tcn加注意力机制进行信用卡欺诈检测代码

以下是使用TCN和注意力机制进行信用卡欺诈检测的代码示例。

首先，我们需要导入所需的库和模块。

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, SpatialDropout1D, Conv1D, MaxPooling1D, Flatten, \
    concatenate, Attention
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split

然后，我们读取数据集并将其拆分为训练集和测试集。

data = pd.read_csv('creditcard.csv')

X = data.drop(['Class'], axis=1)
y = data['Class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们将数据转换为3D张量以适应TCN模型的输入形状。我们还将标准化输入以提高模型的性能。

def prepare_data(X, y):
    n_features = X.shape[1]
    X = np.array(X).reshape((-1, n_features, 1))
    y = np.array(y)
    X_mean = X.mean(axis=0)
    X_std = X.std(axis=0)
    X = (X - X_mean) / X_std
    return X, y

X_train, y_train = prepare_data(X_train, y_train)
X_test, y_test = prepare_data(X_test, y_test)

然后，我们定义TCN模型。这个模型由一系列卷积层和空间丢失层组成，最后通过一个全局最大池化层来提取特征。然后，我们使用注意力机制来加权这些特征，并将其馈入一个密集层进行最终分类。

def build_model(n_features, kernel_size=3):
    input_layer = Input(shape=(n_features, 1))

    conv1 = Conv1D(filters=64, kernel_size=kernel_size, activation='relu')(input_layer)
    conv2 = Conv1D(filters=64, kernel_size=kernel_size, activation='relu')(conv1)
    dropout1 = SpatialDropout1D(rate=0.2)(conv2)
    maxpool1 = MaxPooling1D(pool_size=2)(dropout1)

    conv3 = Conv1D(filters=128, kernel_size=kernel_size, activation='relu')(maxpool1)
    conv4 = Conv1D(filters=128, kernel_size=kernel_size, activation='relu')(conv3)
    dropout2 = SpatialDropout1D(rate=0.2)(conv4)
    maxpool2 = MaxPooling1D(pool_size=2)(dropout2)

    conv5 = Conv1D(filters=256, kernel_size=kernel_size, activation='relu')(maxpool2)
    conv6 = Conv1D(filters=256, kernel_size=kernel_size, activation='relu')(conv5)
    dropout3 = SpatialDropout1D(rate=0.2)(conv6)

    attention = Attention()([dropout3, dropout3])

    flatten = Flatten()(attention)
    dense1 = Dense(units=128, activation='relu')(flatten)
    dense2 = Dense(units=1, activation='sigmoid')(dense1)

    model = Model(inputs=input_layer, outputs=dense2)

    return model

model = build_model(n_features=X_train.shape[1])

接下来，我们编译模型并训练它。

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', patience=3)

model.fit(X_train, y_train, epochs=10, batch_size=64, validation_split=0.2, callbacks=[early_stopping])

最后，我们评估模型的性能并输出分类报告和混淆矩阵。

y_pred = model.predict(X_test)
y_pred = np.round(y_pred)

print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))