以下代码是对NSL-KDD数据集网络入侵检测：model = Sequential() model.add(LSTM(128, return_sequences=True, input_shape=(1, X_train.shape[2]))) model.add(Dropout(0.2)) model.add(LSTM(64, return_sequences=True)) model.add(Attention()) model.add(Flatten()) model.add(Dense(units=50)) model.add(Dense(units=5, activation='softmax')) # Defining loss function, optimizer, metrics and then compiling model model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # Summary of model layers model.summary() # training the model on training dataset history = model.fit(X_train, y_train, epochs=150, batch_size=5000,validation_split=0.2) # predicting target attribute on testing dataset test_results = model.evaluate(X_test, y_test, verbose=1) # Use the trained model to make predictions on the test dataset y_pred = model.predict(X_test) # Convert predictions from one-hot encoding to integers y_pred = np.argmax(y_pred, axis=1) # Convert true labels from one-hot encoding to integers y_test = np.argmax(y_test, axis=1) # Calculate the confusion matrix cm = confusion_matrix(y_test, y_pred) # Calculate the false positive rate (FPR) fpr = cm[0, 1] / (cm[0, 0] + cm[0, 1])如何用代码实现对模型计算量的计算

可以使用`model.count_params()`函数来计算模型的参数数量，而模型的计算量可以通过`model.summary()`函数来获取。在`model.summary()`函数的输出结果中，可以找到模型总参数数和每一层的参数数，从而计算出模型的计算量。具体代码如下：

# 计算模型参数数量
num_params = model.count_params()
print("模型参数数量：", num_params)

# 输出模型结构和计算量
model.summary()

输出结果示例：

模型参数数量： 193741
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_2 (LSTM)                (None, 1, 128)            93696     
_________________________________________________________________
dropout_1 (Dropout)          (None, 1, 128)            0         
_________________________________________________________________
lstm_3 (LSTM)                (None, 1, 64)             49408     
_________________________________________________________________
attention_1 (Attention)      (None, 64)                65        
_________________________________________________________________
flatten_1 (Flatten)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 50)                3250      
_________________________________________________________________
dense_3 (Dense)              (None, 5)                 255       
=================================================================
Total params: 193,741
Trainable params: 193,741
Non-trainable params: 0
_________________________________________________________________