深度学习100例-卷积神经网络(CNN)服装图像分类 | 第3天

本文 GitHub https://github.com/kzbkzb/Python-AI已收录

一、前期工作

我的环境:

  • 语言环境:Python3.6.5
  • 编译器:jupyter notebook
  • 深度学习环境:TensorFlow2.4.1
  • 数据和代码:【传送门】

来自专栏:【深度学习100例】

1. 设置GPU

如果使用的是CPU可以忽略这步

import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")

if gpus:
    gpu0 = gpus[0]                                        #如果有多个GPU,仅使用第0个GPU
    tf.config.experimental.set_memory_growth(gpu0, True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpu0],"GPU")

2. 导入数据

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

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

3. 归一化

# 将像素的值标准化至0到1的区间内。
train_images, test_images = train_images / 255.0, test_images / 255.0

train_images.shape,test_images.shape,train_labels.shape,test_labels.shape
((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))

加载数据集会返回四个 NumPy 数组:

  • train_images 和 train_labels 数组是训练集,模型用于学习的数据。
  • test_images 和 test_labels 数组是测试集,会被用来对模型进行测试。

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 标签
0 T恤/上衣 5 凉鞋
1 裤子 6 衬衫
2 套头衫 7 运动鞋
3 连衣裙 8
4 外套 9 短靴

4.调整图片格式

#调整数据到我们需要的格式
train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))

train_images.shape,test_images.shape,train_labels.shape,test_labels.shape
((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))

5. 可视化

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

plt.figure(figsize=(20,10))
for i in range(20):
    plt.subplot(5,10,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()
在这里插入图片描述

二、构建CNN网络

卷积神经网络(CNN)的输入是张量 (Tensor) 形式的 (image_height, image_width, color_channels),包含了图像高度、宽度及颜色信息。不需要输入batch size。color_channels 为 (R,G,B) 分别对应 RGB 的三个颜色通道(color channel)。在此示例中,我们的 CNN 输入,fashion_mnist 数据集中的图片,形状是 (28, 28, 1)即灰度图像。我们需要在声明第一层时将形状赋值给参数input_shape

model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), #卷积层1,卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层1,2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层2,卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层2,2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层3,卷积核3*3
    
    layers.Flatten(),                      #Flatten层,连接卷积层与全连接层
    layers.Dense(64, activation='relu'),   #全连接层,特征进一步提取
    layers.Dense(10)                       #输出层,输出预期结果
])

model.summary()  # 打印网络结构
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     
_________________________________________________________________
flatten (Flatten)            (None, 576)               0         
_________________________________________________________________
dense (Dense)                (None, 64)                36928     
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
image

三、编译

在准备对模型进行训练之前,还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的:

  • 损失函数(loss):用于测量模型在训练期间的准确率。您会希望最小化此函数,以便将模型“引导”到正确的方向上。
  • 优化器(optimizer):决定模型如何根据其看到的数据和自身的损失函数进行更新。
  • 指标(metrics):用于监控训练和测试步骤。以下示例使用了准确率,即被正确分类的图像的比率。
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

四、训练模型

history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))
Epoch 1/10
1875/1875 [==============================] - 9s 4ms/step - loss: 0.7005 - accuracy: 0.7426 - val_loss: 0.3692 - val_accuracy: 0.8697
Epoch 2/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.3303 - accuracy: 0.8789 - val_loss: 0.3106 - val_accuracy: 0.8855
Epoch 3/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2770 - accuracy: 0.8988 - val_loss: 0.3004 - val_accuracy: 0.8902
Epoch 4/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2398 - accuracy: 0.9097 - val_loss: 0.2898 - val_accuracy: 0.8968
Epoch 5/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.2191 - accuracy: 0.9195 - val_loss: 0.2657 - val_accuracy: 0.9057
Epoch 6/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.1952 - accuracy: 0.9292 - val_loss: 0.2731 - val_accuracy: 0.9036
Epoch 7/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.1791 - accuracy: 0.9322 - val_loss: 0.2747 - val_accuracy: 0.9056
Epoch 8/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.1576 - accuracy: 0.9416 - val_loss: 0.2750 - val_accuracy: 0.9049
Epoch 9/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.1421 - accuracy: 0.9461 - val_loss: 0.2876 - val_accuracy: 0.9032
Epoch 10/10
1875/1875 [==============================] - 6s 3ms/step - loss: 0.1330 - accuracy: 0.9509 - val_loss: 0.2769 - val_accuracy: 0.9144

五、预测

预测结果是一个包含 10 个数字的数组。它们代表模型对 10 种不同服装中每种服装的“置信度”。我们可以看到哪个标签的置信度值最大

plt.imshow(test_images[1])
import numpy as np

pre = model.predict(test_images)
print(class_names[np.argmax(pre[1])])
Pullover

六、模型评估

plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
plt.show()

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
在这里插入图片描述
313/313 - 0s - loss: 0.2769 - accuracy: 0.9144
print("测试准确率为:",test_acc)
测试准确率为: 0.9143999814987183

《深度学习100例》专栏直达:【传送门】

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