解释代码def evaluate_1step_pred(args, model, test_dataset): # Turn on evaluation mode which disables dropout. model.eval() total_loss = 0 with torch.no_grad(): hidden = model.init_hidden(args.eval_batch_size) for nbatch, i in enumerate(range(0, test_dataset.size(0) - 1, args.bptt)): inputSeq, targetSeq = get_batch(args,test_dataset, i) outSeq, hidden = model.forward(inputSeq, hidden) loss = criterion(outSeq.view(args.batch_size,-1), targetSeq.view(args.batch_size,-1)) hidden = model.repackage_hidden(hidden) total_loss+= loss.item() return total_loss / nbatch
时间: 2024-03-07 08:52:12 浏览: 83
这段代码实现了模型在测试集上进行一步预测的评估。首先通过 model.eval() 将模型置于评估模式,禁用了 dropout。然后使用 torch.no_grad() 将梯度计算关闭,提高代码运行效率。在循环中,使用 get_batch() 函数获取输入序列和目标序列,并使用 model.forward() 函数进行一步预测,得到预测结果 outSeq 和隐藏状态 hidden。接着,使用 criterion 计算预测结果和目标序列之间的损失。使用 model.repackage_hidden() 将隐藏状态从计算图中分离出来,防止梯度消失和梯度爆炸问题。最后,将损失累加到 total_loss 中。函数返回平均损失值 total_loss / nbatch,其中 nbatch 表示测试集中可以分成多少个 batch。
相关问题
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复杂度的计算
可以使用模型的参数数量作为模型复杂度的度量,可以通过以下代码实现:
```python
# Counting the number of model parameters
num_params = sum([np.prod(var.shape) for var in model.trainable_variables])
print("Number of model parameters: {}".format(num_params))
```
该代码会输出模型中可训练的参数数量,即模型的复杂度。
阅读全文
相关推荐














