tutorial-9-autoencoders.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly detection with autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This week we will build an autoencoder for detecting anomalous jets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Background:\n",
    "- the goal of anomaly detection here is to find jets which look different from background jets\n",
    "- for us:\n",
    "    - background: QCD jets\n",
    "    - signal: top jets\n",
    "- of course top jets are not Beyond-the-Standard-Model (BSM), but they are a good test case for BSM anomaly detection\n",
    "- autoencoders are one of the main anomaly detection tools we have in particle physics\n",
    "- they consist of an encoder and a decoder\n",
    "    - encoder: maps input image to a lower dimensional latent vector\n",
    "    - decoder: maps lower dimensional latent vector to an output image\n",
    "- the autoencoder is optimised so that the reconstructed output image is as close as possible to the input image\n",
    "- the loss function is the mean-squared-error (MSE) between the input and output pixels\n",
    "- the autoencoder has a bottleneck $\\Rightarrow$ the latent vector has a much lower dimension than the images\n",
    "- this means that the autoencoder will focus on reconstructing only the most prominant features in the training data\n",
    "- therefore, if we train the autoencoder on background (QCD jets), it should poorly reconstruct the signal (top jets)\n",
    "- we can use the reconstruction score (loss) as an anomaly detection measure\n",
    "- larger reconstruction loss $\\Rightarrow$ more anomalous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting paper:\n",
    "- QCD or What?\n",
    "    - https://arxiv.org/abs/1808.08979v1\n",
    "    - A paper by Tilman and others here in Heidelberg that addresses exactly the problem studied in this tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outline:\n",
    "- Imports and plotting set-up\n",
    "- Building the dataset\n",
    "    - Preprocessing\n",
    "    - Dataloaders\n",
    "- Building an autoencoder (AE)\n",
    "- Optimising the AE\n",
    "- Detecting anomalous top jets\n",
    "    - plot the ROC curve for detecting top jets in test data\n",
    "    - plot distribution of the reconstruction loss for QCD jets and top jets\n",
    "- Visualise the input and output QCD and top jets\n",
    "- Are QCD jets reconstructed better than top jets?\n",
    "- Bonus question: can we detect anomalous QCD jets?\n",
    "    - if we train a network on top jets only, can we find QCD jets as anomalies?\n",
    "    - run the same procedure but with background=tops and signal=QCD :-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some tips:\n",
    "- when constructing the dataset, the training and validation data should consist of only QCD jets, while for testing data there should be equal amounts of QCD and top jets\n",
    "    - training on QCD jets only is a good approximation to training on a dataset which is mostly background with a tiny amount of signal (which is what you would have in practice)\n",
    "- when building the autoencoder you can use a fully connected network, or if feeling ambitious, you could use a convolutional neural network\n",
    "- the bottleneck should be small, say between 5 and 20 dimensions\n",
    "- this means that the encoder should be a network that accepts an image (e.g. 40x40 pixels flattened to a 1600d vector) and maps it to a vector of dimension 5-20, with some hidden layers\n",
    "- the decoder should then be a network which takes this 5-20 dimensional vector and maps back to a 1600 dimensional vector\n",
    "- to visualise and compare input and output jets you just pass a jet through the autoencoder and reshape the output to a 40x40 image and plot it like we did in previous tutorials\n",
    "- the loss function you should use is the mean squared error loss averaged over the pixels of the jet image\n",
    "- this is also the anomaly score for each jet\n",
    "- and remember, most of the code you need is in the previous tutorial notebooks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.nn import Sequential, Linear, ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import matplotlib.colors as mcolors\n",
    "import colorsys\n",
    "\n",
    "labelfont = FontProperties()\n",
    "labelfont.set_family('serif')\n",
    "labelfont.set_name('Times New Roman')\n",
    "labelfont.set_size(14)\n",
    "\n",
    "axislabelfont = FontProperties()\n",
    "axislabelfont.set_family('serif')\n",
    "axislabelfont.set_name('Times New Roman')\n",
    "axislabelfont.set_size(22)\n",
    "\n",
    "tickfont = FontProperties()\n",
    "tickfont.set_family('serif')\n",
    "tickfont.set_name('Times New Roman')\n",
    "tickfont.set_size(16)\n",
    "\n",
    "axisfontsize = 16\n",
    "labelfontsize = 16\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"mathtext.default\"] = \"rm\"\n",
    "plt.rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using the top-tagging data again, so let's just load what we saved out before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load( \"tutorial-3-data/TopTagging/x_train_short.npy\")\n",
    "y_train = np.load( \"tutorial-3-data/TopTagging/y_train_short.npy\")\n",
    "X_test = np.load( \"tutorial-3-data/TopTagging/x_test_short.npy\")\n",
    "y_test = np.load( \"tutorial-3-data/TopTagging/y_test_short.npy\")\n",
    "X_val = np.load( \"tutorial-3-data/TopTagging/x_val_short.npy\")\n",
    "y_val = np.load( \"tutorial-3-data/TopTagging/y_val_short.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's re-jig the data so that we have the training and validation data consisting only of background QCD jets, and the testing data being a mixture of both.  This limits our training data but it's fine for this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_qcd = X_train[ y_train == 0.0 ]\n",
    "X_val_qcd = X_val[ y_val == 0.0 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each jet we'll keep the $50$ constituents with the highest $p_T$, with each constituent consisting of $(p_T,\\eta,\\phi)$.\n",
    "So each constituent in the jet has three features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still need to preprocess the jets in much the same way as we did for the CNN study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some initial settings\n",
    "__n_warning__ = 0.7\n",
    "n_shift_phi, n_shift_eta = 0, 0\n",
    "\n",
    "# Grid settings\n",
    "xpixels = np.arange(-2.6, 2.6, 0.029)\n",
    "ypixels = np.arange(-np.pi, np.pi, 0.035)\n",
    "\n",
    "# Calculate the pseudorapidity of pixel entries\n",
    "def eta (pT, pz):\n",
    "    small = 1e-10\n",
    "    small_pT = (np.abs(pT) < small)\n",
    "    small_pz = (np.abs(pz) < small)\n",
    "    not_small = ~(small_pT | small_pz)\n",
    "    theta = np.arctan(pT[not_small]/pz[not_small])\n",
    "    theta[theta < 0] += np.pi\n",
    "    etas = np.zeros_like(pT)\n",
    "    etas[small_pz] = 0\n",
    "    etas[small_pT] = 1e-10\n",
    "    etas[not_small] = np.log(np.tan(theta/2))\n",
    "    return etas\n",
    "\n",
    "# Calculate the azimuthal angle of pixel entries\n",
    "def phi (px, py):\n",
    "    \"\"\"\n",
    "    phis are returned in rad., np.arctan(0,0)=0 -> zero constituents set to -np.pi\n",
    "    \"\"\"\n",
    "    phis = np.arctan2(py,px)\n",
    "    phis[phis < 0] += 2*np.pi\n",
    "    phis[phis > 2*np.pi] -= 2*np.pi\n",
    "    phis = phis - np.pi \n",
    "    return phis\n",
    "\n",
    "# function to calculate masses\n",
    "def mass (E,px,py,pz):\n",
    "    mass = np.sqrt(np.maximum(0.,E**2-px**2-py**2-pz**2))\n",
    "    return mass\n",
    "\n",
    "# function to return the image momenta for centroid and principal axis\n",
    "def img_mom (x, y, weights, x_power, y_power):\n",
    "    return ((x**x_power)*(y**y_power)*weights).sum()\n",
    "\n",
    "# returns the jet image\n",
    "def orig_image (etas, phis, es):\n",
    "    \"\"\"\n",
    "    Gives the value on grid with minimal distance,\n",
    "    eg. for xpixel = (0,1,2,3,..) eta=1.3 -> xpixel=1, eta=1.6 ->xpixel=2\n",
    "    \"\"\"\n",
    "    z = np.zeros((etas.shape[0],len(xpixels),len(ypixels)))\n",
    "    in_grid = ~((etas < xpixels[0]) | (etas > xpixels[-1]) | (phis < ypixels[0]) | (phis > ypixels[-1]))\n",
    "    xcoords = np.argmin(np.abs(etas[:,None,:] - xpixels[None,:,None]),axis=1)\n",
    "    ycoords = np.argmin(np.abs(phis[:,None,:] - ypixels[None,:,None]),axis=1)\n",
    "    ncoords = np.repeat(np.arange(etas.shape[0])[:,None],etas.shape[1],axis=1)\n",
    "    z[ncoords[in_grid],ycoords[in_grid],xcoords[in_grid]] = es[in_grid]\n",
    "    return z\n",
    "\n",
    "# preprocess the jet\n",
    "def preprocessing( x ,y, weights, rotate=True, flip=True ):\n",
    "    \"\"\"\n",
    "    (x,y) are the coordinates and weights the corresponding values, shifts\n",
    "    centroid to origin, rotates image, so that principal axis is vertical,\n",
    "    flips image, so that most weights lay in (x<0, y>0)-plane.\n",
    "    Method for calculating principal axis (similar to tensor of inertia):\n",
    "    https://en.wikipedia.org/wiki/Image_moment\n",
    "    here: y=phi, phi has modulo 2*np.pi but it's not been taken care of hear,\n",
    "    so possible issues with calculating the centroid\n",
    "    -> pre-shifting of events outside of this function solves the problem\n",
    "    for iamge-data with Delta_phi < 2*np.pi\n",
    "    \"\"\"\n",
    "\n",
    "    # Shift\n",
    "    x_centroid = img_mom(x, y, weights, 1, 0) / weights.sum()\n",
    "    y_centroid = img_mom(x, y, weights, 0, 1)/ weights.sum()\n",
    "    x = x - x_centroid\n",
    "    y = y - y_centroid\n",
    "\n",
    "    # Check if shifting worked, there can be problems with modulo variables like phi (y)\n",
    "    # x and y are sorted after highest weight, 0-comp. gives hottest event\n",
    "    # for Jet-like Images Centroid should be close to hottest constituen (pT-sorted arrays)  \n",
    "    global n_shift_phi\n",
    "    global n_shift_eta\n",
    "    if np.abs(x[0]) > __n_warning__:\n",
    "        n_shift_eta += 1  \n",
    "    if np.abs(y[0]) > __n_warning__:\n",
    "        n_shift_phi += 1       \n",
    "\n",
    "    if rotate:\n",
    "        #Ccovariant matrix, eigenvectors corr. to principal axis\n",
    "        u11 = img_mom(x, y, weights, 1, 1) / weights.sum()\n",
    "        u20 = img_mom(x, y, weights, 2, 0) / weights.sum()\n",
    "        u02 = img_mom(x, y, weights, 0, 2) / weights.sum()\n",
    "        cov = np.array([[u20, u11], [u11, u02]])\n",
    "\n",
    "        # Eigenvalues and eigenvectors of covariant matrix\n",
    "        evals, evecs = np.linalg.eig(cov)\n",
    "\n",
    "        # Sorts the eigenvalues, v1, [::-1] turns array around, \n",
    "        sort_indices = np.argsort(evals)[::-1]\n",
    "        e_1 = evecs[:, sort_indices[0]]  # Eigenvector with largest eigenvalue\n",
    "        e_2 = evecs[:, sort_indices[1]]\n",
    "\n",
    "        # Theta to x_asix, arctan2 gives correct angle\n",
    "        theta = np.arctan2(e_1[0], e_1[1])\n",
    "  \n",
    "        # Rotation, so that princple axis is vertical\n",
    "        # anti-clockwise rotation matrix\n",
    "        rotation = np.matrix([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])\n",
    "        transformed_mat = rotation * np.stack([x,y])\n",
    "        x_rot, y_rot = transformed_mat.A\n",
    "    else: \n",
    "        x_rot, y_rot = x, y\n",
    "  \n",
    "    # Flipping\n",
    "    n_flips = 0\n",
    "    if flip:\n",
    "        if weights[x_rot<0.].sum() < weights[x_rot>0.].sum():\n",
    "            x_rot = -x_rot\n",
    "            n_flips  += 1\n",
    "        if weights[y_rot<0.].sum() > weights[y_rot>0.].sum():\n",
    "            y_rot = -y_rot\n",
    "            n_flips += 1\n",
    "            \n",
    "    return x_rot, y_rot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to convert the jet to an image\n",
    "def constit_to_img( jets, n_constit, norm, rotate, flip ):\n",
    "    \n",
    "    print( \"Crop constituents\" )\n",
    "    jets = jets[:,0:n_constit,:]\n",
    "    \n",
    "    print( \"Calculating pT\" )\n",
    "    E     = jets[:,:,0]\n",
    "    pxs   = jets[:,:,1]\n",
    "    pys   = jets[:,:,2]\n",
    "    pzs   = jets[:,:,3]\n",
    "    pT    = np.sqrt(pxs**2+pys**2)\n",
    "    \n",
    "    print( \"Calculating eta\" )\n",
    "    etas  = eta(pT,pzs)\n",
    "    \n",
    "    print( \"Calculating phi\" )\n",
    "    phis  = phi(pxs,pys)\n",
    "    \n",
    "    print( \"Calculating the mass\" )\n",
    "    E_tot = E.sum(axis=1)\n",
    "    px_tot = pxs.sum(axis=1)\n",
    "    py_tot = pys.sum(axis=1)\n",
    "    pz_tot = pzs.sum(axis=1)\n",
    "    j_mass = mass(E_tot, px_tot, py_tot, pz_tot)\n",
    "    \n",
    "    print( \"Pre-shifting the phis\" )\n",
    "    phis = (phis.T - phis[:,0]).T\n",
    "    phis[phis < -np.pi] += 2*np.pi\n",
    "    phis[phis > np.pi] -= 2*np.pi\n",
    "    \n",
    "    print( \"Using pT as weight\" )\n",
    "    weights = pT\n",
    "    \n",
    "    print( \"Preprocessing\" )\n",
    "    \n",
    "    for i in range( np.shape(etas)[0] ):\n",
    "        etas[i,:], phis[i,:] = preprocessing( etas[i,:], phis[i,:], weights[i,:], rotate, flip )\n",
    "    \n",
    "    print( \"Creating images\" )\n",
    "    z_ori = orig_image(etas, phis, weights)\n",
    "    \n",
    "    #return z_ori\n",
    "        \n",
    "    print( \"Cropping and normalising\" )\n",
    "    n_crop = 40\n",
    "    z_new = np.zeros( (z_ori.shape[0],n_crop, n_crop) )\n",
    "    for i in range(z_ori.shape[0]):\n",
    "        Npix = z_ori[i,:,:].shape\n",
    "        z_new[i,:,:] = z_ori[i, int(Npix[0]/2-n_crop/2) : int(Npix[0]/2+n_crop/2), int(Npix[1]/2-n_crop/2) : int(Npix[1]/2+n_crop/2) ]\n",
    "        if norm:\n",
    "            z_sum = z_new[i,:,:].sum()\n",
    "            if z_sum != 0.:\n",
    "                z_new[i,:,:] = z_new[i,:,:]/z_sum\n",
    "    \n",
    "    print( \"Reshaping\" )\n",
    "    z_out = z_new.reshape( (z_new.shape[0],-1) )\n",
    "    \n",
    "    return z_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n"
     ]
    }
   ],
   "source": [
    "z_train = constit_to_img( X_train, 50, True, True, True )\n",
    "z_train_qcd = constit_to_img( X_train_qcd, 50, True, True, True )\n",
    "z_val = constit_to_img( X_val, 50, True, True, True )\n",
    "z_val_qcd = constit_to_img( X_val_qcd, 50, True, True, True )\n",
    "z_test = constit_to_img( X_test, 50, True, True, True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (30000, 1600)\n",
      "Train data (qcd-only) shape: (14960, 1600)\n",
      "Val data shape: (30000, 1600)\n",
      "Val data (qcd-only) shape: (14460, 1600)\n",
      "Test data shape: (30000, 1600)\n"
     ]
    }
   ],
   "source": [
    "print( \"Train data shape: \" + str(z_train.shape) )\n",
    "print( \"Train data (qcd-only) shape: \" + str(z_train_qcd.shape) )\n",
    "print( \"Val data shape: \" + str(z_val.shape) )\n",
    "print( \"Val data (qcd-only) shape: \" + str(z_val_qcd.shape) )\n",
    "print( \"Test data shape: \" + str(z_test.shape) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's cut these so that the training and validation samples are background only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_train_p = torch.Tensor( z_train )\n",
    "z_train_qcd_p = torch.Tensor( z_train_qcd )\n",
    "z_val_p = torch.Tensor( z_val )\n",
    "z_val_qcd_p = torch.Tensor( z_val_qcd )\n",
    "z_test_p = torch.Tensor( z_test )\n",
    "y_train_p = torch.Tensor( y_train )\n",
    "y_val_p = torch.Tensor( y_val )\n",
    "y_test_p = torch.Tensor( y_test )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class topcloud_bkg_dataset( Dataset ):\n",
    "    \n",
    "    def __init__( self, data ):\n",
    "        self.data = data\n",
    "        self.labels = torch.Tensor( [ 0.0 for i in range(len(data)) ] )\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]\n",
    "    \n",
    "class topcloud_sig_dataset( Dataset ):\n",
    "    \n",
    "    def __init__( self, data, labels ):\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.data[idx], self.labels[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_dataset = topcloud_bkg_dataset( z_train_qcd_p )\n",
    "val_dataset = topcloud_bkg_dataset( z_val_qcd_p )\n",
    "tst_dataset = topcloud_sig_dataset( z_test_p, y_test_p.unsqueeze(-1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "trn_dataloader = DataLoader( trn_dataset, batch_size=64, shuffle=True )\n",
    "val_dataloader = DataLoader( val_dataset, batch_size=64, shuffle=True )\n",
    "tst_dataloader = DataLoader( tst_dataset, batch_size=64, shuffle=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building an autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "class JetAutoEncoder( torch.nn.Module ):\n",
    "    \n",
    "    def __init__( self, data_dim, hiddenlayer_size, bottleneck_size ):\n",
    "        super( JetAutoEncoder, self ).__init__()\n",
    "                \n",
    "        self.enc_layer1 = nn.Linear( data_dim, hiddenlayer_size )\n",
    "        self.enc_relu_1 = nn.ReLU()\n",
    "        self.enc_layer2 = nn.Linear( hiddenlayer_size, hiddenlayer_size )\n",
    "        self.enc_relu_2 = nn.ReLU()\n",
    "        self.enc_layer3 = nn.Linear( hiddenlayer_size, bottleneck_size )\n",
    "        \n",
    "        self.dec_layer1 = nn.Linear( bottleneck_size, hiddenlayer_size )\n",
    "        self.dec_relu_1 = nn.ReLU()\n",
    "        self.dec_layer2 = nn.Linear( hiddenlayer_size, hiddenlayer_size )\n",
    "        self.dec_relu_2 = nn.ReLU()\n",
    "        self.dec_layer3 = nn.Linear( hiddenlayer_size, data_dim )\n",
    "\n",
    "        \n",
    "    def forward( self, batch ):\n",
    "\n",
    "        x = self.encode( batch )\n",
    "        x = self.decode( x )\n",
    "\n",
    "        return x\n",
    "    \n",
    "    def encode( self, batch ):\n",
    "        \n",
    "        x = self.enc_layer1( batch )\n",
    "        x = self.enc_relu_1( x )\n",
    "        x = self.enc_layer2( x )\n",
    "        x = self.enc_relu_2( x )\n",
    "        x = self.enc_layer3( x )\n",
    "        \n",
    "        return x\n",
    "    \n",
    "    def decode( self, batch ):\n",
    "        \n",
    "        x = self.dec_layer1( batch )\n",
    "        x = self.dec_relu_1( x )\n",
    "        x = self.dec_layer2( x )\n",
    "        x = self.dec_relu_2( x )\n",
    "        x = self.dec_layer3( x )\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimising the AutoEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_epoch( dataloader, model, loss_fn, optimizer ):\n",
    "    \n",
    "    size = len( dataloader.dataset )\n",
    "    \n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "\n",
    "        # pass data through network\n",
    "        pred = model(X)\n",
    "        \n",
    "        # compute loss\n",
    "        loss = loss_fn( X, pred )\n",
    "\n",
    "        # reset gradients in optimizer\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # compute gradients\n",
    "        loss.backward()\n",
    "        \n",
    "        # update weights with optimizer\n",
    "        optimizer.step()\n",
    "        \n",
    "        # print the training loss every 100 updates\n",
    "        if batch % 100 == 0:\n",
    "            loss, current = loss.item(), batch * len(X)\n",
    "            print( f\"current batch loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def val_pass( dataloader, model, loss_fn ):\n",
    "    \n",
    "    size = len( dataloader.dataset )\n",
    "    num_batches = len( dataloader )\n",
    "    vl = 0.0\n",
    "\n",
    "    # we don't need gradients here since we only use the forward pass\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            pred = model( X )\n",
    "            vl += loss_fn( X, pred ).item()\n",
    "\n",
    "    vl /= num_batches\n",
    "    print( f\"avg val loss per batch: {vl:>8f}\" )\n",
    "    \n",
    "    return vl\n",
    "\n",
    "def trn_pass( dataloader, model, loss_fn ):\n",
    "    \n",
    "    size = len( dataloader.dataset )\n",
    "    num_batches = len( dataloader )\n",
    "    tl = 0.0\n",
    "\n",
    "    # we don't need gradients here since we only use the forward pass\n",
    "    with torch.no_grad():\n",
    "        for X, y in dataloader:\n",
    "            pred = model( X )\n",
    "            tl += loss_fn( X, pred ).item()\n",
    "\n",
    "    tl /= num_batches\n",
    "    print( f\"avg trn loss per batch: {tl:>8f}\" )\n",
    "    \n",
    "    return tl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------\n",
      "model architecture \n",
      "-----------------------------------------------\n",
      "JetAutoEncoder(\n",
      "  (enc_layer1): Linear(in_features=1600, out_features=400, bias=True)\n",
      "  (enc_relu_1): ReLU()\n",
      "  (enc_layer2): Linear(in_features=400, out_features=400, bias=True)\n",
      "  (enc_relu_2): ReLU()\n",
      "  (enc_layer3): Linear(in_features=400, out_features=20, bias=True)\n",
      "  (dec_layer1): Linear(in_features=20, out_features=400, bias=True)\n",
      "  (dec_relu_1): ReLU()\n",
      "  (dec_layer2): Linear(in_features=400, out_features=400, bias=True)\n",
      "  (dec_relu_2): ReLU()\n",
      "  (dec_layer3): Linear(in_features=400, out_features=1600, bias=True)\n",
      ")\n",
      "-----------------------------------------------\n",
      "Epoch 1\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.001540  [    0/14960]\n",
      "current batch loss: 0.000134  [ 6400/14960]\n",
      "current batch loss: 0.000079  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000071\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000073\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 2\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000075  [    0/14960]\n",
      "current batch loss: 0.000047  [ 6400/14960]\n",
      "current batch loss: 0.000039  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000042\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000043\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 3\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000043  [    0/14960]\n",
      "current batch loss: 0.000042  [ 6400/14960]\n",
      "current batch loss: 0.000037  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000033\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000034\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 4\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/14960]\n",
      "current batch loss: 0.000028  [ 6400/14960]\n",
      "current batch loss: 0.000033  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000030\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000030\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 5\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000028  [    0/14960]\n",
      "current batch loss: 0.000021  [ 6400/14960]\n",
      "current batch loss: 0.000025  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 6\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/14960]\n",
      "current batch loss: 0.000023  [ 6400/14960]\n",
      "current batch loss: 0.000032  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 7\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/14960]\n",
      "current batch loss: 0.000029  [ 6400/14960]\n",
      "current batch loss: 0.000027  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 8\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000020  [    0/14960]\n",
      "current batch loss: 0.000028  [ 6400/14960]\n",
      "current batch loss: 0.000020  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 9\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/14960]\n",
      "current batch loss: 0.000024  [ 6400/14960]\n",
      "current batch loss: 0.000024  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000023\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000023\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 10\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000018  [ 6400/14960]\n",
      "current batch loss: 0.000027  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000022\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000023\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 11\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000016  [ 6400/14960]\n",
      "current batch loss: 0.000017  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000022\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000022\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 12\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000020  [    0/14960]\n",
      "current batch loss: 0.000022  [ 6400/14960]\n",
      "current batch loss: 0.000022  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000021\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000022\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 13\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000023  [    0/14960]\n",
      "current batch loss: 0.000019  [ 6400/14960]\n",
      "current batch loss: 0.000018  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000021\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000021\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 14\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000020  [    0/14960]\n",
      "current batch loss: 0.000025  [ 6400/14960]\n",
      "current batch loss: 0.000024  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000021\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 15\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000017  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 16\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/14960]\n",
      "current batch loss: 0.000019  [ 6400/14960]\n",
      "current batch loss: 0.000025  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 17\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/14960]\n",
      "current batch loss: 0.000024  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 18\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/14960]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current batch loss: 0.000022  [ 6400/14960]\n",
      "current batch loss: 0.000018  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 19\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000018  [    0/14960]\n",
      "current batch loss: 0.000020  [ 6400/14960]\n",
      "current batch loss: 0.000012  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000020\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 20\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000019  [ 6400/14960]\n",
      "current batch loss: 0.000015  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 21\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000022  [ 6400/14960]\n",
      "current batch loss: 0.000019  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 22\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000017  [    0/14960]\n",
      "current batch loss: 0.000019  [ 6400/14960]\n",
      "current batch loss: 0.000020  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 23\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/14960]\n",
      "current batch loss: 0.000016  [ 6400/14960]\n",
      "current batch loss: 0.000018  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000019\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 24\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/14960]\n",
      "current batch loss: 0.000013  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 25\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000016  [    0/14960]\n",
      "current batch loss: 0.000020  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 26\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000019  [    0/14960]\n",
      "current batch loss: 0.000022  [ 6400/14960]\n",
      "current batch loss: 0.000013  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 27\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000011  [    0/14960]\n",
      "current batch loss: 0.000016  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 28\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000015  [    0/14960]\n",
      "current batch loss: 0.000016  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000018\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 29\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000015  [    0/14960]\n",
      "current batch loss: 0.000018  [ 6400/14960]\n",
      "current batch loss: 0.000018  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 30\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000017  [    0/14960]\n",
      "current batch loss: 0.000022  [ 6400/14960]\n",
      "current batch loss: 0.000016  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 31\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000013  [    0/14960]\n",
      "current batch loss: 0.000015  [ 6400/14960]\n",
      "current batch loss: 0.000017  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 32\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000018  [    0/14960]\n",
      "current batch loss: 0.000014  [ 6400/14960]\n",
      "current batch loss: 0.000015  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000016\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000017\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 33\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000012  [    0/14960]\n",
      "current batch loss: 0.000014  [ 6400/14960]\n",
      "current batch loss: 0.000019  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000016\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000016\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 34\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000018  [    0/14960]\n",
      "current batch loss: 0.000018  [ 6400/14960]\n",
      "current batch loss: 0.000014  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000016\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000016\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 35\n",
      "-----------------------------------------------\n",
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      "Epoch 36\n",
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     ]
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     "output_type": "stream",
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      "avg trn loss per batch: 0.000015\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000016\n",
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      "Epoch 37\n",
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      "Epoch 38\n",
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      "Epoch 39\n",
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      "Epoch 50\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "current batch loss: 0.000014  [ 6400/14960]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "avg trn loss per batch: 0.000012\n",
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      "Epoch 79\n",
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      "Epoch 80\n",
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      "Epoch 81\n",
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      "Epoch 82\n",
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      "Epoch 84\n",
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      "|\n",
      "-----------------------------------------------\n",
      "Epoch 86\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000010  [    0/14960]\n",
      "current batch loss: 0.000014  [ 6400/14960]\n",
      "current batch loss: 0.000009  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 87\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000013  [    0/14960]\n",
      "current batch loss: 0.000012  [ 6400/14960]\n",
      "current batch loss: 0.000009  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 88\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000010  [    0/14960]\n",
      "current batch loss: 0.000012  [ 6400/14960]\n",
      "current batch loss: 0.000012  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 89\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000008  [    0/14960]\n",
      "current batch loss: 0.000010  [ 6400/14960]\n",
      "current batch loss: 0.000015  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 90\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000012  [    0/14960]\n",
      "current batch loss: 0.000011  [ 6400/14960]\n",
      "current batch loss: 0.000015  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 91\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000011  [    0/14960]\n",
      "current batch loss: 0.000009  [ 6400/14960]\n",
      "current batch loss: 0.000013  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 92\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000013  [    0/14960]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current batch loss: 0.000009  [ 6400/14960]\n",
      "current batch loss: 0.000013  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 93\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000008  [    0/14960]\n",
      "current batch loss: 0.000011  [ 6400/14960]\n",
      "current batch loss: 0.000008  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000011\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 94\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000014  [    0/14960]\n",
      "current batch loss: 0.000011  [ 6400/14960]\n",
      "current batch loss: 0.000013  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 95\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000012  [    0/14960]\n",
      "current batch loss: 0.000009  [ 6400/14960]\n",
      "current batch loss: 0.000010  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 96\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000011  [    0/14960]\n",
      "current batch loss: 0.000011  [ 6400/14960]\n",
      "current batch loss: 0.000008  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 97\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000008  [    0/14960]\n",
      "current batch loss: 0.000008  [ 6400/14960]\n",
      "current batch loss: 0.000012  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 98\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000010  [    0/14960]\n",
      "current batch loss: 0.000010  [ 6400/14960]\n",
      "current batch loss: 0.000012  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 99\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000009  [    0/14960]\n",
      "current batch loss: 0.000012  [ 6400/14960]\n",
      "current batch loss: 0.000011  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 100\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000011  [    0/14960]\n",
      "current batch loss: 0.000010  [ 6400/14960]\n",
      "current batch loss: 0.000010  [12800/14960]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000010\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000012\n",
      "-----------------------------------------------\n",
      "|\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# a useful function to present things clearer\n",
    "def separator():\n",
    "    print( \"-----------------------------------------------\" )\n",
    "\n",
    "epochs = 100\n",
    "\n",
    "# re-initialise the model and the optimizer\n",
    "model = JetAutoEncoder( 1600, 400, 20 )\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.Adam( model.parameters(), lr=learning_rate )\n",
    "separator()\n",
    "print( \"model architecture \")\n",
    "separator()\n",
    "print( model )\n",
    "\n",
    "# track train and val losses\n",
    "trn_losses = []\n",
    "val_losses = []\n",
    "\n",
    "for t in range(epochs):\n",
    "    separator()\n",
    "    print( f\"Epoch {t+1}\" )\n",
    "    separator()\n",
    "    train_epoch( trn_dataloader, model, loss_fn, optimizer )\n",
    "    separator()\n",
    "    trn_loss = trn_pass( trn_dataloader, model, loss_fn )\n",
    "    trn_losses.append( trn_loss )\n",
    "    separator()\n",
    "    val_loss = val_pass( val_dataloader, model, loss_fn )\n",
    "    val_losses.append( val_loss )\n",
    "    separator()\n",
    "    print( \"|\" )\n",
    "    \n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots( 1, 1, figsize=(7,5) )\n",
    "\n",
    "c1 = 'tab:red'\n",
    "c2 = 'tab:green'\n",
    "\n",
    "axs.plot( trn_losses, label=\"train loss\", color=c1 )\n",
    "axs.plot( val_losses, label=\"val   loss\", color=c2 )\n",
    "\n",
    "axs.set_yscale('log')\n",
    "\n",
    "axs.set_xlabel( \"epoch\", fontproperties=axislabelfont )\n",
    "axs.set_ylabel( \"Binary CE\", fontproperties=axislabelfont )\n",
    "\n",
    "xticks = [ int(x) for x in axs.get_xticks() ]\n",
    "axs.set_xticklabels( xticks, fontproperties=tickfont )\n",
    "\n",
    "yticks = axs.get_yticks()\n",
    "axs.set_yticklabels( yticks, fontproperties=tickfont )\n",
    "\n",
    "axs.legend( loc='best', prop=tickfont )\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting anomalous top jets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_size = 10000\n",
    "\n",
    "test_pred = model( z_test_p[:samp_size] ).detach()\n",
    "\n",
    "test_loss = nn.MSELoss( reduce=False )\n",
    "test_mse = test_loss( test_pred[:samp_size], z_test_p[:samp_size] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000, 1600])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mse.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mse = torch.mean( test_mse, dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "def closest_point(array, tpr_p=0.3):\n",
    "    dist = ((array-tpr_p)**2)\n",
    "    return np.argmin(dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14,5))\n",
    "\n",
    "fpr, tpr, th = roc_curve(y_test[0:samp_size], test_mse)\n",
    "auc_score = roc_auc_score(y_test[0:samp_size], test_mse)\n",
    "rnd_class = np.linspace(0, 1, 100)\n",
    "\n",
    "ax[0].plot(fpr, tpr, label='AUC = {:.2f}'.format(auc_score) )\n",
    "ax[0].plot(rnd_class, rnd_class, '--', label='Rnd classifier')\n",
    "ax[1].plot(tpr, 1/fpr, label='AUC = {:.2f}\\n $1/\\epsilon_{{bkg}}$(0.3) = {:.0f}'.format(auc_score, 1/fpr[closest_point(tpr, tpr_p=0.3)]))\n",
    "ax[1].plot(rnd_class, 1/rnd_class, '--', label='Rnd classifier')\n",
    "ax[1].set_yscale('log')\n",
    "\n",
    "ax[0].set_xlabel('$\\epsilon_{bkg}$ - FPR', fontproperties=axislabelfont)\n",
    "ax[0].set_ylabel('$\\epsilon_{s}$ - TPR', fontproperties=axislabelfont)\n",
    "\n",
    "ax[1].set_xlabel('$\\epsilon_{s}$ - TPR', fontproperties=axislabelfont)\n",
    "ax[1].set_ylabel('1/$\\epsilon_{bkg}$ - Inverse FPR', fontproperties=axislabelfont)\n",
    "for i in range(len(ax)):\n",
    "    ax[i].legend(prop=axislabelfont)\n",
    "    ax[i].tick_params(labelsize=axisfontsize)\n",
    "    ax[i].grid('on')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the input and output jets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jets = 4\n",
    "in_qcd_jets = z_test_p[ y_test==0.0 ][ 0:4 ] \n",
    "in_top_jets = z_test_p[ y_test==1.0 ][ 0:4 ]\n",
    "out_qcd_jets = model( in_qcd_jets ).detach().numpy()\n",
    "out_top_jets = model( in_top_jets ).detach().numpy()\n",
    "in_qcd_jets = in_qcd_jets.detach().numpy()\n",
    "in_top_jets = in_top_jets.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( in_qcd_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[1][i].imshow( out_qcd_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"input QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"reconstructed QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( in_top_jets[i].reshape(40,40), cmap='gist_heat_r', vmin=0.0, vmax=0.4 )\n",
    "    axs[1][i].imshow( out_top_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"input top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"reconstructed top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are QCD jets better reconstructed than top jets?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes.\n",
    "\n",
    "We can even plot the MSE-per-pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jets = 4\n",
    "in_qcd_jets = z_test_p[ y_test==0.0 ][ 0:4 ] \n",
    "in_top_jets = z_test_p[ y_test==1.0 ][ 0:4 ]\n",
    "out_qcd_jets = model( in_qcd_jets )\n",
    "out_top_jets = model( in_top_jets )\n",
    "mse_qcd_jets = test_loss( in_qcd_jets, out_qcd_jets ).detach().numpy()\n",
    "mse_top_jets = test_loss( in_top_jets, out_top_jets ).detach().numpy()\n",
    "in_qcd_jets = in_qcd_jets.detach().numpy()\n",
    "in_top_jets = in_top_jets.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( mse_qcd_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.01 )\n",
    "    axs[1][i].imshow( mse_top_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.01 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"MSE QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"MSE top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus question: can we detect anomalous QCD jets?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-do everything for QCD jets as the signal and top jets as the background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Crop constituents\n",
      "Calculating pT\n",
      "Calculating eta\n",
      "Calculating phi\n",
      "Calculating the mass\n",
      "Pre-shifting the phis\n",
      "Using pT as weight\n",
      "Preprocessing\n",
      "Creating images\n",
      "Cropping and normalising\n",
      "Reshaping\n",
      "Train data (top-only) shape: (15040, 1600)\n",
      "Val data (top-only) shape: (15040, 1600)\n",
      "-----------------------------------------------\n",
      "model architecture \n",
      "-----------------------------------------------\n",
      "JetAutoEncoder(\n",
      "  (enc_layer1): Linear(in_features=1600, out_features=400, bias=True)\n",
      "  (enc_relu_1): ReLU()\n",
      "  (enc_layer2): Linear(in_features=400, out_features=400, bias=True)\n",
      "  (enc_relu_2): ReLU()\n",
      "  (enc_layer3): Linear(in_features=400, out_features=20, bias=True)\n",
      "  (dec_layer1): Linear(in_features=20, out_features=400, bias=True)\n",
      "  (dec_relu_1): ReLU()\n",
      "  (dec_layer2): Linear(in_features=400, out_features=400, bias=True)\n",
      "  (dec_relu_2): ReLU()\n",
      "  (dec_layer3): Linear(in_features=400, out_features=1600, bias=True)\n",
      ")\n",
      "-----------------------------------------------\n",
      "Epoch 1\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.001473  [    0/15040]\n",
      "current batch loss: 0.000065  [ 6400/15040]\n",
      "current batch loss: 0.000067  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000067\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000066\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 2\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000065  [    0/15040]\n",
      "current batch loss: 0.000069  [ 6400/15040]\n",
      "current batch loss: 0.000064  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000063\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000063\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 3\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000058  [    0/15040]\n",
      "current batch loss: 0.000057  [ 6400/15040]\n",
      "current batch loss: 0.000059  [12800/15040]\n",
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      "Epoch 4\n",
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      "avg trn loss per batch: 0.000050\n",
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      "Epoch 5\n",
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      "Epoch 6\n",
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      "Epoch 7\n",
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      "avg trn loss per batch: 0.000042\n",
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      "Epoch 8\n",
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      "avg trn loss per batch: 0.000041\n",
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      "Epoch 9\n",
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      "Epoch 10\n",
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      "avg trn loss per batch: 0.000039\n",
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      "Epoch 11\n",
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      "avg trn loss per batch: 0.000038\n",
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      "Epoch 12\n",
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      "Epoch 13\n",
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      "Epoch 14\n",
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      "avg trn loss per batch: 0.000036\n",
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      "Epoch 15\n",
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      "Epoch 16\n",
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      "avg trn loss per batch: 0.000036\n",
      "-----------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg val loss per batch: 0.000036\n",
      "-----------------------------------------------\n",
      "|\n",
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      "Epoch 17\n",
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      "current batch loss: 0.000038  [    0/15040]\n",
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      "avg trn loss per batch: 0.000035\n",
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      "Epoch 18\n",
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      "Epoch 19\n",
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      "avg trn loss per batch: 0.000034\n",
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      "Epoch 20\n",
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      "avg trn loss per batch: 0.000034\n",
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      "Epoch 21\n",
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      "Epoch 22\n",
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      "avg trn loss per batch: 0.000033\n",
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      "Epoch 23\n",
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      "avg trn loss per batch: 0.000033\n",
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      "Epoch 24\n",
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      "avg trn loss per batch: 0.000033\n",
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      "Epoch 25\n",
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      "Epoch 26\n",
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      "avg trn loss per batch: 0.000032\n",
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      "Epoch 27\n",
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      "avg trn loss per batch: 0.000032\n",
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      "Epoch 28\n",
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      "Epoch 29\n",
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      "Epoch 30\n",
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      "Epoch 31\n",
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      "Epoch 32\n",
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      "Epoch 33\n",
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      "Epoch 34\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "current batch loss: 0.000031  [ 6400/15040]\n",
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      "Epoch 36\n",
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      "Epoch 37\n",
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      "Epoch 38\n",
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      "Epoch 39\n",
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      "Epoch 40\n",
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      "avg trn loss per batch: 0.000028\n",
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      "|\n",
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      "Epoch 41\n",
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      "avg trn loss per batch: 0.000028\n",
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      "Epoch 42\n",
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      "avg trn loss per batch: 0.000028\n",
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      "Epoch 43\n",
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      "avg trn loss per batch: 0.000028\n",
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      "Epoch 44\n",
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      "avg trn loss per batch: 0.000027\n",
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      "Epoch 45\n",
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      "Epoch 48\n",
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      "Epoch 50\n",
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      "Epoch 51\n",
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      "Epoch 52\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "avg trn loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
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      "|\n",
      "-----------------------------------------------\n",
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      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000029  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 55\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000026  [ 6400/15040]\n",
      "current batch loss: 0.000029  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 56\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000028  [ 6400/15040]\n",
      "current batch loss: 0.000023  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 57\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000030  [    0/15040]\n",
      "current batch loss: 0.000026  [ 6400/15040]\n",
      "current batch loss: 0.000028  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000026\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000028\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 58\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000023  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 59\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000023  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000023  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 60\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/15040]\n",
      "current batch loss: 0.000030  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 61\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000031  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 62\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000029  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 63\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000029  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 64\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 65\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000027  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 66\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 67\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000026  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 68\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000022  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000025\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 69\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000028  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 70\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000021  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 71\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 72\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current batch loss: 0.000025  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 73\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000028  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 74\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/15040]\n",
      "current batch loss: 0.000022  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 75\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000018  [ 6400/15040]\n",
      "current batch loss: 0.000026  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 76\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/15040]\n",
      "current batch loss: 0.000019  [ 6400/15040]\n",
      "current batch loss: 0.000028  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 77\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000028  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 78\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/15040]\n",
      "current batch loss: 0.000029  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 79\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000025  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 80\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000030  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 81\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000026  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000027  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 82\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000026  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 83\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000029  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000025  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 84\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 85\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000023  [    0/15040]\n",
      "current batch loss: 0.000021  [ 6400/15040]\n",
      "current batch loss: 0.000021  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 86\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/15040]\n",
      "current batch loss: 0.000020  [ 6400/15040]\n",
      "current batch loss: 0.000027  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 87\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000019  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 88\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/15040]\n",
      "current batch loss: 0.000023  [ 6400/15040]\n",
      "current batch loss: 0.000026  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 89\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000022  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 90\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000021  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000020  [12800/15040]\n",
      "-----------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 91\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000032  [ 6400/15040]\n",
      "current batch loss: 0.000027  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 92\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000025  [    0/15040]\n",
      "current batch loss: 0.000026  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 93\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000026  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 94\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000021  [ 6400/15040]\n",
      "current batch loss: 0.000028  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 95\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000023  [    0/15040]\n",
      "current batch loss: 0.000021  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 96\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000020  [    0/15040]\n",
      "current batch loss: 0.000020  [ 6400/15040]\n",
      "current batch loss: 0.000026  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 97\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000027  [    0/15040]\n",
      "current batch loss: 0.000020  [ 6400/15040]\n",
      "current batch loss: 0.000022  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 98\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000028  [    0/15040]\n",
      "current batch loss: 0.000024  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 99\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000028  [    0/15040]\n",
      "current batch loss: 0.000021  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "-----------------------------------------------\n",
      "Epoch 100\n",
      "-----------------------------------------------\n",
      "current batch loss: 0.000024  [    0/15040]\n",
      "current batch loss: 0.000027  [ 6400/15040]\n",
      "current batch loss: 0.000024  [12800/15040]\n",
      "-----------------------------------------------\n",
      "avg trn loss per batch: 0.000024\n",
      "-----------------------------------------------\n",
      "avg val loss per batch: 0.000027\n",
      "-----------------------------------------------\n",
      "|\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "X_train_top = X_train[ y_train == 1.0 ]\n",
    "X_val_top = X_val[ y_val == 1.0 ]\n",
    "\n",
    "z_train_top = constit_to_img( X_train_top, 50, True, True, True )\n",
    "z_val_top = constit_to_img( X_val_top, 50, True, True, True )\n",
    "\n",
    "print( \"Train data (top-only) shape: \" + str(z_train_top.shape) )\n",
    "print( \"Val data (top-only) shape: \" + str(z_train_top.shape) )\n",
    "\n",
    "z_train_top_p = torch.Tensor( z_train_top )\n",
    "z_val_top_p = torch.Tensor( z_val_top )\n",
    "\n",
    "trn_dataset_2 = topcloud_bkg_dataset( z_train_top_p )\n",
    "val_dataset_2 = topcloud_bkg_dataset( z_val_top_p )\n",
    "tst_dataset_2 = topcloud_sig_dataset( z_test_p, y_test_p.unsqueeze(-1) )\n",
    "\n",
    "trn_dataloader_2 = DataLoader( trn_dataset_2, batch_size=64, shuffle=True )\n",
    "val_dataloader_2 = DataLoader( val_dataset_2, batch_size=64, shuffle=True )\n",
    "tst_dataloader_2 = DataLoader( tst_dataset_2, batch_size=64, shuffle=True )\n",
    "\n",
    "# a useful function to present things clearer\n",
    "def separator():\n",
    "    print( \"-----------------------------------------------\" )\n",
    "\n",
    "epochs = 100\n",
    "\n",
    "# re-initialise the model and the optimizer\n",
    "model_2 = JetAutoEncoder( 1600, 400, 20 )\n",
    "learning_rate = 0.001\n",
    "optimizer = torch.optim.Adam( model_2.parameters(), lr=learning_rate )\n",
    "separator()\n",
    "print( \"model architecture \")\n",
    "separator()\n",
    "print( model_2 )\n",
    "\n",
    "# track train and val losses\n",
    "trn_losses_2 = []\n",
    "val_losses_2 = []\n",
    "\n",
    "for t in range(epochs):\n",
    "    separator()\n",
    "    print( f\"Epoch {t+1}\" )\n",
    "    separator()\n",
    "    train_epoch( trn_dataloader_2, model_2, loss_fn, optimizer )\n",
    "    separator()\n",
    "    trn_loss_2 = trn_pass( trn_dataloader_2, model_2, loss_fn )\n",
    "    trn_losses_2.append( trn_loss_2 )\n",
    "    separator()\n",
    "    val_loss_2 = val_pass( val_dataloader_2, model_2, loss_fn )\n",
    "    val_losses_2.append( val_loss_2 )\n",
    "    separator()\n",
    "    print( \"|\" )\n",
    "    \n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots( 1, 1, figsize=(7,5) )\n",
    "\n",
    "c1 = 'tab:red'\n",
    "c2 = 'tab:green'\n",
    "\n",
    "axs.plot( trn_losses_2, label=\"train loss\", color=c1 )\n",
    "axs.plot( val_losses_2, label=\"val   loss\", color=c2 )\n",
    "\n",
    "axs.set_yscale('log')\n",
    "\n",
    "axs.set_xlabel( \"epoch\", fontproperties=axislabelfont )\n",
    "axs.set_ylabel( \"Binary CE\", fontproperties=axislabelfont )\n",
    "\n",
    "xticks = [ int(x) for x in axs.get_xticks() ]\n",
    "axs.set_xticklabels( xticks, fontproperties=tickfont )\n",
    "\n",
    "yticks = axs.get_yticks()\n",
    "axs.set_yticklabels( yticks, fontproperties=tickfont )\n",
    "\n",
    "axs.legend( loc='best', prop=tickfont )\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "samp_size = 10000\n",
    "\n",
    "test_pred_2 = model_2( z_test_p[:samp_size] ).detach()\n",
    "\n",
    "test_loss_2 = nn.MSELoss( reduce=False )\n",
    "test_mse_2 = test_loss_2( test_pred_2[:samp_size], z_test_p[:samp_size] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000, 1600])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mse_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mse_2 = torch.mean( test_mse_2, dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to flip the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_2 = np.abs( y_test-1.0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14,5))\n",
    "\n",
    "fpr, tpr, th = roc_curve(y_test_2[0:samp_size], test_mse_2)\n",
    "auc_score = roc_auc_score(y_test_2[0:samp_size], test_mse_2)\n",
    "rnd_class = np.linspace(0, 1, 100)\n",
    "\n",
    "ax[0].plot(fpr, tpr, label='AUC = {:.2f}'.format(auc_score) )\n",
    "ax[0].plot(rnd_class, rnd_class, '--', label='Rnd classifier')\n",
    "ax[1].plot(tpr, 1/fpr, label='AUC = {:.2f}\\n $1/\\epsilon_{{bkg}}$(0.3) = {:.0f}'.format(auc_score, 1/fpr[closest_point(tpr, tpr_p=0.3)]))\n",
    "ax[1].plot(rnd_class, 1/rnd_class, '--', label='Rnd classifier')\n",
    "ax[1].set_yscale('log')\n",
    "\n",
    "ax[0].set_xlabel('$\\epsilon_{bkg}$ - FPR', fontproperties=axislabelfont)\n",
    "ax[0].set_ylabel('$\\epsilon_{s}$ - TPR', fontproperties=axislabelfont)\n",
    "\n",
    "ax[1].set_xlabel('$\\epsilon_{s}$ - TPR', fontproperties=axislabelfont)\n",
    "ax[1].set_ylabel('1/$\\epsilon_{bkg}$ - Inverse FPR', fontproperties=axislabelfont)\n",
    "for i in range(len(ax)):\n",
    "    ax[i].legend(prop=axislabelfont)\n",
    "    ax[i].tick_params(labelsize=axisfontsize)\n",
    "    ax[i].grid('on')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The AUC is less than 1!!\n",
    "\n",
    "This means we are doing worse than a random tagger...\n",
    "\n",
    "Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well we can look at the per-pixel MSE to see what is going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_jets = 4\n",
    "in_qcd_jets = z_test_p[ y_test==0.0 ][ 0:4 ] \n",
    "in_top_jets = z_test_p[ y_test==1.0 ][ 0:4 ]\n",
    "out_qcd_jets_2 = model_2( in_qcd_jets )\n",
    "out_top_jets_2 = model_2( in_top_jets )\n",
    "mse_qcd_jets_2 = test_loss( in_qcd_jets, out_qcd_jets_2 ).detach().numpy()\n",
    "mse_top_jets_2 = test_loss( in_top_jets, out_top_jets_2 ).detach().numpy()\n",
    "in_qcd_jets = in_qcd_jets.detach().numpy()\n",
    "in_top_jets = in_top_jets.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( mse_qcd_jets_2[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.01 )\n",
    "    axs[1][i].imshow( mse_top_jets_2[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.01 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"MSE QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"MSE top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look explicitly at the input and reconstructed jets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( in_qcd_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[1][i].imshow( out_qcd_jets_2.detach().numpy()[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"input QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"reconstructed QCD\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1512x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4, figsize=(21,8))\n",
    "\n",
    "for i in range(4):\n",
    "    axs[0][i].imshow( in_top_jets[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[1][i].imshow( out_top_jets_2.detach().numpy()[i].reshape(40,40), cmap='gist_heat_r',  vmin=0.0, vmax=0.4 )\n",
    "    axs[0][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[0][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_xlabel( '$\\eta$', fontproperties=axislabelfont )\n",
    "    axs[1][i].set_ylabel( '$\\phi$', fontproperties=axislabelfont )\n",
    "    axs[0][i].text( 38, 4, \"input top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[0][i].tick_params( labelsize=axisfontsize )\n",
    "    axs[1][i].text( 38, 4, \"reconstructed top\", fontproperties=axislabelfont, ha='right' )\n",
    "    axs[1][i].tick_params( labelsize=axisfontsize )\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that despite training the tagger on top jets, the QCD jets are still slightly better reconstructed than the top jets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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