def reset_parameters(self): stdv = 1. / math.sqrt(self.Theta1.shape[1]) self.Theta1.data.uniform_(-stdv, stdv)
时间: 2024-06-12 08:08:07 浏览: 10
This function resets the parameters of a neural network layer. In particular, it initializes the weights of the layer (stored in the `Theta1` attribute) to random values between -`stdv` and `stdv`, where `stdv` is a value calculated based on the number of input features of the layer. This helps to break the symmetry in the weights and prevent the network from getting stuck in local minima during training.
相关问题
print('---> cv train to choose best_num_boost_round') dtrain = xgb.DMatrix(train_X, label=train_Y, feature_names=df_columns) xgb_params = { 'learning_rate': 0.01, 'n_estimators': 1000, 'max_depth': 4, 'min_child_weight': 2, 'eval_metric': 'rmse', 'objective': 'reg:linear', 'nthread': -1, 'silent': 1, 'booster': 'gbtree' } cv_result = xgb.cv(dict(xgb_params), dtrain, num_boost_round=4000, early_stopping_rounds=100, verbose_eval=100, show_stdv=False, ) best_num_boost_rounds = len(cv_result) mean_train_logloss = cv_result.loc[best_num_boost_rounds-11 : best_num_boost_rounds-1, 'train-rmse-mean'].mean() mean_test_logloss = cv_result.loc[best_num_boost_rounds-11 : best_num_boost_rounds-1, 'test-rmse-mean'].mean() print('best_num_boost_rounds = {}'.format(best_num_boost_rounds)) print('mean_train_rmse = {:.7f} , mean_valid_rmse = {:.7f}\n'.format(mean_train_logloss, mean_test_logloss))
这段代码是使用XGBoost库进行机器学习模型的训练,并通过交叉验证选出最佳的boosting迭代次数。具体流程如下:
1. 使用xgb.DMatrix将训练数据转换为DMatrix格式,包括特征矩阵train_X、标签train_Y和特征名字df_columns。
2. 定义XGBoost模型的参数xgb_params,包括学习率、迭代次数、树的最大深度、叶子节点最小权重、评估指标、目标函数、线程数、是否静默和booster类型。
3. 使用xgb.cv进行交叉验证,传入参数包括XGBoost模型参数xgb_params、DMatrix格式的训练数据dtrain、最大迭代次数num_boost_round、早停止迭代次数early_stopping_rounds、是否打印过程verbose_eval和是否显示标准差show_stdv。
4. 通过交叉验证结果选出最佳的boosting迭代次数best_num_boost_rounds。
5. 计算最佳迭代次数下的训练集均方根误差mean_train_rmse和验证集均方根误差mean_valid_rmse。
6. 打印最佳迭代次数和均方根误差。
module 'xgboost.callback' has no attribute 'print_evaluation'
这个错误通常是因为您正在使用的XGBoost版本不支持print_evaluation()函数。这个函数在旧版本中被弃用了。如果您想使用这个函数,您需要使用支持它的旧版本。您可以通过以下命令安装旧版本的XGBoost:
```shell
pip install xgboost==0.90
```
如果您想使用新版本的XGBoost,您可以使用以下代码替代print_evaluation()函数:
```python
def print_evaluation(model, period=1, show_stdv=True):
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(period, epochs)
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['error'][period:], label='Train')
if show_stdv:
ax.fill_between(x_axis,
(results['validation_0']['error'] - results['validation_0']['stderr'])[period:],
(results['validation_0']['error'] + results['validation_0']['stderr'])[period:],
alpha=0.1, color='g')
ax.plot(x_axis, results['validation_1']['error'][period:], label='Test')
if show_stdv:
ax.fill_between(x_axis,
(results['validation_1']['error'] - results['validation_1']['stderr'])[period:],
(results['validation_1']['error'] + results['validation_1']['stderr'])[period:],
alpha=0.1, color='r')
ax.legend()
plt.ylabel('Error')
plt.title('XGBoost Error')
plt.show()
```
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