Fashion_mnist 数据集图像识别

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发布于 2020-01-17 08:17:47

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代码可运行

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代码可运行

fashion_mnist 和 mnist 一样，都是深度学习入门用的简单数据集，两者的图片尺寸一样，都是28x28。fashion_mnist的训练集有6万张图片，测试集有1万张图片，全是衣服、鞋、包包之类的图片，共10个类别：

Label Class：
0    T-shirt/top
1    Trouser
2    Pullover
3    Dress
4    Coat
5    Sandal
6    Shirt
7    Sneaker
8    Bag
9    Ankle boot

下图显示的是训练集中的前25张图片：

下面的代码用于训练CNN:

# -*- coding: utf-8 -*-
"""
Created on Sat Dec 21 18:12:16 2019
@author: Administrator
"""
import tensorflow as tf
print(tf.__version__)
from tensorflow import keras
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()
train_images, test_images = train_images/255.0, test_images/255.0
'''
Label
Class
0    T-shirt/top
1    Trouser
2    Pullover
3    Dress
4    Coat
5    Sandal
6    Shirt
7    Sneaker
8    Bag
9    Ankle boot
'''
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
print(train_images.shape)
print(len(train_labels))

'''
plt.figure(figsize=(10,10))
for i in range(50):
    plt.subplot(5,10,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()
'''
#加入新的维度，Conv2D需要颜色chanels维度 #彩色图片数据集就不需要
train_images = train_images[..., tf.newaxis]
test_images = test_images[..., tf.newaxis]
model = keras.Sequential()
# an `input_shape` passed to the first layer
model.add(keras.layers.Conv2D(input_shape=(28,28,1),
                              filters=32, kernel_size=(3,3),activation='relu'))
model.add(keras.layers.Conv2D(filters=64, kernel_size=(3,3),activation='relu'))
model.add(keras.layers.MaxPool2D(pool_size=(3,3),strides=1,padding='same'))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(250, activation='relu'))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Dense(10, activation='softmax'))
model.compile(optimizer ='adam', loss ='sparse_categorical_crossentropy',
             metrics=['accuracy'])
model.fit(train_images, train_labels, epochs =10, batch_size =200)
tess_loss, test_acc = model.evaluate(test_images,test_labels, verbose=2)
print('\nTest accuracy: ', test_acc)
model.save('my fashion_mnist mode.h5')

10个Epoch后，测试集上的准确度已达93.21%：

下面的代码用于预测一组图片（测试集）：

# -*- coding: utf-8 -*-
"""
Created on Sat Dec 21 18:12:16 2019
@author: Administrator
"""
import tensorflow as tf
print(tf.__version__)
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
fashion_mnist = keras.datasets.fashion_mnist
test_images,test_labels = fashion_mnist.load_data()[1]
test_images = test_images/255.0
test_images = test_images[..., tf.newaxis]
'''
Label
Class
0    T-shirt/top
1    Trouser
2    Pullover
3    Dress
4    Coat
5    Sandal
6    Shirt
7    Sneaker
8    Bag
9    Ankle boot
'''
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
model = keras.models.load_model('my fashion_mnist mode.h5')
#预测一组图片，
predictions = model.predict(test_images)
plt.figure(figsize=(10,10))
for i in range(25):
    index = tf.argmax(predictions[i])
    index = int(index)
    print(index)
    name = class_names[index]
    print(name)
    
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    #plt.imshow(test_images[i], cmap=plt.cm.binary)
    plt.imshow(np.reshape(test_images[i],(28,28)),cmap=plt.cm.binary)
    plt.xlabel("True : %s"%class_names[test_labels[i]])
    plt.title("prediction: %s"%name)
    plt.tight_layout()
plt.show()