python 神经网络解决验证码识别 代码

以下是一个使用Python实现神经网络解决验证码识别的示例代码，仅供参考：

import os
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.utils import to_categorical

# 读取验证码图片和标签
def read_data(data_path):
    data = []
    labels = []
    for label in os.listdir(data_path):
        for image_name in os.listdir(os.path.join(data_path, label)):
            image_path = os.path.join(data_path, label, image_name)
            image = Image.open(image_path).convert('L')  # 转换为灰度图像
            image = np.array(image) / 255.0  # 归一化
            data.append(image)
            labels.append(label)
    return np.array(data), np.array(labels)

# 将标签转换为one-hot编码
def encode_labels(labels):
    label_dict = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9}
    return to_categorical([label_dict[label] for label in labels])

# 构建卷积神经网络模型
def build_model(input_shape, num_classes):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='softmax'))
    return model

# 训练模型
def train_model(model, X_train, y_train, X_val, y_val):
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    early_stop = EarlyStopping(monitor='val_loss', patience=3)
    model.fit(X_train, y_train, batch_size=128, epochs=20, validation_data=(X_val, y_val), callbacks=[early_stop])
    return model

# 评估模型
def evaluate_model(model, X_test, y_test):
    loss, accuracy = model.evaluate(X_test, y_test)
    print('Test loss:', loss)
    print('Test accuracy:', accuracy)

# 加载数据
data_path = 'captcha_data'
data, labels = read_data(data_path)
labels_encoded = encode_labels(labels)

# 划分训练集、验证集和测试集
X_train, X_test, y_train, y_test = train_test_split(data, labels_encoded, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)

# 构建模型
input_shape = (50, 200, 1)
num_classes = 10
model = build_model(input_shape, num_classes)

# 训练模型
model = train_model(model, X_train, y_train, X_val, y_val)

# 评估模型
evaluate_model(model, X_test, y_test)

需要注意的是，上述代码中的`read_data`函数需要根据实际情况进行实现，读取验证码图片和标签的方法需要与具体的数据集相匹配。同时，神经网络模型的构建和训练需要根据实际情况进行调整，如调整网络结构、调整学习率等。