{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "torch.set_printoptions(edgeitems=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_c = torch.tensor([0.5, 14.0, 15.0, 28.0, 11.0, 8.0,\n",
    "                    3.0, -4.0, 6.0, 13.0, 21.0])\n",
    "t_u = torch.tensor([35.7, 55.9, 58.2, 81.9, 56.3, 48.9,\n",
    "                    33.9, 21.8, 48.4, 60.4, 68.4])\n",
    "t_un = 0.1 * t_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(t_u, w, b):\n",
    "    return w * t_u + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn(t_p, t_c):\n",
    "    squared_diffs = (t_p - t_c)**2\n",
    "    return squared_diffs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = torch.tensor([1.0, 0.0], requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params.grad is None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4517.2969,   82.6000])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = loss_fn(model(t_u, *params), t_c)\n",
    "loss.backward()\n",
    "\n",
    "params.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if params.grad is not None:\n",
    "    params.grad.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(n_epochs, learning_rate, params, t_u, t_c):\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        if params.grad is not None:  # <1>\n",
    "            params.grad.zero_()\n",
    "        \n",
    "        t_p = model(t_u, *params) \n",
    "        loss = loss_fn(t_p, t_c)\n",
    "        loss.backward()\n",
    "        \n",
    "        with torch.no_grad():  # <2>\n",
    "            params -= learning_rate * params.grad\n",
    "\n",
    "        if epoch % 500 == 0:\n",
    "            print('Epoch %d, Loss %f' % (epoch, float(loss)))\n",
    "            \n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 500, Loss 7.860116\n",
      "Epoch 1000, Loss 3.828538\n",
      "Epoch 1500, Loss 3.092191\n",
      "Epoch 2000, Loss 2.957697\n",
      "Epoch 2500, Loss 2.933134\n",
      "Epoch 3000, Loss 2.928648\n",
      "Epoch 3500, Loss 2.927830\n",
      "Epoch 4000, Loss 2.927679\n",
      "Epoch 4500, Loss 2.927652\n",
      "Epoch 5000, Loss 2.927647\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([  5.3671, -17.3012], requires_grad=True)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_loop(\n",
    "    n_epochs = 5000, \n",
    "    learning_rate = 1e-2, \n",
    "    params = torch.tensor([1.0, 0.0], requires_grad=True), # <1> \n",
    "    t_u = t_un, # <2> \n",
    "    t_c = t_c)"
   ]
  }
 ],
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