bnn_reg_full.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torchvision.datasets as datasets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pdb\n",
    "from tqdm import tqdm\n",
    "\n",
    "from bayesnn.models.bbb_het_reg import BBBHetRegModel\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "from scipy import stats"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def true_f(x, e=0):\n",
    "    return x + 0.3 * np.sin(2 * np.pi * (x + e)) + 0.3 * np.sin(4. * np.pi * (x + e))\n",
    "    #return np.sin(12.0 * x) + 0.66 * np.cos(25.0 * x) + 3.0\n",
    "    #return np.sin(x)\n",
    "\n",
    "\n",
    "def get_data(train_size=100, test_size=100):\n",
    "    n = train_size + test_size\n",
    "\n",
    "    np.random.seed(42)\n",
    "\n",
    "    #X = np.linspace(-2.0, 2.0, num=n)\n",
    "    X = np.random.uniform(-1.0, 1.0, n)\n",
    "    e = np.random.normal(0, 0.02, n)\n",
    "    Y = true_f(X, e=e) + e\n",
    "\n",
    "    return X[:train_size], Y[:train_size], X[train_size:], Y[train_size:]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_x = np.linspace(-1.0, 1.0, 1000)\n",
    "true_y = true_f(true_x, e=0)\n",
    "\n",
    "X_train, y_train, X_test, y_test = get_data(500, 500)\n",
    "\n",
    "plt.figure(figsize = (6, 5))\n",
    "plt.style.use('default')\n",
    "\n",
    "plt.plot(true_x, true_y, color='black', label='True function')\n",
    "plt.scatter(X_train, y_train, s=10, marker='x', color='blue', label='Train set')\n",
    "plt.scatter(X_test, y_test, s=10, marker='x', color='green', label='Test set')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "n_hidden = 5\n",
    "\n",
    "model_reg = BBBHetRegModel(hidden_size=n_hidden, act_func=F.tanh).to(device)\n",
    "\n",
    "params_reg = list(model_reg.parameters())\n",
    "optimizer_reg = torch.optim.Adam(params_reg, lr=0.01)\n",
    "\n",
    "X_train_tensor = torch.from_numpy(X_train.reshape((-1, 1))).float().to(device)\n",
    "y_train_tensor = torch.from_numpy(y_train.reshape((-1, 1))).float().to(device)\n",
    "\n",
    "X_test_tensor = torch.from_numpy(X_test.reshape((-1, 1))).float().to(device)\n",
    "y_test_tensor = torch.from_numpy(y_test.reshape((-1, 1))).float().to(device)\n",
    "\n",
    "loss_fn = nn.MSELoss()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [02:41<00:00, 18.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50 | ELBO -690.1799926757812 | LL -49.203819274902344 | KL 640.9761962890625\n",
      "MSE 0.08259930461645126\n",
      "Epoch 100 | ELBO -645.1961669921875 | LL -30.078903198242188 | KL 615.1172485351562\n",
      "MSE 0.07829064875841141\n",
      "Epoch 150 | ELBO -546.4310302734375 | LL 43.71381759643555 | KL 590.1448364257812\n",
      "MSE 0.056455496698617935\n",
      "Epoch 200 | ELBO -404.7941589355469 | LL 162.23989868164062 | KL 567.0340576171875\n",
      "MSE 0.03171827271580696\n",
      "Epoch 250 | ELBO -297.0148010253906 | LL 247.85067749023438 | KL 544.865478515625\n",
      "MSE 0.022649073973298073\n",
      "Epoch 300 | ELBO -247.62210083007812 | LL 272.9881286621094 | KL 520.6102294921875\n",
      "MSE 0.0218754131346941\n",
      "Epoch 350 | ELBO -196.28353881835938 | LL 299.5637512207031 | KL 495.8472900390625\n",
      "MSE 0.02214287407696247\n",
      "Epoch 400 | ELBO -152.16439819335938 | LL 318.79705810546875 | KL 470.9614562988281\n",
      "MSE 0.02171524614095688\n",
      "Epoch 450 | ELBO -110.39163208007812 | LL 335.5550842285156 | KL 445.94671630859375\n",
      "MSE 0.02084103412926197\n",
      "Epoch 500 | ELBO -72.47821044921875 | LL 348.23529052734375 | KL 420.7135009765625\n",
      "MSE 0.019942844286561012\n",
      "Epoch 550 | ELBO -28.83514404296875 | LL 366.7796936035156 | KL 395.6148376464844\n",
      "MSE 0.01900552213191986\n",
      "Epoch 600 | ELBO 19.061859130859375 | LL 389.9319152832031 | KL 370.87005615234375\n",
      "MSE 0.018950792029500008\n",
      "Epoch 650 | ELBO 54.681884765625 | LL 401.05682373046875 | KL 346.37493896484375\n",
      "MSE 0.020046530291438103\n",
      "Epoch 700 | ELBO 73.65420532226562 | LL 395.67169189453125 | KL 322.0174865722656\n",
      "MSE 0.020915601402521133\n",
      "Epoch 750 | ELBO 102.14486694335938 | LL 401.1918029785156 | KL 299.04693603515625\n",
      "MSE 0.021522046998143196\n",
      "Epoch 800 | ELBO 77.1451416015625 | LL 355.73675537109375 | KL 278.59161376953125\n",
      "MSE 0.021295316517353058\n",
      "Epoch 850 | ELBO 135.71295166015625 | LL 395.83319091796875 | KL 260.1202392578125\n",
      "MSE 0.022708110511302948\n",
      "Epoch 900 | ELBO 152.40240478515625 | LL 396.65155029296875 | KL 244.2491455078125\n",
      "MSE 0.022379273548722267\n",
      "Epoch 950 | ELBO 154.40386962890625 | LL 386.29217529296875 | KL 231.8883056640625\n",
      "MSE 0.022592613473534584\n",
      "Epoch 1000 | ELBO 164.15721130371094 | LL 386.25506591796875 | KL 222.0978546142578\n",
      "MSE 0.022317716851830482\n",
      "Epoch 1050 | ELBO 167.03421020507812 | LL 382.8770446777344 | KL 215.84283447265625\n",
      "MSE 0.022186703979969025\n",
      "Epoch 1100 | ELBO 170.78628540039062 | LL 382.9137268066406 | KL 212.12744140625\n",
      "MSE 0.023122895509004593\n",
      "Epoch 1150 | ELBO 179.58966064453125 | LL 388.04302978515625 | KL 208.453369140625\n",
      "MSE 0.023023970425128937\n",
      "Epoch 1200 | ELBO 175.26756286621094 | LL 381.51873779296875 | KL 206.2511749267578\n",
      "MSE 0.023118017241358757\n",
      "Epoch 1250 | ELBO 179.76617431640625 | LL 385.5467224121094 | KL 205.78054809570312\n",
      "MSE 0.022260230034589767\n",
      "Epoch 1300 | ELBO 154.73513793945312 | LL 360.02630615234375 | KL 205.29116821289062\n",
      "MSE 0.02197883278131485\n",
      "Epoch 1350 | ELBO 177.40631103515625 | LL 381.5320739746094 | KL 204.12576293945312\n",
      "MSE 0.022638073191046715\n",
      "Epoch 1400 | ELBO 172.12228393554688 | LL 376.557373046875 | KL 204.43508911132812\n",
      "MSE 0.021714666858315468\n",
      "Epoch 1450 | ELBO 177.94642639160156 | LL 382.1625061035156 | KL 204.21607971191406\n",
      "MSE 0.02105412259697914\n",
      "Epoch 1500 | ELBO 175.08055114746094 | LL 378.78472900390625 | KL 203.7041778564453\n",
      "MSE 0.02096380665898323\n",
      "Epoch 1550 | ELBO 170.37893676757812 | LL 372.75970458984375 | KL 202.38076782226562\n",
      "MSE 0.02243519201874733\n",
      "Epoch 1600 | ELBO 178.43572998046875 | LL 380.73919677734375 | KL 202.303466796875\n",
      "MSE 0.022422678768634796\n",
      "Epoch 1650 | ELBO 174.80430603027344 | LL 377.39642333984375 | KL 202.5921173095703\n",
      "MSE 0.020553912967443466\n",
      "Epoch 1700 | ELBO 176.194580078125 | LL 378.2663269042969 | KL 202.07174682617188\n",
      "MSE 0.021004360169172287\n",
      "Epoch 1750 | ELBO 174.13336181640625 | LL 376.45416259765625 | KL 202.32080078125\n",
      "MSE 0.02046233043074608\n",
      "Epoch 1800 | ELBO 185.72088623046875 | LL 388.3699645996094 | KL 202.64907836914062\n",
      "MSE 0.02008388377726078\n",
      "Epoch 1850 | ELBO 176.88388061523438 | LL 379.2313537597656 | KL 202.34747314453125\n",
      "MSE 0.019811809062957764\n",
      "Epoch 1900 | ELBO 176.01185607910156 | LL 377.6500549316406 | KL 201.63819885253906\n",
      "MSE 0.021975107491016388\n",
      "Epoch 1950 | ELBO 168.3365478515625 | LL 370.01171875 | KL 201.6751708984375\n",
      "MSE 0.019439730793237686\n",
      "Epoch 2000 | ELBO 182.71978759765625 | LL 384.59552001953125 | KL 201.875732421875\n",
      "MSE 0.019701236858963966\n",
      "Epoch 2050 | ELBO 177.9839630126953 | LL 378.7911682128906 | KL 200.8072052001953\n",
      "MSE 0.0191006138920784\n",
      "Epoch 2100 | ELBO 185.2998046875 | LL 386.56475830078125 | KL 201.26495361328125\n",
      "MSE 0.018932024016976357\n",
      "Epoch 2150 | ELBO 179.05255126953125 | LL 380.18585205078125 | KL 201.13330078125\n",
      "MSE 0.01927293837070465\n",
      "Epoch 2200 | ELBO 180.60635375976562 | LL 381.6171875 | KL 201.01083374023438\n",
      "MSE 0.019584814086556435\n",
      "Epoch 2250 | ELBO 181.77252197265625 | LL 382.18695068359375 | KL 200.4144287109375\n",
      "MSE 0.01938585191965103\n",
      "Epoch 2300 | ELBO 192.69972229003906 | LL 392.6790771484375 | KL 199.97935485839844\n",
      "MSE 0.018734103068709373\n",
      "Epoch 2350 | ELBO 178.64749145507812 | LL 378.5207214355469 | KL 199.87322998046875\n",
      "MSE 0.018591566011309624\n",
      "Epoch 2400 | ELBO 186.32310485839844 | LL 385.9056091308594 | KL 199.58250427246094\n",
      "MSE 0.02011862024664879\n",
      "Epoch 2450 | ELBO 191.87149047851562 | LL 390.9587707519531 | KL 199.0872802734375\n",
      "MSE 0.0179501511156559\n",
      "Epoch 2500 | ELBO 196.59735107421875 | LL 396.2412109375 | KL 199.64385986328125\n",
      "MSE 0.018369516357779503\n",
      "Epoch 2550 | ELBO 189.2838592529297 | LL 388.74334716796875 | KL 199.45948791503906\n",
      "MSE 0.018063819035887718\n",
      "Epoch 2600 | ELBO 192.49720764160156 | LL 391.77764892578125 | KL 199.2804412841797\n",
      "MSE 0.01867358200252056\n",
      "Epoch 2650 | ELBO 178.27911376953125 | LL 376.6875 | KL 198.40838623046875\n",
      "MSE 0.0194378849118948\n",
      "Epoch 2700 | ELBO 199.1074676513672 | LL 397.4378662109375 | KL 198.3303985595703\n",
      "MSE 0.01770012080669403\n",
      "Epoch 2750 | ELBO 193.99972534179688 | LL 392.5419921875 | KL 198.54226684570312\n",
      "MSE 0.017997490242123604\n",
      "Epoch 2800 | ELBO 188.19970703125 | LL 385.91558837890625 | KL 197.71588134765625\n",
      "MSE 0.019123520702123642\n",
      "Epoch 2850 | ELBO 191.66943359375 | LL 389.2967529296875 | KL 197.6273193359375\n",
      "MSE 0.019453594461083412\n",
      "Epoch 2900 | ELBO 192.97509765625 | LL 390.3636779785156 | KL 197.38858032226562\n",
      "MSE 0.018761614337563515\n",
      "Epoch 2950 | ELBO 193.40670776367188 | LL 390.6341552734375 | KL 197.22744750976562\n",
      "MSE 0.019337108358740807\n",
      "Epoch 3000 | ELBO 189.33145141601562 | LL 386.5133361816406 | KL 197.181884765625\n",
      "MSE 0.017441218718886375\n"
     ]
    }
   ],
   "source": [
    "num_epoches = 3000\n",
    "num_samples = 10\n",
    "\n",
    "model_reg.train(True)\n",
    "\n",
    "for ep in tqdm(range(1, num_epoches+1)):\n",
    "    optimizer_reg.zero_grad()\n",
    "    log_likelihood_total = 0\n",
    "    kl = model_reg.get_kl()\n",
    "\n",
    "    for i in range(num_samples):\n",
    "        out_mu, out_sigma = model_reg(X_train_tensor, sample=True)\n",
    "\n",
    "        log_likelihood = torch.distributions.Normal(loc=out_mu, scale=out_sigma).log_prob(y_train_tensor).sum()\n",
    "        log_likelihood_total += log_likelihood\n",
    "\n",
    "    #elbo = (log_likelihood_total - kl_total) / (num_samples * x_train.shape[0])\n",
    "    log_likelihood_total_s = log_likelihood_total / num_samples\n",
    "    elbo = log_likelihood_total_s - kl\n",
    "    (-elbo).backward()\n",
    "\n",
    "    optimizer_reg.step()\n",
    "    #optimizer_reg.zero_grad()\n",
    "\n",
    "    if ep % 50 == 0:\n",
    "        print(f'Epoch {ep} | '\n",
    "              f'ELBO {elbo.cpu().detach().numpy()} | '\n",
    "              f'LL {log_likelihood_total_s.cpu().detach().numpy()} | '\n",
    "              f'KL {kl.cpu().detach().numpy() if isinstance(kl, torch.Tensor) else kl}')\n",
    "\n",
    "        with torch.no_grad():\n",
    "            y_pred, _ = model_reg(X_test_tensor, sample=False)\n",
    "            mse = loss_fn(y_pred, y_test_tensor).mean().cpu().detach().numpy()\n",
    "\n",
    "        print(f\"MSE {mse}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alex\\anaconda3\\envs\\bayesnn\\lib\\site-packages\\torch\\nn\\functional.py:1614: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[-0.4151],\n",
      "        [ 0.0239],\n",
      "        [ 0.5535]], grad_fn=<SliceBackward>), tensor([[0.2313],\n",
      "        [0.1349],\n",
      "        [0.1438]], grad_fn=<AddBackward0>))\n"
     ]
    }
   ],
   "source": [
    "n_samples = 100\n",
    "model_reg.train(False)\n",
    "\n",
    "true_x_tensor = torch.from_numpy(true_x.reshape((-1, 1))).float().to(device)\n",
    "#true_y_tensor = torch.from_numpy(true_y_train.reshape((-1, 1))).float().to(device)\n",
    "\n",
    "samples_m = np.zeros((true_x_tensor.shape[0], n_samples))\n",
    "samples_s = np.zeros_like(samples_m)\n",
    "for i in range(n_samples):\n",
    "    mus, sigmas = model_reg(true_x_tensor, sample=False)\n",
    "    np_m = mus.cpu().data.numpy()\n",
    "    np_s = sigmas.cpu().data.numpy()\n",
    "    samples_m[:, i] = np_m[:, 0]\n",
    "    samples_s[:, i] = np_s[:, 0]\n",
    "\n",
    "y_pred_mean = samples_m.mean(axis=1)\n",
    "y_pred_std = samples_s.mean(axis=1)\n",
    "\n",
    "ti = stats.t(df=num_samples - 1).ppf((0.025, 0.975))\n",
    "y_err = ti[1] * y_pred_std / np.sqrt(1. + 1. / num_samples)\n",
    "\n",
    "print(model_reg(torch.tensor([[-1.0], [0.0], [1.0]], device=device), sample=False))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6, 5))\n",
    "plt.style.use('default')\n",
    "\n",
    "plt.plot(true_x, true_y, color='black', label='True function')\n",
    "plt.scatter(X_test, y_test, s=10, marker='x', color='green', label='Test set')\n",
    "\n",
    "plt.plot(true_x, y_pred, color='blue', label='Predicted mean')\n",
    "plt.fill_between(true_x, y_pred-y_err, y_pred+y_err, label='+/- predicted std')\n",
    "\n",
    "#plt.scatter(X_test, y_pred, s=10, marker='o', label='Predictions')\n",
    "#plt.errorbar(X_test, y_pred, yerr=y_err, fmt='o', ls='none', label='Predictions')\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "model_simple =  nn.Sequential(\n",
    "          nn.Linear(1, n_hidden),\n",
    "          nn.ReLU(),\n",
    "          nn.Linear(n_hidden, n_hidden),\n",
    "          nn.ReLU(),\n",
    "          nn.Linear(n_hidden, 1)\n",
    "        ).to(device)\n",
    "\n",
    "class Reg2L(nn.Module):\n",
    "    def __init__(self, n_hidden=10, act_func=F.tanh):\n",
    "        super().__init__()\n",
    "        self.n_hidden = n_hidden\n",
    "        self.act_func = act_func\n",
    "        self.layer1 = nn.Linear(1, n_hidden)\n",
    "        self.layer2 = nn.Linear(n_hidden, n_hidden)\n",
    "        self.layer3 = nn.Linear(n_hidden, 2)\n",
    "\n",
    "    def forward(self, input):\n",
    "        h1 = self.layer1(input)\n",
    "        h2 = self.layer2(self.act_func(h1))\n",
    "        h3 = self.layer3(self.act_func(h2))\n",
    "        m = h3[:, :1]\n",
    "        s = F.elu(h3[:, 1:]) + 1.\n",
    "        return m, s\n",
    "\n",
    "class Reg2N(nn.Module):\n",
    "    def __init__(self, n_hidden=10, activation=nn.ReLU):\n",
    "        super().__init__()\n",
    "        self.n_hidden = n_hidden\n",
    "        self.activation = activation\n",
    "        self.mu_net = nn.Sequential(\n",
    "            nn.Linear(1, n_hidden),\n",
    "            activation(),\n",
    "            nn.Linear(n_hidden, n_hidden),\n",
    "            activation(),\n",
    "            nn.Linear(n_hidden, 1)\n",
    "        )\n",
    "\n",
    "        self.sigma_net = nn.Sequential(\n",
    "            nn.Linear(1, n_hidden),\n",
    "            activation(),\n",
    "            nn.Linear(n_hidden, n_hidden),\n",
    "            activation(),\n",
    "            nn.Linear(n_hidden, 1),\n",
    "            nn.Softplus()\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.mu_net(input), self.sigma_net(input)\n",
    "\n",
    "model_simple = Reg2L(n_hidden=n_hidden, act_func=F.relu).to(device)\n",
    "optimizer_simple = torch.optim.Adam(model_simple.parameters(), lr=0.01)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3000/3000 [00:13<00:00, 219.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49 MSE 0.05769817531108856\n",
      "Epoch 99 MSE 0.028818385675549507\n",
      "Epoch 149 MSE 0.022903237491846085\n",
      "Epoch 199 MSE 0.022440308704972267\n",
      "Epoch 249 MSE 0.022236071527004242\n",
      "Epoch 299 MSE 0.022204723209142685\n",
      "Epoch 349 MSE 0.02225394733250141\n",
      "Epoch 399 MSE 0.022330347448587418\n",
      "Epoch 449 MSE 0.022443484514951706\n",
      "Epoch 499 MSE 0.022265268489718437\n",
      "Epoch 549 MSE 0.022469444200396538\n",
      "Epoch 599 MSE 0.02246340550482273\n",
      "Epoch 649 MSE 0.022693436592817307\n",
      "Epoch 699 MSE 0.022442765533924103\n",
      "Epoch 749 MSE 0.02261163666844368\n",
      "Epoch 799 MSE 0.022527145221829414\n",
      "Epoch 849 MSE 0.022709878161549568\n",
      "Epoch 899 MSE 0.022550813853740692\n",
      "Epoch 949 MSE 0.02257147803902626\n",
      "Epoch 999 MSE 0.022675707936286926\n",
      "Epoch 1049 MSE 0.022631928324699402\n",
      "Epoch 1099 MSE 0.022493261843919754\n",
      "Epoch 1149 MSE 0.02249404974281788\n",
      "Epoch 1199 MSE 0.022557582706212997\n",
      "Epoch 1249 MSE 0.02251945622265339\n",
      "Epoch 1299 MSE 0.02251007780432701\n",
      "Epoch 1349 MSE 0.022634699940681458\n",
      "Epoch 1399 MSE 0.022480715066194534\n",
      "Epoch 1449 MSE 0.022509511560201645\n",
      "Epoch 1499 MSE 0.02255476638674736\n",
      "Epoch 1549 MSE 0.022369634360074997\n",
      "Epoch 1599 MSE 0.022518305107951164\n",
      "Epoch 1649 MSE 0.02230180986225605\n",
      "Epoch 1699 MSE 0.022394564002752304\n",
      "Epoch 1749 MSE 0.022497633472085\n",
      "Epoch 1799 MSE 0.02241656742990017\n",
      "Epoch 1849 MSE 0.02226933278143406\n",
      "Epoch 1899 MSE 0.022262167185544968\n",
      "Epoch 1949 MSE 0.022416163235902786\n",
      "Epoch 1999 MSE 0.02240607887506485\n",
      "Epoch 2049 MSE 0.022393647581338882\n",
      "Epoch 2099 MSE 0.022222254425287247\n",
      "Epoch 2149 MSE 0.02227752096951008\n",
      "Epoch 2199 MSE 0.02243540994822979\n",
      "Epoch 2249 MSE 0.022403499111533165\n",
      "Epoch 2299 MSE 0.02229401282966137\n",
      "Epoch 2349 MSE 0.022274211049079895\n",
      "Epoch 2399 MSE 0.0222429558634758\n",
      "Epoch 2449 MSE 0.02241126447916031\n",
      "Epoch 2499 MSE 0.022391190752387047\n",
      "Epoch 2549 MSE 0.02227252535521984\n",
      "Epoch 2599 MSE 0.02232534810900688\n",
      "Epoch 2649 MSE 0.02224430814385414\n",
      "Epoch 2699 MSE 0.022253045812249184\n",
      "Epoch 2749 MSE 0.02242996357381344\n",
      "Epoch 2799 MSE 0.02231624536216259\n",
      "Epoch 2849 MSE 0.022329295054078102\n",
      "Epoch 2899 MSE 0.022221915423870087\n",
      "Epoch 2949 MSE 0.022276299074292183\n",
      "Epoch 2999 MSE 0.022262485697865486\n"
     ]
    }
   ],
   "source": [
    "num_epoches = 3000\n",
    "\n",
    "for ep in tqdm(range(num_epoches)):\n",
    "    optimizer_simple.zero_grad()\n",
    "\n",
    "    mu, sigma = model_simple(X_train_tensor)\n",
    "    #sigma = 1.0\n",
    "    loss = - torch.distributions.Normal(mu, sigma).log_prob(y_train_tensor).sum()\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    optimizer_simple.step()\n",
    "\n",
    "    if (ep + 1) % 50 == 0:\n",
    "        #print(f'ELBO value is {elbo.cpu().detach().numpy()} on epoch number {ep}')\n",
    "\n",
    "        with torch.no_grad():\n",
    "            y_pred, _ = model_simple(X_test_tensor)\n",
    "            mse = loss_fn(y_pred, y_test_tensor).to(torch.float32).cpu().mean().numpy()\n",
    "\n",
    "        print(f\"Epoch {ep} MSE {mse}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 600x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(tensor([[-0.6175],\n",
      "        [ 0.0380],\n",
      "        [ 0.4918]], grad_fn=<SliceBackward>), tensor([[0.2586],\n",
      "        [0.1085],\n",
      "        [0.1918]], grad_fn=<AddBackward0>))\n"
     ]
    }
   ],
   "source": [
    "y_pred, sigma_pred = map(lambda x: x.cpu().data.numpy()[:, 0], model_simple(X_test_tensor))\n",
    "\n",
    "plt.figure(figsize = (6, 5))\n",
    "plt.style.use('default')\n",
    "\n",
    "plt.plot(true_x, true_y, color='black', label='True function')\n",
    "plt.scatter(X_test, y_test, s=10, marker='x', color='green', label='Test set')\n",
    "\n",
    "plt.errorbar(X_test, y_pred, yerr=sigma_pred, ls='none', marker='o', label='Predictions')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(model_simple(torch.tensor([[-1.0], [0.0], [1.0]], device=device)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "w1 = model_simple._modules['0'].weight.data\n",
    "b1 = model_simple._modules['0'].bias.data\n",
    "\n",
    "w2 = model_simple._modules['2'].weight.data\n",
    "b2 = model_simple._modules['2'].bias.data\n",
    "\n",
    "w3 = model_simple._modules['4'].weight.data\n",
    "b3 = model_simple._modules['4'].bias.data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "def copy_weights2(m1, m2):\n",
    "    m2.layer1.W_mu.data = m1.layer1.weight.data\n",
    "    m2.layer1.bias_mu.data = m1.layer1.bias.data\n",
    "\n",
    "    m2.layer2.W_mu.data = m1.layer2.weight.data\n",
    "    m2.layer2.bias_mu.data = m1.layer2.bias.data\n",
    "\n",
    "    m2.layer3.W_mu.data[0:0, ] = m1.layer3.weight.data\n",
    "    m2.layer3.bias_mu.data[0] = m1.layer3.bias.data[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [
    "copy_weights2(model_simple, model_reg)"
   ],
   "metadata": {
    "collapsed": false,
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    "w1_b = model_reg.layer1.W_mu.cpu().data.numpy()\n",
    "w2_b = model_reg.layer2.W_mu.cpu().data.numpy()\n",
    "w3_b = model_reg.layer3.W_mu.cpu().data.numpy()\n",
    "\n",
    "b1_b = model_reg.layer1.bias_mu.cpu().data.numpy()\n",
    "b2_b = model_reg.layer2.bias_mu.cpu().data.numpy()\n",
    "b3_b = model_reg.layer3.bias_mu.cpu().data.numpy()\n"
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