synthetic_aia_mia.mia_synthetic package¶
Submodules¶
synthetic_aia_mia.mia_synthetic.adult module¶
Define structures to manage and interface a fully connected neural network for the mia on synthetic adult.
- class synthetic_aia_mia.mia_synthetic.adult.AdultDataset(data)[source]¶
Bases:
Dataset
Pytorch dataset to handle adult data.
- class synthetic_aia_mia.mia_synthetic.adult.AdultNN[source]¶
Bases:
object
Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.
- fit(dadata)[source]¶
Train and tune hyper parameters.
- Parameters:
data – Dataset the will be split for training and hyper parameter tuning. Dataset must contain a column called “member” used as training label.
- predict(dadata)[source]¶
Use a trained TabularNN to predict label of dataset.
- Parameters:
dadata (fetch_data.Dataset) – Dataset to evaluate.
- Returns:
Input dataset completed with hard labels, soft labels and loss.
- Return type:
synthetic_aia_mia.mia_synthetic.utk module¶
Define structures to manage and interface a CNN for mia on synthetic utk.
- class synthetic_aia_mia.mia_synthetic.utk.CNN(c1, c2, l)[source]¶
Bases:
Module
Convolutional neural network for 50x50x3 images.
- Parameters:
c1 (int) – Output number of channels of the first convolution layer.
c2 (int) – Output number of channels of the second convolution layer.
- Param:
Linear size.
- class synthetic_aia_mia.mia_synthetic.utk.UtkDataset(data)[source]¶
Bases:
Dataset
Pytorch dataset to handle StorageDataset.
- class synthetic_aia_mia.mia_synthetic.utk.UtkNN(epochs=500)[source]¶
Bases:
object
Wrapper arround pytorch neural network. Interfare for hyper parameter optimisation using raytune.
- Parameters:
epochs (int) – Number of epochs.
- fit(data)[source]¶
Train and tune hyper parameters.
- Parameters:
data – Dataset the will be split for training and hyper parameter tuning. Dataset must contain a column called “PINCP” used as training label.