POS_Tagging_FC.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Devininthelab/Toy-Repo/blob/main/POS_Tagging_FC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzv9D-VJdueP",
        "outputId": "83ad59fd-39b7-4031-f368-c74d64ed04cc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torchinfo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lg6hmOgdyQk",
        "outputId": "9e79396c-914d-4a4b-d224-f57f9bca96c7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchinfo\n",
            "  Downloading torchinfo-1.8.0-py3-none-any.whl (23 kB)\n",
            "Installing collected packages: torchinfo\n",
            "Successfully installed torchinfo-1.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "xhvycogTUbxi"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znywtkDfUbxk"
      },
      "source": [
        "## 1. Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_p5TeY_Ubxl"
      },
      "source": [
        "### 1.1 Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        },
        "id": "26RniS0bUbxl",
        "outputId": "a799ea12-c1e6-4105-d094-d290374a1824"
      },
      "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": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98503eaf250549f8b3ca7956e0d6edd4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f8e60a155374659a0ce667e350f3060"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "291e4e61d2ee48cfb4765e4d7f56d5ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c33a384e7ae94177b7fceafc122edacc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7e57c5703bb46edb76bdda3806061d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e08f42da04b14d9784a664c9438e7f76"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"conll2003\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rA6_75pkUbxl",
        "outputId": "6df97924-5371-45a2-cd4c-685c27e7cd96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'pos_tags'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'pos_tags'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'pos_tags'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset = dataset.remove_columns([\"id\", \"chunk_tags\", \"ner_tags\"])\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "eCQutDjyUbxm"
      },
      "outputs": [],
      "source": [
        "dataset_train = dataset[\"train\"]\n",
        "dataset_val = dataset[\"validation\"]\n",
        "dataset_test = dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset_train['tokens'][0])\n",
        "print(dataset_train['pos_tags'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdtvemQmy5Od",
        "outputId": "c1af88f5-6e84-4234-cdd9-81f2d462d46b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']\n",
            "[22, 42, 16, 21, 35, 37, 16, 21, 7]\n"
          ]
        }
      ]
    },
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad3e92df9bce4d4aa4581f11b0b1d63c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32734d53006d4a5f99ad1d693b21f2b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9308d4deedc04380965c30d50565f78e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "568d34dff81c4f02815473e71080fba2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "MAX_LEN = 113"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.convert_tokens_to_ids(dataset_train['tokens'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqNcmbjCzdjc",
        "outputId": "e4af122f-ac83-4f3e-e4d4-6a1cc9632acc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[100, 19164, 100, 2655, 2000, 17757, 100, 12559, 1012]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Wcglmai3Ubxm"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class PosTagging_Dataset(Dataset):\n",
        "    def __init__(self, dataset, tokenizer):\n",
        "        super().__init__()\n",
        "        self.tokens = dataset[\"tokens\"]\n",
        "        self.labels = dataset[\"pos_tags\"]\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_len = MAX_LEN\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.tokens)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        input_token = self.tokens[idx]\n",
        "        label_token = self.labels[idx]\n",
        "\n",
        "        input_token = self.tokenizer.convert_tokens_to_ids(input_token)\n",
        "        input_ids, labels = self.pad_and_truncate(input_token, label_token)\n",
        "\n",
        "        return input_ids, labels\n",
        "\n",
        "    def pad_and_truncate(self, sequence_token, sequence_label):\n",
        "        pad_id = self.tokenizer.pad_token_id # 0\n",
        "        if len(sequence_token) < self.max_len:\n",
        "            padded_sequence_token = sequence_token + [pad_id] * (self.max_len - len(sequence_token))\n",
        "            padded_sequence_label = sequence_label + [47] * (self.max_len - len(sequence_label))\n",
        "        else:\n",
        "            padded_sequence_token = sequence_token[:self.max_len]\n",
        "            padded_sequence_label = sequence_label[:self.max_len]\n",
        "\n",
        "        return torch.tensor(padded_sequence_token), torch.tensor(padded_sequence_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_r66GqA-Ubxn"
      },
      "outputs": [],
      "source": [
        "train_set = PosTagging_Dataset(dataset_train, tokenizer)\n",
        "val_set = PosTagging_Dataset(dataset_val, tokenizer)\n",
        "test_set = PosTagging_Dataset(dataset_test, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "83uQoLtXUbxn"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "batch_size = 512\n",
        "\n",
        "train_loader = DataLoader(train_set, batch_size, shuffle=False)\n",
        "val_loader = DataLoader(val_set, batch_size)\n",
        "test_loader = DataLoader(test_set, batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7jc60t8Ubxn"
      },
      "source": [
        "## 2. Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ySzqDGvRUbxn"
      },
      "outputs": [],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "class POS_Model(nn.Module):\n",
        "    def __init__(self, vocab_size, emb_dim, hidden_size, num_classes):\n",
        "        super().__init__()\n",
        "        self.embedding = nn.Embedding(vocab_size, emb_dim)\n",
        "        self.fc1 = nn.Linear(hidden_size, 2*hidden_size)\n",
        "        self.fc2 = nn.Linear(2*hidden_size, hidden_size)\n",
        "        self.fc3 = nn.Linear(hidden_size, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.embedding(x)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x.permute(0, 2, 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "4GLZ0c-bUbxn"
      },
      "outputs": [],
      "source": [
        "emb_dim = 512\n",
        "hidden_size = 512\n",
        "vocab_size = len(tokenizer)\n",
        "num_classes = 47+1 # 47 classes + 1 padding\n",
        "\n",
        "model = POS_Model(vocab_size, emb_dim, hidden_size, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d62TARxCUbxo",
        "outputId": "aea97986-a476-4aa4-84fe-2f0492222422"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "POS_Model                                [512, 48, 113]            --\n",
              "├─Embedding: 1-1                         [512, 113, 512]           15,627,264\n",
              "├─Linear: 1-2                            [512, 113, 1024]          525,312\n",
              "├─Linear: 1-3                            [512, 113, 512]           524,800\n",
              "├─Linear: 1-4                            [512, 113, 48]            24,624\n",
              "==========================================================================================\n",
              "Total params: 16,702,000\n",
              "Trainable params: 16,702,000\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (G): 8.55\n",
              "==========================================================================================\n",
              "Input size (MB): 0.46\n",
              "Forward/backward pass size (MB): 970.13\n",
              "Params size (MB): 66.81\n",
              "Estimated Total Size (MB): 1037.40\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from torchinfo import  summary\n",
        "\n",
        "input_data = torch.randint(low=0, high=vocab_size-1, size=(512, MAX_LEN))\n",
        "summary(model, input_data = input_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqGCNdFCUbxo"
      },
      "source": [
        "## 3. Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zuflfdNWUbxo"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, test_loader, criterion, device):\n",
        "    model.eval()\n",
        "    test_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in test_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "            total += (labels!=47).sum().item()\n",
        "            test_loss += loss.item()\n",
        "            correct += torch.multiply(predicted == labels, labels!=47).sum().item()\n",
        "\n",
        "    test_loss = test_loss / len(test_loader)\n",
        "    accuracy = 100* correct / total\n",
        "\n",
        "    return test_loss, accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oejywLsqUbxo"
      },
      "source": [
        "## 4. Train model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hZxyBiTeUbxo"
      },
      "outputs": [],
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "EPOCHS = 50\n",
        "LR = 1e-3\n",
        "criterion = nn.CrossEntropyLoss(ignore_index=47)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VEAKd2wvUbxo"
      },
      "outputs": [],
      "source": [
        "def train_model(model):\n",
        "    hist = {\n",
        "        \"train_loss\": [],\n",
        "        \"train_accuracy\": [],\n",
        "        \"val_loss\": [],\n",
        "        \"val_accuracy\": []\n",
        "    }\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n",
        "    model.to(device)\n",
        "    for epoch in range(EPOCHS):\n",
        "        model.train()\n",
        "        running_loss = 0.0\n",
        "        running_correct = 0\n",
        "        total = 0\n",
        "        for inputs, labels in train_loader:\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            running_loss += loss.item()\n",
        "\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            total += (labels!=47).sum().item()\n",
        "            running_correct += torch.multiply(predicted == labels, labels!=47).sum().item()\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "        epoch_loss =  running_loss / len(train_loader)\n",
        "        epoch_accuracy = 100* running_correct / total\n",
        "        val_loss, val_accuracy = evaluate(model, val_loader, criterion, device)\n",
        "\n",
        "        print(f\"Epoch [{epoch + 1}/{EPOCHS}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.2f}%, Val loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.2f}%\")\n",
        "\n",
        "        hist[\"train_loss\"].append(epoch_loss)\n",
        "        hist[\"train_accuracy\"].append(epoch_accuracy)\n",
        "        hist[\"val_loss\"].append(val_loss)\n",
        "        hist[\"val_accuracy\"].append(val_accuracy)\n",
        "\n",
        "    return hist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ED7ldSJiUbxp",
        "outputId": "2e6a0e21-cb62-4c33-ca38-f632c7dafbfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training FC model\n",
            "Epoch [1/50], Loss: 1.7052, Accuracy: 57.69%, Val loss: 1.1403, Val Accuracy: 69.57%\n",
            "Epoch [2/50], Loss: 0.9689, Accuracy: 74.36%, Val loss: 0.8867, Val Accuracy: 77.15%\n",
            "Epoch [3/50], Loss: 0.7951, Accuracy: 79.34%, Val loss: 0.7832, Val Accuracy: 79.91%\n",
            "Epoch [4/50], Loss: 0.7108, Accuracy: 81.58%, Val loss: 0.7328, Val Accuracy: 81.40%\n",
            "Epoch [5/50], Loss: 0.6679, Accuracy: 82.69%, Val loss: 0.7027, Val Accuracy: 82.05%\n",
            "Epoch [6/50], Loss: 0.6453, Accuracy: 83.27%, Val loss: 0.6924, Val Accuracy: 82.41%\n",
            "Epoch [7/50], Loss: 0.6317, Accuracy: 83.62%, Val loss: 0.6822, Val Accuracy: 82.68%\n",
            "Epoch [8/50], Loss: 0.6227, Accuracy: 83.80%, Val loss: 0.6815, Val Accuracy: 82.81%\n",
            "Epoch [9/50], Loss: 0.6174, Accuracy: 83.93%, Val loss: 0.6761, Val Accuracy: 82.83%\n",
            "Epoch [10/50], Loss: 0.6125, Accuracy: 83.98%, Val loss: 0.6781, Val Accuracy: 82.87%\n",
            "Epoch [11/50], Loss: 0.6097, Accuracy: 84.06%, Val loss: 0.6744, Val Accuracy: 82.84%\n",
            "Epoch [12/50], Loss: 0.6072, Accuracy: 84.07%, Val loss: 0.6770, Val Accuracy: 82.88%\n",
            "Epoch [13/50], Loss: 0.6062, Accuracy: 84.11%, Val loss: 0.6752, Val Accuracy: 82.85%\n",
            "Epoch [14/50], Loss: 0.6047, Accuracy: 84.09%, Val loss: 0.6755, Val Accuracy: 82.90%\n",
            "Epoch [15/50], Loss: 0.6036, Accuracy: 84.13%, Val loss: 0.6737, Val Accuracy: 82.88%\n",
            "Epoch [16/50], Loss: 0.6027, Accuracy: 84.10%, Val loss: 0.6756, Val Accuracy: 82.90%\n",
            "Epoch [17/50], Loss: 0.6019, Accuracy: 84.12%, Val loss: 0.6743, Val Accuracy: 82.95%\n",
            "Epoch [18/50], Loss: 0.6015, Accuracy: 84.14%, Val loss: 0.6742, Val Accuracy: 82.96%\n",
            "Epoch [19/50], Loss: 0.6005, Accuracy: 84.16%, Val loss: 0.6735, Val Accuracy: 82.92%\n",
            "Epoch [20/50], Loss: 0.6000, Accuracy: 84.16%, Val loss: 0.6735, Val Accuracy: 82.93%\n",
            "Epoch [21/50], Loss: 0.5991, Accuracy: 84.18%, Val loss: 0.6741, Val Accuracy: 82.94%\n",
            "Epoch [22/50], Loss: 0.5988, Accuracy: 84.18%, Val loss: 0.6729, Val Accuracy: 82.94%\n",
            "Epoch [23/50], Loss: 0.5985, Accuracy: 84.18%, Val loss: 0.6731, Val Accuracy: 82.91%\n",
            "Epoch [24/50], Loss: 0.5983, Accuracy: 84.18%, Val loss: 0.6729, Val Accuracy: 82.95%\n",
            "Epoch [25/50], Loss: 0.5980, Accuracy: 84.18%, Val loss: 0.6727, Val Accuracy: 82.93%\n",
            "Epoch [26/50], Loss: 0.5980, Accuracy: 84.18%, Val loss: 0.6723, Val Accuracy: 82.97%\n",
            "Epoch [27/50], Loss: 0.5978, Accuracy: 84.19%, Val loss: 0.6722, Val Accuracy: 82.96%\n",
            "Epoch [28/50], Loss: 0.5976, Accuracy: 84.18%, Val loss: 0.6718, Val Accuracy: 83.02%\n",
            "Epoch [29/50], Loss: 0.5975, Accuracy: 84.17%, Val loss: 0.6714, Val Accuracy: 83.02%\n",
            "Epoch [30/50], Loss: 0.5971, Accuracy: 84.19%, Val loss: 0.6712, Val Accuracy: 83.03%\n",
            "Epoch [31/50], Loss: 0.5972, Accuracy: 84.20%, Val loss: 0.6711, Val Accuracy: 83.03%\n",
            "Epoch [32/50], Loss: 0.5970, Accuracy: 84.21%, Val loss: 0.6715, Val Accuracy: 83.02%\n",
            "Epoch [33/50], Loss: 0.5969, Accuracy: 84.23%, Val loss: 0.6718, Val Accuracy: 83.04%\n",
            "Epoch [34/50], Loss: 0.5966, Accuracy: 84.24%, Val loss: 0.6722, Val Accuracy: 83.04%\n",
            "Epoch [35/50], Loss: 0.5957, Accuracy: 84.23%, Val loss: 0.6722, Val Accuracy: 83.02%\n",
            "Epoch [36/50], Loss: 0.5958, Accuracy: 84.24%, Val loss: 0.6727, Val Accuracy: 83.05%\n",
            "Epoch [37/50], Loss: 0.5959, Accuracy: 84.24%, Val loss: 0.6733, Val Accuracy: 82.99%\n",
            "Epoch [38/50], Loss: 0.5957, Accuracy: 84.26%, Val loss: 0.6732, Val Accuracy: 83.02%\n",
            "Epoch [39/50], Loss: 0.5953, Accuracy: 84.26%, Val loss: 0.6737, Val Accuracy: 83.01%\n",
            "Epoch [40/50], Loss: 0.5946, Accuracy: 84.27%, Val loss: 0.6738, Val Accuracy: 83.04%\n",
            "Epoch [41/50], Loss: 0.5946, Accuracy: 84.25%, Val loss: 0.6739, Val Accuracy: 83.00%\n",
            "Epoch [42/50], Loss: 0.5946, Accuracy: 84.26%, Val loss: 0.6748, Val Accuracy: 83.03%\n",
            "Epoch [43/50], Loss: 0.5945, Accuracy: 84.26%, Val loss: 0.6745, Val Accuracy: 83.03%\n",
            "Epoch [44/50], Loss: 0.5938, Accuracy: 84.28%, Val loss: 0.6750, Val Accuracy: 83.03%\n",
            "Epoch [45/50], Loss: 0.5937, Accuracy: 84.28%, Val loss: 0.6751, Val Accuracy: 83.03%\n",
            "Epoch [46/50], Loss: 0.5934, Accuracy: 84.29%, Val loss: 0.6752, Val Accuracy: 83.03%\n",
            "Epoch [47/50], Loss: 0.5933, Accuracy: 84.29%, Val loss: 0.6752, Val Accuracy: 83.03%\n",
            "Epoch [48/50], Loss: 0.5930, Accuracy: 84.30%, Val loss: 0.6757, Val Accuracy: 83.03%\n",
            "Epoch [49/50], Loss: 0.5929, Accuracy: 84.29%, Val loss: 0.6755, Val Accuracy: 83.02%\n",
            "Epoch [50/50], Loss: 0.5927, Accuracy: 84.30%, Val loss: 0.6758, Val Accuracy: 83.04%\n"
          ]
        }
      ],
      "source": [
        "print(f\"Training FC model\")\n",
        "hist = train_model(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCejhqbfUbxp"
      },
      "source": [
        "## 5. Plot results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HayZMSCIUbxp",
        "outputId": "d93c61c1-ad02-435a-acc6-65a1719c523a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UJ1IwyBAR+QH5oCFQarPfrDZYrAKlNhtKrZUsswn7cvl1q03AYrXJ9/YDmuNgpZEkaCTIjzWSclBzHMRKbQJW+2dYbfI2rbay55Upf73iUvvnl1oFSuz3FqsNFpsNllJ5mzb7EVqyv9tx0WPHtiRJgk3I7yuxv8fx2GK1KTerTUCv1cCg00Cv1UCvlZTnBq28TKeVIOwHd8dBudTpIG21729HaHAOD87BQQ4MgLX8Prc59rmA1b6/HOFCAMq2lWWO4OH4fKtQ9r2jtpos474UPHhbY1U+m0GGqIYrtdpQYpX/2JutVpgt8vOSUvvN6bG5tOxg4OB8QHEcGGwCMJdaUWyxodhihbm04r3ZYpP/YDv+kNu3IUT573+ARpIgSfIBSCPJn6MskyT7QUC4/NEWcHxzFPbHQlnm+Dzls8p/oGP79p9Jvrcf/qSyg4NNlAUA+SAhb9Mm5G+j5Q/gGkmCVoLTY/nbqmRfr+znK3uvTQAlVmuFfw9zqeMg6wgXQjnYWZ1uSjiw2ly/RbvU7uYvD1E5Gvv/HY1GAkRZK0t1t1U+uPoSgwz5hVKrDVcLS2BRvhnalG+n5Q8IphIrCs2lKDCXoqC4VH5cIt8Xmq0oMJdCCMCoc3xjk+zf1LTQ6yQY7d/YbAIoMFtQYC5FfrG8vUKnxwXmUhRbrDDqtAjQaxCg1yJAp0WAQYsAnf25XgOjTgurzfEtVD6QlZTalG/QJVbhFFbke+eDIA9idCN6rQSdRgOdRoJOK0Gn1UCvkaDVStBrNNBq5GXK605dB45WAsfBrKzbQQ5RAgJa+7a1mrL36rSSstwRWJ1VHHkpoNXYW0XsrSF6bVlriaNmjUZyCbLOG3M8lwD5ffbWFoPztnTyc0mSlBafEqtzoJRDpeOx4yAst0DBpTWqrLXK6cew/6CS61PlPY79otdUfC45WrkkuIRhCc6hH9DZ/80c+1vjuJfK7qWbmDjFEcQle1DXSmVfLCpTviVK7kKSW5+ut1+uty1fY5AhVZVabcgrLsXVQjMu5JpxIa8YF/OKcSG3WHl8PrcYlwvMlfxxrBmKLTbkFvnmsyQJMNibyI32ZnKDzumm1UDnNDdT+SZw5+0E6LUw6jQw2gOYUa+Rg5g9fBn1GuWPuPP3rcr+djm3gAgIpaXFZl8uwfUPePnWG8fr8vbLDhbOXQuO5c4tN45WGyjP5c/W2g8cyjdG++cqf4AhQaDcuAFbWb2O1hu59ch5ufxBzs+d/01c/j3KPdZpJZcDnnxg0rg8d9TnfIDVOLUQaSSUhRKNpPycRNWl0UjQQIK+4pV/ajwGGfKKXJMFBy/k4eD5PJy9VoTcIovLLc9+X1hiveltSpL8TUzn+Hagdf2morUfKAINOoQYtQgx6hBs1Cn38mMtgo06SJCUb2klTt/WlHurDYCEsAD5/SH2+9AAHUKMevtzLYw6rUsXTbHFhiKL1f7YiuJSG8wWq9Inr3wD1bj23eu0UoUDn16rUVqNHH39Oh64iIhcMMhQtVhtAieuFOLQ+XwcPJ+n3M7lFldpO6EBOsSGBSA2PAAxYQGIDQtATLh8Lz82IirYKPfrEhER2THIUJWYSkqx++Q1bMu6gh3Hr+LAuTwUWSpvVWkYEYhWcWFoEhmEiCA9woMMCA/UV7iFBeig06p6tQwiIvJTDDJ0Q0UlVuw+eQ3bj13BtmNXsO90ToXTAAP0GrSIDUPruFC0jA1Dq7gwtIgNRXigXqWqiYiormCQoQou5hVj5c5T+OnoFew5fQ0Wq2twaRgRiNubRuL2pvXRIbEemkQGQ8suHyIiUgGDDClOXzVh6ZYsrP75jH2wqywuPABdm0bi9qaR6JociUb1AjnglIiIagQGGULWpQK8/V0W1u09q0yM1LlJPQzr0AhdkyPRuH4QgwsREdVIDDJ12G/ncvH2d1n4T+Z5ZY6Rns2jMPHOZujSNFLd4oiIiG4Cg0wd9Mupa1j87VFsOpStLLu7dQwm3tkM7RMi1CuMiIioihhk6pBiixUvrf8Nq3adBiBPMDeoXTwm3JmMlrHXv0Q6ERFRTcUgU0eczSnCuI93Y/+ZXGgkYFiHRhjXOxlNo0PULo2IiMhtDDJ1wNajl/H4yj24WliCekF6LHzwVvRsHq12WUQ1m80GCCsgaey3mxjwLgQgbICtFLBZ5ffbSuVtKZcRF5U/BiBf1ltb9nnKZzvdbI5tlpZ7bCl7fjMXJpM0gFYPaA3yTWcoe6zRlf28QgClxYClSL6VFgMWU9lzawlgDAUC6wGB9YHACHm7VWWzyp+l0d7cvnYQAig1yzU56rSVAvogwBgCGEIBbRUOdULI2yopBEoKbm5fVoXj3xX2C41Bcvr9clrmWNflMezPRdnv2nUfo+x5hcdOtWgNgEZv/13Q2x8bAKdrtslXFi2R92+p2f57YC57XloERDYHwuI8u69uEoNMLSaEwDvfH8P8/x6CTQBtG4Zh6UMd0ahekNqlkTNLEVCUI/+hEFb7AdR+ELVZnR7bAH0AYAwDAsIBQ4jrHxtfc9RZ1QOPMyHkg0Vxnvzz64yA1ij/QdUZ5T+o17tKpXKwcbpZCuX9CUA5KCgHDY396pP2fVaUA5iuAIWXAdNl+d75cdFVpwOCY5OVBAvANbjUFloDIGnlgxWqeDA3hsmBJrC+PeDUk/dNians381isj8vLAtIAACpLGBpdPZwpS97DNjXLyoLU39Uny5A/v/iCDbGEEAfKB+Ezfnlfo8Kqv7z1kaSpmx/l97EJWcGLwQ6pnm3putgkKmlCsylmLp6H77OvAAAuL9jI7w8tC0CauKlTW02+dvk9Q5aNyKE/IfMnC//ATLny9sxBMnfyBy36x3whQDMefJBrTjH6f4aUFoif5NzfFvR6Ox/TO3fWhzfnG2l8kHYWgJYLfZvLmbXx45tF11zvRXn3NwfiUpJQIA91BjD5fuAcDkA2ErtLQOOb+j2UOTSSlDZt3r7c6ul7H1ClL3PsU3nA7ZGB+iDAUOwvN8NwfJBQ+94HCxvqzhP3tfFufbHufK/V/mwUJ7WIIcbnUHe346DoRoHG2H743qrRHL9na/QQnMTHL+XGp39d1JbFrBuRFgBq+N311zxdWtJJZ+lt/+fCpCDgD5I/lxzvhz8inPl9cz2f+ucU1X7WeTCyv4/VZWktdeklX9HHNsoLZZvpstV254+WP75PEKU/fuWb0GB/bnz6473/BGXoO7UqqP8Djj/jpV7LKxlf6fKf5awXedvkyQHQ51RvtcH2INi8M3vCg9jkKmFjmYX4LGPd+NodgH0WgkvDm6DUV0ae24uGJsVuHYCyD5Q1pLgcjC3P7ZZ5P8kliJ7yCgoCxvOz0sK7BuWnP5jBJa7D5ADRInJ6f158jZu5luwLtAebuwHW0e4KM718IHJTZLW/g3UcSCSyh4rXQ1a+VtocW7ZH57i3LKDh1pspfZQUo06HN+2Hb9Lzhy/V9c7rumdApQ+WD7AStINmt4h3xvDgOBIICgKCI4GgqOAoEj7fZR8rwsoO8A4Qkz5G4S9K0brFCY0rsFCo0XZAeYP/h8KcePP02jLgsvNdnn9EUdYLR/IHV00jv+Lf9RFY7OWC+xXyx5L2rIvGIZg+73T/0l9sPyzKH9LLE51WMqeQ5SFKF2Avb5A+Va+S6u0pJK/OfnyvcUkv8cQUha+ne9v9AXI1xzBxhF0PPXv7kz597e4/htA2IOL09/hGjavGINMLfPfzAt4evU+FJhLERsWgLcf6oAOjeu5v0HTVeDib/Zbphxesg/avxF7mpAP1KVFAK5V8b2S3E9vCLaHJ5Nrjcp2r1T+dl0AEBAhN4c77nVGp9YJx3/uUtfHwuY6zkB5XG7sQUCEvYk9oqyp3flmCKnaHwdLcVmIKbaHCMfjUnO5g6iu7OAnacqWa+2tShp9xW/15d/j/D6Xx5L8eY4meeexBSVOjzU6ufXIGGa/d7Qg2Zc5wgdQ9ge11Ox6by2RXzMElbX41KSDjacoYceHP5ckySFFqwNQja5njVYOh8E1ZB4qnQHQ1QeC6qtdSfU4/m94M0BotIDGHgj9DINMLfLe98fwyn8OAgC6JNXHW3/pgOhQY9U2UlIIHPwS+G0tcH4/kH+u8vV0AUB0SyA01nWAmFZXcfCYS/90iD1wlHuuNZQNGrMU24OH2WlwYZEcKAxB8vrGUHtft/1W2QHNZpO349wP73is0ZcFi4AI+RunP9HbW65CY9SuxPP8+A8qEfkeg0wt8euZXMz97yEAwJjuSZg+oCX02pv8RicEcGobsHcF8Ns6p64eu4hEIKYtENOm7Fa/qb25vAbTaMrGaIBnaRER1UYMMrVASakNU9fsg9UmMKhdHGYObn1zb7x2Eti3Ctj3iTzmxaFeE6D9X4CmvYEGreTmfyIiohqIQaYWWLolC4cu5KN+sAGz/tzmxitbiuRWl70rgBM/lC03hABthgJ/GgU07lrjBnMRERFVhkHGzx2+kI9F3/4OAHjpz20QGXKdMTElhcDPy4CfFgIFF+0LJSDpDjm8tBqk6ulzRERE7mCQ8WOlVhueWbMPFqtA31YxGNyuklkVzfnAzveAbW/Jk38BQFgjoFM60G4kEJHg05qJiIg8iUHGj73/43HsO5OL0AAdXrm3res8MUU5wM53ge1vy3M4APLYl55PyQFGZ1CjZCIiIo9ikPFTxy4V4I0NRwAAMwa1RkyY/fRh01U5vOx4R54wDgAimwE9nwZShlftmiNEREQ1HI9qfshmE3j28/0wl9rQs3kUhndsJJ9CvX0J8N0rZadPR7cC7ngaaHNvzT9VmoiIyA0MMn7oX9tPYteJawg2aJFxXwokIYBvngN2LJFXiEkBek0FWg6ufbOeEhEROWGQ8TOnr5owzz7x3bT+LdEoVAt88Tcg83N5hbvnAN0e5+nTRERUJzDI+BEhBKZ/8StMJVbcllQfo/5UH/hkBHBss3wtm6FLgHYj1C6TiIjIZxhk/MhnP5/Gj0cvw6jT4B/946BZPgg4v0++auwD/wKa9VG7RCIiIp9ikPETF3KL8fK/5QtCzuoRhMS1Q4Frx4GgKGDUaqBhB3ULJCIiUgGDjJ94YV0m8s2luDf2Mh74dRJQeEm+mOPDa4HIZLXLIyIiUgWDjB84fdWEjQcvoqfmV7xeuBCSpRCITQFGfQ6ExqhdHhERkWoYZPzAfzMvYKBmO940vA2NpVS+PtIDK3hVaiIiqvMYZPzAjn2/YrF+CXQolSe3u/cdQHedi0MSERHVIZwtrYa7kFuMbhc/gVGyoCT+NmDYBwwxREREdgwyNdyW3b/iL9pNAABDn+mcqZeIiMgJj4o1XODuJQiQLLgYlgI0vVPtcoiIiGoUBpka7Er2OfQp+DcAQNPrGV52gIiIqBwGmRrs/DdvIFgy46g2GdEdBqtdDhERUY3DIFNTFV1D8rEVAIAjLR9jawwREVElGGRqqOIf30agMOGQLQEte41UuxwiIqIaiUGmJirOg2bnUgDA58EPomkDTnxHRERUGQaZmmjXezBY8nDUFo/gW+9TuxoiIqIai0GmpikphO2nxQCAt0qHon+7RioXREREVHMxyNQ0P38ATdEVnLDFILNeX9wSE6J2RURERDUWg0xNYikCti4EACy2DsE9KQ0h8WwlIiKi62KQqUl+WQ4UZuOsiMZaaw/0bxundkVEREQ1mqpBxmq1YsaMGUhKSkJgYCCSk5MxZ84cCCGUdYQQmDlzJuLi4hAYGIi+ffvi999/V7FqLyk1Az8uAAC8XToYsfVC0bYhz1YiIiK6EVWDzLx587BkyRK89dZbOHjwIObNm4f58+dj0aJFyjrz58/HwoULsXTpUuzYsQPBwcFITU1FcXGxipV7wZ6PgfxzyNFFYbW1F/q1iWW3EhER0R/QqfnhP/30E4YMGYKBAwcCAJo0aYKVK1di586dAOTWmAULFuCFF17AkCFDAADLly9HTEwM1q1bh5Eja8lEcVaL0hqzxDIIJdCjf0qsujURERH5AVVbZLp164ZNmzbhyJEjAIB9+/bhxx9/RP/+/QEAx48fx4ULF9C3b1/lPeHh4ejSpQu2bdtW6TbNZjPy8vJcbjXevlVA7imYA6Lwobk3YsKMuDWhntpVERER1XiqtshMmzYNeXl5aNmyJbRaLaxWK1555RWMGjUKAHDhwgUAQExMjMv7YmJilNfKy8jIwKxZs7xbuCdZS4EfXgcA/C98BMw5BvRrEwuNht1KREREf0TVFpnPPvsMK1aswCeffIJffvkFH330EV577TV89NFHbm9z+vTpyM3NVW6nT5/2YMVecGobcO04RGA9vHKxKwCgH89WIiIiuimqtshMnToV06ZNU8a6pKSk4OTJk8jIyEBaWhpiY+VxIhcvXkRcXNnB/eLFi/jTn/5U6TaNRiOMRqPXa/eYY5sBANkxd+DCIS0igw24Lam+ujURERH5CVVbZEwmEzQa1xK0Wi1sNhsAICkpCbGxsdi0aZPyel5eHnbs2IGuXbv6tFavsQeZ761tAQD3tImBlt1KREREN0XVFpnBgwfjlVdeQePGjdGmTRvs2bMHb7zxBsaMGQMAkCQJkydPxssvv4zmzZsjKSkJM2bMQHx8PIYOHapm6Z5RlAOc+wUAsOxcYwDsViIiIqoKVYPMokWLMGPGDIwfPx7Z2dmIj4/H3//+d8ycOVNZ55lnnkFhYSHGjh2LnJwc9OjRA//9738REBCgYuUecuJHQNhQFNYUB7JDER6oR7fkSLWrIiIi8huScJ5GtxbKy8tDeHg4cnNzERZWw2bK/c9UYOe72Bk9DCNOD8OwDo3w+oj2aldFRESkups9fvNaS2qyj4/5/FozAED/tpwEj4iIqCoYZNSSexa4fARC0uDrgmYINmjRo3mU2lURERH5FQYZtRzfAgDIiWiLPASjZVwYAvRalYsiIiLyLwwyarF3Kx0N6QgAaBIZrGIxRERE/olBRg1CAMfkFpldUjsAQNNoBhkiIqKqYpBRw6XDQMEFQBeIzUVJANgiQ0RE5A4GGTXYu5WQ2BVHrlgAAElRDDJERERVxSCjBnuQMTXsgRyTHGSaRAWpWBAREZF/YpDxNWupPKMvgJMRtwEAYsMCEGRQdZJlIiIiv8Qg42vnfgFK8oHAejgoEgGwNYaIiMhdDDK+5hgfk9QLx68UyQ+jQtSrh4iIyI8xyPiaI8g07YXjlwsBAElskSEiInILg4wvlRQCp3fKj5v2xokrjiDDFhkiIiJ3MMj40sltgM0CRDSGiGiC45fYIkNERFQdDDK+dOw7+b5pb1wqLEFhiRUaCUiozyBDRETkDgYZX7JflgBJvZTWmIb1AmHU8WKRRERE7mCQ8ZWCS8DFX+XHSb04PoaIiMgDGGR85bi9NSYmBQiJxjHHGUuR7FYiIiJyF4OMrziCTNNeAIAT9iDThNdYIiIichuDjC8IAWRtlh83vRMAnOaQYZAhIiJyF4OML1w7DuSeAjR6ILErbDaBE1dMABhkiIiIqoNBxhccs/km3AYYgnEutwglpTbotRIaRgSqWhoREZE/Y5DxBcdp1017AwBOXJZbYxLqB0Gn5T8BERGRu3gU9TabzWmgb28AwPHLBfJTdisRERFVC4OMt13YDxRdAwyhQHwHAMBxe4tMk0gGGSIioupgkPE2x/iYJj0ArQ5AWYtMUjSDDBERUXUwyHibI8jYu5UAlJ2xxBYZIiKiamGQ8SZLMXBqu/zYHmQsVhtOXbUHGbbIEBERVQuDjDed2QmUFgEhsUB0C3nRtSJYbQIBeg1iQgNULpCIiMi/Mch408Xf5PuEzoAkASgbH9MkMhgajaRWZURERLUCg4w3FVyU78MaKoscZyxxRl8iIqLqY5Dxpnx7kAlpoCw6wWssEREReQyDjDc5WmRCYpVFx3nVayIiIo9hkPEmR5AJjVEWOYIMZ/UlIiKqPgYZb8q/IN+HyEGm2GLFudwiAGyRISIi8gQGGW+xWgDTFfmxvWvp1FUThABCA3SIDDaoWBwREVHtwCDjLYWXAAhA0gJBkQCAY5fKBvpKEk+9JiIiqi4GGW9RupUaABp5N5+4wjOWiIiIPIlBxlsKsuX7EKeBvvYWGV71moiIyDMYZLylwHWgLwAct7fINOU1loiIiDyCQcZbHC0ylZx6zRYZIiIiz2CQ8ZZyp14XmEtxKd8MgKdeExEReQqDjLcos/rKQcZxaYLIYAPCA/VqVUVERFSrMMh4izKrrzyHzHFeY4mIiMjjGGS8Jd+1RYbXWCIiIvI8BhlvEOK6XUtskSEiIvIcBhlvKM4BrPLAXkeQOcYgQ0RE5HEMMt7g6FYKCAf0AQA4qy8REZE3MMh4g9KtJA/0vVZYghyTBQDnkCEiIvIkBhlvUIJMAwBlM/rGhQcg0KBVqyoiIqJah0HGG8qden2CM/oSERF5BYOMN5Sb1VeZQ4bXWCIiIvIoVYNMkyZNIElShduECRMAAL17967w2mOPPaZmyTenoPI5ZJLYIkNERORROjU/fNeuXbBarcrzzMxM3H333Rg+fLiy7NFHH8Xs2bOV50FBQT6t0S2c1ZeIiMgnVA0y0dHRLs/nzp2L5ORk9OrVS1kWFBSE2NhYX5dWPfllg32FEGVjZBhkiIiIPKrGjJEpKSnBxx9/jDFjxkCSJGX5ihUrEBUVhbZt22L69OkwmUw33I7ZbEZeXp7LzeecTr++lG9GYYkVGgloXN8PWpOIiIj8iKotMs7WrVuHnJwcpKenK8v+8pe/IDExEfHx8di/fz+effZZHD58GF988cV1t5ORkYFZs2b5oOLrsBTLM/sCQGgMjp+XW2Ma1QuCQVdjciMREVGtUGOCzPvvv4/+/fsjPj5eWTZ27FjlcUpKCuLi4tCnTx9kZWUhOTm50u1Mnz4dTz75pPI8Ly8PCQkJ3iu8PEdrjNYIBETg+OXTADg+hoiIyBtqRJA5efIkNm7ceMOWFgDo0qULAODo0aPXDTJGoxFGo9HjNd60gmz5PiQGkCRlMjwGGSIiIs+rEX0dy5YtQ4MGDTBw4MAbrrd3714AQFxcnA+qclOBYw4Z+6y+lxhkiIiIvEX1FhmbzYZly5YhLS0NOl1ZOVlZWfjkk08wYMAAREZGYv/+/ZgyZQruuOMOtGvXTsWK/4BjMjzHrL5XeMYSERGRt6geZDZu3IhTp05hzJgxLssNBgM2btyIBQsWoLCwEAkJCRg2bBheeOEFlSq9SU5dSzabwIkr8llWTRlkiIiIPE71IHPPPfdACFFheUJCArZs2aJCRdVUUHZ5gnO5RSgptcGg1SA+IlDduoiIiGqhGjFGplZxtMiExigz+jaODIJWI93gTUREROQOBhlPc7pg5NlrRQA4ER4REZG3MMh4mtMFIwvMpQCA0ADVe/CIiIhqJQYZT7LZnLqWYmEqkS+IGWTQqlgUERFR7cUg40mmK4CwApCA4GglyATq2SJDRETkDQwynuToVgqKBLR6FJXIXUvBRrbIEBEReQODjCc5nXoNoKxFhl1LREREXsEg40n59haZUHuQsdjHyOgZZIiIiLyBQcaTlDOW5MsTmOxnLQUZOEaGiIjIGxhkPEkJMvIFI9m1RERE5F0MMp7kCDL2C0YWWXj6NRERkTcxyHhSfuUtMuxaIiIi8g4GGU9Szlqyt8hwQjwiIiKvYpDxJKdZfQHAVOIY7MsgQ0RE5A0MMp5iLgBKCuTH9q6lQg72JSIi8iq3gsx3333n6Tr8n2Ogrz4YMIbCahMoKbUB4BgZIiIib3EryPTr1w/Jycl4+eWXcfr0aU/X5J8Kyk2GZ+9WAti1RERE5C1uBZmzZ89i4sSJWLNmDZo2bYrU1FR89tlnKCkp8XR9/iPf9fIEjoG+Ggkw6tiDR0RE5A1uHWGjoqIwZcoU7N27Fzt27MAtt9yC8ePHIz4+Hk888QT27dvn6TprPsdA33LXWQoy6CBJklpVERER1WrVbiro0KEDpk+fjokTJ6KgoAAffPABOnbsiJ49e+K3337zRI3+odwFIwvtXUsc6EtEROQ9bgcZi8WCNWvWYMCAAUhMTMQ333yDt956CxcvXsTRo0eRmJiI4cOHe7LWmk059dq1a4njY4iIiLzHrdNpHn/8caxcuRJCCDz88MOYP38+2rZtq7weHByM1157DfHx8R4rtMbLd50Mj7P6EhEReZ9bR9kDBw5g0aJFuO+++2A0GitdJyoqqm6dpq1cMLL8GBm2yBAREXmLW0Fm06ZNf7xhnQ69evVyZ/P+qdzp10UWzupLRETkbW6NkcnIyMAHH3xQYfkHH3yAefPmVbsov2MtBQovy48dg33N9ll99QwyRERE3uJWkHnnnXfQsmXLCsvbtGmDpUuXVrsov1OYDUAAkhYIigLAwb5ERES+4FaQuXDhAuLi4iosj46Oxvnz56tdlN9Rxsc0ADTyLjUp11niYF8iIiJvcSvIJCQkYOvWrRWWb926tW6dqeSQ7xRk7Ez2MTLBbJEhIiLyGreaCx599FFMnjwZFosFd911FwB5APAzzzyDp556yqMF+gWlRSZWWcSuJSIiIu9zK8hMnToVV65cwfjx45XrKwUEBODZZ5/F9OnTPVqgXyio2CKjDPZl1xIREZHXuHWUlSQJ8+bNw4wZM3Dw4EEEBgaiefPm151TptZzTIYX6tQiw9OviYiIvK5azQUhISHo3Lmzp2rxX+UmwwOcB/syyBAREXmL20Hm559/xmeffYZTp04p3UsOX3zxRbUL8ys3CDLB7FoiIiLyGrfOWlq1ahW6deuGgwcPYu3atbBYLPjtt9/w7bffIjw83NM11nzKrL4c7EtERORLbgWZV199Ff/85z/x5ZdfwmAw4M0338ShQ4cwYsQING7c2NM11mxCVH76dYk8RoZdS0RERN7jVpDJysrCwIEDAQAGgwGFhYWQJAlTpkzBu+++69ECa7ziHMBqlh87nX7Ni0YSERF5n1tBpl69esjPzwcANGzYEJmZmQCAnJwcmEwmz1XnDwqy5fuAcEAfoCxmkCEiIvI+t0ai3nHHHdiwYQNSUlIwfPhwTJo0Cd9++y02bNiAPn36eLrGms1x6rXTQF/AeYwMB/sSERF5i1tH2bfeegvFxcUAgOeffx56vR4//fQThg0bhhdeeMGjBdZ4jhYZpyBTarWhxGoDwBYZIiIib6pykCktLcW///1vpKamAgA0Gg2mTZvm8cL8RkHFyfBMFqvymIN9iYiIvKfKY2R0Oh0ee+wxpUWmzquka8lkvzyBViPBoHVrGBIRERHdBLeOsrfddhv27t3r4VL8VCVdS45Tr4P0WkiSpEZVREREdYJbY2TGjx+PJ598EqdPn0bHjh0RHBzs8nq7du08UpxfKKikRYaXJyAiIvIJt4LMyJEjAQBPPPGEskySJAghIEkSrFbr9d5a+zhaZELLgkyRfYxMsJFnLBEREXmTW0fa48ePe7oO/6WMkak4GV6gni0yRERE3uRWkElMTPR0Hf7JUizP7Au4Xp7AbB8jw64lIiIir3IryCxfvvyGr48ePdqtYvxOob1bSWsAAuspizlGhoiIyDfcCjKTJk1yeW6xWGAymWAwGBAUFFR3goxyscgYwOnsJMc8MmyRISIi8i63Tr++du2ay62goACHDx9Gjx49sHLlSk/XWHNVcsYSABTZT78O5uUJiIiIvMpjs7U1b94cc+fOrdBaU6sV2FtknGb1Bdi1RERE5CsenXZWp9Ph3LlzntxkzaZ0LTVwWcwrXxMREfmGW30f69evd3kuhMD58+fx1ltvoXv37h4pzC84WmRCyrfIyF1LgexaIiIi8iq3jrRDhw51eS5JEqKjo3HXXXfh9ddfv+ntNGnSBCdPnqywfPz48Vi8eDGKi4vx1FNPYdWqVTCbzUhNTcXbb7+NmJiYSramggK2yBAREanJrSBjs9k88uG7du1ymQU4MzMTd999N4YPHw4AmDJlCr766iusXr0a4eHhmDhxIu677z5s3brVI59fbfkVr3wNAEX2IBPMIENERORVqvZ9REdHuzyfO3cukpOT0atXL+Tm5uL999/HJ598grvuugsAsGzZMrRq1Qrbt2/H7bffrkbJriq5YCTgPNiXXUtERETe5NZg32HDhmHevHkVls+fP19pTamqkpISfPzxxxgzZgwkScLu3bthsVjQt29fZZ2WLVuicePG2LZt23W3YzabkZeX53LzCputbEK8Cqdfs2uJiIjIF9wKMt9//z0GDBhQYXn//v3x/fffu1XIunXrkJOTg/T0dADAhQsXYDAYEBER4bJeTEwMLly4cN3tZGRkIDw8XLklJCS4Vc8fKroK2ORBveXHyBQqg30ZZIiIiLzJrSBTUFAAg8FQYbler3e7BeT9999H//79ER8f79b7HaZPn47c3Fzldvr06Wpt77oc42OCIgGt3uUlpUWGF40kIiLyKreCTEpKCj799NMKy1etWoXWrVtXeXsnT57Exo0b8be//U1ZFhsbi5KSEuTk5Lise/HiRcTGxuJ6jEYjwsLCXG5eUVDxqtcOZWctcYwMERGRN7l1pJ0xYwbuu+8+ZGVlKQNxN23ahJUrV2L16tVV3t6yZcvQoEEDDBw4UFnWsWNH6PV6bNq0CcOGDQMAHD58GKdOnULXrl3dKduzHAN9QyueCu6YRybIyBYZIiIib3IryAwePBjr1q3Dq6++ijVr1iAwMBDt2rXDxo0b0atXrypty2azYdmyZUhLS4NOV1ZOeHg4HnnkETz55JOoX78+wsLC8Pjjj6Nr164144yl/MqvswQARbxoJBERkU+43fcxcOBAlxYUd23cuBGnTp3CmDFjKrz2z3/+ExqNBsOGDXOZEK9GuM6p1yWlNlisAgAQpGfXEhERkTe5daTdtWsXbDYbunTp4rJ8x44d0Gq16NSp001v65577oEQotLXAgICsHjxYixevNidMr1L0gABEdedDA/gWUtERETe5tZg3wkTJlR6NtDZs2cxYcKEahflF/q9Ckw7CXR5zGWxySKPj9FpJBh0Hr0mJxEREZXj1pH2wIED6NChQ4Xlt956Kw4cOFDtovyKJLk85XWWiIiIfMetIGM0GnHx4sUKy8+fP+8yYLcuKuKp10RERD7jVpC55557lInnHHJycvDcc8/h7rvv9lhx/qjQbD/1mi0yREREXudWs8Frr72GO+64A4mJibj11lsBAHv37kVMTAz+9a9/ebRAf2OyOC4YySBDRETkbW4FmYYNG2L//v1YsWIF9u3bh8DAQPz1r3/Fgw8+CL1e/8cbqMV4wUgiIiLfcXsgR3BwMHr06IHGjRujpKQEAPD1118DAP785z97pjo/xMsTEBER+Y5bR9tjx47h3nvvxa+//gpJkiCEgOR09o7Var3Bu2u3ohKOkSEiIvIVtwb7Tpo0CUlJScjOzkZQUBAyMzOxZcsWdOrUCZs3b/Zwif7F0SLDMTJERETe51aLzLZt2/Dtt98iKioKGo0GWq0WPXr0QEZGBp544gns2bPH03X6jUKOkSEiIvIZt1pkrFYrQkNDAQBRUVE4d+4cACAxMRGHDx/2XHV+qKxriWNkiIiIvM2to23btm2xb98+JCUloUuXLpg/fz4MBgPeffddNG3a1NM1+hWla0nPFhkiIiJvcyvIvPDCCygsLAQAzJ49G4MGDULPnj0RGRmJTz/91KMF+hvH6dfBRgYZIiIib3MryKSmpiqPmzVrhkOHDuHq1auoV6+ey9lLdVHZYF92LREREXmbx4629evX99Sm/FqhY4wMu5aIiIi8zq3BvnR9nNmXiIjIdxhkPIzzyBAREfkOg4yHFVkcg305RoaIiMjbGGQ8zGQfI8PTr4mIiLyPQcbDTGaOkSEiIvIVBhkPEkLAZOHVr4mIiHyFQcaDSqw2WG0CAAf7EhER+QKDjAc5Tr0G2LVERETkCwwyHuQ49dqg1UCv5a4lIiLyNh5tPUg5Y4mtMURERD7BIONBJs7qS0RE5FMMMh7EWX2JiIh8i0HGgxyDfYN56jUREZFPMMh4EFtkiIiIfItBxoMcg305RoaIiMg3GGQ8iIN9iYiIfItBxoOUriU9x8gQERH5AoOMBxWxa4mIiMinGGQ8SOlaMjLIEBER+QKDjAcpV75m1xIREZFPMMh4kMnMriUiIiJfYpDxIM4jQ0RE5FsMMh5UZOHp10RERL7EIONBZfPIcIwMERGRLzDIeBAnxCMiIvItBhkP4iUKiIiIfItBxoM42JeIiMi3GGQ8qIhjZIiIiHyKQcZDhBBK11IwW2SIiIh8gkHGQ8ylNtiE/JhdS0RERL7BIOMhjvExALuWiIiIfIVBxkMc3UoGnQZajaRyNURERHUDg4yHFHEOGSIiIp9jkPEQZTI8PYMMERGRrzDIeIgSZIwcH0NEROQrDDIeUmThrL5ERES+xiDjIYVm+6y+7FoiIiLyGdWDzNmzZ/HQQw8hMjISgYGBSElJwc8//6y8np6eDkmSXG79+vVTseLKcbAvERGR76k6oOPatWvo3r077rzzTnz99deIjo7G77//jnr16rms169fPyxbtkx5bjQafV3qHyq7YCTHyBAREfmKqkfdefPmISEhwSWkJCUlVVjPaDQiNjbWl6VVmcnCFhkiIiJfU7Vraf369ejUqROGDx+OBg0a4NZbb8V7771XYb3NmzejQYMGaNGiBcaNG4crV66oUO2NsWuJiIjI91QNMseOHcOSJUvQvHlzfPPNNxg3bhyeeOIJfPTRR8o6/fr1w/Lly7Fp0ybMmzcPW7ZsQf/+/WG1WivdptlsRl5ensvNF5TBvuxaIiIi8hlVj7o2mw2dOnXCq6++CgC49dZbkZmZiaVLlyItLQ0AMHLkSGX9lJQUtGvXDsnJydi8eTP69OlTYZsZGRmYNWuWb34AJzz9moiIyPdUbZGJi4tD69atXZa1atUKp06duu57mjZtiqioKBw9erTS16dPn47c3Fzldvr0aY/WfD0mdi0RERH5nKotMt27d8fhw4ddlh05cgSJiYnXfc+ZM2dw5coVxMXFVfq60WhU5aymsiDDriUiIiJfUbVFZsqUKdi+fTteffVVHD16FJ988gneffddTJgwAQBQUFCAqVOnYvv27Thx4gQ2bdqEIUOGoFmzZkhNTVWz9Ao42JeIiMj3VA0ynTt3xtq1a7Fy5Uq0bdsWc+bMwYIFCzBq1CgAgFarxf79+/HnP/8Zt9xyCx555BF07NgRP/zwQ42bS6bQPo9MIIMMERGRz6jeDzJo0CAMGjSo0tcCAwPxzTff+Lgi97BFhoiIyPdUv0RBbcHBvkRERL7HIOMhjiATqFe9kYuIiKjOYJDxkCL7GJlgI1tkiIiIfIVBxgOEEMq1ljjYl4iIyHcYZDyg2GKDEPJjziNDRETkOwwyHmCydysBQKCeLTJERES+wiDjAY6BvkadBlqNpHI1REREdQeDjAcU2cfHBBvZrURERORLDDIeUHbqNbuViIiIfIlBxgNMZnmMDCfDIyIi8i0GGQ/grL5ERETqYJDxAM4hQ0REpA4GGQ9QZvXlHDJEREQ+xSDjAcpgX7bIEBER+RSDjAdwjAwREZE6GGQ8wDGzLy9PQERE5FsMMh7AriUiIiJ1MMh4QJGja4kT4hEREfkUg4wHKGNkeIkCIiIin2KQ8YCyMTJskSEiIvIlBhkP4FlLRERE6mCQ8QBeNJKIiEgdDDIeoAz25enXREREPsUg4wEmi32MjJEtMkRERL7EIOMBRRwjQ0REpAoGGQ8oNDvmkWHXEhERkS8xyFSTzSZQZOHMvkRERGpgkKmm4lKr8phdS0RERL7FIFNNjlOvAZ5+TURE5GsMMtVU5DSHjEYjqVwNERFR3cIgU02FvDwBERGRahhkqkmZ1ZdBhoiIyOcYZKqJc8gQERGph0GmmspaZDiHDBERka8xyFSTyT5GJpgtMkRERD7HIFNNJnYtERERqYZBpprYtURERKQeBplqKnKcfs3J8IiIiHyOQaaaePo1ERGRehhkqskRZIKNDDJERES+xiBTTSZlZl+OkSEiIvI1BplqMjlda4mIiIh8i0GmmjizLxERkXoYZKqJg32JiIjUwyBTTSaLfbAvx8gQERH5HINMNSnzyLBFhoiIyOcYZKqp0MyuJSIiIrUwyFRTkcUx2JddS0RERL7GIFNNJnYtERERqYbNCNVgswkUW2wA2LVERFTXWK1WWCwWtcvwW3q9Hlpt9Y+dDDLV4OhWAnjWEhFRXSGEwIULF5CTk6N2KX4vIiICsbGxkCTJ7W3w6FsNhfZuJUkCAvTspSMiqgscIaZBgwYICgqq1kG4rhJCwGQyITs7GwAQFxfn9rZUDzJnz57Fs88+i6+//homkwnNmjXDsmXL0KlTJwDyD/viiy/ivffeQ05ODrp3744lS5agefPmKldeNqtvoF7LX2QiojrAarUqISYyMlLtcvxaYGAgACA7OxsNGjRwu5tJ1WaEa9euoXv37tDr9fj6669x4MABvP7666hXr56yzvz587Fw4UIsXboUO3bsQHBwMFJTU1FcXKxi5TITL09ARFSnOMbEBAUFqVxJ7eDYj9UZa6Rqi8y8efOQkJCAZcuWKcuSkpKUx0IILFiwAC+88AKGDBkCAFi+fDliYmKwbt06jBw50uc1O+PlCYiI6ia2wnuGJ/ajqi0y69evR6dOnTB8+HA0aNAAt956K9577z3l9ePHj+PChQvo27evsiw8PBxdunTBtm3b1CjZhaNriQN9iYioLmrSpAkWLFigag2qBpljx44p412++eYbjBs3Dk888QQ++ugjAPKAKgCIiYlxeV9MTIzyWnlmsxl5eXkuN29xDPZliwwREdVkkiTd8PbSSy+5td1du3Zh7Nixni22ilRtSrDZbOjUqRNeffVVAMCtt96KzMxMLF26FGlpaW5tMyMjA7NmzfJkmddVxDEyRETkB86fP688/vTTTzFz5kwcPnxYWRYSEqI8FkLAarVCp/vjiBAdHe3ZQt2gaotMXFwcWrdu7bKsVatWOHXqFAAgNjYWAHDx4kWXdS5evKi8Vt706dORm5ur3E6fPu2FymXKGBk9u5aIiKjmio2NVW7h4eGQJEl5fujQIYSGhuLrr79Gx44dYTQa8eOPPyIrKwtDhgxBTEwMQkJC0LlzZ2zcuNFlu+W7liRJwv/93//h3nvvRVBQEJo3b47169d79WdTNch0797dJRECwJEjR5CYmAhAHvgbGxuLTZs2Ka/n5eVhx44d6Nq1a6XbNBqNCAsLc7l5Cy9PQEREQgiYSkpVuQkhPPZzTJs2DXPnzsXBgwfRrl07FBQUYMCAAdi0aRP27NmDfv36YfDgwUpjw/XMmjULI0aMwP79+zFgwACMGjUKV69e9Vid5analDBlyhR069YNr776KkaMGIGdO3fi3XffxbvvvgtATnaTJ0/Gyy+/jObNmyMpKQkzZsxAfHw8hg4dqmbpAJwG+xoZZIiI6qoiixWtZ36jymcfmJ3qsYsWz549G3fffbfyvH79+mjfvr3yfM6cOVi7di3Wr1+PiRMnXnc76enpePDBBwEAr776KhYuXIidO3eiX79+HqmzPFWDTOfOnbF27VpMnz4ds2fPRlJSEhYsWIBRo0Yp6zzzzDMoLCzE2LFjkZOTgx49euC///0vAgICVKxcZrKwa4mIiGoHx0S0DgUFBXjppZfw1Vdf4fz58ygtLUVRUdEftsi0a9dOeRwcHIywsDBlBl9vUP0IPGjQIAwaNOi6r0uShNmzZ2P27Nk+rOrmmMzsWiIiqusC9VocmJ2q2md7SnBwsMvzp59+Ghs2bMBrr72GZs2aITAwEPfffz9KSkpuuB29Xu/yXJIk2Gw2j9VZnupBxp9xQjwiIpIkyWPdOzXJ1q1bkZ6ejnvvvReA3EJz4sQJdYuqBK90WA2OriW2yBARUW3TvHlzfPHFF9i7dy/27duHv/zlL15tWXEXg0w1cB4ZIiKqrd544w3Uq1cP3bp1w+DBg5GamooOHTqoXVYFta8tzIfKTr/mbiQiIv+Qnp6O9PR05Xnv3r0rPY27SZMm+Pbbb12WTZgwweV5+a6myraTk5Pjdq03gy0y1cCrXxMREamLQaYaONiXiIhIXQwy1VA2RoZdS0RERGpgkKkGXqKAiIhIXQwy1cAxMkREROpikHGT1SZgLpXPp2fXEhERkToYZNzk6FYC2CJDRESkFgYZNzkG+koSYNRxNxIREamBR2A3KeNj9FpIkqRyNURERHUTg4yblCBj5PgYIiKq/Xr37o3JkyerXUYFDDJu4qnXRETkLwYPHox+/fpV+toPP/wASZKwf/9+H1flGQwyblJm9dUzyBARUc32yCOPYMOGDThz5kyF15YtW4ZOnTqhXbt2KlRWfQwybuIcMkRE5C8GDRqE6OhofPjhhy7LCwoKsHr1agwdOhQPPvggGjZsiKCgIKSkpGDlypXqFFtFDDJuKrLwytdERARACKCkUJ1bJVebroxOp8Po0aPx4YcfulyhevXq1bBarXjooYfQsWNHfPXVV8jMzMTYsWPx8MMPY+fOnd7aax7Do7CbeMFIIiICAFhMwKvx6nz2c+cAQ/BNrTpmzBj84x//wJYtW9C7d28AcrfSsGHDkJiYiKefflpZ9/HHH8c333yDzz77DLfddps3KvcYtsi4yTGPTDCDDBER+YGWLVuiW7du+OCDDwAAR48exQ8//IBHHnkEVqsVc+bMQUpKCurXr4+QkBB88803OHXqlMpV/zG2yLip0OxokeEuJCKq0/RBcsuIWp9dBY888ggef/xxLF68GMuWLUNycjJ69eqFefPm4c0338SCBQuQkpKC4OBgTJ48GSUlJV4q3HN4FHaTycLTr4mICPIU7zfZvaO2ESNGYNKkSfjkk0+wfPlyjBs3DpIkYevWrRgyZAgeeughAIDNZsORI0fQunVrlSv+Y+xaclMRz1oiIiI/ExISggceeADTp0/H+fPnkZ6eDgBo3rw5NmzYgJ9++gkHDx7E3//+d1y8eFHdYm8Sg0w1GHQaDvYlIiK/8sgjj+DatWtITU1FfLw8SPmFF15Ahw4dkJqait69eyM2NhZDhw5Vt9CbJAlxk+du+am8vDyEh4cjNzcXYWFhHt++EILXWiIiqiOKi4tx/PhxJCUlISAgQO1y/N6N9ufNHr/ZIlNNDDFERETqYZAhIiIiv8UgQ0RERH6LQYaIiIj8FoMMERER+S0GGSIioiqq5Sf8+own9iODDBER0U3S6/UAAJPJpHIltYNjPzr2qzt4iQIiIqKbpNVqERERgezsbABAUFAQp+FwgxACJpMJ2dnZiIiIgFbr/uSyDDJERERVEBsbCwBKmCH3RUREKPvTXQwyREREVSBJEuLi4tCgQQNYLBa1y/Fber2+Wi0xDgwyREREbtBqtR45EFP1cLAvERER+S0GGSIiIvJbDDJERETkt2r9GBnHZDt5eXkqV0JEREQ3y3Hc/qNJ82p9kMnPzwcAJCQkqFwJERERVVV+fj7Cw8Ov+7okavk8yzabDefOnUNoaKhHJy3Ky8tDQkICTp8+jbCwMI9tlyrH/e1b3N++x33uW9zfvuXO/hZCID8/H/Hx8dBorj8Spta3yGg0GjRq1Mhr2w8LC+N/Ah/i/vYt7m/f4z73Le5v36rq/r5RS4wDB/sSERGR32KQISIiIr/FIOMmo9GIF198EUajUe1S6gTub9/i/vY97nPf4v72LW/u71o/2JeIiIhqL7bIEBERkd9ikCEiIiK/xSBDREREfotBhoiIiPwWg4ybFi9ejCZNmiAgIABdunTBzp071S6pVvj+++8xePBgxMfHQ5IkrFu3zuV1IQRmzpyJuLg4BAYGom/fvvj999/VKbYWyMjIQOfOnREaGooGDRpg6NChOHz4sMs6xcXFmDBhAiIjIxESEoJhw4bh4sWLKlXs35YsWYJ27dopk4J17doVX3/9tfI697X3zJ07F5IkYfLkycoy7m/PeumllyBJksutZcuWyuve2t8MMm749NNP8eSTT+LFF1/EL7/8gvbt2yM1NRXZ2dlql+b3CgsL0b59eyxevLjS1+fPn4+FCxdi6dKl2LFjB4KDg5Gamori4mIfV1o7bNmyBRMmTMD27duxYcMGWCwW3HPPPSgsLFTWmTJlCr788kusXr0aW7Zswblz53DfffepWLX/atSoEebOnYvdu3fj559/xl133YUhQ4bgt99+A8B97S27du3CO++8g3bt2rks5/72vDZt2uD8+fPK7ccff1Re89r+FlRlt912m5gwYYLy3Gq1ivj4eJGRkaFiVbUPALF27Vrluc1mE7GxseIf//iHsiwnJ0cYjUaxcuVKFSqsfbKzswUAsWXLFiGEvH/1er1YvXq1ss7BgwcFALFt2za1yqxV6tWrJ/7v//6P+9pL8vPzRfPmzcWGDRtEr169xKRJk4QQ/N32hhdffFG0b9++0te8ub/ZIlNFJSUl2L17N/r27ass02g06Nu3L7Zt26ZiZbXf8ePHceHCBZd9Hx4eji5dunDfe0hubi4AoH79+gCA3bt3w2KxuOzzli1bonHjxtzn1WS1WrFq1SoUFhaia9eu3NdeMmHCBAwcONBlvwL83faW33//HfHx8WjatClGjRqFU6dOAfDu/q71F430tMuXL8NqtSImJsZleUxMDA4dOqRSVXXDhQsXAKDSfe94jdxns9kwefJkdO/eHW3btgUg73ODwYCIiAiXdbnP3ffrr7+ia9euKC4uRkhICNauXYvWrVtj79693NcetmrVKvzyyy/YtWtXhdf4u+15Xbp0wYcffogWLVrg/PnzmDVrFnr27InMzEyv7m8GGSICIH9zzczMdOnTJs9r0aIF9u7di9zcXKxZswZpaWnYsmWL2mXVOqdPn8akSZOwYcMGBAQEqF1OndC/f3/lcbt27dClSxckJibis88+Q2BgoNc+l11LVRQVFQWtVlthpPXFixcRGxurUlV1g2P/ct973sSJE/Hvf/8b3333HRo1aqQsj42NRUlJCXJyclzW5z53n8FgQLNmzdCxY0dkZGSgffv2ePPNN7mvPWz37t3Izs5Ghw4doNPpoNPpsGXLFixcuBA6nQ4xMTHc314WERGBW265BUePHvXq7zeDTBUZDAZ07NgRmzZtUpbZbDZs2rQJXbt2VbGy2i8pKQmxsbEu+z4vLw87duzgvneTEAITJ07E2rVr8e233yIpKcnl9Y4dO0Kv17vs88OHD+PUqVPc5x5is9lgNpu5rz2sT58++PXXX7F3717l1qlTJ4waNUp5zP3tXQUFBcjKykJcXJx3f7+rNVS4jlq1apUwGo3iww8/FAcOHBBjx44VERER4sKFC2qX5vfy8/PFnj17xJ49ewQA8cYbb4g9e/aIkydPCiGEmDt3roiIiBD/7//9P7F//34xZMgQkZSUJIqKilSu3D+NGzdOhIeHi82bN4vz588rN5PJpKzz2GOPicaNG4tvv/1W/Pzzz6Jr166ia9euKlbtv6ZNmya2bNkijh8/Lvbv3y+mTZsmJEkS//vf/4QQ3Nfe5nzWkhDc35721FNPic2bN4vjx4+LrVu3ir59+4qoqCiRnZ0thPDe/maQcdOiRYtE48aNhcFgELfddpvYvn272iXVCt99950AUOGWlpYmhJBPwZ4xY4aIiYkRRqNR9OnTRxw+fFjdov1YZfsagFi2bJmyTlFRkRg/fryoV6+eCAoKEvfee684f/68ekX7sTFjxojExERhMBhEdHS06NOnjxJihOC+9rbyQYb727MeeOABERcXJwwGg2jYsKF44IEHxNGjR5XXvbW/JSGEqF6bDhEREZE6OEaGiIiI/BaDDBEREfktBhkiIiLyWwwyRERE5LcYZIiIiMhvMcgQERGR32KQISIiIr/FIENEdc7mzZshSVKF674Qkf9hkCEiIiK/xSBDREREfotBhoh8zmazISMjA0lJSQgMDET79u2xZs0aAGXdPl999RXatWuHgIAA3H777cjMzHTZxueff442bdrAaDSiSZMmeP31111eN5vNePbZZ5GQkACj0YhmzZrh/fffd1ln9+7d6NSpE4KCgtCtWzccPnzYuz84EXkcgwwR+VxGRgaWL1+OpUuX4rfffsOUKVPw0EMPYcuWLco6U6dOxeuvv45du3YhOjoagwcPhsViASAHkBEjRmDkyJH49ddf8dJLL2HGjBn48MMPlfePHj0aK1euxMKFC3Hw4EG88847CAkJcanj+eefx+uvv46ff/4ZOp0OY8aM8cnPT0Sew4tGEpFPmc1m1K9fHxs3bkTXrl2V5X/7299gMpkwduxY3HnnnVi1ahUeeOABAMDVq1fRqFEjfPjhhxgxYgRGjRqFS5cu4X//+5/y/meeeQZfffUVfvvtNxw5cgQtWrTAhg0b0Ldv3wo1bN68GXfeeSc2btyIPn36AAD+85//YODAgSgqKkJAQICX9wIReQpbZIjIp44ePQqTyYS7774bISEhym358uXIyspS1nMOOfXr10eLFi1w8OBBAMDBgwfRvXt3l+12794dv//+O6xWK/bu3QutVotevXrdsJZ27dopj+Pi4gAA2dnZ1f4Zich3dGoXQER1S0FBAQDgq6++QsOGDV1eMxqNLmHGXYGBgTe1nl6vVx5LkgRAHr9DRP6DLTJE5FOtW7eG0WjEqVOn0KxZM5dbQkKCst727duVx9euXcORI0fQqlUrAECrVq2wdetWl+1u3boVt9xyC7RaLVJSUmCz2VzG3BBR7cQWGSLyqdDQUDz99NOYMmUKbDYbevTogdzcXGzduhVhYWFITEwEAMyePRuRkZGIiYnB888/j6ioKAwdOhQA8NRTT6Fz586YM2cOHnjgAWzbtg1vvfUW3n77bQBAkyZNkJaWhjFjxmDhwoVo3749Tp48iezsbIwYMUKtH52IvIBBhoh8bs6cOYiOjkZGRgaOHTuGiIgIdOjQAc8995zStTN37lxMmjQJv//+O/70pz/hyy+/hMFgAAB06NABn332GWbOnIk5c+YgLi4Os2fPRnp6uvIZS5YswXPPPYfx48fjypUraNy4MZ577jk1flwi8iKetURENYrjjKJr164hIiJC7XKIqIbjGBkiIiLyWwwyRERE5LfYtURERER+iy0yRERE5LcYZIiIiMhvMcgQERGR32KQISIiIr/FIENERER+i0GGiIiI/BaDDBEREfktBhkiIiLyWwwyRERE5Lf+P1OXlnITw/XkAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(hist['train_accuracy'], label='Train')\n",
        "plt.plot(hist['val_accuracy'], label='Val')\n",
        "\n",
        "\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Train', 'Val' ], loc='lower right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "08Z7Se1RUbxp",
        "outputId": "1dff676d-3e1f-4cf2-fb89-f5f3ada9fce6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(hist['train_loss'], label='Train')\n",
        "plt.plot(hist['val_loss'], label='Val')\n",
        "\n",
        "\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Train', 'Val' ], loc='upper right')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.5"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "98503eaf250549f8b3ca7956e0d6edd4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_375ff45cbc9c4d8b8167bed4d382ca47",
              "IPY_MODEL_9751ec3267374b45adb613ebfb97a735",
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