Python搭建KerasCNN模型破解网站验证码的实现-创新互联
在本项目中,将会用Keras来搭建一个稍微复杂的CNN模型来破解以上的验证码。验证码如下:
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将数据集分为训练集和测试集,占比为8:2,该模型训练的代码如下:
# -*- coding: utf-8 -*- import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from matplotlib import pyplot as plt from keras.utils import np_utils, plot_model from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.callbacks import EarlyStopping from keras.layers import Conv2D, MaxPooling2D # 读取数据 df = pd.read_csv('./data.csv') # 标签值 vals = range(31) keys = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','J','K','L','N','P','Q','R','S','T','U','V','X','Y','Z'] label_dict = dict(zip(keys, vals)) x_data = df[['v'+str(i+1) for i in range(320)]] y_data = pd.DataFrame({'label':df['label']}) y_data['class'] = y_data['label'].apply(lambda x: label_dict[x]) # 将数据分为训练集和测试集 X_train, X_test, Y_train, Y_test = train_test_split(x_data, y_data['class'], test_size=0.3, random_state=42) x_train = np.array(X_train).reshape((1167, 20, 16, 1)) x_test = np.array(X_test).reshape((501, 20, 16, 1)) # 对标签值进行one-hot encoding n_classes = 31 y_train = np_utils.to_categorical(Y_train, n_classes) y_val = np_utils.to_categorical(Y_test, n_classes) input_shape = x_train[0].shape # CNN模型 model = Sequential() # 卷积层和池化层 model.add(Conv2D(32, kernel_size=(3, 3), input_shape=input_shape, padding='same')) model.add(Activation('relu')) model.add(Conv2D(32, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) # Dropout层 model.add(Dropout(0.25)) model.add(Conv2D(64, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(Conv2D(64, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) model.add(Dropout(0.25)) model.add(Conv2D(128, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(Conv2D(128, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), padding='same')) model.add(Dropout(0.25)) model.add(Flatten()) # 全连接层 model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(128, activation='relu')) model.add(Dense(n_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # plot model ##plot_model(model, to_file=r'./model.png', show_shapes=True) # 模型训练 callbacks = [EarlyStopping(monitor='val_acc', patience=5, verbose=1)] batch_size = 64 n_epochs = 100 history = model.fit(x_train, y_train, batch_size=batch_size, epochs=n_epochs, \ verbose=1, validation_data=(x_test, y_val), callbacks=callbacks) mp = './verifycode_Keras.h6' model.save(mp) # 绘制验证集上的准确率曲线 val_acc = history.history['val_acc'] plt.plot(range(len(val_acc)), val_acc, label='CNN model') plt.title('Validation accuracy on verifycode dataset') plt.xlabel('epochs') plt.ylabel('accuracy') plt.legend() plt.show()
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文章题目:Python搭建KerasCNN模型破解网站验证码的实现-创新互联
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