构建一个lstm模型进行二分类，并绘制分类结果的roc曲线图，绘制的roc曲线为平滑的曲线代码

首先，导入必要的库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

然后，读入数据并进行预处理：

data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接着，构建LSTM模型：

model = Sequential()
model.add(LSTM(units=64, activation='relu', input_shape=(X_train.shape[1], 1)))
model.add(Dense(units=1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train.reshape((X_train.shape[0], X_train.shape[1], 1)), y_train, epochs=50, batch_size=32)

训练完成后，可以使用训练好的模型进行预测并计算ROC曲线：

y_pred = model.predict(X_test.reshape((X_test.shape[0], X_test.shape[1], 1))).ravel()
fpr, tpr, thresholds = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)

plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()

这样就可以得到平滑的ROC曲线图了。