mirror of
https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-05-15 06:52:37 +02:00
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
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import numpy as np
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import torch
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from torch import nn, optim
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from torch.utils.data import DataLoader
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from torch.utils.data.dataset import Dataset
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from apt.utils.datasets import ArrayDataset
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from apt.utils.models import ModelOutputType
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from apt.utils.models.pytorch_model import PyTorchClassifier
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from art.utils import load_nursery
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def test_nursery_pytorch():
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(x_train, y_train), (x_test, y_test), _, _ = load_nursery(test_set=0.5)
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# reduce size of training set to make attack slightly better
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train_set_size = 500
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x_train = x_train[:train_set_size]
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y_train = y_train[:train_set_size]
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x_test = x_test[:train_set_size]
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y_test = y_test[:train_set_size]
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class pytorch_model(nn.Module):
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def __init__(self, num_classes, num_features):
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super(pytorch_model, self).__init__()
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self.fc1 = nn.Sequential(
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nn.Linear(num_features, 1024),
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nn.Tanh(), )
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self.fc2 = nn.Sequential(
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nn.Linear(1024, 512),
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nn.Tanh(), )
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self.fc3 = nn.Sequential(
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nn.Linear(512, 256),
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nn.Tanh(), )
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self.fc4 = nn.Sequential(
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nn.Linear(256, 128),
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nn.Tanh(),
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)
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self.classifier = nn.Linear(128, num_classes)
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def forward(self, x):
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out = self.fc1(x)
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out = self.fc2(out)
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out = self.fc3(out)
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out = self.fc4(out)
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return self.classifier(out)
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mlp_model = pytorch_model(4, 24)
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mlp_model = torch.nn.DataParallel(mlp_model)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(mlp_model.parameters(), lr=0.01)
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mlp_art_model = PyTorchClassifier(model=mlp_model, output_type=ModelOutputType.CLASSIFIER_VECTOR, loss=criterion,
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optimizer=optimizer, input_shape=(24,),
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nb_classes=4)
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mlp_art_model.fit(ArrayDataset(x_train.astype(np.float32), y_train))
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pred = np.array([np.argmax(arr) for arr in mlp_art_model.predict(ArrayDataset(x_test.astype(np.float32)))])
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print('Base model accuracy: ', np.sum(pred == y_test) / len(y_test))
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