PyTorch1.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "def activation(x):\n",
    "    \"\"\" Sigmoid activation function \n",
    "    \n",
    "        Arguments\n",
    "        ---------\n",
    "        x: torch.Tensor\n",
    "    \"\"\"\n",
    "    return 1/(1+torch.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Generate some data\n",
    "torch.manual_seed(7) # Set the random seed so things are predictable\n",
    "\n",
    "# Features are 5 random normal variables\n",
    "features = torch.randn((1, 5))\n",
    "# True weights for our data, random normal variables again\n",
    "weights = torch.randn_like(features)\n",
    "# and a true bias term\n",
    "bias = torch.randn((1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# verify with another resource. not really buying the solution.\n",
    "weights = torch.transpose(weights, 0, 1)\n",
    "result = torch.sum(torch.matmul(features,torch.reshape(weights, (5,1)))) + bias\n",
    "result = activation(result)\n",
    "#transpose vs reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1595]])\n"
     ]
    }
   ],
   "source": [
    "print(result) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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