p1ch5/1_parameter_estimation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "torch.set_printoptions(edgeitems=2, linewidth=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "t_c = [0.5,  14.0, 15.0, 28.0, 11.0,  8.0,  3.0, -4.0,  6.0, 13.0, 21.0]\n",
    "t_u = [35.7, 55.9, 58.2, 81.9, 56.3, 48.9, 33.9, 21.8, 48.4, 60.4, 68.4]\n",
    "t_c = torch.tensor(t_c)\n",
    "t_u = torch.tensor(t_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def model(t_u, w, b):\n",
    "    return w * t_u + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([35.7000, 55.9000, 58.2000, 81.9000, 56.3000, 48.9000, 33.9000,\n",
       "        21.8000, 48.4000, 60.4000, 68.4000])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = torch.ones(())\n",
    "b = torch.zeros(())\n",
    "\n",
    "t_p = model(t_u, w, b)\n",
    "t_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1763.8846)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = loss_fn(t_p, t_c)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shapes: x: torch.Size([]), y: torch.Size([3, 1])\n",
      "        z: torch.Size([1, 3]), a: torch.Size([2, 1, 1])\n",
      "x * y: torch.Size([3, 1])\n",
      "y * z: torch.Size([3, 3])\n",
      "y * z * a: torch.Size([2, 3, 3])\n"
     ]
    }
   ],
   "source": [
    "x = torch.ones(())\n",
    "y = torch.ones(3,1)\n",
    "z = torch.ones(1,3)\n",
    "a = torch.ones(2, 1, 1)\n",
    "print(f\"shapes: x: {x.shape}, y: {y.shape}\")\n",
    "print(f\"        z: {z.shape}, a: {a.shape}\")\n",
    "print(\"x * y:\", (x * y).shape)\n",
    "print(\"y * z:\", (y * z).shape)\n",
    "print(\"y * z * a:\", (y * z * a).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "delta = 0.1\n",
    "\n",
    "loss_rate_of_change_w = \\\n",
    "    (loss_fn(model(t_u, w + delta, b), t_c) - \n",
    "     loss_fn(model(t_u, w - delta, b), t_c)) / (2.0 * delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "learning_rate = 1e-2\n",
    "\n",
    "w = w - learning_rate * loss_rate_of_change_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "loss_rate_of_change_b = \\\n",
    "    (loss_fn(model(t_u, w, b + delta), t_c) - \n",
    "     loss_fn(model(t_u, w, b - delta), t_c)) / (2.0 * delta)\n",
    "\n",
    "b = b - learning_rate * loss_rate_of_change_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def dloss_fn(t_p, t_c):\n",
    "    dsq_diffs = 2 * (t_p - t_c) / t_p.size(0)  # <1>\n",
    "    return dsq_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def dmodel_dw(t_u, w, b):\n",
    "    return t_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def dmodel_db(t_u, w, b):\n",
    "    return 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def grad_fn(t_u, t_c, t_p, w, b):\n",
    "    dloss_dtp = dloss_fn(t_p, t_c)\n",
    "    dloss_dw = dloss_dtp * dmodel_dw(t_u, w, b)\n",
    "    dloss_db = dloss_dtp * dmodel_db(t_u, w, b)\n",
    "    return torch.stack([dloss_dw.sum(), dloss_db.sum()])  # <1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def training_loop(n_epochs, learning_rate, params, t_u, t_c):\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        w, b = params\n",
    "\n",
    "        t_p = model(t_u, w, b)  # <1>\n",
    "        loss = loss_fn(t_p, t_c)\n",
    "        grad = grad_fn(t_u, t_c, t_p, w, b)  # <2>\n",
    "\n",
    "        params = params - learning_rate * grad\n",
    "\n",
    "        print('Epoch %d, Loss %f' % (epoch, float(loss))) # <3>\n",
    "            \n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "def training_loop(n_epochs, learning_rate, params, t_u, t_c,\n",
    "                  print_params=True):\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        w, b = params\n",
    "\n",
    "        t_p = model(t_u, w, b)  # <1>\n",
    "        loss = loss_fn(t_p, t_c)\n",
    "        grad = grad_fn(t_u, t_c, t_p, w, b)  # <2>\n",
    "\n",
    "        params = params - learning_rate * grad\n",
    "\n",
    "        if epoch in {1, 2, 3, 10, 11, 99, 100, 4000, 5000}:  # <3>\n",
    "            print('Epoch %d, Loss %f' % (epoch, float(loss)))\n",
    "            if print_params:\n",
    "                print('    Params:', params)\n",
    "                print('    Grad:  ', grad)\n",
    "        if epoch in {4, 12, 101}:\n",
    "            print('...')\n",
    "\n",
    "        if not torch.isfinite(loss).all():\n",
    "            break  # <3>\n",
    "            \n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss 1763.884644\n",
      "    Params: tensor([-44.1730,  -0.8260])\n",
      "    Grad:   tensor([4517.2969,   82.6000])\n",
      "Epoch 2, Loss 5802485.500000\n",
      "    Params: tensor([2568.4014,   45.1637])\n",
      "    Grad:   tensor([-261257.4219,   -4598.9712])\n",
      "Epoch 3, Loss 19408035840.000000\n",
      "    Params: tensor([-148527.7344,   -2616.3933])\n",
      "    Grad:   tensor([15109614.0000,   266155.7188])\n",
      "...\n",
      "Epoch 10, Loss 90901154706620645225508955521810432.000000\n",
      "    Params: tensor([3.2144e+17, 5.6621e+15])\n",
      "    Grad:   tensor([-3.2700e+19, -5.7600e+17])\n",
      "Epoch 11, Loss inf\n",
      "    Params: tensor([-1.8590e+19, -3.2746e+17])\n",
      "    Grad:   tensor([1.8912e+21, 3.3313e+19])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([-1.8590e+19, -3.2746e+17])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_loop(\n",
    "    n_epochs = 100, \n",
    "    learning_rate = 1e-2, \n",
    "    params = torch.tensor([1.0, 0.0]), \n",
    "    t_u = t_u, \n",
    "    t_c = t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss 1763.884644\n",
      "    Params: tensor([ 0.5483, -0.0083])\n",
      "    Grad:   tensor([4517.2969,   82.6000])\n",
      "Epoch 2, Loss 323.090546\n",
      "    Params: tensor([ 0.3623, -0.0118])\n",
      "    Grad:   tensor([1859.5493,   35.7843])\n",
      "Epoch 3, Loss 78.929634\n",
      "    Params: tensor([ 0.2858, -0.0135])\n",
      "    Grad:   tensor([765.4667,  16.5122])\n",
      "...\n",
      "Epoch 10, Loss 29.105242\n",
      "    Params: tensor([ 0.2324, -0.0166])\n",
      "    Grad:   tensor([1.4803, 3.0544])\n",
      "Epoch 11, Loss 29.104168\n",
      "    Params: tensor([ 0.2323, -0.0169])\n",
      "    Grad:   tensor([0.5781, 3.0384])\n",
      "...\n",
      "Epoch 99, Loss 29.023582\n",
      "    Params: tensor([ 0.2327, -0.0435])\n",
      "    Grad:   tensor([-0.0533,  3.0226])\n",
      "Epoch 100, Loss 29.022669\n",
      "    Params: tensor([ 0.2327, -0.0438])\n",
      "    Grad:   tensor([-0.0532,  3.0226])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([ 0.2327, -0.0438])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_loop(\n",
    "    n_epochs = 100, \n",
    "    learning_rate = 1e-4, \n",
    "    params = torch.tensor([1.0, 0.0]), \n",
    "    t_u = t_u, \n",
    "    t_c = t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "t_un = 0.1 * t_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss 80.364342\n",
      "    Params: tensor([1.7761, 0.1064])\n",
      "    Grad:   tensor([-77.6140, -10.6400])\n",
      "Epoch 2, Loss 37.574917\n",
      "    Params: tensor([2.0848, 0.1303])\n",
      "    Grad:   tensor([-30.8623,  -2.3864])\n",
      "Epoch 3, Loss 30.871077\n",
      "    Params: tensor([2.2094, 0.1217])\n",
      "    Grad:   tensor([-12.4631,   0.8587])\n",
      "...\n",
      "Epoch 10, Loss 29.030487\n",
      "    Params: tensor([ 2.3232, -0.0710])\n",
      "    Grad:   tensor([-0.5355,  2.9295])\n",
      "Epoch 11, Loss 28.941875\n",
      "    Params: tensor([ 2.3284, -0.1003])\n",
      "    Grad:   tensor([-0.5240,  2.9264])\n",
      "...\n",
      "Epoch 99, Loss 22.214186\n",
      "    Params: tensor([ 2.7508, -2.4910])\n",
      "    Grad:   tensor([-0.4453,  2.5208])\n",
      "Epoch 100, Loss 22.148710\n",
      "    Params: tensor([ 2.7553, -2.5162])\n",
      "    Grad:   tensor([-0.4446,  2.5165])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([ 2.7553, -2.5162])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_loop(\n",
    "    n_epochs = 100, \n",
    "    learning_rate = 1e-2, \n",
    "    params = torch.tensor([1.0, 0.0]), \n",
    "    t_u = t_un, # <1>\n",
    "    t_c = t_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss 80.364342\n",
      "Epoch 2, Loss 37.574917\n",
      "Epoch 3, Loss 30.871077\n",
      "...\n",
      "Epoch 10, Loss 29.030487\n",
      "Epoch 11, Loss 28.941875\n",
      "...\n",
      "Epoch 99, Loss 22.214186\n",
      "Epoch 100, Loss 22.148710\n",
      "...\n",
      "Epoch 4000, Loss 2.927680\n",
      "Epoch 5000, Loss 2.927648\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([  5.3671, -17.3012])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = training_loop(\n",
    "    n_epochs = 5000, \n",
    "    learning_rate = 1e-2, \n",
    "    params = torch.tensor([1.0, 0.0]), \n",
    "    t_u = t_un, \n",
    "    t_c = t_c,\n",
    "    print_params = False)\n",
    "\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t_p = model(t_un, *params)  # <1>\n",
    "\n",
    "fig = plt.figure(dpi=600)\n",
    "plt.xlabel(\"Temperature (°Fahrenheit)\")\n",
    "plt.ylabel(\"Temperature (°Celsius)\")\n",
    "plt.plot(t_u.numpy(), t_p.detach().numpy()) # <2>\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.savefig(\"temp_unknown_plot.png\", format=\"png\")  # bookskip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig = plt.figure(dpi=600)\n",
    "plt.xlabel(\"Measurement\")\n",
    "plt.ylabel(\"Temperature (°Celsius)\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "\n",
    "plt.savefig(\"temp_data_plot.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}