AI4Spec_Tutorial3.ipynb

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    "# AI4Spec 3: Predicting spectral curves with NN methods\n",
    "\n",
    "In these tutorials exercises, we review different AI approaches to predicting ground or excited state energy levels simply based on the atomic structure of materials. Here we focus on molecules and molecular orbitals, but the sample principles apply to crystal structures and electronic bands.\n",
    "\n",
    " We start by predicting individual states (Tutorial 1), proceed towards predicting multiple states at once (Tutorial 2) and finish by predicting entire spectral curves (Tutorial 3). The tutorials were prepared by Milica Todorović (University of Turku) and Kunal Ghosh (Aalto University).\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/milicasan/TutorialImages/89238bfff57a38ec78ecbd08765b63bd15ff9e2c/AI4spec.png\" width=\"600px\"/>\n",
    "\n",
    "In the exercise below, our AI objective is to train a model to predict an entire spectral curve. We achieve this by discretising spectra on a grid, then learning the numbers at the same time. This is the most complex objective so far, because we are predicting many floating numbers at the same time. We deploy the same AI model as before, the CNN, but adjust the network architecture to the large output - the spectral curve.\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 1. Using neural networks to predict spectral curves\n",
    "\n",
    "\n",
    "We proceed with the QM9 dataset of 132k molecules for spectral prediction. For the purposes of this tutorial, we take a subset of 7000 molecules, but in your own time you can try the entire dataset.\n",
    "\n",
    "Detailed descriptions and results for a similar dataset (QM9) can be found in [K. Ghosh, et al. \"Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.\" Adv. Sci. 6 1801367 (2019)](https://onlinelibrary.wiley.com/doi/full/10.1002/advs.201801367).\n",
    "\n",
    "## Setup"
   ]
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   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import json\n",
    "import math, random\n",
    "from scipy.sparse import load_npz\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hi4ISia7wrmc"
   },
   "source": [
    "### Load and visualize data\n",
    "\n",
    "First, we load the data. The molecules are represented by their Coulomb matrices, which were computed with the [Dscribe](https://www.sciencedirect.com/science/article/pii/S0010465519303042?via%3Dihub) package. The output data y is a matrix that contains the corresponding spectral curves for all molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jTZ_1hpqLBK5",
    "outputId": "86bd19b3-a735-4334-cbd0-e5416e7bc1db"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-03-04 21:47:36--  https://zenodo.org/records/10069732/files/coulomb_7000.npz\n",
      "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
      "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 47096264 (45M) [application/octet-stream]\n",
      "Saving to: ‘coulomb_7000.npz.3’\n",
      "\n",
      "coulomb_7000.npz.3  100%[===================>]  44.91M  43.4MB/s    in 1.0s    \n",
      "\n",
      "2024-03-04 21:47:38 (43.4 MB/s) - ‘coulomb_7000.npz.3’ saved [47096264/47096264]\n",
      "\n",
      "--2024-03-04 21:47:38--  https://zenodo.org/records/10069732/files/spectra_7000.npz\n",
      "Resolving zenodo.org (zenodo.org)... 188.184.98.238, 188.185.79.172, 188.184.103.159, ...\n",
      "Connecting to zenodo.org (zenodo.org)|188.184.98.238|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 16800264 (16M) [application/octet-stream]\n",
      "Saving to: ‘spectra_7000.npz.1’\n",
      "\n",
      "spectra_7000.npz.1  100%[===================>]  16.02M  27.7MB/s    in 0.6s    \n",
      "\n",
      "2024-03-04 21:47:39 (27.7 MB/s) - ‘spectra_7000.npz.1’ saved [16800264/16800264]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://zenodo.org/records/10069732/files/coulomb_7000.npz\n",
    "!wget https://zenodo.org/records/10069732/files/spectra_7000.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "6tTcc8Mtwrmd"
   },
   "outputs": [],
   "source": [
    "# Here we load all the data\n",
    "x = np.abs(np.load(\"coulomb_7000.npz\")['arr_0'])\n",
    "y = np.load(\"spectra_7000.npz\")['arr_0']\n",
    "\n",
    "## let's shuffle the data immediately\n",
    "c = list(zip(x, y))\n",
    "random.shuffle(c)\n",
    "x, y = zip(*c)\n",
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SfdU22Gv6f6z"
   },
   "source": [
    "As usual, let us check data size and the array lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aHF8AUmF54MI",
    "outputId": "3517a7da-9b21-4070-e13f-8d9f99cc19e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of molecules: 7000\n",
      "(7000, 29, 29)\n",
      "(7000, 300)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of molecules:\", len(y))\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vg4BcE8Kvs0i"
   },
   "source": [
    "The above confirms the 29x29 size of the Coloumb matrix. The spectral values are discretised on a grid of 300 entries, originally in the energy range [-30, 0]eV. Let us review one spectrum entry, e.g. for the 500th molecule:"
   ]
  },
  {
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    "outputId": "1d32167a-bb45-4a69-e39a-fcdf89b886e4"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.04832655e-50 3.06806541e-49 3.01570308e-48 2.89489880e-47\n",
      " 2.71393245e-46 2.48476615e-45 2.22173804e-44 1.94008617e-43\n",
      " 1.65451241e-42 1.37797038e-41 1.12080633e-40 8.90311753e-40\n",
      " 6.90676183e-39 5.23272240e-38 3.87170047e-37 2.79767105e-36\n",
      " 1.97429655e-35 1.36065796e-34 9.15812126e-34 6.01983591e-33\n",
      " 3.86441500e-32 2.42272269e-31 1.48335295e-30 8.86964285e-30\n",
      " 5.17950923e-29 2.95387378e-28 1.64519016e-27 8.94872406e-27\n",
      " 4.75364755e-26 2.46611708e-25 1.24945682e-24 6.18229421e-24\n",
      " 2.98743827e-23 1.40983754e-22 6.49770612e-22 2.92463671e-21\n",
      " 1.28559645e-20 5.51897315e-20 2.31383714e-19 9.47388692e-19\n",
      " 3.78830135e-18 1.47938690e-17 5.64208946e-17 2.10145037e-16\n",
      " 7.64397590e-16 2.71544189e-15 9.42069175e-15 3.19187739e-14\n",
      " 1.05616263e-13 3.41300286e-13 1.07711944e-12 3.31980474e-12\n",
      " 9.99269925e-12 2.93747957e-11 8.43313118e-11 2.36442237e-10\n",
      " 6.47416495e-10 1.73127204e-09 4.52137115e-09 1.15318456e-08\n",
      " 2.87244641e-08 6.98763623e-08 1.66010220e-07 3.85182633e-07\n",
      " 8.72825255e-07 1.93160511e-06 4.17485972e-06 8.81252655e-06\n",
      " 1.81675787e-05 3.65793027e-05 7.19316555e-05 1.38151756e-04\n",
      " 2.59149940e-04 4.74801636e-04 8.49668367e-04 1.48516488e-03\n",
      " 2.53572527e-03 4.22911400e-03 6.89028598e-03 1.09670833e-02\n",
      " 1.70546068e-02 2.59134880e-02 3.84758353e-02 5.58317942e-02\n",
      " 7.91899922e-02 1.09807092e-01 1.48885504e-01 1.97443802e-01\n",
      " 2.56170828e-01 3.25280562e-01 4.04388994e-01 4.92434925e-01\n",
      " 5.87662791e-01 6.87677324e-01 7.89568351e-01 8.90091688e-01\n",
      " 9.85881892e-01 1.07366742e+00 1.15046005e+00 1.21369831e+00\n",
      " 1.26133673e+00 1.29188598e+00 1.30441906e+00 1.29856294e+00\n",
      " 1.27449226e+00 1.23293286e+00 1.17517169e+00 1.10305917e+00\n",
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      " 6.24577682e-01 5.28015551e-01 4.38308338e-01 3.57722671e-01\n",
      " 2.87962132e-01 2.30194342e-01 1.85145792e-01 1.53243404e-01\n",
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      " 2.02812115e-01 2.58077177e-01 3.29626272e-01 4.17349180e-01\n",
      " 5.20390261e-01 6.36914793e-01 7.63949362e-01 8.97337122e-01\n",
      " 1.03183720e+00 1.16137946e+00 1.27946313e+00 1.37966429e+00\n",
      " 1.45619670e+00 1.50445740e+00 1.52148592e+00 1.50627521e+00\n",
      " 1.45989189e+00 1.38539118e+00 1.28754114e+00 1.17239846e+00\n",
      " 1.04679665e+00 9.17816086e-01 7.92301269e-01 6.76476230e-01\n",
      " 5.75687297e-01 4.94278128e-01 4.35579837e-01 4.01982955e-01\n",
      " 3.95050348e-01 4.15631312e-01 4.63945630e-01 5.39619629e-01\n",
      " 6.41671100e-01 7.68453295e-01 9.17577916e-01 1.08584207e+00\n",
      " 1.26918468e+00 1.46269456e+00 1.66068688e+00 1.85685766e+00\n",
      " 2.04451835e+00 2.21690461e+00 2.36754512e+00 2.49066792e+00\n",
      " 2.58161409e+00 2.63722198e+00 2.65614236e+00 2.63904499e+00\n",
      " 2.58868297e+00 2.50979162e+00 2.40881264e+00 2.29345224e+00\n",
      " 2.17209955e+00 2.05314985e+00 1.94429207e+00 1.85183097e+00\n",
      " 1.78011868e+00 1.73116656e+00 1.70449494e+00 1.69725495e+00\n",
      " 1.70462551e+00 1.72045175e+00 1.73805563e+00 1.75111926e+00\n",
      " 1.75452489e+00 1.74503446e+00 1.72170946e+00 1.68600602e+00\n",
      " 1.64152600e+00 1.59345511e+00 1.54776583e+00 1.51029852e+00\n",
      " 1.48585265e+00 1.47741924e+00 1.48566489e+00 1.50874117e+00\n",
      " 1.54244571e+00 1.58071046e+00 1.61634601e+00 1.64193563e+00\n",
      " 1.65075386e+00 1.63758480e+00 1.59933509e+00 1.53537073e+00\n",
      " 1.44755098e+00 1.33997717e+00 1.21851288e+00 1.09015746e+00\n",
      " 9.62364843e-01 8.42392979e-01 7.36750948e-01 6.50784387e-01\n",
      " 5.88413430e-01 5.52015072e-01 5.42427659e-01 5.59049749e-01\n",
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      " 1.63646428e+00 1.60102216e+00 1.56156833e+00 1.51941175e+00\n",
      " 1.47562245e+00 1.43088142e+00 1.38536501e+00 1.33869787e+00\n",
      " 1.28999757e+00 1.23801709e+00 1.18136972e+00 1.11880143e+00\n",
      " 1.04946289e+00 9.73130335e-01 8.90333266e-01 8.02364765e-01\n",
      " 7.11173257e-01 6.19156982e-01 5.28899436e-01 4.42891995e-01\n",
      " 3.63287987e-01 2.91722086e-01 2.29213393e-01 1.76153932e-01\n",
      " 1.32370091e-01 9.72352617e-02 6.98083893e-02 4.89748052e-02\n",
      " 3.35710311e-02 2.24822835e-02 1.47084137e-02 9.39969931e-03\n",
      " 5.86764000e-03 3.57764687e-03 2.13060362e-03 1.23927728e-03\n",
      " 7.04022632e-04 3.90616401e-04 2.11667822e-04 1.12020009e-04\n",
      " 5.78987926e-05 2.92263096e-05 1.44081090e-05 6.93692371e-06\n",
      " 3.26176376e-06 1.49783072e-06 6.71733203e-07 2.94207757e-07\n",
      " 1.25844473e-07 5.25698216e-08 2.14467199e-08 8.54490087e-09\n",
      " 3.32487066e-09 1.26346701e-09 4.68893659e-10 1.69944071e-10\n",
      " 6.01532096e-11 2.07937425e-11 7.01984527e-12 2.31442654e-12]\n"
     ]
    }
   ],
   "source": [
    "print(y[500,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWa4pRHxwrmf"
   },
   "source": [
    "Seeing 300 entries does not tell us much, so next we plot the spectra of two random molecules in the dataset (500 and 1500). This is the type of data we will be predicting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "id": "NIM-5LzW5IoS",
    "outputId": "6e5bdeb5-76a0-4bc9-f587-6cb0c717edb5"
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first we generate the energy x-axis in the range [-30, 0]eV\n",
    "x_ticks = np.linspace(0,-30, 300)\n",
    "\n",
    "plt.figure(1, figsize=(6,9))\n",
    "plt.subplot(311)\n",
    "plt.plot(x_ticks, y[500,:], label='500') # Molecule from training set\n",
    "plt.plot(x_ticks, y[1500,:], label='1500') # Molecule from training set\n",
    "plt.title(\"Example spectra\")\n",
    "plt.xlabel(\"Energy [eV]\")\n",
    "plt.ylabel(\"Spectral intensity\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XQh-MAv5wrml"
   },
   "source": [
    "Now, we divide the data into training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "wmnl8ZzAwrmm"
   },
   "outputs": [],
   "source": [
    "# decide how many samples to take from the database for training, testing and validation\n",
    "n_train = 2000\n",
    "n_test = 500\n",
    "n_val = 500\n",
    "\n",
    "# split data in training and test\n",
    "# take first n_train molecules for training\n",
    "X_train  = x[0:n_train]\n",
    "Y_train = y[0:n_train]\n",
    "\n",
    "# take the next n_test data for testing\n",
    "X_test = x[n_train:n_train + n_test]\n",
    "Y_test = y[n_train:n_train + n_test]\n",
    "\n",
    "# take the next n_val data for validation\n",
    "X_val = x[n_train + n_test: n_train + n_test+ n_val]\n",
    "Y_val = y[n_train + n_test: n_train + n_test+ n_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uEP7iSchwrmm"
   },
   "source": [
    "After the data partition, we should check that the training data resemble the test data well by plotting the distribution of spectra for both datasets. Because we are comparing many spectral curves, for each subset we will plot the mean and +/- 1 standard deviation. If the two mean datasets overlap, it indicates that the training and the test data distributions are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "8cXiavHCuDVM"
   },
   "outputs": [],
   "source": [
    "# Let us first define a plotting function for the [mean +/- stdev] of many spectra\n",
    "def plot_mean_and_1std(plot, data, color, label):\n",
    "    x_ticks = np.linspace(-30, 0, 300)\n",
    "    mean = np.mean(data, axis=0)\n",
    "    std  = np.std(data, axis=0)\n",
    "\n",
    "    plt.plot(x_ticks, mean, color=color, label=label)\n",
    "    plt.fill_between(x_ticks, mean-std, mean+std, facecolor=color, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LGNYVAU6xsQu"
   },
   "source": [
    "Below we plot and compare the training and test sets. What do you think, is the test set representative of the training data? You can also add the validation data to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431
    },
    "id": "t-NggJYtXGGs",
    "outputId": "301254b3-550f-4247-9194-961cbe50087f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plt.figure(1)\n",
    "plot_mean_and_1std(plot, Y_train, color=\"green\", label=\"train\")\n",
    "plot_mean_and_1std(plot, Y_test, color=\"orange\", label=\"test\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0FxS6Kazwrmo"
   },
   "source": [
    "### Implementing the CNN\n",
    "\n",
    "Here, we proceed with the CNN network and build the architecture.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zew4J_tA2jqk"
   },
   "source": [
    "We start by loading PyTorch and setting up a class for loading the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "7GUgjaEUzqkx"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/PUHTI_TYKKY_VU7CbXh/miniconda/envs/env1/lib/python3.7/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from torch import nn\n",
    "from torch.optim import Adam\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
    "\n",
    "import torch\n",
    "import lightning.pytorch as pl\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "\n",
    "# Define the Dataset class\n",
    "class SimpleDataset(Dataset):\n",
    "    def __init__(self, features, labels):\n",
    "        self.x = torch.from_numpy(features).reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "        self.y = torch.from_numpy(labels).float()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\"X\" : self.x[idx], \"y\" : self.y[idx]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bU9XN84t0Fc0"
   },
   "source": [
    "Now we set up a simple CNN model by defining all the functional components this AI model will need, such as:\n",
    "\n",
    "*  network architecture (number of layers and neurons)\n",
    "*  activation function type\n",
    "*  functions to load training and validation data\n",
    "*  training steps on training and validation data\n",
    "*  optimisers for NN training\n",
    "\n",
    "We start by defining a number of network hyperparameters that can later be adjusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "mFuHLTSsXc_X"
   },
   "outputs": [],
   "source": [
    "# Hyperparameters for NN architecture:\n",
    "# Conv2D\n",
    "output_length = 300  # this is size of output\n",
    "nConv = 20           # number of convolutional matrices\n",
    "filter = 3           # size of filter matrix\n",
    "pad = 1              # padding for the filter scan\n",
    "\n",
    "# MaxPool2D\n",
    "maxp = 4             # size of maxpool layer\n",
    "\n",
    "# Hyperparameters for NN training:\n",
    "batch = 64            # total training set is divided into batches for training\n",
    "learning_rate = 1e-3  # the rate of updating the network weights during training\n",
    "\n",
    "# Define network\n",
    "class CNN(pl.LightningModule):\n",
    "    def __init__(self, output_length):\n",
    "        super(CNN, self).__init__()\n",
    "\n",
    "        # Below we define 2 hidden layers (design choice)\n",
    "        # followed by a MaxPool layer and a linear layer\n",
    "        # convolution key parameters: no. of input and output nodes\n",
    "\n",
    "        # 1st convolution, 1 = input matrix\n",
    "        self.conv1_1 = nn.Conv2d(1, nConv, filter, padding=pad)\n",
    "        # 2nd convolution\n",
    "        self.conv1_2 = nn.Conv2d(nConv, nConv, filter, padding=pad)\n",
    "        # 3rd convolution\n",
    "        self.conv1_3 = nn.Conv2d(nConv, maxp, filter, padding=pad)\n",
    "        # MaxPool - different hyperparameters apply\n",
    "        self.pool = nn.MaxPool2d(maxp,maxp)\n",
    "        # The linear layer size is approximately 4x7x7\n",
    "        self.fc1 = nn.Linear(maxp*7*7, output_length)\n",
    "\n",
    "    # this describes how the NN is propagated forward\n",
    "    def forward(self, x, labels=None):\n",
    "\n",
    "        # relu is used after the 1st layer\n",
    "        x = F.relu(self.conv1_1(x))\n",
    "        # relu is used after the 2nd layer\n",
    "        x = F.relu(self.conv1_2(x))\n",
    "        # relu is used after the 3rd layer\n",
    "        x = F.relu(self.conv1_3(x))\n",
    "        # print(x.shape)\n",
    "        x = self.pool(x)\n",
    "        x = x.view(-1, 4*7*7)\n",
    "        outputs = self.fc1(x)\n",
    "\n",
    "        loss = 0\n",
    "        if labels is not None:\n",
    "            loss = nn.functional.mse_loss(outputs.squeeze(), labels.squeeze())\n",
    "        return loss, outputs\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        dataset = SimpleDataset(X_train, Y_train)\n",
    "        return DataLoader(dataset, batch_size=batch)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        dataset = SimpleDataset(X_val, Y_val)\n",
    "        return DataLoader(dataset, batch_size=batch, num_workers=1)\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        coloumb_matrices = batch[\"X\"]\n",
    "        energies = batch[\"y\"]\n",
    "        loss, outputs = self(coloumb_matrices, labels=energies)\n",
    "        self.log(\"val_loss\", loss, prog_bar=True, on_step=False, on_epoch=True)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = Adam(self.parameters(), lr=learning_rate)\n",
    "        return optimizer\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V5clkIUk8_Zj"
   },
   "source": [
    "## Training the CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 780,
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    },
    "id": "a95FhI3519jg",
    "outputId": "dc4c5416-36da-4c1d-db18-8bdea7326864"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name    | Type      | Params\n",
      "--------------------------------------\n",
      "0 | conv1_1 | Conv2d    | 200   \n",
      "1 | conv1_2 | Conv2d    | 3.6 K \n",
      "2 | conv1_3 | Conv2d    | 724   \n",
      "3 | pool    | MaxPool2d | 0     \n",
      "4 | fc1     | Linear    | 59.1 K\n",
      "--------------------------------------\n",
      "63.6 K    Trainable params\n",
      "0         Non-trainable params\n",
      "63.6 K    Total params\n",
      "0.255     Total estimated model params size (MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:  80%|████████  | 32/40 [00:01<00:00, 30.22it/s, loss=0.275, v_num=6]\n",
      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
      "Validation:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Validation DataLoader 0:   0%|          | 0/8 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 0:  82%|████████▎ | 33/40 [00:01<00:00, 24.47it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  85%|████████▌ | 34/40 [00:01<00:00, 24.77it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  88%|████████▊ | 35/40 [00:01<00:00, 25.09it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  90%|█████████ | 36/40 [00:01<00:00, 25.50it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  92%|█████████▎| 37/40 [00:01<00:00, 25.84it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  95%|█████████▌| 38/40 [00:01<00:00, 26.20it/s, loss=0.275, v_num=6]\n",
      "Epoch 0:  98%|█████████▊| 39/40 [00:01<00:00, 26.57it/s, loss=0.275, v_num=6]\n",
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      "Epoch 16:  80%|████████  | 32/40 [00:01<00:00, 30.25it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16:  85%|████████▌ | 34/40 [00:01<00:00, 25.03it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16:  88%|████████▊ | 35/40 [00:01<00:00, 25.42it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16:  90%|█████████ | 36/40 [00:01<00:00, 25.84it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16:  92%|█████████▎| 37/40 [00:01<00:00, 26.19it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16:  95%|█████████▌| 38/40 [00:01<00:00, 26.54it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16:  98%|█████████▊| 39/40 [00:01<00:00, 26.91it/s, loss=0.113, v_num=6, val_loss=0.116]\n",
      "Epoch 16: 100%|██████████| 40/40 [00:01<00:00, 27.18it/s, loss=0.113, v_num=6, val_loss=0.115]\n",
      "Epoch 17:  80%|████████  | 32/40 [00:01<00:00, 30.02it/s, loss=0.112, v_num=6, val_loss=0.115]\n",
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      "Epoch 17:  82%|████████▎ | 33/40 [00:01<00:00, 24.13it/s, loss=0.112, v_num=6, val_loss=0.115]\n",
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      "Epoch 19:  80%|████████  | 32/40 [00:01<00:00, 29.96it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
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      "Epoch 19:  82%|████████▎ | 33/40 [00:01<00:00, 24.10it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  85%|████████▌ | 34/40 [00:01<00:00, 24.30it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  88%|████████▊ | 35/40 [00:01<00:00, 24.71it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  90%|█████████ | 36/40 [00:01<00:00, 25.08it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  92%|█████████▎| 37/40 [00:01<00:00, 25.42it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  95%|█████████▌| 38/40 [00:01<00:00, 25.76it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19:  98%|█████████▊| 39/40 [00:01<00:00, 26.17it/s, loss=0.110, v_num=6, val_loss=0.113]\n",
      "Epoch 19: 100%|██████████| 40/40 [00:01<00:00, 26.45it/s, loss=0.110, v_num=6, val_loss=0.112]\n",
      "Epoch 19: 100%|██████████| 40/40 [00:01<00:00, 26.38it/s, loss=0.109, v_num=6, val_loss=0.112]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=20` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 40/40 [00:01<00:00, 26.19it/s, loss=0.109, v_num=6, val_loss=0.112]\n"
     ]
    }
   ],
   "source": [
    "# We set the output parameter to the entire spectral function\n",
    "cnn = CNN(output_length = output_length)\n",
    "\n",
    "# Next, we define the NN training function, which is then applied to the CNN.\n",
    "# As before, we use the Early Stopping, and you can vary the epochs.\n",
    "trainer = pl.Trainer(max_epochs=20, callbacks=[EarlyStopping(monitor=\"loss\", mode=\"min\", patience=3)])\n",
    "trainer.fit(cnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jWxPSrmw3FTs"
   },
   "source": [
    "Let us check prediction accuracy by computing the MSE on the test set. We now have 300 prediction targets, so the result below is the joint MSE over all of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2BaLjWZXcNSx",
    "outputId": "8c3deaa5-26c3-4506-ab70-ecf36535a29c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1108, grad_fn=<MseLossBackward0>)\n"
     ]
    }
   ],
   "source": [
    "X_test = torch.Tensor(X_test).reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "Y_test = torch.Tensor(Y_test).float()\n",
    "\n",
    "test_loss, Y_pred_test = cnn(X_test, Y_test)\n",
    "print(test_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rtdRLCvh3kJJ"
   },
   "source": [
    "Now let's examine the quality of the predictions on individual examples. We come back to the example spectra we reviewed earlier, with index 500 and 1500, and we compute their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q76RVoRjtNnc",
    "outputId": "3f8051f5-4526-40bb-fceb-a8858a47ff24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1033, grad_fn=<MseLossBackward0>)\n"
     ]
    }
   ],
   "source": [
    "X_train_subset = torch.Tensor(X_train)[[500,1500],:].reshape(-1, 29, 29).unsqueeze(1).float()\n",
    "Y_train_subset = torch.Tensor(Y_train)[[500,1500],:].float()\n",
    "\n",
    "# Here, we compute the MSE on the two spectra only\n",
    "train_loss, Y_pred_subset = cnn(X_train_subset, Y_train_subset)\n",
    "print(train_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7GdimPKX7tH7"
   },
   "source": [
    "Spectra 500 and 1500 are now part of the training set, but we can still review the success of the predictions. What do you observe in these cases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 628
    },
    "id": "DrTsiyuDBjap",
    "outputId": "fa29da0b-28f4-4453-e5ac-3f7e86b6181b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ticks = np.linspace(0,-30, 300)\n",
    "\n",
    "plt.figure(1, figsize=(6,9))\n",
    "plt.subplot(311)\n",
    "plt.plot(x_ticks, Y_train_subset[0,:], color=\"black\", label=\"Reference\") # Molecule from training set\n",
    "plt.plot(x_ticks, Y_pred_subset[0,:].detach().numpy(), color=\"red\", label=\"Prediction\") # Molecule from training set\n",
    "plt.title(\"Spectra for molecule #500 (Training set)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(312)\n",
    "plt.plot(x_ticks, Y_train_subset[1,:], color=\"black\", label=\"Reference\") # Molecule from training set\n",
    "plt.plot(x_ticks, Y_pred_subset[1,:].detach().numpy(), color=\"red\", label=\"Prediction\") # Molecule from training set\n",
    "plt.title(\"Spectra for molecule #1500 (Training set)\")\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Va2KvXlG71ce"
   },
   "source": [
    "Next, let's consider a prediction on a (previously unseen) spectrum in the test set. Is the quality different than for the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "id": "IZMqh36xGEmF",
    "outputId": "7a969154-e43e-455d-e68a-e105323b3497"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f47b3cec5d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_ticks, Y_test[50,:], color=\"black\", label=\"Reference\")\n",
    "plt.plot(x_ticks, Y_pred_test[50,:].detach().numpy(), color=\"red\", label=\"Prediction\")\n",
    "plt.title(\"Spectra for molecule #6550 (Test set)\")\n",
    "plt.legend()\n",
    "#plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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