{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3867293b-8f5e-426a-a223-834c7e04daef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e73a6d0e-4db9-4b0d-ab8f-e421373f1944",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearNet(torch.nn.Module):\n",
    "    def __init__(self, input_size,output_size,layers_size):\n",
    "        super().__init__()\n",
    "        \n",
    "        #So, this next section constructs different layers within the NN\n",
    "        #sinlge linear section\n",
    "        self.linear_step1 = torch.nn.Linear(input_size,layers_size)\n",
    "        #single linear section\n",
    "        self.linear_step2 = torch.nn.Linear(layers_size,output_size)\n",
    "        \n",
    "    def forward(self, input_values):\n",
    "        intermediate_values = self.linear_step1(input_values)\n",
    "        out_values = self.linear_step2(intermediate_values)\n",
    "        \n",
    "        return out_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2870086a-cb32-4f7e-9ee3-2e7a545c86cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearNet2(torch.nn.Module):\n",
    "    def __init__(self, input_size,output_size,layers_size):\n",
    "        super().__init__()\n",
    "        \n",
    "        #combined together into a set of sequential workers\n",
    "        self.sequential_layers = torch.nn.Sequential(\n",
    "            torch.nn.Linear(input_size,layers_size)\n",
    "            torch.nn.Linear(layers_size,output_size)\n",
    "        )\n",
    "        \n",
    "    def forward(self, input_values):\n",
    "        return self.sequential_layers(input_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb6c7507-5abf-428e-8644-d0e42525368d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearNet(input_size = 5, output_size=5, layers_size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3244863-39f7-4fa6-a284-059b858569cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearNet(\n",
      "  (linear_step1): Linear(in_features=5, out_features=15, bias=True)\n",
      "  (linear_step2): Linear(in_features=15, out_features=5, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3480a0ec-7b5b-46d7-bdd2-631e57be9c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_in = torch.tensor([1.5,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1ace0be-9b05-4c77-b076-a05b68cf8f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.3597, 0.7032, 0.0924, 0.7974, 3.1524], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.forward(data_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "464ae8e4-f71e-494f-9845-fefe5c75a7b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('linear_step1.weight',\n",
       "              tensor([[ 0.2498, -0.2177,  0.2323,  0.3493, -0.2541],\n",
       "                      [-0.2169,  0.0535, -0.1223,  0.0237, -0.0184],\n",
       "                      [-0.1554, -0.0134,  0.2918,  0.3542,  0.3464],\n",
       "                      [-0.2378,  0.3828, -0.3026, -0.1545, -0.1484],\n",
       "                      [ 0.3282, -0.1492,  0.3551, -0.0447, -0.3294],\n",
       "                      [ 0.2789, -0.1546,  0.2821,  0.0136,  0.4210],\n",
       "                      [ 0.2911, -0.2191,  0.0493,  0.1006,  0.0470],\n",
       "                      [-0.2269,  0.1705,  0.1198,  0.4040,  0.2512],\n",
       "                      [-0.2696, -0.4259,  0.4229,  0.1412,  0.3553],\n",
       "                      [ 0.0293,  0.4044,  0.3961, -0.3992,  0.2586],\n",
       "                      [-0.3101, -0.0327,  0.1832,  0.0295, -0.3185],\n",
       "                      [ 0.0637, -0.0770,  0.2297, -0.1567,  0.4379],\n",
       "                      [-0.0540, -0.1769,  0.3407,  0.1942,  0.3494],\n",
       "                      [-0.3609, -0.3536,  0.2491, -0.0490, -0.1199],\n",
       "                      [ 0.2946, -0.0782, -0.0580,  0.2313, -0.0696]])),\n",
       "             ('linear_step1.bias',\n",
       "              tensor([-0.4207, -0.1624,  0.0212, -0.0988, -0.2106,  0.2991, -0.3496, -0.1799,\n",
       "                      -0.4257, -0.3384, -0.0020,  0.1267,  0.0252,  0.0037,  0.1784])),\n",
       "             ('linear_step2.weight',\n",
       "              tensor([[ 0.1404, -0.1424,  0.1518, -0.1080,  0.1269, -0.2030, -0.0533, -0.2240,\n",
       "                        0.0364, -0.0393,  0.1619,  0.1242,  0.0731, -0.1545,  0.2024],\n",
       "                      [-0.2529,  0.0578,  0.1629, -0.0352, -0.2128,  0.0429,  0.0261,  0.2264,\n",
       "                       -0.0470,  0.0277,  0.0272, -0.1074, -0.1334,  0.0792, -0.0173],\n",
       "                      [ 0.0459,  0.2224, -0.2272,  0.0123, -0.0676,  0.2378,  0.2166, -0.0981,\n",
       "                        0.1010, -0.1593, -0.2422, -0.1253,  0.0899, -0.0760, -0.0816],\n",
       "                      [ 0.1763,  0.2344,  0.0591, -0.2299,  0.1116,  0.0604,  0.2032,  0.1298,\n",
       "                        0.0509,  0.2581,  0.2425, -0.0920,  0.0098,  0.1353, -0.2110],\n",
       "                      [ 0.0726, -0.1959,  0.2114, -0.0732, -0.1089,  0.0836, -0.1061,  0.1640,\n",
       "                        0.1221,  0.0281, -0.2401,  0.1108,  0.1354,  0.1903, -0.1006]])),\n",
       "             ('linear_step2.bias',\n",
       "              tensor([0.2462, 0.0098, 0.1239, 0.0689, 0.2404]))])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04bca0f6-dfb9-4013-ac2a-f4efd2f93c15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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