FashionMNIST - ConvVAE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "% matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from fashionmnist_cnn import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DISABLE_CUDA = False\n",
    "\n",
    "if not DISABLE_CUDA and torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = FashionMNIST(\n",
    "    os.path.join(*'data/fashionMNIST'.split('/')),\n",
    "    batch_size=32, val_size=.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.920\n",
      "Test error rate: 0.080\n"
     ]
    }
   ],
   "source": [
    "model = load_model(fashionmnist_model(), os.path.join('models', 'fashionmnist.pt'), device)\n",
    "\n",
    "model.eval()\n",
    "test_accuracy = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for X, y, label in dataset.test_loader:\n",
    "        X = X.to(device); y = y.to(device)\n",
    "\n",
    "        pred = model(X)\n",
    "        test_accuracy += (torch.argmax(pred, 1) == y).sum()\n",
    "\n",
    "test_accuracy = test_accuracy.item() / dataset.test_size\n",
    "\n",
    "print('Test accuracy: %.3f' % test_accuracy)\n",
    "print('Test error rate: %.3f' % (1 - test_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 001: train_loss = 379.195, val_loss = 299.368\n",
      "Epoch 002: train_loss = 292.013, val_loss = 267.390\n",
      "Epoch 003: train_loss = 275.424, val_loss = 258.057\n",
      "Epoch 004: train_loss = 269.205, val_loss = 255.717\n",
      "Epoch 005: train_loss = 266.191, val_loss = 251.335\n",
      "Epoch 006: train_loss = 264.174, val_loss = 250.021\n",
      "Epoch 007: train_loss = 263.029, val_loss = 249.103\n",
      "Epoch 008: train_loss = 262.220, val_loss = 248.605\n",
      "Epoch 009: train_loss = 261.528, val_loss = 248.779\n",
      "Epoch 010: train_loss = 260.879, val_loss = 246.879\n",
      "Epoch 011: train_loss = 260.056, val_loss = 246.271\n",
      "Epoch 012: train_loss = 259.831, val_loss = 245.882\n",
      "Epoch 013: train_loss = 259.431, val_loss = 245.849\n",
      "Epoch 014: train_loss = 259.011, val_loss = 245.528\n",
      "Epoch 015: train_loss = 258.601, val_loss = 246.343\n",
      "Epoch 016: train_loss = 257.974, val_loss = 246.017\n",
      "Epoch 017: train_loss = 257.920, val_loss = 246.302\n",
      "Epoch 018: train_loss = 257.527, val_loss = 244.946\n",
      "Epoch 019: train_loss = 257.297, val_loss = 245.279\n",
      "Epoch 020: train_loss = 256.785, val_loss = 244.695\n",
      "Epoch 021: train_loss = 256.526, val_loss = 244.948\n",
      "Epoch 022: train_loss = 256.406, val_loss = 244.539\n",
      "Epoch 023: train_loss = 256.209, val_loss = 244.229\n",
      "Epoch 024: train_loss = 255.907, val_loss = 244.138\n",
      "Epoch 025: train_loss = 255.856, val_loss = 245.110\n",
      "Epoch 026: train_loss = 255.651, val_loss = 244.175\n",
      "Epoch 027: train_loss = 255.546, val_loss = 244.185\n",
      "Epoch 028: train_loss = 255.370, val_loss = 243.661\n",
      "Epoch 029: train_loss = 255.322, val_loss = 243.521\n",
      "Epoch 030: train_loss = 255.289, val_loss = 243.792\n",
      "Epoch 031: train_loss = 254.937, val_loss = 243.189\n",
      "Epoch 032: train_loss = 254.966, val_loss = 243.407\n",
      "Epoch 033: train_loss = 254.747, val_loss = 242.892\n",
      "Epoch 034: train_loss = 254.830, val_loss = 243.306\n",
      "Epoch 035: train_loss = 254.622, val_loss = 243.003\n",
      "Epoch 036: train_loss = 254.555, val_loss = 242.709\n",
      "Epoch 037: train_loss = 254.393, val_loss = 243.090\n",
      "Epoch 038: train_loss = 254.328, val_loss = 243.094\n",
      "Epoch 039: train_loss = 254.325, val_loss = 242.879\n",
      "Epoch 040: train_loss = 254.067, val_loss = 242.498\n",
      "Epoch 041: train_loss = 253.988, val_loss = 243.393\n",
      "Epoch 042: train_loss = 254.023, val_loss = 242.503\n",
      "Epoch 043: train_loss = 254.082, val_loss = 243.040\n",
      "Epoch 044: train_loss = 253.844, val_loss = 242.512\n",
      "Epoch 045: train_loss = 253.816, val_loss = 242.844\n",
      "Epoch 046: train_loss = 253.743, val_loss = 242.762\n",
      "Epoch 047: train_loss = 253.475, val_loss = 242.796\n",
      "Epoch 048: train_loss = 253.624, val_loss = 242.205\n",
      "Epoch 049: train_loss = 253.520, val_loss = 242.430\n",
      "Epoch 050: train_loss = 253.374, val_loss = 242.395\n",
      "Epoch 051: train_loss = 253.465, val_loss = 242.124\n",
      "Epoch 052: train_loss = 253.567, val_loss = 241.841\n",
      "Epoch 053: train_loss = 253.319, val_loss = 242.079\n",
      "Epoch 054: train_loss = 253.136, val_loss = 242.353\n",
      "Epoch 055: train_loss = 253.238, val_loss = 242.162\n",
      "Epoch 056: train_loss = 253.298, val_loss = 242.006\n",
      "Epoch 057: train_loss = 253.093, val_loss = 242.030\n",
      "Epoch 058: train_loss = 253.280, val_loss = 242.404\n",
      "Epoch 059: train_loss = 252.994, val_loss = 241.694\n",
      "Epoch 060: train_loss = 252.909, val_loss = 241.751\n",
      "Epoch 061: train_loss = 252.914, val_loss = 241.894\n",
      "Epoch 062: train_loss = 252.911, val_loss = 242.071\n",
      "Epoch 063: train_loss = 252.831, val_loss = 242.181\n",
      "Epoch 064: train_loss = 252.666, val_loss = 241.874\n",
      "Epoch 065: train_loss = 252.815, val_loss = 241.690\n",
      "Epoch 066: train_loss = 252.710, val_loss = 242.044\n",
      "Epoch 067: train_loss = 252.752, val_loss = 241.628\n",
      "Epoch 068: train_loss = 252.527, val_loss = 241.550\n",
      "Epoch 069: train_loss = 252.443, val_loss = 241.650\n",
      "Epoch 070: train_loss = 252.639, val_loss = 241.665\n",
      "Epoch 071: train_loss = 252.629, val_loss = 241.303\n",
      "Epoch 072: train_loss = 252.459, val_loss = 241.218\n",
      "Epoch 073: train_loss = 252.404, val_loss = 241.664\n",
      "Epoch 074: train_loss = 252.376, val_loss = 241.844\n",
      "Epoch 075: train_loss = 252.486, val_loss = 241.470\n",
      "Epoch 076: train_loss = 252.267, val_loss = 241.301\n",
      "Epoch 077: train_loss = 252.381, val_loss = 241.733\n",
      "Epoch 078: train_loss = 252.200, val_loss = 241.332\n",
      "Epoch 079: train_loss = 252.448, val_loss = 241.477\n",
      "Epoch 080: train_loss = 252.232, val_loss = 241.405\n",
      "Epoch 081: train_loss = 252.070, val_loss = 241.622\n",
      "Epoch 082: train_loss = 252.064, val_loss = 241.172\n",
      "Epoch 083: train_loss = 252.112, val_loss = 241.877\n",
      "Epoch 084: train_loss = 252.101, val_loss = 241.806\n",
      "Epoch 085: train_loss = 252.191, val_loss = 240.958\n",
      "Epoch 086: train_loss = 252.057, val_loss = 241.281\n",
      "Epoch 087: train_loss = 251.931, val_loss = 241.271\n",
      "Epoch 088: train_loss = 251.946, val_loss = 241.113\n",
      "Epoch 089: train_loss = 252.018, val_loss = 241.057\n",
      "Epoch 090: train_loss = 251.893, val_loss = 241.450\n",
      "Epoch 091: train_loss = 251.889, val_loss = 240.847\n",
      "Epoch 092: train_loss = 251.914, val_loss = 241.308\n",
      "Epoch 093: train_loss = 251.734, val_loss = 241.302\n",
      "Epoch 094: train_loss = 251.725, val_loss = 241.574\n",
      "Epoch 095: train_loss = 251.812, val_loss = 241.228\n",
      "Epoch 096: train_loss = 251.642, val_loss = 241.005\n",
      "Epoch 097: train_loss = 251.908, val_loss = 241.435\n",
      "Epoch 098: train_loss = 251.709, val_loss = 241.427\n",
      "Epoch 099: train_loss = 251.719, val_loss = 241.198\n",
      "Epoch 100: train_loss = 251.801, val_loss = 241.153\n",
      "Epoch 101: train_loss = 251.586, val_loss = 241.371\n",
      "Epoch 102: train_loss = 251.715, val_loss = 241.138\n",
      "Epoch 103: train_loss = 251.595, val_loss = 241.304\n",
      "Epoch 104: train_loss = 251.612, val_loss = 241.067\n",
      "Epoch 105: train_loss = 251.552, val_loss = 240.764\n",
      "Epoch 106: train_loss = 251.474, val_loss = 241.165\n",
      "Epoch 107: train_loss = 251.608, val_loss = 240.883\n",
      "Epoch 108: train_loss = 251.538, val_loss = 241.465\n",
      "Epoch 109: train_loss = 251.539, val_loss = 241.486\n",
      "Epoch 110: train_loss = 251.438, val_loss = 240.855\n",
      "Epoch 111: train_loss = 251.448, val_loss = 241.072\n",
      "Epoch 112: train_loss = 251.289, val_loss = 240.966\n",
      "Epoch 113: train_loss = 251.363, val_loss = 241.048\n",
      "Epoch 114: train_loss = 251.376, val_loss = 240.691\n",
      "Epoch 115: train_loss = 251.398, val_loss = 240.791\n",
      "Epoch 116: train_loss = 251.400, val_loss = 241.046\n",
      "Epoch 117: train_loss = 251.328, val_loss = 241.191\n",
      "Epoch 118: train_loss = 251.141, val_loss = 240.552\n",
      "Epoch 119: train_loss = 251.071, val_loss = 241.141\n",
      "Epoch 120: train_loss = 251.243, val_loss = 241.106\n",
      "Epoch 121: train_loss = 251.062, val_loss = 240.476\n",
      "Epoch 122: train_loss = 251.145, val_loss = 240.993\n",
      "Epoch 123: train_loss = 251.371, val_loss = 241.278\n",
      "Epoch 124: train_loss = 251.029, val_loss = 240.916\n",
      "Epoch 125: train_loss = 251.257, val_loss = 240.905\n",
      "Epoch 126: train_loss = 251.233, val_loss = 240.869\n",
      "Epoch 127: train_loss = 251.108, val_loss = 240.908\n",
      "Epoch 128: train_loss = 250.992, val_loss = 241.102\n",
      "Epoch 129: train_loss = 251.026, val_loss = 240.384\n",
      "Epoch 130: train_loss = 251.200, val_loss = 240.630\n",
      "Epoch 131: train_loss = 251.027, val_loss = 240.562\n",
      "Epoch 132: train_loss = 250.948, val_loss = 241.396\n",
      "Epoch 133: train_loss = 250.914, val_loss = 240.750\n",
      "Epoch 134: train_loss = 251.000, val_loss = 240.627\n",
      "Epoch 135: train_loss = 250.975, val_loss = 240.952\n",
      "Epoch 136: train_loss = 250.955, val_loss = 240.739\n",
      "Epoch 137: train_loss = 250.840, val_loss = 240.690\n",
      "Epoch 138: train_loss = 250.846, val_loss = 240.442\n",
      "Epoch 139: train_loss = 251.006, val_loss = 241.457\n",
      "Epoch 140: train_loss = 250.932, val_loss = 240.105\n",
      "Epoch 141: train_loss = 250.806, val_loss = 240.483\n",
      "Epoch 142: train_loss = 250.889, val_loss = 240.859\n",
      "Epoch 143: train_loss = 250.832, val_loss = 241.127\n",
      "Epoch 144: train_loss = 250.936, val_loss = 240.811\n",
      "Epoch 145: train_loss = 250.830, val_loss = 240.506\n",
      "Epoch 146: train_loss = 250.673, val_loss = 240.675\n",
      "Epoch 147: train_loss = 250.700, val_loss = 240.497\n",
      "Epoch 148: train_loss = 250.755, val_loss = 240.351\n",
      "Epoch 149: train_loss = 250.675, val_loss = 241.276\n",
      "Epoch 150: train_loss = 250.690, val_loss = 240.688\n",
      "Epoch 151: train_loss = 250.799, val_loss = 240.828\n",
      "Epoch 152: train_loss = 250.684, val_loss = 240.586\n",
      "Epoch 153: train_loss = 250.740, val_loss = 241.275\n",
      "Epoch 154: train_loss = 250.788, val_loss = 240.261\n",
      "Epoch 155: train_loss = 250.566, val_loss = 240.833\n",
      "Epoch 156: train_loss = 250.688, val_loss = 240.601\n",
      "Epoch 157: train_loss = 250.607, val_loss = 240.275\n",
      "Epoch 158: train_loss = 250.875, val_loss = 240.303\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 159: train_loss = 250.465, val_loss = 240.382\n",
      "Epoch 160: train_loss = 250.718, val_loss = 241.150\n",
      "Epoch 161: train_loss = 250.655, val_loss = 240.451\n",
      "Epoch 162: train_loss = 250.709, val_loss = 240.751\n",
      "Epoch 163: train_loss = 250.672, val_loss = 240.369\n",
      "Epoch 164: train_loss = 250.547, val_loss = 240.934\n",
      "Epoch 165: train_loss = 250.475, val_loss = 240.513\n",
      "Epoch 166: train_loss = 250.438, val_loss = 240.159\n",
      "Epoch 167: train_loss = 250.464, val_loss = 240.397\n",
      "Epoch 168: train_loss = 250.600, val_loss = 240.258\n",
      "Epoch 169: train_loss = 250.523, val_loss = 240.095\n",
      "Epoch 170: train_loss = 250.458, val_loss = 240.357\n",
      "Epoch 171: train_loss = 250.506, val_loss = 240.356\n",
      "Epoch 172: train_loss = 250.527, val_loss = 240.154\n",
      "Epoch 173: train_loss = 250.481, val_loss = 240.263\n",
      "Epoch 174: train_loss = 250.537, val_loss = 240.442\n",
      "Epoch 175: train_loss = 250.467, val_loss = 240.379\n",
      "Epoch 176: train_loss = 250.431, val_loss = 240.647\n",
      "Epoch 177: train_loss = 250.409, val_loss = 240.419\n",
      "Epoch 178: train_loss = 250.466, val_loss = 240.258\n",
      "Epoch 179: train_loss = 250.286, val_loss = 240.614\n",
      "Epoch 180: train_loss = 250.444, val_loss = 239.951\n",
      "Epoch 181: train_loss = 250.366, val_loss = 240.153\n",
      "Epoch 182: train_loss = 250.233, val_loss = 240.389\n",
      "Epoch 183: train_loss = 250.480, val_loss = 240.262\n",
      "Epoch 184: train_loss = 250.276, val_loss = 240.563\n",
      "Epoch 185: train_loss = 250.375, val_loss = 240.270\n",
      "Epoch 186: train_loss = 250.400, val_loss = 240.366\n",
      "Epoch 187: train_loss = 250.179, val_loss = 240.276\n",
      "Epoch 188: train_loss = 250.318, val_loss = 240.188\n",
      "Epoch 189: train_loss = 250.288, val_loss = 240.096\n",
      "Epoch 190: train_loss = 250.292, val_loss = 240.562\n",
      "Epoch 191: train_loss = 250.153, val_loss = 240.242\n",
      "Epoch 192: train_loss = 250.081, val_loss = 240.118\n",
      "Epoch 193: train_loss = 250.120, val_loss = 240.370\n",
      "Epoch 194: train_loss = 250.216, val_loss = 240.357\n",
      "Epoch 195: train_loss = 250.206, val_loss = 240.161\n",
      "Epoch 196: train_loss = 250.225, val_loss = 240.184\n",
      "Epoch 197: train_loss = 250.223, val_loss = 240.308\n",
      "Epoch 198: train_loss = 250.225, val_loss = 239.743\n",
      "Epoch 199: train_loss = 250.185, val_loss = 240.134\n",
      "Epoch 200: train_loss = 250.143, val_loss = 240.505\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd95696c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emb_dim = 100\n",
    "N_EPOCHS = 200\n",
    "\n",
    "vae = ConvVAE(emb_dim).to(device)\n",
    "model_path = os.path.join('models', 'conv_vae_fashionmnist.pt')\n",
    "\n",
    "optimizer = optim.Adam(vae.parameters())\n",
    "\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "# Train model\n",
    "for epoch in range(1, N_EPOCHS + 1):\n",
    "    vae.train()\n",
    "    \n",
    "    train_loss = 0\n",
    "    \n",
    "    for X, _, _ in dataset.train_loader:\n",
    "        X = X.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        recon_X, mu, logvar, code = vae(X)\n",
    "        \n",
    "        loss = vae.loss(recon_X, X, mu, logvar)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item()\n",
    "        \n",
    "    train_loss /= dataset.train_size\n",
    "    train_losses.append(train_loss)\n",
    "    \n",
    "    \n",
    "    vae.eval()\n",
    "    \n",
    "    val_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for X, _, _ in dataset.val_loader:\n",
    "            X = X.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            recon_X, mu, logvar, code = vae(X)\n",
    "            loss = vae.loss(recon_X, X, mu, logvar)\n",
    "\n",
    "            val_loss += loss.item()\n",
    "        \n",
    "    val_loss /= dataset.val_size\n",
    "    val_losses.append(val_loss)\n",
    "    \n",
    "    print('Epoch %.3d: train_loss = %.3f, val_loss = %.3f' % (\n",
    "        epoch, train_loss, val_loss\n",
    "    ))\n",
    "    \n",
    "\n",
    "plt.plot(torch.Tensor(train_losses).cpu().detach().numpy(), label='train_loss')\n",
    "plt.plot(torch.Tensor(val_losses).cpu().detach().numpy(), label='val_loss')\n",
    "plt.legend();\n",
    "\n",
    "torch.save(vae.state_dict(), model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_dim = 100\n",
    "\n",
    "vae = ConvVAE(emb_dim).to(device)\n",
    "model_path = os.path.join('models', 'conv_vae_fashionmnist.pt')\n",
    "vae.load_state_dict(torch.load(model_path))\n",
    "\n",
    "vae = vae.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd956ca2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = subplots(10, 10, plot_size=1).flatten()\n",
    "\n",
    "code = torch.randn(len(axes), emb_dim).to(device)\n",
    "decoded = vae.decode(code)\n",
    "\n",
    "for ax, img in zip(axes, decoded):\n",
    "    imshow(img[0].detach(), ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9380553c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_mat = subplots(10, 10).reshape(50, 2)\n",
    "\n",
    "idxs = np.random.choice(dataset.test_size, len(axes_mat), replace=False)\n",
    "\n",
    "for axes, idx in zip(axes_mat, idxs):\n",
    "    test_idx = dataset.test[idx]\n",
    "    img, y, label = dataset[test_idx]\n",
    "    img = img.to(device).unsqueeze(0)\n",
    "    rec_img = vae(img)[0][0, 0].detach()\n",
    "    \n",
    "    imshow(img[0, 0].detach(), axes[0])\n",
    "    axes[0].set_title('%d (%s)' % (idx, label))\n",
    "    imshow(rec_img, axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the embedding norm as a measure of the likelihood of a code belonging to the manifold.\n",
    "\n",
    "Let's analyze then the norm distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "codes = []\n",
    "ys = []\n",
    "\n",
    "for X, y, _ in dataset.train_loader:\n",
    "    X = X.to(device)\n",
    "    \n",
    "    code, _ = vae.encode(X)\n",
    "    codes.append(code.detach()); ys.append(y)\n",
    "    \n",
    "codes = torch.cat(codes)\n",
    "ys = torch.cat(ys)\n",
    "ys = ys.cpu().detach().numpy().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd938b2f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norms = codes.norm(p=2, dim=1).cpu().detach().numpy()\n",
    "plt.hist(norms);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd9341b1208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = subplots(2, 5, sharex=True).flatten()\n",
    "class_means = {}\n",
    "\n",
    "for y, ax in enumerate(axes):\n",
    "    subset = norms[ys == y]\n",
    "    norm_mean = subset.mean()\n",
    "    class_means[int(y)] = float(norm_mean)\n",
    "    \n",
    "    ax.hist(subset)\n",
    "    ax.axvline(norm_mean, linestyle='dashed', color='red')\n",
    "    ax.set_title('Emb. norm for %s (mean %.2f)' % (dataset.classes[y], norm_mean))\n",
    "    \n",
    "class_means[-1] = float(norms.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is clearly not a normal distribution, even if we split by class. In any case, we'll still work by that assumption for the sake of simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1: 2.1720826625823975,\n",
       " 0: 2.1901679039001465,\n",
       " 1: 2.43392014503479,\n",
       " 2: 2.1937754154205322,\n",
       " 3: 1.9419515132904053,\n",
       " 4: 1.9880778789520264,\n",
       " 5: 2.361179828643799,\n",
       " 6: 1.900506615638733,\n",
       " 7: 2.227470636367798,\n",
       " 8: 2.155830144882202,\n",
       " 9: 2.327273368835449}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('data/fashionmnist_conv_vae_norm_means.json', 'w') as f:\n",
    "    json.dump(class_means, f)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}