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54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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# https://tensorflow.google.cn/beta/tutorials/keras/basic_classification
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow import keras
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# Helper libraries
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import numpy as np
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import matplotlib.pyplot as plt
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# 允许显卡动态分配内存，否则windows上报错
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gpu = tf.config.experimental.list_physical_devices('GPU')[0]
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tf.config.experimental.set_memory_growth(gpu, True)
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fashion_mnist = keras.datasets.fashion_mnist
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(train_images, train_labels), (test_images,
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test_labels) = fashion_mnist.load_data()
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class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
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'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
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train_images = train_images / 255.0
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test_images = test_images / 255.0
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plt.figure(figsize=(10, 10))
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for i in range(25):
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plt.subplot(5, 5, i+1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(train_images[i], cmap=plt.cm.binary)
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plt.xlabel(class_names[train_labels[i]])
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plt.show()
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model = keras.Sequential([
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keras.layers.Flatten(input_shape=(28, 28)),
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keras.layers.Dense(128, activation='relu'),
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keras.layers.Dense(10, activation='softmax')
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])
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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=10)
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test_loss, test_acc = model.evaluate(test_images, test_labels)
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print('\nTest accuracy:', test_acc)
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