different_seeds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import math\n",
    "import numpy as np\n",
    "import random\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from torchtext.data import Field, BucketIterator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "import re\n",
    "from transformers import BertTokenizer\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'cuda'"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset initializing start\n",
      "Tokenizing the data...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\dataloader.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"len\"] = data.iloc[:, 0].apply(lambda x : len(self.tokenize(x)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the data :  29544\n",
      "0\n",
      "review       [[CLS], one, of, the, many, silent, comedies, ...\n",
      "sentiment                                                    0\n",
      "len                                                        186\n",
      "Name: 46539, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23635/23635 [00:01<00:00, 18443.27it/s]\n",
      "100%|██████████| 2954/2954 [00:00<00:00, 19892.29it/s]\n",
      "100%|██████████| 2955/2955 [00:00<00:00, 20664.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset initializing done\n",
      "Vocabulary Size :  23050\n",
      "CPU times: total: 3min 54s\n",
      "Wall time: 3min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# import data\n",
    "data = import_data()\n",
    "\n",
    "# train-test-validation split\n",
    "train_iter, valid_iter, test_iter, voc_size, pad_idx = train_test_val_split(data,\n",
    "                                                                            device,\n",
    "                                                                            batch_size=512)\n",
    "\n",
    "# Creating the embedding matrix\n",
    "embedding = torch.nn.Embedding(voc_size, 16)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive labels ratio (train set): 0.5052253014596996\n",
      "Positive labels ratio (val set): 0.4949221394719025\n",
      "Positive labels ratio (test set): 0.4910321489001692\n"
     ]
    }
   ],
   "source": [
    "# check imbalance\n",
    "check_imbalance(train_iter, name='train set')\n",
    "check_imbalance(valid_iter, name='val set')\n",
    "check_imbalance(test_iter, name='test set')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Training parent models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Idea: We train model A and model B for long enough, s.t. they start overfitting. We use their best models w.r.t. validation set (i.e. not the final model after all training epochs) and fuse them together. The fused model is then trained for long enough as well, saving the best model w.r.t to the same validation set. The fused model is then compared with its parent models on the separate test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# template for training parent models (as we train them the same way)\n",
    "def train_early_stopping(model_name: str, train_iter, valid_iter, epochs, device, lr=2e-4):\n",
    "    # init\n",
    "    model = new_model(embedding, pad_idx, voc_size, device) # init model\n",
    "\n",
    "    opt = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "    loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    # train with early stopping\n",
    "    history, best_model, best_model_score = train_save_best(model=model,\n",
    "                                                            iterator=train_iter,\n",
    "                                                            valid_iter=valid_iter,\n",
    "                                                            optimizer=opt,\n",
    "                                                            criterion=loss_fn,\n",
    "                                                            epoch=epochs,\n",
    "                                                            clip=1,\n",
    "                                                            device=device)\n",
    "\n",
    "    # save model\n",
    "    name = f'parallel_training/model{model_name}_IMDB_256'\n",
    "    save_model(model, name=name)\n",
    "\n",
    "    # save history\n",
    "    name = f'parallel_training/history_model{model_name}_IMDB_256'\n",
    "    save_history(history, name=name)\n",
    "\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "EPOCHS = 100\n",
    "LEARNING_RATE = 2e-4"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/47 [00:00<?, ?it/s]C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:229: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n",
      "100%|██████████| 47/47 [00:02<00:00, 21.47it/s]\n",
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:127: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1  Train Loss: 0.7980               Validation Loss: 0.6945               Train acc: 0.5023,               Val acc: 0.5475,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 2  Train Loss: 0.6598               Validation Loss: 0.6490               Train acc: 0.6033,               Val acc: 0.6161,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 3  Train Loss: 0.5978               Validation Loss: 0.5915               Train acc: 0.6936,               Val acc: 0.6982,               Learning Rate : 0.0002\n"
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    },
    {
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     "text": [
      "Epoch: 4  Train Loss: 0.5361               Validation Loss: 0.5436               Train acc: 0.7580,               Val acc: 0.7268,               Learning Rate : 0.0002\n"
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    {
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     "text": [
      "Epoch: 5  Train Loss: 0.4862               Validation Loss: 0.5231               Train acc: 0.7825,               Val acc: 0.7446,               Learning Rate : 0.0002\n"
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    {
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     "text": [
      "Epoch: 6  Train Loss: 0.4524               Validation Loss: 0.4952               Train acc: 0.7983,               Val acc: 0.7631,               Learning Rate : 0.0002\n"
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     "text": [
      "Epoch: 7  Train Loss: 0.4223               Validation Loss: 0.4721               Train acc: 0.8144,               Val acc: 0.7747,               Learning Rate : 0.0002\n"
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    {
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     "text": [
      "Epoch: 8  Train Loss: 0.3991               Validation Loss: 0.4579               Train acc: 0.8249,               Val acc: 0.7835,               Learning Rate : 0.0002\n"
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    },
    {
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     "text": [
      "Epoch: 9  Train Loss: 0.3744               Validation Loss: 0.4434               Train acc: 0.8341,               Val acc: 0.7940,               Learning Rate : 0.0002\n"
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    },
    {
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     "text": [
      "Epoch: 10  Train Loss: 0.3558               Validation Loss: 0.4446               Train acc: 0.8439,               Val acc: 0.7978,               Learning Rate : 0.0002\n"
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    },
    {
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     "text": [
      "Epoch: 11  Train Loss: 0.3431               Validation Loss: 0.4360               Train acc: 0.8503,               Val acc: 0.7955,               Learning Rate : 0.0002\n"
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    },
    {
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     "text": [
      "Epoch: 12  Train Loss: 0.3309               Validation Loss: 0.4222               Train acc: 0.8551,               Val acc: 0.8067,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 13  Train Loss: 0.3195               Validation Loss: 0.4165               Train acc: 0.8621,               Val acc: 0.8126,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 14  Train Loss: 0.3117               Validation Loss: 0.4393               Train acc: 0.8657,               Val acc: 0.7981,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 15  Train Loss: 0.3046               Validation Loss: 0.4188               Train acc: 0.8710,               Val acc: 0.8143,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 16  Train Loss: 0.2930               Validation Loss: 0.4152               Train acc: 0.8769,               Val acc: 0.8173,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 17  Train Loss: 0.2867               Validation Loss: 0.4140               Train acc: 0.8810,               Val acc: 0.8162,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18  Train Loss: 0.2806               Validation Loss: 0.4154               Train acc: 0.8843,               Val acc: 0.8171,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
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     "output_type": "stream",
     "text": [
      "Epoch: 19  Train Loss: 0.2753               Validation Loss: 0.4142               Train acc: 0.8880,               Val acc: 0.8178,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20  Train Loss: 0.2679               Validation Loss: 0.4105               Train acc: 0.8913,               Val acc: 0.8208,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "text": [
      "Epoch: 21  Train Loss: 0.2635               Validation Loss: 0.4113               Train acc: 0.8935,               Val acc: 0.8224,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 22  Train Loss: 0.2591               Validation Loss: 0.4232               Train acc: 0.8964,               Val acc: 0.8133,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 23  Train Loss: 0.2521               Validation Loss: 0.4124               Train acc: 0.8991,               Val acc: 0.8241,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 24  Train Loss: 0.2476               Validation Loss: 0.4142               Train acc: 0.9026,               Val acc: 0.8260,               Learning Rate : 0.0002\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 25  Train Loss: 0.2437               Validation Loss: 0.4129               Train acc: 0.9031,               Val acc: 0.8234,               Learning Rate : 0.0002\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch: 26  Train Loss: 0.2379               Validation Loss: 0.4185               Train acc: 0.9062,               Val acc: 0.8210,               Learning Rate : 0.0000\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 27  Train Loss: 0.2321               Validation Loss: 0.4112               Train acc: 0.9087,               Val acc: 0.8255,               Learning Rate : 0.0000\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 28  Train Loss: 0.2300               Validation Loss: 0.4115               Train acc: 0.9101,               Val acc: 0.8258,               Learning Rate : 0.0000\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 29  Train Loss: 0.2298               Validation Loss: 0.4115               Train acc: 0.9097,               Val acc: 0.8239,               Learning Rate : 0.0000\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30  Train Loss: 0.2285               Validation Loss: 0.4095               Train acc: 0.9111,               Val acc: 0.8252,               Learning Rate : 0.0000\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31  Train Loss: 0.2277               Validation Loss: 0.4157               Train acc: 0.9112,               Val acc: 0.8231,               Learning Rate : 0.0000\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 32  Train Loss: 0.2270               Validation Loss: 0.4141               Train acc: 0.9127,               Val acc: 0.8246,               Learning Rate : 0.0000\n"
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      "Epoch: 82  Train Loss: 0.2237               Validation Loss: 0.4148               Train acc: 0.9138,               Val acc: 0.8262,               Learning Rate : 0.0000\n"
     ]
    },
    {
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      "Epoch: 83  Train Loss: 0.2248               Validation Loss: 0.4120               Train acc: 0.9130,               Val acc: 0.8263,               Learning Rate : 0.0000\n"
     ]
    },
    {
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      "Epoch: 84  Train Loss: 0.2234               Validation Loss: 0.4105               Train acc: 0.9136,               Val acc: 0.8273,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Epoch: 85  Train Loss: 0.2239               Validation Loss: 0.4117               Train acc: 0.9140,               Val acc: 0.8273,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "Epoch: 86  Train Loss: 0.2251               Validation Loss: 0.4124               Train acc: 0.9136,               Val acc: 0.8256,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 47/47 [00:01<00:00, 23.50it/s]\n"
     ]
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     "output_type": "stream",
     "text": [
      "Epoch: 87  Train Loss: 0.2248               Validation Loss: 0.4105               Train acc: 0.9140,               Val acc: 0.8263,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.73it/s]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 88  Train Loss: 0.2261               Validation Loss: 0.4134               Train acc: 0.9125,               Val acc: 0.8261,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.63it/s]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 89  Train Loss: 0.2223               Validation Loss: 0.4145               Train acc: 0.9145,               Val acc: 0.8258,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 23.47it/s]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 90  Train Loss: 0.2245               Validation Loss: 0.4123               Train acc: 0.9130,               Val acc: 0.8264,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 22.83it/s]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 91  Train Loss: 0.2240               Validation Loss: 0.4112               Train acc: 0.9130,               Val acc: 0.8268,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.68it/s]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 92  Train Loss: 0.2236               Validation Loss: 0.4117               Train acc: 0.9138,               Val acc: 0.8273,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.70it/s]\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 93  Train Loss: 0.2240               Validation Loss: 0.4129               Train acc: 0.9140,               Val acc: 0.8264,               Learning Rate : 0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 94  Train Loss: 0.2247               Validation Loss: 0.4126               Train acc: 0.9131,               Val acc: 0.8259,               Learning Rate : 0.0000\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.65it/s]\n"
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     "output_type": "stream",
     "text": [
      "Epoch: 95  Train Loss: 0.2249               Validation Loss: 0.4108               Train acc: 0.9136,               Val acc: 0.8273,               Learning Rate : 0.0000\n"
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    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.56it/s]\n"
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     "output_type": "stream",
     "text": [
      "Epoch: 96  Train Loss: 0.2254               Validation Loss: 0.4101               Train acc: 0.9127,               Val acc: 0.8268,               Learning Rate : 0.0000\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.58it/s]\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 97  Train Loss: 0.2255               Validation Loss: 0.4124               Train acc: 0.9127,               Val acc: 0.8270,               Learning Rate : 0.0000\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 22.73it/s]\n"
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     "output_type": "stream",
     "text": [
      "Epoch: 98  Train Loss: 0.2232               Validation Loss: 0.4115               Train acc: 0.9142,               Val acc: 0.8271,               Learning Rate : 0.0000\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.65it/s]\n"
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     "text": [
      "Epoch: 99  Train Loss: 0.2247               Validation Loss: 0.4143               Train acc: 0.9132,               Val acc: 0.8259,               Learning Rate : 0.0000\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 23.62it/s]\n"
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     "output_type": "stream",
     "text": [
      "Epoch: 100  Train Loss: 0.2247               Validation Loss: 0.4121               Train acc: 0.9132,               Val acc: 0.8264,               Learning Rate : 0.0000\n"
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     "data": {
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3min 33s\n",
      "Wall time: 3min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# train parent model A\n",
    "modelA = train_early_stopping(model_name='A',\n",
    "                              train_iter=train_iter,\n",
    "                              valid_iter=valid_iter,\n",
    "                              epochs=EPOCHS,\n",
    "                              device=device,\n",
    "                              lr=LEARNING_RATE)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/47 [00:00<?, ?it/s]C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:229: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n",
      "100%|██████████| 47/47 [00:02<00:00, 20.85it/s]\n",
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:127: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1  Train Loss: 0.7869               Validation Loss: 0.6736               Train acc: 0.5152,               Val acc: 0.5824,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 23.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2  Train Loss: 0.6248               Validation Loss: 0.5997               Train acc: 0.6577,               Val acc: 0.6850,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 22.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3  Train Loss: 0.5120               Validation Loss: 0.5015               Train acc: 0.7927,               Val acc: 0.7714,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 23.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4  Train Loss: 0.4165               Validation Loss: 0.4427               Train acc: 0.8347,               Val acc: 0.8039,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:02<00:00, 22.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3min 32s\n",
      "Wall time: 3min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# train parent model B\n",
    "modelB = train_early_stopping(model_name='B',\n",
    "                              train_iter=train_iter,\n",
    "                              valid_iter=valid_iter,\n",
    "                              epochs=EPOCHS,\n",
    "                              device=device,\n",
    "                              lr=LEARNING_RATE)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Model fusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### (Optional) load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load parent models\n",
    "modelA = new_model(embedding, pad_idx, voc_size, device) # init model\n",
    "modelB = new_model(embedding, pad_idx, voc_size, device) # init model\n",
    "\n",
    "modelA = torch.load('./Models/parallel_training/modelA_IMDB_256')\n",
    "modelB = torch.load('./Models/parallel_training/modelB_IMDB_256')\n",
    "\n",
    "modelA = modelA.to(device) # put on CPU/GPU\n",
    "modelB = modelB.to(device) # put on CPU/GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Fusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Vanilla fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fusion successful\n"
     ]
    }
   ],
   "source": [
    "model_fusion_vanilla = vanilla_fusion(modelA=modelA, modelB=modelB, pad_idx=pad_idx, voc_size=voc_size, embedding=embedding, device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Optimal transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fusion successful\n"
     ]
    }
   ],
   "source": [
    "model_fusion = ot_fusion(modelA, modelB, train_iter, embedding, pad_idx, voc_size, device, fusion_ratio=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Test fusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Randomly initialized model for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:144: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test with new randomly initialized transformer\n",
    "model_random = new_model(embedding, pad_idx, voc_size, device) # init model\n",
    "\n",
    "test_fusion(modelA, modelB, model_random, test_iter, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Vanilla fusion model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:144: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test with vanilla fusion\n",
    "test_fusion(modelA, modelB, model_fusion_vanilla, test_iter, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "OT fusion model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:144: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test with OT fusion\n",
    "test_fusion(modelA, modelB, model_fusion, test_iter, device)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Retraining"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/47 [00:00<?, ?it/s]C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:246: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n",
      "100%|██████████| 47/47 [00:02<00:00, 23.28it/s]\n",
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:144: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1  Train Loss: 1.5753               Validation Loss: 2.8600               Train acc: 0.5500,               Val acc: 0.4959,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2  Train Loss: 2.4358               Validation Loss: 2.4859               Train acc: 0.5065,               Val acc: 0.4963,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3  Train Loss: 2.0776               Validation Loss: 2.1202               Train acc: 0.5078,               Val acc: 0.4984,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4  Train Loss: 1.7173               Validation Loss: 1.7603               Train acc: 0.5151,               Val acc: 0.5067,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5  Train Loss: 1.3856               Validation Loss: 1.4232               Train acc: 0.5293,               Val acc: 0.5289,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6  Train Loss: 1.0747               Validation Loss: 1.1249               Train acc: 0.5631,               Val acc: 0.5654,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7  Train Loss: 0.8117               Validation Loss: 0.8749               Train acc: 0.6227,               Val acc: 0.6230,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8  Train Loss: 0.6038               Validation Loss: 0.6969               Train acc: 0.7042,               Val acc: 0.6919,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9  Train Loss: 0.4634               Validation Loss: 0.5869               Train acc: 0.7851,               Val acc: 0.7298,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10  Train Loss: 0.3984               Validation Loss: 0.5541               Train acc: 0.8302,               Val acc: 0.7423,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11  Train Loss: 0.3821               Validation Loss: 0.5390               Train acc: 0.8354,               Val acc: 0.7504,               Learning Rate : 0.0002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:01<00:00, 24.33it/s]\n"
     ]
    },
    {
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    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3min 26s\n",
      "Wall time: 3min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "opt_fusion = torch.optim.SGD(model_fusion.parameters(), lr=LEARNING_RATE)\n",
    "loss_fn_fusion = nn.CrossEntropyLoss()\n",
    "\n",
    "history_fusion, best_model_fusion, best_model_score_fusion = train_save_best(model=model_fusion,\n",
    "                                                                             iterator=train_iter,\n",
    "                                                                             valid_iter=valid_iter,\n",
    "                                                                             optimizer=opt_fusion,\n",
    "                                                                             criterion=loss_fn_fusion,\n",
    "                                                                             epoch=EPOCHS,\n",
    "                                                                             clip=1,\n",
    "                                                                             device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [],
   "source": [
    "model_name = 'fusion_OT_post_retraining'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "outputs": [],
   "source": [
    "# save model\n",
    "name = f'parallel_training/model{model_name}_IMDB_256'\n",
    "save_model(model_fusion, name=name)\n",
    "\n",
    "# save history\n",
    "name = f'parallel_training/history_model{model_name}_IMDB_256'\n",
    "save_history(history_fusion, name=name)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load best model (current model is trained on full epochs)\n",
    "model_fusion = torch.load(f'Models/parallel_training/model{model_name}_IMDB_256')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Test fusion (after retraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jjung\\OneDrive\\Dokumente\\ETH\\MSc 3rd semester\\Deep Learning\\Exploring Model Fusion with Optimal Transport on Transformers\\utils.py:144: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  src, trg = torch.tensor(src).to(device), torch.tensor(trg).to(device)  # put to cpu/gpu\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test with vanilla fusion\n",
    "test_fusion(modelA, modelB, model_fusion, test_iter, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "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": "3.8.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "b1744cd9dc0832a8d503a2c77e6bee76d4493b3bf33a738cf38afd0bb2e60262"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}