Kollektifproje.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/idrisdemir/EnglishExam/blob/master/Kollektifproje.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__oEiqGtWAxP",
        "outputId": "0b5b8ecb-ee09-410f-89ef-1845e10a914b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.16.1-py3-none-any.whl (507 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bsl6ATABWFfp"
      },
      "outputs": [],
      "source": [
        "#libraries\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn.parameter import Parameter\n",
        "import numpy as np\n",
        "from torch.utils.data import DataLoader,StackDataset\n",
        "import matplotlib.pyplot as plt\n",
        "from datasets import load_dataset\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import time\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#from mpl_toolkits.mplot3d import Axes3D\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fFIMvCqHWHsY"
      },
      "outputs": [],
      "source": [
        "batch_size = 16\n",
        "epochs = 10\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "num_ara_agirlik = 5\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fEOHGu12WI8W"
      },
      "outputs": [],
      "source": [
        "# # Setting up the device for GPU usage\n",
        "\n",
        "from torch import cuda\n",
        "device = 'cuda' if cuda.is_available() else 'cpu'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4G9jSwKZlSf"
      },
      "source": [
        "# ***Veri Seti***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hw7Q7W7uWNr8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381,
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        },
        "outputId": "65af520f-ba9a-4ca6-a37b-8e5bb90f9449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/18.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffcca66bfefe4e3db729d853f87aa7da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.23M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "888c59be68724755be2d21a06c6287a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28d3438bc780430c851a7c16b06dcf50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffe891b23e664eae810bd8573610b065"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset(\"ag_news\",split='train')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq_yeU1rZq16"
      },
      "source": [
        "# ***Prepare Veri Seti***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "seQWI7u1ZtGY"
      },
      "outputs": [],
      "source": [
        "#metin kümesinin vektör temsilleri elde ediliyor\n",
        "vectorizer = CountVectorizer(max_features=1000,dtype='float32')\n",
        "vectorizer.fit(dataset['text'])\n",
        "text_vectors = vectorizer.transform(dataset['text']).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BjX6Hm9JZyv4"
      },
      "outputs": [],
      "source": [
        "#Veri setinin %15 'i test için ayrıldı\n",
        "\n",
        "train_x, train_y = text_vectors[:45000],dataset['label'][:45000] #120000'in yüzde 15 i 102000'dir.\n",
        "test_x, test_y = text_vectors[45000:50000],dataset['label'][45000:50000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VO6DvAK6bLOE"
      },
      "outputs": [],
      "source": [
        "train_dataset = StackDataset(text=train_x,label=train_y)\n",
        "test_dataset = StackDataset(text=test_x,label=test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cr4f94WDy52Q"
      },
      "outputs": [],
      "source": [
        "train_dataset = DataLoader(train_dataset,batch_size=batch_size)\n",
        "test_dataset = DataLoader(test_dataset,batch_size=batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zx9uOblvy-eQ"
      },
      "source": [
        "# ***Model***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sh_iHEr3y_tu"
      },
      "outputs": [],
      "source": [
        "# Define model\n",
        "class TextNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.relu = nn.ReLU()\n",
        "        self.linear_1 = nn.Linear(1000,256) #her bir vectör kısmında farklı değerler oluyor\n",
        "        self.linear_2 = nn.Linear(256,256)\n",
        "        self.linear_3 = nn.Linear(256,256)\n",
        "        self.linear_4 = nn.Linear(256,4)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.linear_1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.linear_2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.linear_3(x)\n",
        "        x = self.relu(x)\n",
        "        logits = self.linear_4(x)\n",
        "        return logits\n",
        "    def get_parameters(self):\n",
        "      linear1_w = np.reshape(self.linear_1.weight.cpu().detach().numpy(),(-1,))\n",
        "      linear1_b = np.reshape(self.linear_1.bias.cpu().detach().numpy(),(-1,))\n",
        "      linear2_w = np.reshape(self.linear_2.weight.cpu().detach().numpy(),(-1,))\n",
        "      linear2_b = np.reshape(self.linear_2.bias.cpu().detach().numpy(),(-1,))\n",
        "      #linear3_w = np.reshape(self.linear_3.weight.cpu().detach().numpy(),(-1,))\n",
        "      #linear3_b = np.reshape(self.linear_3.bias.cpu().detach().numpy(),(-1,))\n",
        "\n",
        "      weights = np.concatenate((linear1_w,linear1_b,linear2_w,linear2_b))#,linear3_w,linear3_b))\n",
        "      return weights\n",
        "\n",
        "    def load_parameters(self,parameters):\n",
        "      self.linear_1.weigth = Parameter(torch.tensor(parameters[:1000*32]))\n",
        "      self.linear_1.bias = Parameter(torch.tensor(parameters[1000*32:1000*32 + 32]))\n",
        "      self.linear_2.weight = Parameter(torch.tensor(parameters[1000*32 + 32:1000*32 + 32 + 32*4]))\n",
        "      self.linear_2.bias = Parameter(torch.tensor(parameters[1000*32 + 32 + 32*4:]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeULb8_hzLyF"
      },
      "source": [
        "# ***Training***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bCSX38ySzNSq"
      },
      "outputs": [],
      "source": [
        "#veri kümesi ve model veriliyor. O model o veri kümesi ile eğitiliyor. Eğitilen model döndürülüyor.\n",
        "def train(ds,model):\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
        "  for epoch in range(epochs):\n",
        "    start_time = time.perf_counter()\n",
        "    print(f\"Epoch {epoch+1}\\n-------------------------------\")\n",
        "    size = len(ds.dataset)\n",
        "    model.train()\n",
        "    for batch_no, batch in enumerate(ds):\n",
        "      X, y = batch['text'].to(device), batch['label'].to(device)\n",
        "\n",
        "      # Compute prediction error\n",
        "      pred = model(X)\n",
        "      loss = loss_fn(pred, y)\n",
        "\n",
        "      # Backpropagation\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      if batch_no % 100 == 0:\n",
        "          loss, current = loss.item(), (batch_no + 1) * len(X)\n",
        "          print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a0V-CXuj-pDf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7964b004-94d9-4037-c74d-0a760a4dbf65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 1.406728  [   16/45000]\n",
            "loss: 1.383899  [ 1616/45000]\n",
            "loss: 1.272114  [ 3216/45000]\n",
            "loss: 0.964173  [ 4816/45000]\n",
            "loss: 0.458655  [ 6416/45000]\n",
            "loss: 0.424463  [ 8016/45000]\n",
            "loss: 0.611285  [ 9616/45000]\n",
            "loss: 0.521508  [11216/45000]\n",
            "loss: 0.375147  [12816/45000]\n",
            "loss: 0.661358  [14416/45000]\n",
            "loss: 0.915623  [16016/45000]\n",
            "loss: 0.304290  [17616/45000]\n",
            "loss: 0.105810  [19216/45000]\n",
            "loss: 0.732774  [20816/45000]\n",
            "loss: 0.261520  [22416/45000]\n",
            "loss: 0.360640  [24016/45000]\n",
            "loss: 0.182595  [25616/45000]\n",
            "loss: 0.178499  [27216/45000]\n",
            "loss: 0.409011  [28816/45000]\n",
            "loss: 0.372151  [30416/45000]\n",
            "loss: 0.188707  [32016/45000]\n",
            "loss: 0.153324  [33616/45000]\n",
            "loss: 0.491376  [35216/45000]\n",
            "loss: 0.755903  [36816/45000]\n",
            "loss: 0.410531  [38416/45000]\n",
            "loss: 0.136615  [40016/45000]\n",
            "loss: 0.168791  [41616/45000]\n",
            "loss: 0.333948  [43216/45000]\n",
            "loss: 0.458618  [44816/45000]\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 0.435932  [   16/45000]\n",
            "loss: 0.636636  [ 1616/45000]\n",
            "loss: 0.373106  [ 3216/45000]\n",
            "loss: 0.501364  [ 4816/45000]\n",
            "loss: 0.195673  [ 6416/45000]\n",
            "loss: 0.287209  [ 8016/45000]\n",
            "loss: 0.633091  [ 9616/45000]\n",
            "loss: 0.453408  [11216/45000]\n",
            "loss: 0.351508  [12816/45000]\n",
            "loss: 0.632361  [14416/45000]\n",
            "loss: 0.748933  [16016/45000]\n",
            "loss: 0.204629  [17616/45000]\n",
            "loss: 0.074303  [19216/45000]\n",
            "loss: 0.685719  [20816/45000]\n",
            "loss: 0.155545  [22416/45000]\n",
            "loss: 0.239943  [24016/45000]\n",
            "loss: 0.222791  [25616/45000]\n",
            "loss: 0.125029  [27216/45000]\n",
            "loss: 0.211668  [28816/45000]\n",
            "loss: 0.325383  [30416/45000]\n",
            "loss: 0.181262  [32016/45000]\n",
            "loss: 0.146761  [33616/45000]\n",
            "loss: 0.521026  [35216/45000]\n",
            "loss: 0.674475  [36816/45000]\n",
            "loss: 0.365803  [38416/45000]\n",
            "loss: 0.097124  [40016/45000]\n",
            "loss: 0.140107  [41616/45000]\n",
            "loss: 0.346721  [43216/45000]\n",
            "loss: 0.432782  [44816/45000]\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 0.371159  [   16/45000]\n",
            "loss: 0.613327  [ 1616/45000]\n",
            "loss: 0.344800  [ 3216/45000]\n",
            "loss: 0.425376  [ 4816/45000]\n",
            "loss: 0.168832  [ 6416/45000]\n",
            "loss: 0.227527  [ 8016/45000]\n",
            "loss: 0.614372  [ 9616/45000]\n",
            "loss: 0.396727  [11216/45000]\n",
            "loss: 0.289239  [12816/45000]\n",
            "loss: 0.649505  [14416/45000]\n",
            "loss: 0.712406  [16016/45000]\n",
            "loss: 0.167261  [17616/45000]\n",
            "loss: 0.064654  [19216/45000]\n",
            "loss: 0.639327  [20816/45000]\n",
            "loss: 0.127187  [22416/45000]\n",
            "loss: 0.217481  [24016/45000]\n",
            "loss: 0.220437  [25616/45000]\n",
            "loss: 0.110643  [27216/45000]\n",
            "loss: 0.164914  [28816/45000]\n",
            "loss: 0.302242  [30416/45000]\n",
            "loss: 0.175185  [32016/45000]\n",
            "loss: 0.138714  [33616/45000]\n",
            "loss: 0.503022  [35216/45000]\n",
            "loss: 0.609424  [36816/45000]\n",
            "loss: 0.317724  [38416/45000]\n",
            "loss: 0.087230  [40016/45000]\n",
            "loss: 0.126713  [41616/45000]\n",
            "loss: 0.337387  [43216/45000]\n",
            "loss: 0.402443  [44816/45000]\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 0.343895  [   16/45000]\n",
            "loss: 0.567572  [ 1616/45000]\n",
            "loss: 0.294481  [ 3216/45000]\n",
            "loss: 0.345708  [ 4816/45000]\n",
            "loss: 0.138054  [ 6416/45000]\n",
            "loss: 0.185227  [ 8016/45000]\n",
            "loss: 0.575044  [ 9616/45000]\n",
            "loss: 0.328200  [11216/45000]\n",
            "loss: 0.236893  [12816/45000]\n",
            "loss: 0.637622  [14416/45000]\n",
            "loss: 0.699870  [16016/45000]\n",
            "loss: 0.156584  [17616/45000]\n",
            "loss: 0.053369  [19216/45000]\n",
            "loss: 0.568852  [20816/45000]\n",
            "loss: 0.107570  [22416/45000]\n",
            "loss: 0.204430  [24016/45000]\n",
            "loss: 0.216486  [25616/45000]\n",
            "loss: 0.097211  [27216/45000]\n",
            "loss: 0.130904  [28816/45000]\n",
            "loss: 0.277108  [30416/45000]\n",
            "loss: 0.167991  [32016/45000]\n",
            "loss: 0.125918  [33616/45000]\n",
            "loss: 0.471715  [35216/45000]\n",
            "loss: 0.522744  [36816/45000]\n",
            "loss: 0.250578  [38416/45000]\n",
            "loss: 0.087720  [40016/45000]\n",
            "loss: 0.097763  [41616/45000]\n",
            "loss: 0.293597  [43216/45000]\n",
            "loss: 0.370832  [44816/45000]\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 0.318600  [   16/45000]\n",
            "loss: 0.500651  [ 1616/45000]\n",
            "loss: 0.238819  [ 3216/45000]\n",
            "loss: 0.242321  [ 4816/45000]\n",
            "loss: 0.110175  [ 6416/45000]\n",
            "loss: 0.143822  [ 8016/45000]\n",
            "loss: 0.496077  [ 9616/45000]\n",
            "loss: 0.250933  [11216/45000]\n",
            "loss: 0.186544  [12816/45000]\n",
            "loss: 0.600225  [14416/45000]\n",
            "loss: 0.671314  [16016/45000]\n",
            "loss: 0.140049  [17616/45000]\n",
            "loss: 0.038961  [19216/45000]\n",
            "loss: 0.478437  [20816/45000]\n",
            "loss: 0.085050  [22416/45000]\n",
            "loss: 0.176125  [24016/45000]\n",
            "loss: 0.214365  [25616/45000]\n",
            "loss: 0.077559  [27216/45000]\n",
            "loss: 0.097088  [28816/45000]\n",
            "loss: 0.231227  [30416/45000]\n",
            "loss: 0.163849  [32016/45000]\n",
            "loss: 0.111094  [33616/45000]\n",
            "loss: 0.435643  [35216/45000]\n",
            "loss: 0.437166  [36816/45000]\n",
            "loss: 0.174745  [38416/45000]\n",
            "loss: 0.099463  [40016/45000]\n",
            "loss: 0.065445  [41616/45000]\n",
            "loss: 0.225306  [43216/45000]\n",
            "loss: 0.321862  [44816/45000]\n",
            "Epoch 6\n",
            "-------------------------------\n",
            "loss: 0.260711  [   16/45000]\n",
            "loss: 0.432872  [ 1616/45000]\n",
            "loss: 0.176989  [ 3216/45000]\n",
            "loss: 0.155585  [ 4816/45000]\n",
            "loss: 0.080663  [ 6416/45000]\n",
            "loss: 0.106243  [ 8016/45000]\n",
            "loss: 0.427953  [ 9616/45000]\n",
            "loss: 0.181737  [11216/45000]\n",
            "loss: 0.149983  [12816/45000]\n",
            "loss: 0.546137  [14416/45000]\n",
            "loss: 0.605077  [16016/45000]\n",
            "loss: 0.101499  [17616/45000]\n",
            "loss: 0.025108  [19216/45000]\n",
            "loss: 0.373200  [20816/45000]\n",
            "loss: 0.054863  [22416/45000]\n",
            "loss: 0.145642  [24016/45000]\n",
            "loss: 0.206872  [25616/45000]\n",
            "loss: 0.054650  [27216/45000]\n",
            "loss: 0.066306  [28816/45000]\n",
            "loss: 0.177853  [30416/45000]\n",
            "loss: 0.163084  [32016/45000]\n",
            "loss: 0.088082  [33616/45000]\n",
            "loss: 0.371552  [35216/45000]\n",
            "loss: 0.326610  [36816/45000]\n",
            "loss: 0.114653  [38416/45000]\n",
            "loss: 0.099355  [40016/45000]\n",
            "loss: 0.041277  [41616/45000]\n",
            "loss: 0.154031  [43216/45000]\n",
            "loss: 0.257087  [44816/45000]\n",
            "Epoch 7\n",
            "-------------------------------\n",
            "loss: 0.177507  [   16/45000]\n",
            "loss: 0.355312  [ 1616/45000]\n",
            "loss: 0.119108  [ 3216/45000]\n",
            "loss: 0.083400  [ 4816/45000]\n",
            "loss: 0.051863  [ 6416/45000]\n",
            "loss: 0.073087  [ 8016/45000]\n",
            "loss: 0.385293  [ 9616/45000]\n",
            "loss: 0.081205  [11216/45000]\n",
            "loss: 0.103599  [12816/45000]\n",
            "loss: 0.472967  [14416/45000]\n",
            "loss: 0.475117  [16016/45000]\n",
            "loss: 0.067794  [17616/45000]\n",
            "loss: 0.014538  [19216/45000]\n",
            "loss: 0.291676  [20816/45000]\n",
            "loss: 0.027472  [22416/45000]\n",
            "loss: 0.077336  [24016/45000]\n",
            "loss: 0.192732  [25616/45000]\n",
            "loss: 0.032222  [27216/45000]\n",
            "loss: 0.042397  [28816/45000]\n",
            "loss: 0.098267  [30416/45000]\n",
            "loss: 0.155892  [32016/45000]\n",
            "loss: 0.057791  [33616/45000]\n",
            "loss: 0.213357  [35216/45000]\n",
            "loss: 0.204755  [36816/45000]\n",
            "loss: 0.107989  [38416/45000]\n",
            "loss: 0.057715  [40016/45000]\n",
            "loss: 0.025501  [41616/45000]\n",
            "loss: 0.075232  [43216/45000]\n",
            "loss: 0.173330  [44816/45000]\n",
            "Epoch 8\n",
            "-------------------------------\n",
            "loss: 0.080269  [   16/45000]\n",
            "loss: 0.251508  [ 1616/45000]\n",
            "loss: 0.055662  [ 3216/45000]\n",
            "loss: 0.041144  [ 4816/45000]\n",
            "loss: 0.022911  [ 6416/45000]\n",
            "loss: 0.044158  [ 8016/45000]\n",
            "loss: 0.313033  [ 9616/45000]\n",
            "loss: 0.032983  [11216/45000]\n",
            "loss: 0.070472  [12816/45000]\n",
            "loss: 0.339505  [14416/45000]\n",
            "loss: 0.329956  [16016/45000]\n",
            "loss: 0.038062  [17616/45000]\n",
            "loss: 0.007113  [19216/45000]\n",
            "loss: 0.250542  [20816/45000]\n",
            "loss: 0.012439  [22416/45000]\n",
            "loss: 0.034301  [24016/45000]\n",
            "loss: 0.149622  [25616/45000]\n",
            "loss: 0.015099  [27216/45000]\n",
            "loss: 0.022454  [28816/45000]\n",
            "loss: 0.048859  [30416/45000]\n",
            "loss: 0.121356  [32016/45000]\n",
            "loss: 0.036254  [33616/45000]\n",
            "loss: 0.075605  [35216/45000]\n",
            "loss: 0.107784  [36816/45000]\n",
            "loss: 0.164853  [38416/45000]\n",
            "loss: 0.018914  [40016/45000]\n",
            "loss: 0.011139  [41616/45000]\n",
            "loss: 0.022554  [43216/45000]\n",
            "loss: 0.099787  [44816/45000]\n",
            "Epoch 9\n",
            "-------------------------------\n",
            "loss: 0.046451  [   16/45000]\n",
            "loss: 0.128678  [ 1616/45000]\n",
            "loss: 0.024036  [ 3216/45000]\n",
            "loss: 0.041645  [ 4816/45000]\n",
            "loss: 0.014308  [ 6416/45000]\n",
            "loss: 0.025515  [ 8016/45000]\n",
            "loss: 0.232289  [ 9616/45000]\n",
            "loss: 0.008579  [11216/45000]\n",
            "loss: 0.044833  [12816/45000]\n",
            "loss: 0.232743  [14416/45000]\n",
            "loss: 0.187298  [16016/45000]\n",
            "loss: 0.018798  [17616/45000]\n",
            "loss: 0.005457  [19216/45000]\n",
            "loss: 0.192964  [20816/45000]\n",
            "loss: 0.004406  [22416/45000]\n",
            "loss: 0.019761  [24016/45000]\n",
            "loss: 0.101223  [25616/45000]\n",
            "loss: 0.006804  [27216/45000]\n",
            "loss: 0.016035  [28816/45000]\n",
            "loss: 0.019291  [30416/45000]\n",
            "loss: 0.074085  [32016/45000]\n",
            "loss: 0.024308  [33616/45000]\n",
            "loss: 0.026007  [35216/45000]\n",
            "loss: 0.067563  [36816/45000]\n",
            "loss: 0.229112  [38416/45000]\n",
            "loss: 0.016658  [40016/45000]\n",
            "loss: 0.003372  [41616/45000]\n",
            "loss: 0.013417  [43216/45000]\n",
            "loss: 0.054214  [44816/45000]\n",
            "Epoch 10\n",
            "-------------------------------\n",
            "loss: 0.044380  [   16/45000]\n",
            "loss: 0.062373  [ 1616/45000]\n",
            "loss: 0.010366  [ 3216/45000]\n",
            "loss: 0.024788  [ 4816/45000]\n",
            "loss: 0.005381  [ 6416/45000]\n",
            "loss: 0.015330  [ 8016/45000]\n",
            "loss: 0.160133  [ 9616/45000]\n",
            "loss: 0.005227  [11216/45000]\n",
            "loss: 0.043581  [12816/45000]\n",
            "loss: 0.177965  [14416/45000]\n",
            "loss: 0.048104  [16016/45000]\n",
            "loss: 0.005116  [17616/45000]\n",
            "loss: 0.004239  [19216/45000]\n",
            "loss: 0.065198  [20816/45000]\n",
            "loss: 0.001734  [22416/45000]\n",
            "loss: 0.012504  [24016/45000]\n",
            "loss: 0.010258  [25616/45000]\n",
            "loss: 0.001282  [27216/45000]\n",
            "loss: 0.013491  [28816/45000]\n",
            "loss: 0.006153  [30416/45000]\n",
            "loss: 0.029511  [32016/45000]\n",
            "loss: 0.031840  [33616/45000]\n",
            "loss: 0.021430  [35216/45000]\n",
            "loss: 0.034781  [36816/45000]\n",
            "loss: 0.089211  [38416/45000]\n",
            "loss: 0.001402  [40016/45000]\n",
            "loss: 0.000630  [41616/45000]\n",
            "loss: 0.031738  [43216/45000]\n",
            "loss: 0.053894  [44816/45000]\n"
          ]
        }
      ],
      "source": [
        "model1 = TextNetwork().to(device)\n",
        "model1 = train(train_dataset,model1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = TextNetwork().to(device)\n",
        "model2 = train(train_dataset,model2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9X-otBBhYuZo",
        "outputId": "4f3e29ba-f570-4ef3-9fa8-0a52c66c6398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 1.436506  [   16/45000]\n",
            "loss: 1.388877  [ 1616/45000]\n",
            "loss: 1.277072  [ 3216/45000]\n",
            "loss: 1.012687  [ 4816/45000]\n",
            "loss: 0.446284  [ 6416/45000]\n",
            "loss: 0.392911  [ 8016/45000]\n",
            "loss: 0.617245  [ 9616/45000]\n",
            "loss: 0.562449  [11216/45000]\n",
            "loss: 0.383127  [12816/45000]\n",
            "loss: 0.632760  [14416/45000]\n",
            "loss: 0.954364  [16016/45000]\n",
            "loss: 0.353934  [17616/45000]\n",
            "loss: 0.094388  [19216/45000]\n",
            "loss: 0.748617  [20816/45000]\n",
            "loss: 0.242672  [22416/45000]\n",
            "loss: 0.390976  [24016/45000]\n",
            "loss: 0.157113  [25616/45000]\n",
            "loss: 0.177881  [27216/45000]\n",
            "loss: 0.446204  [28816/45000]\n",
            "loss: 0.391772  [30416/45000]\n",
            "loss: 0.182957  [32016/45000]\n",
            "loss: 0.156912  [33616/45000]\n",
            "loss: 0.467106  [35216/45000]\n",
            "loss: 0.734961  [36816/45000]\n",
            "loss: 0.408248  [38416/45000]\n",
            "loss: 0.125796  [40016/45000]\n",
            "loss: 0.155604  [41616/45000]\n",
            "loss: 0.357471  [43216/45000]\n",
            "loss: 0.461564  [44816/45000]\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 0.437972  [   16/45000]\n",
            "loss: 0.654383  [ 1616/45000]\n",
            "loss: 0.353687  [ 3216/45000]\n",
            "loss: 0.467478  [ 4816/45000]\n",
            "loss: 0.219443  [ 6416/45000]\n",
            "loss: 0.274667  [ 8016/45000]\n",
            "loss: 0.633023  [ 9616/45000]\n",
            "loss: 0.460467  [11216/45000]\n",
            "loss: 0.369835  [12816/45000]\n",
            "loss: 0.656218  [14416/45000]\n",
            "loss: 0.744578  [16016/45000]\n",
            "loss: 0.226574  [17616/45000]\n",
            "loss: 0.076012  [19216/45000]\n",
            "loss: 0.671454  [20816/45000]\n",
            "loss: 0.161426  [22416/45000]\n",
            "loss: 0.254072  [24016/45000]\n",
            "loss: 0.211330  [25616/45000]\n",
            "loss: 0.121270  [27216/45000]\n",
            "loss: 0.228342  [28816/45000]\n",
            "loss: 0.330206  [30416/45000]\n",
            "loss: 0.178475  [32016/45000]\n",
            "loss: 0.148940  [33616/45000]\n",
            "loss: 0.498205  [35216/45000]\n",
            "loss: 0.665274  [36816/45000]\n",
            "loss: 0.377389  [38416/45000]\n",
            "loss: 0.092032  [40016/45000]\n",
            "loss: 0.132383  [41616/45000]\n",
            "loss: 0.386728  [43216/45000]\n",
            "loss: 0.448754  [44816/45000]\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 0.367827  [   16/45000]\n",
            "loss: 0.643872  [ 1616/45000]\n",
            "loss: 0.325676  [ 3216/45000]\n",
            "loss: 0.400278  [ 4816/45000]\n",
            "loss: 0.209429  [ 6416/45000]\n",
            "loss: 0.214594  [ 8016/45000]\n",
            "loss: 0.599853  [ 9616/45000]\n",
            "loss: 0.414109  [11216/45000]\n",
            "loss: 0.307597  [12816/45000]\n",
            "loss: 0.646536  [14416/45000]\n",
            "loss: 0.733872  [16016/45000]\n",
            "loss: 0.176033  [17616/45000]\n",
            "loss: 0.065178  [19216/45000]\n",
            "loss: 0.638690  [20816/45000]\n",
            "loss: 0.133949  [22416/45000]\n",
            "loss: 0.225524  [24016/45000]\n",
            "loss: 0.202174  [25616/45000]\n",
            "loss: 0.102787  [27216/45000]\n",
            "loss: 0.175393  [28816/45000]\n",
            "loss: 0.295177  [30416/45000]\n",
            "loss: 0.177302  [32016/45000]\n",
            "loss: 0.134513  [33616/45000]\n",
            "loss: 0.489073  [35216/45000]\n",
            "loss: 0.605857  [36816/45000]\n",
            "loss: 0.331249  [38416/45000]\n",
            "loss: 0.085127  [40016/45000]\n",
            "loss: 0.116628  [41616/45000]\n",
            "loss: 0.364323  [43216/45000]\n",
            "loss: 0.439042  [44816/45000]\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 0.337892  [   16/45000]\n",
            "loss: 0.631982  [ 1616/45000]\n",
            "loss: 0.275226  [ 3216/45000]\n",
            "loss: 0.326601  [ 4816/45000]\n",
            "loss: 0.177108  [ 6416/45000]\n",
            "loss: 0.177096  [ 8016/45000]\n",
            "loss: 0.533619  [ 9616/45000]\n",
            "loss: 0.350951  [11216/45000]\n",
            "loss: 0.257080  [12816/45000]\n",
            "loss: 0.632645  [14416/45000]\n",
            "loss: 0.718192  [16016/45000]\n",
            "loss: 0.156934  [17616/45000]\n",
            "loss: 0.056008  [19216/45000]\n",
            "loss: 0.612367  [20816/45000]\n",
            "loss: 0.115400  [22416/45000]\n",
            "loss: 0.215291  [24016/45000]\n",
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            "loss: 0.480513  [35216/45000]\n",
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            "loss: 0.316846  [43216/45000]\n",
            "loss: 0.428519  [44816/45000]\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 0.306760  [   16/45000]\n",
            "loss: 0.608201  [ 1616/45000]\n",
            "loss: 0.220507  [ 3216/45000]\n",
            "loss: 0.251528  [ 4816/45000]\n",
            "loss: 0.139470  [ 6416/45000]\n",
            "loss: 0.138051  [ 8016/45000]\n",
            "loss: 0.463936  [ 9616/45000]\n",
            "loss: 0.264447  [11216/45000]\n",
            "loss: 0.222471  [12816/45000]\n",
            "loss: 0.604225  [14416/45000]\n",
            "loss: 0.685748  [16016/45000]\n",
            "loss: 0.138639  [17616/45000]\n",
            "loss: 0.046917  [19216/45000]\n",
            "loss: 0.571768  [20816/45000]\n",
            "loss: 0.093625  [22416/45000]\n",
            "loss: 0.202401  [24016/45000]\n",
            "loss: 0.158374  [25616/45000]\n",
            "loss: 0.059867  [27216/45000]\n",
            "loss: 0.113650  [28816/45000]\n",
            "loss: 0.222088  [30416/45000]\n",
            "loss: 0.182003  [32016/45000]\n",
            "loss: 0.113653  [33616/45000]\n",
            "loss: 0.450097  [35216/45000]\n",
            "loss: 0.449508  [36816/45000]\n",
            "loss: 0.203122  [38416/45000]\n",
            "loss: 0.068049  [40016/45000]\n",
            "loss: 0.062778  [41616/45000]\n",
            "loss: 0.259852  [43216/45000]\n",
            "loss: 0.402130  [44816/45000]\n",
            "Epoch 6\n",
            "-------------------------------\n",
            "loss: 0.271483  [   16/45000]\n",
            "loss: 0.585781  [ 1616/45000]\n",
            "loss: 0.177700  [ 3216/45000]\n",
            "loss: 0.199600  [ 4816/45000]\n",
            "loss: 0.107130  [ 6416/45000]\n",
            "loss: 0.091623  [ 8016/45000]\n",
            "loss: 0.425814  [ 9616/45000]\n",
            "loss: 0.175033  [11216/45000]\n",
            "loss: 0.185367  [12816/45000]\n",
            "loss: 0.542637  [14416/45000]\n",
            "loss: 0.640110  [16016/45000]\n",
            "loss: 0.105504  [17616/45000]\n",
            "loss: 0.038368  [19216/45000]\n",
            "loss: 0.501362  [20816/45000]\n",
            "loss: 0.066193  [22416/45000]\n",
            "loss: 0.157035  [24016/45000]\n",
            "loss: 0.129924  [25616/45000]\n",
            "loss: 0.037373  [27216/45000]\n",
            "loss: 0.088799  [28816/45000]\n",
            "loss: 0.167167  [30416/45000]\n",
            "loss: 0.190092  [32016/45000]\n",
            "loss: 0.099232  [33616/45000]\n",
            "loss: 0.381143  [35216/45000]\n",
            "loss: 0.350903  [36816/45000]\n",
            "loss: 0.145517  [38416/45000]\n",
            "loss: 0.059581  [40016/45000]\n",
            "loss: 0.042327  [41616/45000]\n",
            "loss: 0.225006  [43216/45000]\n",
            "loss: 0.337998  [44816/45000]\n",
            "Epoch 7\n",
            "-------------------------------\n",
            "loss: 0.185663  [   16/45000]\n",
            "loss: 0.529379  [ 1616/45000]\n",
            "loss: 0.129204  [ 3216/45000]\n",
            "loss: 0.166159  [ 4816/45000]\n",
            "loss: 0.075124  [ 6416/45000]\n",
            "loss: 0.054588  [ 8016/45000]\n",
            "loss: 0.401948  [ 9616/45000]\n",
            "loss: 0.087580  [11216/45000]\n",
            "loss: 0.134988  [12816/45000]\n",
            "loss: 0.469152  [14416/45000]\n",
            "loss: 0.586737  [16016/45000]\n",
            "loss: 0.068511  [17616/45000]\n",
            "loss: 0.029893  [19216/45000]\n",
            "loss: 0.401055  [20816/45000]\n",
            "loss: 0.038961  [22416/45000]\n",
            "loss: 0.091137  [24016/45000]\n",
            "loss: 0.085921  [25616/45000]\n",
            "loss: 0.020854  [27216/45000]\n",
            "loss: 0.065607  [28816/45000]\n",
            "loss: 0.116184  [30416/45000]\n",
            "loss: 0.206593  [32016/45000]\n",
            "loss: 0.082305  [33616/45000]\n",
            "loss: 0.275780  [35216/45000]\n",
            "loss: 0.230470  [36816/45000]\n",
            "loss: 0.114770  [38416/45000]\n",
            "loss: 0.032580  [40016/45000]\n",
            "loss: 0.022635  [41616/45000]\n",
            "loss: 0.182750  [43216/45000]\n",
            "loss: 0.287688  [44816/45000]\n",
            "Epoch 8\n",
            "-------------------------------\n",
            "loss: 0.120148  [   16/45000]\n",
            "loss: 0.427775  [ 1616/45000]\n",
            "loss: 0.087239  [ 3216/45000]\n",
            "loss: 0.119403  [ 4816/45000]\n",
            "loss: 0.051079  [ 6416/45000]\n",
            "loss: 0.031965  [ 8016/45000]\n",
            "loss: 0.369682  [ 9616/45000]\n",
            "loss: 0.039979  [11216/45000]\n",
            "loss: 0.060557  [12816/45000]\n",
            "loss: 0.397779  [14416/45000]\n",
            "loss: 0.472463  [16016/45000]\n",
            "loss: 0.039119  [17616/45000]\n",
            "loss: 0.025050  [19216/45000]\n",
            "loss: 0.309634  [20816/45000]\n",
            "loss: 0.020385  [22416/45000]\n",
            "loss: 0.039306  [24016/45000]\n",
            "loss: 0.041203  [25616/45000]\n",
            "loss: 0.010232  [27216/45000]\n",
            "loss: 0.044144  [28816/45000]\n",
            "loss: 0.057941  [30416/45000]\n",
            "loss: 0.214602  [32016/45000]\n",
            "loss: 0.061145  [33616/45000]\n",
            "loss: 0.203347  [35216/45000]\n",
            "loss: 0.111395  [36816/45000]\n",
            "loss: 0.125183  [38416/45000]\n",
            "loss: 0.014251  [40016/45000]\n",
            "loss: 0.010334  [41616/45000]\n",
            "loss: 0.117532  [43216/45000]\n",
            "loss: 0.219721  [44816/45000]\n",
            "Epoch 9\n",
            "-------------------------------\n",
            "loss: 0.063682  [   16/45000]\n",
            "loss: 0.293480  [ 1616/45000]\n",
            "loss: 0.038990  [ 3216/45000]\n",
            "loss: 0.067880  [ 4816/45000]\n",
            "loss: 0.031676  [ 6416/45000]\n",
            "loss: 0.017295  [ 8016/45000]\n",
            "loss: 0.334484  [ 9616/45000]\n",
            "loss: 0.021263  [11216/45000]\n",
            "loss: 0.024377  [12816/45000]\n",
            "loss: 0.322514  [14416/45000]\n",
            "loss: 0.315480  [16016/45000]\n",
            "loss: 0.024979  [17616/45000]\n",
            "loss: 0.024804  [19216/45000]\n",
            "loss: 0.228940  [20816/45000]\n",
            "loss: 0.005979  [22416/45000]\n",
            "loss: 0.021091  [24016/45000]\n",
            "loss: 0.015864  [25616/45000]\n",
            "loss: 0.005057  [27216/45000]\n",
            "loss: 0.020029  [28816/45000]\n",
            "loss: 0.020214  [30416/45000]\n",
            "loss: 0.198312  [32016/45000]\n",
            "loss: 0.037283  [33616/45000]\n",
            "loss: 0.088974  [35216/45000]\n",
            "loss: 0.051788  [36816/45000]\n",
            "loss: 0.152962  [38416/45000]\n",
            "loss: 0.006841  [40016/45000]\n",
            "loss: 0.004025  [41616/45000]\n",
            "loss: 0.058440  [43216/45000]\n",
            "loss: 0.097741  [44816/45000]\n",
            "Epoch 10\n",
            "-------------------------------\n",
            "loss: 0.041878  [   16/45000]\n",
            "loss: 0.141006  [ 1616/45000]\n",
            "loss: 0.017990  [ 3216/45000]\n",
            "loss: 0.046192  [ 4816/45000]\n",
            "loss: 0.016399  [ 6416/45000]\n",
            "loss: 0.009452  [ 8016/45000]\n",
            "loss: 0.301852  [ 9616/45000]\n",
            "loss: 0.024393  [11216/45000]\n",
            "loss: 0.012821  [12816/45000]\n",
            "loss: 0.242585  [14416/45000]\n",
            "loss: 0.130240  [16016/45000]\n",
            "loss: 0.011196  [17616/45000]\n",
            "loss: 0.030923  [19216/45000]\n",
            "loss: 0.109633  [20816/45000]\n",
            "loss: 0.002673  [22416/45000]\n",
            "loss: 0.012506  [24016/45000]\n",
            "loss: 0.012383  [25616/45000]\n",
            "loss: 0.002019  [27216/45000]\n",
            "loss: 0.025152  [28816/45000]\n",
            "loss: 0.005973  [30416/45000]\n",
            "loss: 0.146329  [32016/45000]\n",
            "loss: 0.018432  [33616/45000]\n",
            "loss: 0.015527  [35216/45000]\n",
            "loss: 0.014936  [36816/45000]\n",
            "loss: 0.158356  [38416/45000]\n",
            "loss: 0.001718  [40016/45000]\n",
            "loss: 0.001102  [41616/45000]\n",
            "loss: 0.036374  [43216/45000]\n",
            "loss: 0.171136  [44816/45000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = TextNetwork().to(device)\n",
        "model3 = train(train_dataset,model3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8SmWbTnY0k_",
        "outputId": "6bdd8b14-ef3d-4837-e243-e8fea7279d88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 1.432007  [   16/45000]\n",
            "loss: 1.378582  [ 1616/45000]\n",
            "loss: 1.240448  [ 3216/45000]\n",
            "loss: 1.043665  [ 4816/45000]\n",
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            "loss: 0.462969  [ 8016/45000]\n",
            "loss: 0.687769  [ 9616/45000]\n",
            "loss: 0.493755  [11216/45000]\n",
            "loss: 0.374116  [12816/45000]\n",
            "loss: 0.634559  [14416/45000]\n",
            "loss: 0.939290  [16016/45000]\n",
            "loss: 0.308079  [17616/45000]\n",
            "loss: 0.104184  [19216/45000]\n",
            "loss: 0.703902  [20816/45000]\n",
            "loss: 0.209180  [22416/45000]\n",
            "loss: 0.366999  [24016/45000]\n",
            "loss: 0.193739  [25616/45000]\n",
            "loss: 0.167458  [27216/45000]\n",
            "loss: 0.378231  [28816/45000]\n",
            "loss: 0.397073  [30416/45000]\n",
            "loss: 0.177970  [32016/45000]\n",
            "loss: 0.164025  [33616/45000]\n",
            "loss: 0.470193  [35216/45000]\n",
            "loss: 0.723596  [36816/45000]\n",
            "loss: 0.401524  [38416/45000]\n",
            "loss: 0.120388  [40016/45000]\n",
            "loss: 0.155828  [41616/45000]\n",
            "loss: 0.318112  [43216/45000]\n",
            "loss: 0.458572  [44816/45000]\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 0.445812  [   16/45000]\n",
            "loss: 0.624516  [ 1616/45000]\n",
            "loss: 0.371748  [ 3216/45000]\n",
            "loss: 0.548957  [ 4816/45000]\n",
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            "loss: 0.640050  [ 9616/45000]\n",
            "loss: 0.444636  [11216/45000]\n",
            "loss: 0.357230  [12816/45000]\n",
            "loss: 0.664061  [14416/45000]\n",
            "loss: 0.795675  [16016/45000]\n",
            "loss: 0.207588  [17616/45000]\n",
            "loss: 0.072052  [19216/45000]\n",
            "loss: 0.658031  [20816/45000]\n",
            "loss: 0.131733  [22416/45000]\n",
            "loss: 0.239351  [24016/45000]\n",
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            "loss: 0.118776  [27216/45000]\n",
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            "loss: 0.135067  [41616/45000]\n",
            "loss: 0.324334  [43216/45000]\n",
            "loss: 0.448669  [44816/45000]\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 0.370565  [   16/45000]\n",
            "loss: 0.608082  [ 1616/45000]\n",
            "loss: 0.333540  [ 3216/45000]\n",
            "loss: 0.465251  [ 4816/45000]\n",
            "loss: 0.173005  [ 6416/45000]\n",
            "loss: 0.208787  [ 8016/45000]\n",
            "loss: 0.638215  [ 9616/45000]\n",
            "loss: 0.398524  [11216/45000]\n",
            "loss: 0.292741  [12816/45000]\n",
            "loss: 0.654503  [14416/45000]\n",
            "loss: 0.761312  [16016/45000]\n",
            "loss: 0.174349  [17616/45000]\n",
            "loss: 0.062565  [19216/45000]\n",
            "loss: 0.624653  [20816/45000]\n",
            "loss: 0.117843  [22416/45000]\n",
            "loss: 0.216021  [24016/45000]\n",
            "loss: 0.215537  [25616/45000]\n",
            "loss: 0.104603  [27216/45000]\n",
            "loss: 0.172897  [28816/45000]\n",
            "loss: 0.279191  [30416/45000]\n",
            "loss: 0.182245  [32016/45000]\n",
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            "loss: 0.457401  [35216/45000]\n",
            "loss: 0.590038  [36816/45000]\n",
            "loss: 0.297544  [38416/45000]\n",
            "loss: 0.081497  [40016/45000]\n",
            "loss: 0.114985  [41616/45000]\n",
            "loss: 0.315253  [43216/45000]\n",
            "loss: 0.440608  [44816/45000]\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 0.326462  [   16/45000]\n",
            "loss: 0.590274  [ 1616/45000]\n",
            "loss: 0.273721  [ 3216/45000]\n",
            "loss: 0.391798  [ 4816/45000]\n",
            "loss: 0.150551  [ 6416/45000]\n",
            "loss: 0.169149  [ 8016/45000]\n",
            "loss: 0.630636  [ 9616/45000]\n",
            "loss: 0.341271  [11216/45000]\n",
            "loss: 0.238685  [12816/45000]\n",
            "loss: 0.641970  [14416/45000]\n",
            "loss: 0.716525  [16016/45000]\n",
            "loss: 0.162701  [17616/45000]\n",
            "loss: 0.050515  [19216/45000]\n",
            "loss: 0.591600  [20816/45000]\n",
            "loss: 0.095913  [22416/45000]\n",
            "loss: 0.202492  [24016/45000]\n",
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            "loss: 0.092997  [27216/45000]\n",
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            "loss: 0.494558  [36816/45000]\n",
            "loss: 0.240318  [38416/45000]\n",
            "loss: 0.075786  [40016/45000]\n",
            "loss: 0.080235  [41616/45000]\n",
            "loss: 0.291028  [43216/45000]\n",
            "loss: 0.425241  [44816/45000]\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 0.279618  [   16/45000]\n",
            "loss: 0.560049  [ 1616/45000]\n",
            "loss: 0.221682  [ 3216/45000]\n",
            "loss: 0.315398  [ 4816/45000]\n",
            "loss: 0.121930  [ 6416/45000]\n",
            "loss: 0.135407  [ 8016/45000]\n",
            "loss: 0.589919  [ 9616/45000]\n",
            "loss: 0.278250  [11216/45000]\n",
            "loss: 0.199780  [12816/45000]\n",
            "loss: 0.616006  [14416/45000]\n",
            "loss: 0.656863  [16016/45000]\n",
            "loss: 0.143365  [17616/45000]\n",
            "loss: 0.036923  [19216/45000]\n",
            "loss: 0.531420  [20816/45000]\n",
            "loss: 0.070860  [22416/45000]\n",
            "loss: 0.185530  [24016/45000]\n",
            "loss: 0.175857  [25616/45000]\n",
            "loss: 0.077982  [27216/45000]\n",
            "loss: 0.117648  [28816/45000]\n",
            "loss: 0.197592  [30416/45000]\n",
            "loss: 0.197683  [32016/45000]\n",
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            "loss: 0.177455  [38416/45000]\n",
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            "loss: 0.051554  [41616/45000]\n",
            "loss: 0.250157  [43216/45000]\n",
            "loss: 0.380241  [44816/45000]\n",
            "Epoch 6\n",
            "-------------------------------\n",
            "loss: 0.209801  [   16/45000]\n",
            "loss: 0.523022  [ 1616/45000]\n",
            "loss: 0.174514  [ 3216/45000]\n",
            "loss: 0.245765  [ 4816/45000]\n",
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            "loss: 0.095186  [ 8016/45000]\n",
            "loss: 0.520347  [ 9616/45000]\n",
            "loss: 0.214441  [11216/45000]\n",
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            "loss: 0.584635  [14416/45000]\n",
            "loss: 0.574005  [16016/45000]\n",
            "loss: 0.104241  [17616/45000]\n",
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            "loss: 0.052385  [22416/45000]\n",
            "loss: 0.158130  [24016/45000]\n",
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            "loss: 0.089611  [28816/45000]\n",
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            "loss: 0.204706  [32016/45000]\n",
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            "loss: 0.325191  [35216/45000]\n",
            "loss: 0.222936  [36816/45000]\n",
            "loss: 0.125101  [38416/45000]\n",
            "loss: 0.070094  [40016/45000]\n",
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            "loss: 0.210840  [43216/45000]\n",
            "loss: 0.311227  [44816/45000]\n",
            "Epoch 7\n",
            "-------------------------------\n",
            "loss: 0.104609  [   16/45000]\n",
            "loss: 0.457070  [ 1616/45000]\n",
            "loss: 0.145063  [ 3216/45000]\n",
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            "loss: 0.466187  [ 9616/45000]\n",
            "loss: 0.117436  [11216/45000]\n",
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            "loss: 0.539750  [14416/45000]\n",
            "loss: 0.485905  [16016/45000]\n",
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            "loss: 0.017707  [19216/45000]\n",
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            "loss: 0.034384  [22416/45000]\n",
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            "loss: 0.125659  [30416/45000]\n",
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            "loss: 0.099352  [36816/45000]\n",
            "loss: 0.100775  [38416/45000]\n",
            "loss: 0.041077  [40016/45000]\n",
            "loss: 0.020624  [41616/45000]\n",
            "loss: 0.178534  [43216/45000]\n",
            "loss: 0.240390  [44816/45000]\n",
            "Epoch 8\n",
            "-------------------------------\n",
            "loss: 0.044045  [   16/45000]\n",
            "loss: 0.380058  [ 1616/45000]\n",
            "loss: 0.099359  [ 3216/45000]\n",
            "loss: 0.123724  [ 4816/45000]\n",
            "loss: 0.034535  [ 6416/45000]\n",
            "loss: 0.042489  [ 8016/45000]\n",
            "loss: 0.440971  [ 9616/45000]\n",
            "loss: 0.035869  [11216/45000]\n",
            "loss: 0.078890  [12816/45000]\n",
            "loss: 0.468318  [14416/45000]\n",
            "loss: 0.380560  [16016/45000]\n",
            "loss: 0.026971  [17616/45000]\n",
            "loss: 0.012340  [19216/45000]\n",
            "loss: 0.305064  [20816/45000]\n",
            "loss: 0.023020  [22416/45000]\n",
            "loss: 0.060926  [24016/45000]\n",
            "loss: 0.058960  [25616/45000]\n",
            "loss: 0.025664  [27216/45000]\n",
            "loss: 0.026129  [28816/45000]\n",
            "loss: 0.091632  [30416/45000]\n",
            "loss: 0.220122  [32016/45000]\n",
            "loss: 0.056133  [33616/45000]\n",
            "loss: 0.134556  [35216/45000]\n",
            "loss: 0.036690  [36816/45000]\n",
            "loss: 0.118479  [38416/45000]\n",
            "loss: 0.041612  [40016/45000]\n",
            "loss: 0.009845  [41616/45000]\n",
            "loss: 0.121604  [43216/45000]\n",
            "loss: 0.151647  [44816/45000]\n",
            "Epoch 9\n",
            "-------------------------------\n",
            "loss: 0.022319  [   16/45000]\n",
            "loss: 0.288957  [ 1616/45000]\n",
            "loss: 0.057064  [ 3216/45000]\n",
            "loss: 0.079502  [ 4816/45000]\n",
            "loss: 0.021557  [ 6416/45000]\n",
            "loss: 0.028519  [ 8016/45000]\n",
            "loss: 0.369608  [ 9616/45000]\n",
            "loss: 0.018463  [11216/45000]\n",
            "loss: 0.044492  [12816/45000]\n",
            "loss: 0.342284  [14416/45000]\n",
            "loss: 0.255675  [16016/45000]\n",
            "loss: 0.018182  [17616/45000]\n",
            "loss: 0.013828  [19216/45000]\n",
            "loss: 0.248038  [20816/45000]\n",
            "loss: 0.012190  [22416/45000]\n",
            "loss: 0.031911  [24016/45000]\n",
            "loss: 0.018515  [25616/45000]\n",
            "loss: 0.014878  [27216/45000]\n",
            "loss: 0.015353  [28816/45000]\n",
            "loss: 0.035840  [30416/45000]\n",
            "loss: 0.213055  [32016/45000]\n",
            "loss: 0.029364  [33616/45000]\n",
            "loss: 0.044655  [35216/45000]\n",
            "loss: 0.017921  [36816/45000]\n",
            "loss: 0.125911  [38416/45000]\n",
            "loss: 0.026892  [40016/45000]\n",
            "loss: 0.002280  [41616/45000]\n",
            "loss: 0.089564  [43216/45000]\n",
            "loss: 0.113866  [44816/45000]\n",
            "Epoch 10\n",
            "-------------------------------\n",
            "loss: 0.014037  [   16/45000]\n",
            "loss: 0.193450  [ 1616/45000]\n",
            "loss: 0.016655  [ 3216/45000]\n",
            "loss: 0.076096  [ 4816/45000]\n",
            "loss: 0.014834  [ 6416/45000]\n",
            "loss: 0.017066  [ 8016/45000]\n",
            "loss: 0.259903  [ 9616/45000]\n",
            "loss: 0.010067  [11216/45000]\n",
            "loss: 0.026225  [12816/45000]\n",
            "loss: 0.229256  [14416/45000]\n",
            "loss: 0.091758  [16016/45000]\n",
            "loss: 0.007156  [17616/45000]\n",
            "loss: 0.013255  [19216/45000]\n",
            "loss: 0.175436  [20816/45000]\n",
            "loss: 0.002420  [22416/45000]\n",
            "loss: 0.020803  [24016/45000]\n",
            "loss: 0.007174  [25616/45000]\n",
            "loss: 0.007937  [27216/45000]\n",
            "loss: 0.015173  [28816/45000]\n",
            "loss: 0.015291  [30416/45000]\n",
            "loss: 0.168248  [32016/45000]\n",
            "loss: 0.010687  [33616/45000]\n",
            "loss: 0.010236  [35216/45000]\n",
            "loss: 0.013493  [36816/45000]\n",
            "loss: 0.091142  [38416/45000]\n",
            "loss: 0.024632  [40016/45000]\n",
            "loss: 0.000312  [41616/45000]\n",
            "loss: 0.053743  [43216/45000]\n",
            "loss: 0.028535  [44816/45000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model4 = TextNetwork().to(device)\n",
        "model4 = train(train_dataset,model4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxy10EbTY3GW",
        "outputId": "45879a54-ed2a-4591-d7e5-64207d11f561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 1.422003  [   16/45000]\n",
            "loss: 1.377235  [ 1616/45000]\n",
            "loss: 1.267353  [ 3216/45000]\n",
            "loss: 1.026864  [ 4816/45000]\n",
            "loss: 0.429069  [ 6416/45000]\n",
            "loss: 0.402245  [ 8016/45000]\n",
            "loss: 0.666140  [ 9616/45000]\n",
            "loss: 0.528172  [11216/45000]\n",
            "loss: 0.351584  [12816/45000]\n",
            "loss: 0.695682  [14416/45000]\n",
            "loss: 0.895312  [16016/45000]\n",
            "loss: 0.334760  [17616/45000]\n",
            "loss: 0.102209  [19216/45000]\n",
            "loss: 0.701583  [20816/45000]\n",
            "loss: 0.233439  [22416/45000]\n",
            "loss: 0.375943  [24016/45000]\n",
            "loss: 0.141013  [25616/45000]\n",
            "loss: 0.169679  [27216/45000]\n",
            "loss: 0.411697  [28816/45000]\n",
            "loss: 0.365742  [30416/45000]\n",
            "loss: 0.182647  [32016/45000]\n",
            "loss: 0.149218  [33616/45000]\n",
            "loss: 0.475765  [35216/45000]\n",
            "loss: 0.719365  [36816/45000]\n",
            "loss: 0.404251  [38416/45000]\n",
            "loss: 0.126525  [40016/45000]\n",
            "loss: 0.167869  [41616/45000]\n",
            "loss: 0.331931  [43216/45000]\n",
            "loss: 0.461758  [44816/45000]\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 0.494445  [   16/45000]\n",
            "loss: 0.644995  [ 1616/45000]\n",
            "loss: 0.354812  [ 3216/45000]\n",
            "loss: 0.466853  [ 4816/45000]\n",
            "loss: 0.211712  [ 6416/45000]\n",
            "loss: 0.264101  [ 8016/45000]\n",
            "loss: 0.640807  [ 9616/45000]\n",
            "loss: 0.453649  [11216/45000]\n",
            "loss: 0.355452  [12816/45000]\n",
            "loss: 0.704849  [14416/45000]\n",
            "loss: 0.769217  [16016/45000]\n",
            "loss: 0.225226  [17616/45000]\n",
            "loss: 0.075194  [19216/45000]\n",
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            "loss: 0.148331  [22416/45000]\n",
            "loss: 0.228490  [24016/45000]\n",
            "loss: 0.199481  [25616/45000]\n",
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            "loss: 0.298848  [30416/45000]\n",
            "loss: 0.173250  [32016/45000]\n",
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            "loss: 0.532344  [35216/45000]\n",
            "loss: 0.637553  [36816/45000]\n",
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            "loss: 0.086239  [40016/45000]\n",
            "loss: 0.139972  [41616/45000]\n",
            "loss: 0.354382  [43216/45000]\n",
            "loss: 0.458250  [44816/45000]\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 0.423087  [   16/45000]\n",
            "loss: 0.616972  [ 1616/45000]\n",
            "loss: 0.308069  [ 3216/45000]\n",
            "loss: 0.375807  [ 4816/45000]\n",
            "loss: 0.183429  [ 6416/45000]\n",
            "loss: 0.203957  [ 8016/45000]\n",
            "loss: 0.633054  [ 9616/45000]\n",
            "loss: 0.390173  [11216/45000]\n",
            "loss: 0.309609  [12816/45000]\n",
            "loss: 0.704625  [14416/45000]\n",
            "loss: 0.753050  [16016/45000]\n",
            "loss: 0.183816  [17616/45000]\n",
            "loss: 0.067758  [19216/45000]\n",
            "loss: 0.638450  [20816/45000]\n",
            "loss: 0.121546  [22416/45000]\n",
            "loss: 0.197539  [24016/45000]\n",
            "loss: 0.191425  [25616/45000]\n",
            "loss: 0.105340  [27216/45000]\n",
            "loss: 0.180682  [28816/45000]\n",
            "loss: 0.265856  [30416/45000]\n",
            "loss: 0.170011  [32016/45000]\n",
            "loss: 0.134667  [33616/45000]\n",
            "loss: 0.542007  [35216/45000]\n",
            "loss: 0.565464  [36816/45000]\n",
            "loss: 0.307725  [38416/45000]\n",
            "loss: 0.074593  [40016/45000]\n",
            "loss: 0.120595  [41616/45000]\n",
            "loss: 0.344747  [43216/45000]\n",
            "loss: 0.459073  [44816/45000]\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 0.386289  [   16/45000]\n",
            "loss: 0.581447  [ 1616/45000]\n",
            "loss: 0.249013  [ 3216/45000]\n",
            "loss: 0.295133  [ 4816/45000]\n",
            "loss: 0.151114  [ 6416/45000]\n",
            "loss: 0.170317  [ 8016/45000]\n",
            "loss: 0.599240  [ 9616/45000]\n",
            "loss: 0.331739  [11216/45000]\n",
            "loss: 0.259420  [12816/45000]\n",
            "loss: 0.687378  [14416/45000]\n",
            "loss: 0.729625  [16016/45000]\n",
            "loss: 0.162696  [17616/45000]\n",
            "loss: 0.059364  [19216/45000]\n",
            "loss: 0.590298  [20816/45000]\n",
            "loss: 0.106331  [22416/45000]\n",
            "loss: 0.179486  [24016/45000]\n",
            "loss: 0.159494  [25616/45000]\n",
            "loss: 0.090567  [27216/45000]\n",
            "loss: 0.157443  [28816/45000]\n",
            "loss: 0.234186  [30416/45000]\n",
            "loss: 0.172706  [32016/45000]\n",
            "loss: 0.125669  [33616/45000]\n",
            "loss: 0.541776  [35216/45000]\n",
            "loss: 0.481859  [36816/45000]\n",
            "loss: 0.236219  [38416/45000]\n",
            "loss: 0.065838  [40016/45000]\n",
            "loss: 0.094781  [41616/45000]\n",
            "loss: 0.318088  [43216/45000]\n",
            "loss: 0.460066  [44816/45000]\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 0.359555  [   16/45000]\n",
            "loss: 0.527659  [ 1616/45000]\n",
            "loss: 0.199381  [ 3216/45000]\n",
            "loss: 0.216517  [ 4816/45000]\n",
            "loss: 0.119185  [ 6416/45000]\n",
            "loss: 0.145508  [ 8016/45000]\n",
            "loss: 0.537975  [ 9616/45000]\n",
            "loss: 0.246342  [11216/45000]\n",
            "loss: 0.232679  [12816/45000]\n",
            "loss: 0.649104  [14416/45000]\n",
            "loss: 0.690984  [16016/45000]\n",
            "loss: 0.147348  [17616/45000]\n",
            "loss: 0.047888  [19216/45000]\n",
            "loss: 0.543500  [20816/45000]\n",
            "loss: 0.091556  [22416/45000]\n",
            "loss: 0.145267  [24016/45000]\n",
            "loss: 0.120325  [25616/45000]\n",
            "loss: 0.072328  [27216/45000]\n",
            "loss: 0.136051  [28816/45000]\n",
            "loss: 0.187773  [30416/45000]\n",
            "loss: 0.179501  [32016/45000]\n",
            "loss: 0.117967  [33616/45000]\n",
            "loss: 0.534719  [35216/45000]\n",
            "loss: 0.385795  [36816/45000]\n",
            "loss: 0.162620  [38416/45000]\n",
            "loss: 0.060346  [40016/45000]\n",
            "loss: 0.063674  [41616/45000]\n",
            "loss: 0.279233  [43216/45000]\n",
            "loss: 0.440532  [44816/45000]\n",
            "Epoch 6\n",
            "-------------------------------\n",
            "loss: 0.317053  [   16/45000]\n",
            "loss: 0.455980  [ 1616/45000]\n",
            "loss: 0.149540  [ 3216/45000]\n",
            "loss: 0.139710  [ 4816/45000]\n",
            "loss: 0.090055  [ 6416/45000]\n",
            "loss: 0.108158  [ 8016/45000]\n",
            "loss: 0.436798  [ 9616/45000]\n",
            "loss: 0.154616  [11216/45000]\n",
            "loss: 0.197266  [12816/45000]\n",
            "loss: 0.595463  [14416/45000]\n",
            "loss: 0.633221  [16016/45000]\n",
            "loss: 0.128522  [17616/45000]\n",
            "loss: 0.032775  [19216/45000]\n",
            "loss: 0.479785  [20816/45000]\n",
            "loss: 0.075865  [22416/45000]\n",
            "loss: 0.093112  [24016/45000]\n",
            "loss: 0.086172  [25616/45000]\n",
            "loss: 0.050381  [27216/45000]\n",
            "loss: 0.105605  [28816/45000]\n",
            "loss: 0.148348  [30416/45000]\n",
            "loss: 0.186402  [32016/45000]\n",
            "loss: 0.103242  [33616/45000]\n",
            "loss: 0.485631  [35216/45000]\n",
            "loss: 0.274415  [36816/45000]\n",
            "loss: 0.106088  [38416/45000]\n",
            "loss: 0.054199  [40016/45000]\n",
            "loss: 0.040741  [41616/45000]\n",
            "loss: 0.252391  [43216/45000]\n",
            "loss: 0.380108  [44816/45000]\n",
            "Epoch 7\n",
            "-------------------------------\n",
            "loss: 0.210559  [   16/45000]\n",
            "loss: 0.382649  [ 1616/45000]\n",
            "loss: 0.087076  [ 3216/45000]\n",
            "loss: 0.077346  [ 4816/45000]\n",
            "loss: 0.059994  [ 6416/45000]\n",
            "loss: 0.078883  [ 8016/45000]\n",
            "loss: 0.369639  [ 9616/45000]\n",
            "loss: 0.069549  [11216/45000]\n",
            "loss: 0.155154  [12816/45000]\n",
            "loss: 0.519611  [14416/45000]\n",
            "loss: 0.514739  [16016/45000]\n",
            "loss: 0.090156  [17616/45000]\n",
            "loss: 0.019646  [19216/45000]\n",
            "loss: 0.419566  [20816/45000]\n",
            "loss: 0.062063  [22416/45000]\n",
            "loss: 0.043928  [24016/45000]\n",
            "loss: 0.053371  [25616/45000]\n",
            "loss: 0.033919  [27216/45000]\n",
            "loss: 0.086624  [28816/45000]\n",
            "loss: 0.108168  [30416/45000]\n",
            "loss: 0.183014  [32016/45000]\n",
            "loss: 0.074978  [33616/45000]\n",
            "loss: 0.419206  [35216/45000]\n",
            "loss: 0.146761  [36816/45000]\n",
            "loss: 0.063431  [38416/45000]\n",
            "loss: 0.034351  [40016/45000]\n",
            "loss: 0.024516  [41616/45000]\n",
            "loss: 0.219408  [43216/45000]\n",
            "loss: 0.308842  [44816/45000]\n",
            "Epoch 8\n",
            "-------------------------------\n",
            "loss: 0.067720  [   16/45000]\n",
            "loss: 0.301415  [ 1616/45000]\n",
            "loss: 0.043984  [ 3216/45000]\n",
            "loss: 0.034915  [ 4816/45000]\n",
            "loss: 0.030017  [ 6416/45000]\n",
            "loss: 0.055402  [ 8016/45000]\n",
            "loss: 0.312985  [ 9616/45000]\n",
            "loss: 0.023749  [11216/45000]\n",
            "loss: 0.095208  [12816/45000]\n",
            "loss: 0.414967  [14416/45000]\n",
            "loss: 0.312276  [16016/45000]\n",
            "loss: 0.054511  [17616/45000]\n",
            "loss: 0.014944  [19216/45000]\n",
            "loss: 0.329904  [20816/45000]\n",
            "loss: 0.028085  [22416/45000]\n",
            "loss: 0.018398  [24016/45000]\n",
            "loss: 0.024840  [25616/45000]\n",
            "loss: 0.025061  [27216/45000]\n",
            "loss: 0.067986  [28816/45000]\n",
            "loss: 0.050809  [30416/45000]\n",
            "loss: 0.162776  [32016/45000]\n",
            "loss: 0.030463  [33616/45000]\n",
            "loss: 0.275470  [35216/45000]\n",
            "loss: 0.081785  [36816/45000]\n",
            "loss: 0.055792  [38416/45000]\n",
            "loss: 0.015216  [40016/45000]\n",
            "loss: 0.010723  [41616/45000]\n",
            "loss: 0.167021  [43216/45000]\n",
            "loss: 0.179039  [44816/45000]\n",
            "Epoch 9\n",
            "-------------------------------\n",
            "loss: 0.019516  [   16/45000]\n",
            "loss: 0.147250  [ 1616/45000]\n",
            "loss: 0.022193  [ 3216/45000]\n",
            "loss: 0.022083  [ 4816/45000]\n",
            "loss: 0.010001  [ 6416/45000]\n",
            "loss: 0.032959  [ 8016/45000]\n",
            "loss: 0.257124  [ 9616/45000]\n",
            "loss: 0.016215  [11216/45000]\n",
            "loss: 0.050737  [12816/45000]\n",
            "loss: 0.267339  [14416/45000]\n",
            "loss: 0.179580  [16016/45000]\n",
            "loss: 0.030744  [17616/45000]\n",
            "loss: 0.011380  [19216/45000]\n",
            "loss: 0.223413  [20816/45000]\n",
            "loss: 0.013092  [22416/45000]\n",
            "loss: 0.010576  [24016/45000]\n",
            "loss: 0.008979  [25616/45000]\n",
            "loss: 0.023530  [27216/45000]\n",
            "loss: 0.046276  [28816/45000]\n",
            "loss: 0.015153  [30416/45000]\n",
            "loss: 0.117088  [32016/45000]\n",
            "loss: 0.013283  [33616/45000]\n",
            "loss: 0.100567  [35216/45000]\n",
            "loss: 0.046891  [36816/45000]\n",
            "loss: 0.091326  [38416/45000]\n",
            "loss: 0.003075  [40016/45000]\n",
            "loss: 0.005557  [41616/45000]\n",
            "loss: 0.069735  [43216/45000]\n",
            "loss: 0.103916  [44816/45000]\n",
            "Epoch 10\n",
            "-------------------------------\n",
            "loss: 0.013332  [   16/45000]\n",
            "loss: 0.074778  [ 1616/45000]\n",
            "loss: 0.009963  [ 3216/45000]\n",
            "loss: 0.021426  [ 4816/45000]\n",
            "loss: 0.003887  [ 6416/45000]\n",
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            "loss: 0.022148  [11216/45000]\n",
            "loss: 0.013815  [12816/45000]\n",
            "loss: 0.223512  [14416/45000]\n",
            "loss: 0.058023  [16016/45000]\n",
            "loss: 0.015315  [17616/45000]\n",
            "loss: 0.007661  [19216/45000]\n",
            "loss: 0.122540  [20816/45000]\n",
            "loss: 0.006755  [22416/45000]\n",
            "loss: 0.007596  [24016/45000]\n",
            "loss: 0.004091  [25616/45000]\n",
            "loss: 0.028704  [27216/45000]\n",
            "loss: 0.061656  [28816/45000]\n",
            "loss: 0.005363  [30416/45000]\n",
            "loss: 0.037812  [32016/45000]\n",
            "loss: 0.005191  [33616/45000]\n",
            "loss: 0.012059  [35216/45000]\n",
            "loss: 0.018368  [36816/45000]\n",
            "loss: 0.035015  [38416/45000]\n",
            "loss: 0.000623  [40016/45000]\n",
            "loss: 0.003145  [41616/45000]\n",
            "loss: 0.067669  [43216/45000]\n",
            "loss: 0.097502  [44816/45000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model5 = TextNetwork().to(device)\n",
        "model5 = train(train_dataset,model5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lu8LezHyY5K-",
        "outputId": "5a396ddd-577f-48ce-80f4-0b321300e763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 1.356480  [   16/45000]\n",
            "loss: 1.364033  [ 1616/45000]\n",
            "loss: 1.303742  [ 3216/45000]\n",
            "loss: 0.994202  [ 4816/45000]\n",
            "loss: 0.435677  [ 6416/45000]\n",
            "loss: 0.415779  [ 8016/45000]\n",
            "loss: 0.601479  [ 9616/45000]\n",
            "loss: 0.578302  [11216/45000]\n",
            "loss: 0.384545  [12816/45000]\n",
            "loss: 0.681242  [14416/45000]\n",
            "loss: 0.916897  [16016/45000]\n",
            "loss: 0.299846  [17616/45000]\n",
            "loss: 0.095346  [19216/45000]\n",
            "loss: 0.718825  [20816/45000]\n",
            "loss: 0.235588  [22416/45000]\n",
            "loss: 0.382102  [24016/45000]\n",
            "loss: 0.156249  [25616/45000]\n",
            "loss: 0.174633  [27216/45000]\n",
            "loss: 0.419575  [28816/45000]\n",
            "loss: 0.384692  [30416/45000]\n",
            "loss: 0.193715  [32016/45000]\n",
            "loss: 0.151573  [33616/45000]\n",
            "loss: 0.478307  [35216/45000]\n",
            "loss: 0.730009  [36816/45000]\n",
            "loss: 0.399898  [38416/45000]\n",
            "loss: 0.121062  [40016/45000]\n",
            "loss: 0.156097  [41616/45000]\n",
            "loss: 0.335828  [43216/45000]\n",
            "loss: 0.474661  [44816/45000]\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 0.463931  [   16/45000]\n",
            "loss: 0.658562  [ 1616/45000]\n",
            "loss: 0.368692  [ 3216/45000]\n",
            "loss: 0.536155  [ 4816/45000]\n",
            "loss: 0.192377  [ 6416/45000]\n",
            "loss: 0.260024  [ 8016/45000]\n",
            "loss: 0.608993  [ 9616/45000]\n",
            "loss: 0.498177  [11216/45000]\n",
            "loss: 0.360802  [12816/45000]\n",
            "loss: 0.683331  [14416/45000]\n",
            "loss: 0.758249  [16016/45000]\n",
            "loss: 0.223318  [17616/45000]\n",
            "loss: 0.071380  [19216/45000]\n",
            "loss: 0.671497  [20816/45000]\n",
            "loss: 0.142225  [22416/45000]\n",
            "loss: 0.247806  [24016/45000]\n",
            "loss: 0.197840  [25616/45000]\n",
            "loss: 0.122040  [27216/45000]\n",
            "loss: 0.217148  [28816/45000]\n",
            "loss: 0.321669  [30416/45000]\n",
            "loss: 0.188460  [32016/45000]\n",
            "loss: 0.138460  [33616/45000]\n",
            "loss: 0.513023  [35216/45000]\n",
            "loss: 0.653514  [36816/45000]\n",
            "loss: 0.361235  [38416/45000]\n",
            "loss: 0.089308  [40016/45000]\n",
            "loss: 0.142805  [41616/45000]\n",
            "loss: 0.361888  [43216/45000]\n",
            "loss: 0.465292  [44816/45000]\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 0.395403  [   16/45000]\n",
            "loss: 0.624207  [ 1616/45000]\n",
            "loss: 0.329258  [ 3216/45000]\n",
            "loss: 0.436062  [ 4816/45000]\n",
            "loss: 0.177470  [ 6416/45000]\n",
            "loss: 0.210549  [ 8016/45000]\n",
            "loss: 0.611019  [ 9616/45000]\n",
            "loss: 0.411025  [11216/45000]\n",
            "loss: 0.304159  [12816/45000]\n",
            "loss: 0.695289  [14416/45000]\n",
            "loss: 0.726770  [16016/45000]\n",
            "loss: 0.198931  [17616/45000]\n",
            "loss: 0.064849  [19216/45000]\n",
            "loss: 0.639663  [20816/45000]\n",
            "loss: 0.113759  [22416/45000]\n",
            "loss: 0.225002  [24016/45000]\n",
            "loss: 0.189814  [25616/45000]\n",
            "loss: 0.111473  [27216/45000]\n",
            "loss: 0.171339  [28816/45000]\n",
            "loss: 0.292428  [30416/45000]\n",
            "loss: 0.186510  [32016/45000]\n",
            "loss: 0.128780  [33616/45000]\n",
            "loss: 0.508329  [35216/45000]\n",
            "loss: 0.592523  [36816/45000]\n",
            "loss: 0.315597  [38416/45000]\n",
            "loss: 0.082591  [40016/45000]\n",
            "loss: 0.128198  [41616/45000]\n",
            "loss: 0.368448  [43216/45000]\n",
            "loss: 0.455623  [44816/45000]\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 0.345669  [   16/45000]\n",
            "loss: 0.592301  [ 1616/45000]\n",
            "loss: 0.281565  [ 3216/45000]\n",
            "loss: 0.355865  [ 4816/45000]\n",
            "loss: 0.153511  [ 6416/45000]\n",
            "loss: 0.178020  [ 8016/45000]\n",
            "loss: 0.579709  [ 9616/45000]\n",
            "loss: 0.312177  [11216/45000]\n",
            "loss: 0.252344  [12816/45000]\n",
            "loss: 0.695531  [14416/45000]\n",
            "loss: 0.715125  [16016/45000]\n",
            "loss: 0.187108  [17616/45000]\n",
            "loss: 0.059329  [19216/45000]\n",
            "loss: 0.589973  [20816/45000]\n",
            "loss: 0.094574  [22416/45000]\n",
            "loss: 0.215745  [24016/45000]\n",
            "loss: 0.167618  [25616/45000]\n",
            "loss: 0.104765  [27216/45000]\n",
            "loss: 0.140378  [28816/45000]\n",
            "loss: 0.269553  [30416/45000]\n",
            "loss: 0.187518  [32016/45000]\n",
            "loss: 0.118267  [33616/45000]\n",
            "loss: 0.497940  [35216/45000]\n",
            "loss: 0.516698  [36816/45000]\n",
            "loss: 0.254384  [38416/45000]\n",
            "loss: 0.080918  [40016/45000]\n",
            "loss: 0.089919  [41616/45000]\n",
            "loss: 0.357126  [43216/45000]\n",
            "loss: 0.436809  [44816/45000]\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 0.310927  [   16/45000]\n",
            "loss: 0.547866  [ 1616/45000]\n",
            "loss: 0.235094  [ 3216/45000]\n",
            "loss: 0.253085  [ 4816/45000]\n",
            "loss: 0.130918  [ 6416/45000]\n",
            "loss: 0.151190  [ 8016/45000]\n",
            "loss: 0.531594  [ 9616/45000]\n",
            "loss: 0.211825  [11216/45000]\n",
            "loss: 0.209734  [12816/45000]\n",
            "loss: 0.671909  [14416/45000]\n",
            "loss: 0.665312  [16016/45000]\n",
            "loss: 0.164904  [17616/45000]\n",
            "loss: 0.051090  [19216/45000]\n",
            "loss: 0.525749  [20816/45000]\n",
            "loss: 0.069712  [22416/45000]\n",
            "loss: 0.210040  [24016/45000]\n",
            "loss: 0.149894  [25616/45000]\n",
            "loss: 0.091451  [27216/45000]\n",
            "loss: 0.115335  [28816/45000]\n",
            "loss: 0.235273  [30416/45000]\n",
            "loss: 0.193311  [32016/45000]\n",
            "loss: 0.103097  [33616/45000]\n",
            "loss: 0.475860  [35216/45000]\n",
            "loss: 0.402396  [36816/45000]\n",
            "loss: 0.196652  [38416/45000]\n",
            "loss: 0.074293  [40016/45000]\n",
            "loss: 0.065673  [41616/45000]\n",
            "loss: 0.314411  [43216/45000]\n",
            "loss: 0.397554  [44816/45000]\n",
            "Epoch 6\n",
            "-------------------------------\n",
            "loss: 0.266710  [   16/45000]\n",
            "loss: 0.474791  [ 1616/45000]\n",
            "loss: 0.177228  [ 3216/45000]\n",
            "loss: 0.146485  [ 4816/45000]\n",
            "loss: 0.100975  [ 6416/45000]\n",
            "loss: 0.119873  [ 8016/45000]\n",
            "loss: 0.466685  [ 9616/45000]\n",
            "loss: 0.110558  [11216/45000]\n",
            "loss: 0.166722  [12816/45000]\n",
            "loss: 0.632592  [14416/45000]\n",
            "loss: 0.598173  [16016/45000]\n",
            "loss: 0.136394  [17616/45000]\n",
            "loss: 0.039753  [19216/45000]\n",
            "loss: 0.447335  [20816/45000]\n",
            "loss: 0.046284  [22416/45000]\n",
            "loss: 0.190867  [24016/45000]\n",
            "loss: 0.121947  [25616/45000]\n",
            "loss: 0.071974  [27216/45000]\n",
            "loss: 0.090829  [28816/45000]\n",
            "loss: 0.187918  [30416/45000]\n",
            "loss: 0.205422  [32016/45000]\n",
            "loss: 0.086554  [33616/45000]\n",
            "loss: 0.449606  [35216/45000]\n",
            "loss: 0.266132  [36816/45000]\n",
            "loss: 0.147220  [38416/45000]\n",
            "loss: 0.076622  [40016/45000]\n",
            "loss: 0.048149  [41616/45000]\n",
            "loss: 0.255027  [43216/45000]\n",
            "loss: 0.316369  [44816/45000]\n",
            "Epoch 7\n",
            "-------------------------------\n",
            "loss: 0.192910  [   16/45000]\n",
            "loss: 0.382242  [ 1616/45000]\n",
            "loss: 0.118115  [ 3216/45000]\n",
            "loss: 0.085900  [ 4816/45000]\n",
            "loss: 0.073454  [ 6416/45000]\n",
            "loss: 0.091699  [ 8016/45000]\n",
            "loss: 0.423430  [ 9616/45000]\n",
            "loss: 0.057024  [11216/45000]\n",
            "loss: 0.122826  [12816/45000]\n",
            "loss: 0.564559  [14416/45000]\n",
            "loss: 0.509709  [16016/45000]\n",
            "loss: 0.091011  [17616/45000]\n",
            "loss: 0.028487  [19216/45000]\n",
            "loss: 0.352226  [20816/45000]\n",
            "loss: 0.023518  [22416/45000]\n",
            "loss: 0.157644  [24016/45000]\n",
            "loss: 0.078065  [25616/45000]\n",
            "loss: 0.048254  [27216/45000]\n",
            "loss: 0.071529  [28816/45000]\n",
            "loss: 0.131060  [30416/45000]\n",
            "loss: 0.209227  [32016/45000]\n",
            "loss: 0.056410  [33616/45000]\n",
            "loss: 0.381696  [35216/45000]\n",
            "loss: 0.119210  [36816/45000]\n",
            "loss: 0.093745  [38416/45000]\n",
            "loss: 0.044682  [40016/45000]\n",
            "loss: 0.016439  [41616/45000]\n",
            "loss: 0.188073  [43216/45000]\n",
            "loss: 0.255214  [44816/45000]\n",
            "Epoch 8\n",
            "-------------------------------\n",
            "loss: 0.099467  [   16/45000]\n",
            "loss: 0.298221  [ 1616/45000]\n",
            "loss: 0.068148  [ 3216/45000]\n",
            "loss: 0.060716  [ 4816/45000]\n",
            "loss: 0.051557  [ 6416/45000]\n",
            "loss: 0.066135  [ 8016/45000]\n",
            "loss: 0.419776  [ 9616/45000]\n",
            "loss: 0.025451  [11216/45000]\n",
            "loss: 0.078280  [12816/45000]\n",
            "loss: 0.472191  [14416/45000]\n",
            "loss: 0.402151  [16016/45000]\n",
            "loss: 0.050065  [17616/45000]\n",
            "loss: 0.023979  [19216/45000]\n",
            "loss: 0.260624  [20816/45000]\n",
            "loss: 0.008427  [22416/45000]\n",
            "loss: 0.117761  [24016/45000]\n",
            "loss: 0.048471  [25616/45000]\n",
            "loss: 0.028698  [27216/45000]\n",
            "loss: 0.047929  [28816/45000]\n",
            "loss: 0.065921  [30416/45000]\n",
            "loss: 0.201178  [32016/45000]\n",
            "loss: 0.029658  [33616/45000]\n",
            "loss: 0.286209  [35216/45000]\n",
            "loss: 0.050654  [36816/45000]\n",
            "loss: 0.068845  [38416/45000]\n",
            "loss: 0.029077  [40016/45000]\n",
            "loss: 0.007131  [41616/45000]\n",
            "loss: 0.118649  [43216/45000]\n",
            "loss: 0.204154  [44816/45000]\n",
            "Epoch 9\n",
            "-------------------------------\n",
            "loss: 0.034047  [   16/45000]\n",
            "loss: 0.214295  [ 1616/45000]\n",
            "loss: 0.039034  [ 3216/45000]\n",
            "loss: 0.033309  [ 4816/45000]\n",
            "loss: 0.029410  [ 6416/45000]\n",
            "loss: 0.041219  [ 8016/45000]\n",
            "loss: 0.317999  [ 9616/45000]\n",
            "loss: 0.011935  [11216/45000]\n",
            "loss: 0.047858  [12816/45000]\n",
            "loss: 0.337064  [14416/45000]\n",
            "loss: 0.231883  [16016/45000]\n",
            "loss: 0.022349  [17616/45000]\n",
            "loss: 0.025347  [19216/45000]\n",
            "loss: 0.132621  [20816/45000]\n",
            "loss: 0.003862  [22416/45000]\n",
            "loss: 0.072656  [24016/45000]\n",
            "loss: 0.031925  [25616/45000]\n",
            "loss: 0.015749  [27216/45000]\n",
            "loss: 0.039551  [28816/45000]\n",
            "loss: 0.024759  [30416/45000]\n",
            "loss: 0.185708  [32016/45000]\n",
            "loss: 0.011283  [33616/45000]\n",
            "loss: 0.154936  [35216/45000]\n",
            "loss: 0.024938  [36816/45000]\n",
            "loss: 0.081315  [38416/45000]\n",
            "loss: 0.033076  [40016/45000]\n",
            "loss: 0.004853  [41616/45000]\n",
            "loss: 0.068264  [43216/45000]\n",
            "loss: 0.165003  [44816/45000]\n",
            "Epoch 10\n",
            "-------------------------------\n",
            "loss: 0.019445  [   16/45000]\n",
            "loss: 0.121550  [ 1616/45000]\n",
            "loss: 0.016265  [ 3216/45000]\n",
            "loss: 0.054537  [ 4816/45000]\n",
            "loss: 0.010563  [ 6416/45000]\n",
            "loss: 0.030124  [ 8016/45000]\n",
            "loss: 0.202209  [ 9616/45000]\n",
            "loss: 0.013405  [11216/45000]\n",
            "loss: 0.046625  [12816/45000]\n",
            "loss: 0.184973  [14416/45000]\n",
            "loss: 0.069916  [16016/45000]\n",
            "loss: 0.008011  [17616/45000]\n",
            "loss: 0.016154  [19216/45000]\n",
            "loss: 0.056021  [20816/45000]\n",
            "loss: 0.001300  [22416/45000]\n",
            "loss: 0.048384  [24016/45000]\n",
            "loss: 0.012648  [25616/45000]\n",
            "loss: 0.009877  [27216/45000]\n",
            "loss: 0.029149  [28816/45000]\n",
            "loss: 0.008661  [30416/45000]\n",
            "loss: 0.131231  [32016/45000]\n",
            "loss: 0.003685  [33616/45000]\n",
            "loss: 0.010565  [35216/45000]\n",
            "loss: 0.015981  [36816/45000]\n",
            "loss: 0.029866  [38416/45000]\n",
            "loss: 0.016015  [40016/45000]\n",
            "loss: 0.005445  [41616/45000]\n",
            "loss: 0.043278  [43216/45000]\n",
            "loss: 0.184168  [44816/45000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Test***"
      ],
      "metadata": {
        "id": "mgeemz4TCu8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test_score(ds,model):\n",
        "  size = 0\n",
        "  test_loss, accuracy = 0,0\n",
        "  with torch.no_grad():\n",
        "    for sample in ds:\n",
        "\n",
        "      X = sample['text']\n",
        "      y = sample['label']\n",
        "      X, y = X.to(device), y.to(device)\n",
        "      pred = model(X)\n",
        "      test_loss += loss_fn(pred, y).item()\n",
        "      accuracy += (pred.argmax(-1) == y).type(torch.float).sum().item() #batch_accuracy\n",
        "      size += len(X)\n",
        "  accuracy = accuracy / size\n",
        "  return accuracy, size"
      ],
      "metadata": {
        "id": "2CGC_BhyCw4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, size = test_score(test_dataset,model1)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiiLoTxyCtwj",
        "outputId": "12474417-72a4-43bc-c1b6-402c265767cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8324"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, size = test_score(test_dataset,model2)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0315cOZJZHbl",
        "outputId": "e221ea18-319c-49a4-a2a4-ccded14326b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.838"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, size = test_score(test_dataset,model3)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9pkhVdyZHlk",
        "outputId": "3ebdce5e-829e-4e02-9de3-acd611780cac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.84"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, size = test_score(test_dataset,model4)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1kH9VWUZHs1",
        "outputId": "cbdcf9da-0f51-4342-c713-f001c07a15f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8238"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, size = test_score(test_dataset,model5)\n",
        "accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uztnjf9WZHzx",
        "outputId": "e2726efb-4e3a-4e82-a8eb-e6a27a4e57d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8338"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J9rUiPgr-yfc"
      },
      "outputs": [],
      "source": [
        "PATH = \"drive/MyDrive/Kollektif3modeller/n5\"\n",
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