Training BurstCCN on MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29f2aba8",
   "metadata": {},
   "source": [
    "# Training a BurstCCN on MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be475bc0",
   "metadata": {},
   "source": [
    "Import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a63d78a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "import os\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from modules.networks_burstccn import BurstCCN\n",
    "from modules.optimisers import SGDOptimiser\n",
    "from datasets import get_mnist_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de30539",
   "metadata": {},
   "source": [
    "### Model Schematic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb2ed0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"img/burstcnn_schematic.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e075766",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c138c13",
   "metadata": {},
   "source": [
    "Setting up model parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a6d37c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_baseline = 0.5 # Baseline burst probability\n",
    "n_hidden_layers = 3\n",
    "n_hidden_units = 500 # Number of units in each hidden layer\n",
    "\n",
    "Y_learning = False # Whether to learn the feedback Y weights (default=False corresponding to feedback alignment where they are fixed)\n",
    "\n",
    "# Y_mode controls how the Y_weights are initialised\n",
    "# options are 'tied', 'symmetric_init', random_init'\n",
    "# 'random_init': corresponds to the random feedback weights of feedback alignment (default)\n",
    "# 'tied': feedback Y weights are kept symmetric with the feedforward weights throughout training\n",
    "# 'symmetric_init': feedback Y weights are initialised symmetric with feedforward weights but deviate throughout training\n",
    "Y_mode = 'random_init'\n",
    "\n",
    "# Y_scale controls the strength of the Y weights and it's interpretation depends on the Y_mode:\n",
    "# Y_mode='random_init': the scale is the standard deviation of the normal distribution (mean=0) used for initialisation\n",
    "# Y_mode='symmetric_init' or 'tied': is a controls the ratio of feedforward to feedback weights (Y_scale=1 for exact symmetry)\n",
    "Y_scale = 0.5\n",
    "\n",
    "Q_learning = True # Whether to learn the feedback Q weights (default=True such that the Q weights learn to cancel the Y weights)\n",
    "\n",
    "# Similar to Y_mode, Q_mode controls how the Q weights are initialised\n",
    "# Options are the same as before: 'tied', 'symmetric_init', random_init'\n",
    "# 'random_init': corresponds to initially random Q weights that need to align with Y from scratch \n",
    "# 'tied': feedback Q weights are kept symmetric with the feedback Y weights (with a -p_baseline constant ratio i.e. Q = -p_baselineY) such that the two pathways cancel in the absense of an error signal\n",
    "# 'symmetric_init': Q weights are initialised symmetric with the feedback Y weights (as for the 'tied' case) but deviate through training (default)\n",
    "Q_mode = 'symmetric_init'\n",
    "\n",
    "# Q_scale controls the strength of the Q weights and it's interpretation depends on the Q_mode:\n",
    "# Q_mode='random_init': the scale is the standard deviation of the normal distribution (mean=0) used for initialisation\n",
    "# Q_mode='symmetric_init' or 'tied': is a controls the ratio oY to Q weights (recommended value of Q_scale=1.0 in this case)\n",
    "Q_scale = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0d753b",
   "metadata": {},
   "source": [
    "Setting up training parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b7bdb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "working_directory = os.getcwd()\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "n_epochs = 50\n",
    "batch_size = 32\n",
    "\n",
    "\n",
    "lr = [0.3] * (n_hidden_layers + 1)\n",
    "lr_Y = [0.0] * n_hidden_layers + [None]\n",
    "lr_Q = [0.000731] * n_hidden_layers + [None]\n",
    "momentum = 0.0\n",
    "weight_decay = 0.0\n",
    "\n",
    "\n",
    "data_dir = os.path.join(working_directory, './Data')\n",
    "\n",
    "train_data_loader, _, test_data_loader = get_mnist_dataset(data_dir, train_batch_size=batch_size, use_validation=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f79b2b",
   "metadata": {},
   "source": [
    "Create the model and optimiser:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4ae6e2ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BurstCCN(n_inputs=784,\n",
    "                n_outputs=10,\n",
    "                 p_baseline=p_baseline,\n",
    "                 n_hidden_layers=n_hidden_layers,\n",
    "                 n_hidden_units=n_hidden_units,\n",
    "                 Y_learning=Y_learning,\n",
    "                 Y_mode=Y_mode,\n",
    "                 Y_scale=Y_scale,\n",
    "                 Q_learning=Q_learning,\n",
    "                 Q_mode=Q_mode,\n",
    "                 Q_scale=Q_scale,\n",
    "                 device=device).to(device)\n",
    "\n",
    "optimiser = SGDOptimiser()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d646966",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f879d937",
   "metadata": {},
   "source": [
    "Define the training and testing functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "403ec056",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(data_loader):\n",
    "    model.train()\n",
    "    \n",
    "    train_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    progress_bar = tqdm(data_loader)\n",
    "    for batch_index, (inputs, targets) in enumerate(progress_bar):\n",
    "        inputs, targets = inputs.to(model.device), targets.to(model.device)\n",
    "        t = F.one_hot(targets, num_classes=10).float()\n",
    "\n",
    "        outputs = model(inputs)\n",
    "\n",
    "        loss = model.loss(outputs, t)        \n",
    "        \n",
    "        model.backward(t)\n",
    "        \n",
    "        model.update_weights(lrs=lr, lrs_Y=lr_Y, lrs_Q=lr_Q, weight_decay=weight_decay, optimiser=optimiser)\n",
    "        \n",
    "        train_loss += loss\n",
    "        _, predicted = outputs.max(1)\n",
    "        total += targets.size(0)\n",
    "        correct += predicted.eq(targets).sum().item()\n",
    "\n",
    "        progress_bar.set_description(\"Train Loss: {:.3f} | Acc: {:.3f}% ({:d}/{:d})\".format(train_loss / (batch_index + 1), 100 * correct / total, correct, total))\n",
    "\n",
    "    return 100.0 * (1.0 - correct / total), train_loss / (batch_index + 1)\n",
    "    \n",
    "def test(data_loader):\n",
    "    model.eval()\n",
    "\n",
    "    test_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        progress_bar = tqdm(data_loader)\n",
    "        for batch_idx, (inputs, targets) in enumerate(progress_bar):\n",
    "            inputs, targets = inputs.to(model.device), targets.to(model.device)\n",
    "\n",
    "            t = F.one_hot(targets, num_classes=10).float()\n",
    "\n",
    "            outputs = model(inputs)\n",
    "\n",
    "            loss = model.loss(outputs, t)\n",
    "\n",
    "            test_loss += loss.item()\n",
    "\n",
    "            _, predicted = outputs.max(1)\n",
    "            total += targets.size(0)\n",
    "            correct += predicted.eq(targets).sum().item()\n",
    "\n",
    "            progress_bar.set_description(\"Test Loss: {:.3f} | Acc: {:.3f}% ({:d}/{:d})\".format(test_loss / (batch_idx + 1), 100 * correct / total, correct, total))\n",
    "\n",
    "    return 100 * (1.0 - correct / total), test_loss / (batch_idx + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f840a73",
   "metadata": {},
   "source": [
    "Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d888e2f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.036 | Acc: 76.433% (45860/60000): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1875/1875 [00:11<00:00, 156.28it/s]\n",
      "Test Loss: 0.016 | Acc: 92.410% (9241/10000): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 11.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 23.56666666666667, Test: 7.589999999999996\n",
      "\n",
      "Epoch 2.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.015 | Acc: 92.740% (55644/60000): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1875/1875 [00:12<00:00, 153.60it/s]\n",
      "Test Loss: 0.012 | Acc: 94.130% (9413/10000): 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 12.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 7.26, Test: 5.869999999999997\n",
      "\n",
      "Epoch 3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.012 | Acc: 94.403% (45223/47904):  80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏                                             | 1497/1875 [00:09<00:02, 151.71it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [27]\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, n_epochs \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\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;130;01m\\n\u001b[39;00m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 3\u001b[0m     train_error, train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data_loader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m     test_error, test_loss \u001b[38;5;241m=\u001b[39m test(test_data_loader)\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrain: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrain_error\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, Test: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_error\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Input \u001b[1;32mIn [26]\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(data_loader)\u001b[0m\n\u001b[0;32m      6\u001b[0m total \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m      8\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m tqdm(data_loader)\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch_index, (inputs, targets) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(progress_bar):\n\u001b[0;32m     10\u001b[0m     inputs, targets \u001b[38;5;241m=\u001b[39m inputs\u001b[38;5;241m.\u001b[39mto(model\u001b[38;5;241m.\u001b[39mdevice), targets\u001b[38;5;241m.\u001b[39mto(model\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m     11\u001b[0m     t \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mone_hot(targets, num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\u001b[38;5;241m.\u001b[39mfloat()\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\tqdm\\std.py:1185\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1182\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[0;32m   1184\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1185\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[0;32m   1186\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[0;32m   1187\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[0;32m   1188\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:521\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    520\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[1;32m--> 521\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    522\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    524\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    525\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:561\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    559\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    560\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 561\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    562\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    563\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:44\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m     43\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[1;32m---> 44\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     46\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:44\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m     43\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[1;32m---> 44\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     46\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torchvision\\datasets\\mnist.py:134\u001b[0m, in \u001b[0;36mMNIST.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    131\u001b[0m img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img\u001b[38;5;241m.\u001b[39mnumpy(), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 134\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    137\u001b[0m     target \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtarget_transform(target)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torchvision\\transforms\\transforms.py:60\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     59\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 60\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torchvision\\transforms\\transforms.py:97\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pic):\n\u001b[0;32m     90\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;124;03m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;124;03m        Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m     96\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 97\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpic\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\burstmodels\\lib\\site-packages\\torchvision\\transforms\\functional.py:138\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m    136\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[0;32m    137\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img, torch\u001b[38;5;241m.\u001b[39mByteTensor):\n\u001b[1;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_float_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdiv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m255\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    139\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    140\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(1, n_epochs + 1):\n",
    "    print(f\"\\nEpoch {epoch}.\")\n",
    "    train_error, train_loss = train(train_data_loader)\n",
    "    test_error, test_loss = test(test_data_loader)\n",
    "    \n",
    "    print(f\"Train: {train_error}, Test: {test_error}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24a6cfff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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