将训练一个神经网络模型，对运动鞋和衬衫等服装图像进行分类。本指南使用 Fashion MNIST 数据集，该数据集包含 10 个类别的 70,000 个灰度图像。这些图像以低分辨率（28x28 像素）展示了单件衣物。
Fashion MNIST 旨在临时替代经典 MNIST 数据集，后者常被用作计算机视觉机器学习程序的“Hello, World”。MNIST 数据集包含手写数字（0、1、2 等）的图像，其格式与您将使用的衣物图像的格式相同。
本例使用 Fashion MNIST 来实现多样化，因为它比常规 MNIST 更具挑战性。这两个数据集都相对较小，都用于验证某个算法是否按预期工作。对于代码的测试和调试，它们都是很好的起点。
在本例中，我们使用 60,000 个图像来训练网络，使用 10,000 个图像来评估网络学习对图像分类的准确率。
%matplotlib inline # 本代码在jupterbook中显示，需要加上此行，其他编译器请去掉。
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
#图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。标签是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装类：
# 标签 类
# 0 T恤/上衣
# 1 裤子
# 2 套头衫
# 3 连衣裙
# 4 外套
# 5 凉鞋
# 6 衬衫
# 7 运动鞋
# 8 包
# 9 短靴
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
#在训练网络之前，必须对数据进行预处理。如果您检查训练集中的第一个图像，您会看到像素值处于 0 到 255 之间：
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
#将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对训练集和测试集进行预处理：
train_images = train_images / 255.0
test_images = test_images / 255.0
#为了验证数据的格式是否正确，以及您是否已准备好构建和训练网络，让我们显示训练集中的前 25 个图像，并在每个图像下方显示类名称。
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
#构建神经网络需要先配置模型的层，然后再编译模型。
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
#要开始训练，请调用 model.fit 方法，这样命名是因为该方法会将模型与训练数据进行“拟合”：
model.fit(train_images, train_labels, epochs=10)
#评估准确率
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
#在模型经过训练后，您可以使用它对一些图像进行预测。
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
#可以将其绘制成图表，看看模型对于全部 10 个类的预测。
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array, true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array, true_label[i]
plt.grid(False)
plt.xticks(range(10))
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
#我们来看看第 0 个图像、预测结果和预测数组。正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
#让我们用模型的预测绘制几张图像。请注意，即使置信度很高，模型也可能出错。
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
# MIT License
#
# Copyright (c) 2017 François Chollet
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
output1:
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<pre style="box-sizing: border-box; overflow: auto; font-family: monospace; font-size: inherit; display: block; padding: 1px 0px; margin: 0px; line-height: inherit; color: black; word-break: break-all; overflow-wrap: break-word; background-color: transparent; border: 0px; border-radius: 0px; white-space: pre-wrap; vertical-align: baseline;">Epoch 1/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.4947 - accuracy: 0.8247
Epoch 2/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.3696 - accuracy: 0.8665
Epoch 3/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.3331 - accuracy: 0.8787
Epoch 4/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.3118 - accuracy: 0.8868
Epoch 5/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2939 - accuracy: 0.8910
Epoch 6/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2805 - accuracy: 0.8954
Epoch 7/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.2668 - accuracy: 0.9017
Epoch 8/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2551 - accuracy: 0.9050
Epoch 9/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2471 - accuracy: 0.9079
Epoch 10/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2388 - accuracy: 0.9098
313/313 - 0s - loss: 0.3377 - accuracy: 0.8837
Test accuracy: 0.8837000131607056
</pre>
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