notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d2c631-5289-4ca9-9046-96ac19dc636b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wandb: Currently logged in as: thomashelfer (multimodal-supernovae). Use `wandb login --relogin` to force relogin\n",
      "wandb: wandb version 0.17.4 is available!  To upgrade, please run:\n",
      "wandb:  $ pip install wandb --upgrade\n",
      "wandb: Tracking run with wandb version 0.16.6\n",
      "wandb: Run data is saved locally in /data/tedwar42/thelfer1/TorchGRTL/wandb/run-20240711_153027-ny4w4meh\n",
      "wandb: Run `wandb offline` to turn off syncing.\n",
      "wandb: Syncing run wise-butterfly-2\n",
      "wandb: ⭐️ View project at https://wandb.ai/multimodal-supernovae/TorchGRTL\n",
      "wandb: 🚀 View run at https://wandb.ai/multimodal-supernovae/TorchGRTL/runs/ny4w4meh\n",
      "Folder 'Run15157599' already exists.\n",
      "Copied script to 'Run15157599'.\n",
      "Executing the model on : cuda:0\n",
      "lambda_fac <class 'float'>\n",
      "Loading data\n",
      "100%|██████████| 7/7 [00:01<00:00,  5.64it/s]\n",
      "mean tensor(0.0002, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "Input Shape: torch.Size([336, 16, 16, 16, 25])\n",
      "Output Shape: torch.Size([1, 25, 22, 22, 22])\n",
      "diff -3\n",
      "dx 0.125\n",
      "training\n"
     ]
    }
   ],
   "source": [
    "!srun python learn_error.py configs/config.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e6eda08-ff32-4ee3-9c35-f34b6d3a9a41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing /Users/tom/Desktop/TorchGRTL\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
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      "Building wheels for collected packages: GeneralRelativity\n",
      "  Building wheel for GeneralRelativity (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for GeneralRelativity: filename=GeneralRelativity-0.1-py3-none-any.whl size=3399 sha256=dae5bc717b1053142db7e5015270780cc10a9244ce3543f02b9c4fe68f18e953\n",
      "  Stored in directory: /private/var/folders/br/rfmwms_96jb8rrxcrsp81mqh0000gn/T/pip-ephem-wheel-cache-ho8fhkbd/wheels/7e/56/3a/dbd713ac6a7a2c158d2d7cbf655d63eb4a1db21d5035d47ca4\n",
      "Successfully built GeneralRelativity\n",
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      "Successfully installed GeneralRelativity-0.1\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
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      "Installing collected packages: werkzeug, threadpoolctl, tensorboard-data-server, scipy, pyasn1, oauthlib, joblib, grpcio, cachetools, absl-py, scikit-learn, rsa, requests-oauthlib, pyasn1-modules, markdown, google-auth, google-auth-oauthlib, tensorboard\n",
      "Successfully installed absl-py-2.1.0 cachetools-5.4.0 google-auth-2.32.0 google-auth-oauthlib-1.0.0 grpcio-1.65.4 joblib-1.4.2 markdown-3.6 oauthlib-3.2.2 pyasn1-0.6.0 pyasn1-modules-0.4.0 requests-oauthlib-2.0.0 rsa-4.9 scikit-learn-1.3.2 scipy-1.10.1 tensorboard-2.14.0 tensorboard-data-server-0.7.2 threadpoolctl-3.5.0 werkzeug-3.0.3\n",
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      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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      "Requirement already satisfied: nodeenv>=0.11.1 in ./dev/lib/python3.8/site-packages (from pre-commit->pyinterpx) (1.9.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in ./dev/lib/python3.8/site-packages (from pre-commit->pyinterpx) (6.0.1)\n",
      "Requirement already satisfied: virtualenv>=20.10.0 in ./dev/lib/python3.8/site-packages (from pre-commit->pyinterpx) (20.26.3)\n",
      "Requirement already satisfied: iniconfig in ./dev/lib/python3.8/site-packages (from pytest->pyinterpx) (2.0.0)\n",
      "Requirement already satisfied: pluggy<2,>=1.5 in ./dev/lib/python3.8/site-packages (from pytest->pyinterpx) (1.5.0)\n",
      "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in ./dev/lib/python3.8/site-packages (from pytest->pyinterpx) (1.2.2)\n",
      "Requirement already satisfied: filelock in ./dev/lib/python3.8/site-packages (from torch->pyinterpx) (3.15.4)\n",
      "Requirement already satisfied: sympy in ./dev/lib/python3.8/site-packages (from torch->pyinterpx) (1.13.1)\n",
      "Requirement already satisfied: networkx in ./dev/lib/python3.8/site-packages (from torch->pyinterpx) (3.1)\n",
      "Requirement already satisfied: jinja2 in ./dev/lib/python3.8/site-packages (from torch->pyinterpx) (3.1.4)\n",
      "Requirement already satisfied: fsspec in ./dev/lib/python3.8/site-packages (from torch->pyinterpx) (2024.6.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in ./dev/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib->pyinterpx) (3.19.2)\n",
      "Requirement already satisfied: six>=1.5 in ./dev/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib->pyinterpx) (1.16.0)\n",
      "Requirement already satisfied: distlib<1,>=0.3.7 in ./dev/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->pyinterpx) (0.3.8)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in ./dev/lib/python3.8/site-packages (from jinja2->torch->pyinterpx) (2.1.5)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in ./dev/lib/python3.8/site-packages (from sympy->torch->pyinterpx) (1.3.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: scikit-learn in ./dev/lib/python3.8/site-packages (1.3.2)\n",
      "Requirement already satisfied: numpy<2.0,>=1.17.3 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.24.4)\n",
      "Requirement already satisfied: scipy>=1.5.0 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in ./dev/lib/python3.8/site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in ./dev/lib/python3.8/site-packages (from scikit-learn) (3.5.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install .\n",
    "!pip install pandas scikit-learn tensorboard torch tqdm\n",
    "!pip install pyinterpx\n",
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "113757c9-d051-41d3-9eab-5c65bc8c1098",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch.autograd import Variable\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from tqdm.auto import tqdm, trange\n",
    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
    "\n",
    "%reload_ext tensorboard\n",
    "from GeneralRelativity.Utils import (\n",
    "    get_box_format,\n",
    "    TensorDict,\n",
    "    cut_ghosts,\n",
    "    keys,\n",
    "    keys_all,\n",
    ")\n",
    "from GeneralRelativity.DimensionDefinitions import FOR1, FOR2, FOR3, FOR4\n",
    "from GeneralRelativity.FourthOrderDerivatives import diff1, diff2\n",
    "from pyinterpx.Interpolation import *\n",
    "from GeneralRelativity.TensorAlgebra import (\n",
    "    compute_christoffel,\n",
    "    compute_trace,\n",
    "    compute_christoffel_fast,\n",
    "    raise_all,\n",
    ")\n",
    "from GeneralRelativity.CCZ4Geometry import compute_ricci\n",
    "from GeneralRelativity.Constraints import constraint_equations\n",
    "\n",
    "\n",
    "from SuperResolution.models import (\n",
    "    SuperResolution3DNet,\n",
    "    calculate_test_loss,\n",
    "    load_model,\n",
    "    check_performance,\n",
    ")\n",
    "from SuperResolution.losses import Hamiltonian_loss,Hamiltonian_and_momentum_loss\n",
    "\n",
    "time_stamp = int(time.time())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0581229a-6848-4bde-b6db-8bc5fdff61e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing the model on : cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"Executing the model on :\", device)\n",
    "\n",
    "torch.manual_seed(1)\n",
    "np.random.seed(4)\n",
    "writer = SummaryWriter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4c57d113-0439-4c27-bcbc-c33f963c0c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88c4e018ffac4119a350a667dd6058a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_data = False\n",
    "\n",
    "# Loading small testdata\n",
    "if small_data:\n",
    "    filenamesX = (\n",
    "        \"/home/thelfer1/scr4_tedwar42/thelfer1/data_gen_binary/outputXdata_level1_*\"\n",
    "    )\n",
    "    num_vars = 100\n",
    "    factor = 2  # how much refinement\n",
    "    scaling_factor = 1e-2  # Scaling factor for error\n",
    "    dataX = get_box_format(filenamesX, num_vars)\n",
    "    # Cutting out extra values added for validation\n",
    "    dataX = dataX[:, :, :, :, :25]\n",
    "else:\n",
    "    # Instantiate the model\n",
    "    factor = 1  # how much refinement\n",
    "    res_level = 9\n",
    "    scaling_factor = 0.001\n",
    "    downsample = 1\n",
    "    filenamesX = f\"/home/thelfer1/scr4_tedwar42/thelfer1/high_end_data/outputXdata_level{res_level}_step00[01234]*.dat\"\n",
    "\n",
    "    num_vars = 25\n",
    "    dataX = get_box_format(filenamesX, num_vars)\n",
    "    # Cutting out extra values added for validation\n",
    "    dataX = dataX[:, :, :, :, :25]\n",
    "\n",
    "dataX_shape = dataX.shape\n",
    "# Permute data to put the channel as the second dimension (N, C, H, W, D)\n",
    "dataX = dataX.permute(0, 4, 1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "456b85c7-a008-43bd-9dd5-153b043d7ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# global step counter\n",
    "counter = 0\n",
    "\n",
    "losses_train = []\n",
    "losses_val = []\n",
    "losses_val_interp = []\n",
    "steps_val = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "823b3067-1b41-4cb4-894b-32b3f14884e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 25, 16, 16, 16])\n",
      "0\n",
      "Input Shape: torch.Size([1, 25, 16, 16, 16])\n",
      "Output Shape: torch.Size([1, 25, 16, 16, 16])\n"
     ]
    }
   ],
   "source": [
    "# Create a random 3D low-resolution input tensor (batch size, channels, depth, height, width)\n",
    "input_tensor = torch.randn(\n",
    "    1,\n",
    "    dataX.shape[1],\n",
    "    dataX.shape[2] // downsample,\n",
    "    dataX.shape[3] // downsample,\n",
    "    dataX.shape[4] // downsample,\n",
    ").to(\n",
    "    torch.double\n",
    ")  # Adjust dimensions as needed\n",
    "\n",
    "# Instantiate the model\n",
    "net = SuperResolution3DNet(factor, scaling_factor).to(torch.double)\n",
    "\n",
    "print(input_tensor.shape)\n",
    "# Forward pass to obtain the high-resolution output\n",
    "output_tensor, _ = net(input_tensor)\n",
    "# Relevant for ghosts\n",
    "diff = (dataX_shape[-2] - output_tensor.shape[-1]) // 2\n",
    "print(diff)\n",
    "# Check the shape of the output\n",
    "print(\"Input Shape:\", input_tensor.shape)\n",
    "print(\"Output Shape:\", output_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "748ebfa6-d35a-4e65-8ca0-b7ee0e212d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizerBFGS = torch.optim.LBFGS(\n",
    "    net.parameters(), lr=0.1\n",
    ")  # Use LBFGS sometimes, it really does do magic sometimes, though its a bit expensive\n",
    "optimizerADAM = torch.optim.Adam(net.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6999db8f-b73b-4a36-b0a0-b70caaed8b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the ratio for the split (e.g., 80% train, 20% test)\n",
    "train_ratio = 0.8\n",
    "test_ratio = 1 - train_ratio\n",
    "batch_size = 10\n",
    "\n",
    "# Calculate the number of samples for each split\n",
    "num_samples = len(dataX)\n",
    "num_train = int(train_ratio * num_samples)\n",
    "num_test = num_samples - num_train\n",
    "\n",
    "\n",
    "# Create a dataset from tensors\n",
    "dataset = TensorDataset(dataX)\n",
    "\n",
    "# Split the dataset into training and testing datasets\n",
    "train_dataset, test_dataset = random_split(dataset, [num_train, num_test])\n",
    "\n",
    "# Create DataLoader for batching -- in case data gets larger\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    pin_memory=False,\n",
    "    num_workers=0,\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    num_workers=0,\n",
    ")  # Batch size for test can be larger if you have the memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1a7058ed-a1f3-4af5-ba97-f816558320c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx = 0.015625\n"
     ]
    }
   ],
   "source": [
    "# Magical loss coming from General Relativity\n",
    "if small_data:\n",
    "    oneoverdx = (2 * 32.0) / 16.0\n",
    "else:\n",
    "    oneoverdx = (64.0 * 2**res_level) / 512.0 * float(factor) / float(downsample)\n",
    "\n",
    "print(f\"dx = {1./oneoverdx}\")\n",
    "my_loss = Hamiltonian_loss(oneoverdx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e1b90a1e-79b0-44f0-9d59-65ba9d541581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config file path: models/x1_lvl9/factor_0.yaml\n",
      "model_epoch_counter_0000000039_data_time_1724256680.pth\n",
      "cuda:0\n",
      "loaded model from models/x1_lvl9/model_epoch_counter_0000000039_data_time_1724256680.pth\n"
     ]
    }
   ],
   "source": [
    "restart = True\n",
    "load_restart_data = False\n",
    "if restart and downsample == 1 :\n",
    "    net, config = load_model(f\"models/x{factor}_lvl{res_level}\")\n",
    "if restart and downsample != 1 : \n",
    "    net, config = load_model(f\"models/x{factor}_d{downsample}_lvl{res_level}\")\n",
    "if restart and load_restart_data:\n",
    "    num_vars = 25\n",
    "    dataX = get_box_format(config[\"filenamesX\"], num_vars)\n",
    "    oneoverdx = (64.0 * 2**res_level) / 512.0 * float(factor) / float(downsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f8f10c9c-b601-4ba0-b51e-c8377cc54e9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af9759f866bc464c855f30dd85f06d03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.973876499427724e-05\n"
     ]
    }
   ],
   "source": [
    "# For validation error\n",
    "L1Loss = torch.nn.L1Loss()\n",
    "ham_loss = Hamiltonian_and_momentum_loss(oneoverdx)\n",
    "\n",
    "print(\"training\")\n",
    "pbar = trange(n_steps)\n",
    "for i in pbar:\n",
    "    total_loss_train = 0\n",
    "    for (y_batch,) in train_loader:\n",
    "        net.train()\n",
    "        batchcounter = 0\n",
    "        # for X_batch, y_batch in train_loader:\n",
    "        y_batch = y_batch.to(device)\n",
    "        X_batch = y_batch[:, :, ::downsample, ::downsample, ::downsample].clone()\n",
    "        if diff != 0:\n",
    "            y_batch = y_batch[\n",
    "                :,\n",
    "                :25,\n",
    "                diff - 1 : -diff - 1,\n",
    "                diff - 1 : -diff - 1,\n",
    "                diff - 1 : -diff - 1,\n",
    "            ]\n",
    "        batchcounter += 1\n",
    "\n",
    "        # This is needed for LBFGS\n",
    "        def closure():\n",
    "            if torch.is_grad_enabled():\n",
    "                optimizerBFGS.zero_grad()\n",
    "            y_pred, y_interp = net(X_batch)\n",
    "\n",
    "            loss_train = my_loss(y_pred, y_batch)\n",
    "            if loss_train.requires_grad:\n",
    "                loss_train.backward()\n",
    "            return loss_train\n",
    "\n",
    "        # doing some ADAM first to warm up, sometimes BFGS fuckes up if you start too early\n",
    "        if counter < ADAMsteps:\n",
    "            y_pred, y_interp = net(X_batch)\n",
    "\n",
    "            loss_train = my_loss(y_pred, y_batch)\n",
    "            optimizerADAM.zero_grad()\n",
    "            loss_train.backward()\n",
    "            optimizerADAM.step()\n",
    "            # print(f'ADAM {batchcounter}')\n",
    "\n",
    "        else:\n",
    "            optimizerBFGS.step(closure)\n",
    "            # print(f'BFGS {batchcounter}')\n",
    "\n",
    "        loss_train = closure()\n",
    "        total_loss_train += loss_train.item()\n",
    "\n",
    "    # Calculate the average training loss\n",
    "    average_loss_train = total_loss_train / len(train_loader)\n",
    "    # Log the average training loss\n",
    "    writer.add_scalar(\"loss/train\", average_loss_train, counter)\n",
    "    losses_train.append(average_loss_train)\n",
    "    if np.isnan(average_loss_train):\n",
    "        print(\"we got nans\")\n",
    "\n",
    "    # Validation\n",
    "\n",
    "    if counter % 1 == 0:\n",
    "        with torch.no_grad():\n",
    "            net.eval()\n",
    "            total_loss_val = 0.0\n",
    "            interp_val = 0.0\n",
    "            L1Loss_val = 0.0\n",
    "            L1Loss_val_interp = 0.0\n",
    "            loss_hard_base = 0.0\n",
    "            Ham_loss = 0.0\n",
    "            Ham_loss_interp = 0.0\n",
    "            for (y_val_batch,) in test_loader:\n",
    "                # for X_val_batch, y_val_batch in test_loader:\n",
    "                # Transfer batch to GPU\n",
    "                y_val_batch = y_val_batch.to(device)\n",
    "                X_val_batch = y_val_batch[\n",
    "                    :, :, ::downsample, ::downsample, ::downsample\n",
    "                ].clone()\n",
    "                if diff != 0:\n",
    "                    y_val_batch = y_val_batch[\n",
    "                        :,\n",
    "                        :25,\n",
    "                        diff - 1 : -diff - 1,\n",
    "                        diff - 1 : -diff - 1,\n",
    "                        diff - 1 : -diff - 1,\n",
    "                    ]\n",
    "                y_val_pred, y_val_interp = net(X_val_batch)\n",
    "                loss_val = my_loss(y_val_pred, y_val_batch)\n",
    "                total_loss_val += loss_val.item()\n",
    "                interp_val += my_loss(y_val_interp, y_val_batch).item()\n",
    "                if downsample == factor:\n",
    "                    print(y_val_pred[:, 0, :, :, :].shape)\n",
    "                    print(y_val_batch[:, 0, :, :, :].shape)\n",
    "                    L1Loss_val += L1Loss(\n",
    "                        y_val_pred[:, 0, :, :, :], y_val_batch[:, 0, :, :, :]\n",
    "                    )\n",
    "                    L1Loss_val_interp += L1Loss(\n",
    "                        y_val_interp[:, 0, :, :, :], y_val_batch[:, 0, :, :, :]\n",
    "                    )\n",
    "                    loss_hard_base += ham_loss(y_val_batch, None)\n",
    "\n",
    "                    if config[\"loss\"] == \"L1\":\n",
    "                        Ham_loss_interp += ham_loss(y_val_interp, None)\n",
    "                        Ham_loss += ham_loss(y_val_pred, None)\n",
    "            # Calculate the average loss\n",
    "            average_loss_val = total_loss_val / len(test_loader)\n",
    "            average_interp_val = interp_val / len(test_loader)\n",
    "            losses_val_interp.append(average_interp_val)\n",
    "            losses_val.append(average_loss_val)\n",
    "            steps_val.append(counter)\n",
    "            writer.add_scalar(\"loss/test\", loss_val.item(), counter)\n",
    "\n",
    "    if counter % write_out_freq == 0:\n",
    "        # Writing out network and scaler\n",
    "        torch.save(\n",
    "            net.state_dict(),\n",
    "            f\"{folder_name}/model_epoch_counter_{counter:010d}_data_time_{time_stamp}.pth\",\n",
    "        )\n",
    "    # Advancing global counter\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fdd46a1e-829c-4345-8e9b-0205aeab66a9",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[103], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m9\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfinal val loss \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlosses_val[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m relative \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlosses_val_interp[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(np\u001b[38;5;241m.\u001b[39marray(losses_train), label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrain\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(steps_val, np\u001b[38;5;241m.\u001b[39marray(losses_val), label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVal\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 900x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "print(f\"final val loss {losses_val[-1]} relative {losses_val_interp[-1]}\")\n",
    "plt.plot(np.array(losses_train), label=\"Train\")\n",
    "plt.plot(steps_val, np.array(losses_val), label=\"Val\", linewidth=0.5)\n",
    "plt.plot(steps_val, np.array(losses_val_interp), label=\"baseline\", linewidth=0.5)\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e55340aa-9998-4af8-bdeb-fcd82c6a2757",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\n",
    "    \"chi\",\n",
    "\n",
    "    \"h11\",    \"h12\",    \"h13\",    \"h22\", \"h23\", \"h33\",\n",
    "\n",
    "    \"K\",\n",
    "\n",
    "    \"A11\",    \"A12\",    \"A13\",    \"A22\", \"A23\", \"A33\",\n",
    "\n",
    "    \"Theta\",\n",
    "\n",
    "    \"Gamma1\", \"Gamma2\", \"Gamma3\",\n",
    "\n",
    "    \"lapse\",\n",
    "\n",
    "    \"shift1\", \"shift2\", \"shift3\",\n",
    "\n",
    "    \"B1\",     \"B2\",     \"B3\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2b905e5f-03af-446d-84cb-57d76cc6d147",
   "metadata": {},
   "outputs": [],
   "source": [
    "net.to(device)\n",
    "net.eval();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9b810570-ec00-41e5-a39e-69fb3bec570f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_constraints(output, oneoverdx):\n",
    "    output = output.permute(0, 2, 3, 4, 1)\n",
    "\n",
    "    # cutting ghosts off, otherwise you will run into problems later\n",
    "    dataXcut = cut_ghosts(output)\n",
    "\n",
    "    # creating dict with values\n",
    "    vars = TensorDict(dataXcut, keys)\n",
    "    # creating dict with derivatives\n",
    "    d1 = TensorDict(diff1(output, oneoverdx), keys)\n",
    "    d2 = TensorDict(diff2(output, oneoverdx**2), keys)\n",
    "    # calculating variables required for constraints\n",
    "    h_UU = torch.inverse(vars[\"h\"])\n",
    "    chris = compute_christoffel(d1[\"h\"], h_UU)\n",
    "    # Computing Constraints\n",
    "    out = constraint_equations(vars, d1, d2, h_UU, chris)\n",
    "    loss = out[\"Ham\"] * out[\"Ham\"] + out[\"Mom\"][...,0] * out[\"Mom\"][...,0]+ out[\"Mom\"][...,1] * out[\"Mom\"][...,1]+ out[\"Mom\"][...,2] * out[\"Mom\"][...,2]\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d1111cd8-4e85-4e05-bc75-96eb4281ac91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get comparison with classical methods\n",
    "(y_batch,) = next(iter(test_loader))\n",
    "y_batch = y_batch.to(device)\n",
    "X_batch = y_batch[:, :, ::downsample, ::downsample, ::downsample].clone()\n",
    "if downsample > 1: \n",
    "    y_batch = y_batch[\n",
    "        :,\n",
    "        :25,\n",
    "        diff - 1 : -diff - 1,\n",
    "        diff - 1 : -diff - 1,\n",
    "        diff - 1 : -diff - 1,\n",
    "    ]\n",
    "\n",
    "# Interpolation compared to what is used typically in codes ( we interpolate between 6 values with polynomials x^i y^k z^k containing powers up to 3)\n",
    "shape = X_batch.shape\n",
    "\n",
    "net.eval()\n",
    "y_pred, y_interpolated = net(X_batch.detach())\n",
    "ham = get_constraints(y_pred, oneoverdx)\n",
    "ham_ref = get_constraints(y_interpolated, oneoverdx)\n",
    "\n",
    "\n",
    "# y_interpolated = interpolation(X_batch.detach().cpu()).detach().to(torch.double)\n",
    "# Create subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "121a3b86-3efb-4a63-bd79-9833ab9f8808",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x2100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = 4\n",
    "channel = 0\n",
    "slice = 0\n",
    "fig = plt.figure(figsize=(8, 7),dpi = 300)\n",
    "\n",
    "# Plot ground truth\n",
    "plt.title(\"Diff\")\n",
    "im0 = plt.imshow(\n",
    "    y_batch[box, channel, :, :, slice].cpu().detach().numpy(),\n",
    "    # vmin=-1e-7,\n",
    "    # vmax=1e-7,\n",
    "    cmap=\"gray\",\n",
    ")\n",
    "plt.title(names[channel])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar()\n",
    "\n",
    "\n",
    "plt.savefig(f'x{factor}_d{downsample}_lvl{res_level}_box{box}_slice{slice}_{names[channel]}_input.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "25b7fb22-82ae-4a78-966f-e03ec34d8a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = 4\n",
    "channel = 0\n",
    "slice = 0\n",
    "fig = plt.figure(figsize=(8, 7),dpi = 300)\n",
    "\n",
    "# Plot ground truth\n",
    "plt.title(\"Diff\")\n",
    "im0 = plt.imshow(\n",
    "    y_interpolated[box, channel, :, :, slice].cpu().detach().numpy(),\n",
    "    # vmin=-1e-7,\n",
    "    # vmax=1e-7,\n",
    "    cmap=\"gray\",\n",
    ")\n",
    "plt.title(names[channel])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar()\n",
    "\n",
    "\n",
    "plt.savefig(f'x{factor}_d{downsample}_lvl{res_level}_box{box}_slice{slice}_{names[channel]}_interp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "34e8c8b0-c521-44de-ae88-d0c47a1c0bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = 4\n",
    "channel = 0\n",
    "slice = 0\n",
    "fig = plt.figure(figsize=(8, 7),dpi = 300)\n",
    "\n",
    "# Plot ground truth\n",
    "plt.title(\"Diff\")\n",
    "im0 = plt.imshow(\n",
    "    y_pred[box, channel, :, :, slice].cpu().detach().numpy(),\n",
    "    # vmin=-1e-7,\n",
    "    # vmax=1e-7,\n",
    "    cmap=\"gray\",\n",
    ")\n",
    "plt.title(names[channel])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar()\n",
    "\n",
    "\n",
    "plt.savefig(f'x{factor}_d{downsample}_lvl{res_level}_box{box}_slice{slice}_{names[channel]}_pred.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "34d9710a-014f-446b-82d7-929e6ec7b74d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ground truth\n",
    "\n",
    "fig = plt.figure(figsize=(8, 7),dpi = 300)\n",
    "plt.title(\"Diff\")\n",
    "im0 = plt.imshow(\n",
    "    ((y_pred[box, channel, :, :, slice].cpu().detach().numpy()- y_interpolated[box, channel, :, :, slice].cpu().detach().numpy())),\n",
    "    # vmin=-1e-7,\n",
    "    # vmax=1e-7,\n",
    "    cmap=\"PuBu\",\n",
    ")\n",
    "plt.title(names[channel])\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar()\n",
    "plt.savefig(f'x{factor}_d{downsample}_lvl{res_level}_box{box}_slice{slice}_{names[channel]}_diff.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "eb69ba33-c8e3-4ee5-8c81-b23b28d309f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "\n",
    "fig = plt.figure(figsize=(14, 5),dpi = 300)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Our approach\")\n",
    "\n",
    "\n",
    "vmin = 10**(-8) #np.log10(np.abs(np.min(np.abs(ham_ref[box, :, :, :].cpu().detach().numpy()))))\n",
    "vmax = (np.abs(np.max(np.abs(ham_ref[box, :, :, :].cpu().detach().numpy()))))\n",
    "\n",
    "im0 = plt.imshow(\n",
    "    (np.abs(ham[box, :, :, slice].cpu().detach().numpy())),\n",
    "    #vmin=vmin,\n",
    "    #vmax=vmax,\n",
    "    cmap=\"OrRd\",\n",
    "    #norm=colors.LogNorm()\n",
    ")\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar(im0)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Baseline\")\n",
    "im0 = plt.imshow(\n",
    "    (np.abs(ham_ref[box, :, :, slice].cpu().detach().numpy())),\n",
    "    #vmin=vmin,\n",
    "    #vmax=vmax,\n",
    "    cmap=\"OrRd\",\n",
    "    #norm=colors.LogNorm()\n",
    ")\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar(im0)\n",
    "plt.savefig(f'x{factor}_d{downsample}_lvl{res_level}_box{box}_slice{slice}_{names[channel]}_Hamdiff.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c8d11f32-1b24-4943-936f-d0fefcccc5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "if factor == downsample:\n",
    "    box = 0\n",
    "    channel = 0\n",
    "    slice = 2\n",
    "    fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "    # Plot ground truth\n",
    "    plt.title(\"Diff\")\n",
    "    im0 = plt.imshow(\n",
    "        y_batch[box, channel, :, :, slice].cpu().detach().numpy()\n",
    "        - y_interpolated[box, channel, :, :, slice].cpu().detach().numpy(),\n",
    "        # vmin=-1e-7,\n",
    "        # vmax=1e-7,\n",
    "        cmap=\"viridis\",\n",
    "    )\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f84d754e-bdbc-4818-a526-ac0401015629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1553e062baf0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Plot ground truth\n",
    "plt.title(\"Diff\")\n",
    "im0 = plt.imshow(\n",
    "    X_batch[box, channel, :, :, slice].cpu().detach().numpy(),\n",
    "    # vmin=min_val,\n",
    "    # vmax=max_val,\n",
    "    cmap=\"viridis\",\n",
    ")\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "309d8ad4-fd6d-4376-ace4-3fcb0d65e6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1553a87bed30>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = 2\n",
    "channel = 10\n",
    "slice = 4\n",
    "\n",
    "# net.eval()\n",
    "# y_pred, y_interpolated = net(X_batch.detach())\n",
    "\n",
    "#if downsample == factor:\n",
    "    #plt.plot(\n",
    "    #    y_batch[box, channel, :, slice, slice].detach().cpu().numpy(),\n",
    "    #    label=\"ground truth\",\n",
    "    #)\n",
    "plt.plot(\n",
    "    y_pred[box, channel, :, slice, slice].detach().cpu().numpy(),\n",
    "    label=\"neural network \",\n",
    ")\n",
    "plt.plot(\n",
    "    y_interpolated[box, channel, :, slice, slice].detach().cpu().numpy(),\n",
    "    label=\"interpolation \",\n",
    "    linestyle=\":\",\n",
    "    alpha=0.6,\n",
    "    linewidth=2,\n",
    ")\n",
    "# plt.yscale(\"log\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ddd54cde-bbdc-45ba-8ae4-487b675bdf43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box = 0\n",
    "channel = 2\n",
    "slice = 0\n",
    "\n",
    "# net.eval()\n",
    "# y_pred = net(X_batch.detach())\n",
    "\n",
    "for channel in range(5):\n",
    "    plt.plot(\n",
    "        (\n",
    "            y_pred[box, channel, :, slice, slice].detach().cpu().numpy()\n",
    "            - y_interpolated[box, channel, :, slice, slice].detach().cpu().numpy()\n",
    "        ),\n",
    "        label=\"interpolation residual\",\n",
    "        linestyle=\":\",\n",
    "        alpha=0.6,\n",
    "    )\n",
    "# plt.yscale(\"log\")\n",
    "#plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e2c3a78a-9a54-4772-82b0-bd8d43bcdeb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network L2 Ham 1.8459935305214486e-07\n",
      "\n",
      "Interpolation L2 Ham  3.261893100597274e-06\n",
      "\n",
      "--------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate L2Ham performance\n",
    "my_loss = Hamiltonian_and_momentum_loss(oneoverdx)\n",
    "\n",
    "net.eval()\n",
    "y_pred, _ = net(X_batch.detach())\n",
    "\n",
    "#print(\n",
    "#    f\"Reference data L2 Ham {my_loss(y_batch[:, :, :, :, :], torch.tensor([])).detach().cpu().numpy()}\\n\"\n",
    "#)\n",
    "print(\n",
    "    f\"Neural Network L2 Ham {my_loss(y_pred[:, :, :, :, :], torch.tensor([])).detach().cpu().numpy()}\\n\"\n",
    ")\n",
    "print(\n",
    "    f\"Interpolation L2 Ham  {my_loss(y_interpolated.cpu(), torch.tensor([])).detach().numpy()}\\n\"\n",
    ")\n",
    "print(\"--------------------\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "eb655716-a267-42b1-a6b4-e804d25507ee",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'file_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[99], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Writing out network and scaler\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m torch\u001b[38;5;241m.\u001b[39msave(net\u001b[38;5;241m.\u001b[39mstate_dict(), \u001b[43mfile_path\u001b[49m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Write out model for the C++ code to pick up\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# traced_script_module = torch.jit.trace(net, X)\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# traced_script_module.save(\"model.pt\")\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'file_path' is not defined"
     ]
    }
   ],
   "source": [
    "# Writing out network and scaler\n",
    "torch.save(net.state_dict(), file_path)\n",
    "\n",
    "# Write out model for the C++ code to pick up\n",
    "# traced_script_module = torch.jit.trace(net, X)\n",
    "# traced_script_module.save(\"model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6542c5c-ec7e-485e-9f71-7aadd92982cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90bfe3eb-4255-45b3-bc22-f1f00db3fe2a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}