图像分类入门教程之衣物分类

1.加载相关包：

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

2.加载数据集并把图像和标签赋值给训练集和测试集：

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

3. 制作分类标签：

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

4.查看某一幅图像：

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

5.归一化处理：

train_images = train_images / 255.0

test_images = test_images / 255.0

6.可以查看一下原图片:

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()

7.搭建模型：

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

8.定义优化器和损失函数：

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

9.开始训练，这里训练次数为10轮：

model.fit(train_images, train_labels, epochs=10)

 10.训练完毕查看损失值和准确率：

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

11. softmax进行分类：

probability_model = tf.keras.Sequential([model, 
                                         tf.keras.layers.Softmax()])

12.用测试集进行预测并查看第一个预测结果：

predictions = probability_model.predict(test_images)
predictions[0]