Training-IDCalculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98536375-dc48-4007-bc8b-cbc7a1e36a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib import cm\n",
    "\n",
    "import zarr\n",
    "import xarray as xr\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data as data_utils\n",
    "from torch.nn import Linear, Conv1d, ConvTranspose1d\n",
    "\n",
    "from torch.nn import Sequential as Seq, Linear as Lin, ReLU, Sigmoid, ConstantPad1d\n",
    "from torch.functional import F\n",
    "from torch.optim.lr_scheduler import OneCycleLR, ReduceLROnPlateau, CosineAnnealingWarmRestarts\n",
    "from tqdm import tqdm\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73357f08-c6c9-4643-a03b-7cc27ae24081",
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import DriverDatasets, MicroAutoEncoder\n",
    "#from visualization import latentvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59124eb3-9721-4c50-9156-3859805af39e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-dimension\n",
      "  Downloading scikit_dimension-0.3.3-py3-none-any.whl.metadata (1.3 kB)\n",
      "Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-dimension) (1.26.3)\n",
      "Requirement already satisfied: numba in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-dimension) (0.58.1)\n",
      "Requirement already satisfied: scipy in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-dimension) (1.12.0)\n",
      "Requirement already satisfied: scikit-learn in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-dimension) (1.4.0)\n",
      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from numba->scikit-dimension) (0.41.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-learn->scikit-dimension) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /srv/conda/envs/notebook/lib/python3.11/site-packages (from scikit-learn->scikit-dimension) (3.2.0)\n",
      "Downloading scikit_dimension-0.3.3-py3-none-any.whl (65 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/65.0 kB\u001b[0m \u001b[31m319.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: scikit-dimension\n",
      "Successfully installed scikit-dimension-0.3.3\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2df2d92-29d1-4f5f-8f86-17735f5f6128",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skdim.id import MiND_ML, DANCo, MLE, MOM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e19f31-cddf-41e2-88f2-4e202ea01b0a",
   "metadata": {},
   "source": [
    "## Load the Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4eafc339-3993-4f35-ab35-fb26afd68377",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader=torch.load('/home/jovyan/neural-dsds/neural-dsds/data/trainloader_onecat.pth')\n",
    "valloader=torch.load('/home/jovyan/neural-dsds/neural-dsds/data/valloader_onecat.pth')\n",
    "testloader=torch.load('/home/jovyan/neural-dsds/neural-dsds/data/testloader_onecat.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1dc2f27-c6bc-4b54-a146-380a2d9d1d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([500, 2, 35])\n",
      "torch.Size([500, 4, 3]) torch.Size([500, 2, 35])\n",
      "torch.Size([500, 2, 35])\n",
      "torch.Size([500, 4, 3]) torch.Size([500, 2, 35])\n",
      "torch.Size([500, 2, 35])\n",
      "torch.Size([500, 4, 3]) torch.Size([500, 2, 35])\n"
     ]
    }
   ],
   "source": [
    "model = MicroAutoEncoder(n_latent=10)\n",
    "\n",
    "ii = 0 \n",
    "for data in trainloader:\n",
    "    M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "    print(bin0.shape)\n",
    "    print(M.shape,bin0.shape)\n",
    "    xhat,latent = model(bin0.float())\n",
    "\n",
    "    ii+=1\n",
    "    if ii >2: \n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c433a4-aeac-4353-9749-b6fe6433fb81",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7da8bf42-a401-4340-8a9d-aac66bc418f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recon_loss(recon_x,x):\n",
    "    mseloss = torch.nn.MSELoss()\n",
    "\n",
    "    mse = mseloss(recon_x,x)\n",
    "\n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bd78eff-375b-44f7-ac06-125e608f122d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model,dataloader,criterion,device,epoch):\n",
    "    model.train()\n",
    "    \n",
    "    for data in dataloader:\n",
    "        M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "        bin1 = bin1coal #bin1condevap #\n",
    "\n",
    "        bin0 = bin0.to(device)\n",
    "        bin1 = bin1.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        xhat,latent = model(bin0.float())\n",
    "        \n",
    "        \n",
    "        mseloss = recon_loss(xhat,bin1.float())\n",
    "    \n",
    "        loss = mseloss\n",
    "        \n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "        \n",
    "    return model\n",
    "def test(model,dataloader,criterion,device,val=False):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    recon = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data in dataloader:\n",
    "            M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "            bin1 = bin1coal #bin1condevap #\n",
    "            bin0 = bin0.to(device)\n",
    "            bin1 = bin1.to(device)\n",
    "            \n",
    "            xhat,latent = model(bin0.float())\n",
    "\n",
    "            mse = recon_loss(xhat.float(),bin1.float())\n",
    "            \n",
    "            #correct = correct+loss\n",
    "            nn = len(dataloader.dataset)\n",
    "            recon = recon+mse\n",
    "\n",
    "                        \n",
    "        if val==True:\n",
    "            sched.step(recon/nn)\n",
    "            \n",
    "        return recon/nn\n",
    "\n",
    "def inference(model,dataloader,device):\n",
    "    model.eval()\n",
    "    dataset = dataloader.dataset\n",
    "    ntargets = 4\n",
    "    nrates = 3\n",
    "    bs = 500\n",
    "    \n",
    "    pred = np.zeros((len(dataset)*bs,ntargets,nrates))\n",
    "    real = np.zeros((len(dataset)*bs,ntargets,nrates))\n",
    "    realbin1 = np.zeros((len(dataset)*bs,2,35))\n",
    "    realbin0 = np.zeros((len(dataset)*bs,2,35))\n",
    "    predbin1 = np.zeros((len(dataset)*bs,2,35))\n",
    "    predM = np.zeros((len(dataset)*bs,ntargets))\n",
    "    realM = np.zeros((len(dataset)*bs,ntargets))\n",
    "\n",
    "    jj = 0\n",
    "    i = 0\n",
    "\n",
    "    for data in dataloader:\n",
    "\n",
    "        M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "        bin1 = bin1coal \n",
    "        \n",
    "        xhat,latent = model(bin0.float())\n",
    "        \n",
    "        bs = xhat.shape[0]\n",
    "        if xhat is not(None):\n",
    "            realbin0[jj:jj+bs,:]=bin0.cpu().detach().numpy().reshape(bs,2,35)\n",
    "            realbin1[jj:jj+bs,:]=bin1.cpu().detach().numpy().reshape(bs,2,35)\n",
    "            predbin1[jj:jj+bs,:]=xhat.cpu().detach().numpy().reshape(bs,2,35)\n",
    "            \n",
    "            jj=jj+bs\n",
    "            i = i+1\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    return realbin1,predbin1,realbin0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e66ef715-b5d7-4640-a991-fb551f0d579d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlat = [2,3,4,5,8,10,12,14,18,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad63b1df-2ee8-4fe8-aedd-2b8567e1ca29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.current_device()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffee219a-9f66-4fd5-bd74-13d90a2af286",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model nlat =  2\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000, Train MSE: 1.35731525e-05, Val. MSE: 1.35853852e-05\n",
      "Epoch: 020, Train MSE: 1.92297784e-06, Val. MSE: 1.93293295e-06\n",
      "Epoch: 040, Train MSE: 9.83005975e-07, Val. MSE: 1.00200282e-06\n",
      "Epoch: 060, Train MSE: 6.86499561e-07, Val. MSE: 7.07015090e-07\n",
      "Epoch: 080, Train MSE: 5.99901455e-07, Val. MSE: 6.19257378e-07\n",
      "Epoch: 100, Train MSE: 5.27177292e-07, Val. MSE: 5.42037355e-07\n",
      "Epoch: 120, Train MSE: 4.93782181e-07, Val. MSE: 5.09456413e-07\n",
      "Epoch: 140, Train MSE: 4.62902165e-07, Val. MSE: 4.78913705e-07\n",
      "Epoch: 160, Train MSE: 4.45907972e-07, Val. MSE: 4.62656118e-07\n",
      "Epoch: 180, Train MSE: 4.18634642e-07, Val. MSE: 4.35803287e-07\n",
      "Epoch: 200, Train MSE: 4.09570362e-07, Val. MSE: 4.26941511e-07\n",
      "Epoch: 220, Train MSE: 4.07022213e-07, Val. MSE: 4.24798316e-07\n",
      "Epoch: 240, Train MSE: 3.87871836e-07, Val. MSE: 4.05941762e-07\n",
      "Epoch: 260, Train MSE: 3.82483421e-07, Val. MSE: 4.01092819e-07\n",
      "Epoch: 280, Train MSE: 3.77807652e-07, Val. MSE: 3.95954004e-07\n",
      "Epoch: 300, Train MSE: 3.75968398e-07, Val. MSE: 3.93707467e-07\n",
      "Epoch: 320, Train MSE: 3.73385291e-07, Val. MSE: 3.92026465e-07\n",
      "Epoch: 340, Train MSE: 3.73093826e-07, Val. MSE: 3.91400704e-07\n",
      "Epoch: 360, Train MSE: 3.55489306e-07, Val. MSE: 3.73750879e-07\n",
      "Epoch: 380, Train MSE: 3.54139047e-07, Val. MSE: 3.72524767e-07\n",
      "Epoch: 400, Train MSE: 3.53999894e-07, Val. MSE: 3.72357022e-07\n",
      "Epoch: 420, Train MSE: 3.53991766e-07, Val. MSE: 3.72342896e-07\n",
      "Epoch: 440, Train MSE: 3.53991652e-07, Val. MSE: 3.72343436e-07\n",
      "Epoch: 460, Train MSE: 3.53991737e-07, Val. MSE: 3.72343294e-07\n",
      "Epoch: 480, Train MSE: 3.53991652e-07, Val. MSE: 3.72343237e-07\n",
      "Epoch: 500, Train MSE: 3.53991595e-07, Val. MSE: 3.72343180e-07\n",
      "Epoch: 520, Train MSE: 3.53991595e-07, Val. MSE: 3.72343095e-07\n",
      "Epoch: 540, Train MSE: 3.53991624e-07, Val. MSE: 3.72343038e-07\n",
      "Epoch: 560, Train MSE: 3.53991624e-07, Val. MSE: 3.72342981e-07\n",
      "Epoch: 580, Train MSE: 3.53991453e-07, Val. MSE: 3.72342981e-07\n",
      "Epoch: 600, Train MSE: 3.53991624e-07, Val. MSE: 3.72342868e-07\n",
      "Epoch: 620, Train MSE: 3.53991453e-07, Val. MSE: 3.72342839e-07\n",
      "Epoch: 640, Train MSE: 3.53991595e-07, Val. MSE: 3.72342726e-07\n",
      "Epoch: 660, Train MSE: 3.53991510e-07, Val. MSE: 3.72342583e-07\n",
      "Epoch: 680, Train MSE: 3.53991538e-07, Val. MSE: 3.72342640e-07\n",
      "Epoch: 700, Train MSE: 3.53991368e-07, Val. MSE: 3.72342612e-07\n",
      "Epoch: 720, Train MSE: 3.53991396e-07, Val. MSE: 3.72342498e-07\n",
      "Epoch: 740, Train MSE: 3.53991453e-07, Val. MSE: 3.72342612e-07\n",
      "Epoch: 760, Train MSE: 3.53991339e-07, Val. MSE: 3.72342498e-07\n",
      "Epoch: 780, Train MSE: 3.53991339e-07, Val. MSE: 3.72342498e-07\n",
      "Epoch: 800, Train MSE: 3.53991396e-07, Val. MSE: 3.72342498e-07\n",
      "Epoch: 820, Train MSE: 3.53991396e-07, Val. MSE: 3.72342583e-07\n",
      "Epoch: 840, Train MSE: 3.53991311e-07, Val. MSE: 3.72342555e-07\n",
      "Epoch: 860, Train MSE: 3.53991311e-07, Val. MSE: 3.72342328e-07\n",
      "Epoch: 880, Train MSE: 3.53991339e-07, Val. MSE: 3.72342441e-07\n",
      "Epoch: 900, Train MSE: 3.53991197e-07, Val. MSE: 3.72342498e-07\n",
      "Epoch: 920, Train MSE: 3.53991311e-07, Val. MSE: 3.72342356e-07\n",
      "Epoch: 940, Train MSE: 3.53991197e-07, Val. MSE: 3.72342328e-07\n",
      "Epoch: 960, Train MSE: 3.53991197e-07, Val. MSE: 3.72342271e-07\n",
      "Epoch: 980, Train MSE: 3.53991169e-07, Val. MSE: 3.72342299e-07\n",
      "IDModels/AE_z2_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z2_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z2_mse_1000epochs_updatedata\n",
      "407\n",
      "Training model nlat =  3\n"
     ]
    },
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     "metadata": {},
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000, Train MSE: 1.38136538e-05, Val. MSE: 1.38235564e-05\n",
      "Epoch: 020, Train MSE: 1.20861682e-06, Val. MSE: 1.22518475e-06\n",
      "Epoch: 040, Train MSE: 8.35653282e-07, Val. MSE: 8.54984989e-07\n",
      "Epoch: 060, Train MSE: 7.21853098e-07, Val. MSE: 7.36838786e-07\n",
      "Epoch: 080, Train MSE: 6.89141814e-07, Val. MSE: 7.04714864e-07\n",
      "Epoch: 100, Train MSE: 6.03622595e-07, Val. MSE: 6.18386593e-07\n",
      "Epoch: 120, Train MSE: 5.74697765e-07, Val. MSE: 5.89413560e-07\n",
      "Epoch: 140, Train MSE: 5.56053067e-07, Val. MSE: 5.68920370e-07\n",
      "Epoch: 160, Train MSE: 4.86305225e-07, Val. MSE: 4.99163377e-07\n",
      "Epoch: 180, Train MSE: 4.32307075e-07, Val. MSE: 4.44658667e-07\n",
      "Epoch: 200, Train MSE: 3.73566564e-07, Val. MSE: 3.87537511e-07\n",
      "Epoch: 220, Train MSE: 3.45163329e-07, Val. MSE: 3.59379811e-07\n",
      "Epoch: 240, Train MSE: 3.26392126e-07, Val. MSE: 3.40208516e-07\n",
      "Epoch: 260, Train MSE: 3.20453040e-07, Val. MSE: 3.34381326e-07\n",
      "Epoch: 280, Train MSE: 3.09876384e-07, Val. MSE: 3.23642723e-07\n",
      "Epoch: 300, Train MSE: 3.00387001e-07, Val. MSE: 3.14360847e-07\n",
      "Epoch: 320, Train MSE: 3.00164658e-07, Val. MSE: 3.14207284e-07\n",
      "Epoch: 340, Train MSE: 2.99363251e-07, Val. MSE: 3.13216930e-07\n",
      "Epoch: 360, Train MSE: 2.98849983e-07, Val. MSE: 3.13038583e-07\n",
      "Epoch: 380, Train MSE: 2.98232635e-07, Val. MSE: 3.12220010e-07\n",
      "Epoch: 400, Train MSE: 2.96766586e-07, Val. MSE: 3.10830899e-07\n",
      "Epoch: 420, Train MSE: 2.96830848e-07, Val. MSE: 3.11084420e-07\n",
      "Epoch: 440, Train MSE: 2.96513804e-07, Val. MSE: 3.10589655e-07\n",
      "Epoch: 460, Train MSE: 2.95291244e-07, Val. MSE: 3.09351321e-07\n",
      "Epoch: 480, Train MSE: 2.94599459e-07, Val. MSE: 3.08723457e-07\n",
      "Epoch: 500, Train MSE: 2.93653471e-07, Val. MSE: 3.07707069e-07\n",
      "Epoch: 520, Train MSE: 2.93570196e-07, Val. MSE: 3.07609582e-07\n",
      "Epoch: 540, Train MSE: 2.93563176e-07, Val. MSE: 3.07615494e-07\n",
      "Epoch: 560, Train MSE: 2.93563147e-07, Val. MSE: 3.07615664e-07\n",
      "Epoch: 580, Train MSE: 2.93563119e-07, Val. MSE: 3.07615778e-07\n",
      "Epoch: 600, Train MSE: 2.93563119e-07, Val. MSE: 3.07615835e-07\n",
      "Epoch: 620, Train MSE: 2.93563147e-07, Val. MSE: 3.07616062e-07\n",
      "Epoch: 640, Train MSE: 2.93563090e-07, Val. MSE: 3.07615949e-07\n",
      "Epoch: 660, Train MSE: 2.93562977e-07, Val. MSE: 3.07615977e-07\n",
      "Epoch: 680, Train MSE: 2.93563033e-07, Val. MSE: 3.07616119e-07\n",
      "Epoch: 700, Train MSE: 2.93562948e-07, Val. MSE: 3.07616176e-07\n",
      "Epoch: 720, Train MSE: 2.93562977e-07, Val. MSE: 3.07616119e-07\n",
      "Epoch: 740, Train MSE: 2.93562948e-07, Val. MSE: 3.07616176e-07\n",
      "Epoch: 760, Train MSE: 2.93563005e-07, Val. MSE: 3.07616034e-07\n",
      "Epoch: 780, Train MSE: 2.93562891e-07, Val. MSE: 3.07616091e-07\n",
      "Epoch: 800, Train MSE: 2.93562863e-07, Val. MSE: 3.07616261e-07\n",
      "Epoch: 820, Train MSE: 2.93562891e-07, Val. MSE: 3.07616261e-07\n",
      "Epoch: 840, Train MSE: 2.93562863e-07, Val. MSE: 3.07616403e-07\n",
      "Epoch: 860, Train MSE: 2.93562891e-07, Val. MSE: 3.07616347e-07\n",
      "Epoch: 880, Train MSE: 2.93562834e-07, Val. MSE: 3.07616176e-07\n",
      "Epoch: 900, Train MSE: 2.93562749e-07, Val. MSE: 3.07616205e-07\n",
      "Epoch: 920, Train MSE: 2.93562778e-07, Val. MSE: 3.07616119e-07\n",
      "Epoch: 940, Train MSE: 2.93562721e-07, Val. MSE: 3.07615977e-07\n",
      "Epoch: 960, Train MSE: 2.93562692e-07, Val. MSE: 3.07616091e-07\n",
      "Epoch: 980, Train MSE: 2.93562749e-07, Val. MSE: 3.07616091e-07\n",
      "IDModels/AE_z3_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z3_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z3_mse_1000epochs_updatedata\n",
      "520\n",
      "Training model nlat =  4\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.36710196e-05, Val. MSE: 1.36837489e-05\n",
      "Epoch: 020, Train MSE: 3.78627806e-06, Val. MSE: 3.82695816e-06\n",
      "Epoch: 040, Train MSE: 2.67095493e-06, Val. MSE: 2.68957365e-06\n",
      "Epoch: 060, Train MSE: 2.50237463e-06, Val. MSE: 2.52322525e-06\n",
      "Epoch: 080, Train MSE: 2.46032459e-06, Val. MSE: 2.48155288e-06\n",
      "Epoch: 100, Train MSE: 2.42699571e-06, Val. MSE: 2.44844387e-06\n",
      "Epoch: 120, Train MSE: 2.40946929e-06, Val. MSE: 2.43034037e-06\n",
      "Epoch: 140, Train MSE: 2.41446355e-06, Val. MSE: 2.43534123e-06\n",
      "Epoch: 160, Train MSE: 1.82189285e-06, Val. MSE: 1.84007376e-06\n",
      "Epoch: 180, Train MSE: 9.26317853e-07, Val. MSE: 9.43588930e-07\n",
      "Epoch: 200, Train MSE: 6.05495643e-07, Val. MSE: 6.18965089e-07\n",
      "Epoch: 220, Train MSE: 5.94281744e-07, Val. MSE: 6.08305015e-07\n",
      "Epoch: 240, Train MSE: 5.80058156e-07, Val. MSE: 5.93706147e-07\n",
      "Epoch: 260, Train MSE: 4.89806155e-07, Val. MSE: 5.00699628e-07\n",
      "Epoch: 280, Train MSE: 4.21693670e-07, Val. MSE: 4.33088388e-07\n",
      "Epoch: 300, Train MSE: 3.61830814e-07, Val. MSE: 3.73468907e-07\n",
      "Epoch: 320, Train MSE: 3.60856518e-07, Val. MSE: 3.72171030e-07\n",
      "Epoch: 340, Train MSE: 3.49381395e-07, Val. MSE: 3.60205036e-07\n",
      "Epoch: 360, Train MSE: 3.47303086e-07, Val. MSE: 3.57516200e-07\n",
      "Epoch: 380, Train MSE: 3.43061714e-07, Val. MSE: 3.53089348e-07\n",
      "Epoch: 400, Train MSE: 3.40865000e-07, Val. MSE: 3.50892748e-07\n",
      "Epoch: 420, Train MSE: 3.39349867e-07, Val. MSE: 3.49608086e-07\n",
      "Epoch: 440, Train MSE: 3.29827884e-07, Val. MSE: 3.40028350e-07\n",
      "Epoch: 460, Train MSE: 3.29057968e-07, Val. MSE: 3.39221430e-07\n",
      "Epoch: 480, Train MSE: 3.29045434e-07, Val. MSE: 3.39200454e-07\n",
      "Epoch: 500, Train MSE: 3.29045292e-07, Val. MSE: 3.39200312e-07\n",
      "Epoch: 520, Train MSE: 3.29045292e-07, Val. MSE: 3.39200540e-07\n",
      "Epoch: 540, Train MSE: 3.29045349e-07, Val. MSE: 3.39200540e-07\n",
      "Epoch: 560, Train MSE: 3.29045264e-07, Val. MSE: 3.39200398e-07\n",
      "Epoch: 580, Train MSE: 3.29045264e-07, Val. MSE: 3.39200398e-07\n",
      "Epoch: 600, Train MSE: 3.29045179e-07, Val. MSE: 3.39200511e-07\n",
      "Epoch: 620, Train MSE: 3.29045150e-07, Val. MSE: 3.39200398e-07\n",
      "Epoch: 640, Train MSE: 3.29045093e-07, Val. MSE: 3.39200426e-07\n",
      "Epoch: 660, Train MSE: 3.29045093e-07, Val. MSE: 3.39200596e-07\n",
      "Epoch: 680, Train MSE: 3.29045065e-07, Val. MSE: 3.39200568e-07\n",
      "Epoch: 700, Train MSE: 3.29045065e-07, Val. MSE: 3.39200511e-07\n",
      "Epoch: 720, Train MSE: 3.29045037e-07, Val. MSE: 3.39200426e-07\n",
      "Epoch: 740, Train MSE: 3.29044923e-07, Val. MSE: 3.39200426e-07\n",
      "Epoch: 760, Train MSE: 3.29044894e-07, Val. MSE: 3.39200369e-07\n",
      "Epoch: 780, Train MSE: 3.29044894e-07, Val. MSE: 3.39200369e-07\n",
      "Epoch: 800, Train MSE: 3.29044894e-07, Val. MSE: 3.39200284e-07\n",
      "Epoch: 820, Train MSE: 3.29044894e-07, Val. MSE: 3.39200284e-07\n",
      "Epoch: 840, Train MSE: 3.29044724e-07, Val. MSE: 3.39200284e-07\n",
      "Epoch: 860, Train MSE: 3.29044752e-07, Val. MSE: 3.39200199e-07\n",
      "Epoch: 880, Train MSE: 3.29044724e-07, Val. MSE: 3.39200199e-07\n",
      "Epoch: 900, Train MSE: 3.29044639e-07, Val. MSE: 3.39200255e-07\n",
      "Epoch: 920, Train MSE: 3.29044667e-07, Val. MSE: 3.39200255e-07\n",
      "Epoch: 940, Train MSE: 3.29044582e-07, Val. MSE: 3.39200170e-07\n",
      "Epoch: 960, Train MSE: 3.29044639e-07, Val. MSE: 3.39200199e-07\n",
      "Epoch: 980, Train MSE: 3.29044525e-07, Val. MSE: 3.39200170e-07\n",
      "IDModels/AE_z4_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z4_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z4_mse_1000epochs_updatedata\n",
      "941\n",
      "Training model nlat =  5\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.48140280e-05, Val. MSE: 1.48284080e-05\n",
      "Epoch: 020, Train MSE: 7.44841600e-07, Val. MSE: 7.59871739e-07\n",
      "Epoch: 040, Train MSE: 4.69216786e-07, Val. MSE: 4.84281259e-07\n",
      "Epoch: 060, Train MSE: 3.97592061e-07, Val. MSE: 4.11585063e-07\n",
      "Epoch: 080, Train MSE: 3.61420348e-07, Val. MSE: 3.75607613e-07\n",
      "Epoch: 100, Train MSE: 3.37589967e-07, Val. MSE: 3.51310092e-07\n",
      "Epoch: 120, Train MSE: 3.23441185e-07, Val. MSE: 3.37225231e-07\n",
      "Epoch: 140, Train MSE: 2.99787018e-07, Val. MSE: 3.12962356e-07\n",
      "Epoch: 160, Train MSE: 2.89009080e-07, Val. MSE: 3.02324793e-07\n",
      "Epoch: 180, Train MSE: 2.82160443e-07, Val. MSE: 2.94975990e-07\n",
      "Epoch: 200, Train MSE: 2.21510803e-07, Val. MSE: 2.34183702e-07\n",
      "Epoch: 220, Train MSE: 2.14245489e-07, Val. MSE: 2.26205728e-07\n",
      "Epoch: 240, Train MSE: 2.10533130e-07, Val. MSE: 2.22191886e-07\n",
      "Epoch: 260, Train MSE: 2.04149913e-07, Val. MSE: 2.16049102e-07\n",
      "Epoch: 280, Train MSE: 1.99859983e-07, Val. MSE: 2.11541845e-07\n",
      "Epoch: 300, Train MSE: 1.99106822e-07, Val. MSE: 2.10755147e-07\n",
      "Epoch: 320, Train MSE: 1.98767367e-07, Val. MSE: 2.10391008e-07\n",
      "Epoch: 340, Train MSE: 1.97834552e-07, Val. MSE: 2.09419710e-07\n",
      "Epoch: 360, Train MSE: 1.97443100e-07, Val. MSE: 2.09026297e-07\n",
      "Epoch: 380, Train MSE: 1.96902718e-07, Val. MSE: 2.08418584e-07\n",
      "Epoch: 400, Train MSE: 1.95846454e-07, Val. MSE: 2.07388496e-07\n",
      "Epoch: 420, Train MSE: 1.95767441e-07, Val. MSE: 2.07340250e-07\n",
      "Epoch: 440, Train MSE: 1.95766773e-07, Val. MSE: 2.07339568e-07\n",
      "Epoch: 460, Train MSE: 1.95766745e-07, Val. MSE: 2.07339539e-07\n",
      "Epoch: 480, Train MSE: 1.95766816e-07, Val. MSE: 2.07339554e-07\n",
      "Epoch: 500, Train MSE: 1.95766731e-07, Val. MSE: 2.07339497e-07\n",
      "Epoch: 520, Train MSE: 1.95766745e-07, Val. MSE: 2.07339497e-07\n",
      "Epoch: 540, Train MSE: 1.95766745e-07, Val. MSE: 2.07339468e-07\n",
      "Epoch: 560, Train MSE: 1.95766702e-07, Val. MSE: 2.07339426e-07\n",
      "Epoch: 580, Train MSE: 1.95766646e-07, Val. MSE: 2.07339369e-07\n",
      "Epoch: 600, Train MSE: 1.95766646e-07, Val. MSE: 2.07339326e-07\n",
      "Epoch: 620, Train MSE: 1.95766603e-07, Val. MSE: 2.07339340e-07\n",
      "Epoch: 640, Train MSE: 1.95766560e-07, Val. MSE: 2.07339212e-07\n",
      "Epoch: 660, Train MSE: 1.95766603e-07, Val. MSE: 2.07339156e-07\n",
      "Epoch: 680, Train MSE: 1.95766546e-07, Val. MSE: 2.07339156e-07\n",
      "Epoch: 700, Train MSE: 1.95766560e-07, Val. MSE: 2.07339141e-07\n",
      "Epoch: 720, Train MSE: 1.95766503e-07, Val. MSE: 2.07339141e-07\n",
      "Epoch: 740, Train MSE: 1.95766475e-07, Val. MSE: 2.07339028e-07\n",
      "Epoch: 760, Train MSE: 1.95766503e-07, Val. MSE: 2.07338957e-07\n",
      "Epoch: 780, Train MSE: 1.95766404e-07, Val. MSE: 2.07338886e-07\n",
      "Epoch: 800, Train MSE: 1.95766475e-07, Val. MSE: 2.07338928e-07\n",
      "Epoch: 820, Train MSE: 1.95766461e-07, Val. MSE: 2.07338914e-07\n",
      "Epoch: 840, Train MSE: 1.95766404e-07, Val. MSE: 2.07338857e-07\n",
      "Epoch: 860, Train MSE: 1.95766418e-07, Val. MSE: 2.07338800e-07\n",
      "Epoch: 880, Train MSE: 1.95766390e-07, Val. MSE: 2.07338786e-07\n",
      "Epoch: 900, Train MSE: 1.95766333e-07, Val. MSE: 2.07338786e-07\n",
      "Epoch: 920, Train MSE: 1.95766390e-07, Val. MSE: 2.07338786e-07\n",
      "Epoch: 940, Train MSE: 1.95766347e-07, Val. MSE: 2.07338672e-07\n",
      "Epoch: 960, Train MSE: 1.95766347e-07, Val. MSE: 2.07338687e-07\n",
      "Epoch: 980, Train MSE: 1.95766233e-07, Val. MSE: 2.07338616e-07\n",
      "IDModels/AE_z5_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z5_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z5_mse_1000epochs_updatedata\n",
      "990\n",
      "Training model nlat =  8\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.31020752e-05, Val. MSE: 1.31109000e-05\n",
      "Epoch: 020, Train MSE: 1.25976726e-06, Val. MSE: 1.26712769e-06\n",
      "Epoch: 040, Train MSE: 4.69975390e-07, Val. MSE: 4.86259864e-07\n",
      "Epoch: 060, Train MSE: 3.46952106e-07, Val. MSE: 3.64201554e-07\n",
      "Epoch: 080, Train MSE: 2.87501734e-07, Val. MSE: 3.03851465e-07\n",
      "Epoch: 100, Train MSE: 2.25385151e-07, Val. MSE: 2.39793280e-07\n",
      "Epoch: 120, Train MSE: 1.93072097e-07, Val. MSE: 2.05501962e-07\n",
      "Epoch: 140, Train MSE: 1.67605180e-07, Val. MSE: 1.77271019e-07\n",
      "Epoch: 160, Train MSE: 1.53089587e-07, Val. MSE: 1.62045211e-07\n",
      "Epoch: 180, Train MSE: 1.43046009e-07, Val. MSE: 1.51160677e-07\n",
      "Epoch: 200, Train MSE: 1.37050421e-07, Val. MSE: 1.45345737e-07\n",
      "Epoch: 220, Train MSE: 1.29665992e-07, Val. MSE: 1.37626913e-07\n",
      "Epoch: 240, Train MSE: 1.23098417e-07, Val. MSE: 1.30844384e-07\n",
      "Epoch: 260, Train MSE: 1.23320532e-07, Val. MSE: 1.30771937e-07\n",
      "Epoch: 280, Train MSE: 1.14695808e-07, Val. MSE: 1.22048320e-07\n",
      "Epoch: 300, Train MSE: 1.11035277e-07, Val. MSE: 1.18031558e-07\n",
      "Epoch: 320, Train MSE: 1.07403466e-07, Val. MSE: 1.14211787e-07\n",
      "Epoch: 340, Train MSE: 1.05424597e-07, Val. MSE: 1.11804681e-07\n",
      "Epoch: 360, Train MSE: 1.03327721e-07, Val. MSE: 1.09572007e-07\n",
      "Epoch: 380, Train MSE: 1.00865016e-07, Val. MSE: 1.07218241e-07\n",
      "Epoch: 400, Train MSE: 9.86464457e-08, Val. MSE: 1.04687302e-07\n",
      "Epoch: 420, Train MSE: 9.57076338e-08, Val. MSE: 1.01681707e-07\n",
      "Epoch: 440, Train MSE: 9.29000450e-08, Val. MSE: 9.88088189e-08\n",
      "Epoch: 460, Train MSE: 9.25362826e-08, Val. MSE: 9.84754678e-08\n",
      "Epoch: 480, Train MSE: 9.24451840e-08, Val. MSE: 9.83702577e-08\n",
      "Epoch: 500, Train MSE: 9.24421713e-08, Val. MSE: 9.83683677e-08\n",
      "Epoch: 520, Train MSE: 9.24421641e-08, Val. MSE: 9.83683321e-08\n",
      "Epoch: 540, Train MSE: 9.24421357e-08, Val. MSE: 9.83683677e-08\n",
      "Epoch: 560, Train MSE: 9.24421570e-08, Val. MSE: 9.83683748e-08\n",
      "Epoch: 580, Train MSE: 9.24420860e-08, Val. MSE: 9.83683819e-08\n",
      "Epoch: 600, Train MSE: 9.24421002e-08, Val. MSE: 9.83683108e-08\n",
      "Epoch: 620, Train MSE: 9.24420576e-08, Val. MSE: 9.83683819e-08\n",
      "Epoch: 640, Train MSE: 9.24420576e-08, Val. MSE: 9.83683321e-08\n",
      "Epoch: 660, Train MSE: 9.24420149e-08, Val. MSE: 9.83683464e-08\n",
      "Epoch: 680, Train MSE: 9.24420291e-08, Val. MSE: 9.83683179e-08\n",
      "Epoch: 700, Train MSE: 9.24420007e-08, Val. MSE: 9.83683037e-08\n",
      "Epoch: 720, Train MSE: 9.24419865e-08, Val. MSE: 9.83683606e-08\n",
      "Epoch: 740, Train MSE: 9.24419865e-08, Val. MSE: 9.83683108e-08\n",
      "Epoch: 760, Train MSE: 9.24419652e-08, Val. MSE: 9.83683108e-08\n",
      "Epoch: 780, Train MSE: 9.24419510e-08, Val. MSE: 9.83682824e-08\n",
      "Epoch: 800, Train MSE: 9.24419368e-08, Val. MSE: 9.83682469e-08\n",
      "Epoch: 820, Train MSE: 9.24419012e-08, Val. MSE: 9.83682042e-08\n",
      "Epoch: 840, Train MSE: 9.24418799e-08, Val. MSE: 9.83681900e-08\n",
      "Epoch: 860, Train MSE: 9.24418941e-08, Val. MSE: 9.83681687e-08\n",
      "Epoch: 880, Train MSE: 9.24418657e-08, Val. MSE: 9.83681687e-08\n",
      "Epoch: 900, Train MSE: 9.24418799e-08, Val. MSE: 9.83681403e-08\n",
      "Epoch: 920, Train MSE: 9.24418515e-08, Val. MSE: 9.83681758e-08\n",
      "Epoch: 940, Train MSE: 9.24418302e-08, Val. MSE: 9.83682327e-08\n",
      "Epoch: 960, Train MSE: 9.24418160e-08, Val. MSE: 9.83681900e-08\n",
      "Epoch: 980, Train MSE: 9.24418160e-08, Val. MSE: 9.83682042e-08\n",
      "IDModels/AE_z8_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z8_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z8_mse_1000epochs_updatedata\n",
      "886\n",
      "Training model nlat =  10\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.43348625e-05, Val. MSE: 1.43461384e-05\n",
      "Epoch: 020, Train MSE: 1.21982123e-06, Val. MSE: 1.23232610e-06\n",
      "Epoch: 040, Train MSE: 6.16427599e-07, Val. MSE: 6.32956528e-07\n",
      "Epoch: 060, Train MSE: 3.62981865e-07, Val. MSE: 3.79710571e-07\n",
      "Epoch: 080, Train MSE: 2.58715971e-07, Val. MSE: 2.73991645e-07\n",
      "Epoch: 100, Train MSE: 2.15862727e-07, Val. MSE: 2.30193081e-07\n",
      "Epoch: 120, Train MSE: 1.78407987e-07, Val. MSE: 1.91441487e-07\n",
      "Epoch: 140, Train MSE: 1.57396443e-07, Val. MSE: 1.68174424e-07\n",
      "Epoch: 160, Train MSE: 1.41939864e-07, Val. MSE: 1.50653491e-07\n",
      "Epoch: 180, Train MSE: 1.28386176e-07, Val. MSE: 1.36627676e-07\n",
      "Epoch: 200, Train MSE: 1.23554898e-07, Val. MSE: 1.30683148e-07\n",
      "Epoch: 220, Train MSE: 1.13254394e-07, Val. MSE: 1.19964440e-07\n",
      "Epoch: 240, Train MSE: 1.06622878e-07, Val. MSE: 1.12807868e-07\n",
      "Epoch: 260, Train MSE: 1.01399223e-07, Val. MSE: 1.07310036e-07\n",
      "Epoch: 280, Train MSE: 9.56821538e-08, Val. MSE: 1.01350132e-07\n",
      "Epoch: 300, Train MSE: 9.52774428e-08, Val. MSE: 1.00841326e-07\n",
      "Epoch: 320, Train MSE: 9.48479197e-08, Val. MSE: 1.00396456e-07\n",
      "Epoch: 340, Train MSE: 9.44358902e-08, Val. MSE: 9.99682683e-08\n",
      "Epoch: 360, Train MSE: 9.36480617e-08, Val. MSE: 9.91374591e-08\n",
      "Epoch: 380, Train MSE: 9.28264754e-08, Val. MSE: 9.82755921e-08\n",
      "Epoch: 400, Train MSE: 9.27127601e-08, Val. MSE: 9.80828361e-08\n",
      "Epoch: 420, Train MSE: 9.18015459e-08, Val. MSE: 9.71695684e-08\n",
      "Epoch: 440, Train MSE: 9.14469922e-08, Val. MSE: 9.68306395e-08\n",
      "Epoch: 460, Train MSE: 9.08865232e-08, Val. MSE: 9.62106057e-08\n",
      "Epoch: 480, Train MSE: 9.04107083e-08, Val. MSE: 9.57711919e-08\n",
      "Epoch: 500, Train MSE: 9.01690811e-08, Val. MSE: 9.54251291e-08\n",
      "Epoch: 520, Train MSE: 8.97250061e-08, Val. MSE: 9.50525632e-08\n",
      "Epoch: 540, Train MSE: 8.92644110e-08, Val. MSE: 9.45220933e-08\n",
      "Epoch: 560, Train MSE: 8.89482479e-08, Val. MSE: 9.41518721e-08\n",
      "Epoch: 580, Train MSE: 8.83549802e-08, Val. MSE: 9.36038447e-08\n",
      "Epoch: 600, Train MSE: 8.81492213e-08, Val. MSE: 9.33811037e-08\n",
      "Epoch: 620, Train MSE: 8.76997319e-08, Val. MSE: 9.29361335e-08\n",
      "Epoch: 640, Train MSE: 8.74549926e-08, Val. MSE: 9.26150747e-08\n",
      "Epoch: 660, Train MSE: 8.73416468e-08, Val. MSE: 9.24954193e-08\n",
      "Epoch: 680, Train MSE: 8.69642136e-08, Val. MSE: 9.21273795e-08\n",
      "Epoch: 700, Train MSE: 8.63464891e-08, Val. MSE: 9.14931206e-08\n",
      "Epoch: 720, Train MSE: 8.60847962e-08, Val. MSE: 9.11647930e-08\n",
      "Epoch: 740, Train MSE: 8.55728146e-08, Val. MSE: 9.06825406e-08\n",
      "Epoch: 760, Train MSE: 8.55037783e-08, Val. MSE: 9.06009490e-08\n",
      "Epoch: 780, Train MSE: 8.54991171e-08, Val. MSE: 9.05966857e-08\n",
      "Epoch: 800, Train MSE: 8.54991171e-08, Val. MSE: 9.05966786e-08\n",
      "Epoch: 820, Train MSE: 8.54990745e-08, Val. MSE: 9.05965791e-08\n",
      "Epoch: 840, Train MSE: 8.54990674e-08, Val. MSE: 9.05965720e-08\n",
      "Epoch: 860, Train MSE: 8.54990532e-08, Val. MSE: 9.05965223e-08\n",
      "Epoch: 880, Train MSE: 8.54990461e-08, Val. MSE: 9.05965223e-08\n",
      "Epoch: 900, Train MSE: 8.54990390e-08, Val. MSE: 9.05964725e-08\n",
      "Epoch: 920, Train MSE: 8.54990105e-08, Val. MSE: 9.05964725e-08\n",
      "Epoch: 940, Train MSE: 8.54990034e-08, Val. MSE: 9.05964157e-08\n",
      "Epoch: 960, Train MSE: 8.54990105e-08, Val. MSE: 9.05963660e-08\n",
      "Epoch: 980, Train MSE: 8.54989821e-08, Val. MSE: 9.05963304e-08\n",
      "IDModels/AE_z10_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z10_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z10_mse_1000epochs_updatedata\n",
      "983\n",
      "Training model nlat =  12\n"
     ]
    },
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      "Epoch: 000, Train MSE: 1.40264765e-05, Val. MSE: 1.40387383e-05\n",
      "Epoch: 020, Train MSE: 9.38926235e-07, Val. MSE: 9.59552494e-07\n",
      "Epoch: 040, Train MSE: 4.89924844e-07, Val. MSE: 5.14600288e-07\n",
      "Epoch: 060, Train MSE: 3.65635145e-07, Val. MSE: 3.87144411e-07\n",
      "Epoch: 080, Train MSE: 2.77124684e-07, Val. MSE: 2.92173269e-07\n",
      "Epoch: 100, Train MSE: 2.26434778e-07, Val. MSE: 2.39380171e-07\n",
      "Epoch: 120, Train MSE: 1.96092458e-07, Val. MSE: 2.07995512e-07\n",
      "Epoch: 140, Train MSE: 1.71438515e-07, Val. MSE: 1.82266163e-07\n",
      "Epoch: 160, Train MSE: 1.55608149e-07, Val. MSE: 1.64630293e-07\n",
      "Epoch: 180, Train MSE: 1.42549140e-07, Val. MSE: 1.50551870e-07\n",
      "Epoch: 200, Train MSE: 1.34081517e-07, Val. MSE: 1.41070288e-07\n",
      "Epoch: 220, Train MSE: 1.28620471e-07, Val. MSE: 1.35551730e-07\n",
      "Epoch: 240, Train MSE: 1.24111850e-07, Val. MSE: 1.30465551e-07\n",
      "Epoch: 260, Train MSE: 1.17039043e-07, Val. MSE: 1.23352834e-07\n",
      "Epoch: 280, Train MSE: 1.14871632e-07, Val. MSE: 1.20981568e-07\n",
      "Epoch: 300, Train MSE: 1.13074577e-07, Val. MSE: 1.18909085e-07\n",
      "Epoch: 320, Train MSE: 1.09096135e-07, Val. MSE: 1.14628961e-07\n",
      "Epoch: 340, Train MSE: 1.03563025e-07, Val. MSE: 1.09076531e-07\n",
      "Epoch: 360, Train MSE: 1.01589201e-07, Val. MSE: 1.06552058e-07\n",
      "Epoch: 380, Train MSE: 1.00949428e-07, Val. MSE: 1.05885860e-07\n",
      "Epoch: 400, Train MSE: 9.61276641e-08, Val. MSE: 1.00843380e-07\n",
      "Epoch: 420, Train MSE: 9.37709075e-08, Val. MSE: 9.83111548e-08\n",
      "Epoch: 440, Train MSE: 8.98053898e-08, Val. MSE: 9.40932452e-08\n",
      "Epoch: 460, Train MSE: 8.92803982e-08, Val. MSE: 9.35029405e-08\n",
      "Epoch: 480, Train MSE: 8.45152286e-08, Val. MSE: 8.87480667e-08\n",
      "Epoch: 500, Train MSE: 8.41627141e-08, Val. MSE: 8.83621922e-08\n",
      "Epoch: 520, Train MSE: 8.39239505e-08, Val. MSE: 8.81096227e-08\n",
      "Epoch: 540, Train MSE: 8.33528517e-08, Val. MSE: 8.76157529e-08\n",
      "Epoch: 560, Train MSE: 8.28462703e-08, Val. MSE: 8.70419825e-08\n",
      "Epoch: 580, Train MSE: 8.22217459e-08, Val. MSE: 8.63872955e-08\n",
      "Epoch: 600, Train MSE: 8.21138926e-08, Val. MSE: 8.63210019e-08\n",
      "Epoch: 620, Train MSE: 8.16129173e-08, Val. MSE: 8.57765698e-08\n",
      "Epoch: 640, Train MSE: 8.11711800e-08, Val. MSE: 8.53391242e-08\n",
      "Epoch: 660, Train MSE: 8.07668883e-08, Val. MSE: 8.49105817e-08\n",
      "Epoch: 680, Train MSE: 8.05343205e-08, Val. MSE: 8.47108694e-08\n",
      "Epoch: 700, Train MSE: 7.99016533e-08, Val. MSE: 8.40031475e-08\n",
      "Epoch: 720, Train MSE: 7.93393085e-08, Val. MSE: 8.34569889e-08\n",
      "Epoch: 740, Train MSE: 7.91083039e-08, Val. MSE: 8.32319742e-08\n",
      "Epoch: 760, Train MSE: 7.85889469e-08, Val. MSE: 8.27055260e-08\n",
      "Epoch: 780, Train MSE: 7.83591787e-08, Val. MSE: 8.25028863e-08\n",
      "Epoch: 800, Train MSE: 7.78670284e-08, Val. MSE: 8.19461405e-08\n",
      "Epoch: 820, Train MSE: 7.75895899e-08, Val. MSE: 8.17528161e-08\n",
      "Epoch: 840, Train MSE: 7.70930839e-08, Val. MSE: 8.12062666e-08\n",
      "Epoch: 860, Train MSE: 7.66436443e-08, Val. MSE: 8.06387845e-08\n",
      "Epoch: 880, Train MSE: 7.59481722e-08, Val. MSE: 8.00012288e-08\n",
      "Epoch: 900, Train MSE: 7.59217258e-08, Val. MSE: 7.99566564e-08\n",
      "Epoch: 920, Train MSE: 7.58763363e-08, Val. MSE: 7.99171076e-08\n",
      "Epoch: 940, Train MSE: 7.57657403e-08, Val. MSE: 7.97995057e-08\n",
      "Epoch: 960, Train MSE: 7.57362528e-08, Val. MSE: 7.97822466e-08\n",
      "Epoch: 980, Train MSE: 7.56643814e-08, Val. MSE: 7.96966901e-08\n",
      "IDModels/AE_z12_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z12_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z12_mse_1000epochs_updatedata\n",
      "998\n",
      "Training model nlat =  14\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.50129636e-05, Val. MSE: 1.50252690e-05\n",
      "Epoch: 020, Train MSE: 8.90905255e-07, Val. MSE: 9.03229420e-07\n",
      "Epoch: 040, Train MSE: 4.82619214e-07, Val. MSE: 5.01662214e-07\n",
      "Epoch: 060, Train MSE: 3.92052016e-07, Val. MSE: 4.09952918e-07\n",
      "Epoch: 080, Train MSE: 3.44892129e-07, Val. MSE: 3.60477856e-07\n",
      "Epoch: 100, Train MSE: 3.14548203e-07, Val. MSE: 3.28188548e-07\n",
      "Epoch: 120, Train MSE: 2.29659307e-07, Val. MSE: 2.42176725e-07\n",
      "Epoch: 140, Train MSE: 2.06667707e-07, Val. MSE: 2.16370367e-07\n",
      "Epoch: 160, Train MSE: 1.95003537e-07, Val. MSE: 2.01942214e-07\n",
      "Epoch: 180, Train MSE: 1.87353564e-07, Val. MSE: 1.92849697e-07\n",
      "Epoch: 200, Train MSE: 1.73613174e-07, Val. MSE: 1.78841859e-07\n",
      "Epoch: 220, Train MSE: 1.66505217e-07, Val. MSE: 1.71810029e-07\n",
      "Epoch: 240, Train MSE: 1.45419477e-07, Val. MSE: 1.51273071e-07\n",
      "Epoch: 260, Train MSE: 1.39976549e-07, Val. MSE: 1.45447316e-07\n",
      "Epoch: 280, Train MSE: 1.35164882e-07, Val. MSE: 1.40470405e-07\n",
      "Epoch: 300, Train MSE: 1.32756213e-07, Val. MSE: 1.37856446e-07\n",
      "Epoch: 320, Train MSE: 1.30615831e-07, Val. MSE: 1.35755656e-07\n",
      "Epoch: 340, Train MSE: 1.27788141e-07, Val. MSE: 1.32898037e-07\n",
      "Epoch: 360, Train MSE: 1.26503920e-07, Val. MSE: 1.31894339e-07\n",
      "Epoch: 380, Train MSE: 1.23654061e-07, Val. MSE: 1.28972999e-07\n",
      "Epoch: 400, Train MSE: 1.22599630e-07, Val. MSE: 1.28226375e-07\n",
      "Epoch: 420, Train MSE: 1.19830105e-07, Val. MSE: 1.25252413e-07\n",
      "Epoch: 440, Train MSE: 1.19805094e-07, Val. MSE: 1.25246601e-07\n",
      "Epoch: 460, Train MSE: 1.19723282e-07, Val. MSE: 1.25147878e-07\n",
      "Epoch: 480, Train MSE: 1.19718777e-07, Val. MSE: 1.25142819e-07\n",
      "Epoch: 500, Train MSE: 1.19718706e-07, Val. MSE: 1.25142890e-07\n",
      "Epoch: 520, Train MSE: 1.19718706e-07, Val. MSE: 1.25142890e-07\n",
      "Epoch: 540, Train MSE: 1.19718678e-07, Val. MSE: 1.25142918e-07\n",
      "Epoch: 560, Train MSE: 1.19718692e-07, Val. MSE: 1.25142932e-07\n",
      "Epoch: 580, Train MSE: 1.19718663e-07, Val. MSE: 1.25142961e-07\n",
      "Epoch: 600, Train MSE: 1.19718621e-07, Val. MSE: 1.25142947e-07\n",
      "Epoch: 620, Train MSE: 1.19718692e-07, Val. MSE: 1.25142947e-07\n",
      "Epoch: 640, Train MSE: 1.19718607e-07, Val. MSE: 1.25142861e-07\n",
      "Epoch: 660, Train MSE: 1.19718607e-07, Val. MSE: 1.25142819e-07\n",
      "Epoch: 680, Train MSE: 1.19718607e-07, Val. MSE: 1.25142805e-07\n",
      "Epoch: 700, Train MSE: 1.19718592e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 720, Train MSE: 1.19718536e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 740, Train MSE: 1.19718536e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 760, Train MSE: 1.19718536e-07, Val. MSE: 1.25142819e-07\n",
      "Epoch: 780, Train MSE: 1.19718521e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 800, Train MSE: 1.19718493e-07, Val. MSE: 1.25142819e-07\n",
      "Epoch: 820, Train MSE: 1.19718507e-07, Val. MSE: 1.25142833e-07\n",
      "Epoch: 840, Train MSE: 1.19718479e-07, Val. MSE: 1.25142776e-07\n",
      "Epoch: 860, Train MSE: 1.19718507e-07, Val. MSE: 1.25142819e-07\n",
      "Epoch: 880, Train MSE: 1.19718464e-07, Val. MSE: 1.25142833e-07\n",
      "Epoch: 900, Train MSE: 1.19718450e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 920, Train MSE: 1.19718422e-07, Val. MSE: 1.25142790e-07\n",
      "Epoch: 940, Train MSE: 1.19718408e-07, Val. MSE: 1.25142805e-07\n",
      "Epoch: 960, Train MSE: 1.19718436e-07, Val. MSE: 1.25142776e-07\n",
      "Epoch: 980, Train MSE: 1.19718408e-07, Val. MSE: 1.25142719e-07\n",
      "IDModels/AE_z14_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z14_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z14_mse_1000epochs_updatedata\n",
      "476\n",
      "Training model nlat =  18\n"
     ]
    },
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     "text": [
      "Epoch: 000, Train MSE: 1.42016934e-05, Val. MSE: 1.42211366e-05\n",
      "Epoch: 020, Train MSE: 1.99890997e-06, Val. MSE: 2.01772082e-06\n",
      "Epoch: 040, Train MSE: 8.03113039e-07, Val. MSE: 8.16172133e-07\n",
      "Epoch: 060, Train MSE: 4.52148242e-07, Val. MSE: 4.66424609e-07\n",
      "Epoch: 080, Train MSE: 3.27667493e-07, Val. MSE: 3.40278035e-07\n",
      "Epoch: 100, Train MSE: 2.53708464e-07, Val. MSE: 2.66474757e-07\n",
      "Epoch: 120, Train MSE: 2.20558746e-07, Val. MSE: 2.32392310e-07\n",
      "Epoch: 140, Train MSE: 1.91806564e-07, Val. MSE: 2.02314965e-07\n",
      "Epoch: 160, Train MSE: 1.80520175e-07, Val. MSE: 1.89658408e-07\n",
      "Epoch: 180, Train MSE: 1.68216800e-07, Val. MSE: 1.75991360e-07\n",
      "Epoch: 200, Train MSE: 1.58834737e-07, Val. MSE: 1.66349167e-07\n",
      "Epoch: 220, Train MSE: 1.47237998e-07, Val. MSE: 1.54403736e-07\n",
      "Epoch: 240, Train MSE: 1.41294109e-07, Val. MSE: 1.48095822e-07\n",
      "Epoch: 260, Train MSE: 1.36201336e-07, Val. MSE: 1.42540657e-07\n",
      "Epoch: 280, Train MSE: 1.31366221e-07, Val. MSE: 1.37668906e-07\n",
      "Epoch: 300, Train MSE: 1.26837065e-07, Val. MSE: 1.32554163e-07\n",
      "Epoch: 320, Train MSE: 1.23316013e-07, Val. MSE: 1.28808551e-07\n",
      "Epoch: 340, Train MSE: 1.19561903e-07, Val. MSE: 1.25038483e-07\n",
      "Epoch: 360, Train MSE: 1.11740405e-07, Val. MSE: 1.17042788e-07\n",
      "Epoch: 380, Train MSE: 1.06940163e-07, Val. MSE: 1.11764194e-07\n",
      "Epoch: 400, Train MSE: 1.04292219e-07, Val. MSE: 1.09226555e-07\n",
      "Epoch: 420, Train MSE: 1.05479145e-07, Val. MSE: 1.10282990e-07\n",
      "Epoch: 440, Train MSE: 9.83931869e-08, Val. MSE: 1.03045622e-07\n",
      "Epoch: 460, Train MSE: 9.68089182e-08, Val. MSE: 1.01099147e-07\n",
      "Epoch: 480, Train MSE: 9.19132148e-08, Val. MSE: 9.62398801e-08\n",
      "Epoch: 500, Train MSE: 9.23877010e-08, Val. MSE: 9.65559934e-08\n",
      "Epoch: 520, Train MSE: 8.93119889e-08, Val. MSE: 9.36454398e-08\n",
      "Epoch: 540, Train MSE: 9.03322857e-08, Val. MSE: 9.46778158e-08\n",
      "Epoch: 560, Train MSE: 8.64425829e-08, Val. MSE: 9.09017501e-08\n",
      "Epoch: 580, Train MSE: 8.31406126e-08, Val. MSE: 8.75492887e-08\n",
      "Epoch: 600, Train MSE: 8.27984294e-08, Val. MSE: 8.72026931e-08\n",
      "Epoch: 620, Train MSE: 8.27934201e-08, Val. MSE: 8.71960850e-08\n",
      "Epoch: 640, Train MSE: 8.27932212e-08, Val. MSE: 8.71955308e-08\n",
      "Epoch: 660, Train MSE: 8.27931856e-08, Val. MSE: 8.71955592e-08\n",
      "Epoch: 680, Train MSE: 8.27931501e-08, Val. MSE: 8.71955379e-08\n",
      "Epoch: 700, Train MSE: 8.27931501e-08, Val. MSE: 8.71954953e-08\n",
      "Epoch: 720, Train MSE: 8.27931430e-08, Val. MSE: 8.71955024e-08\n",
      "Epoch: 740, Train MSE: 8.27931359e-08, Val. MSE: 8.71954668e-08\n",
      "Epoch: 760, Train MSE: 8.27931075e-08, Val. MSE: 8.71954882e-08\n",
      "Epoch: 780, Train MSE: 8.27930791e-08, Val. MSE: 8.71954597e-08\n",
      "Epoch: 800, Train MSE: 8.27930862e-08, Val. MSE: 8.71954313e-08\n",
      "Epoch: 820, Train MSE: 8.27930648e-08, Val. MSE: 8.71954171e-08\n",
      "Epoch: 840, Train MSE: 8.27930364e-08, Val. MSE: 8.71953887e-08\n",
      "Epoch: 860, Train MSE: 8.27930293e-08, Val. MSE: 8.71953887e-08\n",
      "Epoch: 880, Train MSE: 8.27930009e-08, Val. MSE: 8.71953318e-08\n",
      "Epoch: 900, Train MSE: 8.27930151e-08, Val. MSE: 8.71953532e-08\n",
      "Epoch: 920, Train MSE: 8.27929654e-08, Val. MSE: 8.71953318e-08\n",
      "Epoch: 940, Train MSE: 8.27929654e-08, Val. MSE: 8.71952750e-08\n",
      "Epoch: 960, Train MSE: 8.27929654e-08, Val. MSE: 8.71952750e-08\n",
      "Epoch: 980, Train MSE: 8.27929085e-08, Val. MSE: 8.71952750e-08\n",
      "IDModels/AE_z18_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z18_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z18_mse_1000epochs_updatedata\n",
      "998\n",
      "Training model nlat =  20\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "773ad47246134468924e601e4e9fccd1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000, Train MSE: 1.44430705e-05, Val. MSE: 1.44560645e-05\n",
      "Epoch: 020, Train MSE: 1.12776354e-06, Val. MSE: 1.14129148e-06\n",
      "Epoch: 040, Train MSE: 5.31377850e-07, Val. MSE: 5.45766966e-07\n",
      "Epoch: 060, Train MSE: 4.09449399e-07, Val. MSE: 4.20916422e-07\n",
      "Epoch: 080, Train MSE: 3.40092953e-07, Val. MSE: 3.50516331e-07\n",
      "Epoch: 100, Train MSE: 2.98494768e-07, Val. MSE: 3.09712817e-07\n",
      "Epoch: 120, Train MSE: 2.28007281e-07, Val. MSE: 2.39055197e-07\n",
      "Epoch: 140, Train MSE: 2.08293187e-07, Val. MSE: 2.18929543e-07\n",
      "Epoch: 160, Train MSE: 1.99013243e-07, Val. MSE: 2.08835800e-07\n",
      "Epoch: 180, Train MSE: 1.90166574e-07, Val. MSE: 1.99870556e-07\n",
      "Epoch: 200, Train MSE: 1.79062027e-07, Val. MSE: 1.88121390e-07\n",
      "Epoch: 220, Train MSE: 1.47154978e-07, Val. MSE: 1.53037632e-07\n",
      "Epoch: 240, Train MSE: 1.37756160e-07, Val. MSE: 1.43197553e-07\n",
      "Epoch: 260, Train MSE: 1.36099828e-07, Val. MSE: 1.41241671e-07\n",
      "Epoch: 280, Train MSE: 1.28904503e-07, Val. MSE: 1.33640071e-07\n",
      "Epoch: 300, Train MSE: 1.26987814e-07, Val. MSE: 1.31573799e-07\n",
      "Epoch: 320, Train MSE: 1.19717754e-07, Val. MSE: 1.24416275e-07\n",
      "Epoch: 340, Train MSE: 1.19491176e-07, Val. MSE: 1.24116369e-07\n",
      "Epoch: 360, Train MSE: 1.18879946e-07, Val. MSE: 1.23577678e-07\n",
      "Epoch: 380, Train MSE: 1.18383589e-07, Val. MSE: 1.23020826e-07\n",
      "Epoch: 400, Train MSE: 1.18204092e-07, Val. MSE: 1.22763609e-07\n",
      "Epoch: 420, Train MSE: 1.17534455e-07, Val. MSE: 1.22037193e-07\n",
      "Epoch: 440, Train MSE: 1.16802518e-07, Val. MSE: 1.21269821e-07\n",
      "Epoch: 460, Train MSE: 1.16280788e-07, Val. MSE: 1.20776761e-07\n",
      "Epoch: 480, Train MSE: 1.16191096e-07, Val. MSE: 1.20676546e-07\n",
      "Epoch: 500, Train MSE: 1.16179926e-07, Val. MSE: 1.20666158e-07\n",
      "Epoch: 520, Train MSE: 1.16179734e-07, Val. MSE: 1.20665163e-07\n",
      "Epoch: 540, Train MSE: 1.16179720e-07, Val. MSE: 1.20665135e-07\n",
      "Epoch: 560, Train MSE: 1.16179685e-07, Val. MSE: 1.20665092e-07\n",
      "Epoch: 580, Train MSE: 1.16179670e-07, Val. MSE: 1.20665021e-07\n",
      "Epoch: 600, Train MSE: 1.16179635e-07, Val. MSE: 1.20665021e-07\n",
      "Epoch: 620, Train MSE: 1.16179621e-07, Val. MSE: 1.20664978e-07\n",
      "Epoch: 640, Train MSE: 1.16179564e-07, Val. MSE: 1.20664964e-07\n",
      "Epoch: 660, Train MSE: 1.16179599e-07, Val. MSE: 1.20664936e-07\n",
      "Epoch: 680, Train MSE: 1.16179550e-07, Val. MSE: 1.20664893e-07\n",
      "Epoch: 700, Train MSE: 1.16179599e-07, Val. MSE: 1.20664950e-07\n",
      "Epoch: 720, Train MSE: 1.16179507e-07, Val. MSE: 1.20664978e-07\n",
      "Epoch: 740, Train MSE: 1.16179528e-07, Val. MSE: 1.20664879e-07\n",
      "Epoch: 760, Train MSE: 1.16179493e-07, Val. MSE: 1.20664865e-07\n",
      "Epoch: 780, Train MSE: 1.16179478e-07, Val. MSE: 1.20664851e-07\n",
      "Epoch: 800, Train MSE: 1.16179415e-07, Val. MSE: 1.20664851e-07\n",
      "Epoch: 820, Train MSE: 1.16179407e-07, Val. MSE: 1.20664851e-07\n",
      "Epoch: 840, Train MSE: 1.16179407e-07, Val. MSE: 1.20664808e-07\n",
      "Epoch: 860, Train MSE: 1.16179407e-07, Val. MSE: 1.20664794e-07\n",
      "Epoch: 880, Train MSE: 1.16179343e-07, Val. MSE: 1.20664765e-07\n",
      "Epoch: 900, Train MSE: 1.16179372e-07, Val. MSE: 1.20664751e-07\n",
      "Epoch: 920, Train MSE: 1.16179343e-07, Val. MSE: 1.20664737e-07\n",
      "Epoch: 940, Train MSE: 1.16179343e-07, Val. MSE: 1.20664652e-07\n",
      "Epoch: 960, Train MSE: 1.16179301e-07, Val. MSE: 1.20664637e-07\n",
      "Epoch: 980, Train MSE: 1.16179258e-07, Val. MSE: 1.20664581e-07\n",
      "IDModels/AE_z20_mse_1000epochs_losses_updatedata.npz\n",
      "IDModels/AE_z20_mse_1000epochs_valloss_updatedata.npz\n",
      "IDModels/AE_z20_mse_1000epochs_updatedata\n",
      "981\n"
     ]
    }
   ],
   "source": [
    "for j in nlat:\n",
    "    print(\"Training model nlat = \",str(j))\n",
    "    init_lr = 1e-3\n",
    "    weight_decay = 1e-3\n",
    "    total_epochs = 1000\n",
    "\n",
    "    # Learning rate strings so its in the correct format for the filenames\n",
    "    lrval='{:3.0e}'.format(init_lr)[0]\n",
    "    lrexp = '{:3.0e}'.format(init_lr)[4]\n",
    "\n",
    "    model = MicroAutoEncoder(n_latent=j)\n",
    "\n",
    "    # Check if there is a gpu\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    model = model.to(device)\n",
    "\n",
    "    optimizer = torch.optim.AdamW(model.parameters(), lr=init_lr, weight_decay=weight_decay)\n",
    "    criterion = torch.nn.MSELoss()\n",
    "    sched =  ReduceLROnPlateau(optimizer, 'min')\n",
    "    \n",
    "    val_accur = []\n",
    "    train_accur = []\n",
    "\n",
    "    train_mse_recs = []\n",
    "    val_mse_recs = []\n",
    "    \n",
    "    e0 = 0\n",
    "    epochs = 1000\n",
    "    best_epoch = 0\n",
    "    \n",
    "    min_loss = 1e10\n",
    "\n",
    "    for epoch in tqdm(range(e0,epochs)):\n",
    "        mod = train(model,trainloader,criterion,device,epoch)\n",
    "        train_mse = test(model,trainloader,criterion,device)\n",
    "        val_mse = test(model,valloader,criterion,device,val=True)\n",
    "\n",
    "        train_mse_recs.append(train_mse)\n",
    "        val_mse_recs.append(val_mse)\n",
    "        if epoch%20 == 0:\n",
    "            print(f'Epoch: {epoch:03d}, Train MSE: {train_mse:.8e}, Val. MSE: {val_mse:.8e}')\n",
    "        if val_mse<min_loss:\n",
    "            bestmodel = model\n",
    "            min_loss = val_mse\n",
    "            best_epoch = epoch\n",
    "    \n",
    "    tmp_trainloss = \"IDModels/AE_z{}_mse_{}epochs_losses_updatedata.npz\".format(j,epochs)\n",
    "    tmp_valloss = \"IDModels/AE_z{}_mse_{}epochs_valloss_updatedata.npz\".format(j,epochs)\n",
    "    mod_weights = \"IDModels/AE_z{}_mse_{}epochs_updatedata\".format(j,epochs)\n",
    "    \n",
    "    print(tmp_trainloss)\n",
    "    print(tmp_valloss)\n",
    "    print(mod_weights)\n",
    "    print(best_epoch)\n",
    "    \n",
    "    torch.save(bestmodel.state_dict(),mod_weights)\n",
    "    \n",
    "    train_mse_np = np.array([train_mse_recs[i].detach().cpu().numpy() for i in range(0,len(train_mse_recs))])\n",
    "    val_mse_np = np.array([val_mse_recs[i].detach().cpu().numpy() for i in range(0,len(val_mse_recs))])\n",
    "\n",
    "    np.savez(tmp_trainloss,trainl=train_mse_np,vall=val_mse_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1b3ca57a-86fb-4164-857f-ba4f6c63ae94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainloss = []\n",
    "valloss = []\n",
    "\n",
    "fig = plt.figure(figsize=(8,6.5))\n",
    "norm = matplotlib.colors.Normalize(vmin=2.0, vmax=20.0)\n",
    "cols = cm.jet(norm(nlat),bytes=True)/256\n",
    "#print(cols)\n",
    "for i in range(0,len(nlat)):\n",
    "    j = nlat[i]\n",
    "    fnloss = 'IDModels/AE_z{}_mse_1000epochs_losses_updatedata.npz'.format(j)\n",
    "    \n",
    "    loss = np.load(fnloss)\n",
    "    trainloss.append(loss['trainl'])\n",
    "    valloss.append(loss['vall'])\n",
    "    \n",
    "    plt.plot(trainloss[i],label=r\"Train, ($N_{lat}$ = \"+str(j)+\")\",c = tuple(cols[i]))\n",
    "for i in range(0,len(nlat)):\n",
    "    j = nlat[i]\n",
    "    fnloss = 'IDModels/AE_z{}_mse_1000epochs_losses_updatedata.npz'.format(j)\n",
    "    \n",
    "    loss = np.load(fnloss)\n",
    "    trainloss.append(loss['trainl'])\n",
    "    valloss.append(loss['vall'])\n",
    "    \n",
    "    plt.plot(valloss[i],label=r\"Val., ($N_{lat}$ = \"+str(j)+\")\", linestyle='dashed',c = tuple(cols[i]))\n",
    "    \n",
    "#plt.legend(bbox_to_anchor=(1.0, 0.95),ncol=2,frameon=False)\n",
    "plt.legend(loc='upper right',ncol=2,frameon=False)\n",
    "plt.yscale('log')\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "#plt.show()\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"MSELoss_nlat_AE_sc.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "064e9f38-6d7c-4451-85b3-bd61bee2894e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def latentvars(model,dataloader,device,nlatent=12):\n",
    "    dataset = dataloader.dataset\n",
    "    bs = 500\n",
    "    nmoments = 4\n",
    "    nmoms2 = 3\n",
    "    nrates = 3\n",
    "\n",
    "\n",
    "    latents = np.zeros((len(dataset),nlatent))\n",
    "    moments = np.zeros((len(dataset),nmoments,nrates))\n",
    "    rates = np.zeros((len(dataset),nmoments,nrates))\n",
    "    M1s = np.zeros((len(dataset),nmoments,nrates))\n",
    "    dM1dts = np.zeros((len(dataset),nmoments,nrates))\n",
    "    M2s = np.zeros((len(dataset),nmoms2,nrates,2))\n",
    "    dM2dts = np.zeros((len(dataset),nmoms2,nrates,2))\n",
    "\n",
    "    jj = 0\n",
    "    i = 0\n",
    "    for data in dataloader:\n",
    "        #M,dMdt,bin0,bin1coal,bin1condevap,M1,dM1dt,M2,dM2dt=data\n",
    "        #M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "        M,dMdt,bin0,bin1coal,bin1condevap,binmag,M1,dM1dt,M2,dM2dt=data\n",
    "        bin1 = bin1coal.to(device)\n",
    "        bin0 = bin0.to(device)\n",
    "        #print(M.shape,M2.shape,dM2dt.shape)\n",
    "        # z, mu, logvar\n",
    "        xhat,latent = model(bin0.float())\n",
    "        #print(latent.shape)\n",
    "        #print(out[0].shape)\n",
    "        bs = xhat.shape[0]\n",
    "        #print(bs)\n",
    "        if bs>0:\n",
    "            latents[jj:jj+bs,:]=latent.detach().cpu().numpy().reshape(bs,nlatent)\n",
    "            moments[jj:jj+bs,:,:]=M.cpu().numpy().reshape(bs,nmoments,nrates)\n",
    "            rates[jj:jj+bs,:,:]=dMdt.cpu().numpy().reshape(bs,nmoments,nrates)\n",
    "            M1s[jj:jj+bs,:]=M1.cpu().numpy().reshape(bs,nmoments,nrates)\n",
    "            dM1dts[jj:jj+bs,:]=dM1dt.cpu().numpy().reshape(bs,nmoments,nrates)\n",
    "            M2s[jj:jj+bs,:]=M2.cpu().numpy().reshape(bs,nmoms2,nrates,2)\n",
    "            dM2dts[jj:jj+bs,:]=dM2dt.cpu().numpy().reshape(bs,nmoms2,nrates,2)\n",
    "            jj=jj+bs\n",
    "            i = i+1\n",
    "        else:\n",
    "            break\n",
    "        #trainlatentvar,trainmoments,trainrates,trainM1,traindM1dt,trainM2,traindM2dt\n",
    "    return latents,moments,rates,M1s,dM1dts,M2s,dM2dts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "320f340f-a5cf-4705-9ea1-2082aeb57b4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_duplicates(X):\n",
    "    return np.unique(X, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73f256a3-9683-4003-9171-3986d07e13ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def kNN(X, n_neighbors, n_jobs):\n",
    "    neigh = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=n_jobs).fit(X)\n",
    "    dists, inds = neigh.kneighbors(X)\n",
    "    return dists, inds\n",
    "def Levina_Bickel(X, dists, k):\n",
    "    m = np.log(dists[:, k:k+1] / dists[:, 1:k])\n",
    "    m = (k-2) / np.sum(m, axis=1)\n",
    "    dim = np.mean(m)\n",
    "    return dim\n",
    "def MiND_MLk(X,dists,k):\n",
    "    ss = MiND_ML(k=k,ver=\"MLk\")\n",
    "    \n",
    "    return ss.fit_transform(X)\n",
    "def MiND_MLi(X,dists,k):\n",
    "    ss = MiND_ML(k=k,ver=\"MLi\")\n",
    "    \n",
    "    return ss.fit_transform(X)\n",
    "def DimfromAngleNormConc(X,dists,k):\n",
    "    ss = DANCo(k=k,ver=\"DANCo\")\n",
    "    \n",
    "    return ss.fit_transform(X)\n",
    "def MethodofMoments(X,dists,k):\n",
    "    ss = Mom()\n",
    "    \n",
    "    return ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53032308-bfa0-436c-a2bd-c339e6a6fe08",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_list = (latent.shape[0] * np.linspace(0.008, 0.016, 5)).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30565f6c-423d-4c45-aaca-43c0720f35d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the k nearest neighbors (for the Levina-Bickel algorithm calculation)\n",
    "if np.isscalar(k_list):\n",
    "    k_list = np.array([k_list])\n",
    "else:\n",
    "    k_list = np.array(k_list)\n",
    "kmax = np.max(k_list) + 2\n",
    "n_jobs=4\n",
    "dists, inds = kNN(latent.detach().numpy(), kmax,n_jobs)\n",
    "dims = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8fa41b8-318a-4770-8069-9a82af686c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlat = [3,4,5,8,10,14,18,20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e7bed6d8-2f9d-4bec-b736-3866a38b7de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples: (26000, 3); Latent dimension: 3\n",
      "Number of samples (duplicates removed): 26000\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "j = nlat[i]\n",
    "model = MicroAutoEncoder(n_latent=j)\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model = model.to(device)\n",
    "savedmodelweights = 'IDModels/AE_z{}_mse_1000epochs_updatedata'.format(j)\n",
    "\n",
    "model.load_state_dict(torch.load(savedmodelweights,map_location=device))\n",
    "\n",
    "vallatentvar,valmoments,valrates,M1s,dM1dts,M2s,dM2dts = latentvars(model,valloader,device,nlatent=j)\n",
    "\n",
    "latent = vallatentvar\n",
    "\n",
    "print(f'Number of samples: {latent.shape}; Latent dimension: {latent.shape[1]}')\n",
    "\n",
    "latent = remove_duplicates(latent)\n",
    "print(f'Number of samples (duplicates removed): {latent.shape[0]}')\n",
    "\n",
    "#k_list = (latent.shape[0] * np.linspace(0.008, 0.016, 5)).astype('int')\n",
    "\n",
    "#k_list = [100, 200, 500, 1000, 2000]\n",
    "k_list = np.linspace(10,100,10).astype('int')\n",
    "\n",
    "# get the k nearest neighbors (for the Levina-Bickel algorithm calculation)\n",
    "if np.isscalar(k_list):\n",
    "    k_list = np.array([k_list])\n",
    "else:\n",
    "    k_list = np.array(k_list)\n",
    "kmax = np.max(k_list) + 2\n",
    "n_jobs=4\n",
    "dists, inds = kNN(latent, kmax,n_jobs)\n",
    "dims = []\n",
    "dims2 = []\n",
    "for k in k_list:\n",
    "    dims.append(Levina_Bickel(latent, dists, k))\n",
    "    dims2.append(MiND_MLk(latent,dists,k))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee956598-3273-4559-ace1-398ad7d6c308",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4a780ba9-5a04-4308-83b9-67f124d1dc58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_id_estimates(nlats):\n",
    "    nmeth = 4\n",
    "    val_ids = np.zeros((len(nlats),nmeth))\n",
    "    val_ids_sdev = np.zeros((len(nlats),nmeth))\n",
    "    \n",
    "    for i in range(0,len(nlats)):\n",
    "        j = nlats[i]\n",
    "        model = MicroAutoEncoder(n_latent=j)\n",
    "\n",
    "        device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "        model = model.to(device)\n",
    "\n",
    "        savedmodelweights = 'IDModels/AE_z{}_mse_1000epochs_updatedata'.format(j)\n",
    "        model.load_state_dict(torch.load(savedmodelweights,map_location=device))\n",
    "        \n",
    "        vallatentvar,valmoments,valrates,M1s,dM1dts,M2s,dM2dts = latentvars(model,valloader,device,nlatent=j)\n",
    "        \n",
    "        latent = vallatentvar\n",
    "        print(f'Number of samples: {latent.shape}; Latent dimension: {latent.shape[1]}')\n",
    "        \n",
    "        latent = remove_duplicates(latent)\n",
    "        print(f'Number of samples (duplicates removed): {latent.shape[0]}')\n",
    "        \n",
    "        k_list = np.linspace(10,100,20).astype('int')\n",
    "        \n",
    "        # get the k nearest neighbors (for the Levina-Bickel algorithm calculation)\n",
    "        if np.isscalar(k_list):\n",
    "            k_list = np.array([k_list])\n",
    "        else:\n",
    "            k_list = np.array(k_list)\n",
    "        kmax = np.max(k_list) + 2\n",
    "        n_jobs=4\n",
    "        dists, inds = kNN(latent, kmax,n_jobs)\n",
    "        dims = []\n",
    "        dims_DANCo = []\n",
    "        dims_MiND_MLk = []\n",
    "        dims_MiND_MLi = []\n",
    "        print(\"Levina Bickel\")\n",
    "        for k in k_list:\n",
    "            dims.append(Levina_Bickel(latent, dists, k))\n",
    "\n",
    "        \n",
    "        print(\"MiND MLk\")\n",
    "        for k in k_list:\n",
    "            dims_MiND_MLk.append(MiND_MLk(latent,dists,k))\n",
    "        \n",
    "        print(\"MiND MLi\")\n",
    "        for k in k_list:\n",
    "            dims_MiND_MLi.append(MiND_MLi(latent,dists,k))\n",
    "            \n",
    "        val_ids[i,0] = np.mean(dims)\n",
    "        val_ids_sdev[i,0]=np.std(dims)\n",
    "        \n",
    "        ss = MOM()\n",
    "        val_ids[i,1] = ss.fit_transform(latent) #np.mean(dims_DANCo)\n",
    "        val_ids_sdev[i,1]= 0 #np.std(dims_DANCo)\n",
    "        \n",
    "        val_ids[i,2] = np.mean(dims_MiND_MLk)\n",
    "        val_ids_sdev[i,2]=np.std(dims_MiND_MLk)\n",
    "        \n",
    "        val_ids[i,3] = np.mean(dims_MiND_MLi)\n",
    "        val_ids_sdev[i,3]=np.std(dims_MiND_MLi)\n",
    "        \n",
    "    return val_ids,val_ids_sdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65c3218f-e8dd-4bc7-a07c-0ea16eb20d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples: (26000, 3); Latent dimension: 3\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 4); Latent dimension: 4\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 5); Latent dimension: 5\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 8); Latent dimension: 8\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 10); Latent dimension: 10\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 14); Latent dimension: 14\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 18); Latent dimension: 18\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n",
      "Number of samples: (26000, 20); Latent dimension: 20\n",
      "Number of samples (duplicates removed): 26000\n",
      "Levina Bickel\n",
      "MiND MLk\n",
      "MiND MLi\n"
     ]
    }
   ],
   "source": [
    "val_ids,val_ids_sdev = get_id_estimates(nlat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07bac9e7-908a-4988-91f4-c40bd304d1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(nlat,val_ids[:,1], yerr=val_ids_sdev[:,1], fmt=\"o\",c='r',marker=\"X\",label=\"MOM\")\n",
    "plt.errorbar(nlat,val_ids[:,1], yerr=val_ids_sdev[:,2], fmt=\"o\",c='g',marker=\"v\",label=\"MiND MLk\")\n",
    "plt.errorbar(nlat,val_ids[:,2], yerr=val_ids_sdev[:,3], fmt=\"o\",c='c',marker='s',label=\"MiND MLi\")\n",
    "plt.errorbar(nlat,val_ids[:,0], yerr=val_ids_sdev[:,0], fmt=\"o\",c='b',label=\"Levina Bickel\")\n",
    "plt.ylabel(\"Intrinsic Dimension\",fontsize=18)\n",
    "plt.xlabel(r\"$n_{lat}$\",fontsize=18)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlim((2,21))\n",
    "plt.ylim((0,4))\n",
    "plt.tight_layout()\n",
    "plt.legend(frameon=False,loc=\"lower right\")\n",
    "plt.savefig(\"ID_calc_comparison_updateddata.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f929dd-0e02-4261-ad43-fa1c58e77bc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fbe7171-0d92-4d93-8ca1-c0b5a2523e62",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}