nn-parameter-changes-input-tensor.ipynb

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  {
   "cell_type": "markdown",
   "id": "spare-light",
   "metadata": {},
   "source": [
    "# nn-parameter-changes-input-tensor/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "manufactured-squad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "charming-patch",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "w0 tensor([[-0.8809, -0.1315,  0.7930],\n",
      "        [-0.2604, -1.3041,  0.6648]])\n",
      "model.w Parameter containing:\n",
      "tensor([[-0.8809, -0.1315,  0.7930],\n",
      "        [-0.2604, -1.3041,  0.6648]], requires_grad=True)\n",
      "w0 tensor([[-0.8809, -0.1315,  0.7930],\n",
      "        [-0.2604, -1.3041,  0.6648]])\n",
      "model.w Parameter containing:\n",
      "tensor([[-0.7809, -0.2315,  0.8930],\n",
      "        [-0.3604, -1.2041,  0.5648]], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "class mymodel(nn.Module):\n",
    "    def __init__(self, w0):\n",
    "        super().__init__() \n",
    "        self.w = nn.Parameter(w0.clone().detach())\n",
    "    def forward(self, x):\n",
    "        diff = self.w * x - 10\n",
    "        self.loss = torch.mean(diff * diff)\n",
    "        return self.loss\n",
    "\n",
    "w0 = torch.randn(2, 3)\n",
    "model = mymodel(w0)\n",
    "print(\"w0\", w0)\n",
    "print(\"model.w\", model.w)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = 0.1)\n",
    "inputs = torch.randn(2, 3)\n",
    "loss = model(inputs)\n",
    "optimizer.zero_grad()\n",
    "loss.backward()\n",
    "optimizer.step()\n",
    "print(\"w0\", w0)\n",
    "print(\"model.w\", model.w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "excited-program",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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