Projeto_1º_unidade_TEIA.ipynb

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      "source": [
        "**IFPE Campus Paulista**\n",
        "\n",
        "**Curso Análise e Desenvolvimento de Sistemas - Tópicos especiais em IA**\n",
        "\n",
        "**Prof. Dr. Antônio Barreto**\n",
        "\n",
        "**Discentes:** Adriel Leite, Hilda Miranda, Kamila Rocha e Murilo Alves.\n",
        "\n",
        "**Objetivo:** Prever o valor das ações da Netflix baseado no seu histórico."
      ],
      "metadata": {
        "id": "4hGTN0oSr_VJ"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "## Imports do projeto"
      ],
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        "id": "94HTx4-hsb2K"
      }
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      "cell_type": "code",
      "source": [
        "!pip install -U skorch"
      ],
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            "Collecting skorch\n",
            "  Downloading skorch-1.0.0-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from skorch) (1.26.4)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from skorch) (1.5.2)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from skorch) (1.13.1)\n",
            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from skorch) (0.9.0)\n",
            "Requirement already satisfied: tqdm>=4.14.0 in /usr/local/lib/python3.10/dist-packages (from skorch) (4.66.6)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->skorch) (1.4.2)\n",
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            "Downloading skorch-1.0.0-py3-none-any.whl (239 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.4/239.4 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: skorch\n",
            "Successfully installed skorch-1.0.0\n"
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from plotly.subplots import make_subplots\n",
        "import matplotlib.dates as dates\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.graph_objects as go\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "from skorch import NeuralNetRegressor\n",
        "from torch.utils.data import DataLoader, TensorDataset\n"
      ],
      "metadata": {
        "id": "HGeN7dpTsffT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Criação do DataFrame original\n"
      ],
      "metadata": {
        "id": "fRvKFKwUu6k6"
      }
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/NFLX.csv')"
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        "id": "I2GolhU5shqD"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "## Analisando e explorando a base de dados para obter informações\n",
        "\n",
        "* **Date:** Dados temporais;\n",
        "* **Open:** Preço da ação no momento de abertura do mercado;\n",
        "* **High:** Maior preço da ação durante o dia;\n",
        "* **Low** Menor preço da ação durante o dia;\n",
        "* **Close:** Preço da ação no momento de fechamento do mercado;\n",
        "* **Adj Close:** Preço ajustado da ação;\n",
        "* **Volume:** Número de ações negociadas durante o dia."
      ],
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      "source": [
        "df.head()"
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              "         Date      Open      High       Low     Close  Adj Close     Volume\n",
              "0  2002-05-23  1.156429  1.242857  1.145714  1.196429   1.196429  104790000\n",
              "1  2002-05-24  1.214286  1.225000  1.197143  1.210000   1.210000   11104800\n",
              "2  2002-05-28  1.213571  1.232143  1.157143  1.157143   1.157143    6609400\n",
              "3  2002-05-29  1.164286  1.164286  1.085714  1.103571   1.103571    6757800\n",
              "4  2002-05-30  1.107857  1.107857  1.071429  1.071429   1.071429   10154200"
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              "      <td>2002-05-24</td>\n",
              "      <td>1.214286</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5044 entries, 0 to 5043\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       5044 non-null   object \n",
            " 1   Open       5044 non-null   float64\n",
            " 2   High       5044 non-null   float64\n",
            " 3   Low        5044 non-null   float64\n",
            " 4   Close      5044 non-null   float64\n",
            " 5   Adj Close  5044 non-null   float64\n",
            " 6   Volume     5044 non-null   int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 276.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com a informação acima vemos que não temos na nossa base de dados nenhum dado faltante\n"
      ],
      "metadata": {
        "id": "Sy9UTNjG2Biv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUu4XRrC18t_",
        "outputId": "9a80c8f1-09b4-4ec0-e3e5-eff6704c5113"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Também sem dados duplicados"
      ],
      "metadata": {
        "id": "LiFO0mDk2NOY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tratamento nas datas"
      ],
      "metadata": {
        "id": "XOK037eo38fG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['year'] = df['Date'].dt.year\n",
        "df['month'] = df['Date'].dt.month\n",
        "df['day_of_week'] = df['Date'].dt.dayofweek"
      ],
      "metadata": {
        "id": "UOh5fVTy7M8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gerando gráfico para visualização do preço da ação ao longo dos anos."
      ],
      "metadata": {
        "id": "fafPh42C7xk1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig=make_subplots(specs=[[{\"secondary_y\":False}]])\n",
        "fig.add_trace(go.Scatter(x=df['Date'],y=df['Close'].rolling(window=14).mean(),name=\"netflix\"),secondary_y=False,)\n",
        "fig.update_layout(autosize=False,width=700,height=500,title_text=\"NETFLIX\")\n",
        "fig.update_xaxes(title_text=\"Ano\")\n",
        "fig.update_yaxes(title_text=\"preço\",secondary_y=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "lTNbw0JN7Log",
        "outputId": "6ea6f2ce-4ee4-4b99-d178-7984e8a13834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Definição de X e y e Normalização"
      ],
      "metadata": {
        "id": "zvsXCGlB_bA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(labels=['Date','Close'], axis=1)\n",
        "y = df['Close']\n",
        "scaler = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "y_scaled = scaler_y.fit_transform(y.values.reshape(-1, 1))\n",
        "y_scaled = scaler_y.fit_transform(y_scaled.reshape(-1, 1))"
      ],
      "metadata": {
        "id": "66qTLHyS_Rtx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Divisão dos dados : Treinamento e Teste\n",
        "Como o banco de dados só possui 5.044 linhas foi esscolhido a proporção: 70% treinamento e 30% teste.\n"
      ],
      "metadata": {
        "id": "X6FGBzvhylUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled,y_scaled,test_size=0.3,random_state=42)"
      ],
      "metadata": {
        "id": "Fkn6wc628oDx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('\\033[1m' + 'X_train' + '\\033[0m', np.shape(X_train),':', f'{(len(X_train) / len(df) * 100):.0f}%')\n",
        "print('\\033[1m' + 'y_train' + '\\033[0m',np.shape(y_train),':', f'{(len(y_train) / len(df) * 100):.0f}%')\n",
        "print('\\033[1m' + 'X_test' + '\\033[0m',np.shape(X_test),':', f'{(len(X_test) / len(df) * 100):.0f}%')\n",
        "print('\\033[1m' + 'y_test' + '\\033[0m',np.shape(y_test),':', f'{(len(y_test) / len(df) * 100):.0f}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_YNqWK7_mdC",
        "outputId": "3adc90f2-0703-479e-eae1-ed5977c4538b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mX_train\u001b[0m (3530, 8) : 70%\n",
            "\u001b[1my_train\u001b[0m (3530, 1) : 70%\n",
            "\u001b[1mX_test\u001b[0m (1514, 8) : 30%\n",
            "\u001b[1my_test\u001b[0m (1514, 1) : 30%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RegressorNeuralNet model com 2 camadas ocultas 64 neurônios em 300 epochs"
      ],
      "metadata": {
        "id": "RACsVsWQDL7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class RegressorNeuralNet(nn.Module):\n",
        "  def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "    super(RegressorNeuralNet, self).__init__()\n",
        "    self.dense0 = nn.Linear(input_dim, hidden_dim)\n",
        "    self.relu = nn.ReLU()\n",
        "    self.dense1 = nn.Linear(hidden_dim, hidden_dim)\n",
        "    self.dropout = nn.Dropout(0.2)\n",
        "    self.dense2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.dense0(x)\n",
        "    x = self.relu(x)\n",
        "    x = self.dense1(x)\n",
        "    x = self.relu(x)\n",
        "    x = self.dropout(x)\n",
        "    x = self.dense2(x)\n",
        "    return x"
      ],
      "metadata": {
        "id": "3m4BETOtDJLT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net = NeuralNetRegressor(\n",
        "    module=RegressorNeuralNet,\n",
        "    module__input_dim=X.shape[1],\n",
        "    module__hidden_dim=64,\n",
        "    module__output_dim=1,\n",
        "    criterion=nn.MSELoss,\n",
        "    optimizer=torch.optim.Adam,\n",
        "    optimizer__lr=0.001,\n",
        "    max_epochs=300\n",
        ")"
      ],
      "metadata": {
        "id": "kO-v3MqaDQ5b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "net.fit(X_train.astype(np.float32), y_train.astype(np.float32).reshape(-1,1))"
      ],
      "metadata": {
        "id": "A9m2ok6-DaWr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f1f8751-f5f0-4a3d-bd80-1f4a13b6df13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  epoch    train_loss    valid_loss     dur\n",
            "-------  ------------  ------------  ------\n",
            "      1        \u001b[36m0.5568\u001b[0m        \u001b[32m0.1009\u001b[0m  0.4065\n",
            "      2        \u001b[36m0.0552\u001b[0m        \u001b[32m0.0094\u001b[0m  0.2105\n",
            "      3        \u001b[36m0.0235\u001b[0m        \u001b[32m0.0031\u001b[0m  0.1827\n",
            "      4        \u001b[36m0.0218\u001b[0m        \u001b[32m0.0027\u001b[0m  0.2377\n",
            "      5        \u001b[36m0.0195\u001b[0m        \u001b[32m0.0026\u001b[0m  0.4262\n",
            "      6        \u001b[36m0.0185\u001b[0m        \u001b[32m0.0017\u001b[0m  0.1537\n",
            "      7        \u001b[36m0.0178\u001b[0m        \u001b[32m0.0014\u001b[0m  0.1743\n",
            "      8        \u001b[36m0.0145\u001b[0m        0.0017  0.1703\n",
            "      9        0.0149        0.0019  0.2197\n",
            "     10        \u001b[36m0.0144\u001b[0m        \u001b[32m0.0013\u001b[0m  0.1741\n",
            "     11        \u001b[36m0.0136\u001b[0m        \u001b[32m0.0008\u001b[0m  0.2250\n",
            "     12        0.0137        0.0008  0.2220\n",
            "     13        0.0136        \u001b[32m0.0006\u001b[0m  0.2203\n",
            "     14        \u001b[36m0.0133\u001b[0m        0.0012  0.2176\n",
            "     15        \u001b[36m0.0130\u001b[0m        0.0008  0.2199\n",
            "     16        \u001b[36m0.0119\u001b[0m        \u001b[32m0.0006\u001b[0m  0.1892\n",
            "     17        0.0127        0.0010  0.2794\n",
            "     18        \u001b[36m0.0116\u001b[0m        0.0007  0.1634\n",
            "     19        \u001b[36m0.0111\u001b[0m        \u001b[32m0.0004\u001b[0m  0.2930\n",
            "     20        \u001b[36m0.0108\u001b[0m        0.0010  0.2291\n",
            "     21        0.0119        0.0005  0.1879\n",
            "     22        0.0115        \u001b[32m0.0004\u001b[0m  0.3046\n",
            "     23        0.0128        0.0005  0.2148\n",
            "     24        0.0123        0.0006  0.2375\n",
            "     25        0.0125        0.0006  0.1914\n",
            "     26        0.0115        \u001b[32m0.0004\u001b[0m  0.3231\n",
            "     27        0.0124        0.0007  0.1649\n",
            "     28        0.0119        0.0007  0.1904\n",
            "     29        0.0121        0.0007  0.1694\n",
            "     30        0.0113        0.0004  0.2008\n",
            "     31        0.0114        0.0007  0.2617\n",
            "     32        0.0114        0.0006  0.2462\n",
            "     33        0.0120        0.0005  0.2524\n",
            "     34        0.0115        \u001b[32m0.0003\u001b[0m  0.1584\n",
            "     35        0.0116        0.0006  0.1693\n",
            "     36        0.0120        0.0004  0.1813\n",
            "     37        0.0118        0.0007  0.1697\n",
            "     38        0.0115        0.0012  0.1666\n",
            "     39        0.0111        0.0005  0.3204\n",
            "     40        0.0109        0.0003  0.4052\n",
            "     41        \u001b[36m0.0105\u001b[0m        \u001b[32m0.0003\u001b[0m  0.5734\n",
            "     42        0.0117        0.0006  0.4976\n",
            "     43        0.0105        0.0006  0.3871\n",
            "     44        0.0121        0.0004  0.2406\n",
            "     45        0.0114        0.0008  0.3016\n",
            "     46        0.0125        0.0005  0.3458\n",
            "     47        0.0113        0.0007  0.4453\n",
            "     48        \u001b[36m0.0104\u001b[0m        0.0010  0.2947\n",
            "     49        0.0116        0.0006  0.2896\n",
            "     50        \u001b[36m0.0103\u001b[0m        0.0021  0.2450\n",
            "     51        0.0111        0.0005  0.1610\n",
            "     52        0.0106        0.0003  0.3130\n",
            "     53        0.0113        0.0003  0.1754\n",
            "     54        0.0109        0.0009  0.2006\n",
            "     55        0.0120        \u001b[32m0.0002\u001b[0m  0.2250\n",
            "     56        0.0112        0.0004  0.1725\n",
            "     57        0.0103        0.0006  0.4303\n",
            "     58        0.0112        0.0003  0.2623\n",
            "     59        0.0115        0.0017  0.2565\n",
            "     60        0.0107        0.0005  0.2254\n",
            "     61        0.0113        0.0005  0.1924\n",
            "     62        0.0104        0.0006  0.1924\n",
            "     63        0.0108        0.0005  0.3702\n",
            "     64        0.0111        0.0004  0.2497\n",
            "     65        0.0116        0.0005  0.1266\n",
            "     66        0.0112        0.0005  0.0886\n",
            "     67        0.0104        0.0003  0.0869\n",
            "     68        0.0112        0.0004  0.0900\n",
            "     69        \u001b[36m0.0101\u001b[0m        \u001b[32m0.0002\u001b[0m  0.1067\n",
            "     70        0.0112        0.0004  0.0885\n",
            "     71        0.0103        0.0008  0.1024\n",
            "     72        0.0102        0.0004  0.0915\n",
            "     73        0.0123        0.0005  0.1009\n",
            "     74        0.0114        0.0007  0.0955\n",
            "     75        0.0102        0.0013  0.0920\n",
            "     76        0.0109        0.0004  0.0852\n",
            "     77        0.0106        0.0008  0.0903\n",
            "     78        0.0111        0.0016  0.0852\n",
            "     79        0.0112        0.0004  0.0947\n",
            "     80        \u001b[36m0.0101\u001b[0m        0.0007  0.1013\n",
            "     81        0.0102        0.0005  0.0946\n",
            "     82        0.0107        0.0003  0.0994\n",
            "     83        0.0107        \u001b[32m0.0002\u001b[0m  0.0916\n",
            "     84        0.0113        0.0010  0.0909\n",
            "     85        0.0111        0.0008  0.0908\n",
            "     86        0.0114        \u001b[32m0.0002\u001b[0m  0.0885\n",
            "     87        0.0105        0.0003  0.0943\n",
            "     88        0.0109        0.0002  0.0912\n",
            "     89        \u001b[36m0.0098\u001b[0m        0.0016  0.0902\n",
            "     90        0.0109        0.0011  0.0993\n",
            "     91        0.0116        0.0003  0.0873\n",
            "     92        \u001b[36m0.0097\u001b[0m        0.0008  0.1007\n",
            "     93        0.0112        0.0002  0.0892\n",
            "     94        0.0115        0.0007  0.1173\n",
            "     95        0.0107        0.0003  0.0945\n",
            "     96        0.0112        0.0002  0.0923\n",
            "     97        0.0103        0.0003  0.0909\n",
            "     98        \u001b[36m0.0088\u001b[0m        0.0011  0.0954\n",
            "     99        0.0108        0.0008  0.0913\n",
            "    100        0.0107        0.0006  0.0909\n",
            "    101        0.0091        0.0003  0.1018\n",
            "    102        0.0101        0.0011  0.1054\n",
            "    103        0.0099        0.0003  0.0906\n",
            "    104        0.0106        0.0005  0.0898\n",
            "    105        0.0112        0.0007  0.0868\n",
            "    106        0.0107        0.0005  0.0960\n",
            "    107        0.0114        0.0005  0.0903\n",
            "    108        0.0104        0.0004  0.0936\n",
            "    109        0.0101        0.0004  0.0948\n",
            "    110        0.0098        0.0002  0.0918\n",
            "    111        0.0096        0.0008  0.0899\n",
            "    112        0.0104        0.0006  0.1147\n",
            "    113        0.0107        0.0006  0.1026\n",
            "    114        0.0089        0.0004  0.1096\n",
            "    115        0.0111        0.0007  0.0946\n",
            "    116        0.0099        0.0008  0.0922\n",
            "    117        0.0104        0.0002  0.0906\n",
            "    118        0.0102        0.0004  0.0898\n",
            "    119        0.0115        0.0003  0.0963\n",
            "    120        0.0108        0.0006  0.0891\n",
            "    121        0.0101        0.0003  0.0901\n",
            "    122        0.0104        0.0004  0.1099\n",
            "    123        0.0098        0.0006  0.1000\n",
            "    124        0.0102        0.0011  0.0953\n",
            "    125        0.0104        0.0004  0.1308\n",
            "    126        0.0112        0.0011  0.1501\n",
            "    127        0.0106        0.0005  0.1443\n",
            "    128        0.0106        0.0007  0.1343\n",
            "    129        0.0114        0.0003  0.1262\n",
            "    130        0.0098        0.0005  0.1642\n",
            "    131        0.0107        0.0009  0.1676\n",
            "    132        0.0109        0.0003  0.1364\n",
            "    133        0.0120        0.0005  0.1358\n",
            "    134        0.0106        0.0002  0.1338\n",
            "    135        0.0105        0.0024  0.1358\n",
            "    136        0.0115        \u001b[32m0.0002\u001b[0m  0.1430\n",
            "    137        0.0103        0.0005  0.1584\n",
            "    138        0.0103        0.0002  0.1414\n",
            "    139        0.0110        0.0003  0.1526\n",
            "    140        0.0103        0.0004  0.1407\n",
            "    141        0.0097        0.0002  0.1549\n",
            "    142        0.0102        0.0013  0.1403\n",
            "    143        0.0111        0.0003  0.1762\n",
            "    144        0.0104        0.0006  0.1646\n",
            "    145        0.0101        0.0011  0.1396\n",
            "    146        0.0107        0.0004  0.1421\n",
            "    147        0.0110        0.0013  0.0929\n",
            "    148        0.0110        0.0008  0.0826\n",
            "    149        0.0106        0.0004  0.0892\n",
            "    150        0.0101        0.0004  0.0832\n",
            "    151        0.0105        0.0002  0.0878\n",
            "    152        0.0105        0.0002  0.1026\n",
            "    153        0.0106        \u001b[32m0.0002\u001b[0m  0.0922\n",
            "    154        0.0121        0.0008  0.1041\n",
            "    155        0.0109        0.0016  0.0894\n",
            "    156        0.0104        0.0003  0.0968\n",
            "    157        0.0105        0.0008  0.0941\n",
            "    158        0.0101        0.0009  0.0929\n",
            "    159        0.0096        0.0004  0.0875\n",
            "    160        0.0095        0.0005  0.0889\n",
            "    161        0.0109        0.0003  0.0884\n",
            "    162        0.0116        0.0018  0.0897\n",
            "    163        0.0110        0.0018  0.1015\n",
            "    164        0.0119        0.0010  0.0924\n",
            "    165        0.0111        0.0002  0.1125\n",
            "    166        0.0102        0.0009  0.0899\n",
            "    167        0.0103        0.0004  0.1007\n",
            "    168        0.0103        0.0002  0.0918\n",
            "    169        0.0108        0.0014  0.0874\n",
            "    170        0.0104        0.0013  0.0930\n",
            "    171        0.0113        0.0005  0.0968\n",
            "    172        0.0099        0.0011  0.0831\n",
            "    173        0.0101        0.0004  0.1077\n",
            "    174        0.0100        0.0005  0.0912\n",
            "    175        0.0092        0.0003  0.0991\n",
            "    176        0.0096        0.0004  0.0900\n",
            "    177        0.0099        0.0002  0.0927\n",
            "    178        0.0104        0.0020  0.0914\n",
            "    179        0.0104        0.0007  0.0914\n",
            "    180        0.0109        0.0005  0.0921\n",
            "    181        0.0101        0.0004  0.0913\n",
            "    182        0.0103        0.0003  0.0865\n",
            "    183        0.0101        0.0002  0.0947\n",
            "    184        0.0103        0.0004  0.1139\n",
            "    185        0.0101        0.0003  0.0889\n",
            "    186        0.0097        0.0002  0.1326\n",
            "    187        0.0096        0.0008  0.1010\n",
            "    188        0.0115        0.0007  0.0952\n",
            "    189        0.0114        0.0003  0.0826\n",
            "    190        0.0107        0.0008  0.0866\n",
            "    191        0.0108        0.0004  0.0875\n",
            "    192        0.0107        0.0004  0.0846\n",
            "    193        0.0099        0.0002  0.0905\n",
            "    194        0.0101        0.0002  0.0998\n",
            "    195        0.0098        0.0007  0.0959\n",
            "    196        0.0108        0.0002  0.0932\n",
            "    197        0.0095        0.0006  0.1047\n",
            "    198        0.0096        0.0002  0.1001\n",
            "    199        0.0096        0.0005  0.0923\n",
            "    200        0.0111        0.0006  0.0872\n",
            "    201        0.0117        0.0005  0.1010\n",
            "    202        0.0106        0.0004  0.0870\n",
            "    203        0.0111        0.0005  0.0890\n",
            "    204        0.0102        0.0004  0.1132\n",
            "    205        0.0104        0.0011  0.1032\n",
            "    206        0.0106        0.0002  0.0894\n",
            "    207        0.0095        0.0020  0.1061\n",
            "    208        0.0098        0.0003  0.0958\n",
            "    209        0.0100        0.0006  0.0882\n",
            "    210        0.0102        0.0009  0.0891\n",
            "    211        0.0104        0.0002  0.0903\n",
            "    212        0.0105        0.0003  0.0888\n",
            "    213        0.0100        0.0010  0.0895\n",
            "    214        0.0104        0.0005  0.1109\n",
            "    215        0.0100        0.0007  0.1087\n",
            "    216        0.0100        0.0010  0.0900\n",
            "    217        0.0104        0.0005  0.0931\n",
            "    218        0.0119        0.0005  0.1198\n",
            "    219        0.0115        0.0003  0.0974\n",
            "    220        0.0109        0.0003  0.0871\n",
            "    221        0.0100        0.0003  0.0931\n",
            "    222        0.0104        0.0004  0.0883\n",
            "    223        0.0098        0.0004  0.0857\n",
            "    224        0.0094        0.0003  0.0986\n",
            "    225        0.0103        0.0007  0.0890\n",
            "    226        0.0106        0.0002  0.0933\n",
            "    227        0.0101        0.0012  0.0949\n",
            "    228        0.0106        0.0015  0.0960\n",
            "    229        0.0103        0.0003  0.0989\n",
            "    230        0.0104        0.0008  0.0886\n",
            "    231        0.0110        0.0006  0.0846\n",
            "    232        0.0108        0.0013  0.0871\n",
            "    233        0.0109        0.0003  0.0862\n",
            "    234        0.0109        0.0012  0.0866\n",
            "    235        0.0100        0.0002  0.1069\n",
            "    236        0.0101        0.0022  0.0902\n",
            "    237        0.0106        0.0002  0.0929\n",
            "    238        0.0103        0.0010  0.0935\n",
            "    239        0.0113        0.0010  0.1050\n",
            "    240        0.0103        0.0003  0.0983\n",
            "    241        0.0098        0.0011  0.0942\n",
            "    242        0.0116        \u001b[32m0.0001\u001b[0m  0.0878\n",
            "    243        0.0104        0.0006  0.0870\n",
            "    244        0.0097        0.0008  0.0900\n",
            "    245        0.0101        0.0002  0.1060\n",
            "    246        0.0115        0.0002  0.0909\n",
            "    247        0.0101        0.0007  0.0871\n",
            "    248        0.0101        0.0005  0.0929\n",
            "    249        0.0106        0.0002  0.0996\n",
            "    250        0.0105        0.0017  0.1345\n",
            "    251        0.0103        0.0002  0.1483\n",
            "    252        0.0117        0.0018  0.1523\n",
            "    253        0.0104        0.0003  0.1505\n",
            "    254        0.0099        0.0006  0.1356\n",
            "    255        0.0104        0.0007  0.1346\n",
            "    256        0.0103        0.0033  0.1432\n",
            "    257        0.0120        0.0016  0.1461\n",
            "    258        0.0110        0.0006  0.1253\n",
            "    259        0.0098        0.0008  0.1301\n",
            "    260        0.0101        0.0004  0.1347\n",
            "    261        0.0096        0.0007  0.1366\n",
            "    262        0.0094        0.0009  0.1549\n",
            "    263        0.0096        0.0011  0.1375\n",
            "    264        0.0105        0.0005  0.1633\n",
            "    265        0.0095        0.0003  0.1487\n",
            "    266        0.0102        0.0004  0.1444\n",
            "    267        0.0104        0.0005  0.1438\n",
            "    268        0.0096        0.0013  0.1498\n",
            "    269        0.0112        0.0008  0.1458\n",
            "    270        0.0110        0.0002  0.1424\n",
            "    271        0.0110        0.0003  0.1532\n",
            "    272        0.0104        0.0010  0.1516\n",
            "    273        0.0109        0.0006  0.0962\n",
            "    274        0.0102        0.0012  0.1065\n",
            "    275        0.0109        0.0009  0.0919\n",
            "    276        0.0104        0.0015  0.0902\n",
            "    277        0.0117        0.0005  0.0958\n",
            "    278        0.0106        0.0002  0.0897\n",
            "    279        0.0108        0.0002  0.0907\n",
            "    280        0.0097        0.0007  0.0884\n",
            "    281        0.0099        0.0005  0.1020\n",
            "    282        0.0098        0.0001  0.0897\n",
            "    283        0.0103        0.0007  0.0910\n",
            "    284        0.0100        0.0001  0.1065\n",
            "    285        0.0104        0.0007  0.0962\n",
            "    286        0.0108        0.0007  0.0998\n",
            "    287        0.0096        0.0002  0.1041\n",
            "    288        0.0097        0.0003  0.0971\n",
            "    289        0.0106        0.0002  0.0925\n",
            "    290        0.0102        0.0006  0.0949\n",
            "    291        0.0107        0.0002  0.0966\n",
            "    292        0.0107        0.0005  0.0937\n",
            "    293        0.0095        0.0007  0.0923\n",
            "    294        0.0102        0.0002  0.1032\n",
            "    295        0.0099        0.0006  0.0932\n",
            "    296        0.0098        0.0003  0.0919\n",
            "    297        0.0104        0.0007  0.0932\n",
            "    298        0.0097        0.0001  0.0947\n",
            "    299        0.0099        0.0003  0.0907\n",
            "    300        0.0111        0.0005  0.0934\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'skorch.regressor.NeuralNetRegressor'>[initialized](\n",
              "  module_=RegressorNeuralNet(\n",
              "    (dense0): Linear(in_features=8, out_features=64, bias=True)\n",
              "    (relu): ReLU()\n",
              "    (dense1): Linear(in_features=64, out_features=64, bias=True)\n",
              "    (dropout): Dropout(p=0.2, inplace=False)\n",
              "    (dense2): Linear(in_features=64, out_features=1, bias=True)\n",
              "  ),\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Avaliação do RegressorNeuralNet model"
      ],
      "metadata": {
        "id": "79ikO0EjDdGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_y_pred_scaled = net.predict(X_train.astype(np.float32))\n",
        "train_y_pred = scaler_y.inverse_transform(train_y_pred_scaled)\n",
        "y_train_orig = scaler_y.inverse_transform(y_train.reshape(-1, 1))\n",
        "\n",
        "mae_RegressorNeuralNet_train = mean_absolute_error(y_train_orig, train_y_pred)\n",
        "mse_RegressorNeuralNet_train = mean_squared_error(y_train_orig, train_y_pred)\n",
        "\n",
        "print(f\"MAE (Treino): {mae_RegressorNeuralNet_train:.4f}\")\n",
        "print(f\"MSE (Treino): {mse_RegressorNeuralNet_train:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8dFXXxfhFwC",
        "outputId": "b5fe7ee1-3ba8-48a0-fdf6-9fd290cd9239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE (Treino): 0.0159\n",
            "MSE (Treino): 0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_y_pred_scaled = net.predict(X_test.astype(np.float32))\n",
        "test_y_pred = scaler_y.inverse_transform(test_y_pred_scaled)\n",
        "y_test_orig = scaler_y.inverse_transform(y_test.reshape(-1, 1))\n",
        "\n",
        "mae_RegressorNeuralNet_test = mean_absolute_error(y_test_orig, test_y_pred)\n",
        "mse_RegressorNeuralNet_test = mean_squared_error(y_test_orig, test_y_pred)\n",
        "\n",
        "print(f\"MAE (Teste): {mae_RegressorNeuralNet_test:.4f}\")\n",
        "print(f\"MSE (Teste): {mse_RegressorNeuralNet_test:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQjOzewchIDi",
        "outputId": "f3a79b53-f24b-4325-8a18-ab200a8e3e44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE (Teste): 0.0156\n",
            "MSE (Teste): 0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MultiLayerPerceptron model com 2 camadas ocultas e 64 neurônios em 1001 epochs com batch size de 32"
      ],
      "metadata": {
        "id": "EAZXppQ9HmAe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load e tratamento do dataset para o novo modelo"
      ],
      "metadata": {
        "id": "6QcsEr5qsoW7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/NFLX.csv')\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['year'] = df['Date'].dt.year\n",
        "df['month'] = df['Date'].dt.month\n",
        "df['day_of_week'] = df['Date'].dt.dayofweek\n",
        "X = df.drop(labels=['Date','Close'], axis=1)\n",
        "y = df['Close']\n",
        "scaler = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "y_scaled = scaler_y.fit_transform(y.values.reshape(-1, 1))\n",
        "y_scaled = scaler_y.fit_transform(y_scaled.reshape(-1, 1))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled,\n",
        "                                                    y_scaled,\n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=42)"
      ],
      "metadata": {
        "id": "FS3O0Ph0snSc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checagem para aproveitar GPU acceleration"
      ],
      "metadata": {
        "id": "TYhtldl2zIFk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "wckDMoxbzJ_z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conversão para PyTorch tensor e envio para o device disponível (CPU OU GPU)"
      ],
      "metadata": {
        "id": "M_JszMEQzQEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_t = torch.tensor(X_train, dtype=torch.float32).to(device)\n",
        "X_test_t = torch.tensor(X_test, dtype=torch.float32).to(device)\n",
        "y_train_t = torch.tensor(y_train, dtype=torch.float32).to(device)\n",
        "y_test_t = torch.tensor(y_test, dtype=torch.float32).to(device)"
      ],
      "metadata": {
        "id": "KwkYCB4wzZtn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = TensorDataset(X_train_t, y_train_t)\n",
        "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "ljy5vrI7Sk-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = X.shape[1]\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(input_dim, 64),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(64, 64),\n",
        "    nn.ReLU(),\n",
        "    nn.Dropout(0.2),\n",
        "    nn.Linear(64, 1)\n",
        ").to(device)"
      ],
      "metadata": {
        "id": "ITGQFa2iHlQt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "3FItAf0dKzbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs = 1001\n",
        "train_losses = []\n",
        "test_losses = []"
      ],
      "metadata": {
        "id": "ipFKnFCxzBIW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(n_epochs):\n",
        "  total_loss = 0\n",
        "  for X_scaled, y_scaled in dataloader:\n",
        "    optimizer.zero_grad()\n",
        "    y_pred = model(X_scaled)\n",
        "    loss = criterion(y_pred, y_scaled)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    total_loss += loss.item()\n",
        "\n",
        "    if epoch % 100 == 0:\n",
        "      y_pred_test = model(X_test_t)\n",
        "      loss_test = criterion(y_pred_test, y_test_t)\n",
        "      print(f\"Epoch {epoch+1}/{n_epochs}, Loss: {total_loss/len(dataloader):.4f}, Test Loss: {loss_test:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9arl3PPuzFQW",
        "outputId": "81481660-b054-427d-d63c-03a083dff00b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1001, Loss: 0.0105, Test Loss: 0.8037\n",
            "Epoch 1/1001, Loss: 0.0196, Test Loss: 0.7467\n",
            "Epoch 1/1001, Loss: 0.0282, Test Loss: 0.6807\n",
            "Epoch 1/1001, Loss: 0.0354, Test Loss: 0.6287\n",
            "Epoch 1/1001, Loss: 0.0394, Test Loss: 0.5845\n",
            "Epoch 1/1001, Loss: 0.0464, Test Loss: 0.5307\n",
            "Epoch 1/1001, Loss: 0.0529, Test Loss: 0.4834\n",
            "Epoch 1/1001, Loss: 0.0599, Test Loss: 0.4340\n",
            "Epoch 1/1001, Loss: 0.0637, Test Loss: 0.3983\n",
            "Epoch 1/1001, Loss: 0.0658, Test Loss: 0.3512\n",
            "Epoch 1/1001, Loss: 0.0700, Test Loss: 0.3173\n",
            "Epoch 1/1001, Loss: 0.0734, Test Loss: 0.2867\n",
            "Epoch 1/1001, Loss: 0.0754, Test Loss: 0.2550\n",
            "Epoch 1/1001, Loss: 0.0779, Test Loss: 0.2240\n",
            "Epoch 1/1001, Loss: 0.0794, Test Loss: 0.1886\n",
            "Epoch 1/1001, Loss: 0.0818, Test Loss: 0.1627\n",
            "Epoch 1/1001, Loss: 0.0825, Test Loss: 0.1384\n",
            "Epoch 1/1001, Loss: 0.0833, Test Loss: 0.1207\n",
            "Epoch 1/1001, Loss: 0.0840, Test Loss: 0.1024\n",
            "Epoch 1/1001, Loss: 0.0859, Test Loss: 0.0876\n",
            "Epoch 1/1001, Loss: 0.0866, Test Loss: 0.0716\n",
            "Epoch 1/1001, Loss: 0.0873, Test Loss: 0.0604\n",
            "Epoch 1/1001, Loss: 0.0879, Test Loss: 0.0583\n",
            "Epoch 1/1001, Loss: 0.0890, Test Loss: 0.0496\n",
            "Epoch 1/1001, Loss: 0.0893, Test Loss: 0.0399\n",
            "Epoch 1/1001, Loss: 0.0898, Test Loss: 0.0373\n",
            "Epoch 1/1001, Loss: 0.0901, Test Loss: 0.0373\n",
            "Epoch 1/1001, Loss: 0.0902, Test Loss: 0.0313\n",
            "Epoch 1/1001, Loss: 0.0905, Test Loss: 0.0333\n",
            "Epoch 1/1001, Loss: 0.0907, Test Loss: 0.0323\n",
            "Epoch 1/1001, Loss: 0.0909, Test Loss: 0.0335\n",
            "Epoch 1/1001, Loss: 0.0911, Test Loss: 0.0324\n",
            "Epoch 1/1001, Loss: 0.0915, Test Loss: 0.0361\n",
            "Epoch 1/1001, Loss: 0.0918, Test Loss: 0.0362\n",
            "Epoch 1/1001, Loss: 0.0922, Test Loss: 0.0372\n",
            "Epoch 1/1001, Loss: 0.0924, Test Loss: 0.0321\n",
            "Epoch 1/1001, Loss: 0.0927, Test Loss: 0.0326\n",
            "Epoch 1/1001, Loss: 0.0929, Test Loss: 0.0359\n",
            "Epoch 1/1001, Loss: 0.0933, Test Loss: 0.0323\n",
            "Epoch 1/1001, Loss: 0.0938, Test Loss: 0.0308\n",
            "Epoch 1/1001, Loss: 0.0941, Test Loss: 0.0268\n",
            "Epoch 1/1001, Loss: 0.0944, Test Loss: 0.0292\n",
            "Epoch 1/1001, Loss: 0.0948, Test Loss: 0.0275\n",
            "Epoch 1/1001, Loss: 0.0951, Test Loss: 0.0234\n",
            "Epoch 1/1001, Loss: 0.0955, Test Loss: 0.0221\n",
            "Epoch 1/1001, Loss: 0.0956, Test Loss: 0.0225\n",
            "Epoch 1/1001, Loss: 0.0960, Test Loss: 0.0208\n",
            "Epoch 1/1001, Loss: 0.0961, Test Loss: 0.0176\n",
            "Epoch 1/1001, Loss: 0.0964, Test Loss: 0.0205\n",
            "Epoch 1/1001, Loss: 0.0966, Test Loss: 0.0206\n",
            "Epoch 1/1001, Loss: 0.0968, Test Loss: 0.0196\n",
            "Epoch 1/1001, Loss: 0.0969, Test Loss: 0.0198\n",
            "Epoch 1/1001, Loss: 0.0970, Test Loss: 0.0190\n",
            "Epoch 1/1001, Loss: 0.0972, Test Loss: 0.0198\n",
            "Epoch 1/1001, Loss: 0.0972, Test Loss: 0.0209\n",
            "Epoch 1/1001, Loss: 0.0976, Test Loss: 0.0210\n",
            "Epoch 1/1001, Loss: 0.0977, Test Loss: 0.0211\n",
            "Epoch 1/1001, Loss: 0.0980, Test Loss: 0.0216\n",
            "Epoch 1/1001, Loss: 0.0981, Test Loss: 0.0190\n",
            "Epoch 1/1001, Loss: 0.0982, Test Loss: 0.0177\n",
            "Epoch 1/1001, Loss: 0.0985, Test Loss: 0.0186\n",
            "Epoch 1/1001, Loss: 0.0988, Test Loss: 0.0157\n",
            "Epoch 1/1001, Loss: 0.0990, Test Loss: 0.0187\n",
            "Epoch 1/1001, Loss: 0.0991, Test Loss: 0.0182\n",
            "Epoch 1/1001, Loss: 0.0993, Test Loss: 0.0163\n",
            "Epoch 1/1001, Loss: 0.0994, Test Loss: 0.0193\n",
            "Epoch 1/1001, Loss: 0.0995, Test Loss: 0.0183\n",
            "Epoch 1/1001, Loss: 0.0996, Test Loss: 0.0172\n",
            "Epoch 1/1001, Loss: 0.0998, Test Loss: 0.0164\n",
            "Epoch 1/1001, Loss: 0.1000, Test Loss: 0.0166\n",
            "Epoch 1/1001, Loss: 0.1002, Test Loss: 0.0165\n",
            "Epoch 1/1001, Loss: 0.1004, Test Loss: 0.0184\n",
            "Epoch 1/1001, Loss: 0.1004, Test Loss: 0.0160\n",
            "Epoch 1/1001, Loss: 0.1007, Test Loss: 0.0180\n",
            "Epoch 1/1001, Loss: 0.1007, Test Loss: 0.0179\n",
            "Epoch 1/1001, Loss: 0.1008, Test Loss: 0.0162\n",
            "Epoch 1/1001, Loss: 0.1010, Test Loss: 0.0177\n",
            "Epoch 1/1001, Loss: 0.1012, Test Loss: 0.0172\n",
            "Epoch 1/1001, Loss: 0.1013, Test Loss: 0.0180\n",
            "Epoch 1/1001, Loss: 0.1014, Test Loss: 0.0168\n",
            "Epoch 1/1001, Loss: 0.1017, Test Loss: 0.0157\n",
            "Epoch 1/1001, Loss: 0.1018, Test Loss: 0.0168\n",
            "Epoch 1/1001, Loss: 0.1019, Test Loss: 0.0152\n",
            "Epoch 1/1001, Loss: 0.1021, Test Loss: 0.0155\n",
            "Epoch 1/1001, Loss: 0.1023, Test Loss: 0.0169\n",
            "Epoch 1/1001, Loss: 0.1026, Test Loss: 0.0169\n",
            "Epoch 1/1001, Loss: 0.1029, Test Loss: 0.0160\n",
            "Epoch 1/1001, Loss: 0.1030, Test Loss: 0.0170\n",
            "Epoch 1/1001, Loss: 0.1032, Test Loss: 0.0154\n",
            "Epoch 1/1001, Loss: 0.1033, Test Loss: 0.0171\n",
            "Epoch 1/1001, Loss: 0.1034, Test Loss: 0.0161\n",
            "Epoch 1/1001, Loss: 0.1036, Test Loss: 0.0157\n",
            "Epoch 1/1001, Loss: 0.1037, Test Loss: 0.0163\n",
            "Epoch 1/1001, Loss: 0.1038, Test Loss: 0.0137\n",
            "Epoch 1/1001, Loss: 0.1040, Test Loss: 0.0163\n",
            "Epoch 1/1001, Loss: 0.1041, Test Loss: 0.0150\n",
            "Epoch 1/1001, Loss: 0.1042, Test Loss: 0.0153\n",
            "Epoch 1/1001, Loss: 0.1044, Test Loss: 0.0169\n",
            "Epoch 1/1001, Loss: 0.1045, Test Loss: 0.0165\n",
            "Epoch 1/1001, Loss: 0.1048, Test Loss: 0.0151\n",
            "Epoch 1/1001, Loss: 0.1051, Test Loss: 0.0174\n",
            "Epoch 1/1001, Loss: 0.1054, Test Loss: 0.0164\n",
            "Epoch 1/1001, Loss: 0.1055, Test Loss: 0.0148\n",
            "Epoch 1/1001, Loss: 0.1058, Test Loss: 0.0169\n",
            "Epoch 1/1001, Loss: 0.1060, Test Loss: 0.0158\n",
            "Epoch 1/1001, Loss: 0.1063, Test Loss: 0.0139\n",
            "Epoch 1/1001, Loss: 0.1066, Test Loss: 0.0165\n",
            "Epoch 1/1001, Loss: 0.1068, Test Loss: 0.0151\n",
            "Epoch 1/1001, Loss: 0.1070, Test Loss: 0.0144\n",
            "Epoch 1/1001, Loss: 0.1071, Test Loss: 0.0146\n",
            "Epoch 1/1001, Loss: 0.1072, Test Loss: 0.0151\n",
            "Epoch 101/1001, Loss: 0.0001, Test Loss: 0.0105\n",
            "Epoch 101/1001, Loss: 0.0001, Test Loss: 0.0098\n",
            "Epoch 101/1001, Loss: 0.0002, Test Loss: 0.0099\n",
            "Epoch 101/1001, Loss: 0.0003, Test Loss: 0.0100\n",
            "Epoch 101/1001, Loss: 0.0005, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0006, Test Loss: 0.0096\n",
            "Epoch 101/1001, Loss: 0.0007, Test Loss: 0.0116\n",
            "Epoch 101/1001, Loss: 0.0007, Test Loss: 0.0102\n",
            "Epoch 101/1001, Loss: 0.0008, Test Loss: 0.0111\n",
            "Epoch 101/1001, Loss: 0.0009, Test Loss: 0.0100\n",
            "Epoch 101/1001, Loss: 0.0010, Test Loss: 0.0120\n",
            "Epoch 101/1001, Loss: 0.0012, Test Loss: 0.0115\n",
            "Epoch 101/1001, Loss: 0.0013, Test Loss: 0.0092\n",
            "Epoch 101/1001, Loss: 0.0014, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0014, Test Loss: 0.0098\n",
            "Epoch 101/1001, Loss: 0.0015, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0016, Test Loss: 0.0103\n",
            "Epoch 101/1001, Loss: 0.0017, Test Loss: 0.0097\n",
            "Epoch 101/1001, Loss: 0.0017, Test Loss: 0.0082\n",
            "Epoch 101/1001, Loss: 0.0018, Test Loss: 0.0092\n",
            "Epoch 101/1001, Loss: 0.0018, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0019, Test Loss: 0.0096\n",
            "Epoch 101/1001, Loss: 0.0020, Test Loss: 0.0091\n",
            "Epoch 101/1001, Loss: 0.0021, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0022, Test Loss: 0.0104\n",
            "Epoch 101/1001, Loss: 0.0022, Test Loss: 0.0104\n",
            "Epoch 101/1001, Loss: 0.0023, Test Loss: 0.0100\n",
            "Epoch 101/1001, Loss: 0.0024, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0024, Test Loss: 0.0082\n",
            "Epoch 101/1001, Loss: 0.0025, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0025, Test Loss: 0.0106\n",
            "Epoch 101/1001, Loss: 0.0026, Test Loss: 0.0086\n",
            "Epoch 101/1001, Loss: 0.0027, Test Loss: 0.0106\n",
            "Epoch 101/1001, Loss: 0.0028, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0029, Test Loss: 0.0086\n",
            "Epoch 101/1001, Loss: 0.0031, Test Loss: 0.0102\n",
            "Epoch 101/1001, Loss: 0.0032, Test Loss: 0.0095\n",
            "Epoch 101/1001, Loss: 0.0033, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0035, Test Loss: 0.0087\n",
            "Epoch 101/1001, Loss: 0.0035, Test Loss: 0.0087\n",
            "Epoch 101/1001, Loss: 0.0035, Test Loss: 0.0082\n",
            "Epoch 101/1001, Loss: 0.0036, Test Loss: 0.0083\n",
            "Epoch 101/1001, Loss: 0.0037, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0038, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0038, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0040, Test Loss: 0.0084\n",
            "Epoch 101/1001, Loss: 0.0040, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0042, Test Loss: 0.0095\n",
            "Epoch 101/1001, Loss: 0.0043, Test Loss: 0.0097\n",
            "Epoch 101/1001, Loss: 0.0043, Test Loss: 0.0083\n",
            "Epoch 101/1001, Loss: 0.0044, Test Loss: 0.0085\n",
            "Epoch 101/1001, Loss: 0.0045, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0045, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0047, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0048, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0048, Test Loss: 0.0085\n",
            "Epoch 101/1001, Loss: 0.0050, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0050, Test Loss: 0.0086\n",
            "Epoch 101/1001, Loss: 0.0051, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0052, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0054, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0054, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0055, Test Loss: 0.0086\n",
            "Epoch 101/1001, Loss: 0.0055, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0056, Test Loss: 0.0082\n",
            "Epoch 101/1001, Loss: 0.0059, Test Loss: 0.0073\n",
            "Epoch 101/1001, Loss: 0.0061, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0061, Test Loss: 0.0086\n",
            "Epoch 101/1001, Loss: 0.0062, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0064, Test Loss: 0.0094\n",
            "Epoch 101/1001, Loss: 0.0065, Test Loss: 0.0113\n",
            "Epoch 101/1001, Loss: 0.0066, Test Loss: 0.0124\n",
            "Epoch 101/1001, Loss: 0.0069, Test Loss: 0.0108\n",
            "Epoch 101/1001, Loss: 0.0071, Test Loss: 0.0097\n",
            "Epoch 101/1001, Loss: 0.0072, Test Loss: 0.0081\n",
            "Epoch 101/1001, Loss: 0.0073, Test Loss: 0.0097\n",
            "Epoch 101/1001, Loss: 0.0073, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0074, Test Loss: 0.0088\n",
            "Epoch 101/1001, Loss: 0.0074, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0076, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0077, Test Loss: 0.0122\n",
            "Epoch 101/1001, Loss: 0.0078, Test Loss: 0.0100\n",
            "Epoch 101/1001, Loss: 0.0080, Test Loss: 0.0115\n",
            "Epoch 101/1001, Loss: 0.0082, Test Loss: 0.0107\n",
            "Epoch 101/1001, Loss: 0.0082, Test Loss: 0.0110\n",
            "Epoch 101/1001, Loss: 0.0083, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0084, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0085, Test Loss: 0.0093\n",
            "Epoch 101/1001, Loss: 0.0085, Test Loss: 0.0084\n",
            "Epoch 101/1001, Loss: 0.0086, Test Loss: 0.0082\n",
            "Epoch 101/1001, Loss: 0.0087, Test Loss: 0.0101\n",
            "Epoch 101/1001, Loss: 0.0088, Test Loss: 0.0102\n",
            "Epoch 101/1001, Loss: 0.0089, Test Loss: 0.0099\n",
            "Epoch 101/1001, Loss: 0.0092, Test Loss: 0.0083\n",
            "Epoch 101/1001, Loss: 0.0092, Test Loss: 0.0090\n",
            "Epoch 101/1001, Loss: 0.0093, Test Loss: 0.0089\n",
            "Epoch 101/1001, Loss: 0.0094, Test Loss: 0.0083\n",
            "Epoch 101/1001, Loss: 0.0095, Test Loss: 0.0091\n",
            "Epoch 101/1001, Loss: 0.0095, Test Loss: 0.0091\n",
            "Epoch 101/1001, Loss: 0.0096, Test Loss: 0.0100\n",
            "Epoch 101/1001, Loss: 0.0097, Test Loss: 0.0101\n",
            "Epoch 101/1001, Loss: 0.0098, Test Loss: 0.0083\n",
            "Epoch 101/1001, Loss: 0.0099, Test Loss: 0.0099\n",
            "Epoch 101/1001, Loss: 0.0099, Test Loss: 0.0081\n",
            "Epoch 101/1001, Loss: 0.0100, Test Loss: 0.0080\n",
            "Epoch 101/1001, Loss: 0.0101, Test Loss: 0.0085\n",
            "Epoch 101/1001, Loss: 0.0102, Test Loss: 0.0092\n",
            "Epoch 101/1001, Loss: 0.0104, Test Loss: 0.0081\n",
            "Epoch 101/1001, Loss: 0.0104, Test Loss: 0.0107\n",
            "Epoch 101/1001, Loss: 0.0105, Test Loss: 0.0095\n",
            "Epoch 101/1001, Loss: 0.0105, Test Loss: 0.0107\n",
            "Epoch 201/1001, Loss: 0.0000, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0001, Test Loss: 0.0099\n",
            "Epoch 201/1001, Loss: 0.0002, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0003, Test Loss: 0.0099\n",
            "Epoch 201/1001, Loss: 0.0004, Test Loss: 0.0083\n",
            "Epoch 201/1001, Loss: 0.0005, Test Loss: 0.0099\n",
            "Epoch 201/1001, Loss: 0.0006, Test Loss: 0.0092\n",
            "Epoch 201/1001, Loss: 0.0006, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0007, Test Loss: 0.0093\n",
            "Epoch 201/1001, Loss: 0.0007, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0008, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0009, Test Loss: 0.0090\n",
            "Epoch 201/1001, Loss: 0.0009, Test Loss: 0.0092\n",
            "Epoch 201/1001, Loss: 0.0010, Test Loss: 0.0097\n",
            "Epoch 201/1001, Loss: 0.0010, Test Loss: 0.0082\n",
            "Epoch 201/1001, Loss: 0.0011, Test Loss: 0.0089\n",
            "Epoch 201/1001, Loss: 0.0011, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0012, Test Loss: 0.0098\n",
            "Epoch 201/1001, Loss: 0.0012, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0013, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0013, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0014, Test Loss: 0.0101\n",
            "Epoch 201/1001, Loss: 0.0015, Test Loss: 0.0086\n",
            "Epoch 201/1001, Loss: 0.0015, Test Loss: 0.0092\n",
            "Epoch 201/1001, Loss: 0.0016, Test Loss: 0.0088\n",
            "Epoch 201/1001, Loss: 0.0017, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0017, Test Loss: 0.0091\n",
            "Epoch 201/1001, Loss: 0.0018, Test Loss: 0.0098\n",
            "Epoch 201/1001, Loss: 0.0020, Test Loss: 0.0088\n",
            "Epoch 201/1001, Loss: 0.0021, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0021, Test Loss: 0.0088\n",
            "Epoch 201/1001, Loss: 0.0022, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0023, Test Loss: 0.0085\n",
            "Epoch 201/1001, Loss: 0.0025, Test Loss: 0.0086\n",
            "Epoch 201/1001, Loss: 0.0026, Test Loss: 0.0088\n",
            "Epoch 201/1001, Loss: 0.0027, Test Loss: 0.0075\n",
            "Epoch 201/1001, Loss: 0.0027, Test Loss: 0.0098\n",
            "Epoch 201/1001, Loss: 0.0028, Test Loss: 0.0091\n",
            "Epoch 201/1001, Loss: 0.0028, Test Loss: 0.0085\n",
            "Epoch 201/1001, Loss: 0.0029, Test Loss: 0.0091\n",
            "Epoch 201/1001, Loss: 0.0029, Test Loss: 0.0085\n",
            "Epoch 201/1001, Loss: 0.0030, Test Loss: 0.0091\n",
            "Epoch 201/1001, Loss: 0.0031, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0032, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0033, Test Loss: 0.0092\n",
            "Epoch 201/1001, Loss: 0.0034, Test Loss: 0.0087\n",
            "Epoch 201/1001, Loss: 0.0034, Test Loss: 0.0093\n",
            "Epoch 201/1001, Loss: 0.0036, Test Loss: 0.0081\n",
            "Epoch 201/1001, Loss: 0.0036, Test Loss: 0.0089\n",
            "Epoch 201/1001, Loss: 0.0037, Test Loss: 0.0093\n",
            "Epoch 201/1001, Loss: 0.0038, Test Loss: 0.0082\n",
            "Epoch 201/1001, Loss: 0.0038, Test Loss: 0.0090\n",
            "Epoch 201/1001, Loss: 0.0038, Test Loss: 0.0085\n",
            "Epoch 201/1001, Loss: 0.0040, Test Loss: 0.0094\n",
            "Epoch 201/1001, Loss: 0.0041, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0042, Test Loss: 0.0088\n",
            "Epoch 201/1001, Loss: 0.0042, Test Loss: 0.0093\n",
            "Epoch 201/1001, Loss: 0.0043, Test Loss: 0.0091\n",
            "Epoch 201/1001, Loss: 0.0044, Test Loss: 0.0092\n",
            "Epoch 201/1001, Loss: 0.0045, Test Loss: 0.0083\n",
            "Epoch 201/1001, Loss: 0.0046, Test Loss: 0.0078\n",
            "Epoch 201/1001, Loss: 0.0047, Test Loss: 0.0095\n",
            "Epoch 201/1001, Loss: 0.0047, Test Loss: 0.0090\n",
            "Epoch 201/1001, Loss: 0.0048, Test Loss: 0.0107\n",
            "Epoch 201/1001, Loss: 0.0049, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0050, Test Loss: 0.0096\n",
            "Epoch 201/1001, Loss: 0.0051, Test Loss: 0.0084\n",
            "Epoch 201/1001, Loss: 0.0051, Test Loss: 0.0090\n",
            "Epoch 201/1001, Loss: 0.0053, Test Loss: 0.0081\n",
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            "Epoch 701/1001, Loss: 0.0087, Test Loss: 0.0101\n",
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            "Epoch 801/1001, Loss: 0.0001, Test Loss: 0.0081\n",
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            "Epoch 801/1001, Loss: 0.0014, Test Loss: 0.0087\n",
            "Epoch 801/1001, Loss: 0.0015, Test Loss: 0.0094\n",
            "Epoch 801/1001, Loss: 0.0016, Test Loss: 0.0081\n",
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            "Epoch 801/1001, Loss: 0.0017, Test Loss: 0.0084\n",
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            "Epoch 801/1001, Loss: 0.0019, Test Loss: 0.0089\n",
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            "Epoch 801/1001, Loss: 0.0026, Test Loss: 0.0088\n",
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            "Epoch 801/1001, Loss: 0.0028, Test Loss: 0.0092\n",
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            "Epoch 801/1001, Loss: 0.0029, Test Loss: 0.0074\n",
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            "Epoch 801/1001, Loss: 0.0030, Test Loss: 0.0096\n",
            "Epoch 801/1001, Loss: 0.0032, Test Loss: 0.0093\n",
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            "Epoch 801/1001, Loss: 0.0033, Test Loss: 0.0084\n",
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            "Epoch 801/1001, Loss: 0.0034, Test Loss: 0.0087\n",
            "Epoch 801/1001, Loss: 0.0034, Test Loss: 0.0099\n",
            "Epoch 801/1001, Loss: 0.0035, Test Loss: 0.0082\n",
            "Epoch 801/1001, Loss: 0.0036, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0036, Test Loss: 0.0083\n",
            "Epoch 801/1001, Loss: 0.0037, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0038, Test Loss: 0.0078\n",
            "Epoch 801/1001, Loss: 0.0038, Test Loss: 0.0079\n",
            "Epoch 801/1001, Loss: 0.0039, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0039, Test Loss: 0.0095\n",
            "Epoch 801/1001, Loss: 0.0040, Test Loss: 0.0086\n",
            "Epoch 801/1001, Loss: 0.0041, Test Loss: 0.0086\n",
            "Epoch 801/1001, Loss: 0.0042, Test Loss: 0.0105\n",
            "Epoch 801/1001, Loss: 0.0043, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0043, Test Loss: 0.0096\n",
            "Epoch 801/1001, Loss: 0.0046, Test Loss: 0.0104\n",
            "Epoch 801/1001, Loss: 0.0046, Test Loss: 0.0087\n",
            "Epoch 801/1001, Loss: 0.0046, Test Loss: 0.0086\n",
            "Epoch 801/1001, Loss: 0.0047, Test Loss: 0.0085\n",
            "Epoch 801/1001, Loss: 0.0047, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0049, Test Loss: 0.0088\n",
            "Epoch 801/1001, Loss: 0.0050, Test Loss: 0.0087\n",
            "Epoch 801/1001, Loss: 0.0051, Test Loss: 0.0089\n",
            "Epoch 801/1001, Loss: 0.0052, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0053, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0053, Test Loss: 0.0096\n",
            "Epoch 801/1001, Loss: 0.0054, Test Loss: 0.0079\n",
            "Epoch 801/1001, Loss: 0.0054, Test Loss: 0.0086\n",
            "Epoch 801/1001, Loss: 0.0055, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0056, Test Loss: 0.0084\n",
            "Epoch 801/1001, Loss: 0.0057, Test Loss: 0.0101\n",
            "Epoch 801/1001, Loss: 0.0058, Test Loss: 0.0097\n",
            "Epoch 801/1001, Loss: 0.0059, Test Loss: 0.0081\n",
            "Epoch 801/1001, Loss: 0.0060, Test Loss: 0.0082\n",
            "Epoch 801/1001, Loss: 0.0061, Test Loss: 0.0085\n",
            "Epoch 801/1001, Loss: 0.0063, Test Loss: 0.0101\n",
            "Epoch 801/1001, Loss: 0.0064, Test Loss: 0.0092\n",
            "Epoch 801/1001, Loss: 0.0065, Test Loss: 0.0085\n",
            "Epoch 801/1001, Loss: 0.0067, Test Loss: 0.0091\n",
            "Epoch 801/1001, Loss: 0.0068, Test Loss: 0.0088\n",
            "Epoch 801/1001, Loss: 0.0069, Test Loss: 0.0104\n",
            "Epoch 801/1001, Loss: 0.0070, Test Loss: 0.0094\n",
            "Epoch 801/1001, Loss: 0.0070, Test Loss: 0.0091\n",
            "Epoch 801/1001, Loss: 0.0071, Test Loss: 0.0091\n",
            "Epoch 801/1001, Loss: 0.0073, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0074, Test Loss: 0.0096\n",
            "Epoch 801/1001, Loss: 0.0074, Test Loss: 0.0080\n",
            "Epoch 801/1001, Loss: 0.0075, Test Loss: 0.0086\n",
            "Epoch 801/1001, Loss: 0.0076, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0077, Test Loss: 0.0085\n",
            "Epoch 801/1001, Loss: 0.0078, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0079, Test Loss: 0.0094\n",
            "Epoch 801/1001, Loss: 0.0080, Test Loss: 0.0088\n",
            "Epoch 801/1001, Loss: 0.0082, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0083, Test Loss: 0.0091\n",
            "Epoch 801/1001, Loss: 0.0085, Test Loss: 0.0081\n",
            "Epoch 801/1001, Loss: 0.0085, Test Loss: 0.0098\n",
            "Epoch 801/1001, Loss: 0.0086, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0088, Test Loss: 0.0082\n",
            "Epoch 801/1001, Loss: 0.0089, Test Loss: 0.0098\n",
            "Epoch 801/1001, Loss: 0.0090, Test Loss: 0.0086\n",
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            "Epoch 801/1001, Loss: 0.0091, Test Loss: 0.0102\n",
            "Epoch 801/1001, Loss: 0.0092, Test Loss: 0.0097\n",
            "Epoch 801/1001, Loss: 0.0093, Test Loss: 0.0090\n",
            "Epoch 801/1001, Loss: 0.0094, Test Loss: 0.0089\n",
            "Epoch 801/1001, Loss: 0.0095, Test Loss: 0.0084\n",
            "Epoch 801/1001, Loss: 0.0096, Test Loss: 0.0081\n",
            "Epoch 801/1001, Loss: 0.0097, Test Loss: 0.0088\n",
            "Epoch 801/1001, Loss: 0.0098, Test Loss: 0.0093\n",
            "Epoch 801/1001, Loss: 0.0099, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0001, Test Loss: 0.0081\n",
            "Epoch 901/1001, Loss: 0.0002, Test Loss: 0.0096\n",
            "Epoch 901/1001, Loss: 0.0003, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0003, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0004, Test Loss: 0.0080\n",
            "Epoch 901/1001, Loss: 0.0005, Test Loss: 0.0092\n",
            "Epoch 901/1001, Loss: 0.0008, Test Loss: 0.0096\n",
            "Epoch 901/1001, Loss: 0.0010, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0011, Test Loss: 0.0084\n",
            "Epoch 901/1001, Loss: 0.0011, Test Loss: 0.0084\n",
            "Epoch 901/1001, Loss: 0.0013, Test Loss: 0.0092\n",
            "Epoch 901/1001, Loss: 0.0013, Test Loss: 0.0101\n",
            "Epoch 901/1001, Loss: 0.0014, Test Loss: 0.0084\n",
            "Epoch 901/1001, Loss: 0.0015, Test Loss: 0.0092\n",
            "Epoch 901/1001, Loss: 0.0015, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0016, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0017, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0018, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0019, Test Loss: 0.0088\n",
            "Epoch 901/1001, Loss: 0.0020, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0021, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0021, Test Loss: 0.0093\n",
            "Epoch 901/1001, Loss: 0.0023, Test Loss: 0.0078\n",
            "Epoch 901/1001, Loss: 0.0023, Test Loss: 0.0071\n",
            "Epoch 901/1001, Loss: 0.0024, Test Loss: 0.0078\n",
            "Epoch 901/1001, Loss: 0.0024, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0025, Test Loss: 0.0089\n",
            "Epoch 901/1001, Loss: 0.0026, Test Loss: 0.0093\n",
            "Epoch 901/1001, Loss: 0.0027, Test Loss: 0.0100\n",
            "Epoch 901/1001, Loss: 0.0028, Test Loss: 0.0080\n",
            "Epoch 901/1001, Loss: 0.0029, Test Loss: 0.0096\n",
            "Epoch 901/1001, Loss: 0.0029, Test Loss: 0.0097\n",
            "Epoch 901/1001, Loss: 0.0030, Test Loss: 0.0103\n",
            "Epoch 901/1001, Loss: 0.0031, Test Loss: 0.0100\n",
            "Epoch 901/1001, Loss: 0.0031, Test Loss: 0.0089\n",
            "Epoch 901/1001, Loss: 0.0033, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0033, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0033, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0034, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0034, Test Loss: 0.0089\n",
            "Epoch 901/1001, Loss: 0.0035, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0036, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0037, Test Loss: 0.0088\n",
            "Epoch 901/1001, Loss: 0.0038, Test Loss: 0.0101\n",
            "Epoch 901/1001, Loss: 0.0039, Test Loss: 0.0083\n",
            "Epoch 901/1001, Loss: 0.0040, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0041, Test Loss: 0.0084\n",
            "Epoch 901/1001, Loss: 0.0041, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0042, Test Loss: 0.0093\n",
            "Epoch 901/1001, Loss: 0.0043, Test Loss: 0.0085\n",
            "Epoch 901/1001, Loss: 0.0044, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0044, Test Loss: 0.0077\n",
            "Epoch 901/1001, Loss: 0.0045, Test Loss: 0.0083\n",
            "Epoch 901/1001, Loss: 0.0045, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0046, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0047, Test Loss: 0.0079\n",
            "Epoch 901/1001, Loss: 0.0047, Test Loss: 0.0096\n",
            "Epoch 901/1001, Loss: 0.0048, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0050, Test Loss: 0.0085\n",
            "Epoch 901/1001, Loss: 0.0051, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0052, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0053, Test Loss: 0.0095\n",
            "Epoch 901/1001, Loss: 0.0055, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0057, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0057, Test Loss: 0.0081\n",
            "Epoch 901/1001, Loss: 0.0058, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0059, Test Loss: 0.0099\n",
            "Epoch 901/1001, Loss: 0.0060, Test Loss: 0.0091\n",
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            "Epoch 901/1001, Loss: 0.0061, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0061, Test Loss: 0.0085\n",
            "Epoch 901/1001, Loss: 0.0062, Test Loss: 0.0085\n",
            "Epoch 901/1001, Loss: 0.0063, Test Loss: 0.0095\n",
            "Epoch 901/1001, Loss: 0.0065, Test Loss: 0.0097\n",
            "Epoch 901/1001, Loss: 0.0065, Test Loss: 0.0098\n",
            "Epoch 901/1001, Loss: 0.0066, Test Loss: 0.0085\n",
            "Epoch 901/1001, Loss: 0.0066, Test Loss: 0.0081\n",
            "Epoch 901/1001, Loss: 0.0067, Test Loss: 0.0101\n",
            "Epoch 901/1001, Loss: 0.0068, Test Loss: 0.0092\n",
            "Epoch 901/1001, Loss: 0.0068, Test Loss: 0.0079\n",
            "Epoch 901/1001, Loss: 0.0069, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0069, Test Loss: 0.0104\n",
            "Epoch 901/1001, Loss: 0.0070, Test Loss: 0.0088\n",
            "Epoch 901/1001, Loss: 0.0070, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0071, Test Loss: 0.0091\n",
            "Epoch 901/1001, Loss: 0.0072, Test Loss: 0.0082\n",
            "Epoch 901/1001, Loss: 0.0073, Test Loss: 0.0075\n",
            "Epoch 901/1001, Loss: 0.0074, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0075, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0076, Test Loss: 0.0099\n",
            "Epoch 901/1001, Loss: 0.0077, Test Loss: 0.0110\n",
            "Epoch 901/1001, Loss: 0.0077, Test Loss: 0.0112\n",
            "Epoch 901/1001, Loss: 0.0078, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0078, Test Loss: 0.0092\n",
            "Epoch 901/1001, Loss: 0.0079, Test Loss: 0.0101\n",
            "Epoch 901/1001, Loss: 0.0079, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0081, Test Loss: 0.0091\n",
            "Epoch 901/1001, Loss: 0.0082, Test Loss: 0.0081\n",
            "Epoch 901/1001, Loss: 0.0082, Test Loss: 0.0076\n",
            "Epoch 901/1001, Loss: 0.0082, Test Loss: 0.0094\n",
            "Epoch 901/1001, Loss: 0.0083, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0084, Test Loss: 0.0084\n",
            "Epoch 901/1001, Loss: 0.0085, Test Loss: 0.0101\n",
            "Epoch 901/1001, Loss: 0.0086, Test Loss: 0.0090\n",
            "Epoch 901/1001, Loss: 0.0087, Test Loss: 0.0087\n",
            "Epoch 901/1001, Loss: 0.0087, Test Loss: 0.0078\n",
            "Epoch 901/1001, Loss: 0.0088, Test Loss: 0.0086\n",
            "Epoch 901/1001, Loss: 0.0090, Test Loss: 0.0088\n",
            "Epoch 901/1001, Loss: 0.0090, Test Loss: 0.0089\n",
            "Epoch 901/1001, Loss: 0.0091, Test Loss: 0.0081\n",
            "Epoch 901/1001, Loss: 0.0092, Test Loss: 0.0096\n",
            "Epoch 1001/1001, Loss: 0.0001, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0001, Test Loss: 0.0078\n",
            "Epoch 1001/1001, Loss: 0.0002, Test Loss: 0.0083\n",
            "Epoch 1001/1001, Loss: 0.0003, Test Loss: 0.0080\n",
            "Epoch 1001/1001, Loss: 0.0004, Test Loss: 0.0088\n",
            "Epoch 1001/1001, Loss: 0.0004, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0005, Test Loss: 0.0077\n",
            "Epoch 1001/1001, Loss: 0.0007, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0008, Test Loss: 0.0095\n",
            "Epoch 1001/1001, Loss: 0.0009, Test Loss: 0.0103\n",
            "Epoch 1001/1001, Loss: 0.0010, Test Loss: 0.0084\n",
            "Epoch 1001/1001, Loss: 0.0010, Test Loss: 0.0101\n",
            "Epoch 1001/1001, Loss: 0.0011, Test Loss: 0.0079\n",
            "Epoch 1001/1001, Loss: 0.0012, Test Loss: 0.0082\n",
            "Epoch 1001/1001, Loss: 0.0012, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0013, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0013, Test Loss: 0.0103\n",
            "Epoch 1001/1001, Loss: 0.0014, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0015, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0016, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0017, Test Loss: 0.0103\n",
            "Epoch 1001/1001, Loss: 0.0018, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0018, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0019, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0020, Test Loss: 0.0088\n",
            "Epoch 1001/1001, Loss: 0.0020, Test Loss: 0.0089\n",
            "Epoch 1001/1001, Loss: 0.0022, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0023, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0024, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0024, Test Loss: 0.0096\n",
            "Epoch 1001/1001, Loss: 0.0025, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0025, Test Loss: 0.0083\n",
            "Epoch 1001/1001, Loss: 0.0026, Test Loss: 0.0078\n",
            "Epoch 1001/1001, Loss: 0.0026, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0027, Test Loss: 0.0093\n",
            "Epoch 1001/1001, Loss: 0.0027, Test Loss: 0.0085\n",
            "Epoch 1001/1001, Loss: 0.0027, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0028, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0029, Test Loss: 0.0085\n",
            "Epoch 1001/1001, Loss: 0.0029, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0030, Test Loss: 0.0084\n",
            "Epoch 1001/1001, Loss: 0.0031, Test Loss: 0.0105\n",
            "Epoch 1001/1001, Loss: 0.0031, Test Loss: 0.0089\n",
            "Epoch 1001/1001, Loss: 0.0032, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0032, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0033, Test Loss: 0.0079\n",
            "Epoch 1001/1001, Loss: 0.0035, Test Loss: 0.0081\n",
            "Epoch 1001/1001, Loss: 0.0035, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0036, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0037, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0041, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0041, Test Loss: 0.0085\n",
            "Epoch 1001/1001, Loss: 0.0043, Test Loss: 0.0093\n",
            "Epoch 1001/1001, Loss: 0.0045, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0046, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0047, Test Loss: 0.0080\n",
            "Epoch 1001/1001, Loss: 0.0049, Test Loss: 0.0085\n",
            "Epoch 1001/1001, Loss: 0.0050, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0051, Test Loss: 0.0088\n",
            "Epoch 1001/1001, Loss: 0.0052, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0053, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0054, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0055, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0056, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0056, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0058, Test Loss: 0.0101\n",
            "Epoch 1001/1001, Loss: 0.0058, Test Loss: 0.0105\n",
            "Epoch 1001/1001, Loss: 0.0059, Test Loss: 0.0079\n",
            "Epoch 1001/1001, Loss: 0.0060, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0061, Test Loss: 0.0084\n",
            "Epoch 1001/1001, Loss: 0.0061, Test Loss: 0.0099\n",
            "Epoch 1001/1001, Loss: 0.0062, Test Loss: 0.0096\n",
            "Epoch 1001/1001, Loss: 0.0063, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0064, Test Loss: 0.0075\n",
            "Epoch 1001/1001, Loss: 0.0065, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0065, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0066, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0067, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0068, Test Loss: 0.0095\n",
            "Epoch 1001/1001, Loss: 0.0069, Test Loss: 0.0081\n",
            "Epoch 1001/1001, Loss: 0.0069, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0071, Test Loss: 0.0080\n",
            "Epoch 1001/1001, Loss: 0.0072, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0073, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0074, Test Loss: 0.0089\n",
            "Epoch 1001/1001, Loss: 0.0076, Test Loss: 0.0091\n",
            "Epoch 1001/1001, Loss: 0.0076, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0077, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0079, Test Loss: 0.0079\n",
            "Epoch 1001/1001, Loss: 0.0080, Test Loss: 0.0091\n",
            "Epoch 1001/1001, Loss: 0.0081, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0082, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0083, Test Loss: 0.0095\n",
            "Epoch 1001/1001, Loss: 0.0083, Test Loss: 0.0091\n",
            "Epoch 1001/1001, Loss: 0.0084, Test Loss: 0.0092\n",
            "Epoch 1001/1001, Loss: 0.0085, Test Loss: 0.0097\n",
            "Epoch 1001/1001, Loss: 0.0086, Test Loss: 0.0095\n",
            "Epoch 1001/1001, Loss: 0.0087, Test Loss: 0.0093\n",
            "Epoch 1001/1001, Loss: 0.0089, Test Loss: 0.0084\n",
            "Epoch 1001/1001, Loss: 0.0090, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0090, Test Loss: 0.0090\n",
            "Epoch 1001/1001, Loss: 0.0091, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0091, Test Loss: 0.0087\n",
            "Epoch 1001/1001, Loss: 0.0093, Test Loss: 0.0091\n",
            "Epoch 1001/1001, Loss: 0.0094, Test Loss: 0.0086\n",
            "Epoch 1001/1001, Loss: 0.0095, Test Loss: 0.0101\n",
            "Epoch 1001/1001, Loss: 0.0096, Test Loss: 0.0089\n",
            "Epoch 1001/1001, Loss: 0.0097, Test Loss: 0.0084\n",
            "Epoch 1001/1001, Loss: 0.0097, Test Loss: 0.0103\n",
            "Epoch 1001/1001, Loss: 0.0098, Test Loss: 0.0094\n",
            "Epoch 1001/1001, Loss: 0.0098, Test Loss: 0.0105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "  train_pred = model(X_train_t).cpu().numpy()\n",
        "  test_pred = model(X_test_t).cpu().numpy()"
      ],
      "metadata": {
        "id": "QG5-sOWXzJEu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pred_orig = scaler_y.inverse_transform(train_pred)\n",
        "test_pred_orig = scaler_y.inverse_transform(test_pred)\n",
        "y_train_orig = scaler_y.inverse_transform(y_train_t.cpu().numpy())\n",
        "y_test_orig = scaler_y.inverse_transform(y_test_t.cpu().numpy())"
      ],
      "metadata": {
        "id": "M9KATpLSzNRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae_train_mlp2 = mean_absolute_error(y_train_orig, train_pred_orig)\n",
        "mse_train_mlp2 = mean_squared_error(y_train_orig, train_pred_orig)\n",
        "mae_test_mlp2 = mean_absolute_error(y_test_orig, test_pred_orig)\n",
        "mse_test_mlp2 = mean_squared_error(y_test_orig, test_pred_orig)"
      ],
      "metadata": {
        "id": "oF51WXoRzP4G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"MAE (Treinamento): {mae_train_mlp2:.4f}\")\n",
        "print(f\"MSE (Treinamento): {mse_train_mlp2:.4f}\")\n",
        "print(f\"MAE (Teste): {mae_test_mlp2:.4f}\")\n",
        "print(f\"MSE (Teste): {mse_test_mlp2:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNtkPutbzoS_",
        "outputId": "1911ba2c-a350-4161-b1cf-d95615b0507c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE (Treinamento): 0.0235\n",
            "MSE (Treinamento): 0.0020\n",
            "MAE (Teste): 0.0212\n",
            "MSE (Teste): 0.0018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.median(y_train_orig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IZmDdos035-",
        "outputId": "675a62f5-9ac9-4127-bfb8-8849181df654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.53078544"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_quantiles = np.percentile(y_train_orig, [25, 75])\n",
        "iqr_train = train_quantiles[1] - train_quantiles[0]\n",
        "train_lower_bound = train_quantiles[0] - 1.5 * iqr_train\n",
        "train_upper_bound = train_quantiles[1] + 1.5 * iqr_train"
      ],
      "metadata": {
        "id": "UtTNLNUk0TzO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.median(y_test_orig)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VP3raJ0L16z2",
        "outputId": "75fb1870-ef1a-4bcb-d682-c716bd82b601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.5675222"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_quantiles = np.percentile(y_test_orig, [25, 75])\n",
        "iqr_test = test_quantiles[1] - test_quantiles[0]\n",
        "test_lower_bound = test_quantiles[0] - 1.5 * iqr_test\n",
        "test_upper_bound = test_quantiles[1] + 1.5 * iqr_test"
      ],
      "metadata": {
        "id": "4tva8RqA1Er2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"IQR do Treino: {iqr_train}\")\n",
        "print(f\"IQR do Teste: {iqr_test}\")\n",
        "print(f\"Treinamento Lower Bound: {train_lower_bound:.4f}, Upper Bound: {train_upper_bound:.4f}\")\n",
        "print(f\"Teste Lower Bound: {test_lower_bound:.4f}, Upper Bound: {test_upper_bound:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k7gHqne0VgF",
        "outputId": "15a5db41-6f52-4c1e-9d93-a4b5d5cfc302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IQR do Treino: 0.977793961763382\n",
            "IQR do Teste: 0.8228887058794498\n",
            "Treinamento Lower Bound: -2.1334, Upper Bound: 1.7778\n",
            "Teste Lower Bound: -1.9020, Upper Bound: 1.3896\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MultiLayerPerceptron model com 3 camadas ocultas e 32 neurônios em 600 epochs com batch size de 16"
      ],
      "metadata": {
        "id": "MWPPE1XooLpZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load e tratamento do dataset para o novo modelo"
      ],
      "metadata": {
        "id": "2eTLu7hr8ejx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/NFLX.csv')\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['year'] = df['Date'].dt.year\n",
        "df['month'] = df['Date'].dt.month\n",
        "df['day_of_week'] = df['Date'].dt.dayofweek\n",
        "X = df.drop(labels=['Date','Close'], axis=1)\n",
        "y = df['Close']\n",
        "scaler = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "y_scaled = scaler_y.fit_transform(y.values.reshape(-1, 1))\n",
        "y_scaled = scaler_y.fit_transform(y_scaled.reshape(-1, 1))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled,\n",
        "                                                    y_scaled,\n",
        "                                                    test_size=0.3,\n",
        "                                                    random_state=42)"
      ],
      "metadata": {
        "id": "lhLIyriVP50a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " # Checagem para aproveitar GPU acceleration, Conversão para PyTorch tensor e envio para o device disponível (CPU OU GPU)"
      ],
      "metadata": {
        "id": "-5skYFaX-DsD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "IKq0udyMAKm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_t = torch.tensor(X_train, dtype=torch.float32).to(device)\n",
        "X_test_t = torch.tensor(X_test, dtype=torch.float32).to(device)\n",
        "y_train_t = torch.tensor(y_train, dtype=torch.float32).to(device)\n",
        "y_test_t = torch.tensor(y_test, dtype=torch.float32).to(device)"
      ],
      "metadata": {
        "id": "1z6HBZnE-NvC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = TensorDataset(X_train_t, y_train_t)\n",
        "dataloader = DataLoader(dataset, batch_size=16, shuffle=True)"
      ],
      "metadata": {
        "id": "uZXBQxk0AURE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = X.shape[1]\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(input_dim, 32),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(32, 32),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(32, 32),\n",
        "    nn.ReLU(),\n",
        "    nn.Dropout(0.2),\n",
        "    nn.Linear(32, 1)\n",
        ").to(device)\n"
      ],
      "metadata": {
        "id": "a-8GP3cp-Qia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "fviSwRzU1cmm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs = 600\n",
        "train_losses = []\n",
        "test_losses = []"
      ],
      "metadata": {
        "id": "O82LRigfAZOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(n_epochs):\n",
        "  total_loss = 0\n",
        "  for X_scaled, y_scaled in dataloader:\n",
        "    optimizer.zero_grad()\n",
        "    y_pred = model(X_scaled)\n",
        "    loss = criterion(y_pred, y_scaled)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    total_loss += loss.item()\n",
        "\n",
        "    if epoch % 100 == 0:\n",
        "      y_pred_test = model(X_test_t)\n",
        "      loss_test = criterion(y_pred_test, y_test_t)\n",
        "      print(f\"Epoch {epoch+1}/{n_epochs}, Loss: {total_loss/len(dataloader):.4f}, Test Loss: {loss_test:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRcHKJk_1iy1",
        "outputId": "c866c21e-850f-47cb-d13b-8696705ba12e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/600, Loss: 0.0050, Test Loss: 0.9641\n",
            "Epoch 1/600, Loss: 0.0124, Test Loss: 0.9502\n",
            "Epoch 1/600, Loss: 0.0176, Test Loss: 0.9407\n",
            "Epoch 1/600, Loss: 0.0268, Test Loss: 0.9310\n",
            "Epoch 1/600, Loss: 0.0310, Test Loss: 0.9230\n",
            "Epoch 1/600, Loss: 0.0370, Test Loss: 0.9108\n",
            "Epoch 1/600, Loss: 0.0397, Test Loss: 0.9017\n",
            "Epoch 1/600, Loss: 0.0424, Test Loss: 0.8928\n",
            "Epoch 1/600, Loss: 0.0456, Test Loss: 0.8793\n",
            "Epoch 1/600, Loss: 0.0500, Test Loss: 0.8730\n",
            "Epoch 1/600, Loss: 0.0555, Test Loss: 0.8639\n",
            "Epoch 1/600, Loss: 0.0584, Test Loss: 0.8524\n",
            "Epoch 1/600, Loss: 0.0621, Test Loss: 0.8432\n",
            "Epoch 1/600, Loss: 0.0664, Test Loss: 0.8323\n",
            "Epoch 1/600, Loss: 0.0686, Test Loss: 0.8230\n",
            "Epoch 1/600, Loss: 0.0726, Test Loss: 0.8137\n",
            "Epoch 1/600, Loss: 0.0762, Test Loss: 0.8053\n",
            "Epoch 1/600, Loss: 0.0778, Test Loss: 0.7899\n",
            "Epoch 1/600, Loss: 0.0804, Test Loss: 0.7791\n",
            "Epoch 1/600, Loss: 0.0820, Test Loss: 0.7683\n",
            "Epoch 1/600, Loss: 0.0840, Test Loss: 0.7554\n",
            "Epoch 1/600, Loss: 0.0899, Test Loss: 0.7417\n",
            "Epoch 1/600, Loss: 0.0913, Test Loss: 0.7318\n",
            "Epoch 1/600, Loss: 0.0931, Test Loss: 0.7228\n",
            "Epoch 1/600, Loss: 0.0978, Test Loss: 0.7070\n",
            "Epoch 1/600, Loss: 0.1012, Test Loss: 0.6977\n",
            "Epoch 1/600, Loss: 0.1095, Test Loss: 0.6796\n",
            "Epoch 1/600, Loss: 0.1114, Test Loss: 0.6683\n",
            "Epoch 1/600, Loss: 0.1142, Test Loss: 0.6506\n",
            "Epoch 1/600, Loss: 0.1152, Test Loss: 0.6363\n",
            "Epoch 1/600, Loss: 0.1193, Test Loss: 0.6223\n",
            "Epoch 1/600, Loss: 0.1217, Test Loss: 0.6034\n",
            "Epoch 1/600, Loss: 0.1260, Test Loss: 0.5858\n",
            "Epoch 1/600, Loss: 0.1281, Test Loss: 0.5755\n",
            "Epoch 1/600, Loss: 0.1339, Test Loss: 0.5514\n",
            "Epoch 1/600, Loss: 0.1359, Test Loss: 0.5403\n",
            "Epoch 1/600, Loss: 0.1373, Test Loss: 0.5210\n",
            "Epoch 1/600, Loss: 0.1396, Test Loss: 0.5071\n",
            "Epoch 1/600, Loss: 0.1425, Test Loss: 0.4876\n",
            "Epoch 1/600, Loss: 0.1456, Test Loss: 0.4737\n",
            "Epoch 1/600, Loss: 0.1487, Test Loss: 0.4537\n",
            "Epoch 1/600, Loss: 0.1510, Test Loss: 0.4254\n",
            "Epoch 1/600, Loss: 0.1560, Test Loss: 0.4097\n",
            "Epoch 1/600, Loss: 0.1571, Test Loss: 0.3887\n",
            "Epoch 1/600, Loss: 0.1597, Test Loss: 0.3653\n",
            "Epoch 1/600, Loss: 0.1633, Test Loss: 0.3430\n",
            "Epoch 1/600, Loss: 0.1647, Test Loss: 0.3254\n",
            "Epoch 1/600, Loss: 0.1663, Test Loss: 0.2993\n",
            "Epoch 1/600, Loss: 0.1683, Test Loss: 0.2862\n",
            "Epoch 1/600, Loss: 0.1699, Test Loss: 0.2558\n",
            "Epoch 1/600, Loss: 0.1710, Test Loss: 0.2424\n",
            "Epoch 1/600, Loss: 0.1723, Test Loss: 0.2268\n",
            "Epoch 1/600, Loss: 0.1735, Test Loss: 0.2145\n",
            "Epoch 1/600, Loss: 0.1754, Test Loss: 0.1793\n",
            "Epoch 1/600, Loss: 0.1768, Test Loss: 0.1678\n",
            "Epoch 1/600, Loss: 0.1771, Test Loss: 0.1533\n",
            "Epoch 1/600, Loss: 0.1786, Test Loss: 0.1255\n",
            "Epoch 1/600, Loss: 0.1790, Test Loss: 0.1184\n",
            "Epoch 1/600, Loss: 0.1792, Test Loss: 0.1140\n",
            "Epoch 1/600, Loss: 0.1794, Test Loss: 0.1024\n",
            "Epoch 1/600, Loss: 0.1800, Test Loss: 0.0854\n",
            "Epoch 1/600, Loss: 0.1804, Test Loss: 0.0847\n",
            "Epoch 1/600, Loss: 0.1805, Test Loss: 0.0703\n",
            "Epoch 1/600, Loss: 0.1814, Test Loss: 0.0715\n",
            "Epoch 1/600, Loss: 0.1819, Test Loss: 0.0619\n",
            "Epoch 1/600, Loss: 0.1821, Test Loss: 0.0615\n",
            "Epoch 1/600, Loss: 0.1822, Test Loss: 0.0601\n",
            "Epoch 1/600, Loss: 0.1829, Test Loss: 0.0583\n",
            "Epoch 1/600, Loss: 0.1833, Test Loss: 0.0612\n",
            "Epoch 1/600, Loss: 0.1837, Test Loss: 0.0567\n",
            "Epoch 1/600, Loss: 0.1840, Test Loss: 0.0596\n",
            "Epoch 1/600, Loss: 0.1841, Test Loss: 0.0643\n",
            "Epoch 1/600, Loss: 0.1843, Test Loss: 0.0611\n",
            "Epoch 1/600, Loss: 0.1846, Test Loss: 0.0698\n",
            "Epoch 1/600, Loss: 0.1848, Test Loss: 0.0723\n",
            "Epoch 1/600, Loss: 0.1854, Test Loss: 0.0728\n",
            "Epoch 1/600, Loss: 0.1859, Test Loss: 0.0705\n",
            "Epoch 1/600, Loss: 0.1864, Test Loss: 0.0620\n",
            "Epoch 1/600, Loss: 0.1868, Test Loss: 0.0657\n",
            "Epoch 1/600, Loss: 0.1874, Test Loss: 0.0595\n",
            "Epoch 1/600, Loss: 0.1877, Test Loss: 0.0572\n",
            "Epoch 1/600, Loss: 0.1879, Test Loss: 0.0479\n",
            "Epoch 1/600, Loss: 0.1880, Test Loss: 0.0428\n",
            "Epoch 1/600, Loss: 0.1882, Test Loss: 0.0426\n",
            "Epoch 1/600, Loss: 0.1885, Test Loss: 0.0437\n",
            "Epoch 1/600, Loss: 0.1895, Test Loss: 0.0432\n",
            "Epoch 1/600, Loss: 0.1897, Test Loss: 0.0447\n",
            "Epoch 1/600, Loss: 0.1901, Test Loss: 0.0409\n",
            "Epoch 1/600, Loss: 0.1903, Test Loss: 0.0479\n",
            "Epoch 1/600, Loss: 0.1903, Test Loss: 0.0387\n",
            "Epoch 1/600, Loss: 0.1904, Test Loss: 0.0422\n",
            "Epoch 1/600, Loss: 0.1906, Test Loss: 0.0484\n",
            "Epoch 1/600, Loss: 0.1908, Test Loss: 0.0450\n",
            "Epoch 1/600, Loss: 0.1909, Test Loss: 0.0510\n",
            "Epoch 1/600, Loss: 0.1911, Test Loss: 0.0493\n",
            "Epoch 1/600, Loss: 0.1913, Test Loss: 0.0477\n",
            "Epoch 1/600, Loss: 0.1917, Test Loss: 0.0466\n",
            "Epoch 1/600, Loss: 0.1919, Test Loss: 0.0439\n",
            "Epoch 1/600, Loss: 0.1919, Test Loss: 0.0404\n",
            "Epoch 1/600, Loss: 0.1926, Test Loss: 0.0406\n",
            "Epoch 1/600, Loss: 0.1927, Test Loss: 0.0347\n",
            "Epoch 1/600, Loss: 0.1928, Test Loss: 0.0369\n",
            "Epoch 1/600, Loss: 0.1930, Test Loss: 0.0383\n",
            "Epoch 1/600, Loss: 0.1932, Test Loss: 0.0347\n",
            "Epoch 1/600, Loss: 0.1934, Test Loss: 0.0298\n",
            "Epoch 1/600, Loss: 0.1936, Test Loss: 0.0359\n",
            "Epoch 1/600, Loss: 0.1940, Test Loss: 0.0402\n",
            "Epoch 1/600, Loss: 0.1941, Test Loss: 0.0405\n",
            "Epoch 1/600, Loss: 0.1942, Test Loss: 0.0400\n",
            "Epoch 1/600, Loss: 0.1943, Test Loss: 0.0360\n",
            "Epoch 1/600, Loss: 0.1945, Test Loss: 0.0313\n",
            "Epoch 1/600, Loss: 0.1948, Test Loss: 0.0360\n",
            "Epoch 1/600, Loss: 0.1948, Test Loss: 0.0315\n",
            "Epoch 1/600, Loss: 0.1949, Test Loss: 0.0370\n",
            "Epoch 1/600, Loss: 0.1951, Test Loss: 0.0392\n",
            "Epoch 1/600, Loss: 0.1951, Test Loss: 0.0355\n",
            "Epoch 1/600, Loss: 0.1953, Test Loss: 0.0341\n",
            "Epoch 1/600, Loss: 0.1954, Test Loss: 0.0295\n",
            "Epoch 1/600, Loss: 0.1957, Test Loss: 0.0321\n",
            "Epoch 1/600, Loss: 0.1958, Test Loss: 0.0308\n",
            "Epoch 1/600, Loss: 0.1959, Test Loss: 0.0296\n",
            "Epoch 1/600, Loss: 0.1959, Test Loss: 0.0328\n",
            "Epoch 1/600, Loss: 0.1960, Test Loss: 0.0305\n",
            "Epoch 1/600, Loss: 0.1962, Test Loss: 0.0368\n",
            "Epoch 1/600, Loss: 0.1967, Test Loss: 0.0355\n",
            "Epoch 1/600, Loss: 0.1968, Test Loss: 0.0339\n",
            "Epoch 1/600, Loss: 0.1969, Test Loss: 0.0372\n",
            "Epoch 1/600, Loss: 0.1972, Test Loss: 0.0327\n",
            "Epoch 1/600, Loss: 0.1974, Test Loss: 0.0313\n",
            "Epoch 1/600, Loss: 0.1974, Test Loss: 0.0323\n",
            "Epoch 1/600, Loss: 0.1977, Test Loss: 0.0354\n",
            "Epoch 1/600, Loss: 0.1979, Test Loss: 0.0349\n",
            "Epoch 1/600, Loss: 0.1980, Test Loss: 0.0317\n",
            "Epoch 1/600, Loss: 0.1984, Test Loss: 0.0306\n",
            "Epoch 1/600, Loss: 0.1985, Test Loss: 0.0287\n",
            "Epoch 1/600, Loss: 0.1986, Test Loss: 0.0376\n",
            "Epoch 1/600, Loss: 0.1989, Test Loss: 0.0344\n",
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            "Epoch 101/600, Loss: 0.0206, Test Loss: 0.0192\n",
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            "Epoch 401/600, Loss: 0.0127, Test Loss: 0.0181\n",
            "Epoch 401/600, Loss: 0.0128, Test Loss: 0.0167\n",
            "Epoch 401/600, Loss: 0.0131, Test Loss: 0.0177\n",
            "Epoch 401/600, Loss: 0.0131, Test Loss: 0.0172\n",
            "Epoch 401/600, Loss: 0.0132, Test Loss: 0.0172\n",
            "Epoch 401/600, Loss: 0.0133, Test Loss: 0.0183\n",
            "Epoch 401/600, Loss: 0.0133, Test Loss: 0.0181\n",
            "Epoch 401/600, Loss: 0.0134, Test Loss: 0.0185\n",
            "Epoch 401/600, Loss: 0.0136, Test Loss: 0.0201\n",
            "Epoch 401/600, Loss: 0.0137, Test Loss: 0.0185\n",
            "Epoch 401/600, Loss: 0.0137, Test Loss: 0.0205\n",
            "Epoch 401/600, Loss: 0.0137, Test Loss: 0.0165\n",
            "Epoch 401/600, Loss: 0.0138, Test Loss: 0.0202\n",
            "Epoch 401/600, Loss: 0.0139, Test Loss: 0.0199\n",
            "Epoch 401/600, Loss: 0.0141, Test Loss: 0.0181\n",
            "Epoch 401/600, Loss: 0.0141, Test Loss: 0.0186\n",
            "Epoch 401/600, Loss: 0.0145, Test Loss: 0.0197\n",
            "Epoch 401/600, Loss: 0.0145, Test Loss: 0.0202\n",
            "Epoch 401/600, Loss: 0.0146, Test Loss: 0.0178\n",
            "Epoch 401/600, Loss: 0.0147, Test Loss: 0.0176\n",
            "Epoch 401/600, Loss: 0.0148, Test Loss: 0.0180\n",
            "Epoch 401/600, Loss: 0.0149, Test Loss: 0.0169\n",
            "Epoch 401/600, Loss: 0.0149, Test Loss: 0.0165\n",
            "Epoch 401/600, Loss: 0.0150, Test Loss: 0.0146\n",
            "Epoch 401/600, Loss: 0.0153, Test Loss: 0.0160\n",
            "Epoch 401/600, Loss: 0.0154, Test Loss: 0.0195\n",
            "Epoch 401/600, Loss: 0.0155, Test Loss: 0.0166\n",
            "Epoch 401/600, Loss: 0.0155, Test Loss: 0.0204\n",
            "Epoch 401/600, Loss: 0.0156, Test Loss: 0.0210\n",
            "Epoch 401/600, Loss: 0.0157, Test Loss: 0.0186\n",
            "Epoch 401/600, Loss: 0.0159, Test Loss: 0.0211\n",
            "Epoch 401/600, Loss: 0.0160, Test Loss: 0.0180\n",
            "Epoch 401/600, Loss: 0.0161, Test Loss: 0.0203\n",
            "Epoch 401/600, Loss: 0.0162, Test Loss: 0.0203\n",
            "Epoch 401/600, Loss: 0.0162, Test Loss: 0.0210\n",
            "Epoch 401/600, Loss: 0.0163, Test Loss: 0.0205\n",
            "Epoch 401/600, Loss: 0.0163, Test Loss: 0.0188\n",
            "Epoch 401/600, Loss: 0.0165, Test Loss: 0.0176\n",
            "Epoch 401/600, Loss: 0.0166, Test Loss: 0.0196\n",
            "Epoch 401/600, Loss: 0.0169, Test Loss: 0.0209\n",
            "Epoch 401/600, Loss: 0.0171, Test Loss: 0.0181\n",
            "Epoch 401/600, Loss: 0.0172, Test Loss: 0.0167\n",
            "Epoch 401/600, Loss: 0.0173, Test Loss: 0.0156\n",
            "Epoch 401/600, Loss: 0.0175, Test Loss: 0.0176\n",
            "Epoch 401/600, Loss: 0.0175, Test Loss: 0.0181\n",
            "Epoch 401/600, Loss: 0.0176, Test Loss: 0.0187\n",
            "Epoch 401/600, Loss: 0.0177, Test Loss: 0.0185\n",
            "Epoch 401/600, Loss: 0.0178, Test Loss: 0.0196\n",
            "Epoch 401/600, Loss: 0.0178, Test Loss: 0.0198\n",
            "Epoch 401/600, Loss: 0.0179, Test Loss: 0.0199\n",
            "Epoch 401/600, Loss: 0.0180, Test Loss: 0.0219\n",
            "Epoch 401/600, Loss: 0.0181, Test Loss: 0.0203\n",
            "Epoch 401/600, Loss: 0.0181, Test Loss: 0.0232\n",
            "Epoch 401/600, Loss: 0.0183, Test Loss: 0.0202\n",
            "Epoch 401/600, Loss: 0.0185, Test Loss: 0.0162\n",
            "Epoch 401/600, Loss: 0.0186, Test Loss: 0.0197\n",
            "Epoch 501/600, Loss: 0.0000, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0001, Test Loss: 0.0214\n",
            "Epoch 501/600, Loss: 0.0001, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0003, Test Loss: 0.0233\n",
            "Epoch 501/600, Loss: 0.0004, Test Loss: 0.0222\n",
            "Epoch 501/600, Loss: 0.0004, Test Loss: 0.0201\n",
            "Epoch 501/600, Loss: 0.0006, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0006, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0007, Test Loss: 0.0203\n",
            "Epoch 501/600, Loss: 0.0007, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0010, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0012, Test Loss: 0.0186\n",
            "Epoch 501/600, Loss: 0.0012, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0012, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0013, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0014, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0015, Test Loss: 0.0169\n",
            "Epoch 501/600, Loss: 0.0015, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0018, Test Loss: 0.0174\n",
            "Epoch 501/600, Loss: 0.0019, Test Loss: 0.0161\n",
            "Epoch 501/600, Loss: 0.0020, Test Loss: 0.0166\n",
            "Epoch 501/600, Loss: 0.0020, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0022, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0023, Test Loss: 0.0185\n",
            "Epoch 501/600, Loss: 0.0023, Test Loss: 0.0160\n",
            "Epoch 501/600, Loss: 0.0024, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0025, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0026, Test Loss: 0.0146\n",
            "Epoch 501/600, Loss: 0.0027, Test Loss: 0.0167\n",
            "Epoch 501/600, Loss: 0.0028, Test Loss: 0.0182\n",
            "Epoch 501/600, Loss: 0.0030, Test Loss: 0.0187\n",
            "Epoch 501/600, Loss: 0.0030, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0031, Test Loss: 0.0167\n",
            "Epoch 501/600, Loss: 0.0031, Test Loss: 0.0188\n",
            "Epoch 501/600, Loss: 0.0033, Test Loss: 0.0190\n",
            "Epoch 501/600, Loss: 0.0033, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0035, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0036, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0036, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0037, Test Loss: 0.0167\n",
            "Epoch 501/600, Loss: 0.0040, Test Loss: 0.0192\n",
            "Epoch 501/600, Loss: 0.0042, Test Loss: 0.0197\n",
            "Epoch 501/600, Loss: 0.0042, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0043, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0044, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0045, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0046, Test Loss: 0.0173\n",
            "Epoch 501/600, Loss: 0.0046, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0047, Test Loss: 0.0147\n",
            "Epoch 501/600, Loss: 0.0049, Test Loss: 0.0196\n",
            "Epoch 501/600, Loss: 0.0050, Test Loss: 0.0203\n",
            "Epoch 501/600, Loss: 0.0050, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0050, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0051, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0052, Test Loss: 0.0157\n",
            "Epoch 501/600, Loss: 0.0052, Test Loss: 0.0184\n",
            "Epoch 501/600, Loss: 0.0052, Test Loss: 0.0210\n",
            "Epoch 501/600, Loss: 0.0053, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0054, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0054, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0055, Test Loss: 0.0201\n",
            "Epoch 501/600, Loss: 0.0055, Test Loss: 0.0181\n",
            "Epoch 501/600, Loss: 0.0055, Test Loss: 0.0194\n",
            "Epoch 501/600, Loss: 0.0056, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0056, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0056, Test Loss: 0.0190\n",
            "Epoch 501/600, Loss: 0.0056, Test Loss: 0.0155\n",
            "Epoch 501/600, Loss: 0.0057, Test Loss: 0.0199\n",
            "Epoch 501/600, Loss: 0.0057, Test Loss: 0.0202\n",
            "Epoch 501/600, Loss: 0.0058, Test Loss: 0.0190\n",
            "Epoch 501/600, Loss: 0.0059, Test Loss: 0.0181\n",
            "Epoch 501/600, Loss: 0.0060, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0061, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0062, Test Loss: 0.0186\n",
            "Epoch 501/600, Loss: 0.0062, Test Loss: 0.0201\n",
            "Epoch 501/600, Loss: 0.0063, Test Loss: 0.0174\n",
            "Epoch 501/600, Loss: 0.0064, Test Loss: 0.0211\n",
            "Epoch 501/600, Loss: 0.0067, Test Loss: 0.0173\n",
            "Epoch 501/600, Loss: 0.0067, Test Loss: 0.0142\n",
            "Epoch 501/600, Loss: 0.0068, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0068, Test Loss: 0.0166\n",
            "Epoch 501/600, Loss: 0.0069, Test Loss: 0.0193\n",
            "Epoch 501/600, Loss: 0.0070, Test Loss: 0.0182\n",
            "Epoch 501/600, Loss: 0.0071, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0072, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0073, Test Loss: 0.0163\n",
            "Epoch 501/600, Loss: 0.0074, Test Loss: 0.0167\n",
            "Epoch 501/600, Loss: 0.0074, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0075, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0076, Test Loss: 0.0181\n",
            "Epoch 501/600, Loss: 0.0076, Test Loss: 0.0184\n",
            "Epoch 501/600, Loss: 0.0077, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0079, Test Loss: 0.0173\n",
            "Epoch 501/600, Loss: 0.0080, Test Loss: 0.0164\n",
            "Epoch 501/600, Loss: 0.0080, Test Loss: 0.0182\n",
            "Epoch 501/600, Loss: 0.0081, Test Loss: 0.0151\n",
            "Epoch 501/600, Loss: 0.0082, Test Loss: 0.0181\n",
            "Epoch 501/600, Loss: 0.0083, Test Loss: 0.0216\n",
            "Epoch 501/600, Loss: 0.0083, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0084, Test Loss: 0.0203\n",
            "Epoch 501/600, Loss: 0.0085, Test Loss: 0.0164\n",
            "Epoch 501/600, Loss: 0.0086, Test Loss: 0.0187\n",
            "Epoch 501/600, Loss: 0.0086, Test Loss: 0.0207\n",
            "Epoch 501/600, Loss: 0.0087, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0088, Test Loss: 0.0192\n",
            "Epoch 501/600, Loss: 0.0088, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0089, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0090, Test Loss: 0.0169\n",
            "Epoch 501/600, Loss: 0.0091, Test Loss: 0.0160\n",
            "Epoch 501/600, Loss: 0.0091, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0091, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0092, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0092, Test Loss: 0.0162\n",
            "Epoch 501/600, Loss: 0.0094, Test Loss: 0.0201\n",
            "Epoch 501/600, Loss: 0.0094, Test Loss: 0.0166\n",
            "Epoch 501/600, Loss: 0.0094, Test Loss: 0.0193\n",
            "Epoch 501/600, Loss: 0.0095, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0095, Test Loss: 0.0161\n",
            "Epoch 501/600, Loss: 0.0096, Test Loss: 0.0182\n",
            "Epoch 501/600, Loss: 0.0096, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0096, Test Loss: 0.0165\n",
            "Epoch 501/600, Loss: 0.0097, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0097, Test Loss: 0.0197\n",
            "Epoch 501/600, Loss: 0.0098, Test Loss: 0.0193\n",
            "Epoch 501/600, Loss: 0.0098, Test Loss: 0.0161\n",
            "Epoch 501/600, Loss: 0.0098, Test Loss: 0.0205\n",
            "Epoch 501/600, Loss: 0.0099, Test Loss: 0.0173\n",
            "Epoch 501/600, Loss: 0.0100, Test Loss: 0.0207\n",
            "Epoch 501/600, Loss: 0.0101, Test Loss: 0.0162\n",
            "Epoch 501/600, Loss: 0.0102, Test Loss: 0.0169\n",
            "Epoch 501/600, Loss: 0.0102, Test Loss: 0.0184\n",
            "Epoch 501/600, Loss: 0.0102, Test Loss: 0.0169\n",
            "Epoch 501/600, Loss: 0.0104, Test Loss: 0.0186\n",
            "Epoch 501/600, Loss: 0.0105, Test Loss: 0.0147\n",
            "Epoch 501/600, Loss: 0.0107, Test Loss: 0.0194\n",
            "Epoch 501/600, Loss: 0.0107, Test Loss: 0.0153\n",
            "Epoch 501/600, Loss: 0.0108, Test Loss: 0.0165\n",
            "Epoch 501/600, Loss: 0.0109, Test Loss: 0.0192\n",
            "Epoch 501/600, Loss: 0.0109, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0110, Test Loss: 0.0163\n",
            "Epoch 501/600, Loss: 0.0111, Test Loss: 0.0162\n",
            "Epoch 501/600, Loss: 0.0111, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0112, Test Loss: 0.0159\n",
            "Epoch 501/600, Loss: 0.0114, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0114, Test Loss: 0.0184\n",
            "Epoch 501/600, Loss: 0.0115, Test Loss: 0.0166\n",
            "Epoch 501/600, Loss: 0.0116, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0117, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0117, Test Loss: 0.0166\n",
            "Epoch 501/600, Loss: 0.0117, Test Loss: 0.0164\n",
            "Epoch 501/600, Loss: 0.0118, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0120, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0120, Test Loss: 0.0171\n",
            "Epoch 501/600, Loss: 0.0121, Test Loss: 0.0156\n",
            "Epoch 501/600, Loss: 0.0122, Test Loss: 0.0185\n",
            "Epoch 501/600, Loss: 0.0123, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0124, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0124, Test Loss: 0.0188\n",
            "Epoch 501/600, Loss: 0.0125, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0126, Test Loss: 0.0185\n",
            "Epoch 501/600, Loss: 0.0127, Test Loss: 0.0205\n",
            "Epoch 501/600, Loss: 0.0130, Test Loss: 0.0170\n",
            "Epoch 501/600, Loss: 0.0131, Test Loss: 0.0164\n",
            "Epoch 501/600, Loss: 0.0136, Test Loss: 0.0159\n",
            "Epoch 501/600, Loss: 0.0136, Test Loss: 0.0196\n",
            "Epoch 501/600, Loss: 0.0137, Test Loss: 0.0163\n",
            "Epoch 501/600, Loss: 0.0138, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0138, Test Loss: 0.0173\n",
            "Epoch 501/600, Loss: 0.0139, Test Loss: 0.0187\n",
            "Epoch 501/600, Loss: 0.0139, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0140, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0141, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0142, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0143, Test Loss: 0.0154\n",
            "Epoch 501/600, Loss: 0.0144, Test Loss: 0.0174\n",
            "Epoch 501/600, Loss: 0.0145, Test Loss: 0.0188\n",
            "Epoch 501/600, Loss: 0.0146, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0146, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0147, Test Loss: 0.0205\n",
            "Epoch 501/600, Loss: 0.0148, Test Loss: 0.0150\n",
            "Epoch 501/600, Loss: 0.0150, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0151, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0153, Test Loss: 0.0196\n",
            "Epoch 501/600, Loss: 0.0155, Test Loss: 0.0183\n",
            "Epoch 501/600, Loss: 0.0155, Test Loss: 0.0165\n",
            "Epoch 501/600, Loss: 0.0156, Test Loss: 0.0175\n",
            "Epoch 501/600, Loss: 0.0158, Test Loss: 0.0156\n",
            "Epoch 501/600, Loss: 0.0158, Test Loss: 0.0155\n",
            "Epoch 501/600, Loss: 0.0160, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0161, Test Loss: 0.0161\n",
            "Epoch 501/600, Loss: 0.0161, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0161, Test Loss: 0.0179\n",
            "Epoch 501/600, Loss: 0.0162, Test Loss: 0.0193\n",
            "Epoch 501/600, Loss: 0.0163, Test Loss: 0.0168\n",
            "Epoch 501/600, Loss: 0.0164, Test Loss: 0.0201\n",
            "Epoch 501/600, Loss: 0.0164, Test Loss: 0.0204\n",
            "Epoch 501/600, Loss: 0.0165, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0166, Test Loss: 0.0197\n",
            "Epoch 501/600, Loss: 0.0168, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0171, Test Loss: 0.0209\n",
            "Epoch 501/600, Loss: 0.0171, Test Loss: 0.0155\n",
            "Epoch 501/600, Loss: 0.0171, Test Loss: 0.0163\n",
            "Epoch 501/600, Loss: 0.0172, Test Loss: 0.0186\n",
            "Epoch 501/600, Loss: 0.0172, Test Loss: 0.0202\n",
            "Epoch 501/600, Loss: 0.0172, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0173, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0176, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0177, Test Loss: 0.0181\n",
            "Epoch 501/600, Loss: 0.0177, Test Loss: 0.0195\n",
            "Epoch 501/600, Loss: 0.0177, Test Loss: 0.0210\n",
            "Epoch 501/600, Loss: 0.0178, Test Loss: 0.0191\n",
            "Epoch 501/600, Loss: 0.0178, Test Loss: 0.0189\n",
            "Epoch 501/600, Loss: 0.0178, Test Loss: 0.0185\n",
            "Epoch 501/600, Loss: 0.0179, Test Loss: 0.0178\n",
            "Epoch 501/600, Loss: 0.0180, Test Loss: 0.0217\n",
            "Epoch 501/600, Loss: 0.0181, Test Loss: 0.0180\n",
            "Epoch 501/600, Loss: 0.0181, Test Loss: 0.0167\n",
            "Epoch 501/600, Loss: 0.0182, Test Loss: 0.0172\n",
            "Epoch 501/600, Loss: 0.0183, Test Loss: 0.0177\n",
            "Epoch 501/600, Loss: 0.0183, Test Loss: 0.0176\n",
            "Epoch 501/600, Loss: 0.0186, Test Loss: 0.0172\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "  train_pred = model(X_train_t).cpu().numpy()\n",
        "  test_pred = model(X_test_t).cpu().numpy()"
      ],
      "metadata": {
        "id": "XJgV048tAkvr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pred_orig = scaler_y.inverse_transform(train_pred)\n",
        "test_pred_orig = scaler_y.inverse_transform(test_pred)\n",
        "y_train_orig = scaler_y.inverse_transform(y_train_t.cpu().numpy())\n",
        "y_test_orig = scaler_y.inverse_transform(y_test_t.cpu().numpy())"
      ],
      "metadata": {
        "id": "X06x3jtRAnSz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae_train_mlp3 = mean_absolute_error(y_train_orig, train_pred_orig)\n",
        "mse_train_mlp3 = mean_squared_error(y_train_orig, train_pred_orig)\n",
        "mae_test_mlp3 = mean_absolute_error(y_test_orig, test_pred_orig)\n",
        "mse_test_mlp3 = mean_squared_error(y_test_orig, test_pred_orig)"
      ],
      "metadata": {
        "id": "AmjrGEH0BEYS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"MAE (Treinamento): {mae_train_mlp3:.4f}\")\n",
        "print(f\"MSE (Treinamento): {mse_train_mlp3:.4f}\")\n",
        "print(f\"MAE (Teste): {mae_test_mlp3:.4f}\")\n",
        "print(f\"MSE (Teste): {mse_test_mlp3:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLZRE-wOBKz6",
        "outputId": "9fca875b-8a24-418f-e3a5-73dc285c5f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE (Treinamento): 0.0229\n",
            "MSE (Treinamento): 0.0007\n",
            "MAE (Teste): 0.0230\n",
            "MSE (Teste): 0.0007\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_quantiles = np.percentile(y_train_orig, [25, 75])\n",
        "iqr_train = train_quantiles[1] - train_quantiles[0]\n",
        "train_lower_bound = train_quantiles[0] - 1.5 * iqr_train\n",
        "train_upper_bound = train_quantiles[1] + 1.5 * iqr_train"
      ],
      "metadata": {
        "id": "AUNcv4JEL8Vh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_quantiles = np.percentile(y_test_orig, [25, 75])\n",
        "iqr_test = test_quantiles[1] - test_quantiles[0]\n",
        "test_lower_bound = test_quantiles[0] - 1.5 * iqr_test\n",
        "test_upper_bound = test_quantiles[1] + 1.5 * iqr_test"
      ],
      "metadata": {
        "id": "0Sx5QtDKL5sJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"IQR do Treino: {iqr_train}\")\n",
        "print(f\"IQR do Teste: {iqr_test}\")\n",
        "print(f\"Treinamento Lower Bound: {train_lower_bound:.4f}, Upper Bound: {train_upper_bound:.4f}\")\n",
        "print(f\"Teste Lower Bound: {test_lower_bound:.4f}, Upper Bound: {test_upper_bound:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fPmeQrQBNTj",
        "outputId": "4a961988-2690-487a-fc37-140546b1ba96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IQR do Treino: 0.977793961763382\n",
            "IQR do Teste: 0.8228887058794498\n",
            "Treinamento Lower Bound: -2.1334, Upper Bound: 1.7778\n",
            "Teste Lower Bound: -1.9020, Upper Bound: 1.3896\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame({\n",
        "    'Modelo': ['Regressor NeuralNet - 2 Camadas', 'MLP - 2 Camadas', 'MLP - 3 Camadas'],\n",
        "    'MAE Treino': [mae_RegressorNeuralNet_train, mae_train_mlp2, mae_train_mlp3],\n",
        "    'MAE Teste': [mae_RegressorNeuralNet_test, mae_test_mlp2, mae_test_mlp3],\n",
        "    'MSE Treino': [mse_RegressorNeuralNet_train, mse_train_mlp2, mse_train_mlp3],\n",
        "    'MSE Teste': [mse_RegressorNeuralNet_test, mse_test_mlp2, mse_test_mlp3]\n",
        "})\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g09svjBg1Ql9",
        "outputId": "09d88749-af08-4376-dc73-f7af8e4bcd24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            Modelo  MAE Treino  MAE Teste  MSE Treino  \\\n",
            "0  Regressor NeuralNet - 2 Camadas    0.015942   0.015559    0.000504   \n",
            "1                  MLP - 2 Camadas    0.023453   0.021242    0.002005   \n",
            "2                  MLP - 3 Camadas    0.022945   0.023006    0.000676   \n",
            "\n",
            "   MSE Teste  \n",
            "0   0.000498  \n",
            "1   0.001780  \n",
            "2   0.000678  \n"
          ]
        }
      ]
    }
  ]
}