#预处理数据 import tensorflow as tf #加载数据 fashion_mnist = tf.keras.datasets.fashion_mnist (train_images,train_labels),(test_images,test_labels) = fashion_mnist.load_data() #映射数据分类 class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] #归一化数据 train_images,test_images = train_images/255.0,test_images/255.0 # 构建模型 model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(128,activation='relu'), tf.keras.layers.Dense(10,activation='softmax') ]) #编译模型 model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) #训练模型 model.fit(train_images,train_labels,epochs=5) #评估模型 test_loss,test_acc = model.evaluate(test_images,test_labels,verbose=2) #挑选单个数据测试 import numpy as np img = test_images[5] img = np.expand_dims(img,0) single = model.predict(img) single_index = np.argmax(single[0]) class_names[single_index] 输出：Trouser 裤子 #查看具体的图像，看看是否正确 import matplotlib.pyplot as plt plt.figure(figsize=(1,1)) plt.imshow(test_images[5]) plt.show()