{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tested-harassment",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "absolute-psychiatry",
   "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": 29,
   "id": "exciting-pipeline",
   "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)\n",
    "    \n",
    "    def fwd_wrapper(self,input1,input2):\n",
    "        #Combines two layers together.\n",
    "        #This supports the thought that I can create a \"launch\" nn that takes two values as input.\n",
    "        return self.sequential_layers(torch.cat((input1,input2),0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "romantic-visit",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearNet2(input_size = 5, output_size=5, layers_size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "finished-taste",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearNet2(\n",
      "  (sequential_layers): Sequential(\n",
      "    (0): Linear(in_features=5, out_features=15, bias=True)\n",
      "    (1): Linear(in_features=15, out_features=5, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dress-baltimore",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_in = torch.tensor([1.5,2,3,4,5])\n",
    "data_in1 = torch.tensor([1.5,2])\n",
    "data_in2 = torch.tensor([3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "korean-width",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([8.7445e-04, 5.1066e-02, 5.5142e-01, 3.3832e-01, 1.8822e+00],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.forward(data_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "novel-composer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([8.7445e-04, 5.1066e-02, 5.5142e-01, 3.3832e-01, 1.8822e+00],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = model.fwd_wrapper(data_in1,data_in2)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "accepting-fireplace",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "grad can be implicitly created only for scalar outputs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-b26b7a7d68aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/pytorch-CPU/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pytorch-CPU/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tensor_or_tensors_to_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m     \u001b[0mgrad_tensors_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_make_grads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mretain_graph\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/pytorch-CPU/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36m_make_grads\u001b[0;34m(outputs, grads)\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequires_grad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"grad can be implicitly created only for scalar outputs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m                 \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreserve_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: grad can be implicitly created only for scalar outputs"
     ]
    }
   ],
   "source": [
    "test.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "rapid-spoke",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('linear_step1.weight',\n",
       "              tensor([[ 0.1832, -0.0033, -0.0508, -0.3904,  0.3259],\n",
       "                      [-0.1713,  0.0096, -0.0525,  0.4356, -0.2567],\n",
       "                      [ 0.3026, -0.4448,  0.2032, -0.2238, -0.1767],\n",
       "                      [ 0.4211,  0.3466,  0.4140,  0.1798,  0.0438],\n",
       "                      [ 0.2925,  0.2805, -0.1709, -0.0526,  0.1170],\n",
       "                      [ 0.3011, -0.1513, -0.1232, -0.3212,  0.1243],\n",
       "                      [-0.3001,  0.4073, -0.3111,  0.4319, -0.1216],\n",
       "                      [ 0.2732,  0.2345,  0.0355,  0.3887,  0.4408],\n",
       "                      [-0.4345, -0.0974,  0.0280,  0.2111, -0.3547],\n",
       "                      [ 0.0311,  0.3230,  0.0452,  0.0421, -0.3895],\n",
       "                      [-0.4149, -0.0149,  0.2047,  0.3821, -0.1537],\n",
       "                      [-0.3941, -0.1030, -0.3153,  0.1546, -0.2481],\n",
       "                      [-0.2590, -0.3550, -0.3910, -0.1634,  0.0569],\n",
       "                      [-0.2632, -0.3178, -0.1942, -0.2556, -0.0210],\n",
       "                      [-0.1319, -0.4315,  0.2441, -0.3021, -0.2024]])),\n",
       "             ('linear_step1.bias',\n",
       "              tensor([ 0.4211,  0.3796, -0.0749,  0.1055,  0.2941, -0.0833,  0.2162,  0.4153,\n",
       "                      -0.0988,  0.3072, -0.2772,  0.0678,  0.3566,  0.1708, -0.0892])),\n",
       "             ('linear_step2.weight',\n",
       "              tensor([[ 0.0047,  0.0587,  0.1857, -0.2414,  0.0850,  0.2135,  0.0963,  0.0224,\n",
       "                        0.0328,  0.0438, -0.2483,  0.0613,  0.2572,  0.2077,  0.0558],\n",
       "                      [ 0.2141,  0.0382,  0.1007, -0.0127,  0.2511,  0.0085,  0.1415,  0.1731,\n",
       "                       -0.0145,  0.0885, -0.0495,  0.1828,  0.2506, -0.1226, -0.1583],\n",
       "                      [ 0.0772,  0.1059,  0.0391, -0.0915, -0.2160,  0.1320,  0.0727,  0.1585,\n",
       "                        0.1454, -0.0973, -0.0960, -0.0753, -0.2022,  0.0092,  0.1243],\n",
       "                      [-0.2120,  0.1741, -0.0886,  0.0816,  0.0032,  0.1702,  0.1375, -0.1119,\n",
       "                       -0.0059, -0.1185,  0.2243, -0.1849, -0.1739, -0.0298, -0.1838],\n",
       "                      [ 0.0095,  0.0369,  0.0736,  0.1567, -0.1493,  0.2170,  0.1332,  0.1684,\n",
       "                        0.1232,  0.1393, -0.2525, -0.1990, -0.1293, -0.1827,  0.1345]])),\n",
       "             ('linear_step2.bias',\n",
       "              tensor([ 0.1185, -0.1514,  0.1662,  0.0849, -0.1373]))])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "compatible-least",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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