{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "torch.set_printoptions(edgeitems=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([11, 1])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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).unsqueeze(1) # <1>\n",
    "t_u = torch.tensor(t_u).unsqueeze(1) # <1>\n",
    "\n",
    "t_u.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 3,  6,  2,  5,  8,  0, 10,  9,  4]), tensor([7, 1]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_samples = t_u.shape[0]\n",
    "n_val = int(0.2 * n_samples)\n",
    "\n",
    "shuffled_indices = torch.randperm(n_samples)\n",
    "\n",
    "train_indices = shuffled_indices[:-n_val]\n",
    "val_indices = shuffled_indices[-n_val:]\n",
    "\n",
    "train_indices, val_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_u_train = t_u[train_indices]\n",
    "t_c_train = t_c[train_indices]\n",
    "\n",
    "t_u_val = t_u[val_indices]\n",
    "t_c_val = t_c[val_indices]\n",
    "\n",
    "t_un_train = 0.1 * t_u_train\n",
    "t_un_val = 0.1 * t_u_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.9852],\n",
       "        [-2.6876]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "linear_model(t_un_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[-0.4992]], requires_grad=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0.1031], requires_grad=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.3961], grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.ones(1)\n",
    "linear_model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961],\n",
       "        [-0.3961]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.ones(10, 1)\n",
    "linear_model(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "optimizer = optim.SGD(\n",
    "    linear_model.parameters(), # <2>\n",
    "    lr=1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Module.parameters at 0x0000020A2B022D58>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[0.3791]], requires_grad=True), Parameter containing:\n",
       " tensor([-0.5349], requires_grad=True)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(linear_model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(n_epochs, optimizer, model, loss_fn, t_u_train, t_u_val, t_c_train, t_c_val):\n",
    "    for epoch in range(1, n_epochs + 1):\n",
    "        t_p_train = model(t_u_train) # <1>\n",
    "        loss_train = loss_fn(t_p_train, t_c_train)\n",
    "\n",
    "        t_p_val = model(t_u_val) # <1>\n",
    "        loss_val = loss_fn(t_p_val, t_c_val)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss_train.backward() # <2>\n",
    "        optimizer.step()\n",
    "\n",
    "        if epoch == 1 or epoch % 1000 == 0:\n",
    "            print('Epoch {}, Training loss {}, Validation loss {}'.format(\n",
    "                epoch, float(loss_train), float(loss_val)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 82.00872802734375, Validation loss 55.06585693359375\n",
      "Epoch 1000, Training loss 4.997957229614258, Validation loss 1.2232692241668701\n",
      "Epoch 2000, Training loss 3.021897077560425, Validation loss 2.7614946365356445\n",
      "Epoch 3000, Training loss 2.8582355976104736, Validation loss 4.448704719543457\n",
      "\n",
      "Parameter containing:\n",
      "tensor([[5.5630]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([-18.6652], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "def loss_fn(t_p, t_c):\n",
    "    squared_diffs = (t_p - t_c)**2\n",
    "    return squared_diffs.mean()\n",
    "\n",
    "linear_model = nn.Linear(1, 1) # <1>\n",
    "optimizer = optim.SGD(linear_model.parameters(), lr=1e-2)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 3000, \n",
    "    optimizer = optimizer,\n",
    "    model = linear_model,\n",
    "    loss_fn = loss_fn,\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "print()\n",
    "print(linear_model.weight)\n",
    "print(linear_model.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 92.3511962890625, Validation loss 57.714385986328125\n",
      "Epoch 1000, Training loss 4.910993576049805, Validation loss 1.173926591873169\n",
      "Epoch 2000, Training loss 3.014694929122925, Validation loss 2.8020541667938232\n",
      "Epoch 3000, Training loss 2.857640504837036, Validation loss 4.464878559112549\n",
      "\n",
      "Parameter containing:\n",
      "tensor([[5.5647]], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([-18.6750], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "linear_model = nn.Linear(1, 1)\n",
    "optimizer = optim.SGD(linear_model.parameters(), lr=1e-2)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 3000, \n",
    "    optimizer = optimizer,\n",
    "    model = linear_model,\n",
    "    loss_fn = nn.MSELoss(), # <1>\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "print()\n",
    "print(linear_model.weight)\n",
    "print(linear_model.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=13, bias=True)\n",
       "  (1): Tanh()\n",
       "  (2): Linear(in_features=13, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_model = nn.Sequential(\n",
    "            nn.Linear(1, 13), # <1>\n",
    "            nn.Tanh(),\n",
    "            nn.Linear(13, 1)) # <2>\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[torch.Size([13, 1]), torch.Size([13]), torch.Size([1, 13]), torch.Size([1])]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[param.shape for param in seq_model.parameters()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.weight torch.Size([13, 1])\n",
      "0.bias torch.Size([13])\n",
      "2.weight torch.Size([1, 13])\n",
      "2.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "for name, param in seq_model.named_parameters():\n",
    "    print(name, param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (hidden_linear): Linear(in_features=1, out_features=8, bias=True)\n",
       "  (hidden_activation): Tanh()\n",
       "  (output_linear): Linear(in_features=8, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "seq_model = nn.Sequential(OrderedDict([\n",
    "    ('hidden_linear', nn.Linear(1, 8)),\n",
    "    ('hidden_activation', nn.Tanh()),\n",
    "    ('output_linear', nn.Linear(8, 1))\n",
    "]))\n",
    "\n",
    "seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden_linear.weight torch.Size([8, 1])\n",
      "hidden_linear.bias torch.Size([8])\n",
      "output_linear.weight torch.Size([1, 8])\n",
      "output_linear.bias torch.Size([1])\n"
     ]
    }
   ],
   "source": [
    "for name, param in seq_model.named_parameters():\n",
    "    print(name, param.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([-0.2194], requires_grad=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_model.output_linear.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 207.2268524169922, Validation loss 106.6062240600586\n",
      "Epoch 1000, Training loss 6.121204376220703, Validation loss 2.213937759399414\n",
      "Epoch 2000, Training loss 5.273784637451172, Validation loss 0.0025627268478274345\n",
      "Epoch 3000, Training loss 2.4436306953430176, Validation loss 1.9463319778442383\n",
      "Epoch 4000, Training loss 1.6909029483795166, Validation loss 4.027190685272217\n",
      "Epoch 5000, Training loss 1.4900192022323608, Validation loss 5.368413925170898\n",
      "output tensor([[-1.8966],\n",
      "        [11.1774]], grad_fn=<AddmmBackward>)\n",
      "answer tensor([[-4.],\n",
      "        [14.]])\n",
      "hidden tensor([[-0.0073],\n",
      "        [ 4.0584],\n",
      "        [-4.5080],\n",
      "        [-4.4498],\n",
      "        [ 0.0127],\n",
      "        [-0.0073],\n",
      "        [-4.1530],\n",
      "        [-0.6572]])\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.SGD(seq_model.parameters(), lr=1e-3) # <1>\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 5000, \n",
    "    optimizer = optimizer,\n",
    "    model = seq_model,\n",
    "    loss_fn = nn.MSELoss(),\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "    \n",
    "print('output', seq_model(t_un_val))\n",
    "print('answer', t_c_val)\n",
    "print('hidden', seq_model.hidden_linear.weight.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20a2b0fff60>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OIFEUxcwkz6hx2T9UvO03kjzczfqQJK+oeE8AAAAAAOi1sizz4fvvz2/fdVc6yrLXeX89bVo+PXNmmouigukAAAAAAODElEQO664IMqLfpuhbw0/w845+nYJ6eVmN9dvKslxX5YZlWXYl+XKNy2rNBQAAAAAA/epQV1fefNdd+dP77+91VltR5Pq5c/P+pz0thYIIAAAAAAD9QEnksL3drJ2oXDHQnKjs0t29M3i8sMb6N/to31q5v1gURXMf7Q0AAAAAACdlV0dHXnbnnfncI4/0OmtsS0u+fcEF+c2JEyuYDAAAAAAAekZJ5LDuihLn9tsUfWvSCX6uJDLIFUXRkuR5NS77Th9tf0uS/d2sj0nyjD7aGwAAAAAAemzTgQN53vLl+c/HH+911tOGDMn/XHhhnj92bAWTAQAAAABAzymJHPZoN2vn99sUfevY+zh6pvnW/h6EfjcvJz5JJkkOJflJX2xcluX+JLfXuExJBAAAAACAulq1Z0+euWxZlu/e3eusC0eOzI8WL077iO4+mgcAAAAAgL6hJHLY/d2sze2vIfpYe5LymJ+VSTbUYRb61+Ia66vLsjzQh/vfWmP9wj7cGwAAAAAAuvXfjz+eZy9blgcO9P6j8peceWa+v2hRzhkypILJAAAAAADg5CmJJCnLcluSo18NdWyR4tn9PE7liqI4J8mUo388Zvn+/p2GOlhUY31FH+9/R411JREAAAAAAOri+s2b8+IVK7Kjs7PXWW8+55x8ff78jGxpqWAyAAAAAAA4NUoiP3NXnlqgKI/8+ZlFUQz0T/Nf0M3a2v4agrqZVWN9XR/vf0+N9Zl9vD8AAAAAADxFWZb5qwceyGvXrMnB8tjvDzt5H5k6NUtnzUprk0dvAAAAAADUl0+qf+aHT/r9k8sio5O8pJ9nqdqru1n7Ub9NQb2cX2N9fR/vXyt/RFEU4/t4BgAAAAAASJJ0dHXl7evW5Y/uvbfXWS1FkS/MmZM/mTo1RXHsYe4AAAAAAND/lER+pruyxBX9NkXFiqIYl8Mll6Nfg/Xkr8Pak2RFvw9FvykOP5GaUuOyTX08xsNJumpcU6vIAgAAAAAAvbanszOXr1qVz2zq/Ufjo5qb860FC/KGs8+uYDIAAAAAAKiGksjP/Hd+vkhR5vCpIq8oimJ6PYaqwLuTDDny++JJv5ZJbinLCs5Qp5GdkWRojWse6csByrLsTPJojcsm9eUMAAAAAACw+eDB/OLy5fnGY4/1Ouvctrb84MIL88Izz6xgMgAAAAAAqI6SyBFlWW5K8sM8tUhxVHOSD/X7UL1UFMVZSd6Rp54e8mRf6cdxqI9xPbhmS59PUXuPnswJAAAAAACn5O69e/PMZcvy0127ep21YMSI/Gjx4iwcObKCyQAAAAAAoFpKIk91bGni6IkbRZLXFUXxi/0/Uq/8bZKxR35/9F6O6khyU79PRH/ryVeY7ezzKWrv4avWAAAAAADoE/+zY0eeuWxZ7tu/v9dZvzx2bG658MJMHlrrEG8AAAAAAKiPlnoP0GC+lOQvkgzJz8ohedLvP1cUxdPLsuz9OeR9rCiKVye5Ik+9j+RnZZGbyrJ8vB6z0a/OqLG+tyzLzn6YY8CVRIqieHuSt/XDVtP7YQ8AAAAAgNPSV7duzRWrV+dAeaJD13vuDRMn5trZs9PW5DvYAAAAAABoXEoiT1KW5daiKL6Y5C352akbTz6B42lJvlIUxYvKsjxUjxl7oiiKxUk+l6eeHHKsj/fTONRXra8y29svUyR7aqw34leujU/SXu8hAAAAAAA4NZ988MG89557un1Y0lN/MmVKPjx1aoqiqH0xAAAAAADUka86+nkfS3L0ZIVjiyJFkucl+VpRFI34l9pTFMXTk3w7yfCjPzry69H5yyS3lGX5kzqMR/9rq7He0S9T1N6n1pwAAAAAANAjnWWZ96xfn/dUUBBpTrJ01qx85PzzFUQAAAAAABgQlESOUZbluiR/n5+VK456clHkxUm+UxTFpH4er1tFUVyW5DtJzszPZk2eeqJIV5L39PNo1I+SCAAAAAAAp43dHR25fOXKfHLjxl5njWhqyjcWLMhbJjXU4yAAAAAAAOiWksjxfTDJliO/f3LB4slFkWclWV4UxW/082w/pyiKEUVRfCrJV5OMTo77xVhHZ7+2LMvb+3M+6qrWf+OdNdarUmuf5n6ZAgAAAACAQWvj/v157vLl+fpjj/U6a2Jra7534YX5lXHjKpgMAAAAAAD6j5LIcZRluTPJVfn500SSn5UtkuSsJF8piuI/iqJY2F/zPTFIUTQVRfHGJHcleccxsx17ikiZ5L4kf9SvQ1JvtU7waOmXKWrvc6hfpgAAAAAAYFC6defO/MKyZVm+e3evs+YMH54fLV6ci0aNqmAyAAAAAADoX/31F8QHnLIsv1YUxceS/H5+dnrIUceWMS5NcntRFN9O8ukk/1mWZa2/nH/KiqKYmOSKJO9Kcl5+vhByvD/vT/LKIwUYTh8Ha6z31/8DWmus15qzHrYmWd0P+0xPMqQf9gEAAAAAGJS+smVL3rB2bfZ1dfU663ljxuSm+fNzRmutj7UBAAAAAKAxKYl074+TLEzyopy4KPLkn7/oyGtHURRfT/L/Jfnvsiwf6M0QRVE0JbkoyQuSvCTJ83L4FJgTlUOO/VlXkivLslzemzkYkGqd0NHWL1MMwJJIWZb/J8n/6et9iqJYlaS9r/cBAAAAABhsyrLMRx94IB+4775K8l4zfnw+P2dOhjY3V5IHAAAAAAD1oCTSjbIsO4uiuCzJf+RwMeN4RZHk50saY5O8/sgrRVFsy+ETCdYm2ZTkkSSP5vDpHvuTdOTwSQJDkoxMMiHJ2Umm5PBfHp+Vp540cOy+T/5ZjvlZmeRdZVle18PbZnDZXWN9ZL9MkYyusV5rTgAAAAAAeMKBrq5cdddd+cLmzZXkvf+88/LRadPSVBzvcQsAAAAAAAwcSiI1lGW5vyiKX03yjRw+yeN4p3bUKm2MS/KcI6+TdbynEd2VQ449QeT3yrL8+1PYl8FhW4311qIohpZlub+P5xhVY73WnAAAAAAAkCR59ODBXL5qVW7ZsaPXWU1J/m7mzLz93HN7PxgAAAAAADSApnoPMBCUZbknyYuS/EOOXwg5qjhm/cmv4hRfx8s6dq886bqja7uTXFaW5adP9n4ZVB7rwTVj+3yK2nv0ZE4AAAAAAE5za/fsySXLllVSEBnW1JQb589XEAEAAAAAYFBREumhsiw7yrK8MsnvJtmTw0WMJ5c2nqwnRY+TeZ0o84nxjrnujiTPLMvy30/5hhksHu3BNWf3+RTJOTXWlUQAAAAAAOjWd7ZtyyXLluWe/b0/HHt8a2u+u2hRXn7WWRVMBgAAAAAAjUNJ5CSVZXlNkguS/HeOXwA5nlM9ReREpZAnxslTyyEdSZYkeUZZlqtO4fYYZMqy3JvaBYyJfTlDURTDk4yscdmGvpwBAAAAAICB7ZpNm/KSFSuyo7Oz11mzhw3L/73wwlw8enQFkwEAAAAAQGNREjkFZVneV5blLyX5jSQrc+ITQ/pk+/x8OaRMcl2SOWVZfrgsy44+2puB6f4a61P6eP+e5N/fxzMAAAAAADAAdZZl3rN+AQ5rNgAAIABJREFUfd56993pfT0k+eWxY/PDxYszY/jwCtIAAAAAAKDxKIn0QlmWX0uyKMmrkvzXkR+fqDByKsWRE73/6B47k3wmyYKyLN9QluV9p3YnDHK1/r2Y2cf718rffOTEEwAAAAAAeMKujo5ctnJlPrlxYyV5V55zTr61cGHOaG2tJA8AAAAAABpRS70HGOjKsiyTfDXJV4uimJbktUl+Nckz8tQSTnnMryejeNLvdya5OcnXk3ylLMt9p5DH6WVVkld2sz67j/efVWN9VR/vDwAAAADAAPPA/v152Z13ZsWePb3OKpJ8fPr0/N7kySmKoub1AAAAAAAwkCmJVKgsy3uT/FmSPyuKYlyS5ye5KMmFSRYkOScnd3rL/iT3Jlme5PYkP0nyw7IsO6qcm0FvWY31C/t4/8U11m/v4/0BAAAAABhAfrJzZ379zjuz+dChXmeNaGrK9e3tedlZZ1UwGQAAAAAAND4lkT5SluVjSW488kqSFEXRnGRSknOTjEkyNMmwHP7ncCDJviOvR5NsPJIBvVWrJDK5KIoJZVlu6aP9L6qxriQCAAAAAECS5MtbtuSNa9dmf1dXr7MmDxmSf1+wIBeMHFnBZAAAAAAAMDAoifSjsiw7kzx45AX9oizLjUVRbEgypZvLXpDky1XvXRTFpCSzalz2g6r3BQAAAABgYCnLMn++YUM+eP/9leQ9Y9SofG3+/JwzZEgleQAAAAAAMFA01XsAoF98p8b6pX207wtrrK8ry3JDH+0NAAAAAMAAcKCrK29Yu7aygsgrx4/Pfy9apCACAAAAAMBpSUkETg8311j/9aIomvtg31fWWP92H+wJAAAAAMAAsfXgwfzy8uW5bvPmSvL+ZMqU/Ft7e4Y398VH3gAAAAAA0PiUROD08M0ke7tZn5Dap36clKIozkzy4hqX3VDlngAAAAAADByr9+zJxcuW5X927ux1VltR5J/nzMlHzj8/TUVRwXQAAAAAADAwKYnAaaAsy91Jvl7jsndWvO1bk7R1s74xyfcr3hMAAAAAgAHg29u25ZnLluW+/ft7nXVWa2v+64IL8vqzz65gMgAAAAAAGNiUROD08bka6y8timJRFRsVRTEytUsnXyjLsqxiPwAAAAAABo7PPPRQXrpiRXZ2dvY6a87w4fnx4sV5ztixFUwGAAAAAAADn5IInCbKsrw5yYpuLimSfLKi7f44SXdf2XYgyacr2gsAAAAAgAGgo6sr7163Lm9bty69r4ckl55xRn544YWZNmxYBWkAAAAAADA4KInA6eWvaqw/vyiK9/Rmg6IonpnkD2pc9vmyLB/pzT4AAAAAAAwcOzs68usrV+bvHnqokry3TpqUby5YkLG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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t_range = torch.arange(20., 90.).unsqueeze(1)\n",
    "\n",
    "fig = plt.figure(dpi=600)\n",
    "plt.xlabel(\"Fahrenheit\")\n",
    "plt.ylabel(\"Celsius\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.plot(t_range.numpy(), seq_model(0.1 * t_range).detach().numpy(), 'c-')\n",
    "plt.plot(t_u.numpy(), seq_model(0.1 * t_u).detach().numpy(), 'kx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Training loss 203.22389221191406, Validation loss 104.12740325927734\n",
      "Epoch 1000, Training loss 49.626617431640625, Validation loss 56.38867950439453\n",
      "Epoch 2000, Training loss 35.23250198364258, Validation loss 37.46330642700195\n",
      "Epoch 3000, Training loss 23.670225143432617, Validation loss 19.621896743774414\n",
      "Epoch 4000, Training loss 15.505778312683105, Validation loss 8.775274276733398\n",
      "Epoch 5000, Training loss 10.130289077758789, Validation loss 3.130990743637085\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20a2b1b0e10>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercises here!\n",
    "\n",
    "neuron_count = 20\n",
    "\n",
    "seq_model = nn.Sequential(OrderedDict([\n",
    "    ('hidden_linear', nn.Linear(1, neuron_count)),\n",
    "    ('hidden_activation', nn.Tanh()),\n",
    "    ('output_linear', nn.Linear(neuron_count, 1))\n",
    "]))\n",
    "\n",
    "optimizer = optim.SGD(seq_model.parameters(), lr=1e-4)\n",
    "\n",
    "training_loop(\n",
    "    n_epochs = 5000, \n",
    "    optimizer = optimizer,\n",
    "    model = seq_model,\n",
    "    loss_fn = nn.MSELoss(),\n",
    "    t_u_train = t_un_train,\n",
    "    t_u_val = t_un_val, \n",
    "    t_c_train = t_c_train,\n",
    "    t_c_val = t_c_val)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "t_range = torch.arange(20., 90.).unsqueeze(1)\n",
    "\n",
    "fig = plt.figure(dpi=150)\n",
    "plt.xlabel(\"Fahrenheit\")\n",
    "plt.ylabel(\"Celsius\")\n",
    "plt.plot(t_u.numpy(), t_c.numpy(), 'o')\n",
    "plt.plot(t_range.numpy(), seq_model(0.1 * t_range).detach().numpy(), 'c-')\n",
    "plt.plot(t_u.numpy(), seq_model(0.1 * t_u).detach().numpy(), 'kx')\n"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}