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{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GRU_REG_EMB.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zn9maoCzLyT",
        "colab_type": "code",
        "outputId": "7a7c7bc2-02b8-4951-c738-6769535b5e2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "GPU = True\n",
        "device_idx = 0\n",
        "if GPU:\n",
        "    device = torch.device(\"cuda:\" + str(device_idx) if torch.cuda.is_available() else \"cpu\")\n",
        "else:\n",
        "    device = torch.device(\"cpu\")\n",
        "print(device)\n",
        "\n",
        "# Set default dtype for model weights\n",
        "torch.set_default_dtype(torch.double)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lhv5MZ3JyH59",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the data\n",
        "import os\n",
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        "# os.chdir(\"/content/drive/My Drive/Colab Notebooks/NLP/coursework\")\n",
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        "import pickle\n",
        "with open(\"en_indices.pk\", \"rb\") as f:\n",
        "    en_sentences_vectors = pickle.load(f)\n",
        "with open(\"de_indices.pk\", \"rb\") as f:\n",
        "    de_sentences_vectors = pickle.load(f)\n",
        "with open(\"scores.pk\", \"rb\") as f:\n",
        "    scores = pickle.load(f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTm0ONuPB7Z2",
        "colab_type": "code",
        "outputId": "fb608a1d-3655-4352-c4b9-9f762c0c879e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "en_lengths = np.array([len(en_sentences_vectors[i]) for i in range(len(en_sentences_vectors))])\n",
        "de_lengths = np.array([len(de_sentences_vectors[i]) for i in range(len(de_sentences_vectors))])\n",
        "scores = np.array(scores)\n",
        "\n",
        "# Plotting the lengths of sentences to see where we should pad\n",
        "sns.distplot(en_lengths)\n",
        "plt.title(\"Lengths of sentences EN\")\n",
        "plt.show()\n",
        "sns.distplot(de_lengths)\n",
        "plt.title(\"Lengths of sentences DE\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5xJp0Gq73DnBMM2sUEZFm0J2xIiIhp6AXEQk5Bb2ISMjpefQd2N6aOhaUb2Pzrmpe+2gj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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8ro/uUipxoStjW1l1bT1vfLKZ/B6ZnKKHi3Qol53Yn8Lpp5GX3YlvPbWAaU98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\noY4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-pmaN-XE-4o",
        "colab_type": "code",
        "outputId": "8138fed1-312a-47f8-90b6-8bad1c44a74d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        }
      },
      "source": [
        "# Split into 2 group so that inputs are not too sparse\n",
        "# Check whether the proposed splits have the same distribution of scores\n",
        "sns.distplot(scores)\n",
        "plt.title(\"Full Distribution of scores\")\n",
        "plt.show()\n",
        "\n",
        "sns.distplot(scores[en_lengths < 17])\n",
        "plt.title(\"EN_SENT, len < 17\")\n",
        "plt.show()\n",
        "\n",
        "sns.distplot(scores[en_lengths >= 17])\n",
        "plt.title(\"EN_SENT, len >= 17\")\n",
        "plt.show()\n",
        "\n",
        "# sns.distplot(scores[de_lengths < 17])\n",
        "# plt.title(\"DE_SENT, len < 17\")\n",
        "# plt.show()\n",
        "\n",
        "# sns.distplot(scores[de_lengths >= 17])\n",
        "# plt.title(\"DE_SENT, len >= 17\")\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T/yOmQBeRqhwfy7Fu2YFeaaGryyVKCnQRWZS70z+WW0ELPYgaLaEbLQW6iCxqdHKafLG8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\n0EVEmoQCXUSkSSjQRUSahAJdRKRJ/H/K9cc5Tbdd8AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jEX78hRvUWy+zRl3NbMeOHb5nz56GHFskTF/dd4Lf+sKjDE/k2NLTxkW97azv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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JLVNEMk2yFxVFLS0v5JiGXCaVSKDXAgdjHh+KPBfrNOA0M/ujmT1iZpdPdiAzu97MdprZ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\nyRIKdBGRLPH/ASdZhBlG4tYVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5q6FF8aXXfr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split up into two seperate sets and pad\n",
        "pad = [0]\n",
        "\n",
        "scores_set_1 = scores[en_lengths < 17]\n",
        "scores_set_2 = scores[en_lengths >= 17]\n",
        "\n",
        "en_set_1 = []\n",
        "en_set_2 = []\n",
        "idx_set_1 = []\n",
        "idx_set_2 = []\n",
        "\n",
        "max_len_en = max(len(sent) for sent in en_sentences_vectors)\n",
        "\n",
        "for i, en_sentence in enumerate(en_sentences_vectors):\n",
        "    sent_len = len(en_sentence)\n",
        "    if sent_len < 17:\n",
        "        en_set_1.append(en_sentence + pad * (16 - sent_len))\n",
        "        idx_set_1.append(i)\n",
        "    else:\n",
        "        en_set_2.append(en_sentence + pad * (max_len_en - sent_len))\n",
        "        idx_set_2.append(i)\n",
        "\n",
        "\n",
        "de_set_1 = []\n",
        "de_set_2 = []\n",
        "max_len_de_set_1 = max(de_lengths[idx_set_1])\n",
        "max_len_de_set_2 = max(len(sent) for sent in de_sentences_vectors)\n",
        "\n",
        "for idx in idx_set_1:\n",
        "    de_set_1.append(de_sentences_vectors[idx] + pad * (max_len_de_set_1 - len(de_sentences_vectors[idx])))\n",
        "for idx in idx_set_2:\n",
        "    de_set_2.append(de_sentences_vectors[idx] + pad * (max_len_de_set_2 - len(de_sentences_vectors[idx])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q43POV9QytX_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a dataset\n",
        "class Sentences(torch.utils.data.Dataset):\n",
        "    def __init__(self, en_sentences, de_sentences, scores):\n",
        "        super(Sentences, self).__init__()\n",
        "        self.en_sentences_vectors = torch.tensor(en_sentences, device=device)\n",
        "        self.de_sentences_vectors = torch.tensor(de_sentences, device=device)\n",
        "        self.scores = torch.tensor(scores, device=device)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.scores)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "        \n",
        "        en, de, scores = self.en_sentences_vectors[idx], self.de_sentences_vectors[idx], self.scores[idx]\n",
        "        return en, de, scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOd75PS_fGoA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create train and test sets\n",
        "train_pct = 0.8\n",
        "first_set_train_len = int(train_pct * len(en_set_1))\n",
        "second_set_train_len = int(train_pct * len(en_set_2))\n",
        "\n",
        "first_set = Sentences(en_set_1, de_set_1, scores_set_1)\n",
        "second_set = Sentences(en_set_2, de_set_2, scores_set_2)\n",
        "\n",
        "first_set_train, first_set_test = torch.utils.data.random_split(first_set, [first_set_train_len, len(en_set_1) - first_set_train_len])\n",
        "second_set_train, second_set_test = torch.utils.data.random_split(second_set, [second_set_train_len, len(en_set_2) - second_set_train_len])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkw5SZx1fTZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create Dataloaders\n",
        "batch_size = 32\n",
        "\n",
        "loader_first_train = torch.utils.data.DataLoader(first_set_train, batch_size=batch_size, shuffle=True)\n",
        "loader_first_test = torch.utils.data.DataLoader(first_set_test, batch_size=batch_size, shuffle=False)\n",
        "\n",
        "loader_second_train = torch.utils.data.DataLoader(second_set_train, batch_size=batch_size, shuffle=True)\n",
        "loader_second_test = torch.utils.data.DataLoader(second_set_test, batch_size=batch_size, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7keA4QQ0x0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Hyper Parameters\n",
        "en_vocab_size = 25237\n",
        "de_vocab_size = 27854\n",
        "embed_dim = 300\n",
        "lstm_hidden_dim = 256\n",
        "num_layers = 2\n",
        "reg_inputs = 4 * lstm_hidden_dim\n",
        "reg_hid_dim1 = 2 * lstm_hidden_dim\n",
        "reg_hid_dim2 = lstm_hidden_dim\n",
        "learning_rate = 1e-4\n",
        "\n",
        "class LSTM_REG(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(LSTM_REG, self).__init__()\n",
        "        self.en_emb = torch.nn.Embedding(en_vocab_size, embed_dim, padding_idx=0)\n",
        "        self.de_emb = torch.nn.Embedding(de_vocab_size, embed_dim, padding_idx=0)\n",
        "        self.en_lstm = torch.nn.GRU(embed_dim, lstm_hidden_dim, num_layers, bidirectional=True, batch_first=True)\n",
        "        self.de_lstm = torch.nn.GRU(embed_dim, lstm_hidden_dim, num_layers, bidirectional=True, batch_first=True)\n",
        "        self.reg = torch.nn.Sequential(\n",
        "                    nn.Dropout(),\n",
        "                    nn.Linear(reg_inputs, reg_hid_dim1),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Linear(reg_hid_dim1, reg_hid_dim2),\n",
        "                    nn.ReLU(),\n",
        "                    nn.Linear(reg_hid_dim2, 1)\n",
        "        )\n",
        "\n",
        "    def forward(self, en_batch, de_batch):\n",
        "        # inputs should be 3D, BATCH, NUM_WORDS, lstm_hidden_dim\n",
        "        en_emb = self.en_emb(en_batch)\n",
        "        de_emb = self.de_emb(de_batch)\n",
        "        en_all_hids, en_last_hid = self.en_lstm(en_emb)\n",
        "        de_all_hids, de_last_hid = self.de_lstm(de_emb)\n",
        "\n",
        "        # using last state of last layer in each direction\n",
        "        en_last_hid_resh = en_last_hid.view(num_layers, 2, en_batch.size(0), lstm_hidden_dim)\n",
        "        en_reg_input_1 = en_last_hid_resh[-1, 0, :, :]\n",
        "        en_reg_input_2 = en_last_hid_resh[-1, 1, :, :]\n",
        "\n",
        "        de_last_hid_resh = de_last_hid.view(num_layers, 2, de_batch.size(0), lstm_hidden_dim)\n",
        "        de_reg_input_1 = de_last_hid_resh[-1, 0, :, :]\n",
        "        de_reg_input_2 = de_last_hid_resh[-1, 1, :, :]\n",
        "\n",
        "        reg_input = torch.cat((en_reg_input_1, en_reg_input_2, de_reg_input_1, de_reg_input_2), dim=-1)\n",
        "\n",
        "        out = self.reg(reg_input)\n",
        "        return out\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFgnl4bonSGg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create model\n",
        "model = LSTM_REG()\n",
        "model = model.to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDNWHcEh0lxP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimiser\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3IepQDap5AW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loss function\n",
        "def calc_loss(output, target):\n",
        "  loss = torch.nn.functional.mse_loss(output.squeeze(), target, reduction=\"mean\")\n",
        "  return loss\n",
        "\n",
        "# Test function\n",
        "def eval_target(output, target):\n",
        "    acc = torch.nn.functional.l1_loss(output.squeeze(), target, reduction=\"mean\")\n",
        "    return acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VieQzJimED7Y",
        "colab_type": "code",
        "outputId": "4cbde0f8-cb80-4e37-f048-0344ea756542",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "num_epochs = 100\n",
        "\n",
        "verbose_print = False\n",
        "\n",
        "train_losses = []\n",
        "test_losses = []\n",
        "test_acc = []\n",
        "test_pearson = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    epoch_loss = []\n",
        "    total_steps = len(loader_first_train) + len(loader_second_train)\n",
        "    for idx_1, (en, de, labels) in enumerate(loader_first_train):\n",
        "        model.zero_grad()\n",
        "        en = en.to(device)\n",
        "        de = de.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        output = model(en, de)\n",
        "        loss = calc_loss(output, labels)\n",
        "        loss.backward()\n",
        "        avg_batch_loss = loss.item()\n",
        "        optimizer.step()\n",
        "        epoch_loss.append(avg_batch_loss)\n",
        "        if verbose_print:\n",
        "            print(f\"Epoch {epoch}, Batch {idx_1 + 1} Train Loss: {avg_batch_loss:.4f}\")\n",
        "\n",
        "    for idx_2, (en, de, labels) in enumerate(loader_second_train):\n",
        "        model.zero_grad()\n",
        "        en = en.to(device)\n",
        "        de = de.to(device)\n",
        "        labels = labels.to(device)\n",
        "        \n",
        "        output = model(en, de)\n",
        "        loss = calc_loss(output, labels)\n",
        "        loss.backward()\n",
        "        avg_batch_loss = loss.item()\n",
        "        optimizer.step()\n",
        "        epoch_loss.append(avg_batch_loss)\n",
        "        if verbose_print:\n",
        "            print(f\"Epoch {epoch}, Batch {idx_1 + idx_2 + 2} Train Loss: {avg_batch_loss:.4f}\")\n",
        "    \n",
        "    avg_epoch_loss = sum(epoch_loss) / total_steps\n",
        "    train_losses.append(avg_epoch_loss)\n",
        "    print(f\"Average Train Loss in Epoch {epoch}: {avg_epoch_loss:.4f}\")\n",
        "\n",
        "    # Test on test set\n",
        "\n",
        "    model.eval()\n",
        "    epoch_loss = []\n",
        "    epoch_acc = []\n",
        "\n",
        "    all_outputs = []\n",
        "    all_labels = []\n",
        "\n",
        "    total_steps = len(loader_first_test) + len(loader_second_test)\n",
        "    with torch.no_grad():\n",
        "        for idx_1, (en, de, labels) in enumerate(loader_first_test):\n",
        "            # Record for Pearson\n",
        "            all_labels.extend(labels.tolist())\n",
        "\n",
        "            en = en.to(device)\n",
        "            de = de.to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            output = model(en, de)\n",
        "            # Record for Pearson\n",
        "            all_outputs.extend(output.squeeze().tolist())\n",
        "\n",
        "            loss = calc_loss(output, labels)\n",
        "            avg_batch_loss = loss.item()\n",
        "            acc = eval_target(output, labels)\n",
        "            avg_batch_acc= acc.item()\n",
        "            epoch_loss.append(avg_batch_loss)\n",
        "            epoch_acc.append(avg_batch_acc)\n",
        "            if verbose_print:\n",
        "                print(f\"Epoch {epoch}, Batch {idx_1 + 1} Test Loss: {avg_batch_loss:.4f}, Test Acc: {avg_batch_acc:.4f}\")\n",
        "\n",
        "        for idx_2, (en, de, labels) in enumerate(loader_second_test):\n",
        "            en = en.to(device)\n",
        "            de = de.to(device)\n",
        "            labels = labels.to(device)\n",
        "            \n",
        "            output = model(en, de)\n",
        "            loss = calc_loss(output, labels)\n",
        "            avg_batch_loss = loss.item()\n",
        "            acc = eval_target(output, labels)\n",
        "            avg_batch_acc= acc.item()\n",
        "            epoch_loss.append(avg_batch_loss)\n",
        "            epoch_acc.append(avg_batch_acc)\n",
        "            if verbose_print:\n",
        "                print(f\"Epoch {epoch}, Batch {idx_1 + idx_2 + 2} Test Loss: {avg_batch_loss:.4f}, Test Acc: {avg_batch_acc:.4f}\")\n",
        "    \n",
        "    avg_epoch_loss = sum(epoch_loss) / total_steps\n",
        "    avg_epoch_acc = sum(epoch_acc) / total_steps\n",
        "    test_losses.append(avg_epoch_loss)\n",
        "    test_acc.append(avg_epoch_acc)\n",
        "    print(f\"Average Test Loss in Epoch {epoch}: {avg_epoch_loss:.4f}\")\n",
        "    print(f\"Average Test Acc in Epoch {epoch}: {avg_epoch_acc:.4f}\")\n",
        "\n",
        "    # Calc Pearson\n",
        "    pearson = np.corrcoef(all_outputs, all_labels)[0, 1]\n",
        "    test_pearson.append(pearson)\n",
        "    print(f\"Pearson Coeff in Epoch {epoch}: {pearson}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Train Loss in Epoch 0: 0.0064\n",
            "Average Test Loss in Epoch 0: 0.7037\n",
            "Average Test Acc in Epoch 0: 0.5604\n",
            "Pearson Coeff in Epoch 0: -0.00640668765047935\n",
            "Average Train Loss in Epoch 1: 0.0070\n",
            "Average Test Loss in Epoch 1: 0.7356\n",
            "Average Test Acc in Epoch 1: 0.5793\n",
            "Pearson Coeff in Epoch 1: -0.006979045487867788\n",
            "Average Train Loss in Epoch 2: 0.0070\n",
            "Average Test Loss in Epoch 2: 0.7183\n",
            "Average Test Acc in Epoch 2: 0.5663\n",
            "Pearson Coeff in Epoch 2: -0.008282282412511641\n",
            "Average Train Loss in Epoch 3: 0.0070\n",
            "Average Test Loss in Epoch 3: 0.7177\n",
            "Average Test Acc in Epoch 3: 0.5695\n",
            "Pearson Coeff in Epoch 3: -0.0060183350042013065\n",
            "Average Train Loss in Epoch 4: 0.0069\n",
            "Average Test Loss in Epoch 4: 0.7513\n",
            "Average Test Acc in Epoch 4: 0.5851\n",
            "Pearson Coeff in Epoch 4: -0.01662392594265492\n",
            "Average Train Loss in Epoch 5: 0.0084\n",
            "Average Test Loss in Epoch 5: 0.7393\n",
            "Average Test Acc in Epoch 5: 0.5792\n",
            "Pearson Coeff in Epoch 5: -0.010780362453402855\n",
            "Average Train Loss in Epoch 6: 0.0062\n",
            "Average Test Loss in Epoch 6: 0.7309\n",
            "Average Test Acc in Epoch 6: 0.5760\n",
            "Pearson Coeff in Epoch 6: -0.007079517514670253\n",
            "Average Train Loss in Epoch 7: 0.0061\n",
            "Average Test Loss in Epoch 7: 0.7319\n",
            "Average Test Acc in Epoch 7: 0.5740\n",
            "Pearson Coeff in Epoch 7: -0.013774120205408132\n",
            "Average Train Loss in Epoch 8: 0.0065\n",
            "Average Test Loss in Epoch 8: 0.7202\n",
            "Average Test Acc in Epoch 8: 0.5689\n",
            "Pearson Coeff in Epoch 8: -0.012118685158348044\n",
            "Average Train Loss in Epoch 9: 0.0066\n",
            "Average Test Loss in Epoch 9: 0.7192\n",
            "Average Test Acc in Epoch 9: 0.5707\n",
            "Pearson Coeff in Epoch 9: -0.0071289457160448495\n",
            "Average Train Loss in Epoch 10: 0.0069\n",
            "Average Test Loss in Epoch 10: 0.7193\n",
            "Average Test Acc in Epoch 10: 0.5656\n",
            "Pearson Coeff in Epoch 10: -0.011515248369711595\n",
            "Average Train Loss in Epoch 11: 0.0060\n",
            "Average Test Loss in Epoch 11: 0.7236\n",
            "Average Test Acc in Epoch 11: 0.5703\n",
            "Pearson Coeff in Epoch 11: -0.005069177373407349\n",
            "Average Train Loss in Epoch 12: 0.0062\n",
            "Average Test Loss in Epoch 12: 0.7340\n",
            "Average Test Acc in Epoch 12: 0.5758\n",
            "Pearson Coeff in Epoch 12: -0.011562655037911231\n",
            "Average Train Loss in Epoch 13: 0.0079\n",
            "Average Test Loss in Epoch 13: 0.7214\n",
            "Average Test Acc in Epoch 13: 0.5684\n",
            "Pearson Coeff in Epoch 13: -0.008449650566705465\n",
            "Average Train Loss in Epoch 14: 0.0072\n",
            "Average Test Loss in Epoch 14: 0.7141\n",
            "Average Test Acc in Epoch 14: 0.5676\n",
            "Pearson Coeff in Epoch 14: -0.006097211910686325\n",
            "Average Train Loss in Epoch 15: 0.0059\n",
            "Average Test Loss in Epoch 15: 0.7171\n",
            "Average Test Acc in Epoch 15: 0.5663\n",
            "Pearson Coeff in Epoch 15: -0.00868391500578421\n",
            "Average Train Loss in Epoch 16: 0.0063\n",
            "Average Test Loss in Epoch 16: 0.7132\n",
            "Average Test Acc in Epoch 16: 0.5626\n",
            "Pearson Coeff in Epoch 16: -0.0068288151125823265\n",
            "Average Train Loss in Epoch 17: 0.0073\n",
            "Average Test Loss in Epoch 17: 0.7285\n",
            "Average Test Acc in Epoch 17: 0.5689\n",
            "Pearson Coeff in Epoch 17: -0.015800308472356018\n",
            "Average Train Loss in Epoch 18: 0.0079\n",
            "Average Test Loss in Epoch 18: 0.7174\n",
            "Average Test Acc in Epoch 18: 0.5664\n",
            "Pearson Coeff in Epoch 18: -0.006678423310916171\n",
            "Average Train Loss in Epoch 19: 0.0070\n",
            "Average Test Loss in Epoch 19: 0.7112\n",
            "Average Test Acc in Epoch 19: 0.5584\n",
            "Pearson Coeff in Epoch 19: -0.012301081725153455\n",
            "Average Train Loss in Epoch 20: 0.0066\n",
            "Average Test Loss in Epoch 20: 0.7375\n",
            "Average Test Acc in Epoch 20: 0.5748\n",
            "Pearson Coeff in Epoch 20: -0.011193149754333446\n",
            "Average Train Loss in Epoch 21: 0.0058\n",
            "Average Test Loss in Epoch 21: 0.7168\n",
            "Average Test Acc in Epoch 21: 0.5616\n",
            "Pearson Coeff in Epoch 21: -0.016230629153410902\n",
            "Average Train Loss in Epoch 22: 0.0061\n",
            "Average Test Loss in Epoch 22: 0.7361\n",
            "Average Test Acc in Epoch 22: 0.5745\n",
            "Pearson Coeff in Epoch 22: -0.008928321861264556\n",
            "Average Train Loss in Epoch 23: 0.0061\n",
            "Average Test Loss in Epoch 23: 0.7311\n",
            "Average Test Acc in Epoch 23: 0.5724\n",
            "Pearson Coeff in Epoch 23: -0.010055173007708168\n",
            "Average Train Loss in Epoch 24: 0.0055\n",
            "Average Test Loss in Epoch 24: 0.7179\n",
            "Average Test Acc in Epoch 24: 0.5661\n",
            "Pearson Coeff in Epoch 24: -0.010154973152838248\n",
            "Average Train Loss in Epoch 25: 0.0058\n",
            "Average Test Loss in Epoch 25: 0.7151\n",
            "Average Test Acc in Epoch 25: 0.5665\n",
            "Pearson Coeff in Epoch 25: -0.014390283954534358\n",
            "Average Train Loss in Epoch 26: 0.0065\n",
            "Average Test Loss in Epoch 26: 0.6966\n",
            "Average Test Acc in Epoch 26: 0.5547\n",
            "Pearson Coeff in Epoch 26: -0.006887657576059487\n",
            "Average Train Loss in Epoch 27: 0.0074\n",
            "Average Test Loss in Epoch 27: 0.7502\n",
            "Average Test Acc in Epoch 27: 0.5811\n",
            "Pearson Coeff in Epoch 27: -0.013991437861377506\n",
            "Average Train Loss in Epoch 28: 0.0083\n",
            "Average Test Loss in Epoch 28: 0.7345\n",
            "Average Test Acc in Epoch 28: 0.5756\n",
            "Pearson Coeff in Epoch 28: -0.01275095297448143\n",
            "Average Train Loss in Epoch 29: 0.0070\n",
            "Average Test Loss in Epoch 29: 0.7228\n",
            "Average Test Acc in Epoch 29: 0.5699\n",
            "Pearson Coeff in Epoch 29: -0.010738972002671059\n",
            "Average Train Loss in Epoch 30: 0.0058\n",
            "Average Test Loss in Epoch 30: 0.7384\n",
            "Average Test Acc in Epoch 30: 0.5746\n",
            "Pearson Coeff in Epoch 30: -0.014365453459400542\n",
            "Average Train Loss in Epoch 31: 0.0048\n",
            "Average Test Loss in Epoch 31: 0.7270\n",
            "Average Test Acc in Epoch 31: 0.5708\n",
            "Pearson Coeff in Epoch 31: -0.01160351295848988\n",
            "Average Train Loss in Epoch 32: 0.0044\n",
            "Average Test Loss in Epoch 32: 0.7284\n",
            "Average Test Acc in Epoch 32: 0.5734\n",
            "Pearson Coeff in Epoch 32: -0.013472051296738327\n",
            "Average Train Loss in Epoch 33: 0.0056\n",
            "Average Test Loss in Epoch 33: 0.7188\n",
            "Average Test Acc in Epoch 33: 0.5686\n",
            "Pearson Coeff in Epoch 33: -0.00952966769478103\n",
            "Average Train Loss in Epoch 34: 0.0051\n",
            "Average Test Loss in Epoch 34: 0.7328\n",
            "Average Test Acc in Epoch 34: 0.5728\n",
            "Pearson Coeff in Epoch 34: -0.014258350328160824\n",
            "Average Train Loss in Epoch 35: 0.0052\n",
            "Average Test Loss in Epoch 35: 0.7503\n",
            "Average Test Acc in Epoch 35: 0.5829\n",
            "Pearson Coeff in Epoch 35: -0.013481927715847445\n",
            "Average Train Loss in Epoch 36: 0.0056\n",
            "Average Test Loss in Epoch 36: 0.7332\n",
            "Average Test Acc in Epoch 36: 0.5736\n",
            "Pearson Coeff in Epoch 36: -0.011264977647855326\n",
            "Average Train Loss in Epoch 37: 0.0046\n",
            "Average Test Loss in Epoch 37: 0.7137\n",
            "Average Test Acc in Epoch 37: 0.5610\n",
            "Pearson Coeff in Epoch 37: -0.0145935856302308\n",
            "Average Train Loss in Epoch 38: 0.0048\n",
            "Average Test Loss in Epoch 38: 0.7094\n",
            "Average Test Acc in Epoch 38: 0.5559\n",
            "Pearson Coeff in Epoch 38: -0.015018139630774197\n",
            "Average Train Loss in Epoch 39: 0.0050\n",
            "Average Test Loss in Epoch 39: 0.7175\n",
            "Average Test Acc in Epoch 39: 0.5611\n",
            "Pearson Coeff in Epoch 39: -0.01683060454278484\n",
            "Average Train Loss in Epoch 40: 0.0056\n",
            "Average Test Loss in Epoch 40: 0.7006\n",
            "Average Test Acc in Epoch 40: 0.5604\n",
            "Pearson Coeff in Epoch 40: -0.014162116495078265\n",
            "Average Train Loss in Epoch 41: 0.0071\n",
            "Average Test Loss in Epoch 41: 0.7132\n",
            "Average Test Acc in Epoch 41: 0.5651\n",
            "Pearson Coeff in Epoch 41: -0.011043106471982304\n",
            "Average Train Loss in Epoch 42: 0.0057\n",
            "Average Test Loss in Epoch 42: 0.7186\n",
            "Average Test Acc in Epoch 42: 0.5623\n",
            "Pearson Coeff in Epoch 42: -0.01730310071553439\n",
            "Average Train Loss in Epoch 43: 0.0046\n",
            "Average Test Loss in Epoch 43: 0.7365\n",
            "Average Test Acc in Epoch 43: 0.5735\n",
            "Pearson Coeff in Epoch 43: -0.015166682443291847\n",
            "Average Train Loss in Epoch 44: 0.0053\n",
            "Average Test Loss in Epoch 44: 0.7442\n",
            "Average Test Acc in Epoch 44: 0.5783\n",
            "Pearson Coeff in Epoch 44: -0.016477045219500264\n",
            "Average Train Loss in Epoch 45: 0.0049\n",
            "Average Test Loss in Epoch 45: 0.7381\n",
            "Average Test Acc in Epoch 45: 0.5715\n",
            "Pearson Coeff in Epoch 45: -0.014855397963026226\n",
            "Average Train Loss in Epoch 46: 0.0045\n",
            "Average Test Loss in Epoch 46: 0.7301\n",
            "Average Test Acc in Epoch 46: 0.5700\n",
            "Pearson Coeff in Epoch 46: -0.014198007048672636\n",
            "Average Train Loss in Epoch 47: 0.0046\n",
            "Average Test Loss in Epoch 47: 0.7421\n",
            "Average Test Acc in Epoch 47: 0.5746\n",
            "Pearson Coeff in Epoch 47: -0.014543215716162435\n",
            "Average Train Loss in Epoch 48: 0.0051\n",
            "Average Test Loss in Epoch 48: 0.7382\n",
            "Average Test Acc in Epoch 48: 0.5737\n",
            "Pearson Coeff in Epoch 48: -0.018195214360679147\n",
            "Average Train Loss in Epoch 49: 0.0058\n",
            "Average Test Loss in Epoch 49: 0.7369\n",
            "Average Test Acc in Epoch 49: 0.5736\n",
            "Pearson Coeff in Epoch 49: -0.0187835210579788\n",
            "Average Train Loss in Epoch 50: 0.0060\n",
            "Average Test Loss in Epoch 50: 0.7327\n",
            "Average Test Acc in Epoch 50: 0.5713\n",
            "Pearson Coeff in Epoch 50: -0.016376628604220102\n",
            "Average Train Loss in Epoch 51: 0.0047\n",
            "Average Test Loss in Epoch 51: 0.7333\n",
            "Average Test Acc in Epoch 51: 0.5698\n",
            "Pearson Coeff in Epoch 51: -0.021823389604178886\n",
            "Average Train Loss in Epoch 52: 0.0039\n",
            "Average Test Loss in Epoch 52: 0.7330\n",
            "Average Test Acc in Epoch 52: 0.5661\n",
            "Pearson Coeff in Epoch 52: -0.021463282019656534\n",
            "Average Train Loss in Epoch 53: 0.0048\n",
            "Average Test Loss in Epoch 53: 0.7222\n",
            "Average Test Acc in Epoch 53: 0.5625\n",
            "Pearson Coeff in Epoch 53: -0.018696313913313105\n",
            "Average Train Loss in Epoch 54: 0.0056\n",
            "Average Test Loss in Epoch 54: 0.7278\n",
            "Average Test Acc in Epoch 54: 0.5663\n",
            "Pearson Coeff in Epoch 54: -0.017137352732088478\n",
            "Average Train Loss in Epoch 55: 0.0051\n",
            "Average Test Loss in Epoch 55: 0.7280\n",
            "Average Test Acc in Epoch 55: 0.5682\n",
            "Pearson Coeff in Epoch 55: -0.018891591467467665\n",
            "Average Train Loss in Epoch 56: 0.0040\n",
            "Average Test Loss in Epoch 56: 0.7340\n",
            "Average Test Acc in Epoch 56: 0.5694\n",
            "Pearson Coeff in Epoch 56: -0.019951237621963408\n",
            "Average Train Loss in Epoch 57: 0.0041\n",
            "Average Test Loss in Epoch 57: 0.7418\n",
            "Average Test Acc in Epoch 57: 0.5753\n",
            "Pearson Coeff in Epoch 57: -0.019676796008937927\n",
            "Average Train Loss in Epoch 58: 0.0041\n",
            "Average Test Loss in Epoch 58: 0.7390\n",
            "Average Test Acc in Epoch 58: 0.5707\n",
            "Pearson Coeff in Epoch 58: -0.020598112045259128\n",
            "Average Train Loss in Epoch 59: 0.0043\n",
            "Average Test Loss in Epoch 59: 0.7237\n",
            "Average Test Acc in Epoch 59: 0.5626\n",
            "Pearson Coeff in Epoch 59: -0.018634062292781854\n",
            "Average Train Loss in Epoch 60: 0.0043\n",
            "Average Test Loss in Epoch 60: 0.7294\n",
            "Average Test Acc in Epoch 60: 0.5690\n",
            "Pearson Coeff in Epoch 60: -0.019200184562833688\n",
            "Average Train Loss in Epoch 61: 0.0048\n",
            "Average Test Loss in Epoch 61: 0.7250\n",
            "Average Test Acc in Epoch 61: 0.5645\n",
            "Pearson Coeff in Epoch 61: -0.017169600118991837\n",
            "Average Train Loss in Epoch 62: 0.0046\n",
            "Average Test Loss in Epoch 62: 0.7429\n",
            "Average Test Acc in Epoch 62: 0.5717\n",
            "Pearson Coeff in Epoch 62: -0.02105909092450887\n",
            "Average Train Loss in Epoch 63: 0.0040\n",
            "Average Test Loss in Epoch 63: 0.7397\n",
            "Average Test Acc in Epoch 63: 0.5708\n",
            "Pearson Coeff in Epoch 63: -0.020242108494791737\n",
            "Average Train Loss in Epoch 64: 0.0042\n",
            "Average Test Loss in Epoch 64: 0.7207\n",
            "Average Test Acc in Epoch 64: 0.5620\n",
            "Pearson Coeff in Epoch 64: -0.02208794678246374\n",
            "Average Train Loss in Epoch 65: 0.0042\n",
            "Average Test Loss in Epoch 65: 0.7232\n",
            "Average Test Acc in Epoch 65: 0.5612\n",
            "Pearson Coeff in Epoch 65: -0.019729349599685995\n",
            "Average Train Loss in Epoch 66: 0.0048\n",
            "Average Test Loss in Epoch 66: 0.7218\n",
            "Average Test Acc in Epoch 66: 0.5619\n",
            "Pearson Coeff in Epoch 66: -0.01763264710866364\n",
            "Average Train Loss in Epoch 67: 0.0045\n",
            "Average Test Loss in Epoch 67: 0.7217\n",
            "Average Test Acc in Epoch 67: 0.5601\n",
            "Pearson Coeff in Epoch 67: -0.019551360501773796\n",
            "Average Train Loss in Epoch 68: 0.0050\n",
            "Average Test Loss in Epoch 68: 0.7119\n",
            "Average Test Acc in Epoch 68: 0.5567\n",
            "Pearson Coeff in Epoch 68: -0.018149361915137767\n",
            "Average Train Loss in Epoch 69: 0.0042\n",
            "Average Test Loss in Epoch 69: 0.7361\n",
            "Average Test Acc in Epoch 69: 0.5679\n",
            "Pearson Coeff in Epoch 69: -0.019791509404197764\n",
            "Average Train Loss in Epoch 70: 0.0047\n",
            "Average Test Loss in Epoch 70: 0.7192\n",
            "Average Test Acc in Epoch 70: 0.5590\n",
            "Pearson Coeff in Epoch 70: -0.02263321301328681\n",
            "Average Train Loss in Epoch 71: 0.0050\n",
            "Average Test Loss in Epoch 71: 0.7246\n",
            "Average Test Acc in Epoch 71: 0.5600\n",
            "Pearson Coeff in Epoch 71: -0.01887427602274251\n",
            "Average Train Loss in Epoch 72: 0.0043\n",
            "Average Test Loss in Epoch 72: 0.7290\n",
            "Average Test Acc in Epoch 72: 0.5655\n",
            "Pearson Coeff in Epoch 72: -0.01975682382762401\n",
            "Average Train Loss in Epoch 73: 0.0045\n",
            "Average Test Loss in Epoch 73: 0.7275\n",
            "Average Test Acc in Epoch 73: 0.5611\n",
            "Pearson Coeff in Epoch 73: -0.022303661384655567\n",
            "Average Train Loss in Epoch 74: 0.0044\n",
            "Average Test Loss in Epoch 74: 0.7345\n",
            "Average Test Acc in Epoch 74: 0.5697\n",
            "Pearson Coeff in Epoch 74: -0.013561320865527042\n",
            "Average Train Loss in Epoch 75: 0.0046\n",
            "Average Test Loss in Epoch 75: 0.7555\n",
            "Average Test Acc in Epoch 75: 0.5764\n",
            "Pearson Coeff in Epoch 75: -0.02202401637066137\n",
            "Average Train Loss in Epoch 76: 0.0033\n",
            "Average Test Loss in Epoch 76: 0.7241\n",
            "Average Test Acc in Epoch 76: 0.5617\n",
            "Pearson Coeff in Epoch 76: -0.019334440020621756\n",
            "Average Train Loss in Epoch 77: 0.0037\n",
            "Average Test Loss in Epoch 77: 0.7241\n",
            "Average Test Acc in Epoch 77: 0.5612\n",
            "Pearson Coeff in Epoch 77: -0.01860456967621103\n",
            "Average Train Loss in Epoch 78: 0.0041\n",
            "Average Test Loss in Epoch 78: 0.7257\n",
            "Average Test Acc in Epoch 78: 0.5617\n",
            "Pearson Coeff in Epoch 78: -0.020251074135047984\n",
            "Average Train Loss in Epoch 79: 0.0039\n",
            "Average Test Loss in Epoch 79: 0.7314\n",
            "Average Test Acc in Epoch 79: 0.5637\n",
            "Pearson Coeff in Epoch 79: -0.01813485602271905\n",
            "Average Train Loss in Epoch 80: 0.0036\n",
            "Average Test Loss in Epoch 80: 0.7216\n",
            "Average Test Acc in Epoch 80: 0.5600\n",
            "Pearson Coeff in Epoch 80: -0.02119288567892971\n",
            "Average Train Loss in Epoch 81: 0.0040\n",
            "Average Test Loss in Epoch 81: 0.7204\n",
            "Average Test Acc in Epoch 81: 0.5569\n",
            "Pearson Coeff in Epoch 81: -0.01891174788038295\n",
            "Average Train Loss in Epoch 82: 0.0040\n",
            "Average Test Loss in Epoch 82: 0.7121\n",
            "Average Test Acc in Epoch 82: 0.5535\n",
            "Pearson Coeff in Epoch 82: -0.025232018269285464\n",
            "Average Train Loss in Epoch 83: 0.0041\n",
            "Average Test Loss in Epoch 83: 0.7225\n",
            "Average Test Acc in Epoch 83: 0.5599\n",
            "Pearson Coeff in Epoch 83: -0.017284696851279386\n",
            "Average Train Loss in Epoch 84: 0.0038\n",
            "Average Test Loss in Epoch 84: 0.7079\n",
            "Average Test Acc in Epoch 84: 0.5509\n",
            "Pearson Coeff in Epoch 84: -0.01465603118938496\n",
            "Average Train Loss in Epoch 85: 0.0041\n",
            "Average Test Loss in Epoch 85: 0.7082\n",
            "Average Test Acc in Epoch 85: 0.5533\n",
            "Pearson Coeff in Epoch 85: -0.020592868840659445\n",
            "Average Train Loss in Epoch 86: 0.0037\n",
            "Average Test Loss in Epoch 86: 0.7177\n",
            "Average Test Acc in Epoch 86: 0.5548\n",
            "Pearson Coeff in Epoch 86: -0.013848488095631058\n",
            "Average Train Loss in Epoch 87: 0.0046\n",
            "Average Test Loss in Epoch 87: 0.7191\n",
            "Average Test Acc in Epoch 87: 0.5614\n",
            "Pearson Coeff in Epoch 87: -0.020433791133147864\n",
            "Average Train Loss in Epoch 88: 0.0039\n",
            "Average Test Loss in Epoch 88: 0.7168\n",
            "Average Test Acc in Epoch 88: 0.5562\n",
            "Pearson Coeff in Epoch 88: -0.0209650485342702\n",
            "Average Train Loss in Epoch 89: 0.0039\n",
            "Average Test Loss in Epoch 89: 0.7073\n",
            "Average Test Acc in Epoch 89: 0.5538\n",
            "Pearson Coeff in Epoch 89: -0.01930070775427866\n",
            "Average Train Loss in Epoch 90: 0.0033\n",
            "Average Test Loss in Epoch 90: 0.7176\n",
            "Average Test Acc in Epoch 90: 0.5562\n",
            "Pearson Coeff in Epoch 90: -0.018268362264179574\n",
            "Average Train Loss in Epoch 91: 0.0036\n",
            "Average Test Loss in Epoch 91: 0.7092\n",
            "Average Test Acc in Epoch 91: 0.5495\n",
            "Pearson Coeff in Epoch 91: -0.01695721266439781\n",
            "Average Train Loss in Epoch 92: 0.0035\n",
            "Average Test Loss in Epoch 92: 0.7220\n",
            "Average Test Acc in Epoch 92: 0.5597\n",
            "Pearson Coeff in Epoch 92: -0.020730753494440576\n",
            "Average Train Loss in Epoch 93: 0.0032\n",
            "Average Test Loss in Epoch 93: 0.7041\n",
            "Average Test Acc in Epoch 93: 0.5497\n",
            "Pearson Coeff in Epoch 93: -0.023294490975846783\n",
            "Average Train Loss in Epoch 94: 0.0034\n",
            "Average Test Loss in Epoch 94: 0.7133\n",
            "Average Test Acc in Epoch 94: 0.5538\n",
            "Pearson Coeff in Epoch 94: -0.021937212324890532\n",
            "Average Train Loss in Epoch 95: 0.0036\n",
            "Average Test Loss in Epoch 95: 0.7207\n",
            "Average Test Acc in Epoch 95: 0.5584\n",
            "Pearson Coeff in Epoch 95: -0.023256931584865477\n",
            "Average Train Loss in Epoch 96: 0.0038\n",
            "Average Test Loss in Epoch 96: 0.7115\n",
            "Average Test Acc in Epoch 96: 0.5523\n",
            "Pearson Coeff in Epoch 96: -0.023386304406968088\n",
            "Average Train Loss in Epoch 97: 0.0044\n",
            "Average Test Loss in Epoch 97: 0.7186\n",
            "Average Test Acc in Epoch 97: 0.5548\n",
            "Pearson Coeff in Epoch 97: -0.017680577356088172\n",
            "Average Train Loss in Epoch 98: 0.0044\n",
            "Average Test Loss in Epoch 98: 0.7276\n",
            "Average Test Acc in Epoch 98: 0.5588\n",
            "Pearson Coeff in Epoch 98: -0.027775749690502043\n",
            "Average Train Loss in Epoch 99: 0.0043\n",
            "Average Test Loss in Epoch 99: 0.7179\n",
            "Average Test Acc in Epoch 99: 0.5514\n",
            "Pearson Coeff in Epoch 99: -0.024401396445606256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OV-ZxbuwBw76",
        "colab_type": "code",
        "outputId": "6b2f1823-f5b0-4f1b-dc49-57921d85b898",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# plot stats\n",
        "plt.plot(range(len(train_losses)), train_losses)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.title('Train losses')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(len(test_losses)), test_losses)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.title('Test losses')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(len(test_acc)), test_acc)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.title('Test acc')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(range(len(test_pearson)), test_pearson)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.title('Test pearson')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q5e5b3D0H3ApcXt7A3fvKFhuBkyJBm+s0ha6IxEclgT4f2FG2vDNc9wJm9l4z20zQQ3/f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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GDS8cauOS+lLml+TQ5/FHjdgD2Gf30F892jEn5g8eVymMMX7gduBxYB9wvzFm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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aShroR+EsaDan1Oqwi+6QbTk5QFFOFs998TJ++mfnsLCqILLmhsOp4c0ZJdBD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\nSqU5DfRKKZXmNNArpVSa+/9ElHGQRcDEKAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rTXNNiOFpyyLw+8R1C3mFwIkPOC68/KKyruGF9f0ZmUq6v8tyXEMbPQ38WqKW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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}