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Make sklearn classifiers in parallel.

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It's done only for those classifiers that run a significant amount of time.

PiperOrigin-RevId: 326215987
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A. Unique TensorFlower 2020-08-12 05:53:12 -07:00
parent 37ff5d502e
commit 0fd06493cc
1 changed files with 4 additions and 2 deletions
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6
tensorflow_privacy/privacy/membership_inference_attack/models.py
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@ -149,8 +149,9 @@ class MultilayerPerceptronAttacker(TrainedAttacker):
'solver': ['adam'], 'solver': ['adam'],
'alpha': [0.0001, 0.001, 0.01], 'alpha': [0.0001, 0.001, 0.01],
} }
n_jobs = -1
model = model_selection.GridSearchCV( model = model_selection.GridSearchCV(
mlp_model, param_grid=param_grid, cv=3, n_jobs=1, verbose=0) mlp_model, param_grid=param_grid, cv=3, n_jobs=n_jobs, verbose=0)
model.fit(input_features, is_training_labels) model.fit(input_features, is_training_labels)
self.model = model self.model = model
@ -175,8 +176,9 @@ class RandomForestAttacker(TrainedAttacker):
'min_samples_split': [2, 5, 10], 'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4] 'min_samples_leaf': [1, 2, 4]
} }
n_jobs = -1
model = model_selection.GridSearchCV( model = model_selection.GridSearchCV(
rf_model, param_grid=param_grid, cv=3, n_jobs=1, verbose=0) rf_model, param_grid=param_grid, cv=3, n_jobs=n_jobs, verbose=0)
model.fit(input_features, is_training_labels) model.fit(input_features, is_training_labels)
self.model = model self.model = model