Orbits/Code/BasicNeuralNet.ipynb

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   "cell_type": "markdown",
   "id": "usual-deviation",
   "metadata": {},
   "source": [
    "Note on pytorch. NN optimization acts imperitively/by side effect as follows.\n",
    " - Define model\n",
    " - loop\n",
    "     - Calculate loss\n",
    "     - Zero gradients\n",
    "     - backprop to model\n",
    "     - check conditions for exit\n",
    " - report diagnostics\n",
    " - disect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "generous-alexandria",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "surrounded-jurisdiction",
   "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": 3,
   "id": "legal-character",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearNet(input_size = 5, output_size=5, layers_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "statutory-bachelor",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_in = torch.tensor([1.5,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "informational-bennett",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.5000, 2.0000, 3.0000, 4.0000, 5.0000])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "basic-printer",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = torch.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "nonprofit-sponsorship",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn2(output,target):\n",
    "    return torch.nn.MSELoss()(2*output,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "logical-conversation",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prep Optimizer\n",
    "optimizer = torch.optim.SGD(model.parameters(),lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "inner-stations",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "tensor([-0.2090, -0.2099,  0.9129, -1.1506,  0.9998])\n",
      "tensor([-0.2780,  0.1246, -0.0349,  0.0779, -0.1034])\n",
      "tensor([-0.0678,  0.0177,  0.0479, -0.0193,  0.0401])\n",
      "tensor([-0.0338,  0.0143,  0.0187,  0.0088,  0.0175])\n",
      "tensor([-0.0157,  0.0081,  0.0111,  0.0089,  0.0140])\n",
      "tensor([-0.0083,  0.0051,  0.0063,  0.0076,  0.0101])\n",
      "tensor([-0.0049,  0.0033,  0.0038,  0.0058,  0.0074])\n",
      "tensor([-0.0032,  0.0022,  0.0023,  0.0043,  0.0054])\n",
      "tensor([-0.0022,  0.0015,  0.0014,  0.0031,  0.0040])\n",
      "tensor([-0.0016,  0.0010,  0.0009,  0.0023,  0.0030])\n",
      "tensor([-0.0012,  0.0007,  0.0006,  0.0017,  0.0022])\n",
      "tensor([-0.0009,  0.0005,  0.0004,  0.0013,  0.0017])\n",
      "tensor([-0.0007,  0.0003,  0.0003,  0.0010,  0.0013])\n",
      "tensor([-0.0005,  0.0002,  0.0002,  0.0007,  0.0010])\n",
      "tensor([-0.0004,  0.0002,  0.0002,  0.0005,  0.0007])\n",
      "tensor([-0.0003,  0.0001,  0.0001,  0.0004,  0.0006])\n",
      "tensor([-2.4156e-04,  9.8367e-05,  8.3392e-05,  3.1965e-04,  4.3452e-04])\n",
      "tensor([-1.8733e-04,  7.3545e-05,  6.3141e-05,  2.4562e-04,  3.3559e-04])\n",
      "tensor([-1.4602e-04,  5.5818e-05,  4.7690e-05,  1.8978e-04,  2.5897e-04])\n",
      "tensor([-1.1313e-04,  4.2276e-05,  3.6786e-05,  1.4635e-04,  2.0107e-04])\n",
      "tensor([-8.8156e-05,  3.2514e-05,  2.8075e-05,  1.1362e-04,  1.5562e-04])\n",
      "tensor([-6.8587e-05,  2.5118e-05,  2.1561e-05,  8.8054e-05,  1.2085e-04])\n",
      "tensor([-5.3484e-05,  1.9454e-05,  1.6658e-05,  6.8377e-05,  9.3834e-05])\n",
      "tensor([-4.1497e-05,  1.4939e-05,  1.3124e-05,  5.3061e-05,  7.3066e-05])\n",
      "tensor([-3.2303e-05,  1.1659e-05,  1.0152e-05,  4.1244e-05,  5.6852e-05])\n",
      "\n",
      " tensor(1.3901e-06, grad_fn=<MseLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    #training loop\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    output = model.forward(data_in)\n",
    "    output,output.shape\n",
    "\n",
    "    l = loss_fn2(output, target)\n",
    "\n",
    "    l.backward()\n",
    "\n",
    "    optimizer.step()\n",
    "\n",
    "    print(model.linear_step1.bias.grad)\n",
    "print(\"\\n\",l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "native-bristol",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moral-apollo",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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