Image VAE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.utils.data\n",
    "from torch import nn, optim\n",
    "from torch.nn import functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from torchvision.utils import save_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image VAE.ipynb                 \u001b[1m\u001b[34mpoemfiles\u001b[m\u001b[m/\r\n",
      "Study Data.ipynb                poetry_foundation_scraper.py\r\n",
      "\u001b[1m\u001b[31mchromedriver\u001b[m\u001b[m*                   poetry_scraper_improved.py\r\n",
      "edited_poems.csv                poetry_scraper_improved_old.py\r\n",
      "full_scraper.py                 \u001b[1m\u001b[34mresults\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mglove\u001b[m\u001b[m/                          rhyme_dict_full.txt\r\n",
      "\u001b[1m\u001b[31mlyrics_billboard.csv\u001b[m\u001b[m*           rhyme_dict_simple.txt\r\n",
      "\u001b[1m\u001b[31mlyrics_large.csv\u001b[m\u001b[m*               rhyme_phones.txt\r\n",
      "\u001b[1m\u001b[31mlyrics_small.csv\u001b[m\u001b[m*               \u001b[1m\u001b[34mscraper\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mmlcourse\u001b[m\u001b[m/                       scraper.py\r\n",
      "poem_VAE.py                     small_poem_set.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.ToTensor()),\n",
    "    batch_size=32, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),\n",
    "    batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mcifar-10-batches-py\u001b[m\u001b[m/          \u001b[1m\u001b[34mprocessed\u001b[m\u001b[m/\r\n",
      "\u001b[1m\u001b[34mcornell movie-dialogs corpus\u001b[m\u001b[m/ \u001b[1m\u001b[34mraw\u001b[m\u001b[m/\r\n"
     ]
    }
   ],
   "source": [
    "ls ../data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "log_interval = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VAE, self).__init__()\n",
    "\n",
    "        self.encode1 = nn.Linear(784, 400)\n",
    "        self.encode2 = nn.Linear(400, 100)\n",
    "        self.encode31 = nn.Linear(100, 10)\n",
    "        self.encode32 = nn.Linear(100, 10)\n",
    "        self.decode1 = nn.Linear(10, 100)\n",
    "        self.decode2 = nn.Linear(100, 400)\n",
    "        self.decode3 = nn.Linear(400, 784)\n",
    "\n",
    "    def encode(self, x):\n",
    "        h1 = F.relu(self.encode1(x))\n",
    "        h2 = F.relu(self.encode2(h1))\n",
    "        return self.encode31(h2), self.encode32(h2)\n",
    "\n",
    "    def reparameterize(self, mu, logvar):\n",
    "        std = torch.exp(0.5*logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return eps.mul(std).add_(mu)\n",
    "\n",
    "    def decode(self, z):\n",
    "        h3 = F.relu(self.decode1(z))\n",
    "        h4 = F.relu(self.decode2(h3))\n",
    "        return torch.sigmoid(self.decode3(h4))\n",
    "\n",
    "    def forward(self, x):\n",
    "        mu, logvar = self.encode(x.view(-1, 784))\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        return self.decode(z), mu, logvar\n",
    "    \n",
    "\n",
    "# Reconstruction + KL divergence losses summed over all elements and batch\n",
    "def loss_function(recon_x, x, mu, logvar):\n",
    "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction=\"sum\")\n",
    "\n",
    "    # see Appendix B from VAE paper:\n",
    "    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n",
    "    # https://arxiv.org/abs/1312.6114\n",
    "    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "\n",
    "    return BCE + KLD\n",
    "\n",
    "\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    train_loss = 0\n",
    "    for batch_idx, (data, _) in enumerate(train_loader):\n",
    "        data = data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        recon_batch, mu, logvar = model(data)\n",
    "        loss = loss_function(recon_batch, data, mu, logvar)\n",
    "        loss.backward()\n",
    "        train_loss += loss.item()\n",
    "        optimizer.step()\n",
    "        if batch_idx % log_interval == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader),\n",
    "                loss.item() / len(data)))\n",
    "\n",
    "    print('====> Epoch: {} Average loss: {:.4f}'.format(\n",
    "          epoch, train_loss / len(train_loader.dataset)))\n",
    "    \n",
    "\n",
    "def test(epoch):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    with torch.no_grad():\n",
    "        for i, (data, _) in enumerate(test_loader):\n",
    "            data = data.to(device)\n",
    "            recon_batch, mu, logvar = model(data)\n",
    "            test_loss += loss_function(recon_batch, data, mu, logvar).item()\n",
    "            if i == 0:\n",
    "                n = min(data.size(0), 8)\n",
    "                comparison = torch.cat([data[:n],\n",
    "                                      recon_batch.view(batch_size, 1, 28, 28)[:n]])\n",
    "                save_image(comparison.cpu(),\n",
    "                         'results/reconstruction_' + str(epoch) + '.png', nrow=n)\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('====> Test set loss: {:.4f}'.format(test_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 544.654419\n",
      "Train Epoch: 1 [320/60000 (1%)]\tLoss: 518.053528\n",
      "Train Epoch: 1 [640/60000 (1%)]\tLoss: 484.653290\n",
      "Train Epoch: 1 [960/60000 (2%)]\tLoss: 395.094452\n",
      "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 290.077484\n",
      "Train Epoch: 1 [1600/60000 (3%)]\tLoss: 245.589462\n",
      "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 243.209015\n",
      "Train Epoch: 1 [2240/60000 (4%)]\tLoss: 229.086670\n",
      "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 237.467743\n",
      "Train Epoch: 1 [2880/60000 (5%)]\tLoss: 226.884521\n",
      "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 221.428833\n",
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      "Train Epoch: 1 [50880/60000 (85%)]\tLoss: 163.159668\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 147.625412\n",
      "Train Epoch: 1 [51520/60000 (86%)]\tLoss: 157.491577\n",
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      "Train Epoch: 1 [56640/60000 (94%)]\tLoss: 138.925446\n",
      "Train Epoch: 1 [56960/60000 (95%)]\tLoss: 148.482101\n",
      "Train Epoch: 1 [57280/60000 (95%)]\tLoss: 152.629486\n",
      "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 147.806274\n",
      "Train Epoch: 1 [57920/60000 (97%)]\tLoss: 158.189941\n",
      "Train Epoch: 1 [58240/60000 (97%)]\tLoss: 150.135681\n",
      "Train Epoch: 1 [58560/60000 (98%)]\tLoss: 148.824951\n",
      "Train Epoch: 1 [58880/60000 (98%)]\tLoss: 143.460358\n",
      "Train Epoch: 1 [59200/60000 (99%)]\tLoss: 132.563400\n",
      "Train Epoch: 1 [59520/60000 (99%)]\tLoss: 145.112686\n",
      "Train Epoch: 1 [59840/60000 (100%)]\tLoss: 142.353683\n",
      "====> Epoch: 1 Average loss: 183.9455\n",
      "====> Test set loss: 146.9550\n",
      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 156.233032\n",
      "Train Epoch: 2 [320/60000 (1%)]\tLoss: 134.375336\n",
      "Train Epoch: 2 [640/60000 (1%)]\tLoss: 141.927750\n",
      "Train Epoch: 2 [960/60000 (2%)]\tLoss: 152.945435\n",
      "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 161.871124\n",
      "Train Epoch: 2 [1600/60000 (3%)]\tLoss: 133.449402\n",
      "Train Epoch: 2 [1920/60000 (3%)]\tLoss: 135.470901\n",
      "Train Epoch: 2 [2240/60000 (4%)]\tLoss: 149.902817\n",
      "Train Epoch: 2 [2560/60000 (4%)]\tLoss: 153.186890\n",
      "Train Epoch: 2 [2880/60000 (5%)]\tLoss: 150.400665\n",
      "Train Epoch: 2 [3200/60000 (5%)]\tLoss: 143.572388\n",
      "Train Epoch: 2 [3520/60000 (6%)]\tLoss: 139.307648\n",
      "Train Epoch: 2 [3840/60000 (6%)]\tLoss: 142.465286\n",
      "Train Epoch: 2 [4160/60000 (7%)]\tLoss: 160.830231\n",
      "Train Epoch: 2 [4480/60000 (7%)]\tLoss: 139.060425\n",
      "Train Epoch: 2 [4800/60000 (8%)]\tLoss: 135.948685\n",
      "Train Epoch: 2 [5120/60000 (9%)]\tLoss: 140.192062\n",
      "Train Epoch: 2 [5440/60000 (9%)]\tLoss: 133.544235\n",
      "Train Epoch: 2 [5760/60000 (10%)]\tLoss: 137.903397\n",
      "Train Epoch: 2 [6080/60000 (10%)]\tLoss: 147.455994\n",
      "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 139.306717\n",
      "Train Epoch: 2 [6720/60000 (11%)]\tLoss: 135.562012\n",
      "Train Epoch: 2 [7040/60000 (12%)]\tLoss: 137.116562\n",
      "Train Epoch: 2 [7360/60000 (12%)]\tLoss: 138.354294\n",
      "Train Epoch: 2 [7680/60000 (13%)]\tLoss: 143.536804\n",
      "Train Epoch: 2 [8000/60000 (13%)]\tLoss: 141.855804\n",
      "Train Epoch: 2 [8320/60000 (14%)]\tLoss: 148.981064\n",
      "Train Epoch: 2 [8640/60000 (14%)]\tLoss: 136.463959\n",
      "Train Epoch: 2 [8960/60000 (15%)]\tLoss: 135.035477\n",
      "Train Epoch: 2 [9280/60000 (15%)]\tLoss: 149.818375\n",
      "Train Epoch: 2 [9600/60000 (16%)]\tLoss: 145.733536\n",
      "Train Epoch: 2 [9920/60000 (17%)]\tLoss: 138.486496\n",
      "Train Epoch: 2 [10240/60000 (17%)]\tLoss: 147.446823\n",
      "Train Epoch: 2 [10560/60000 (18%)]\tLoss: 147.531769\n",
      "Train Epoch: 2 [10880/60000 (18%)]\tLoss: 142.975189\n",
      "Train Epoch: 2 [11200/60000 (19%)]\tLoss: 143.681030\n",
      "Train Epoch: 2 [11520/60000 (19%)]\tLoss: 135.253021\n",
      "Train Epoch: 2 [11840/60000 (20%)]\tLoss: 145.862122\n",
      "Train Epoch: 2 [12160/60000 (20%)]\tLoss: 139.958237\n",
      "Train Epoch: 2 [12480/60000 (21%)]\tLoss: 138.922012\n",
      "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 141.636276\n",
      "Train Epoch: 2 [13120/60000 (22%)]\tLoss: 140.501755\n",
      "Train Epoch: 2 [13440/60000 (22%)]\tLoss: 139.355225\n",
      "Train Epoch: 2 [13760/60000 (23%)]\tLoss: 139.953613\n",
      "Train Epoch: 2 [14080/60000 (23%)]\tLoss: 145.926651\n",
      "Train Epoch: 2 [14400/60000 (24%)]\tLoss: 141.783218\n",
      "Train Epoch: 2 [14720/60000 (25%)]\tLoss: 144.545959\n",
      "Train Epoch: 2 [15040/60000 (25%)]\tLoss: 147.718872\n",
      "Train Epoch: 2 [15360/60000 (26%)]\tLoss: 141.676407\n",
      "Train Epoch: 2 [15680/60000 (26%)]\tLoss: 144.615799\n",
      "Train Epoch: 2 [16000/60000 (27%)]\tLoss: 138.336166\n",
      "Train Epoch: 2 [16320/60000 (27%)]\tLoss: 119.087730\n",
      "Train Epoch: 2 [16640/60000 (28%)]\tLoss: 134.950226\n",
      "Train Epoch: 2 [16960/60000 (28%)]\tLoss: 147.334793\n",
      "Train Epoch: 2 [17280/60000 (29%)]\tLoss: 150.015076\n",
      "Train Epoch: 2 [17600/60000 (29%)]\tLoss: 146.561646\n",
      "Train Epoch: 2 [17920/60000 (30%)]\tLoss: 147.643417\n",
      "Train Epoch: 2 [18240/60000 (30%)]\tLoss: 141.953888\n",
      "Train Epoch: 2 [18560/60000 (31%)]\tLoss: 133.977432\n",
      "Train Epoch: 2 [18880/60000 (31%)]\tLoss: 156.577469\n",
      "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 136.082535\n",
      "Train Epoch: 2 [19520/60000 (33%)]\tLoss: 135.926636\n",
      "Train Epoch: 2 [19840/60000 (33%)]\tLoss: 139.648407\n",
      "Train Epoch: 2 [20160/60000 (34%)]\tLoss: 138.778076\n",
      "Train Epoch: 2 [20480/60000 (34%)]\tLoss: 134.166550\n",
      "Train Epoch: 2 [20800/60000 (35%)]\tLoss: 150.758942\n",
      "Train Epoch: 2 [21120/60000 (35%)]\tLoss: 136.898468\n",
      "Train Epoch: 2 [21440/60000 (36%)]\tLoss: 129.269485\n",
      "Train Epoch: 2 [21760/60000 (36%)]\tLoss: 137.595932\n",
      "Train Epoch: 2 [22080/60000 (37%)]\tLoss: 143.714355\n",
      "Train Epoch: 2 [22400/60000 (37%)]\tLoss: 131.163177\n",
      "Train Epoch: 2 [22720/60000 (38%)]\tLoss: 145.635010\n",
      "Train Epoch: 2 [23040/60000 (38%)]\tLoss: 141.366959\n",
      "Train Epoch: 2 [23360/60000 (39%)]\tLoss: 137.662369\n",
      "Train Epoch: 2 [23680/60000 (39%)]\tLoss: 136.485092\n",
      "Train Epoch: 2 [24000/60000 (40%)]\tLoss: 134.099823\n",
      "Train Epoch: 2 [24320/60000 (41%)]\tLoss: 136.879547\n",
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      "Train Epoch: 2 [24960/60000 (42%)]\tLoss: 148.431503\n",
      "Train Epoch: 2 [25280/60000 (42%)]\tLoss: 140.298019\n",
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      "Train Epoch: 2 [25920/60000 (43%)]\tLoss: 115.418884\n",
      "Train Epoch: 2 [26240/60000 (44%)]\tLoss: 122.158615\n",
      "Train Epoch: 2 [26560/60000 (44%)]\tLoss: 146.469635\n",
      "Train Epoch: 2 [26880/60000 (45%)]\tLoss: 145.133377\n",
      "Train Epoch: 2 [27200/60000 (45%)]\tLoss: 154.182388\n",
      "Train Epoch: 2 [27520/60000 (46%)]\tLoss: 139.463257\n",
      "Train Epoch: 2 [27840/60000 (46%)]\tLoss: 139.189819\n",
      "Train Epoch: 2 [28160/60000 (47%)]\tLoss: 135.624603\n",
      "Train Epoch: 2 [28480/60000 (47%)]\tLoss: 135.776657\n",
      "Train Epoch: 2 [28800/60000 (48%)]\tLoss: 141.576233\n",
      "Train Epoch: 2 [29120/60000 (49%)]\tLoss: 140.628784\n",
      "Train Epoch: 2 [29440/60000 (49%)]\tLoss: 132.208633\n",
      "Train Epoch: 2 [29760/60000 (50%)]\tLoss: 140.098831\n",
      "Train Epoch: 2 [30080/60000 (50%)]\tLoss: 140.843842\n",
      "Train Epoch: 2 [30400/60000 (51%)]\tLoss: 130.664749\n",
      "Train Epoch: 2 [30720/60000 (51%)]\tLoss: 140.393387\n",
      "Train Epoch: 2 [31040/60000 (52%)]\tLoss: 135.646179\n",
      "Train Epoch: 2 [31360/60000 (52%)]\tLoss: 137.613831\n",
      "Train Epoch: 2 [31680/60000 (53%)]\tLoss: 133.919586\n",
      "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 148.437592\n",
      "Train Epoch: 2 [32320/60000 (54%)]\tLoss: 139.294662\n",
      "Train Epoch: 2 [32640/60000 (54%)]\tLoss: 135.069687\n",
      "Train Epoch: 2 [32960/60000 (55%)]\tLoss: 129.916809\n",
      "Train Epoch: 2 [33280/60000 (55%)]\tLoss: 124.751228\n",
      "Train Epoch: 2 [33600/60000 (56%)]\tLoss: 136.593781\n",
      "Train Epoch: 2 [33920/60000 (57%)]\tLoss: 127.941536\n",
      "Train Epoch: 2 [34240/60000 (57%)]\tLoss: 127.988228\n",
      "Train Epoch: 2 [34560/60000 (58%)]\tLoss: 136.212082\n",
      "Train Epoch: 2 [34880/60000 (58%)]\tLoss: 134.664551\n",
      "Train Epoch: 2 [35200/60000 (59%)]\tLoss: 141.582336\n",
      "Train Epoch: 2 [35520/60000 (59%)]\tLoss: 127.879608\n",
      "Train Epoch: 2 [35840/60000 (60%)]\tLoss: 132.364624\n",
      "Train Epoch: 2 [36160/60000 (60%)]\tLoss: 141.445892\n",
      "Train Epoch: 2 [36480/60000 (61%)]\tLoss: 128.288330\n",
      "Train Epoch: 2 [36800/60000 (61%)]\tLoss: 135.587692\n",
      "Train Epoch: 2 [37120/60000 (62%)]\tLoss: 122.270844\n",
      "Train Epoch: 2 [37440/60000 (62%)]\tLoss: 139.634323\n",
      "Train Epoch: 2 [37760/60000 (63%)]\tLoss: 130.375992\n",
      "Train Epoch: 2 [38080/60000 (63%)]\tLoss: 121.044861\n",
      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 141.290436\n",
      "Train Epoch: 2 [38720/60000 (65%)]\tLoss: 128.575500\n",
      "Train Epoch: 2 [39040/60000 (65%)]\tLoss: 136.373276\n",
      "Train Epoch: 2 [39360/60000 (66%)]\tLoss: 127.112701\n",
      "Train Epoch: 2 [39680/60000 (66%)]\tLoss: 147.846619\n",
      "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 132.088562\n",
      "Train Epoch: 2 [40320/60000 (67%)]\tLoss: 139.737427\n",
      "Train Epoch: 2 [40640/60000 (68%)]\tLoss: 135.533981\n",
      "Train Epoch: 2 [40960/60000 (68%)]\tLoss: 124.531326\n",
      "Train Epoch: 2 [41280/60000 (69%)]\tLoss: 133.843353\n",
      "Train Epoch: 2 [41600/60000 (69%)]\tLoss: 129.118927\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [41920/60000 (70%)]\tLoss: 133.095291\n",
      "Train Epoch: 2 [42240/60000 (70%)]\tLoss: 133.520935\n",
      "Train Epoch: 2 [42560/60000 (71%)]\tLoss: 132.036453\n",
      "Train Epoch: 2 [42880/60000 (71%)]\tLoss: 122.419640\n",
      "Train Epoch: 2 [43200/60000 (72%)]\tLoss: 140.259277\n",
      "Train Epoch: 2 [43520/60000 (73%)]\tLoss: 120.931122\n",
      "Train Epoch: 2 [43840/60000 (73%)]\tLoss: 128.490356\n",
      "Train Epoch: 2 [44160/60000 (74%)]\tLoss: 133.120193\n",
      "Train Epoch: 2 [44480/60000 (74%)]\tLoss: 137.761261\n",
      "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 133.586716\n",
      "Train Epoch: 2 [45120/60000 (75%)]\tLoss: 136.818726\n",
      "Train Epoch: 2 [45440/60000 (76%)]\tLoss: 131.076141\n",
      "Train Epoch: 2 [45760/60000 (76%)]\tLoss: 139.124664\n",
      "Train Epoch: 2 [46080/60000 (77%)]\tLoss: 135.457947\n",
      "Train Epoch: 2 [46400/60000 (77%)]\tLoss: 127.648766\n",
      "Train Epoch: 2 [46720/60000 (78%)]\tLoss: 139.488037\n",
      "Train Epoch: 2 [47040/60000 (78%)]\tLoss: 134.073975\n",
      "Train Epoch: 2 [47360/60000 (79%)]\tLoss: 133.715714\n",
      "Train Epoch: 2 [47680/60000 (79%)]\tLoss: 134.060699\n",
      "Train Epoch: 2 [48000/60000 (80%)]\tLoss: 133.504501\n",
      "Train Epoch: 2 [48320/60000 (81%)]\tLoss: 118.338943\n",
      "Train Epoch: 2 [48640/60000 (81%)]\tLoss: 129.482040\n",
      "Train Epoch: 2 [48960/60000 (82%)]\tLoss: 129.482697\n",
      "Train Epoch: 2 [49280/60000 (82%)]\tLoss: 127.003761\n",
      "Train Epoch: 2 [49600/60000 (83%)]\tLoss: 131.661438\n",
      "Train Epoch: 2 [49920/60000 (83%)]\tLoss: 132.710236\n",
      "Train Epoch: 2 [50240/60000 (84%)]\tLoss: 137.392349\n",
      "Train Epoch: 2 [50560/60000 (84%)]\tLoss: 124.016838\n",
      "Train Epoch: 2 [50880/60000 (85%)]\tLoss: 124.823349\n",
      "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 121.512207\n",
      "Train Epoch: 2 [51520/60000 (86%)]\tLoss: 136.715912\n",
      "Train Epoch: 2 [51840/60000 (86%)]\tLoss: 129.715652\n",
      "Train Epoch: 2 [52160/60000 (87%)]\tLoss: 123.242760\n",
      "Train Epoch: 2 [52480/60000 (87%)]\tLoss: 121.426651\n",
      "Train Epoch: 2 [52800/60000 (88%)]\tLoss: 137.533310\n",
      "Train Epoch: 2 [53120/60000 (89%)]\tLoss: 131.839157\n",
      "Train Epoch: 2 [53440/60000 (89%)]\tLoss: 133.233292\n",
      "Train Epoch: 2 [53760/60000 (90%)]\tLoss: 130.200867\n",
      "Train Epoch: 2 [54080/60000 (90%)]\tLoss: 131.390701\n",
      "Train Epoch: 2 [54400/60000 (91%)]\tLoss: 129.316559\n",
      "Train Epoch: 2 [54720/60000 (91%)]\tLoss: 126.414124\n",
      "Train Epoch: 2 [55040/60000 (92%)]\tLoss: 127.467224\n",
      "Train Epoch: 2 [55360/60000 (92%)]\tLoss: 118.485588\n",
      "Train Epoch: 2 [55680/60000 (93%)]\tLoss: 130.455063\n",
      "Train Epoch: 2 [56000/60000 (93%)]\tLoss: 140.030121\n",
      "Train Epoch: 2 [56320/60000 (94%)]\tLoss: 127.004524\n",
      "Train Epoch: 2 [56640/60000 (94%)]\tLoss: 124.935654\n",
      "Train Epoch: 2 [56960/60000 (95%)]\tLoss: 143.305878\n",
      "Train Epoch: 2 [57280/60000 (95%)]\tLoss: 127.961243\n",
      "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 132.796158\n",
      "Train Epoch: 2 [57920/60000 (97%)]\tLoss: 122.530823\n",
      "Train Epoch: 2 [58240/60000 (97%)]\tLoss: 111.762047\n",
      "Train Epoch: 2 [58560/60000 (98%)]\tLoss: 129.138367\n",
      "Train Epoch: 2 [58880/60000 (98%)]\tLoss: 129.024155\n",
      "Train Epoch: 2 [59200/60000 (99%)]\tLoss: 125.611839\n",
      "Train Epoch: 2 [59520/60000 (99%)]\tLoss: 119.064133\n",
      "Train Epoch: 2 [59840/60000 (100%)]\tLoss: 128.013672\n",
      "====> Epoch: 2 Average loss: 136.4788\n",
      "====> Test set loss: 127.7730\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 126.638168\n",
      "Train Epoch: 3 [320/60000 (1%)]\tLoss: 142.625488\n",
      "Train Epoch: 3 [640/60000 (1%)]\tLoss: 123.596863\n",
      "Train Epoch: 3 [960/60000 (2%)]\tLoss: 137.081192\n",
      "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 123.770905\n",
      "Train Epoch: 3 [1600/60000 (3%)]\tLoss: 123.385406\n",
      "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 124.026237\n",
      "Train Epoch: 3 [2240/60000 (4%)]\tLoss: 132.697128\n",
      "Train Epoch: 3 [2560/60000 (4%)]\tLoss: 129.235474\n",
      "Train Epoch: 3 [2880/60000 (5%)]\tLoss: 129.357651\n",
      "Train Epoch: 3 [3200/60000 (5%)]\tLoss: 137.094025\n",
      "Train Epoch: 3 [3520/60000 (6%)]\tLoss: 127.613014\n",
      "Train Epoch: 3 [3840/60000 (6%)]\tLoss: 124.997665\n",
      "Train Epoch: 3 [4160/60000 (7%)]\tLoss: 129.663635\n",
      "Train Epoch: 3 [4480/60000 (7%)]\tLoss: 128.361374\n",
      "Train Epoch: 3 [4800/60000 (8%)]\tLoss: 126.062660\n",
      "Train Epoch: 3 [5120/60000 (9%)]\tLoss: 120.950943\n",
      "Train Epoch: 3 [5440/60000 (9%)]\tLoss: 126.953133\n",
      "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 131.564178\n",
      "Train Epoch: 3 [6080/60000 (10%)]\tLoss: 126.991394\n",
      "Train Epoch: 3 [6400/60000 (11%)]\tLoss: 124.732964\n",
      "Train Epoch: 3 [6720/60000 (11%)]\tLoss: 118.671043\n",
      "Train Epoch: 3 [7040/60000 (12%)]\tLoss: 133.842377\n",
      "Train Epoch: 3 [7360/60000 (12%)]\tLoss: 124.553070\n",
      "Train Epoch: 3 [7680/60000 (13%)]\tLoss: 128.195587\n",
      "Train Epoch: 3 [8000/60000 (13%)]\tLoss: 124.774857\n",
      "Train Epoch: 3 [8320/60000 (14%)]\tLoss: 123.291214\n",
      "Train Epoch: 3 [8640/60000 (14%)]\tLoss: 129.277008\n",
      "Train Epoch: 3 [8960/60000 (15%)]\tLoss: 119.742798\n",
      "Train Epoch: 3 [9280/60000 (15%)]\tLoss: 125.871498\n",
      "Train Epoch: 3 [9600/60000 (16%)]\tLoss: 109.684456\n",
      "Train Epoch: 3 [9920/60000 (17%)]\tLoss: 135.674530\n",
      "Train Epoch: 3 [10240/60000 (17%)]\tLoss: 117.203239\n",
      "Train Epoch: 3 [10560/60000 (18%)]\tLoss: 128.205414\n",
      "Train Epoch: 3 [10880/60000 (18%)]\tLoss: 129.115204\n",
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      "Train Epoch: 3 [11520/60000 (19%)]\tLoss: 121.342270\n",
      "Train Epoch: 3 [11840/60000 (20%)]\tLoss: 139.183121\n",
      "Train Epoch: 3 [12160/60000 (20%)]\tLoss: 122.789856\n",
      "Train Epoch: 3 [12480/60000 (21%)]\tLoss: 122.662399\n",
      "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 119.766678\n",
      "Train Epoch: 3 [13120/60000 (22%)]\tLoss: 131.568481\n",
      "Train Epoch: 3 [13440/60000 (22%)]\tLoss: 129.552795\n",
      "Train Epoch: 3 [13760/60000 (23%)]\tLoss: 123.961189\n",
      "Train Epoch: 3 [14080/60000 (23%)]\tLoss: 124.723061\n",
      "Train Epoch: 3 [14400/60000 (24%)]\tLoss: 130.875656\n",
      "Train Epoch: 3 [14720/60000 (25%)]\tLoss: 125.171074\n",
      "Train Epoch: 3 [15040/60000 (25%)]\tLoss: 117.742569\n",
      "Train Epoch: 3 [15360/60000 (26%)]\tLoss: 122.139664\n",
      "Train Epoch: 3 [15680/60000 (26%)]\tLoss: 130.594086\n",
      "Train Epoch: 3 [16000/60000 (27%)]\tLoss: 127.758301\n",
      "Train Epoch: 3 [16320/60000 (27%)]\tLoss: 128.829330\n",
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      "Train Epoch: 3 [19200/60000 (32%)]\tLoss: 123.952194\n",
      "Train Epoch: 3 [19520/60000 (33%)]\tLoss: 119.052391\n",
      "Train Epoch: 3 [19840/60000 (33%)]\tLoss: 124.288986\n",
      "Train Epoch: 3 [20160/60000 (34%)]\tLoss: 112.164078\n",
      "Train Epoch: 3 [20480/60000 (34%)]\tLoss: 132.533020\n",
      "Train Epoch: 3 [20800/60000 (35%)]\tLoss: 128.041733\n",
      "Train Epoch: 3 [21120/60000 (35%)]\tLoss: 133.129562\n",
      "Train Epoch: 3 [21440/60000 (36%)]\tLoss: 122.386780\n",
      "Train Epoch: 3 [21760/60000 (36%)]\tLoss: 121.690704\n",
      "Train Epoch: 3 [22080/60000 (37%)]\tLoss: 124.888489\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 3 [32640/60000 (54%)]\tLoss: 131.354446\n",
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      "Train Epoch: 3 [59840/60000 (100%)]\tLoss: 124.271667\n",
      "====> Epoch: 3 Average loss: 124.8090\n",
      "====> Test set loss: 120.7261\n",
      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 129.325577\n",
      "Train Epoch: 4 [320/60000 (1%)]\tLoss: 127.529846\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [23040/60000 (38%)]\tLoss: 107.808228\n",
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      "Train Epoch: 4 [59840/60000 (100%)]\tLoss: 119.590729\n",
      "====> Epoch: 4 Average loss: 119.8247\n",
      "====> Test set loss: 117.0948\n",
      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 119.798523\n",
      "Train Epoch: 5 [320/60000 (1%)]\tLoss: 119.699554\n",
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      "Train Epoch: 5 [13120/60000 (22%)]\tLoss: 128.585114\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 5 [13440/60000 (22%)]\tLoss: 124.840744\n",
      "Train Epoch: 5 [13760/60000 (23%)]\tLoss: 118.913216\n",
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      "Train Epoch: 5 [55360/60000 (92%)]\tLoss: 109.915634\n",
      "Train Epoch: 5 [55680/60000 (93%)]\tLoss: 106.087029\n",
      "Train Epoch: 5 [56000/60000 (93%)]\tLoss: 116.651840\n",
      "Train Epoch: 5 [56320/60000 (94%)]\tLoss: 123.362259\n",
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      "Train Epoch: 5 [56960/60000 (95%)]\tLoss: 110.946838\n",
      "Train Epoch: 5 [57280/60000 (95%)]\tLoss: 100.773598\n",
      "Train Epoch: 5 [57600/60000 (96%)]\tLoss: 111.786026\n",
      "Train Epoch: 5 [57920/60000 (97%)]\tLoss: 121.712708\n",
      "Train Epoch: 5 [58240/60000 (97%)]\tLoss: 107.863037\n",
      "Train Epoch: 5 [58560/60000 (98%)]\tLoss: 112.801331\n",
      "Train Epoch: 5 [58880/60000 (98%)]\tLoss: 124.303802\n",
      "Train Epoch: 5 [59200/60000 (99%)]\tLoss: 116.508118\n",
      "Train Epoch: 5 [59520/60000 (99%)]\tLoss: 113.318314\n",
      "Train Epoch: 5 [59840/60000 (100%)]\tLoss: 117.415108\n",
      "====> Epoch: 5 Average loss: 116.9115\n",
      "====> Test set loss: 114.6563\n",
      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 111.582809\n",
      "Train Epoch: 6 [320/60000 (1%)]\tLoss: 123.005775\n",
      "Train Epoch: 6 [640/60000 (1%)]\tLoss: 113.286560\n",
      "Train Epoch: 6 [960/60000 (2%)]\tLoss: 110.119125\n",
      "Train Epoch: 6 [1280/60000 (2%)]\tLoss: 114.634949\n",
      "Train Epoch: 6 [1600/60000 (3%)]\tLoss: 116.377281\n",
      "Train Epoch: 6 [1920/60000 (3%)]\tLoss: 120.632584\n",
      "Train Epoch: 6 [2240/60000 (4%)]\tLoss: 110.924225\n",
      "Train Epoch: 6 [2560/60000 (4%)]\tLoss: 113.089378\n",
      "Train Epoch: 6 [2880/60000 (5%)]\tLoss: 113.396469\n",
      "Train Epoch: 6 [3200/60000 (5%)]\tLoss: 114.852158\n",
      "Train Epoch: 6 [3520/60000 (6%)]\tLoss: 131.416061\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [3840/60000 (6%)]\tLoss: 109.146507\n",
      "Train Epoch: 6 [4160/60000 (7%)]\tLoss: 123.241966\n",
      "Train Epoch: 6 [4480/60000 (7%)]\tLoss: 107.595612\n",
      "Train Epoch: 6 [4800/60000 (8%)]\tLoss: 113.351837\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [55040/60000 (92%)]\tLoss: 123.042755\n",
      "Train Epoch: 6 [55360/60000 (92%)]\tLoss: 110.920418\n",
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      "Train Epoch: 6 [59840/60000 (100%)]\tLoss: 110.303146\n",
      "====> Epoch: 6 Average loss: 114.9139\n",
      "====> Test set loss: 113.3354\n",
      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 117.784348\n",
      "Train Epoch: 7 [320/60000 (1%)]\tLoss: 109.071091\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 7 [45440/60000 (76%)]\tLoss: 111.168671\n",
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      "Train Epoch: 7 [59840/60000 (100%)]\tLoss: 114.172684\n",
      "====> Epoch: 7 Average loss: 113.3942\n",
      "====> Test set loss: 111.9163\n",
      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 111.752716\n",
      "Train Epoch: 8 [320/60000 (1%)]\tLoss: 117.791901\n",
      "Train Epoch: 8 [640/60000 (1%)]\tLoss: 114.648499\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 8 [35840/60000 (60%)]\tLoss: 114.964378\n",
      "Train Epoch: 8 [36160/60000 (60%)]\tLoss: 111.325241\n",
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      "Train Epoch: 8 [51520/60000 (86%)]\tLoss: 107.835175\n",
      "Train Epoch: 8 [51840/60000 (86%)]\tLoss: 99.178467\n",
      "Train Epoch: 8 [52160/60000 (87%)]\tLoss: 112.508728\n",
      "Train Epoch: 8 [52480/60000 (87%)]\tLoss: 114.082458\n",
      "Train Epoch: 8 [52800/60000 (88%)]\tLoss: 114.890053\n",
      "Train Epoch: 8 [53120/60000 (89%)]\tLoss: 110.368530\n",
      "Train Epoch: 8 [53440/60000 (89%)]\tLoss: 119.564453\n",
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      "Train Epoch: 8 [54400/60000 (91%)]\tLoss: 107.182869\n",
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      "Train Epoch: 8 [55360/60000 (92%)]\tLoss: 109.238380\n",
      "Train Epoch: 8 [55680/60000 (93%)]\tLoss: 109.774773\n",
      "Train Epoch: 8 [56000/60000 (93%)]\tLoss: 112.605927\n",
      "Train Epoch: 8 [56320/60000 (94%)]\tLoss: 106.372971\n",
      "Train Epoch: 8 [56640/60000 (94%)]\tLoss: 103.449493\n",
      "Train Epoch: 8 [56960/60000 (95%)]\tLoss: 109.607521\n",
      "Train Epoch: 8 [57280/60000 (95%)]\tLoss: 114.747177\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss: 115.403496\n",
      "Train Epoch: 8 [57920/60000 (97%)]\tLoss: 109.764725\n",
      "Train Epoch: 8 [58240/60000 (97%)]\tLoss: 107.894356\n",
      "Train Epoch: 8 [58560/60000 (98%)]\tLoss: 119.063141\n",
      "Train Epoch: 8 [58880/60000 (98%)]\tLoss: 107.513916\n",
      "Train Epoch: 8 [59200/60000 (99%)]\tLoss: 99.040482\n",
      "Train Epoch: 8 [59520/60000 (99%)]\tLoss: 117.101837\n",
      "Train Epoch: 8 [59840/60000 (100%)]\tLoss: 112.374603\n",
      "====> Epoch: 8 Average loss: 112.1851\n",
      "====> Test set loss: 110.8714\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 103.290062\n",
      "Train Epoch: 9 [320/60000 (1%)]\tLoss: 104.507904\n",
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      "Train Epoch: 9 [960/60000 (2%)]\tLoss: 114.911568\n",
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      "Train Epoch: 9 [1600/60000 (3%)]\tLoss: 108.352715\n",
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      "Train Epoch: 9 [2240/60000 (4%)]\tLoss: 116.569931\n",
      "Train Epoch: 9 [2560/60000 (4%)]\tLoss: 115.906082\n",
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      "Train Epoch: 9 [3520/60000 (6%)]\tLoss: 111.646896\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [26240/60000 (44%)]\tLoss: 109.719734\n",
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      "Train Epoch: 9 [59840/60000 (100%)]\tLoss: 106.899124\n",
      "====> Epoch: 9 Average loss: 111.1614\n",
      "====> Test set loss: 110.0759\n",
      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 118.465515\n",
      "Train Epoch: 10 [320/60000 (1%)]\tLoss: 117.424866\n",
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      "Train Epoch: 10 [16320/60000 (27%)]\tLoss: 112.966400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 10 [16640/60000 (28%)]\tLoss: 113.053864\n",
      "Train Epoch: 10 [16960/60000 (28%)]\tLoss: 101.721657\n",
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      "Train Epoch: 10 [33280/60000 (55%)]\tLoss: 116.631500\n",
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      "Train Epoch: 10 [51840/60000 (86%)]\tLoss: 118.163788\n",
      "Train Epoch: 10 [52160/60000 (87%)]\tLoss: 100.676247\n",
      "Train Epoch: 10 [52480/60000 (87%)]\tLoss: 101.215530\n",
      "Train Epoch: 10 [52800/60000 (88%)]\tLoss: 116.428268\n",
      "Train Epoch: 10 [53120/60000 (89%)]\tLoss: 111.240326\n",
      "Train Epoch: 10 [53440/60000 (89%)]\tLoss: 118.576706\n",
      "Train Epoch: 10 [53760/60000 (90%)]\tLoss: 116.182297\n",
      "Train Epoch: 10 [54080/60000 (90%)]\tLoss: 112.975777\n",
      "Train Epoch: 10 [54400/60000 (91%)]\tLoss: 118.924515\n",
      "Train Epoch: 10 [54720/60000 (91%)]\tLoss: 101.298630\n",
      "Train Epoch: 10 [55040/60000 (92%)]\tLoss: 111.975464\n",
      "Train Epoch: 10 [55360/60000 (92%)]\tLoss: 103.980377\n",
      "Train Epoch: 10 [55680/60000 (93%)]\tLoss: 114.739105\n",
      "Train Epoch: 10 [56000/60000 (93%)]\tLoss: 116.495094\n",
      "Train Epoch: 10 [56320/60000 (94%)]\tLoss: 117.470001\n",
      "Train Epoch: 10 [56640/60000 (94%)]\tLoss: 112.095840\n",
      "Train Epoch: 10 [56960/60000 (95%)]\tLoss: 103.742859\n",
      "Train Epoch: 10 [57280/60000 (95%)]\tLoss: 108.051407\n",
      "Train Epoch: 10 [57600/60000 (96%)]\tLoss: 104.307518\n",
      "Train Epoch: 10 [57920/60000 (97%)]\tLoss: 108.815643\n",
      "Train Epoch: 10 [58240/60000 (97%)]\tLoss: 106.209045\n",
      "Train Epoch: 10 [58560/60000 (98%)]\tLoss: 110.181976\n",
      "Train Epoch: 10 [58880/60000 (98%)]\tLoss: 113.682526\n",
      "Train Epoch: 10 [59200/60000 (99%)]\tLoss: 106.900810\n",
      "Train Epoch: 10 [59520/60000 (99%)]\tLoss: 113.146660\n",
      "Train Epoch: 10 [59840/60000 (100%)]\tLoss: 117.717720\n",
      "====> Epoch: 10 Average loss: 110.3688\n",
      "====> Test set loss: 109.3066\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cpu\")\n",
    "model = VAE().to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=2e-4)\n",
    "\n",
    "epochs = 10\n",
    "for epoch in range(1, epochs + 1):\n",
    "    train(epoch)\n",
    "    test(epoch)\n",
    "    with torch.no_grad():\n",
    "        sample = torch.randn(64, 10).to(device)\n",
    "        sample = model.decode(sample).cpu()\n",
    "        save_image(sample.view(64, 1, 28, 28),\n",
    "                   'results/sample_' + str(epoch) + '.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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