train_images.reshape((60000, 28, 28, 1))
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test_images = test_images.reshape((10000, 28, 28, 1))
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# Normalize pixel values to be between 0 and 1
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train_images, test_images = train_images / 255.0, test_images / 255.0
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model = models.Sequential()
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model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.summary()
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model.add(layers.Flatten())
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model.add(layers.Dense(64, activation='relu'))
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model.add(layers.Dense(10, activation='softmax'))
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model.summary()
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.fit(train_images, train_labels, epochs=5)
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test_loss, test_acc = model.evaluate(test_images, test_labels)
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