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使用tensorflow实现Fashion Mnist商品分类

2021/8/20 16:55:42 浏览:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# 导入Fashin mnist 数据集(大小28*28)

fashion_mnist = tf.keras.datasets.fashion_mnist

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

# 对应图像打上相应的标签
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
# 打印数据信息
# print(train_images.shape)
# print(len(train_labels))
# print(train_labels)
# print(test_images.shape)
# print(len(test_labels))
# print(test_labels)

# 显示图像示例
# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid()
# plt.show()

# 预处理数据,是数据在0~1范围之间

train_images = train_images / 255.0
test_images = test_images / 255.0

# 验证是否准备好数据
# 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()

#  构建模型

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),  # 将图像格式从 28*28 转换成一维数组 ==>格式化数据
    tf.keras.layers.Dense(128, activation='relu'),  # 128节点,处理数据
    tf.keras.layers.Dense(10)  # 返回长度为10的logits数组,每个节点都表示一个分数,表明当前图像10个类别之一
])

# 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=10)

# 评估准确性
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

# # 可视化预测
# 添加一个softmax,将logits转换为更容易解释的概率
probability_model = tf.keras.Sequential([model,
                                         tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

print(predictions[12])
print(test_labels[np.argmax(predictions[0])])


# 绘图查看完整的10个类别预测集
def plot_image(i, predictions_array, true_label, img, class_names=class_names):
    true_label, img = true_label[i], img[i]
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(img, cmap=plt.cm.binary)

    predicted_label = test_labels[np.argmax(predictions_array)]
    if predicted_label.all() == true_label.all():
        color = 'blue'
    else:
        color = 'red'
    class_names = np.array(class_names)
    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):
    true_label = 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])
    predictions_label = np.argmax(predictions_array)

    thisplot[predictions_label].set_color('red')
    thisplot[true_label].set_color('blue')


# 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()

i = 12
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()


num_rows = 5
num_cols = 3
num_images = num_cols*num_rows
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()

# 记录自己学习tensorflow过程,大佬们不喜勿喷

# 每天学习一点点,路在脚下! 加油!

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