src/main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from finetune import Resnet18Finetuner, Resnet18FintunerForCaseD\n",
    "from dataloader import load_augmented_data\n",
    "import matplotlib.pyplot as plt\n",
    "from trainer import Trainer\n",
    "from torch import optim, nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "finetuner_base = Resnet18Finetuner(pretrained=True, feature_extract=True)\n",
    "finetuner_base.add_final_layers()\n",
    "\n",
    "finetuner_A = Resnet18Finetuner(pretrained=True, feature_extract=True)\n",
    "finetuner_A.add_final_layers()\n",
    "\n",
    "finetuner_B = Resnet18Finetuner(pretrained=True, feature_extract=False)  # make all conv blocks trainable\n",
    "finetuner_B.add_final_layers()\n",
    "\n",
    "finetuner_C = Resnet18FintunerForCaseD(pretrained=True, feature_extract=True)\n",
    "finetuner_C.add_final_layers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "Sequential(\n  (0): BasicBlock(\n    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    (relu): ReLU(inplace=True)\n    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  )\n  (1): BasicBlock(\n    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    (relu): ReLU(inplace=True)\n    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  )\n)"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finetuner_base.model.layer1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = \"../face_dataset\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Set hyperparameters and share with all cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "input_size = 64\n",
    "batch_size = 256\n",
    "num_epochs = 50\n",
    "lr = 0.002\n",
    "num_workers = 4  # how many subprocesses to use for data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Datasets and Dataloaders...\n"
     ]
    },
    {
     "data": {
      "text/plain": "{'train': <torch.utils.data.dataloader.DataLoader at 0x1951d1c1b20>,\n 'test': <torch.utils.data.dataloader.DataLoader at 0x1951d1c1c40>}"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataloaders_dict = load_augmented_data(input_size, batch_size, data_dir, num_workers)\n",
    "dataloaders_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "device(type='cuda', index=0)"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect if we have a GPU available\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Case A: Re-train the softmax layer (Baseline model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "When finetuning all resnet layers, feature_extract = False, the model is finetuned and all model parameters are updated.  \n",
    "If feature_extract = True, only the last layer parameters are updated, the others remain fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n  (batch_norm): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (classifier): Softmax(dim=None)\n)"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finetuner_base.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t fc.weight\n",
      "\t fc.bias\n",
      "\t batch_norm.weight\n",
      "\t batch_norm.bias\n",
      "Epoch 0/49\n",
      "----------\n",
      "train Loss: 4.0530 Acc: 0.1150\n",
      "test Loss: 3.8707 Acc: 0.1570\n",
      "\n",
      "Epoch 1/49\n",
      "----------\n",
      "train Loss: 3.9766 Acc: 0.1262\n",
      "test Loss: 3.8448 Acc: 0.1350\n",
      "\n",
      "Epoch 2/49\n",
      "----------\n",
      "train Loss: 3.9472 Acc: 0.1250\n",
      "test Loss: 3.8411 Acc: 0.1380\n",
      "\n",
      "Epoch 3/49\n",
      "----------\n",
      "train Loss: 3.9122 Acc: 0.1393\n",
      "test Loss: 3.7976 Acc: 0.1520\n",
      "\n",
      "Epoch 4/49\n",
      "----------\n",
      "train Loss: 3.9118 Acc: 0.1438\n",
      "test Loss: 3.7572 Acc: 0.1560\n",
      "\n",
      "Epoch 5/49\n",
      "----------\n",
      "train Loss: 3.8833 Acc: 0.1438\n",
      "test Loss: 3.7336 Acc: 0.1460\n",
      "\n",
      "Epoch 6/49\n",
      "----------\n",
      "train Loss: 3.8658 Acc: 0.1523\n",
      "test Loss: 3.7240 Acc: 0.1660\n",
      "\n",
      "Epoch 7/49\n",
      "----------\n",
      "train Loss: 3.8507 Acc: 0.1483\n",
      "test Loss: 3.7043 Acc: 0.1890\n",
      "\n",
      "Epoch 8/49\n",
      "----------\n",
      "train Loss: 3.8342 Acc: 0.1570\n",
      "test Loss: 3.6849 Acc: 0.1910\n",
      "\n",
      "Epoch 9/49\n",
      "----------\n",
      "train Loss: 3.8368 Acc: 0.1550\n",
      "test Loss: 3.6772 Acc: 0.1920\n",
      "\n",
      "Epoch 10/49\n",
      "----------\n",
      "train Loss: 3.8430 Acc: 0.1500\n",
      "test Loss: 3.7652 Acc: 0.1480\n",
      "\n",
      "Epoch 11/49\n",
      "----------\n",
      "train Loss: 3.8594 Acc: 0.1453\n",
      "test Loss: 3.6994 Acc: 0.1730\n",
      "\n",
      "Epoch 12/49\n",
      "----------\n",
      "train Loss: 3.8142 Acc: 0.1505\n",
      "test Loss: 3.7132 Acc: 0.1750\n",
      "\n",
      "Epoch 13/49\n",
      "----------\n",
      "train Loss: 3.8222 Acc: 0.1473\n",
      "test Loss: 3.7126 Acc: 0.1510\n",
      "\n",
      "Epoch 14/49\n",
      "----------\n",
      "train Loss: 3.8342 Acc: 0.1468\n",
      "test Loss: 3.6497 Acc: 0.1850\n",
      "\n",
      "Epoch 15/49\n",
      "----------\n",
      "train Loss: 3.7619 Acc: 0.1723\n",
      "test Loss: 3.6323 Acc: 0.1750\n",
      "\n",
      "Epoch 16/49\n",
      "----------\n",
      "train Loss: 3.7912 Acc: 0.1585\n",
      "test Loss: 3.6341 Acc: 0.1860\n",
      "\n",
      "Epoch 17/49\n",
      "----------\n",
      "train Loss: 3.7426 Acc: 0.1690\n",
      "test Loss: 3.5966 Acc: 0.1900\n",
      "\n",
      "Epoch 18/49\n",
      "----------\n",
      "train Loss: 3.7385 Acc: 0.1738\n",
      "test Loss: 3.5848 Acc: 0.1900\n",
      "\n",
      "Epoch 19/49\n",
      "----------\n",
      "train Loss: 3.7232 Acc: 0.1748\n",
      "test Loss: 3.5704 Acc: 0.1960\n",
      "\n",
      "Epoch 20/49\n",
      "----------\n",
      "train Loss: 3.7336 Acc: 0.1778\n",
      "test Loss: 3.6358 Acc: 0.1870\n",
      "\n",
      "Epoch 21/49\n",
      "----------\n",
      "train Loss: 3.8047 Acc: 0.1475\n",
      "test Loss: 3.6210 Acc: 0.1800\n",
      "\n",
      "Epoch 22/49\n",
      "----------\n",
      "train Loss: 3.7322 Acc: 0.1755\n",
      "test Loss: 3.6518 Acc: 0.1760\n",
      "\n",
      "Epoch 23/49\n",
      "----------\n",
      "train Loss: 3.7367 Acc: 0.1775\n",
      "test Loss: 3.5715 Acc: 0.1950\n",
      "\n",
      "Epoch 24/49\n",
      "----------\n",
      "train Loss: 3.7180 Acc: 0.1778\n",
      "test Loss: 3.5686 Acc: 0.1920\n",
      "\n",
      "Epoch 25/49\n",
      "----------\n",
      "train Loss: 3.7023 Acc: 0.1848\n",
      "test Loss: 3.5598 Acc: 0.2020\n",
      "\n",
      "Epoch 26/49\n",
      "----------\n",
      "train Loss: 3.6968 Acc: 0.1775\n",
      "test Loss: 3.5500 Acc: 0.1930\n",
      "\n",
      "Epoch 27/49\n",
      "----------\n",
      "train Loss: 3.6593 Acc: 0.1843\n",
      "test Loss: 3.5003 Acc: 0.2130\n",
      "\n",
      "Epoch 28/49\n",
      "----------\n",
      "train Loss: 3.6593 Acc: 0.1825\n",
      "test Loss: 3.5044 Acc: 0.2050\n",
      "\n",
      "Epoch 29/49\n",
      "----------\n",
      "train Loss: 3.6734 Acc: 0.1885\n",
      "test Loss: 3.4941 Acc: 0.2100\n",
      "\n",
      "Epoch 30/49\n",
      "----------\n",
      "train Loss: 3.7082 Acc: 0.1740\n",
      "test Loss: 3.5564 Acc: 0.1960\n",
      "\n",
      "Epoch 31/49\n",
      "----------\n",
      "train Loss: 3.7268 Acc: 0.1680\n",
      "test Loss: 3.5667 Acc: 0.1730\n",
      "\n",
      "Epoch 32/49\n",
      "----------\n",
      "train Loss: 3.6911 Acc: 0.1853\n",
      "test Loss: 3.5749 Acc: 0.1940\n",
      "\n",
      "Epoch 33/49\n",
      "----------\n",
      "train Loss: 3.6868 Acc: 0.1848\n",
      "test Loss: 3.5564 Acc: 0.1900\n",
      "\n",
      "Epoch 34/49\n",
      "----------\n",
      "train Loss: 3.6790 Acc: 0.1858\n",
      "test Loss: 3.4805 Acc: 0.2220\n",
      "\n",
      "Epoch 35/49\n",
      "----------\n",
      "train Loss: 3.6414 Acc: 0.1815\n",
      "test Loss: 3.4912 Acc: 0.2030\n",
      "\n",
      "Epoch 36/49\n",
      "----------\n",
      "train Loss: 3.6707 Acc: 0.1870\n",
      "test Loss: 3.5044 Acc: 0.1950\n",
      "\n",
      "Epoch 37/49\n",
      "----------\n",
      "train Loss: 3.6445 Acc: 0.1920\n",
      "test Loss: 3.4682 Acc: 0.2220\n",
      "\n",
      "Epoch 38/49\n",
      "----------\n",
      "train Loss: 3.6162 Acc: 0.1930\n",
      "test Loss: 3.4555 Acc: 0.2230\n",
      "\n",
      "Epoch 39/49\n",
      "----------\n",
      "train Loss: 3.6011 Acc: 0.1940\n",
      "test Loss: 3.4583 Acc: 0.2060\n",
      "\n",
      "Epoch 40/49\n",
      "----------\n",
      "train Loss: 3.6328 Acc: 0.1885\n",
      "test Loss: 3.5212 Acc: 0.2020\n",
      "\n",
      "Epoch 41/49\n",
      "----------\n",
      "train Loss: 3.6559 Acc: 0.1815\n",
      "test Loss: 3.4973 Acc: 0.2150\n",
      "\n",
      "Epoch 42/49\n",
      "----------\n",
      "train Loss: 3.6615 Acc: 0.1803\n",
      "test Loss: 3.5301 Acc: 0.1840\n",
      "\n",
      "Epoch 43/49\n",
      "----------\n",
      "train Loss: 3.6339 Acc: 0.1915\n",
      "test Loss: 3.4505 Acc: 0.2110\n",
      "\n",
      "Epoch 44/49\n",
      "----------\n",
      "train Loss: 3.6381 Acc: 0.1948\n",
      "test Loss: 3.5191 Acc: 0.2040\n",
      "\n",
      "Epoch 45/49\n",
      "----------\n",
      "train Loss: 3.6597 Acc: 0.1860\n",
      "test Loss: 3.4548 Acc: 0.2090\n",
      "\n",
      "Epoch 46/49\n",
      "----------\n",
      "train Loss: 3.6173 Acc: 0.1990\n",
      "test Loss: 3.4783 Acc: 0.2130\n",
      "\n",
      "Epoch 47/49\n",
      "----------\n",
      "train Loss: 3.5875 Acc: 0.1918\n",
      "test Loss: 3.4499 Acc: 0.2070\n",
      "\n",
      "Epoch 48/49\n",
      "----------\n",
      "train Loss: 3.6200 Acc: 0.1950\n",
      "test Loss: 3.4247 Acc: 0.2200\n",
      "\n",
      "Epoch 49/49\n",
      "----------\n",
      "train Loss: 3.5829 Acc: 0.2035\n",
      "test Loss: 3.4275 Acc: 0.2120\n",
      "\n",
      "Training complete in 8m 25s\n",
      "Best test Acc: 0.223000\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate\n",
    "trainer_base = Trainer(finetuner_base)\n",
    "fine_tuned_model_base, hist_base = trainer_base.fit(\n",
    "    dataloaders=dataloaders_dict,\n",
    "    num_epochs=num_epochs,\n",
    "    lr=lr,\n",
    "    optimizer=optim.Adam,\n",
    "    criterion=nn.CrossEntropyLoss(),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "train_loss_hist_base, train_acc_hist_base, test_loss_hist_base, test_acc_hist_base = hist_base"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Case B: Fine tune Conv5_x and freeze the rest of Conv blocks"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Unfreeze layer4 in Resnet18\n",
    "finetuner_A.unfreeze_layer(\"layer4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t fc.weight\n",
      "\t fc.bias\n",
      "\t batch_norm.weight\n",
      "\t batch_norm.bias\n",
      "Epoch 0/49\n",
      "----------\n",
      "train Loss: 4.0579 Acc: 0.1105\n",
      "test Loss: 3.9105 Acc: 0.1410\n",
      "\n",
      "Epoch 1/49\n",
      "----------\n",
      "train Loss: 4.0118 Acc: 0.1205\n",
      "test Loss: 3.8579 Acc: 0.1430\n",
      "\n",
      "Epoch 2/49\n",
      "----------\n",
      "train Loss: 3.9594 Acc: 0.1295\n",
      "test Loss: 3.8005 Acc: 0.1780\n",
      "\n",
      "Epoch 3/49\n",
      "----------\n",
      "train Loss: 3.9371 Acc: 0.1345\n",
      "test Loss: 3.8029 Acc: 0.1570\n",
      "\n",
      "Epoch 4/49\n",
      "----------\n",
      "train Loss: 3.9495 Acc: 0.1305\n",
      "test Loss: 3.7390 Acc: 0.1680\n",
      "\n",
      "Epoch 5/49\n",
      "----------\n",
      "train Loss: 3.8818 Acc: 0.1380\n",
      "test Loss: 3.7373 Acc: 0.1730\n",
      "\n",
      "Epoch 6/49\n",
      "----------\n",
      "train Loss: 3.8849 Acc: 0.1430\n",
      "test Loss: 3.7100 Acc: 0.1850\n",
      "\n",
      "Epoch 7/49\n",
      "----------\n",
      "train Loss: 3.8656 Acc: 0.1463\n",
      "test Loss: 3.6824 Acc: 0.1760\n",
      "\n",
      "Epoch 8/49\n",
      "----------\n",
      "train Loss: 3.8484 Acc: 0.1525\n",
      "test Loss: 3.6800 Acc: 0.1870\n",
      "\n",
      "Epoch 9/49\n",
      "----------\n",
      "train Loss: 3.8520 Acc: 0.1458\n",
      "test Loss: 3.6588 Acc: 0.1840\n",
      "\n",
      "Epoch 10/49\n",
      "----------\n",
      "train Loss: 3.8173 Acc: 0.1533\n",
      "test Loss: 3.7326 Acc: 0.1460\n",
      "\n",
      "Epoch 11/49\n",
      "----------\n",
      "train Loss: 3.8384 Acc: 0.1558\n",
      "test Loss: 3.6998 Acc: 0.1690\n",
      "\n",
      "Epoch 12/49\n",
      "----------\n",
      "train Loss: 3.8393 Acc: 0.1440\n",
      "test Loss: 3.6598 Acc: 0.1880\n",
      "\n",
      "Epoch 13/49\n",
      "----------\n",
      "train Loss: 3.8160 Acc: 0.1562\n",
      "test Loss: 3.6602 Acc: 0.1790\n",
      "\n",
      "Epoch 14/49\n",
      "----------\n",
      "train Loss: 3.8729 Acc: 0.1453\n",
      "test Loss: 3.6495 Acc: 0.1730\n",
      "\n",
      "Epoch 15/49\n",
      "----------\n",
      "train Loss: 3.8031 Acc: 0.1603\n",
      "test Loss: 3.6304 Acc: 0.1950\n",
      "\n",
      "Epoch 16/49\n",
      "----------\n",
      "train Loss: 3.7722 Acc: 0.1748\n",
      "test Loss: 3.5933 Acc: 0.1880\n",
      "\n",
      "Epoch 17/49\n",
      "----------\n",
      "train Loss: 3.7661 Acc: 0.1718\n",
      "test Loss: 3.5886 Acc: 0.1930\n",
      "\n",
      "Epoch 18/49\n",
      "----------\n",
      "train Loss: 3.7510 Acc: 0.1723\n",
      "test Loss: 3.5765 Acc: 0.1900\n",
      "\n",
      "Epoch 19/49\n",
      "----------\n",
      "train Loss: 3.7551 Acc: 0.1660\n",
      "test Loss: 3.5593 Acc: 0.2010\n",
      "\n",
      "Epoch 20/49\n",
      "----------\n",
      "train Loss: 3.7718 Acc: 0.1668\n",
      "test Loss: 3.6285 Acc: 0.1770\n",
      "\n",
      "Epoch 21/49\n",
      "----------\n",
      "train Loss: 3.8161 Acc: 0.1565\n",
      "test Loss: 3.6408 Acc: 0.1750\n",
      "\n",
      "Epoch 22/49\n",
      "----------\n",
      "train Loss: 3.7787 Acc: 0.1565\n",
      "test Loss: 3.5776 Acc: 0.1870\n",
      "\n",
      "Epoch 23/49\n",
      "----------\n",
      "train Loss: 3.7247 Acc: 0.1585\n",
      "test Loss: 3.6079 Acc: 0.1830\n",
      "\n",
      "Epoch 24/49\n",
      "----------\n",
      "train Loss: 3.7858 Acc: 0.1653\n",
      "test Loss: 3.5848 Acc: 0.1840\n",
      "\n",
      "Epoch 25/49\n",
      "----------\n",
      "train Loss: 3.7241 Acc: 0.1693\n",
      "test Loss: 3.5305 Acc: 0.1960\n",
      "\n",
      "Epoch 26/49\n",
      "----------\n",
      "train Loss: 3.6884 Acc: 0.1840\n",
      "test Loss: 3.5366 Acc: 0.2050\n",
      "\n",
      "Epoch 27/49\n",
      "----------\n",
      "train Loss: 3.7387 Acc: 0.1723\n",
      "test Loss: 3.5116 Acc: 0.2070\n",
      "\n",
      "Epoch 28/49\n",
      "----------\n",
      "train Loss: 3.7241 Acc: 0.1708\n",
      "test Loss: 3.5020 Acc: 0.2060\n",
      "\n",
      "Epoch 29/49\n",
      "----------\n",
      "train Loss: 3.6957 Acc: 0.1793\n",
      "test Loss: 3.4822 Acc: 0.2180\n",
      "\n",
      "Epoch 30/49\n",
      "----------\n",
      "train Loss: 3.7153 Acc: 0.1733\n",
      "test Loss: 3.5235 Acc: 0.2030\n",
      "\n",
      "Epoch 31/49\n",
      "----------\n",
      "train Loss: 3.7162 Acc: 0.1738\n",
      "test Loss: 3.5116 Acc: 0.2060\n",
      "\n",
      "Epoch 32/49\n",
      "----------\n",
      "train Loss: 3.7615 Acc: 0.1605\n",
      "test Loss: 3.5544 Acc: 0.1920\n",
      "\n",
      "Epoch 33/49\n",
      "----------\n",
      "train Loss: 3.7579 Acc: 0.1553\n",
      "test Loss: 3.5463 Acc: 0.1900\n",
      "\n",
      "Epoch 34/49\n",
      "----------\n",
      "train Loss: 3.6857 Acc: 0.1828\n",
      "test Loss: 3.5075 Acc: 0.2010\n",
      "\n",
      "Epoch 35/49\n",
      "----------\n",
      "train Loss: 3.6802 Acc: 0.1735\n",
      "test Loss: 3.5025 Acc: 0.2100\n",
      "\n",
      "Epoch 36/49\n",
      "----------\n",
      "train Loss: 3.6743 Acc: 0.1838\n",
      "test Loss: 3.4425 Acc: 0.2190\n",
      "\n",
      "Epoch 37/49\n",
      "----------\n",
      "train Loss: 3.6664 Acc: 0.1800\n",
      "test Loss: 3.4637 Acc: 0.2100\n",
      "\n",
      "Epoch 38/49\n",
      "----------\n",
      "train Loss: 3.6039 Acc: 0.1893\n",
      "test Loss: 3.4144 Acc: 0.2260\n",
      "\n",
      "Epoch 39/49\n",
      "----------\n",
      "train Loss: 3.6393 Acc: 0.1880\n",
      "test Loss: 3.4502 Acc: 0.2140\n",
      "\n",
      "Epoch 40/49\n",
      "----------\n",
      "train Loss: 3.6499 Acc: 0.1828\n",
      "test Loss: 3.5138 Acc: 0.2100\n",
      "\n",
      "Epoch 41/49\n",
      "----------\n",
      "train Loss: 3.7054 Acc: 0.1720\n",
      "test Loss: 3.4806 Acc: 0.2160\n",
      "\n",
      "Epoch 42/49\n",
      "----------\n",
      "train Loss: 3.7042 Acc: 0.1728\n",
      "test Loss: 3.4589 Acc: 0.2140\n",
      "\n",
      "Epoch 43/49\n",
      "----------\n",
      "train Loss: 3.6487 Acc: 0.1783\n",
      "test Loss: 3.4667 Acc: 0.2060\n",
      "\n",
      "Epoch 44/49\n",
      "----------\n",
      "train Loss: 3.6163 Acc: 0.1905\n",
      "test Loss: 3.4629 Acc: 0.2140\n",
      "\n",
      "Epoch 45/49\n",
      "----------\n",
      "train Loss: 3.6255 Acc: 0.1815\n",
      "test Loss: 3.4278 Acc: 0.2020\n",
      "\n",
      "Epoch 46/49\n",
      "----------\n",
      "train Loss: 3.5933 Acc: 0.2043\n",
      "test Loss: 3.4523 Acc: 0.2040\n",
      "\n",
      "Epoch 47/49\n",
      "----------\n",
      "train Loss: 3.6266 Acc: 0.1898\n",
      "test Loss: 3.4163 Acc: 0.2170\n",
      "\n",
      "Epoch 48/49\n",
      "----------\n",
      "train Loss: 3.6175 Acc: 0.1940\n",
      "test Loss: 3.3976 Acc: 0.2310\n",
      "\n",
      "Epoch 49/49\n",
      "----------\n",
      "train Loss: 3.6224 Acc: 0.1880\n",
      "test Loss: 3.3997 Acc: 0.2350\n",
      "\n",
      "Training complete in 8m 27s\n",
      "Best test Acc: 0.235000\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate\n",
    "trainer_A = Trainer(finetuner_A)\n",
    "fine_tuned_model_A, hist_A = trainer_A.fit(\n",
    "    dataloaders=dataloaders_dict,\n",
    "    num_epochs=num_epochs,\n",
    "    lr=lr,\n",
    "    optimizer=optim.Adam,\n",
    "    criterion=nn.CrossEntropyLoss(),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "train_loss_hist_A, train_acc_hist_A, test_loss_hist_A, test_acc_hist_A = hist_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Case C: Fine tune ALL convolution layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t conv1.weight\n",
      "\t bn1.weight\n",
      "\t bn1.bias\n",
      "\t layer1.0.conv1.weight\n",
      "\t layer1.0.bn1.weight\n",
      "\t layer1.0.bn1.bias\n",
      "\t layer1.0.conv2.weight\n",
      "\t layer1.0.bn2.weight\n",
      "\t layer1.0.bn2.bias\n",
      "\t layer1.1.conv1.weight\n",
      "\t layer1.1.bn1.weight\n",
      "\t layer1.1.bn1.bias\n",
      "\t layer1.1.conv2.weight\n",
      "\t layer1.1.bn2.weight\n",
      "\t layer1.1.bn2.bias\n",
      "\t layer2.0.conv1.weight\n",
      "\t layer2.0.bn1.weight\n",
      "\t layer2.0.bn1.bias\n",
      "\t layer2.0.conv2.weight\n",
      "\t layer2.0.bn2.weight\n",
      "\t layer2.0.bn2.bias\n",
      "\t layer2.0.downsample.0.weight\n",
      "\t layer2.0.downsample.1.weight\n",
      "\t layer2.0.downsample.1.bias\n",
      "\t layer2.1.conv1.weight\n",
      "\t layer2.1.bn1.weight\n",
      "\t layer2.1.bn1.bias\n",
      "\t layer2.1.conv2.weight\n",
      "\t layer2.1.bn2.weight\n",
      "\t layer2.1.bn2.bias\n",
      "\t layer3.0.conv1.weight\n",
      "\t layer3.0.bn1.weight\n",
      "\t layer3.0.bn1.bias\n",
      "\t layer3.0.conv2.weight\n",
      "\t layer3.0.bn2.weight\n",
      "\t layer3.0.bn2.bias\n",
      "\t layer3.0.downsample.0.weight\n",
      "\t layer3.0.downsample.1.weight\n",
      "\t layer3.0.downsample.1.bias\n",
      "\t layer3.1.conv1.weight\n",
      "\t layer3.1.bn1.weight\n",
      "\t layer3.1.bn1.bias\n",
      "\t layer3.1.conv2.weight\n",
      "\t layer3.1.bn2.weight\n",
      "\t layer3.1.bn2.bias\n",
      "\t layer4.0.conv1.weight\n",
      "\t layer4.0.bn1.weight\n",
      "\t layer4.0.bn1.bias\n",
      "\t layer4.0.conv2.weight\n",
      "\t layer4.0.bn2.weight\n",
      "\t layer4.0.bn2.bias\n",
      "\t layer4.0.downsample.0.weight\n",
      "\t layer4.0.downsample.1.weight\n",
      "\t layer4.0.downsample.1.bias\n",
      "\t layer4.1.conv1.weight\n",
      "\t layer4.1.bn1.weight\n",
      "\t layer4.1.bn1.bias\n",
      "\t layer4.1.conv2.weight\n",
      "\t layer4.1.bn2.weight\n",
      "\t layer4.1.bn2.bias\n",
      "\t fc.weight\n",
      "\t fc.bias\n",
      "\t batch_norm.weight\n",
      "\t batch_norm.bias\n",
      "Epoch 0/49\n",
      "----------\n",
      "train Loss: 1.6104 Acc: 0.5838\n",
      "test Loss: 3.3446 Acc: 0.3760\n",
      "\n",
      "Epoch 1/49\n",
      "----------\n",
      "train Loss: 1.3627 Acc: 0.6440\n",
      "test Loss: 2.3633 Acc: 0.4770\n",
      "\n",
      "Epoch 2/49\n",
      "----------\n",
      "train Loss: 1.1423 Acc: 0.7010\n",
      "test Loss: 1.3058 Acc: 0.6640\n",
      "\n",
      "Epoch 3/49\n",
      "----------\n",
      "train Loss: 0.9744 Acc: 0.7420\n",
      "test Loss: 1.2263 Acc: 0.6940\n",
      "\n",
      "Epoch 4/49\n",
      "----------\n",
      "train Loss: 0.9279 Acc: 0.7498\n",
      "test Loss: 1.0854 Acc: 0.7190\n",
      "\n",
      "Epoch 5/49\n",
      "----------\n",
      "train Loss: 0.8218 Acc: 0.7733\n",
      "test Loss: 1.0657 Acc: 0.7340\n",
      "\n",
      "Epoch 6/49\n",
      "----------\n",
      "train Loss: 0.7213 Acc: 0.8060\n",
      "test Loss: 1.0140 Acc: 0.7450\n",
      "\n",
      "Epoch 7/49\n",
      "----------\n",
      "train Loss: 0.7080 Acc: 0.8048\n",
      "test Loss: 0.9609 Acc: 0.7620\n",
      "\n",
      "Epoch 8/49\n",
      "----------\n",
      "train Loss: 0.6548 Acc: 0.8218\n",
      "test Loss: 0.8680 Acc: 0.7610\n",
      "\n",
      "Epoch 9/49\n",
      "----------\n",
      "train Loss: 0.6038 Acc: 0.8395\n",
      "test Loss: 0.7975 Acc: 0.8010\n",
      "\n",
      "Epoch 10/49\n",
      "----------\n",
      "train Loss: 0.6837 Acc: 0.8108\n",
      "test Loss: 1.2241 Acc: 0.7070\n",
      "\n",
      "Epoch 11/49\n",
      "----------\n",
      "train Loss: 0.8573 Acc: 0.7675\n",
      "test Loss: 1.5582 Acc: 0.6270\n",
      "\n",
      "Epoch 12/49\n",
      "----------\n",
      "train Loss: 0.8693 Acc: 0.7650\n",
      "test Loss: 1.2579 Acc: 0.6880\n",
      "\n",
      "Epoch 13/49\n",
      "----------\n",
      "train Loss: 0.8013 Acc: 0.7895\n",
      "test Loss: 1.0763 Acc: 0.7220\n",
      "\n",
      "Epoch 14/49\n",
      "----------\n",
      "train Loss: 0.7354 Acc: 0.7998\n",
      "test Loss: 1.0971 Acc: 0.7260\n",
      "\n",
      "Epoch 15/49\n",
      "----------\n",
      "train Loss: 0.6580 Acc: 0.8193\n",
      "test Loss: 0.9732 Acc: 0.7380\n",
      "\n",
      "Epoch 16/49\n",
      "----------\n",
      "train Loss: 0.5816 Acc: 0.8433\n",
      "test Loss: 0.8218 Acc: 0.7900\n",
      "\n",
      "Epoch 17/49\n",
      "----------\n",
      "train Loss: 0.5332 Acc: 0.8575\n",
      "test Loss: 0.7137 Acc: 0.8170\n",
      "\n",
      "Epoch 18/49\n",
      "----------\n",
      "train Loss: 0.5525 Acc: 0.8548\n",
      "test Loss: 0.8190 Acc: 0.7950\n",
      "\n",
      "Epoch 19/49\n",
      "----------\n",
      "train Loss: 0.4557 Acc: 0.8788\n",
      "test Loss: 0.6723 Acc: 0.8350\n",
      "\n",
      "Epoch 20/49\n",
      "----------\n",
      "train Loss: 0.5865 Acc: 0.8378\n",
      "test Loss: 1.0218 Acc: 0.7650\n",
      "\n",
      "Epoch 21/49\n",
      "----------\n",
      "train Loss: 0.6406 Acc: 0.8233\n",
      "test Loss: 1.2935 Acc: 0.6800\n",
      "\n",
      "Epoch 22/49\n",
      "----------\n",
      "train Loss: 0.6978 Acc: 0.8120\n",
      "test Loss: 0.9915 Acc: 0.7310\n",
      "\n",
      "Epoch 23/49\n",
      "----------\n",
      "train Loss: 0.6607 Acc: 0.8215\n",
      "test Loss: 1.1300 Acc: 0.7400\n",
      "\n",
      "Epoch 24/49\n",
      "----------\n",
      "train Loss: 0.6128 Acc: 0.8300\n",
      "test Loss: 0.9985 Acc: 0.7350\n",
      "\n",
      "Epoch 25/49\n",
      "----------\n",
      "train Loss: 0.5851 Acc: 0.8408\n",
      "test Loss: 0.7497 Acc: 0.8080\n",
      "\n",
      "Epoch 26/49\n",
      "----------\n",
      "train Loss: 0.5377 Acc: 0.8545\n",
      "test Loss: 0.8381 Acc: 0.7840\n",
      "\n",
      "Epoch 27/49\n",
      "----------\n",
      "train Loss: 0.4391 Acc: 0.8785\n",
      "test Loss: 0.8234 Acc: 0.7840\n",
      "\n",
      "Epoch 28/49\n",
      "----------\n",
      "train Loss: 0.4058 Acc: 0.8925\n",
      "test Loss: 0.6894 Acc: 0.8400\n",
      "\n",
      "Epoch 29/49\n",
      "----------\n",
      "train Loss: 0.4062 Acc: 0.8915\n",
      "test Loss: 0.6885 Acc: 0.8260\n",
      "\n",
      "Epoch 30/49\n",
      "----------\n",
      "train Loss: 0.4817 Acc: 0.8728\n",
      "test Loss: 1.0401 Acc: 0.7390\n",
      "\n",
      "Epoch 31/49\n",
      "----------\n",
      "train Loss: 0.5553 Acc: 0.8530\n",
      "test Loss: 1.0430 Acc: 0.7410\n",
      "\n",
      "Epoch 32/49\n",
      "----------\n",
      "train Loss: 0.5414 Acc: 0.8545\n",
      "test Loss: 1.2084 Acc: 0.7220\n",
      "\n",
      "Epoch 33/49\n",
      "----------\n",
      "train Loss: 0.5136 Acc: 0.8575\n",
      "test Loss: 1.2604 Acc: 0.7070\n",
      "\n",
      "Epoch 34/49\n",
      "----------\n",
      "train Loss: 0.5067 Acc: 0.8598\n",
      "test Loss: 0.9448 Acc: 0.7890\n",
      "\n",
      "Epoch 35/49\n",
      "----------\n",
      "train Loss: 0.5139 Acc: 0.8638\n",
      "test Loss: 0.8519 Acc: 0.7970\n",
      "\n",
      "Epoch 36/49\n",
      "----------\n",
      "train Loss: 0.4513 Acc: 0.8768\n",
      "test Loss: 0.8987 Acc: 0.7690\n",
      "\n",
      "Epoch 37/49\n",
      "----------\n",
      "train Loss: 0.4123 Acc: 0.8848\n",
      "test Loss: 0.8354 Acc: 0.7990\n",
      "\n",
      "Epoch 38/49\n",
      "----------\n",
      "train Loss: 0.3831 Acc: 0.8980\n",
      "test Loss: 0.7058 Acc: 0.8260\n",
      "\n",
      "Epoch 39/49\n",
      "----------\n",
      "train Loss: 0.3528 Acc: 0.9030\n",
      "test Loss: 0.7477 Acc: 0.8310\n",
      "\n",
      "Epoch 40/49\n",
      "----------\n",
      "train Loss: 0.4138 Acc: 0.8853\n",
      "test Loss: 0.9619 Acc: 0.7740\n",
      "\n",
      "Epoch 41/49\n",
      "----------\n",
      "train Loss: 0.4696 Acc: 0.8703\n",
      "test Loss: 0.9331 Acc: 0.7880\n",
      "\n",
      "Epoch 42/49\n",
      "----------\n",
      "train Loss: 0.5221 Acc: 0.8613\n",
      "test Loss: 1.1556 Acc: 0.7340\n",
      "\n",
      "Epoch 43/49\n",
      "----------\n",
      "train Loss: 0.4904 Acc: 0.8620\n",
      "test Loss: 1.3384 Acc: 0.6980\n",
      "\n",
      "Epoch 44/49\n",
      "----------\n",
      "train Loss: 0.4869 Acc: 0.8648\n",
      "test Loss: 1.3258 Acc: 0.6760\n",
      "\n",
      "Epoch 45/49\n",
      "----------\n",
      "train Loss: 0.4599 Acc: 0.8725\n",
      "test Loss: 0.7712 Acc: 0.8200\n",
      "\n",
      "Epoch 46/49\n",
      "----------\n",
      "train Loss: 0.3993 Acc: 0.8930\n",
      "test Loss: 0.7988 Acc: 0.8100\n",
      "\n",
      "Epoch 47/49\n",
      "----------\n",
      "train Loss: 0.3733 Acc: 0.8970\n",
      "test Loss: 0.6637 Acc: 0.8350\n",
      "\n",
      "Epoch 48/49\n",
      "----------\n",
      "train Loss: 0.3742 Acc: 0.8973\n",
      "test Loss: 0.6549 Acc: 0.8350\n",
      "\n",
      "Epoch 49/49\n",
      "----------\n",
      "train Loss: 0.3157 Acc: 0.9125\n",
      "test Loss: 0.7008 Acc: 0.8350\n",
      "\n",
      "Training complete in 9m 52s\n",
      "Best test Acc: 0.840000\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate\n",
    "trainer_B = Trainer(finetuner_B)\n",
    "fine_tuned_model_B, hist_B = trainer_B.fit(\n",
    "    dataloaders=dataloaders_dict,\n",
    "    num_epochs=num_epochs,\n",
    "    lr=lr,\n",
    "    optimizer=optim.Adam,\n",
    "    criterion=nn.CrossEntropyLoss(),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "train_loss_hist_B, train_acc_hist_B, test_loss_hist_B, test_acc_hist_B = hist_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Case D: Freeze all the convolution blocks, introduce two FC layers prior to the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params to learn:\n",
      "\t fc.weight\n",
      "\t fc.bias\n",
      "\t batch_norm.weight\n",
      "\t batch_norm.bias\n",
      "\t fc_2.weight\n",
      "\t fc_2.bias\n",
      "\t batch_norm_2.weight\n",
      "\t batch_norm_2.bias\n",
      "Epoch 0/49\n",
      "----------\n",
      "train Loss: 4.8325 Acc: 0.0085\n",
      "test Loss: 4.7036 Acc: 0.0110\n",
      "\n",
      "Epoch 1/49\n",
      "----------\n",
      "train Loss: 4.6518 Acc: 0.0213\n",
      "test Loss: 4.5648 Acc: 0.0240\n",
      "\n",
      "Epoch 2/49\n",
      "----------\n",
      "train Loss: 4.5471 Acc: 0.0285\n",
      "test Loss: 4.4695 Acc: 0.0420\n",
      "\n",
      "Epoch 3/49\n",
      "----------\n",
      "train Loss: 4.4893 Acc: 0.0415\n",
      "test Loss: 4.4181 Acc: 0.0530\n",
      "\n",
      "Epoch 4/49\n",
      "----------\n",
      "train Loss: 4.3991 Acc: 0.0518\n",
      "test Loss: 4.3748 Acc: 0.0560\n",
      "\n",
      "Epoch 5/49\n",
      "----------\n",
      "train Loss: 4.3629 Acc: 0.0558\n",
      "test Loss: 4.2671 Acc: 0.0690\n",
      "\n",
      "Epoch 6/49\n",
      "----------\n",
      "train Loss: 4.2993 Acc: 0.0690\n",
      "test Loss: 4.2187 Acc: 0.0840\n",
      "\n",
      "Epoch 7/49\n",
      "----------\n",
      "train Loss: 4.2722 Acc: 0.0750\n",
      "test Loss: 4.1752 Acc: 0.0880\n",
      "\n",
      "Epoch 8/49\n",
      "----------\n",
      "train Loss: 4.2246 Acc: 0.0925\n",
      "test Loss: 4.1178 Acc: 0.0860\n",
      "\n",
      "Epoch 9/49\n",
      "----------\n",
      "train Loss: 4.2047 Acc: 0.0855\n",
      "test Loss: 4.0830 Acc: 0.1030\n",
      "\n",
      "Epoch 10/49\n",
      "----------\n",
      "train Loss: 4.2034 Acc: 0.0845\n",
      "test Loss: 4.1108 Acc: 0.0980\n",
      "\n",
      "Epoch 11/49\n",
      "----------\n",
      "train Loss: 4.1949 Acc: 0.0860\n",
      "test Loss: 4.0827 Acc: 0.1130\n",
      "\n",
      "Epoch 12/49\n",
      "----------\n",
      "train Loss: 4.1589 Acc: 0.0955\n",
      "test Loss: 4.0657 Acc: 0.1140\n",
      "\n",
      "Epoch 13/49\n",
      "----------\n",
      "train Loss: 4.1334 Acc: 0.0990\n",
      "test Loss: 4.0061 Acc: 0.1130\n",
      "\n",
      "Epoch 14/49\n",
      "----------\n",
      "train Loss: 4.0751 Acc: 0.1105\n",
      "test Loss: 3.9408 Acc: 0.1170\n",
      "\n",
      "Epoch 15/49\n",
      "----------\n",
      "train Loss: 4.0575 Acc: 0.1188\n",
      "test Loss: 3.9363 Acc: 0.1310\n",
      "\n",
      "Epoch 16/49\n",
      "----------\n",
      "train Loss: 4.0327 Acc: 0.1095\n",
      "test Loss: 3.8834 Acc: 0.1490\n",
      "\n",
      "Epoch 17/49\n",
      "----------\n",
      "train Loss: 3.9735 Acc: 0.1325\n",
      "test Loss: 3.8624 Acc: 0.1300\n",
      "\n",
      "Epoch 18/49\n",
      "----------\n",
      "train Loss: 3.9837 Acc: 0.1238\n",
      "test Loss: 3.8191 Acc: 0.1650\n",
      "\n",
      "Epoch 19/49\n",
      "----------\n",
      "train Loss: 3.9591 Acc: 0.1385\n",
      "test Loss: 3.7950 Acc: 0.1680\n",
      "\n",
      "Epoch 20/49\n",
      "----------\n",
      "train Loss: 3.9743 Acc: 0.1280\n",
      "test Loss: 3.8420 Acc: 0.1470\n",
      "\n",
      "Epoch 21/49\n",
      "----------\n",
      "train Loss: 3.9790 Acc: 0.1288\n",
      "test Loss: 3.8388 Acc: 0.1490\n",
      "\n",
      "Epoch 22/49\n",
      "----------\n",
      "train Loss: 3.9658 Acc: 0.1268\n",
      "test Loss: 3.8064 Acc: 0.1490\n",
      "\n",
      "Epoch 23/49\n",
      "----------\n",
      "train Loss: 3.9050 Acc: 0.1428\n",
      "test Loss: 3.8150 Acc: 0.1540\n",
      "\n",
      "Epoch 24/49\n",
      "----------\n",
      "train Loss: 3.9262 Acc: 0.1360\n",
      "test Loss: 3.7885 Acc: 0.1550\n",
      "\n",
      "Epoch 25/49\n",
      "----------\n",
      "train Loss: 3.8754 Acc: 0.1545\n",
      "test Loss: 3.7339 Acc: 0.1740\n",
      "\n",
      "Epoch 26/49\n",
      "----------\n",
      "train Loss: 3.8814 Acc: 0.1453\n",
      "test Loss: 3.7099 Acc: 0.1600\n",
      "\n",
      "Epoch 27/49\n",
      "----------\n",
      "train Loss: 3.8356 Acc: 0.1568\n",
      "test Loss: 3.6679 Acc: 0.1850\n",
      "\n",
      "Epoch 28/49\n",
      "----------\n",
      "train Loss: 3.8199 Acc: 0.1653\n",
      "test Loss: 3.6356 Acc: 0.2030\n",
      "\n",
      "Epoch 29/49\n",
      "----------\n",
      "train Loss: 3.8282 Acc: 0.1583\n",
      "test Loss: 3.6533 Acc: 0.1620\n",
      "\n",
      "Epoch 30/49\n",
      "----------\n",
      "train Loss: 3.8522 Acc: 0.1523\n",
      "test Loss: 3.7183 Acc: 0.1670\n",
      "\n",
      "Epoch 31/49\n",
      "----------\n",
      "train Loss: 3.8515 Acc: 0.1500\n",
      "test Loss: 3.7420 Acc: 0.1580\n",
      "\n",
      "Epoch 32/49\n",
      "----------\n",
      "train Loss: 3.8424 Acc: 0.1540\n",
      "test Loss: 3.6773 Acc: 0.1870\n",
      "\n",
      "Epoch 33/49\n",
      "----------\n",
      "train Loss: 3.8142 Acc: 0.1543\n",
      "test Loss: 3.6545 Acc: 0.1800\n",
      "\n",
      "Epoch 34/49\n",
      "----------\n",
      "train Loss: 3.7978 Acc: 0.1605\n",
      "test Loss: 3.5758 Acc: 0.1960\n",
      "\n",
      "Epoch 35/49\n",
      "----------\n",
      "train Loss: 3.7631 Acc: 0.1610\n",
      "test Loss: 3.6081 Acc: 0.1900\n",
      "\n",
      "Epoch 36/49\n",
      "----------\n",
      "train Loss: 3.7720 Acc: 0.1683\n",
      "test Loss: 3.6094 Acc: 0.1860\n",
      "\n",
      "Epoch 37/49\n",
      "----------\n",
      "train Loss: 3.7232 Acc: 0.1695\n",
      "test Loss: 3.5536 Acc: 0.2000\n",
      "\n",
      "Epoch 38/49\n",
      "----------\n",
      "train Loss: 3.7401 Acc: 0.1658\n",
      "test Loss: 3.5477 Acc: 0.2040\n",
      "\n",
      "Epoch 39/49\n",
      "----------\n",
      "train Loss: 3.7558 Acc: 0.1770\n",
      "test Loss: 3.5272 Acc: 0.1950\n",
      "\n",
      "Epoch 40/49\n",
      "----------\n",
      "train Loss: 3.7775 Acc: 0.1645\n",
      "test Loss: 3.6409 Acc: 0.1680\n",
      "\n",
      "Epoch 41/49\n",
      "----------\n",
      "train Loss: 3.7472 Acc: 0.1693\n",
      "test Loss: 3.5872 Acc: 0.1900\n",
      "\n",
      "Epoch 42/49\n",
      "----------\n",
      "train Loss: 3.7585 Acc: 0.1683\n",
      "test Loss: 3.6126 Acc: 0.1880\n",
      "\n",
      "Epoch 43/49\n",
      "----------\n",
      "train Loss: 3.7403 Acc: 0.1748\n",
      "test Loss: 3.5619 Acc: 0.1930\n",
      "\n",
      "Epoch 44/49\n",
      "----------\n",
      "train Loss: 3.7358 Acc: 0.1673\n",
      "test Loss: 3.5287 Acc: 0.2070\n",
      "\n",
      "Epoch 45/49\n",
      "----------\n",
      "train Loss: 3.7189 Acc: 0.1805\n",
      "test Loss: 3.5381 Acc: 0.1900\n",
      "\n",
      "Epoch 46/49\n",
      "----------\n",
      "train Loss: 3.6999 Acc: 0.1710\n",
      "test Loss: 3.5513 Acc: 0.1850\n",
      "\n",
      "Epoch 47/49\n",
      "----------\n",
      "train Loss: 3.6750 Acc: 0.1745\n",
      "test Loss: 3.4820 Acc: 0.2080\n",
      "\n",
      "Epoch 48/49\n",
      "----------\n",
      "train Loss: 3.7072 Acc: 0.1723\n",
      "test Loss: 3.4785 Acc: 0.2010\n",
      "\n",
      "Epoch 49/49\n",
      "----------\n",
      "train Loss: 3.6567 Acc: 0.1853\n",
      "test Loss: 3.4747 Acc: 0.2030\n",
      "\n",
      "Training complete in 8m 23s\n",
      "Best test Acc: 0.208000\n"
     ]
    }
   ],
   "source": [
    "trainer_C = Trainer(finetuner_C)\n",
    "fine_tuned_model_C, hist_C = trainer_C.fit(\n",
    "    dataloaders=dataloaders_dict,\n",
    "    num_epochs=num_epochs,\n",
    "    lr=lr,\n",
    "    optimizer=optim.Adam,\n",
    "    criterion=nn.CrossEntropyLoss(),\n",
    "    device=device\n",
    ")\n",
    "\n",
    "train_loss_hist_C, train_acc_hist_C, test_loss_hist_C, test_acc_hist_C = hist_C"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Results"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss_hist_base, label='Baseline (Train)')\n",
    "plt.plot(train_loss_hist_A, label='Model A (Train)')\n",
    "plt.plot(train_loss_hist_B, label='Model B (Train)')\n",
    "plt.plot(train_loss_hist_C, label='Model C (Train)')\n",
    "plt.plot(test_loss_hist_base, label='Baseline (Test)')\n",
    "plt.plot(test_loss_hist_A, label='Model A (Test)')\n",
    "plt.plot(test_loss_hist_B, label='Model B (Test)')\n",
    "plt.plot(test_loss_hist_C, label='Model C (Test)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_acc_hist_base, label='Baseline (Train)')\n",
    "plt.plot(train_acc_hist_A, label='Model A (Train)')\n",
    "plt.plot(train_acc_hist_B, label='Model B (Train)')\n",
    "plt.plot(train_acc_hist_C, label='Model C (Train)')\n",
    "plt.plot(test_acc_hist_base, label='Baseline (Test)')\n",
    "plt.plot(test_acc_hist_A, label='Model A (Test)')\n",
    "plt.plot(test_acc_hist_B, label='Model B (Test)')\n",
    "plt.plot(test_acc_hist_C, label='Model C (Test)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "783c5a5cff0fe5c79904b741d009b7c58ee2faf8ae465d9cae8287e59a309cd7"
  },
  "kernelspec": {
   "display_name": "Python 3.8.0 ('dac')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}