{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "from utils import mnist, plot_graphs, plot_mnist\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_transform = transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize((0.5,), (0.5,)),\n",
    "           ])\n",
    "train_loader, valid_loader, test_loader = mnist(valid=10000, transform=mnist_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, latent_size=10):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 128)\n",
    "        self.fc2 = nn.Linear(128, latent_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = torch.sigmoid(self.fc2(x))\n",
    "        return x\n",
    "    \n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, latent_size=10):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.fc1 = nn.Linear(latent_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 28*28)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = torch.tanh(self.fc2(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, latent_size=10, loss_fn=F.mse_loss, lr=1e-4, l2=0.):\n",
    "        super(Net, self).__init__()\n",
    "        self.latent_size = latent_size\n",
    "        self.E = Encoder(latent_size)\n",
    "        self.D = Decoder(latent_size)\n",
    "        self.loss_fn = loss_fn\n",
    "        self._rho_loss = None\n",
    "        self._loss = None\n",
    "        self.optim = optim.Adam(self.parameters(), lr=lr, weight_decay=l2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 28*28)\n",
    "        h = self.E(x)\n",
    "        self.data_rho = h.mean(0)\n",
    "        out = self.D(h)\n",
    "        return out\n",
    "    \n",
    "    def decode(self, h):\n",
    "        with torch.no_grad():\n",
    "            return self.D(h)\n",
    "    \n",
    "    def rho_loss(self, rho, size_average=True):\n",
    "        \"\"\"\n",
    "        D_KL(P||Q) = sum(p*log(p/q)) = -sum(p*log(q/p)) = -p*log(q/p) - (1-p)log((1-q)/(1-p))\n",
    "        \"\"\"\n",
    "        dkl = - torch.log(self.data_rho/rho) * rho - torch.log((1-self.data_rho)/(1-rho)) * (1-rho)\n",
    "        if size_average:\n",
    "            self._rho_loss = dkl.mean()\n",
    "        else:\n",
    "            self._rho_loss = dkl.sum()\n",
    "        return self._rho_loss\n",
    "    \n",
    "    def loss(self, x, target, **kwargs):\n",
    "        target = target.view(-1, 28*28)\n",
    "        self._loss = self.loss_fn(x, target, **kwargs)\n",
    "        return self._loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {'16': Net(16), '32': Net(32), '64': Net(64)}\n",
    "rho = 0.05\n",
    "train_log = {k: [] for k in models}\n",
    "test_log = {k: [] for k in models}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch, models, log=None):\n",
    "    train_size = len(train_loader.sampler)\n",
    "    for batch_idx, (data, _) in enumerate(train_loader):\n",
    "        for model in models.values():\n",
    "            model.optim.zero_grad()\n",
    "            output = model(data)\n",
    "            rho_loss = model.rho_loss(rho)\n",
    "            loss = model.loss(output, data) + rho_loss\n",
    "            loss.backward()\n",
    "            model.optim.step()\n",
    "            \n",
    "        if batch_idx % 200 == 0:\n",
    "            line = 'Train Epoch: {} [{}/{} ({:.0f}%)]\\tLosses '.format(\n",
    "                epoch, batch_idx * len(data), train_size, 100. * batch_idx / len(train_loader))\n",
    "            losses = ' '.join(['{}: {:.6f}'.format(k, m._loss.item()) for k, m in models.items()])\n",
    "            print(line + losses)\n",
    "            \n",
    "    else:\n",
    "        batch_idx += 1\n",
    "        line = 'Train Epoch: {} [{}/{} ({:.0f}%)]\\tLosses '.format(\n",
    "            epoch, batch_idx * len(data), train_size, 100. * batch_idx / len(train_loader))\n",
    "        losses = ' '.join(['{}: {:.6f}'.format(k, m._loss.item()) for k, m in models.items()])\n",
    "        if log is not None:\n",
    "            for k in models:\n",
    "                log[k].append((models[k]._loss, models[k]._rho_loss))\n",
    "        print(line + losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_lambda = lambda l: 'loss: {:.4f}'.format(l)\n",
    "rho_lambda = lambda p: 'rho_loss: {:.4f}'.format(p)\n",
    "line = lambda i, l, p: '{}: '.format(i) + avg_lambda(l) + '\\t' + rho_lambda(p)\n",
    "    \n",
    "def test(models, loader, log=None):\n",
    "    test_size = len(loader.sampler)\n",
    "\n",
    "\n",
    "    test_loss = {k: 0. for k in models}\n",
    "    rho_loss = {k: 0. for k in models}\n",
    "    with torch.no_grad():\n",
    "        for data, _ in loader:\n",
    "            output = {k: m(data) for k, m in models.items()}\n",
    "            for k, m in models.items():\n",
    "                test_loss[k] += m.loss(output[k], data, reduction='sum').item() # sum up batch loss\n",
    "                rho_loss[k] += m.rho_loss(rho, size_average=False).item()\n",
    "    \n",
    "    for k in models:\n",
    "        test_loss[k] /= (test_size * 784)\n",
    "        rho_loss[k] /= (test_size * models[k].latent_size)\n",
    "        if log is not None:\n",
    "            log[k].append((test_loss[k], rho_loss[k]))\n",
    "    \n",
    "    lines = '\\n'.join([line(k, test_loss[k], rho_loss[k]) for k in models]) + '\\n'\n",
    "    report = 'Test set:\\n' + lines        \n",
    "    print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/50000 (0%)]\tLosses 16: 0.953925 32: 0.947870 64: 0.937362\n",
      "Train Epoch: 1 [10000/50000 (20%)]\tLosses 16: 0.645819 32: 0.522220 64: 0.378696\n",
      "Train Epoch: 1 [20000/50000 (40%)]\tLosses 16: 0.345602 32: 0.306340 64: 0.284809\n",
      "Train Epoch: 1 [30000/50000 (60%)]\tLosses 16: 0.296734 32: 0.285375 64: 0.273486\n",
      "Train Epoch: 1 [40000/50000 (80%)]\tLosses 16: 0.266420 32: 0.263494 64: 0.252917\n",
      "Train Epoch: 1 [50000/50000 (100%)]\tLosses 16: 0.254062 32: 0.256088 64: 0.243908\n",
      "Test set:\n",
      "16: loss: 0.2612\trho_loss: 0.0002\n",
      "32: loss: 0.2634\trho_loss: 0.0002\n",
      "64: loss: 0.2504\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 2 [0/50000 (0%)]\tLosses 16: 0.279840 32: 0.281536 64: 0.266739\n",
      "Train Epoch: 2 [10000/50000 (20%)]\tLosses 16: 0.256081 32: 0.259067 64: 0.244765\n",
      "Train Epoch: 2 [20000/50000 (40%)]\tLosses 16: 0.253985 32: 0.259011 64: 0.244021\n",
      "Train Epoch: 2 [30000/50000 (60%)]\tLosses 16: 0.230968 32: 0.240006 64: 0.222519\n",
      "Train Epoch: 2 [40000/50000 (80%)]\tLosses 16: 0.214168 32: 0.227934 64: 0.200735\n",
      "Train Epoch: 2 [50000/50000 (100%)]\tLosses 16: 0.199336 32: 0.210531 64: 0.182559\n",
      "Test set:\n",
      "16: loss: 0.2056\trho_loss: 0.0001\n",
      "32: loss: 0.2160\trho_loss: 0.0001\n",
      "64: loss: 0.1877\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 3 [0/50000 (0%)]\tLosses 16: 0.184957 32: 0.191155 64: 0.164449\n",
      "Train Epoch: 3 [10000/50000 (20%)]\tLosses 16: 0.198242 32: 0.203935 64: 0.173858\n",
      "Train Epoch: 3 [20000/50000 (40%)]\tLosses 16: 0.189167 32: 0.199288 64: 0.163940\n",
      "Train Epoch: 3 [30000/50000 (60%)]\tLosses 16: 0.185622 32: 0.185278 64: 0.163163\n",
      "Train Epoch: 3 [40000/50000 (80%)]\tLosses 16: 0.174077 32: 0.173215 64: 0.153314\n",
      "Train Epoch: 3 [50000/50000 (100%)]\tLosses 16: 0.175689 32: 0.168438 64: 0.150324\n",
      "Test set:\n",
      "16: loss: 0.1807\trho_loss: 0.0001\n",
      "32: loss: 0.1737\trho_loss: 0.0001\n",
      "64: loss: 0.1557\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 4 [0/50000 (0%)]\tLosses 16: 0.170082 32: 0.164745 64: 0.148080\n",
      "Train Epoch: 4 [10000/50000 (20%)]\tLosses 16: 0.180207 32: 0.171387 64: 0.152456\n",
      "Train Epoch: 4 [20000/50000 (40%)]\tLosses 16: 0.184123 32: 0.174308 64: 0.156428\n",
      "Train Epoch: 4 [30000/50000 (60%)]\tLosses 16: 0.181712 32: 0.171389 64: 0.154001\n",
      "Train Epoch: 4 [40000/50000 (80%)]\tLosses 16: 0.150750 32: 0.136512 64: 0.121947\n",
      "Train Epoch: 4 [50000/50000 (100%)]\tLosses 16: 0.181383 32: 0.169586 64: 0.149427\n",
      "Test set:\n",
      "16: loss: 0.1664\trho_loss: 0.0001\n",
      "32: loss: 0.1559\trho_loss: 0.0001\n",
      "64: loss: 0.1387\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 5 [0/50000 (0%)]\tLosses 16: 0.164791 32: 0.154693 64: 0.136364\n",
      "Train Epoch: 5 [10000/50000 (20%)]\tLosses 16: 0.169234 32: 0.160086 64: 0.142669\n",
      "Train Epoch: 5 [20000/50000 (40%)]\tLosses 16: 0.165629 32: 0.155296 64: 0.140030\n",
      "Train Epoch: 5 [30000/50000 (60%)]\tLosses 16: 0.167042 32: 0.156601 64: 0.139339\n",
      "Train Epoch: 5 [40000/50000 (80%)]\tLosses 16: 0.159171 32: 0.142096 64: 0.126172\n",
      "Train Epoch: 5 [50000/50000 (100%)]\tLosses 16: 0.159630 32: 0.143411 64: 0.129436\n",
      "Test set:\n",
      "16: loss: 0.1557\trho_loss: 0.0001\n",
      "32: loss: 0.1414\trho_loss: 0.0001\n",
      "64: loss: 0.1243\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 6 [0/50000 (0%)]\tLosses 16: 0.153137 32: 0.138477 64: 0.122848\n",
      "Train Epoch: 6 [10000/50000 (20%)]\tLosses 16: 0.152264 32: 0.141909 64: 0.124340\n",
      "Train Epoch: 6 [20000/50000 (40%)]\tLosses 16: 0.145895 32: 0.129753 64: 0.111323\n",
      "Train Epoch: 6 [30000/50000 (60%)]\tLosses 16: 0.148462 32: 0.130057 64: 0.113242\n",
      "Train Epoch: 6 [40000/50000 (80%)]\tLosses 16: 0.150556 32: 0.132123 64: 0.115244\n",
      "Train Epoch: 6 [50000/50000 (100%)]\tLosses 16: 0.152061 32: 0.135862 64: 0.117707\n",
      "Test set:\n",
      "16: loss: 0.1475\trho_loss: 0.0001\n",
      "32: loss: 0.1305\trho_loss: 0.0001\n",
      "64: loss: 0.1139\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 7 [0/50000 (0%)]\tLosses 16: 0.146426 32: 0.130057 64: 0.113463\n",
      "Train Epoch: 7 [10000/50000 (20%)]\tLosses 16: 0.133004 32: 0.117759 64: 0.101869\n",
      "Train Epoch: 7 [20000/50000 (40%)]\tLosses 16: 0.147351 32: 0.132197 64: 0.114449\n",
      "Train Epoch: 7 [30000/50000 (60%)]\tLosses 16: 0.151908 32: 0.136478 64: 0.117885\n",
      "Train Epoch: 7 [40000/50000 (80%)]\tLosses 16: 0.139505 32: 0.124568 64: 0.108110\n",
      "Train Epoch: 7 [50000/50000 (100%)]\tLosses 16: 0.136747 32: 0.118188 64: 0.102165\n",
      "Test set:\n",
      "16: loss: 0.1414\trho_loss: 0.0001\n",
      "32: loss: 0.1235\trho_loss: 0.0001\n",
      "64: loss: 0.1059\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 8 [0/50000 (0%)]\tLosses 16: 0.139620 32: 0.125786 64: 0.109292\n",
      "Train Epoch: 8 [10000/50000 (20%)]\tLosses 16: 0.137299 32: 0.119092 64: 0.100087\n",
      "Train Epoch: 8 [20000/50000 (40%)]\tLosses 16: 0.135253 32: 0.116980 64: 0.099279\n",
      "Train Epoch: 8 [30000/50000 (60%)]\tLosses 16: 0.137829 32: 0.120243 64: 0.102382\n",
      "Train Epoch: 8 [40000/50000 (80%)]\tLosses 16: 0.137184 32: 0.122077 64: 0.103960\n",
      "Train Epoch: 8 [50000/50000 (100%)]\tLosses 16: 0.129073 32: 0.108442 64: 0.089146\n",
      "Test set:\n",
      "16: loss: 0.1365\trho_loss: 0.0001\n",
      "32: loss: 0.1176\trho_loss: 0.0001\n",
      "64: loss: 0.0992\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 9 [0/50000 (0%)]\tLosses 16: 0.129185 32: 0.110087 64: 0.093358\n",
      "Train Epoch: 9 [10000/50000 (20%)]\tLosses 16: 0.145361 32: 0.122004 64: 0.102391\n",
      "Train Epoch: 9 [20000/50000 (40%)]\tLosses 16: 0.145692 32: 0.127730 64: 0.107305\n",
      "Train Epoch: 9 [30000/50000 (60%)]\tLosses 16: 0.134186 32: 0.117907 64: 0.101171\n",
      "Train Epoch: 9 [40000/50000 (80%)]\tLosses 16: 0.119309 32: 0.102634 64: 0.086703\n",
      "Train Epoch: 9 [50000/50000 (100%)]\tLosses 16: 0.115476 32: 0.101321 64: 0.083627\n",
      "Test set:\n",
      "16: loss: 0.1307\trho_loss: 0.0001\n",
      "32: loss: 0.1122\trho_loss: 0.0001\n",
      "64: loss: 0.0939\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 10 [0/50000 (0%)]\tLosses 16: 0.127160 32: 0.108751 64: 0.090074\n",
      "Train Epoch: 10 [10000/50000 (20%)]\tLosses 16: 0.133967 32: 0.114102 64: 0.090894\n",
      "Train Epoch: 10 [20000/50000 (40%)]\tLosses 16: 0.130966 32: 0.110369 64: 0.089768\n",
      "Train Epoch: 10 [30000/50000 (60%)]\tLosses 16: 0.128737 32: 0.107266 64: 0.087543\n",
      "Train Epoch: 10 [40000/50000 (80%)]\tLosses 16: 0.143689 32: 0.120103 64: 0.098450\n",
      "Train Epoch: 10 [50000/50000 (100%)]\tLosses 16: 0.124661 32: 0.105255 64: 0.088351\n",
      "Test set:\n",
      "16: loss: 0.1265\trho_loss: 0.0001\n",
      "32: loss: 0.1076\trho_loss: 0.0001\n",
      "64: loss: 0.0896\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 11 [0/50000 (0%)]\tLosses 16: 0.121949 32: 0.105601 64: 0.088571\n",
      "Train Epoch: 11 [10000/50000 (20%)]\tLosses 16: 0.118782 32: 0.099130 64: 0.080567\n",
      "Train Epoch: 11 [20000/50000 (40%)]\tLosses 16: 0.125857 32: 0.105337 64: 0.087596\n",
      "Train Epoch: 11 [30000/50000 (60%)]\tLosses 16: 0.127182 32: 0.108320 64: 0.092583\n",
      "Train Epoch: 11 [40000/50000 (80%)]\tLosses 16: 0.129008 32: 0.110212 64: 0.091836\n",
      "Train Epoch: 11 [50000/50000 (100%)]\tLosses 16: 0.123519 32: 0.102500 64: 0.081984\n",
      "Test set:\n",
      "16: loss: 0.1226\trho_loss: 0.0001\n",
      "32: loss: 0.1040\trho_loss: 0.0001\n",
      "64: loss: 0.0857\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 12 [0/50000 (0%)]\tLosses 16: 0.115716 32: 0.098628 64: 0.079470\n",
      "Train Epoch: 12 [10000/50000 (20%)]\tLosses 16: 0.121505 32: 0.106177 64: 0.087998\n",
      "Train Epoch: 12 [20000/50000 (40%)]\tLosses 16: 0.115814 32: 0.094314 64: 0.077147\n",
      "Train Epoch: 12 [30000/50000 (60%)]\tLosses 16: 0.121955 32: 0.106223 64: 0.086010\n",
      "Train Epoch: 12 [40000/50000 (80%)]\tLosses 16: 0.128630 32: 0.110150 64: 0.091541\n",
      "Train Epoch: 12 [50000/50000 (100%)]\tLosses 16: 0.126704 32: 0.108567 64: 0.087865\n",
      "Test set:\n",
      "16: loss: 0.1194\trho_loss: 0.0001\n",
      "32: loss: 0.1007\trho_loss: 0.0001\n",
      "64: loss: 0.0820\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 13 [0/50000 (0%)]\tLosses 16: 0.111184 32: 0.095287 64: 0.077021\n",
      "Train Epoch: 13 [10000/50000 (20%)]\tLosses 16: 0.120663 32: 0.099834 64: 0.078475\n",
      "Train Epoch: 13 [20000/50000 (40%)]\tLosses 16: 0.106305 32: 0.089085 64: 0.074426\n",
      "Train Epoch: 13 [30000/50000 (60%)]\tLosses 16: 0.104998 32: 0.087741 64: 0.070928\n",
      "Train Epoch: 13 [40000/50000 (80%)]\tLosses 16: 0.120778 32: 0.105300 64: 0.086263\n",
      "Train Epoch: 13 [50000/50000 (100%)]\tLosses 16: 0.122306 32: 0.101849 64: 0.081698\n",
      "Test set:\n",
      "16: loss: 0.1169\trho_loss: 0.0001\n",
      "32: loss: 0.0977\trho_loss: 0.0001\n",
      "64: loss: 0.0790\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 14 [0/50000 (0%)]\tLosses 16: 0.114561 32: 0.095083 64: 0.078096\n",
      "Train Epoch: 14 [10000/50000 (20%)]\tLosses 16: 0.117372 32: 0.097257 64: 0.077534\n",
      "Train Epoch: 14 [20000/50000 (40%)]\tLosses 16: 0.105050 32: 0.089431 64: 0.071008\n",
      "Train Epoch: 14 [30000/50000 (60%)]\tLosses 16: 0.118237 32: 0.101801 64: 0.082864\n",
      "Train Epoch: 14 [40000/50000 (80%)]\tLosses 16: 0.123549 32: 0.103693 64: 0.082023\n",
      "Train Epoch: 14 [50000/50000 (100%)]\tLosses 16: 0.117566 32: 0.095660 64: 0.073424\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set:\n",
      "16: loss: 0.1139\trho_loss: 0.0001\n",
      "32: loss: 0.0952\trho_loss: 0.0001\n",
      "64: loss: 0.0761\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 15 [0/50000 (0%)]\tLosses 16: 0.113232 32: 0.091922 64: 0.070434\n",
      "Train Epoch: 15 [10000/50000 (20%)]\tLosses 16: 0.112933 32: 0.094300 64: 0.075566\n",
      "Train Epoch: 15 [20000/50000 (40%)]\tLosses 16: 0.114163 32: 0.097540 64: 0.076038\n",
      "Train Epoch: 15 [30000/50000 (60%)]\tLosses 16: 0.121012 32: 0.100206 64: 0.079916\n",
      "Train Epoch: 15 [40000/50000 (80%)]\tLosses 16: 0.106346 32: 0.087579 64: 0.069862\n",
      "Train Epoch: 15 [50000/50000 (100%)]\tLosses 16: 0.118455 32: 0.097337 64: 0.075758\n",
      "Test set:\n",
      "16: loss: 0.1121\trho_loss: 0.0001\n",
      "32: loss: 0.0928\trho_loss: 0.0001\n",
      "64: loss: 0.0737\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 16 [0/50000 (0%)]\tLosses 16: 0.117660 32: 0.097672 64: 0.079800\n",
      "Train Epoch: 16 [10000/50000 (20%)]\tLosses 16: 0.114630 32: 0.097654 64: 0.077293\n",
      "Train Epoch: 16 [20000/50000 (40%)]\tLosses 16: 0.105366 32: 0.088356 64: 0.071398\n",
      "Train Epoch: 16 [30000/50000 (60%)]\tLosses 16: 0.110238 32: 0.089191 64: 0.069487\n",
      "Train Epoch: 16 [40000/50000 (80%)]\tLosses 16: 0.115939 32: 0.096112 64: 0.073816\n",
      "Train Epoch: 16 [50000/50000 (100%)]\tLosses 16: 0.110717 32: 0.088902 64: 0.069705\n",
      "Test set:\n",
      "16: loss: 0.1097\trho_loss: 0.0001\n",
      "32: loss: 0.0905\trho_loss: 0.0001\n",
      "64: loss: 0.0714\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 17 [0/50000 (0%)]\tLosses 16: 0.109264 32: 0.088515 64: 0.069142\n",
      "Train Epoch: 17 [10000/50000 (20%)]\tLosses 16: 0.121832 32: 0.101174 64: 0.078574\n",
      "Train Epoch: 17 [20000/50000 (40%)]\tLosses 16: 0.105844 32: 0.087670 64: 0.066618\n",
      "Train Epoch: 17 [30000/50000 (60%)]\tLosses 16: 0.099810 32: 0.082939 64: 0.064199\n",
      "Train Epoch: 17 [40000/50000 (80%)]\tLosses 16: 0.112100 32: 0.089848 64: 0.071383\n",
      "Train Epoch: 17 [50000/50000 (100%)]\tLosses 16: 0.096535 32: 0.078806 64: 0.061451\n",
      "Test set:\n",
      "16: loss: 0.1075\trho_loss: 0.0001\n",
      "32: loss: 0.0883\trho_loss: 0.0001\n",
      "64: loss: 0.0696\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 18 [0/50000 (0%)]\tLosses 16: 0.110249 32: 0.094789 64: 0.072316\n",
      "Train Epoch: 18 [10000/50000 (20%)]\tLosses 16: 0.092586 32: 0.077355 64: 0.057924\n",
      "Train Epoch: 18 [20000/50000 (40%)]\tLosses 16: 0.107966 32: 0.087013 64: 0.065444\n",
      "Train Epoch: 18 [30000/50000 (60%)]\tLosses 16: 0.102992 32: 0.084707 64: 0.066004\n",
      "Train Epoch: 18 [40000/50000 (80%)]\tLosses 16: 0.101526 32: 0.080701 64: 0.063619\n",
      "Train Epoch: 18 [50000/50000 (100%)]\tLosses 16: 0.097754 32: 0.080701 64: 0.060891\n",
      "Test set:\n",
      "16: loss: 0.1059\trho_loss: 0.0001\n",
      "32: loss: 0.0864\trho_loss: 0.0001\n",
      "64: loss: 0.0676\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 19 [0/50000 (0%)]\tLosses 16: 0.092190 32: 0.074462 64: 0.055444\n",
      "Train Epoch: 19 [10000/50000 (20%)]\tLosses 16: 0.101534 32: 0.082541 64: 0.066575\n",
      "Train Epoch: 19 [20000/50000 (40%)]\tLosses 16: 0.095729 32: 0.077866 64: 0.062705\n",
      "Train Epoch: 19 [30000/50000 (60%)]\tLosses 16: 0.110980 32: 0.089101 64: 0.067637\n",
      "Train Epoch: 19 [40000/50000 (80%)]\tLosses 16: 0.099115 32: 0.078831 64: 0.062426\n",
      "Train Epoch: 19 [50000/50000 (100%)]\tLosses 16: 0.106124 32: 0.084844 64: 0.065467\n",
      "Test set:\n",
      "16: loss: 0.1039\trho_loss: 0.0001\n",
      "32: loss: 0.0845\trho_loss: 0.0001\n",
      "64: loss: 0.0660\trho_loss: 0.0001\n",
      "\n",
      "Train Epoch: 20 [0/50000 (0%)]\tLosses 16: 0.107699 32: 0.087606 64: 0.067164\n",
      "Train Epoch: 20 [10000/50000 (20%)]\tLosses 16: 0.102842 32: 0.079509 64: 0.062259\n",
      "Train Epoch: 20 [20000/50000 (40%)]\tLosses 16: 0.093442 32: 0.076532 64: 0.058247\n",
      "Train Epoch: 20 [30000/50000 (60%)]\tLosses 16: 0.097581 32: 0.079217 64: 0.062076\n",
      "Train Epoch: 20 [40000/50000 (80%)]\tLosses 16: 0.098890 32: 0.081489 64: 0.061894\n",
      "Train Epoch: 20 [50000/50000 (100%)]\tLosses 16: 0.092545 32: 0.073345 64: 0.057717\n",
      "Test set:\n",
      "16: loss: 0.1020\trho_loss: 0.0001\n",
      "32: loss: 0.0822\trho_loss: 0.0001\n",
      "64: loss: 0.0643\trho_loss: 0.0001\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 21):\n",
    "    for model in models.values():\n",
    "        model.train()\n",
    "    train(epoch, models, train_log)\n",
    "    for model in models.values():\n",
    "        model.eval()\n",
    "    test(models, valid_loader, test_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 5,  6,  5,  6,  3,  4,  4,  4,  7,  6,  8,  4,  2,  8,  5,  4,  3,  4,\n",
      "         4,  5,  4,  3,  5,  3,  3,  6,  5,  4, 10, 10,  6,  6,  4,  4,  6,  6,\n",
      "         4,  8,  2,  5,  4,  4,  4,  1,  5,  3,  6,  3,  6,  7])\n"
     ]
    }
   ],
   "source": [
    "data, _ = next(iter(test_loader))\n",
    "output = models['64'](data)\n",
    "to_plot = output.view(-1, 1, 28, 28).clamp(0, 1).data.numpy()\n",
    "decoded = models['64'].decode(torch.eye(64))\n",
    "dec_to_plot = ((decoded.view(-1, 1, 28, 28)+1)*0.5).clamp(0, 1).data.numpy()\n",
    "with torch.no_grad():\n",
    "    encoded = models['64'].E(data.view(-1, 28*28))\n",
    "    print((encoded > 0.2).sum(1))\n",
    "    encoded[encoded < 0.2] = 0.\n",
    "    decoded_f = models['64'].decode(encoded)\n",
    "    f_to_plot = ((decoded_f.view(-1, 1, 28, 28)+1)*0.5).clamp(0, 1).data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x640 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mnist(data.data.numpy(), (5, 10))\n",
    "plot_mnist(to_plot, (5, 10))\n",
    "plot_mnist(f_to_plot, (5, 10))\n",
    "plot_mnist(dec_to_plot, (8, 8))"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
