深度学习100例 —— 卷积神经网络(CNN)识别验证码

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深度学习100例——卷积神经网络(CNN)识别验证码

• 本文为365天深度学习训练营 中的学习记录博客
• 参考文章地址： 深度学习100例-卷积神经网络（CNN）识别验证码 | 第12天

我的环境

1. 前期准备工作

1.1 设置GPU

import tensorflow as tf

gpus = tf.config.list_physical_devices("GPU")

if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpus[0]],"GPU")

1.2 导入数据

import matplotlib.pyplot as plt
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

import os,PIL,random,pathlib

# 设置随机种子尽可能使结果可以重现
import numpy as np
np.random.seed(1)

# 设置随机种子尽可能使结果可以重现
import tensorflow as tf
tf.random.set_seed(1)

data_dir = "第12天/captcha/"
data_dir = pathlib.Path(data_dir)

all_image_paths = list(data_dir.glob('*'))
all_image_paths = [str(path) for path in all_image_paths]

# 打乱数据
random.shuffle(all_image_paths)

# 获取数据标签
all_label_names = [path.split("/")[2].split(".")[0] for path in all_image_paths]

image_count = len(all_image_paths)
print("图片总数为：",image_count)

1.3 数据可视化

plt.figure(figsize=(10,5))

for i in range(20):
    plt.subplot(5,4,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    
    # 显示图片
    images = plt.imread(all_image_paths[i])
    plt.imshow(images)
    # 显示标签
    plt.xlabel(all_label_names[i])

plt.show()

1.4 标签数字化

number   = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
char_set       = number + alphabet
char_set_len   = len(char_set)
label_name_len = len(all_label_names[0])


# 将字符串数字化
def text2vec(text):
    vector = np.zeros([label_name_len, char_set_len])
    for i, c in enumerate(text):
        idx = char_set.index(c)
        vector[i][idx] = 1.0
    return vector

all_labels = [text2vec(i) for i in all_label_names]

2. 构建一个tf.data.Dataset

2.1 预处理函数

def preprocess_image(image):
    image = tf.image.decode_jpeg(image, channels=1)
    image = tf.image.resize(image, [50, 200])
    return image/255.0

def load_and_preprocess_image(path):
    image = tf.io.read_file(path)
    return preprocess_image(image)

2.2 加载数据

AUTOTUNE = tf.data.experimental.AUTOTUNE

path_ds  = tf.data.Dataset.from_tensor_slices(all_image_paths)
image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
label_ds = tf.data.Dataset.from_tensor_slices(all_labels)

image_label_ds = tf.data.Dataset.zip((image_ds, label_ds))
image_label_ds

train_ds = image_label_ds.take(1000)  # 前1000个batch
val_ds   = image_label_ds.skip(1000)  # 跳过前1000，选取后面的

2.3 配置数据

prefetch()功能详细介绍:CPU正在准备数据时，加速器处于空闲状态。相反，当加速器正在训练模型时，CPU处于空闲状态。因此，训练所用的时间是CPU预处理时间和加速器训练时间的总和。prefetch()将训练步骤的预处理和模型执行过程重叠到一起。当加速器正在执行第N个训练步时，CPU正在准备第N+1步的数据。这样做不仅可以最大限度地缩短训练的单步用时(而不是总用时)，而且可以缩短提取和转换数据所需的时间。如果不使用prefetch( ) ,CPU和GPU/TPU在大部分时间都处于空闲状态:

使用prefetch( )可显著减少空闲时间:

BATCH_SIZE = 16

train_ds = train_ds.batch(BATCH_SIZE)
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)

val_ds = val_ds.batch(BATCH_SIZE)
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)
val_ds

3. 搭建网络模型

from tensorflow.keras import datasets, layers, models

model = models.Sequential([
    
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(50, 200, 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.Flatten(),                              #Flatten层，连接卷积层与全连接层
    layers.Dense(1000, activation='relu'),         #全连接层，特征进一步提取
    
    layers.Dense(label_name_len * char_set_len),
    layers.Reshape([label_name_len, char_set_len]),
    layers.Softmax()                               #输出层，输出预期结果
])
# 打印网络结构
model.summary()

4. 编译

model.compile(optimizer="adam",loss='categorical_crossentropy',metrics=['accuracy'])

5. 训练

epochs = 20

history = model.fit(train_ds,validation_data=val_ds,epochs=epochs)

6. 模型评估

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()

7. 保存和加载模型

# 保存模型
model.save('model/12_model.h5')
# 加载模型
new_model = tf.keras.models.load_model('model/12_model.h5')

8. 预测

def vec2text(vec):
    """ 还原标签（向量->字符串） """
    text = []
    for i, c in enumerate(vec):
        text.append(char_set[c])
    return "".join(text)

plt.figure(figsize=(10, 8))            # 图形的宽为10高为8

for images, labels in val_ds.take(1):
    for i in range(6):
        ax = plt.subplot(5, 2, i + 1)  
        # 显示图片
        plt.imshow(images[i])

        # 需要给图片增加一个维度
        img_array = tf.expand_dims(images[i], 0) 

        # 使用模型预测验证码
        predictions = model.predict(img_array)
        plt.title(vec2text(np.argmax(predictions, axis=2)[0]))

        plt.axis("off")