Created
July 24, 2023 23:48
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minimal working example
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import tensorflow as tf | |
from tensorflow.keras import datasets, layers, models | |
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() | |
# Normalize pixel values to be between 0 and 1 | |
train_images, test_images = train_images / 255.0, test_images / 255.0 | |
model = models.Sequential() | |
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
model.add(layers.MaxPooling2D((2, 2))) | |
model.add(layers.Conv2D(64, (3, 3), activation='relu')) | |
model.add(layers.Flatten()) | |
model.add(layers.Dense(64, activation='relu')) | |
model.add(layers.Dense(10)) | |
model.compile(optimizer='adam', | |
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), | |
metrics=['accuracy']) | |
history = model.fit(train_images, train_labels, epochs=10, | |
validation_data=(test_images, test_labels)) | |
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2) | |
print(test_acc) |
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