Part 12 - Train an Encrypted Neural Network on Encrypted Data

PySyft/examples/Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Falling back to insecure randomness since the required custom op could not be found for the installed version of TensorFlow. Fix this by compiling custom ops. Missing file was '/opt/conda/lib/python3.7/site-packages/tf_encrypted/operations/secure_random/secure_random_module_tf_1.15.2.so'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tf_encrypted/session.py:24: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "import syft as sy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set everything up\n",
+    "hook = sy.TorchHook(torch) \n",
+    "\n",
+    "alice = sy.VirtualWorker(id=\"alice\", hook=hook)\n",
+    "bob = sy.VirtualWorker(id=\"bob\", hook=hook)\n",
+    "james = sy.VirtualWorker(id=\"james\", hook=hook)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A Toy Dataset\n",
+    "data = torch.tensor([[0,0],[0,1],[1,0],[1,1.]])\n",
+    "target = torch.tensor([[0],[0],[1],[1.]])\n",
+    "\n",
+    "# A Toy Model\n",
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Net, self).__init__()\n",
+    "        self.fc1 = nn.Linear(2, 2)\n",
+    "        self.fc2 = nn.Linear(2, 1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.fc1(x)\n",
+    "        x = F.relu(x)\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "model = Net()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/additive_shared.py:79: UserWarning: Use dtype instead of field\n",
+      "  warnings.warn(\"Use dtype instead of field\")\n",
+      "/opt/conda/lib/python3.7/site-packages/syft/frameworks/torch/tensors/interpreters/additive_shared.py:91: UserWarning: Default args selected\n",
+      "  warnings.warn(\"Default args selected\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We encode everything\n",
+    "data = data.fix_precision().share(bob, alice, crypto_provider=james, requires_grad=True)\n",
+    "target = target.fix_precision().share(bob, alice, crypto_provider=james, requires_grad=True)\n",
+    "model = model.fix_precision().share(bob, alice, crypto_provider=james, requires_grad=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.8350)\n",
+      "tensor(0.7070)\n",
+      "tensor(0.6110)\n",
+      "tensor(0.5240)\n",
+      "tensor(0.4390)\n",
+      "tensor(0.3560)\n",
+      "tensor(0.2800)\n",
+      "tensor(0.2140)\n",
+      "tensor(0.1660)\n",
+      "tensor(0.1350)\n",
+      "tensor(0.1010)\n",
+      "tensor(0.0740)\n",
+      "tensor(0.0610)\n",
+      "tensor(0.0380)\n",
+      "tensor(0.0330)\n",
+      "tensor(0.0210)\n",
+      "tensor(0.0140)\n",
+      "tensor(0.0110)\n",
+      "tensor(0.0080)\n",
+      "tensor(0.0070)\n"
+     ]
+    }
+   ],
+   "source": [
+    "opt = optim.SGD(params=model.parameters(),lr=0.1).fix_precision()\n",
+    "\n",
+    "for iter in range(20):\n",
+    "    # 1) erase previous gradients (if they exist)\n",
+    "    opt.zero_grad()\n",
+    "\n",
+    "    # 2) make a prediction\n",
+    "    pred = model(data)\n",
+    "\n",
+    "    # 3) calculate how much we missed\n",
+    "    loss = ((pred - target)**2).sum()\n",
+    "\n",
+    "    # 4) figure out which weights caused us to miss\n",
+    "    loss.backward()\n",
+    "\n",
+    "    # 5) change those weights\n",
+    "    opt.step()\n",
+    "\n",
+    "    # 6) print our progress\n",
+    "    print(loss.get().float_precision())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
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+ "nbformat": 4,
+ "nbformat_minor": 4
+}