analyze_trained_en.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f5f1abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import json\n",
    "import os\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import scipy.io\n",
    "\n",
    "from experiment import lesion\n",
    "from experiment import mRNN\n",
    "from experiment import michaels_load\n",
    "from experiment import config, experiment, utils\n",
    "import cpn_model\n",
    "import stim_model\n",
    "\n",
    "#CUDA = torch.device(0)\n",
    "CUDA = None\n",
    "\n",
    "MODEL_PATHS = \"models/gaussian20.1.75_connectionsIdxs[(1, 2, 0, 1), (0, 1, 1, 2), (2, 3, 0, 1), (0, 1, 2, 3)]_gaussianExp16.2.175_enActTanh_cpnActTanh/c7583779-4622-40a6-a259-59f2e8c0cfd9\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4a38297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_path = os.path.join(MODEL_PATHS, \"log\")\n",
    "\n",
    "log = json.load(open(log_path, \"r\"))\n",
    "\n",
    "pred_losses = []\n",
    "pred_val_losses = []\n",
    "cur_type = \"EN\"\n",
    "cur = []\n",
    "cur_val = []\n",
    "for r in log:\n",
    "    next_type = r['user']['epoch_type']\n",
    "    \n",
    "    if next_type == \"EN\" and cur_type == \"CPN\":\n",
    "        pred_losses.append(cur)\n",
    "        cur = []\n",
    "        \n",
    "        pred_val_losses.append(cur_val)\n",
    "        cur_val = []\n",
    "    if next_type == \"CPN\":\n",
    "        cur.append(float(r['user']['pred_loss']))\n",
    "        cur_val.append(float(r['user']['pred_val_loss']))\n",
    "\n",
    "    cur_type = next_type\n",
    "\n",
    "for t in pred_losses:\n",
    "    #cur = []\n",
    "    #ll = len(t)\n",
    "    #for sidx in range(ll):\n",
    "    #    curx = sidx / ll\n",
    "        \n",
    "    plt.plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78315f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Michaels session data\n",
    "dataset = mRNN.MichaelsDataset(michaels_load.get_default_path(),\n",
    "    cuda=CUDA, with_label=True)\n",
    "\n",
    "# Dataset items are:\n",
    "#   (michaels network input, trial end indicator, trial len (scalar),\n",
    "#    target output, class label)\n",
    "\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=len(dataset))\n",
    "batch = next(iter(dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f36abf4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH_MRNN = os.path.join(MODEL_PATHS, \"mike.model\")\n",
    "PATH_CPN = os.path.join(MODEL_PATHS, \"cpn.model\")\n",
    "PATH_EN = os.path.join(MODEL_PATHS, \"en.model\")\n",
    "\n",
    "cfg = experiment.get_vanilla_config(coadapt=False, cuda=CUDA)\n",
    "in_dim, stim_dim, out_dim, _ = cfg.unpack()\n",
    "observer = cfg.observer_instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3263374",
   "metadata": {},
   "outputs": [],
   "source": [
    "mike = mRNN.MichaelsRNN(init_data_path=michaels_load.get_default_path(),\n",
    "                        stimulus=cfg.stim_instance,\n",
    "                        cuda=CUDA)\n",
    "\n",
    "mike.load_state_dict(torch.load(PATH_MRNN, map_location=torch.device('cpu')))\n",
    "\n",
    "mike.set_lesion(cfg.lesion_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c3cdecad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_in_dim = cfg.observer_instance.out_dim + stim_dim + 1\n",
    "\n",
    "en, _ = stim_model.get_stim_model(\n",
    "  en_in_dim, out_dim, activation=cfg.en_activation, cuda=CUDA\n",
    ")\n",
    "\n",
    "en.load_state_dict(torch.load(PATH_EN, map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a9c32c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpn = cpn_model.CPNModelLSTM(                                    \n",
    "            in_dim,                                            \n",
    "            stim_dim,\n",
    "            num_neurons=in_dim,                     \n",
    "            activation_func=cfg.cpn_activation,\n",
    "            cuda=CUDA)\n",
    "cpn.load_state_dict(torch.load(PATH_CPN, map_location=torch.device('cpu')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38af7b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run experiment forward, and get EN results too.\n",
    "\n",
    "din, trial_end, _, dout, labels = batch\n",
    "steps = din.shape[1]\n",
    "\n",
    "actuals = []\n",
    "preds = []\n",
    "\n",
    "mike_out = mike(din[:, 0, :].T)\n",
    "\n",
    "for tidx in range(steps - 1):\n",
    "    obs_raw = mike.observe(observer)\n",
    "    obs = obs_raw + (trial_end[:, tidx, :],)\n",
    "    cpn_in = torch.cat(obs, axis=1)\n",
    "\n",
    "    stim = cpn(cpn_in)\n",
    "\n",
    "    # new_stim will be cloned in here, to prevent accidentally\n",
    "    # backprop-ing through the \"brain\", aka mike.\n",
    "    mike.stimulate(stim)\n",
    "\n",
    "    mike_out = mike(din[:, tidx + 1, :].T)\n",
    "    actuals.append(mike_out.unsqueeze(dim=1))\n",
    "    \n",
    "    # Now let's get the EN output too.\n",
    "    en_in = torch.cat((obs[0], stim, obs[-1]), axis=1)\n",
    "    pred = en(en_in)\n",
    "    preds.append(pred.unsqueeze(dim=1))\n",
    "    \n",
    "actuals = torch.cat(actuals, axis=1)\n",
    "actuals = utils.trunc_to_trial_end(actuals, trial_end[:, :-1, :])\n",
    "\n",
    "preds = torch.cat(preds, axis=1)\n",
    "preds = utils.trunc_to_trial_end(preds, trial_end[:, :-1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1ef4ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9fb00f55a0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cidx = random.randrange(preds.shape[-1])\n",
    "ridx = random.randrange(preds.shape[0])\n",
    "\n",
    "plt.plot(actuals[ridx, :, cidx].detach().numpy())\n",
    "plt.plot(preds[ridx, :, cidx].detach().numpy())\n",
    "plt.plot(dout[ridx, 1:, cidx].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f426c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196 8.411065937252715e-05 417 0.00034606328699737787 0 1\n",
      "std: 1.91e-05\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cidx = 7  # TMAJ_l from the graphs earlier; a hand muscle\n",
    "\n",
    "pred_losses = []\n",
    "\n",
    "max_loss = -11111\n",
    "min_loss = 1111111\n",
    "ridx_min = None\n",
    "ridx_max = None\n",
    "for ridx in range(preds.shape[0]):\n",
    "    cur_loss = torch.nn.MSELoss()(preds[ridx, :, cidx], actuals[ridx, :, cidx]).detach().numpy().item()\n",
    "    pred_losses.append(cur_loss)\n",
    "    \n",
    "    if cur_loss > max_loss:\n",
    "        max_loss = cur_loss\n",
    "        ridx_max = ridx\n",
    "    if cur_loss < min_loss:\n",
    "        min_loss = cur_loss\n",
    "        ridx_min = ridx\n",
    "        \n",
    "ridx_other1 = None\n",
    "other1_loss = None\n",
    "ridx_other2 = None\n",
    "other2_loss = None\n",
    "losses = torch.zeros(preds.shape[0])\n",
    "for ridx in range(preds.shape[0]):\n",
    "    if ridx != ridx_min and ridx != ridx_max:\n",
    "        cur_loss = torch.nn.MSELoss()(preds[ridx, :, cidx], actuals[ridx, :, cidx]).detach().numpy().item()\n",
    "        losses[ridx] = cur_loss\n",
    "        if ridx_other1 is None:\n",
    "            ridx_other1 = ridx\n",
    "            other1_loss = cur_loss\n",
    "        else:\n",
    "            ridx_other2 = ridx\n",
    "            other2_loss = cur_loss\n",
    "            break\n",
    "            \n",
    "if other1_loss > other2_loss:\n",
    "    blahR, blahL = ridx_other1, other1_loss\n",
    "    ridx_other1, other1_loss = ridx_other2, other2_loss\n",
    "    ridx_other2, other2_loss = blahR, blahL\n",
    "\n",
    "print(ridx_min, min_loss, ridx_max, max_loss, ridx_other1, ridx_other2)\n",
    "\n",
    "tmaj_std = torch.std(losses).item()\n",
    "print(f\"std: {tmaj_std:.2e}\")\n",
    "\n",
    "plt.hist(pred_losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "488dc007",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "03-21 15:16:44 The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cidx = 7  # TMAJ_l from the graphs earlier; a hand muscle\n",
    "linewidth = 0.9\n",
    "tx = 0.1\n",
    "ty = 0.88\n",
    "\n",
    "fig,axs = plt.subplots(2,2, figsize=(7, 7))\n",
    "\n",
    "axs[0,0].plot(actuals[ridx_min, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:orange', label=\"Actual trajectory\")\n",
    "axs[0,0].plot(preds[ridx_min, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:blue', label=\"Predicted trajectory\")\n",
    "#axs[0,0].plot(dout[ridx_min, 1:, cidx].detach().numpy(), linewidth=linewidth, color='tab:purple', label=\"Target trajectory\")\n",
    "axs[0,0].set_title(f\"a. Loss: {min_loss:.2e}\\n(minimum prediction error)\")\n",
    "axs[0,0].set_ylim(-0.1, 1.0)\n",
    "#axs[0,0].text(tx, ty, \"a.\", fontsize='x-large', fontfamily='serif')\n",
    "\n",
    "axs[0,1].plot(actuals[ridx_other1, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:orange', label=\"Actual trajectory\")\n",
    "axs[0,1].plot(preds[ridx_other1, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:blue', label=\"Predicted trajectory\")\n",
    "#axs[0,1].plot(dout[ridx_other1, 1:, cidx].detach().numpy(), linewidth=linewidth, color='tab:purple', label=\"Target trajectory\")\n",
    "axs[0,1].set_title(f\"b. Loss: {other1_loss:.2e}\")\n",
    "axs[0,1].set_ylim(-0.1, 1.0)\n",
    "#axs[0,1].text(tx, ty, \"b.\", fontsize='x-large', fontfamily='serif')\n",
    "\n",
    "axs[1,0].plot(actuals[ridx_other2, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:orange', label=\"Actual trajectory\")\n",
    "axs[1,0].plot(preds[ridx_other2, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:blue', label=\"Predicted trajectory\")\n",
    "#axs[1,0].plot(dout[ridx_other2, 1:, cidx].detach().numpy(), linewidth=linewidth, color='tab:purple', label=\"Target trajectory\")\n",
    "axs[1,0].set_title(f\"c. Loss: {other2_loss:.2e}\")\n",
    "axs[1,0].set_ylim(-0.1, 1.0)\n",
    "#axs[1,0].text(tx, ty, \"b.\", fontsize='x-large', fontfamily='serif')\n",
    "\n",
    "axs[1,1].plot(actuals[ridx_max, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:orange', label=\"Actual trajectory\")\n",
    "axs[1,1].plot(preds[ridx_max, :, cidx].detach().numpy(), linewidth=linewidth, color='tab:blue', label=\"Predicted trajectory\")\n",
    "#axs[1,1].plot(dout[ridx_max, 1:, cidx].detach().numpy(), linewidth=linewidth, color='tab:purple', label=\"Target trajectory\")\n",
    "axs[1,1].set_title(f\"d. Loss: {max_loss:.2e}\\n(maximum prediction error)\")\n",
    "axs[1,1].set_ylim(-0.1, 1)\n",
    "#axs[1,1].text(tx, ty, \"b.\", fontsize='x-large', fontfamily='serif')\n",
    "\n",
    "\n",
    "orange_patch = mpatches.Patch(color='tab:orange', label='Actual trajectory')\n",
    "blue_patch = mpatches.Patch(color='tab:blue', label='Predicted trajectory')\n",
    "#purple_patch = mpatches.Patch(color='tab:purple', label='Target trajectory')\n",
    "    \n",
    "    \n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(bottom=0.1)\n",
    "plt.legend(handles=[orange_patch, blue_patch], loc=\"lower center\", ncol=3,\n",
    "            bbox_to_anchor=(-0.1, -0.3))\n",
    "\n",
    "plt.savefig('paper/figs/pred_loss_min_max.eps', format='eps', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "defaddd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0001712009689072147 4.5191314711701125e-05 0.2639664658451398\n",
      "1 0.00027507072081789374 0.00012137940939282998 0.4412661915885527\n",
      "2 0.00027187596424482763 6.354276411002502e-05 0.23371968274769586\n",
      "3 0.000700824661180377 0.00023860133660491556 0.34045796305604714\n",
      "4 0.0004486797843128443 0.00011002044630004093 0.24520927874773296\n",
      "5 0.0005623489851132035 0.00013825151836499572 0.2458464797214225\n",
      "6 0.00028355230460874736 7.657078094780445e-05 0.27004111658855584\n",
      "7 0.00020341073104646057 6.607479008380324e-05 0.32483433761767094\n",
      "8 0.00027872639475390315 9.067688370123506e-05 0.32532578689326036\n",
      "9 0.0002628886140882969 9.610909910406917e-05 0.36558867122251565\n",
      "10 0.00039352080784738064 9.597298048902303e-05 0.24388286102076787\n",
      "11 0.00027996490825898945 8.429156878264621e-05 0.30107905060969253\n",
      "12 0.00018224101222585887 5.1657094445545226e-05 0.28345482619205625\n",
      "13 0.00020400018547661602 4.5145017793402076e-05 0.22129890562563692\n",
      "14 0.00027436017990112305 6.617533654207364e-05 0.24119876494439768\n",
      "15 0.0003982381313107908 0.00013369607040658593 0.3357189075956355\n",
      "16 0.0002369473222643137 7.138119690353051e-05 0.30125344410478333\n",
      "17 0.0002586949849501252 6.89325388520956e-05 0.26646260214663753\n",
      "18 0.00025902173365466297 6.776226655347273e-05 0.26160842025640907\n",
      "19 9.56072035478428e-05 8.39505300973542e-05 0.8780774563220491\n",
      "20 0.0003358207759447396 0.0001892543223220855 0.563557516028216\n",
      "21 0.0003282231919001788 7.322737656068057e-05 0.22310238388928627\n",
      "22 0.0002943906874861568 8.388466812903062e-05 0.2849433480567388\n",
      "23 0.00036551826633512974 8.724578947294503e-05 0.23869064150395347\n",
      "24 0.0001696848194114864 4.199911199975759e-05 0.2475124890100485\n",
      "25 0.0001678064145380631 7.918723713373765e-05 0.47189636553360603\n",
      "26 0.00025469984393566847 5.3081148507772014e-05 0.20840667857330583\n",
      "27 0.00020267597574274987 6.520293391076848e-05 0.3217102257523036\n",
      "28 0.0002530124329496175 5.942374627920799e-05 0.234864925752645\n",
      "29 0.00016435507859569043 4.9499205488245934e-05 0.30117235141855764\n",
      "30 0.00010521488729864359 8.79128638189286e-05 0.8355553674585551\n",
      "31 7.800752791808918e-05 7.099554204614833e-05 0.9101114205374679\n",
      "32 0.00034432069514878094 7.153605838539079e-05 0.20775997316826997\n",
      "33 0.0003626885882113129 7.003123027971014e-05 0.1930891474283385\n",
      "34 0.00015980283205863088 9.761297405930236e-05 0.6108338181609236\n",
      "35 0.00030114632681943476 0.00012072228855686262 0.40087584607746807\n",
      "36 0.0002242153132101521 0.0001181100815301761 0.526770807217231\n",
      "37 0.00032427278347313404 6.911651144037023e-05 0.21314311580545126\n",
      "38 0.00020576643873937428 6.687379209324718e-05 0.32499853962069175\n",
      "39 0.00013600422244053334 4.413294664118439e-05 0.3244968858263289\n",
      "40 0.00019512024300638586 7.841378828743473e-05 0.4018741832177219\n",
      "41 0.0001647812605369836 5.5217184126377106e-05 0.3350938325537577\n",
      "42 0.0001581568649271503 6.121525802882388e-05 0.38705406848460644\n",
      "43 0.00022830654052086174 6.824643787695095e-05 0.2989245849954739\n",
      "44 0.0001646791642997414 5.0448652473278344e-05 0.3063450843207708\n",
      "45 0.00021765090059489012 8.429302397416905e-05 0.38728543619060046\n",
      "46 0.0005154125392436981 0.00014583610754925758 0.28295025139134833\n",
      "47 0.0015124765923246741 0.0001421070337528363 0.0939565177233044\n",
      "48 0.00026747435913421214 6.755453068763018e-05 0.2525645108798371\n",
      "49 0.0003679646470118314 8.908873132895678e-05 0.24211220304023462\n"
     ]
    }
   ],
   "source": [
    "# One for each channel\n",
    "pred_mse = []\n",
    "pred_stdev = []\n",
    "pred_cv = []\n",
    "\n",
    "for cidx in range(preds.shape[-1]):\n",
    "    cur_loss = torch.nn.MSELoss()(preds[:, :, cidx], actuals[:, :, cidx]).detach().numpy().item()\n",
    "    pred_mse.append(cur_loss)\n",
    "    \n",
    "    cls = torch.zeros(preds.shape[0])\n",
    "    for ridx in range(preds.shape[0]):\n",
    "        cur_loss = torch.nn.MSELoss()(preds[ridx, :, cidx], actuals[ridx, :, cidx])\n",
    "        cls[ridx] = cur_loss\n",
    "        \n",
    "    pred_stdev.append(torch.std(cls).item())\n",
    "    pred_cv.append(pred_stdev[-1] / pred_mse[-1])\n",
    "    \n",
    "    print(cidx, pred_mse[-1], pred_stdev[-1], pred_cv[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cae00534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525x525 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.340438874649274\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525x525 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3.5, 3.5), dpi=150)\n",
    "plt.hist(pred_cv)\n",
    "plt.xlabel(\"Coefficient of Variation (CV)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.savefig('paper/figs/pred_loss_cv_hist.eps', format='eps', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(sum(pred_cv) / len(pred_cv))\n",
    "\n",
    "plt.figure(figsize=(3.5, 3.5), dpi=150)\n",
    "plt.scatter(pred_mse, pred_cv)\n",
    "plt.xlabel(\"MSE Loss\")\n",
    "plt.ylabel(\"Coefficient of Variation (CV)\")\n",
    "plt.savefig('paper/figs/pred_loss_cv_v_mse.eps', format='eps', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d817bc9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000292216514935717 0.0009513962431810796\n"
     ]
    }
   ],
   "source": [
    "losses = torch.zeros(preds.shape[0] * preds.shape[1] * preds.shape[2])\n",
    "\n",
    "mle = torch.nn.MSELoss()\n",
    "mse = mle(preds, actuals).detach().numpy().item()\n",
    "\n",
    "idx = 0\n",
    "for ridx in range(preds.shape[0]):\n",
    " for tidx in range(preds.shape[1]):\n",
    "  for cidx in range(preds.shape[2]):\n",
    "    cur_loss = mle(preds[ridx, tidx, cidx], actuals[ridx, tidx, cidx]).detach().numpy().item()\n",
    "    losses[idx] = cur_loss\n",
    "    idx += 1\n",
    "    \n",
    "print(mse, torch.std(losses).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc0374a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = torch.zeros(preds.shape[0] * preds.shape[1] * preds.shape[2])\n",
    "\n",
    "mle = torch.nn.MSELoss()\n",
    "mse = mle(preds, actuals).detach().numpy().item()\n",
    "\n",
    "idx = 0\n",
    "for ridx in range(preds.shape[0]):\n",
    " for tidx in range(preds.shape[1]):\n",
    "  for cidx in range(preds.shape[2]):\n",
    "    cur_loss = mle(preds[ridx, tidx, cidx], actuals[ridx, tidx, cidx]).detach().numpy().item()\n",
    "    losses[idx] = cur_loss\n",
    "    idx += 1\n",
    "    \n",
    "print(mse, torch.std(losses).item())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}