36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras import Sequential
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from tensorflow.keras.layers import Flatten, Dense
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from tensorflow.keras.datasets import fashion_mnist
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np.random.seed(10)
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device = "gpu" if tf.config.list_physical_devices("GPU") else "cpu"
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print(device)
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fashion_mnist = tf.keras.datasets.fashion_mnist
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(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
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train_images = train_images / 255.0
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test_images = test_images / 255.0
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train_images = train_images.reshape(-1, 28, 28, 1)
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test_images = test_images.reshape(-1, 28, 28, 1)
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neurons = 300
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model = Sequential([
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Flatten(input_shape=(28, 28)),
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Dense(neurons, activation='relu'),
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Dense(10, activation='softmax')
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])
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model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
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model.fit(train_images, train_labels, epochs=10)
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test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
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print('\naccuracy:', test_acc)
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