Training is very slow

[mixml]

$python examples/demo_mnist.py
Using TensorFlow backend.
[ 2018-12-14 13:34:10,577][cascade_classifier.fit_transform] X_groups_train.shape=[(60000, 1, 28, 28)],y_train.shape=(60000,),X_groups_test.shape=no_test,y_test.shape=no_test
[ 2018-12-14 13:34:10,588][cascade_classifier.fit_transform] group_dims=[784]
[ 2018-12-14 13:34:10,588][cascade_classifier.fit_transform] group_starts=[0]
[ 2018-12-14 13:34:10,588][cascade_classifier.fit_transform] group_ends=[784]
[ 2018-12-14 13:34:10,588][cascade_classifier.fit_transform] X_train.shape=(60000, 784),X_test.shape=(0, 784)
[ 2018-12-14 13:34:10,645][cascade_classifier.fit_transform] [layer=0] look_indexs=[0], X_cur_train.shape=(60000, 784), X_cur_test.shape=(0, 784)
[ 2018-12-14 13:34:27,575][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_0.predict)=90.02%
[ 2018-12-14 13:34:41,561][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_1.predict)=90.07%
[ 2018-12-14 13:34:55,516][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_2.predict)=90.22%
[ 2018-12-14 13:35:09,470][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_3.predict)=90.11%
[ 2018-12-14 13:35:23,300][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_4.predict)=89.24%
[ 2018-12-14 13:35:23,303][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_0 - 5_folds.train_cv.predict)=89.93%
[ 2018-12-14 13:35:24,074][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_0.predict)=94.64%
[ 2018-12-14 13:35:24,841][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_1.predict)=94.12%
[ 2018-12-14 13:35:25,624][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_2.predict)=93.92%
[ 2018-12-14 13:35:26,382][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_3.predict)=94.61%
[ 2018-12-14 13:35:27,138][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_4.predict)=94.35%
[ 2018-12-14 13:35:27,144][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_1 - 5_folds.train_cv.predict)=94.33%
[ 2018-12-14 13:35:27,924][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_0.predict)=94.49%
[ 2018-12-14 13:35:28,705][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_1.predict)=94.85%
[ 2018-12-14 13:35:29,484][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_2.predict)=94.59%
[ 2018-12-14 13:35:30,255][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_3.predict)=94.96%
[ 2018-12-14 13:35:31,042][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_4.predict)=95.02%
[ 2018-12-14 13:35:31,048][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_2 - 5_folds.train_cv.predict)=94.78%
[ 2018-12-14 14:12:52,985][kfold_wrapper.log_eval_metrics] Accuracy(layer_0 - estimator_3 - 5_folds.train_0.predict)=91.26%

estimator0~estimator2 is fast， but estimator3 is very slow，how to fixed this problem？

[machao199271]

Use other estimator, which can be parallel computed. such as estimator0-2, to replace Logistic Regression, estimator3, which can not be parallel computed.