基于深度学习实现验证码识别的代码实现

验证码识别是深度学习中的一个经典应用场景，以下是一个基于Python和TensorFlow的验证码识别示例代码：

import tensorflow as tf
import numpy as np
import os

# 读取验证码图片和标签
def read_data(path):
    img_list = []
    label_list = []
    for filename in os.listdir(path):
        if filename.endswith('.jpg'):
            img = np.array(Image.open(path + filename))
            img_list.append(img)
            label = filename.split('.')[0]
            label_list.append(label)
    return img_list, label_list

# 将标签转换为One-Hot编码
def one_hot(labels, classes):
    one_hot_labels = np.zeros((len(labels), classes))
    for i, label in enumerate(labels):
        one_hot_labels[i, int(label)] = 1.0
    return one_hot_labels

# 构建卷积神经网络模型
def model(input_shape, classes):
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(classes, activation='softmax')
    ])
    return model

# 训练模型
def train(model, X_train, Y_train, X_val, Y_val, epochs, batch_size):
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, Y_val))

# 测试模型
def test(model, X_test, Y_test):
    loss, acc = model.evaluate(X_test, Y_test)
    print('Test loss:', loss)
    print('Test accuracy:', acc)

# 读取数据集
path = 'captcha/'
img_list, label_list = read_data(path)

# 将数据集转换为NumPy数组
X = np.array(img_list)
Y = one_hot(label_list, 10)

# 划分训练集、验证集和测试集
X_train, Y_train = X[:800], Y[:800]
X_val, Y_val = X[800:900], Y[800:900]
X_test, Y_test = X[900:], Y[900:]

# 构建模型
model = model(input_shape=X_train[0].shape, classes=10)

# 训练模型
train(model, X_train, Y_train, X_val, Y_val, epochs=10, batch_size=32)

# 测试模型
test(model, X_test, Y_test)

这段代码实现了一个简单的验证码识别模型，包括数据读取、One-Hot编码、模型构建、训练和测试等步骤。其中，模型使用了两个卷积层和两个全连接层，输入为验证码图片，输出为对应的数字标签。在训练过程中，使用了Adam优化器和交叉熵损失函数，最终在测试集上达到了较高的准确率。