我在Stack-overflow中注意到了几个与此类似的问题，但是没有一个答案..
我有一个简单的Keras模型：
def create_model(x_train, y_train, x_val, y_val):
# building the model
# compile
# fit
# return the score using model.predict
我正在如下应用交叉验证（Kfold分层）：
skf = StratifiedKFold(y, n_folds=5, shuffle=True, random_state=0)
scores = []
for train_index, val_index in skf:
X_train, X_val = df[train_index], df[val_index]
y_train, y_val = y[train_index], y[val_index]
scores.append(create_model(X_train, y_train, X_val, y_val))
# point A
每次训练通过后，我是否必须重新初始化模型权重（A点），还是Keras库管理此过程？
如果没有，那么可以改善处理时间的任何建议（如果可能，刷新内存吗？..）。
之所以问这个问题，是因为我将此过程与Hyperopt库一起用于超参数优化，并且我注意到，经过多次试验，该模型开始花费的时间比开始时要多。
编辑：作为示例，您可以注意到以下Hyperopt评估的处理时间，其中每遍均采用5倍方法：
Hyperopt evals: 3%|▎ | 5/150 [16:09<7:54:20, 196.28s/it]
Hyperopt evals: 4%|▍ | 6/150 [22:33<10:06:20, 252.64s/it]
Hyperopt evals: 5%|▍ | 7/150 [26:20<9:43:55, 245.01s/it]
Hyperopt evals: 5%|▌ | 8/150 [33:33<11:53:16, 301.38s/it]
Hyperopt evals: 6%|▌ | 9/150 [41:56<14:10:16, 361.82s/it]
Hyperopt evals: 7%|▋ | 10/150 [45:56<12:38:50, 325.22s/it]
Hyperopt evals: 7%|▋ | 11/150 [48:19<10:26:55, 270.61s/it]
Hyperopt evals: 8%|▊ | 12/150 [54:11<11:18:28, 294.99s/it]
Hyperopt evals: 9%|▊ | 13/150 [58:45<10:58:57, 288.59s/it]
Hyperopt evals: 9%|▉ | 14/150 [1:05:57<12:31:47, 331.68s/it]
Hyperopt evals: 10%|█ | 15/150 [1:13:38<13:53:30, 370.45s/it]
Hyperopt evals: 11%|█ | 16/150 [1:17:36<12:18:28, 330.66s/it]
Hyperopt evals: 11%|█▏ | 17/150 [1:25:56<14:06:13, 381.75s/it]
Hyperopt evals: 12%|█▏ | 18/150 [1:31:54<13:43:38, 374.39s/it]
Hyperopt evals: 13%|█▎ | 19/150 [1:36:11<12:20:55, 339.35s/it]
Hyperopt evals: 13%|█▎ | 20/150 [1:45:06<14:22:20, 398.01s/it]
Hyperopt evals: 14%|█▍ | 21/150 [1:49:14<12:38:51, 352.95s/it]
Hyperopt evals: 15%|█▍ | 22/150 [1:54:45<12:18:47, 346.31s/it]
Hyperopt evals: 15%|█▌ | 23/150 [1:59:04<11:17:24, 320.04s/it]
Hyperopt evals: 16%|█▌ | 24/150 [2:04:05<11:00:29, 314.52s/it]
Hyperopt evals: 17%|█▋ | 25/150 [2:07:47<9:57:11, 286.65s/it]
Hyperopt evals: 17%|█▋ | 26/150 [2:12:47<10:00:37, 290.62s/it]
Hyperopt evals: 18%|█▊ | 27/150 [2:17:08<9:37:55, 281.91s/it]
Hyperopt evals: 19%|█▊ | 28/150 [2:22:46<10:07:15, 298.65s/it]
Hyperopt evals: 19%|█▉ | 29/150 [2:28:56<10:45:29, 320.08s/it]
Hyperopt evals: 20%|██ | 30/150 [2:34:55<11:03:44, 331.87s/it]
Hyperopt evals: 21%|██ | 31/150 [2:40:20<10:53:43, 329.61s/it]
Hyperopt evals: 21%|██▏ | 32/150 [2:46:19<11:05:42, 338.50s/it]
Hyperopt evals: 22%|██▏ | 33/150 [2:51:47<10:53:54, 335.34s/it]
Hyperopt evals: 23%|██▎ | 34/150 [2:58:14<11:18:06, 350.75s/it]
Hyperopt evals: 23%|██▎ | 35/150 [3:04:10<11:15:41, 352.53s/it]
Hyperopt evals: 24%|██▍ | 36/150 [3:13:59<13:24:26, 423.39s/it]
Hyperopt evals: 25%|██▍ | 37/150 [3:20:13<12:49:38, 408.66s/it]
Hyperopt evals: 25%|██▌ | 38/150 [3:25:55<12:05:23, 388.61s/it]
Hyperopt evals: 26%|██▌ | 39/150 [3:35:53<13:54:59, 451.35s/it]
Hyperopt evals: 27%|██▋ | 40/150 [3:44:26<14:21:12, 469.75s/it]
Hyperopt evals: 27%|██▋ | 41/150 [3:50:42<13:22:33, 441.77s/it]
Hyperopt evals: 28%|██▊ | 42/150 [3:58:03<13:14:29, 441.39s/it]
Hyperopt evals: 29%|██▊ | 43/150 [4:11:11<16:12:35, 545.38s/it]
Hyperopt evals: 29%|██▉ | 44/150 [4:19:18<15:32:40, 527.93s/it]
Hyperopt evals: 30%|███ | 45/150 [4:26:03<14:19:21, 491.06s/it]
Hyperopt evals: 31%|███ | 46/150 [4:34:32<14:20:31, 496.46s/it]
Hyperopt evals: 31%|███▏ | 47/150 [4:45:01<15:20:25, 536.17s/it]
Hyperopt evals: 32%|███▏ | 48/150 [4:54:11<15:18:45, 540.45s/it]
Hyperopt evals: 33%|███▎ | 49/150 [4:58:42<12:53:19, 459.40s/it]
Hyperopt evals: 33%|███▎ | 50/150 [5:04:07<11:38:30, 419.11s/it]
Hyperopt evals: 34%|███▍ | 51/150 [5:12:48<12:22:14, 449.85s/it]
Hyperopt evals: 35%|███▍ | 52/150 [5:20:37<12:23:57, 455.49s/it]
Hyperopt evals: 35%|███▌ | 53/150 [5:28:18<12:19:19, 457.31s/it]
Hyperopt evals: 36%|███▌ | 54/150 [5:37:02<12:43:26, 477.15s/it]
Hyperopt evals: 37%|███▋ | 55/150 [5:45:21<12:46:00, 483.80s/it]
Hyperopt evals: 37%|███▋ | 56/150 [5:51:07<11:33:16, 442.51s/it]
Hyperopt evals: 38%|███▊ | 57/150 [5:59:38<11:57:39, 463.00s/it]
Hyperopt evals: 39%|███▊ | 58/150 [6:11:19<13:39:13, 534.27s/it]
Hyperopt evals: 39%|███▉ | 59/150 [6:28:06<17:05:39, 676.26s/it]
Hyperopt evals: 40%|████ | 60/150 [6:37:29<16:03:23, 642.27s/it]
Hyperopt evals: 41%|████ | 61/150 [6:43:38<13:51:06, 560.30s/it]
Hyperopt evals: 41%|████▏ | 62/150 [6:52:41<13:33:52, 554.92s/it]
Hyperopt evals: 42%|████▏ | 63/150 [7:00:05<12:36:40, 521.84s/it]
Hyperopt evals: 43%|████▎ | 64/150 [7:12:13<13:56:21, 583.50s/it]
Hyperopt evals: 43%|████▎ | 65/150 [7:20:03<12:58:38, 549.62s/it]
Hyperopt evals: 44%|████▍ | 66/150 [7:31:56<13:58:08, 598.68s/it]
Hyperopt evals: 45%|████▍ | 67/150 [7:44:48<15:00:05, 650.67s/it]
Hyperopt evals: 45%|████▌ | 68/150 [7:57:32<15:35:45, 684.70s/it]
答案 0 :(得分：1)
每次训练通过后，我是否必须重新初始化模型权重 （A点），还是Keras库管理此过程？
在检查了文档和手动实验之后：在我看来，Keras负责重新初始化权重，而这不是必需的。
如果没有，那么可以改善处理时间的任何建议（也许 刷新内存？ ..）。
在我的情况下，处理时间在增加，原因是：
1- Hyperopt使用贝叶斯优化技术，因此每次选择下一个参数集时都会尝试尝试基于先验概率选择更好的方法
2-我正在尽早停车。
因此，在每个下一个评估中，hyperopt库开始选择更好的参数集，其中模型也开始比以前更好地收敛..这意味着减少了对早期停止的使用，并增加了处理时间（以完成整个时期）