深度学习 Day 16——利用卷神经网络实现咖啡豆的识别

深度学习 Day 16——利用卷神经网络实现咖啡豆的识别

一、前言

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍦 参考文章：365天深度学习训练营-第7周：咖啡豆识别（训练营内部成员可读）
• 🍖 原作者：K同学啊|接辅导、项目定制

因为种种原因，我已经差不多两个星期没有更新有关深度学习方面的博客了，因为我拿不到我这个性能好的电脑，手上只有一个轻薄本没有深度学习环境也带不动这些程序。

不过现在都解决了，可以接着开始学习了，本期我们承接上文，我们仍然学习的是RNN，本期我们将自己搭建VGG-16网络框架进行操作。

浪费了两个星期的时间，我们也不再废话了直接开始工作。

二、我的环境

• 电脑系统：Windows 11
• 语言环境：Python 3.8.5
• 编译器：DataSpell 2022.2
• 深度学习环境：TensorFlow 2.3.4
• 显卡及显存：RTX 3070 8G

三、前期工作

1、导入依赖项并设置GPU

导入依赖项：

from tensorflow import keras
from tensorflow.keras import layers,models
import os, PIL, pathlib
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
1
2
3
4
5
6

和之前一样，如果你GPU很好就只使用GPU进行训练，如果GPU不行就推荐使用CPU训练加GPU加速。

只使用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")
1
2
3
4

使用CPU+GPU：

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
1

2、导入数据集

data_dir = "E:\Deep_Learning\data\Day16"
data_dir = pathlib.Path(data_dir)
1
2

3、查看数据集

查看数据集内有多少张图片：

image_count = len(list(data_dir.glob('*/*.png')))

print("图片总数为：",image_count)
1
2
3

运行的结果是：

图片总数为： 1200
1

从数据集内返回一张图片查看一下：

roses = list(data_dir.glob('Dark/*.png'))
PIL.Image.open(str(roses[0]))
1
2

四、数据预处理

1、加载数据

我们使用image_dataset_from_directory方法将我们本地的数据加载到tf.data.Dataset

中，并设置训练图片模型参数：

batch_size = 32
img_height = 224
img_width = 224
1
2
3

接下来加载数据：

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)


val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

然后我们再利用class_name输出我们本地数据集的标签，标签也就是对应数据所在的文件目录名：

class_names = train_ds.class_names
print(class_names)
1
2

['Dark', 'Green', 'Light', 'Medium']
1

2、检查数据并可视化数据

在可视化数据前，我们来检查一下我们的数据信息是否是正确的：

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break
1
2
3
4

(32, 224, 224, 3)
(32,)
1
2

这是一批形状224x224x3的32张图片，我们将数据进行可视化看看：

plt.figure(figsize=(10, 4))  # 图形的宽为10高为5

for images, labels in train_ds.take(1):
    for i in range(10):
        
        ax = plt.subplot(2, 5, i + 1)  

        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        
        plt.axis("off")
1
2
3
4
5
6
7
8
9
10
11

3、配置数据集并进行归一化处理

AUTOTUNE = tf.data.experimental.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
1
2
3
4

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)

train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds   = val_ds.map(lambda x, y: (normalization_layer(x), y))
1
2
3
4

image_batch, labels_batch = next(iter(val_ds))
first_image = image_batch[0]

# 查看归一化后的数据
print(np.min(first_image), np.max(first_image)) 
1
2
3
4
5

打印结果是：

0.0 1.0
1

上面layers.experimental.preprocessing.Rescaling函数原型是：

tf.keras.layers.experimental.preprocessing.Rescaling(
    scale, offset=0.0, name=None, **kwargs
)
1
2
3

其中参数：

这里设置scale=1./255的作用是要将[0, 255]范围内的输入重新调整为范围[0, 1]内，也就是缩放层将像素标准化为[0,1]，在后面打印的结果也看出来我们成功将像素标准化为[0,1]。

如果我们要将[0, 255]范围内的输入重新调整为范围[-1, 1]内，我们需要使通过设置scale=1./127.5, offset=-1来实现。

五、构建VGG-16网络

官方模型我在上一期博客中就用到过，训练非常的缓慢，不知道你们是不是和我一样的情况，在这里我们今天主要来试试我们自己搭建VGG-16网络。

from tensorflow.keras import layers, models, Input
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

def VGG16(nb_classes, input_shape):
    input_tensor = Input(shape=input_shape)
    # 1st block
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv1')(input_tensor)
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='block1_conv2')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block1_pool')(x)
    # 2nd block
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv1')(x)
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='block2_conv2')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block2_pool')(x)
    # 3rd block
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv1')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv2')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='block3_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block3_pool')(x)
    # 4th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv1')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv2')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block4_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block4_pool')(x)
    # 5th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv1')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv2')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='block5_conv3')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'block5_pool')(x)
    # full connection
    x = Flatten()(x)
    x = Dense(4096, activation='relu',  name='fc1')(x)
    x = Dense(4096, activation='relu', name='fc2')(x)
    output_tensor = Dense(nb_classes, activation='softmax', name='predictions')(x)

    model = Model(input_tensor, output_tensor)
    return model

model=VGG16(len(class_names), (img_width, img_height, 3))
model.summary()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

打印的结果是：

Model: "functional_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 4)                 16388     
=================================================================
Total params: 134,276,932
Trainable params: 134,276,932
Non-trainable params: 0
_________________________________________________________________
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54

六、设置动态学习率、损失函数、优化器，指标为准确率

# 设置初始学习率
initial_learning_rate = 1e-4

lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
        initial_learning_rate, 
        decay_steps=30,      # 敲黑板！！！这里是指 steps，不是指epochs
        decay_rate=0.92,     # lr经过一次衰减就会变成 decay_rate*lr
        staircase=True)

# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)

model.compile(optimizer=opt,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

七、训练模型

epochs = 20

history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)
1
2
3
4
5
6
7

Epoch 1/20
30/30 [==============================] - 13s 187ms/step - loss: 1.3438 - accuracy: 0.3208 - val_loss: 0.9648 - val_accuracy: 0.6750
Epoch 2/20
 ......
30/30 [==============================] - 4s 137ms/step - loss: 0.0537 - accuracy: 0.9781 - val_loss: 0.1639 - val_accuracy: 0.9667
Epoch 19/20
30/30 [==============================] - 4s 138ms/step - loss: 0.0580 - accuracy: 0.9781 - val_loss: 0.1093 - val_accuracy: 0.9625
Epoch 20/20
30/30 [==============================] - 4s 136ms/step - loss: 0.0765 - accuracy: 0.9740 - val_loss: 0.1346 - val_accuracy: 0.9667
1
2
3
4
5
6
7
8
9

八、可视化结果

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

九、最后我想说

本次项目的结果我并没有运行出来，上面的结果是截取K老师的博客，供大家参考一下。

我也借这次博客来记录一下这个目前无法解决的问题，我自己的结果如下：

这个图跟K老师的完全不一样，代码跟K老师的一样，然后我还在本地跑了一遍K老师项目源码，源码都跑不动，跟K老师讨论了一会，K老师说他之前也遇见过类似的情况，那是3080显卡刚出来的时候，配置的环境没有错，代码也没错但是就是运行结果异常低下，他当时重装了系统解决了问题，我也不知道我的是不是重装系统也能结果，我觉得也有可是跟显卡驱动更新有关，总之这个层面的问题很麻烦，最近快要考试了加上事挺多的，就暂时不去折腾了，如果你们也遇见过类似的问题，可以说说你们的解决办法，谢谢！