注释以下代码 def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size): train_l_sum, valid_l_sum = 0.0, 0.0 for i in range(k): data = get_k_fold_data(k, i, X_train, y_train) train_ls, valid_ls = train(model, *data, num_epochs, learning_rate, weight_decay, batch_size) train_l_sum += train_ls[-1] valid_l_sum += valid_ls[-1] print("fold %d, train rmse: %.4f, valid rmse: %.4f" % (i, train_ls[-1], valid_ls[-1])) return train_l_sum / k, valid_l_sum / k

这段代码实现了一个 k 折交叉验证的训练过程，其输入包括：k 表示折数，X_train 表示训练数据集的特征，y_train 表示训练数据集的标签，num_epochs 表示训练模型的轮数，learning_rate 表示学习率，weight_decay 表示权重衰减参数，batch_size 表示每个小批量的样本数。其输出包括：训练数据集的平均 RMSE 和验证数据集的平均 RMSE。

具体实现过程为：首先，通过 get_k_fold_data 函数将训练数据集分为 k 折，取其中一折作为验证数据集，其余折作为训练数据集。然后，将分好的数据集传入 train 函数进行模型训练，得到训练数据集和验证数据集的 RMSE，累加每一折的 RMSE，并输出当前折数、训练数据集 RMSE 和验证数据集 RMSE。最后，返回训练数据集的平均 RMSE 和验证数据集的平均 RMSE。

相关问题

k, num_epochs, lr, weight_decay, batch_size = 5, 100, 2, 0, 64 train_l, valid_l, = k_fold(k, train_features, train_labels, num_epochs, lr, weight_decay, batch_size) print("%d-fold validation: avg train rmse: %f, avg valid rmse：%f" % (k, train_l, valid_l)) 对上面的代码进行注释

这段代码实现了一个 5 折交叉验证的训练过程，并输出训练数据集和验证数据集的平均 RMSE。

具体实现过程为：首先，将 k（折数）、num_epochs（训练轮数）、lr（学习率）、weight_decay（权重衰减参数）、batch_size（每个小批量的样本数）设定为 5、100、2、0、64。然后，将这些参数传入 k_fold 函数中进行模型训练和验证，得到训练数据集和验证数据集的 RMSE。最后，通过字符串格式化输出 5 折交叉验证的结果，其中包括折数、训练数据集平均 RMSE 和验证数据集平均 RMSE。

在以下这段代码后面继续添加输出测试集、训练集AUC、f1_score、准确率的代码：# 定义模型参数 input_dim = X_train.shape[1] epochs = 100 batch_size = 32 learning_rate = 0.1 dropout_rate = 0.5 # 定义模型结构 def create_model(): model = Sequential() model.add(Dense(128, input_dim=input_dim, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(32, activation='relu')) model.add(Dropout(dropout_rate)) model.add(Dense(1, activation='sigmoid')) optimizer = Adam(learning_rate=learning_rate) model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model # 5折交叉验证 kf = KFold(n_splits=5, shuffle=True, random_state=42) cv_scores = [] for train_index, test_index in kf.split(X_train): # 划分训练集和验证集 X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[test_index] y_train_fold, y_val_fold = y_train_forced_turnover_nolimited.iloc[train_index], y_train_forced_turnover_nolimited.iloc[test_index] # 创建模型 model = create_model() # 定义早停策略 early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # 训练模型 model.fit(X_train_fold, y_train_fold, validation_data=(X_val_fold, y_val_fold), epochs=epochs, batch_size=batch_size, callbacks=[early_stopping], verbose=1) # 预测验证集 y_pred = model.predict(X_val_fold) # 计算AUC指标 auc = roc_auc_score(y_val_fold, y_pred) cv_scores.append(auc) # 输出交叉验证结果 print('CV AUC:', np.mean(cv_scores)) # 在全量数据上重新训练模型 model = create_model() model.fit(X_train, y_train_forced_turnover_nolimited, epochs=epochs, batch_size=batch_size, verbose=1)

# 测试集结果
test_pred = model.predict(X_test)
test_auc = roc_auc_score(y_test_forced_turnover_nolimited, test_pred)
test_f1_score = f1_score(y_test_forced_turnover_nolimited, np.round(test_pred))
test_accuracy = accuracy_score(y_test_forced_turnover_nolimited, np.round(test_pred))
print('Test AUC:', test_auc)
print('Test F1 Score:', test_f1_score)
print('Test Accuracy:', test_accuracy)

# 训练集结果
train_pred = model.predict(X_train)
train_auc = roc_auc_score(y_train_forced_turnover_nolimited, train_pred)
train_f1_score = f1_score(y_train_forced_turnover_nolimited, np.round(train_pred))
train_accuracy = accuracy_score(y_train_forced_turnover_nolimited, np.round(train_pred))
print('Train AUC:', train_auc)
print('Train F1 Score:', train_f1_score)
print('Train Accuracy:', train_accuracy)