This is a warm up code of what the possibilites are in the present library. It gives a snippet of the vision of the OpenMined project
In [11]:
from capsule.client import LocalCapsuleClient
import syft as sy
from sklearn.datasets import load_diabetes
from syft.nn.linear import LinearClassifier
In [12]:
cc = LocalCapsuleClient()
pk = cc.keygen()
b"\x80\x03cphe.paillier\nPaillierPublicKey\nq\x00)\x81q\x01}q\x02(X\x01\x00\x00\x00gq\x03\x8a\x81h\x91bJ\xd1\xbc\xfe\x11T\xa1\xbb\x17\xc0\x06b\xe7\x1f\xdd^\xf1x\xbb\xf4\xf2-\xe2\xe5\x95|\xe0\x04F\x133\x9e\x08\xf4W\xb6u<~\xef\xf3$\x84Bz\xd1\xb9\xbb\xe3<\xd7\x12\xe3\x9c9\\\x07*\x13\x13\xd6-|L\x01\tS\xc6\xc4}\xfb\x87:\x0c\x02d\xdabNX\x8f~\xe4\xf05\x04\xe5\x0e\xc5^\x8d\xab\xd1\xa9Z\xe3-u\x13\xda\xa0\xdd\xd1*\x89\x986h\xe8L\xb9RY\xddw\xfep n\xc5\xf7OWb\xad\x00X\x01\x00\x00\x00nq\x04\x8a\x81g\x91bJ\xd1\xbc\xfe\x11T\xa1\xbb\x17\xc0\x06b\xe7\x1f\xdd^\xf1x\xbb\xf4\xf2-\xe2\xe5\x95|\xe0\x04F\x133\x9e\x08\xf4W\xb6u<~\xef\xf3$\x84Bz\xd1\xb9\xbb\xe3<\xd7\x12\xe3\x9c9\\\x07*\x13\x13\xd6-|L\x01\tS\xc6\xc4}\xfb\x87:\x0c\x02d\xdabNX\x8f~\xe4\xf05\x04\xe5\x0e\xc5^\x8d\xab\xd1\xa9Z\xe3-u\x13\xda\xa0\xdd\xd1*\x89\x986h\xe8L\xb9RY\xddw\xfep n\xc5\xf7OWb\xad\x00X\x07\x00\x00\x00nsquareq\x05\x8b\x00\x01\x00\x00q\xd7q11\xd4\x81\x86\xdb\xbc\x81\xc0\x18L\x96\x16\x91\xd6\xb7M\x14\xc5\xac.\xf4Q\x99+\xbd\xdeF\x89Io/O\xefUH\x13\x1c\xbeRr\x1d\x86\x91V\tt\xbeF-g`\x97\x0c\xd9\xaa\x8f\x18|N\xe6/\xc0\xe0\xf1m\xfdg\xdd\x1d\x8c\xb0&\xa90\xcd\x92:V\x06\xd4\xc9\xd0\x8dNV\xaf*\x06U\x0b)\x8e dP\x86|\xba.\xe9\xb24\xbd\x91\\\x0chL\xbb\xa9\x86\x139S\xdd\xbd\x08\x11P\xb5\xa2m\x0f\x99CgA\x14YC)\x8e\xf8\\Cwz\xc4<\xbf'\xff\x9c\x15\x98\xc2\x17R\xd9\xd5\x19-f\xea\xbaS\\%w\xed;\x06`\x82\x0f!q\xf7#H\xd9\xae(\x9dXY\xbe4c\x08f\xb4Q\xfb6[\xc6?)\xb99\xa0\xc6\xc8\x97\xed\xe1\xf93\xf8\xb8\xbe\xde{x\xbd\xeb\x96\x88h\xd4C\x88\xe9\xa0\xc7\xd8\xa8\xec\xf5&\xb4~2C\xf5\xd0D9\xa3\xe2\x01\xd3]\x88\xe9Cw\x11\xeal\x08\x116c\xfd\xe1\x0b\xc9\x0fnuX\x07\x00\x00\x00max_intq\x06\x8a\x80v0v\xc3E\x94\xff\x05\x1c\x8b>]\x95W\xcb\xf7_\xf4tP(\xe9\xa6\xfb\xb9\xa0L\x87)\xa0\x01\xc2[f\xdf\x02\xfc\xc7<'\x14*\xa5\xa6a\x81k\xd3E\x93>\xa1i\xf2\xb0K4\x13t\x02\x0e\xb1[G\x0f\xd4n\x00\x03q\x97A\x7f\xfe\xd7hY\xabv\xf3 \x1a\xc8/*LPg\x01\xf7\x04\x97\x1f/9\x9b8\x1e\xa1d|\x06\x9e5\x9f\xf0\xb8-\x88g\xcd\xa2\x19\x93\x1bs\xf4'\xaa%`\xcfA\xfdor\xcb9ub."
In [13]:
model = LinearClassifier(n_inputs=10,n_labels=1).encrypt(pk)
In [14]:
model
Out[14]:
Linear Model (10,1):
In [15]:
diabetes = load_diabetes()
target = diabetes.target
input = diabetes.data
In [16]:
for i in range(5):
print("Grads:" + str((model.learn(input=input[i],target=target[i],alpha=0.5))))
Grads:PaillierTensor: [[e]
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Grads:PaillierTensor: [[e]
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Grads:PaillierTensor: [[e]
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Grads:PaillierTensor: [[e]
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Grads:PaillierTensor: [[e]
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In [17]:
model = model.decrypt(cc)
In [18]:
for i in range(len(input)):
print(model.forward(input[i]))
BaseTensor: [ 0.72394632]
BaseTensor: [ 0.65432927]
BaseTensor: [ 0.78078893]
BaseTensor: [ 0.37791087]
BaseTensor: [ 0.38056636]
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BaseTensor: [-1.07454611]
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BaseTensor: [ 0.97099631]
BaseTensor: [-1.48426783]
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BaseTensor: [ 0.42552349]
BaseTensor: [ 1.35244225]
BaseTensor: [ 1.66138972]
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BaseTensor: [ 0.48137219]
BaseTensor: [ 0.11992176]
BaseTensor: [ 1.25639549]
BaseTensor: [-1.38745814]
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BaseTensor: [ 0.27516012]
BaseTensor: [-0.15791325]
BaseTensor: [-1.3157857]
BaseTensor: [-0.91622524]
BaseTensor: [-0.53126227]
BaseTensor: [ 0.73871972]
BaseTensor: [ 0.14669851]
BaseTensor: [-0.08073748]
BaseTensor: [-0.66734655]
BaseTensor: [ 0.0520421]
BaseTensor: [ 0.51215727]
BaseTensor: [-1.52147642]
Content source: sajalsubodh22/PySyft
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