NN_Comparisons.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJEx7uptNtXP",
        "outputId": "f60bf07a-a103-43bb-efc1-34090c6d7c38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python version: 3.11.11 (main, Dec  4 2024, 08:55:07) [GCC 11.4.0]\n",
            "PyTorch version: 2.6.0+cu124\n",
            "cuda\n"
          ]
        }
      ],
      "source": [
        "# Daniel Armani, PhD : April, 2025\n",
        "\n",
        "import sys\n",
        "print(f\"Python version: {sys.version}\")\n",
        "\n",
        "import time\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "print(f\"PyTorch version: {torch.__version__}\")\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.nn.utils import clip_grad_norm_\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uffa3TYvV-sF",
        "outputId": "3ebf2917-efa8-4ec8-8da4-128e5c570072"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1. ,  0.6,  0. ,  0. ,  0. ],\n",
              "       [ 0.6,  1. ,  0. ,  0. ,  0. ],\n",
              "       [ 0. ,  0. ,  1. , -0.2,  0. ],\n",
              "       [ 0. ,  0. , -0.2,  1. ,  0. ],\n",
              "       [ 0. ,  0. ,  0. ,  0. ,  1. ]])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Generate Some fake data:\n",
        "\n",
        "np.random.seed(2025)\n",
        "torch.manual_seed(2025)\n",
        "\n",
        "n_samples = 10000\n",
        "n_inputs = 5\n",
        "n_outputs = 3\n",
        "\n",
        "# Introduce correlations among X0, X1, X2 & X3 but no correlation with X4!\n",
        "correlation_matrix = np.eye(n_inputs)\n",
        "correlation_matrix[0, 1] = correlation_matrix[1, 0] = 0.6\n",
        "correlation_matrix[2, 3] = correlation_matrix[3, 2] = -0.2\n",
        "\n",
        "correlation_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMqNDD9xSqzG",
        "outputId": "0c7583e1-e797-4db8-ec6e-d1e2c6c34e2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Cholesky decomposition for correlated random variables\n",
        "cov_matrix = np.linalg.cholesky(correlation_matrix)\n",
        "X = np.dot(np.random.randn(n_samples, n_inputs), cov_matrix)\n",
        "\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bwHHtydCSheC",
        "outputId": "6d963a0b-3a76-496c-cf46-8504acb77381"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Linear relationship:\n",
        "raw_y1 = 1.5*X[:, 0] - 0.5*X[:, 1] + 0.1*X[:, 2] + np.random.normal(0, 0.5, size=(n_samples,))\n",
        "\n",
        "# With interactions:\n",
        "raw_y2 = 0.7*X[:, 1] + 0.3*X[:, 2] + 0.5*X[:, 0]*X[:, 3] + np.random.uniform(-0.5, 0.5, size=(n_samples,))\n",
        "\n",
        "# Complex non-linear interactions:\n",
        "epsilon = 1e-10\n",
        "raw_y3 = (\n",
        "    np.sin(2*X[:, 0]) * np.cos(X[:, 1])  # Trigonometric interaction\n",
        "    + np.tanh(X[:, 2] + X[:, 3])  # Non-linear transformation of sum\n",
        "    + np.exp(X[:, 4]/5) / (1 + np.exp(X[:, 4]/5))  # Sigmoid-like function\n",
        "    + 0.3 * np.log(np.abs(X[:, 0]*X[:, 2]) + epsilon)  # Log of interaction\n",
        "    - 0.5 * (X[:, 1]**2 * X[:, 3])  # Higher-order interaction\n",
        "    + 0.2 * np.maximum(0, X[:, 2])  # ReLU-like function\n",
        "    + np.random.normal(0, 0.3, size=(n_samples,))  # Noise\n",
        ")\n",
        "\n",
        "# Get thresholds for exactly 50% split binary outcome\n",
        "threshold_y1 = np.median(raw_y1)\n",
        "threshold_y2 = np.median(raw_y2)\n",
        "threshold_y3 = np.median(raw_y3)\n",
        "\n",
        "y1 = (raw_y1 > threshold_y1).astype(int)\n",
        "y2 = (raw_y2 > threshold_y2).astype(int)\n",
        "y3 = (raw_y3 > threshold_y3).astype(int)\n",
        "\n",
        "Y = np.column_stack([y1, y2, y3])\n",
        "\n",
        "Y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIBThUqglTNe",
        "outputId": "899e9c93-b6db-4a15-ce56-bd9a52a6f108"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000, 5), (2000, 5), (8000, 3), (2000, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Split and prepare data:\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2025)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "X_train_tensor = torch.FloatTensor(X_train_scaled)\n",
        "Y_train_tensor = torch.FloatTensor(Y_train)\n",
        "X_test_tensor = torch.FloatTensor(X_test_scaled)\n",
        "Y_test_tensor = torch.FloatTensor(Y_test)\n",
        "\n",
        "X_train.shape, X_test.shape, Y_train.shape, Y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "GlFRvfaBJ2Id"
      },
      "outputs": [],
      "source": [
        "def num_param(model):\n",
        "  return sum(p.numel() for p in model.parameters() if p.requires_grad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ver1LXRsbuI",
        "outputId": "8ad73e34-bfa2-415e-d48d-f1463642dad6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Coefficients: [ 6.17462127 -1.41605674  0.38145854 -0.03377563  0.0152006 ], Intercept: -0.019\n",
            "Output y1 accuracy: 0.9015\n",
            "\n",
            " Coefficients: [-0.23303093  1.75072474  0.99059278  0.07096124  0.01632559], Intercept: -0.011\n",
            "Output y2 accuracy: 0.781\n",
            "\n",
            " Coefficients: [ 0.3401595  -0.08341299  1.09054417  0.36270869  0.07834484], Intercept: -0.006\n",
            "Output y3 accuracy: 0.709\n"
          ]
        }
      ],
      "source": [
        "# 1. Multivariate Logistic Regression:\n",
        "\n",
        "lr_models = []\n",
        "lr_predictions = np.zeros((X_test.shape[0], n_outputs))\n",
        "\n",
        "for i in range(n_outputs):\n",
        "    lr = LogisticRegression(C=1e6, random_state=2, max_iter=10000, tol=1e-8)\n",
        "    lr.fit(X_train_scaled, Y_train[:, i])\n",
        "    lr_models.append(lr)\n",
        "    print(f\"\\n Coefficients: {lr.coef_[0]}, Intercept: {lr.intercept_[0]:.3f}\")\n",
        "\n",
        "    lr_predictions[:, i] = lr.predict(X_test_scaled)\n",
        "\n",
        "    print(f\"Output y{i+1} accuracy: {lr.score(X_test_scaled, Y_test[:, i])}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RggydVV7vRVO",
        "outputId": "92bb014a-12b3-4dfb-b57f-103c89ff3321"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 18\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SimpleNN(\n",
              "  (linear): Linear(in_features=5, out_features=3, bias=True)\n",
              "  (sigmoid): Sigmoid()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# 2. Simple Neural Network:\n",
        "\n",
        "class SimpleNN(nn.Module):\n",
        "    def __init__(self, input_size, output_size):\n",
        "        super(SimpleNN, self).__init__()\n",
        "        self.linear = nn.Linear(input_size, output_size)\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.sigmoid(self.linear(x))\n",
        "\n",
        "simple_nn = SimpleNN(n_inputs, n_outputs)\n",
        "\n",
        "print(f\"Number of parameters: {num_param(simple_nn)}\")\n",
        "simple_nn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfdUoLDFwwCz",
        "outputId": "d92fe306-605d-472f-baa3-e6453786463e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 30\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearHiddenNN(\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=5, out_features=3, bias=True)\n",
              "    (1): Linear(in_features=3, out_features=3, bias=True)\n",
              "    (2): Sigmoid()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# 3. Neural Network with one linear hidden layer with no activation function:\n",
        "\n",
        "class LinearHiddenNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(LinearHiddenNN, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_size, hidden_size),\n",
        "            nn.Linear(hidden_size, output_size),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "linear_hidden = LinearHiddenNN(n_inputs, 3, n_outputs)\n",
        "print(f\"Number of parameters: {num_param(linear_hidden)}\")\n",
        "linear_hidden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "J3rBCpAs5YQz"
      },
      "outputs": [],
      "source": [
        "# 4. Neural Network with a hidden layer with ReLU activation function:\n",
        "\n",
        "class ReluHiddenNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(ReluHiddenNN, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_size, hidden_size),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden_size, output_size),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "MAX_NEURONS = 30\n",
        "MAX_NEURONS\n",
        "\n",
        "# Create an array of grayscale values from light to dark\n",
        "grays = np.linspace(0.7, 0.2,MAX_NEURONS)\n",
        "colors = [(g, g, g) for g in grays]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "4WTjA3NcsW76",
        "outputId": "59956ce4-028b-4c7d-81f8-5482621510fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Linear Hidden with 1 Neurons and 12 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=1, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=1, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 2 Neurons and 21 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=2, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=2, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 3 Neurons and 30 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=3, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=3, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 4 Neurons and 39 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=4, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=4, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 5 Neurons and 48 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=5, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=5, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 6 Neurons and 57 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=6, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=6, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 7 Neurons and 66 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=7, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=7, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 8 Neurons and 75 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=8, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=8, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 9 Neurons and 84 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=9, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=9, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 10 Neurons and 93 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=10, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=10, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 11 Neurons and 102 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=11, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=11, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 12 Neurons and 111 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=12, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=12, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 13 Neurons and 120 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=13, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=13, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 14 Neurons and 129 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=14, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=14, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 15 Neurons and 138 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=15, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=15, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 16 Neurons and 147 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=16, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=16, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 17 Neurons and 156 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=17, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=17, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 18 Neurons and 165 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=18, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=18, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 19 Neurons and 174 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=19, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=19, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 20 Neurons and 183 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=20, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=20, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 21 Neurons and 192 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=21, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=21, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 22 Neurons and 201 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=22, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=22, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 23 Neurons and 210 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=23, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=23, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 24 Neurons and 219 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=24, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=24, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 25 Neurons and 228 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=25, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=25, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 26 Neurons and 237 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=26, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=26, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 27 Neurons and 246 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=27, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=27, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 28 Neurons and 255 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=28, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=28, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 29 Neurons and 264 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=29, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=29, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n",
            "\n",
            " Linear Hidden with 30 Neurons and 273 parameters:\n",
            "ReluHiddenNN(\n",
            "  (model): Sequential(\n",
            "    (0): Linear(in_features=5, out_features=30, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=30, out_features=3, bias=True)\n",
            "    (3): Sigmoid()\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create instances with different hidden sizes\n",
        "\n",
        "relu_hidden = []\n",
        "for hidden_size in range(1, MAX_NEURONS+1):\n",
        "    instance = ReluHiddenNN(n_inputs, hidden_size, n_outputs)\n",
        "    relu_hidden.append(instance)\n",
        "    print(f\"\\n Linear Hidden with {hidden_size} Neurons and {num_param(instance)} parameters:\")\n",
        "    print(instance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9q9_K_uVaqsa",
        "outputId": "959c8757-a298-457c-a678-7e056ba041a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 147\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TwoHiddenNN(\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=5, out_features=8, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=8, out_features=8, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=8, out_features=3, bias=True)\n",
              "    (5): Sigmoid()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# 5. The Neural Network with two hidden layers (ReLU):\n",
        "\n",
        "class TwoHiddenNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden1_size, hidden2_size, output_size):\n",
        "        super(TwoHiddenNN, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_size, hidden1_size),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden1_size, hidden2_size),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(hidden2_size, output_size),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "two_hidden = TwoHiddenNN(n_inputs, 8, 8, n_outputs) # 16 Neurons in the hidden layers\n",
        "print(f\"Number of parameters: {num_param(two_hidden)}\")\n",
        "two_hidden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "mhE6kG_PeuD4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "768f9834-96b4-42df-a3e2-07ec482bd6eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.train_model(model, X_train, Y_train, max_epochs=9000, lr=0.1, tol=1e-20)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>train_model</b><br/>def train_model(model, X_train, Y_train, max_epochs=9000, lr=0.1, tol=1e-20)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-13-4ba94b472c61&gt;</a>&lt;no docstring&gt;</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Function to train the Neural Network models:\n",
        "\n",
        "def train_model(model, X_train, Y_train, max_epochs=9000, lr=0.1, tol=1e-20):\n",
        "    model = model.to(device)\n",
        "\n",
        "    X_train = X_train.to(device)\n",
        "    Y_train = Y_train.to(device)\n",
        "\n",
        "    criterion = nn.BCELoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
        "    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20,gamma=0.9)\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    losses = []\n",
        "    prev_loss = float('inf')\n",
        "\n",
        "    start_time = time.time()\n",
        "\n",
        "    for epoch in range(max_epochs):\n",
        "        y_pred = model(X_train)\n",
        "        loss = criterion(y_pred, Y_train)\n",
        "        losses.append(loss.item())\n",
        "        current_loss = loss.item()\n",
        "\n",
        "        if abs(prev_loss - current_loss) < tol:\n",
        "            print(f\"Converged at epoch {epoch} with loss {current_loss:.8f}\")\n",
        "            break\n",
        "\n",
        "        if (epoch) % 100 == 0:\n",
        "            print(f\"Epoch {epoch}/{max_epochs}, Loss: {loss.item():.4f}\")\n",
        "\n",
        "        prev_loss = current_loss\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "        optimizer.step()\n",
        "\n",
        "        scheduler.step()\n",
        "\n",
        "    if epoch == max_epochs - 1:\n",
        "        print(f\"Reached max epochs {max_epochs} with loss {current_loss:.8f}\")\n",
        "\n",
        "    print(f\"Training duration = {time.time() - start_time} seconds\")\n",
        "\n",
        "    return losses\n",
        "\n",
        "train_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "5xYRJAfvhLyZ",
        "outputId": "72b809d9-d772-4e2b-dbf1-9bd1c411cc44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Simple NN, training 18 parameters: \n",
            "Epoch 0/9000, Loss: 0.7577\n",
            "Epoch 100/9000, Loss: 0.4302\n",
            "Epoch 200/9000, Loss: 0.4274\n",
            "Epoch 300/9000, Loss: 0.4268\n",
            "Epoch 400/9000, Loss: 0.4266\n",
            "Epoch 500/9000, Loss: 0.4265\n",
            "Epoch 600/9000, Loss: 0.4265\n",
            "Epoch 700/9000, Loss: 0.4265\n",
            "Epoch 800/9000, Loss: 0.4264\n",
            "Converged at epoch 844 with loss 0.42643547\n",
            "Training duration = 2.2663040161132812 seconds\n",
            "\n",
            " One Linear Hidden NN, training 30 parameters: \n",
            "Epoch 0/9000, Loss: 0.7325\n",
            "Epoch 100/9000, Loss: 0.4263\n",
            "Converged at epoch 102 with loss 0.42630339\n",
            "Training duration = 0.15150141716003418 seconds\n",
            "\n",
            " ReLU Hidden Layer with 1 Neurons, training 12 parameters: \n",
            "Epoch 0/9000, Loss: 0.7404\n",
            "Epoch 100/9000, Loss: 0.5295\n",
            "Epoch 200/9000, Loss: 0.5288\n",
            "Epoch 300/9000, Loss: 0.5285\n",
            "Epoch 400/9000, Loss: 0.5284\n",
            "Epoch 500/9000, Loss: 0.5284\n",
            "Epoch 600/9000, Loss: 0.5284\n",
            "Epoch 700/9000, Loss: 0.5284\n",
            "Converged at epoch 762 with loss 0.52835983\n",
            "Training duration = 1.4312818050384521 seconds\n",
            "\n",
            " ReLU Hidden Layer with 2 Neurons, training 21 parameters: \n",
            "Epoch 0/9000, Loss: 0.7167\n",
            "Epoch 100/9000, Loss: 0.4471\n",
            "Epoch 200/9000, Loss: 0.4448\n",
            "Epoch 300/9000, Loss: 0.4444\n",
            "Epoch 400/9000, Loss: 0.4442\n",
            "Epoch 500/9000, Loss: 0.4442\n",
            "Epoch 600/9000, Loss: 0.4442\n",
            "Epoch 700/9000, Loss: 0.4442\n",
            "Converged at epoch 747 with loss 0.44416261\n",
            "Training duration = 1.403425931930542 seconds\n",
            "\n",
            " ReLU Hidden Layer with 3 Neurons, training 30 parameters: \n",
            "Epoch 0/9000, Loss: 0.7213\n",
            "Epoch 100/9000, Loss: 0.3981\n",
            "Epoch 200/9000, Loss: 0.3950\n",
            "Epoch 300/9000, Loss: 0.3919\n",
            "Epoch 400/9000, Loss: 0.3907\n",
            "Epoch 500/9000, Loss: 0.3901\n",
            "Epoch 600/9000, Loss: 0.3897\n",
            "Epoch 700/9000, Loss: 0.3895\n",
            "Epoch 800/9000, Loss: 0.3893\n",
            "Epoch 900/9000, Loss: 0.3893\n",
            "Epoch 1000/9000, Loss: 0.3892\n",
            "Epoch 1100/9000, Loss: 0.3892\n",
            "Epoch 1200/9000, Loss: 0.3892\n",
            "Epoch 1300/9000, Loss: 0.3892\n",
            "Converged at epoch 1365 with loss 0.38917154\n",
            "Training duration = 2.188732385635376 seconds\n",
            "\n",
            " ReLU Hidden Layer with 4 Neurons, training 39 parameters: \n",
            "Epoch 0/9000, Loss: 0.7154\n",
            "Epoch 100/9000, Loss: 0.3718\n",
            "Epoch 200/9000, Loss: 0.3569\n",
            "Epoch 300/9000, Loss: 0.3514\n",
            "Epoch 400/9000, Loss: 0.3503\n",
            "Epoch 500/9000, Loss: 0.3497\n",
            "Epoch 600/9000, Loss: 0.3496\n",
            "Epoch 700/9000, Loss: 0.3495\n",
            "Epoch 800/9000, Loss: 0.3494\n",
            "Epoch 900/9000, Loss: 0.3494\n",
            "Epoch 1000/9000, Loss: 0.3494\n",
            "Converged at epoch 1044 with loss 0.34937760\n",
            "Training duration = 1.5950658321380615 seconds\n",
            "\n",
            " ReLU Hidden Layer with 5 Neurons, training 48 parameters: \n",
            "Epoch 0/9000, Loss: 0.6985\n",
            "Epoch 100/9000, Loss: 0.3508\n",
            "Epoch 200/9000, Loss: 0.3475\n",
            "Epoch 300/9000, Loss: 0.3468\n",
            "Epoch 400/9000, Loss: 0.3465\n",
            "Epoch 500/9000, Loss: 0.3463\n",
            "Epoch 600/9000, Loss: 0.3463\n",
            "Epoch 700/9000, Loss: 0.3462\n",
            "Epoch 800/9000, Loss: 0.3461\n",
            "Epoch 900/9000, Loss: 0.3461\n",
            "Epoch 1000/9000, Loss: 0.3461\n",
            "Epoch 1100/9000, Loss: 0.3461\n",
            "Converged at epoch 1122 with loss 0.34609219\n",
            "Training duration = 1.7474117279052734 seconds\n",
            "\n",
            " ReLU Hidden Layer with 6 Neurons, training 57 parameters: \n",
            "Epoch 0/9000, Loss: 0.7166\n",
            "Epoch 100/9000, Loss: 0.3203\n",
            "Epoch 200/9000, Loss: 0.3082\n",
            "Epoch 300/9000, Loss: 0.3075\n",
            "Epoch 400/9000, Loss: 0.3072\n",
            "Epoch 500/9000, Loss: 0.3071\n",
            "Epoch 600/9000, Loss: 0.3070\n",
            "Epoch 700/9000, Loss: 0.3070\n",
            "Epoch 800/9000, Loss: 0.3070\n",
            "Epoch 900/9000, Loss: 0.3069\n",
            "Epoch 1000/9000, Loss: 0.3069\n",
            "Epoch 1100/9000, Loss: 0.3069\n",
            "Converged at epoch 1142 with loss 0.30691698\n",
            "Training duration = 1.769449234008789 seconds\n",
            "\n",
            " ReLU Hidden Layer with 7 Neurons, training 66 parameters: \n",
            "Epoch 0/9000, Loss: 0.7120\n",
            "Epoch 100/9000, Loss: 0.3068\n",
            "Epoch 200/9000, Loss: 0.2993\n",
            "Epoch 300/9000, Loss: 0.2985\n",
            "Epoch 400/9000, Loss: 0.2979\n",
            "Epoch 500/9000, Loss: 0.2977\n",
            "Epoch 600/9000, Loss: 0.2976\n",
            "Epoch 700/9000, Loss: 0.2975\n",
            "Epoch 800/9000, Loss: 0.2975\n",
            "Epoch 900/9000, Loss: 0.2975\n",
            "Epoch 1000/9000, Loss: 0.2975\n",
            "Epoch 1100/9000, Loss: 0.2974\n",
            "Converged at epoch 1118 with loss 0.29744357\n",
            "Training duration = 1.7489750385284424 seconds\n",
            "\n",
            " ReLU Hidden Layer with 8 Neurons, training 75 parameters: \n",
            "Epoch 0/9000, Loss: 0.6944\n",
            "Epoch 100/9000, Loss: 0.3161\n",
            "Epoch 200/9000, Loss: 0.3052\n",
            "Epoch 300/9000, Loss: 0.3035\n",
            "Epoch 400/9000, Loss: 0.3028\n",
            "Epoch 500/9000, Loss: 0.3026\n",
            "Epoch 600/9000, Loss: 0.3022\n",
            "Epoch 700/9000, Loss: 0.3019\n",
            "Epoch 800/9000, Loss: 0.3018\n",
            "Epoch 900/9000, Loss: 0.3017\n",
            "Epoch 1000/9000, Loss: 0.3017\n",
            "Epoch 1100/9000, Loss: 0.3016\n",
            "Epoch 1200/9000, Loss: 0.3016\n",
            "Epoch 1300/9000, Loss: 0.3016\n",
            "Epoch 1400/9000, Loss: 0.3016\n",
            "Epoch 1500/9000, Loss: 0.3016\n",
            "Converged at epoch 1598 with loss 0.30156723\n",
            "Training duration = 3.1256210803985596 seconds\n",
            "\n",
            " ReLU Hidden Layer with 9 Neurons, training 84 parameters: \n",
            "Epoch 0/9000, Loss: 0.7267\n",
            "Epoch 100/9000, Loss: 0.3051\n",
            "Epoch 200/9000, Loss: 0.2970\n",
            "Epoch 300/9000, Loss: 0.2954\n",
            "Epoch 400/9000, Loss: 0.2947\n",
            "Epoch 500/9000, Loss: 0.2945\n",
            "Epoch 600/9000, Loss: 0.2944\n",
            "Epoch 700/9000, Loss: 0.2943\n",
            "Epoch 800/9000, Loss: 0.2943\n",
            "Epoch 900/9000, Loss: 0.2943\n",
            "Epoch 1000/9000, Loss: 0.2943\n",
            "Epoch 1100/9000, Loss: 0.2943\n",
            "Converged at epoch 1114 with loss 0.29425666\n",
            "Training duration = 1.7479259967803955 seconds\n",
            "\n",
            " ReLU Hidden Layer with 10 Neurons, training 93 parameters: \n",
            "Epoch 0/9000, Loss: 0.7120\n",
            "Epoch 100/9000, Loss: 0.2897\n",
            "Epoch 200/9000, Loss: 0.2801\n",
            "Epoch 300/9000, Loss: 0.2786\n",
            "Epoch 400/9000, Loss: 0.2780\n",
            "Epoch 500/9000, Loss: 0.2777\n",
            "Epoch 600/9000, Loss: 0.2775\n",
            "Epoch 700/9000, Loss: 0.2775\n",
            "Epoch 800/9000, Loss: 0.2774\n",
            "Epoch 900/9000, Loss: 0.2774\n",
            "Epoch 1000/9000, Loss: 0.2774\n",
            "Epoch 1100/9000, Loss: 0.2774\n",
            "Epoch 1200/9000, Loss: 0.2774\n",
            "Converged at epoch 1202 with loss 0.27735648\n",
            "Training duration = 1.9099221229553223 seconds\n",
            "\n",
            " ReLU Hidden Layer with 11 Neurons, training 102 parameters: \n",
            "Epoch 0/9000, Loss: 0.7061\n",
            "Epoch 100/9000, Loss: 0.2864\n",
            "Epoch 200/9000, Loss: 0.2766\n",
            "Epoch 300/9000, Loss: 0.2748\n",
            "Epoch 400/9000, Loss: 0.2744\n",
            "Epoch 500/9000, Loss: 0.2741\n",
            "Epoch 600/9000, Loss: 0.2740\n",
            "Epoch 700/9000, Loss: 0.2740\n",
            "Epoch 800/9000, Loss: 0.2739\n",
            "Epoch 900/9000, Loss: 0.2739\n",
            "Epoch 1000/9000, Loss: 0.2739\n",
            "Epoch 1100/9000, Loss: 0.2739\n",
            "Converged at epoch 1125 with loss 0.27386439\n",
            "Training duration = 1.839982509613037 seconds\n",
            "\n",
            " ReLU Hidden Layer with 12 Neurons, training 111 parameters: \n",
            "Epoch 0/9000, Loss: 0.7280\n",
            "Epoch 100/9000, Loss: 0.2864\n",
            "Epoch 200/9000, Loss: 0.2733\n",
            "Epoch 300/9000, Loss: 0.2722\n",
            "Epoch 400/9000, Loss: 0.2720\n",
            "Epoch 500/9000, Loss: 0.2719\n",
            "Epoch 600/9000, Loss: 0.2718\n",
            "Epoch 700/9000, Loss: 0.2717\n",
            "Epoch 800/9000, Loss: 0.2716\n",
            "Epoch 900/9000, Loss: 0.2716\n",
            "Epoch 1000/9000, Loss: 0.2716\n",
            "Epoch 1100/9000, Loss: 0.2716\n",
            "Converged at epoch 1167 with loss 0.27161115\n",
            "Training duration = 3.2580671310424805 seconds\n",
            "\n",
            " ReLU Hidden Layer with 13 Neurons, training 120 parameters: \n",
            "Epoch 0/9000, Loss: 0.6845\n",
            "Epoch 100/9000, Loss: 0.2736\n",
            "Epoch 200/9000, Loss: 0.2666\n",
            "Epoch 300/9000, Loss: 0.2649\n",
            "Epoch 400/9000, Loss: 0.2637\n",
            "Epoch 500/9000, Loss: 0.2632\n",
            "Epoch 600/9000, Loss: 0.2630\n",
            "Epoch 700/9000, Loss: 0.2629\n",
            "Epoch 800/9000, Loss: 0.2628\n",
            "Epoch 900/9000, Loss: 0.2628\n",
            "Epoch 1000/9000, Loss: 0.2628\n",
            "Epoch 1100/9000, Loss: 0.2628\n",
            "Epoch 1200/9000, Loss: 0.2627\n",
            "Converged at epoch 1267 with loss 0.26274174\n",
            "Training duration = 4.750821828842163 seconds\n",
            "\n",
            " ReLU Hidden Layer with 14 Neurons, training 129 parameters: \n",
            "Epoch 0/9000, Loss: 0.7089\n",
            "Epoch 100/9000, Loss: 0.2780\n",
            "Epoch 200/9000, Loss: 0.2672\n",
            "Epoch 300/9000, Loss: 0.2646\n",
            "Epoch 400/9000, Loss: 0.2630\n",
            "Epoch 500/9000, Loss: 0.2621\n",
            "Epoch 600/9000, Loss: 0.2613\n",
            "Epoch 700/9000, Loss: 0.2609\n",
            "Epoch 800/9000, Loss: 0.2607\n",
            "Epoch 900/9000, Loss: 0.2606\n",
            "Epoch 1000/9000, Loss: 0.2605\n",
            "Epoch 1100/9000, Loss: 0.2605\n",
            "Epoch 1200/9000, Loss: 0.2605\n",
            "Epoch 1300/9000, Loss: 0.2605\n",
            "Converged at epoch 1335 with loss 0.26049751\n",
            "Training duration = 2.0981051921844482 seconds\n",
            "\n",
            " ReLU Hidden Layer with 15 Neurons, training 138 parameters: \n",
            "Epoch 0/9000, Loss: 0.7096\n",
            "Epoch 100/9000, Loss: 0.2734\n",
            "Epoch 200/9000, Loss: 0.2695\n",
            "Epoch 300/9000, Loss: 0.2687\n",
            "Epoch 400/9000, Loss: 0.2685\n",
            "Epoch 500/9000, Loss: 0.2682\n",
            "Epoch 600/9000, Loss: 0.2680\n",
            "Epoch 700/9000, Loss: 0.2679\n",
            "Epoch 800/9000, Loss: 0.2678\n",
            "Epoch 900/9000, Loss: 0.2677\n",
            "Epoch 1000/9000, Loss: 0.2676\n",
            "Epoch 1100/9000, Loss: 0.2676\n",
            "Epoch 1200/9000, Loss: 0.2676\n",
            "Epoch 1300/9000, Loss: 0.2676\n",
            "Epoch 1400/9000, Loss: 0.2676\n",
            "Converged at epoch 1428 with loss 0.26755372\n",
            "Training duration = 2.2624547481536865 seconds\n",
            "\n",
            " ReLU Hidden Layer with 16 Neurons, training 147 parameters: \n",
            "Epoch 0/9000, Loss: 0.6993\n",
            "Epoch 100/9000, Loss: 0.2769\n",
            "Epoch 200/9000, Loss: 0.2710\n",
            "Epoch 300/9000, Loss: 0.2674\n",
            "Epoch 400/9000, Loss: 0.2648\n",
            "Epoch 500/9000, Loss: 0.2644\n",
            "Epoch 600/9000, Loss: 0.2642\n",
            "Epoch 700/9000, Loss: 0.2640\n",
            "Epoch 800/9000, Loss: 0.2638\n",
            "Epoch 900/9000, Loss: 0.2637\n",
            "Epoch 1000/9000, Loss: 0.2637\n",
            "Epoch 1100/9000, Loss: 0.2636\n",
            "Epoch 1200/9000, Loss: 0.2636\n",
            "Epoch 1300/9000, Loss: 0.2636\n",
            "Epoch 1400/9000, Loss: 0.2636\n",
            "Epoch 1500/9000, Loss: 0.2636\n",
            "Converged at epoch 1551 with loss 0.26355758\n",
            "Training duration = 2.4016263484954834 seconds\n",
            "\n",
            " ReLU Hidden Layer with 17 Neurons, training 156 parameters: \n",
            "Epoch 0/9000, Loss: 0.6913\n",
            "Epoch 100/9000, Loss: 0.2724\n",
            "Epoch 200/9000, Loss: 0.2612\n",
            "Epoch 300/9000, Loss: 0.2586\n",
            "Epoch 400/9000, Loss: 0.2581\n",
            "Epoch 500/9000, Loss: 0.2578\n",
            "Epoch 600/9000, Loss: 0.2577\n",
            "Epoch 700/9000, Loss: 0.2577\n",
            "Epoch 800/9000, Loss: 0.2577\n",
            "Epoch 900/9000, Loss: 0.2576\n",
            "Epoch 1000/9000, Loss: 0.2576\n",
            "Converged at epoch 1077 with loss 0.25763616\n",
            "Training duration = 1.7077429294586182 seconds\n",
            "\n",
            " ReLU Hidden Layer with 18 Neurons, training 165 parameters: \n",
            "Epoch 0/9000, Loss: 0.6922\n",
            "Epoch 100/9000, Loss: 0.2628\n",
            "Epoch 200/9000, Loss: 0.2573\n",
            "Epoch 300/9000, Loss: 0.2560\n",
            "Epoch 400/9000, Loss: 0.2554\n",
            "Epoch 500/9000, Loss: 0.2550\n",
            "Epoch 600/9000, Loss: 0.2541\n",
            "Epoch 700/9000, Loss: 0.2534\n",
            "Epoch 800/9000, Loss: 0.2530\n",
            "Epoch 900/9000, Loss: 0.2528\n",
            "Epoch 1000/9000, Loss: 0.2526\n",
            "Epoch 1100/9000, Loss: 0.2526\n",
            "Epoch 1200/9000, Loss: 0.2525\n",
            "Epoch 1300/9000, Loss: 0.2525\n",
            "Epoch 1400/9000, Loss: 0.2525\n",
            "Epoch 1500/9000, Loss: 0.2525\n",
            "Epoch 1600/9000, Loss: 0.2525\n",
            "Converged at epoch 1612 with loss 0.25246924\n",
            "Training duration = 2.952190637588501 seconds\n",
            "\n",
            " ReLU Hidden Layer with 19 Neurons, training 174 parameters: \n",
            "Epoch 0/9000, Loss: 0.7165\n",
            "Epoch 100/9000, Loss: 0.2649\n",
            "Epoch 200/9000, Loss: 0.2568\n",
            "Epoch 300/9000, Loss: 0.2541\n",
            "Epoch 400/9000, Loss: 0.2531\n",
            "Epoch 500/9000, Loss: 0.2527\n",
            "Epoch 600/9000, Loss: 0.2524\n",
            "Epoch 700/9000, Loss: 0.2522\n",
            "Epoch 800/9000, Loss: 0.2520\n",
            "Epoch 900/9000, Loss: 0.2519\n",
            "Epoch 1000/9000, Loss: 0.2518\n",
            "Epoch 1100/9000, Loss: 0.2518\n",
            "Epoch 1200/9000, Loss: 0.2518\n",
            "Epoch 1300/9000, Loss: 0.2518\n",
            "Converged at epoch 1312 with loss 0.25178269\n",
            "Training duration = 2.102100133895874 seconds\n",
            "\n",
            " ReLU Hidden Layer with 20 Neurons, training 183 parameters: \n",
            "Epoch 0/9000, Loss: 0.6829\n",
            "Epoch 100/9000, Loss: 0.2648\n",
            "Epoch 200/9000, Loss: 0.2549\n",
            "Epoch 300/9000, Loss: 0.2500\n",
            "Epoch 400/9000, Loss: 0.2480\n",
            "Epoch 500/9000, Loss: 0.2473\n",
            "Epoch 600/9000, Loss: 0.2469\n",
            "Epoch 700/9000, Loss: 0.2468\n",
            "Epoch 800/9000, Loss: 0.2467\n",
            "Epoch 900/9000, Loss: 0.2466\n",
            "Epoch 1000/9000, Loss: 0.2466\n",
            "Epoch 1100/9000, Loss: 0.2466\n",
            "Epoch 1200/9000, Loss: 0.2466\n",
            "Converged at epoch 1259 with loss 0.24656986\n",
            "Training duration = 1.977158546447754 seconds\n",
            "\n",
            " ReLU Hidden Layer with 21 Neurons, training 192 parameters: \n",
            "Epoch 0/9000, Loss: 0.7058\n",
            "Epoch 100/9000, Loss: 0.2582\n",
            "Epoch 200/9000, Loss: 0.2513\n",
            "Epoch 300/9000, Loss: 0.2504\n",
            "Epoch 400/9000, Loss: 0.2501\n",
            "Epoch 500/9000, Loss: 0.2500\n",
            "Epoch 600/9000, Loss: 0.2500\n",
            "Converged at epoch 642 with loss 0.25000310\n",
            "Training duration = 1.0042259693145752 seconds\n",
            "\n",
            " ReLU Hidden Layer with 22 Neurons, training 201 parameters: \n",
            "Epoch 0/9000, Loss: 0.7168\n",
            "Epoch 100/9000, Loss: 0.2551\n",
            "Epoch 200/9000, Loss: 0.2486\n",
            "Epoch 300/9000, Loss: 0.2472\n",
            "Epoch 400/9000, Loss: 0.2468\n",
            "Epoch 500/9000, Loss: 0.2466\n",
            "Epoch 600/9000, Loss: 0.2464\n",
            "Epoch 700/9000, Loss: 0.2461\n",
            "Epoch 800/9000, Loss: 0.2459\n",
            "Epoch 900/9000, Loss: 0.2458\n",
            "Epoch 1000/9000, Loss: 0.2458\n",
            "Epoch 1100/9000, Loss: 0.2457\n",
            "Epoch 1200/9000, Loss: 0.2457\n",
            "Epoch 1300/9000, Loss: 0.2457\n",
            "Epoch 1400/9000, Loss: 0.2457\n",
            "Converged at epoch 1432 with loss 0.24570304\n",
            "Training duration = 2.2468411922454834 seconds\n",
            "\n",
            " ReLU Hidden Layer with 23 Neurons, training 210 parameters: \n",
            "Epoch 0/9000, Loss: 0.7112\n",
            "Epoch 100/9000, Loss: 0.2655\n",
            "Epoch 200/9000, Loss: 0.2556\n",
            "Epoch 300/9000, Loss: 0.2544\n",
            "Epoch 400/9000, Loss: 0.2540\n",
            "Epoch 500/9000, Loss: 0.2537\n",
            "Epoch 600/9000, Loss: 0.2536\n",
            "Epoch 700/9000, Loss: 0.2535\n",
            "Epoch 800/9000, Loss: 0.2534\n",
            "Epoch 900/9000, Loss: 0.2534\n",
            "Epoch 1000/9000, Loss: 0.2534\n",
            "Converged at epoch 1028 with loss 0.25341550\n",
            "Training duration = 1.5958826541900635 seconds\n",
            "\n",
            " ReLU Hidden Layer with 24 Neurons, training 219 parameters: \n",
            "Epoch 0/9000, Loss: 0.6838\n",
            "Epoch 100/9000, Loss: 0.2623\n",
            "Epoch 200/9000, Loss: 0.2536\n",
            "Epoch 300/9000, Loss: 0.2505\n",
            "Epoch 400/9000, Loss: 0.2492\n",
            "Epoch 500/9000, Loss: 0.2482\n",
            "Epoch 600/9000, Loss: 0.2476\n",
            "Epoch 700/9000, Loss: 0.2474\n",
            "Epoch 800/9000, Loss: 0.2472\n",
            "Epoch 900/9000, Loss: 0.2470\n",
            "Epoch 1000/9000, Loss: 0.2469\n",
            "Epoch 1100/9000, Loss: 0.2469\n",
            "Epoch 1200/9000, Loss: 0.2469\n",
            "Epoch 1300/9000, Loss: 0.2468\n",
            "Epoch 1400/9000, Loss: 0.2468\n",
            "Converged at epoch 1462 with loss 0.24680375\n",
            "Training duration = 2.5766232013702393 seconds\n",
            "\n",
            " ReLU Hidden Layer with 25 Neurons, training 228 parameters: \n",
            "Epoch 0/9000, Loss: 0.7187\n",
            "Epoch 100/9000, Loss: 0.2566\n",
            "Epoch 200/9000, Loss: 0.2503\n",
            "Epoch 300/9000, Loss: 0.2479\n",
            "Epoch 400/9000, Loss: 0.2465\n",
            "Epoch 500/9000, Loss: 0.2455\n",
            "Epoch 600/9000, Loss: 0.2449\n",
            "Epoch 700/9000, Loss: 0.2443\n",
            "Epoch 800/9000, Loss: 0.2440\n",
            "Epoch 900/9000, Loss: 0.2438\n",
            "Epoch 1000/9000, Loss: 0.2437\n",
            "Epoch 1100/9000, Loss: 0.2437\n",
            "Epoch 1200/9000, Loss: 0.2436\n",
            "Epoch 1300/9000, Loss: 0.2436\n",
            "Epoch 1400/9000, Loss: 0.2436\n",
            "Epoch 1500/9000, Loss: 0.2436\n",
            "Epoch 1600/9000, Loss: 0.2436\n",
            "Converged at epoch 1605 with loss 0.24357828\n",
            "Training duration = 2.5379605293273926 seconds\n",
            "\n",
            " ReLU Hidden Layer with 26 Neurons, training 237 parameters: \n",
            "Epoch 0/9000, Loss: 0.7127\n",
            "Epoch 100/9000, Loss: 0.2607\n",
            "Epoch 200/9000, Loss: 0.2538\n",
            "Epoch 300/9000, Loss: 0.2501\n",
            "Epoch 400/9000, Loss: 0.2482\n",
            "Epoch 500/9000, Loss: 0.2472\n",
            "Epoch 600/9000, Loss: 0.2465\n",
            "Epoch 700/9000, Loss: 0.2461\n",
            "Epoch 800/9000, Loss: 0.2457\n",
            "Epoch 900/9000, Loss: 0.2456\n",
            "Epoch 1000/9000, Loss: 0.2455\n",
            "Epoch 1100/9000, Loss: 0.2455\n",
            "Epoch 1200/9000, Loss: 0.2455\n",
            "Epoch 1300/9000, Loss: 0.2455\n",
            "Epoch 1400/9000, Loss: 0.2454\n",
            "Epoch 1500/9000, Loss: 0.2454\n",
            "Epoch 1600/9000, Loss: 0.2454\n",
            "Converged at epoch 1657 with loss 0.24542196\n",
            "Training duration = 2.5949602127075195 seconds\n",
            "\n",
            " ReLU Hidden Layer with 27 Neurons, training 246 parameters: \n",
            "Epoch 0/9000, Loss: 0.7125\n",
            "Epoch 100/9000, Loss: 0.2573\n",
            "Epoch 200/9000, Loss: 0.2489\n",
            "Epoch 300/9000, Loss: 0.2459\n",
            "Epoch 400/9000, Loss: 0.2441\n",
            "Epoch 500/9000, Loss: 0.2423\n",
            "Epoch 600/9000, Loss: 0.2415\n",
            "Epoch 700/9000, Loss: 0.2411\n",
            "Epoch 800/9000, Loss: 0.2409\n",
            "Epoch 900/9000, Loss: 0.2407\n",
            "Epoch 1000/9000, Loss: 0.2406\n",
            "Epoch 1100/9000, Loss: 0.2406\n",
            "Epoch 1200/9000, Loss: 0.2405\n",
            "Epoch 1300/9000, Loss: 0.2405\n",
            "Epoch 1400/9000, Loss: 0.2405\n",
            "Epoch 1500/9000, Loss: 0.2405\n",
            "Epoch 1600/9000, Loss: 0.2405\n",
            "Converged at epoch 1608 with loss 0.24048026\n",
            "Training duration = 2.5494446754455566 seconds\n",
            "\n",
            " ReLU Hidden Layer with 28 Neurons, training 255 parameters: \n",
            "Epoch 0/9000, Loss: 0.6969\n",
            "Epoch 100/9000, Loss: 0.2564\n",
            "Epoch 200/9000, Loss: 0.2506\n",
            "Epoch 300/9000, Loss: 0.2480\n",
            "Epoch 400/9000, Loss: 0.2470\n",
            "Epoch 500/9000, Loss: 0.2461\n",
            "Epoch 600/9000, Loss: 0.2458\n",
            "Epoch 700/9000, Loss: 0.2455\n",
            "Epoch 800/9000, Loss: 0.2454\n",
            "Epoch 900/9000, Loss: 0.2453\n",
            "Epoch 1000/9000, Loss: 0.2452\n",
            "Epoch 1100/9000, Loss: 0.2452\n",
            "Epoch 1200/9000, Loss: 0.2451\n",
            "Epoch 1300/9000, Loss: 0.2451\n",
            "Epoch 1400/9000, Loss: 0.2451\n",
            "Converged at epoch 1446 with loss 0.24511950\n",
            "Training duration = 2.273514747619629 seconds\n",
            "\n",
            " ReLU Hidden Layer with 29 Neurons, training 264 parameters: \n",
            "Epoch 0/9000, Loss: 0.7021\n",
            "Epoch 100/9000, Loss: 0.2511\n",
            "Epoch 200/9000, Loss: 0.2421\n",
            "Epoch 300/9000, Loss: 0.2398\n",
            "Epoch 400/9000, Loss: 0.2383\n",
            "Epoch 500/9000, Loss: 0.2378\n",
            "Epoch 600/9000, Loss: 0.2376\n",
            "Epoch 700/9000, Loss: 0.2373\n",
            "Epoch 800/9000, Loss: 0.2371\n",
            "Epoch 900/9000, Loss: 0.2370\n",
            "Epoch 1000/9000, Loss: 0.2370\n",
            "Epoch 1100/9000, Loss: 0.2369\n",
            "Epoch 1200/9000, Loss: 0.2369\n",
            "Epoch 1300/9000, Loss: 0.2369\n",
            "Epoch 1400/9000, Loss: 0.2369\n",
            "Converged at epoch 1500 with loss 0.23689456\n",
            "Training duration = 2.727484941482544 seconds\n",
            "\n",
            " ReLU Hidden Layer with 30 Neurons, training 273 parameters: \n",
            "Epoch 0/9000, Loss: 0.7033\n",
            "Epoch 100/9000, Loss: 0.2616\n",
            "Epoch 200/9000, Loss: 0.2467\n",
            "Epoch 300/9000, Loss: 0.2428\n",
            "Epoch 400/9000, Loss: 0.2412\n",
            "Epoch 500/9000, Loss: 0.2405\n",
            "Epoch 600/9000, Loss: 0.2401\n",
            "Epoch 700/9000, Loss: 0.2398\n",
            "Epoch 800/9000, Loss: 0.2397\n",
            "Epoch 900/9000, Loss: 0.2396\n",
            "Epoch 1000/9000, Loss: 0.2395\n",
            "Epoch 1100/9000, Loss: 0.2395\n",
            "Epoch 1200/9000, Loss: 0.2394\n",
            "Epoch 1300/9000, Loss: 0.2394\n",
            "Epoch 1400/9000, Loss: 0.2394\n",
            "Epoch 1500/9000, Loss: 0.2394\n",
            "Epoch 1600/9000, Loss: 0.2394\n",
            "Converged at epoch 1619 with loss 0.23938452\n",
            "Training duration = 2.5269551277160645 seconds\n",
            "\n",
            " Two ReLU Hidden NN, training 147 parameters: \n",
            "Epoch 0/9000, Loss: 0.6990\n",
            "Epoch 100/9000, Loss: 0.3078\n",
            "Epoch 200/9000, Loss: 0.2873\n",
            "Epoch 300/9000, Loss: 0.2845\n",
            "Epoch 400/9000, Loss: 0.2832\n",
            "Epoch 500/9000, Loss: 0.2827\n",
            "Epoch 600/9000, Loss: 0.2825\n",
            "Epoch 700/9000, Loss: 0.2823\n",
            "Epoch 800/9000, Loss: 0.2823\n",
            "Epoch 900/9000, Loss: 0.2822\n",
            "Epoch 1000/9000, Loss: 0.2822\n",
            "Epoch 1100/9000, Loss: 0.2822\n",
            "Epoch 1200/9000, Loss: 0.2821\n",
            "Epoch 1300/9000, Loss: 0.2821\n",
            "Converged at epoch 1353 with loss 0.28212842\n",
            "Training duration = 2.444042921066284 seconds\n"
          ]
        }
      ],
      "source": [
        "# Training Models and Obtaining Losses:\n",
        "\n",
        "print(f\"\\n Simple NN, training {num_param(simple_nn)} parameters: \")\n",
        "simple_losses = train_model(simple_nn, X_train_tensor, Y_train_tensor)\n",
        "\n",
        "print(f\"\\n One Linear Hidden NN, training {num_param(linear_hidden)} parameters: \")\n",
        "linear_hidden_losses= train_model(linear_hidden, X_train_tensor, Y_train_tensor)\n",
        "\n",
        "relu_hidden_losses = [[] for _ in range(len(relu_hidden))]\n",
        "for i, instance in enumerate(relu_hidden):\n",
        "  print(f\"\\n ReLU Hidden Layer with {i+1} Neurons, training {num_param(instance)} parameters: \")\n",
        "  relu_hidden_losses[i] = train_model(instance, X_train_tensor, Y_train_tensor)\n",
        "\n",
        "print(f\"\\n Two ReLU Hidden NN, training {num_param(two_hidden)} parameters: \")\n",
        "two_hidden_losses = train_model(two_hidden, X_train_tensor, Y_train_tensor)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "pZ0CxSQ_qzxq",
        "outputId": "537a36e3-a3a4-4065-a984-d22d1ccd4dfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simple No Hidden Layer: 0.4264354705810547\n",
            "One Linear Hidden Layer : 0.4263033866882324\n",
            "One Relu Hidden 1: 0.5283598303794861\n",
            "One Relu Hidden 2: 0.44416260719299316\n",
            "One Relu Hidden 3: 0.3891715407371521\n",
            "One Relu Hidden 4: 0.3493776023387909\n",
            "One Relu Hidden 5: 0.34609219431877136\n",
            "One Relu Hidden 6: 0.3069169819355011\n",
            "One Relu Hidden 7: 0.29744356870651245\n",
            "One Relu Hidden 8: 0.3015672266483307\n",
            "One Relu Hidden 9: 0.29425665736198425\n",
            "One Relu Hidden 10: 0.27735647559165955\n",
            "One Relu Hidden 11: 0.27386438846588135\n",
            "One Relu Hidden 12: 0.27161115407943726\n",
            "One Relu Hidden 13: 0.26274174451828003\n",
            "One Relu Hidden 14: 0.260497510433197\n",
            "One Relu Hidden 15: 0.2675537168979645\n",
            "One Relu Hidden 16: 0.2635575830936432\n",
            "One Relu Hidden 17: 0.257636159658432\n",
            "One Relu Hidden 18: 0.2524692416191101\n",
            "One Relu Hidden 19: 0.25178268551826477\n",
            "One Relu Hidden 20: 0.24656985700130463\n",
            "One Relu Hidden 21: 0.2500030994415283\n",
            "One Relu Hidden 22: 0.24570304155349731\n",
            "One Relu Hidden 23: 0.2534154951572418\n",
            "One Relu Hidden 24: 0.24680374562740326\n",
            "One Relu Hidden 25: 0.24357828497886658\n",
            "One Relu Hidden 26: 0.24542196094989777\n",
            "One Relu Hidden 27: 0.24048025906085968\n",
            "One Relu Hidden 28: 0.24511949717998505\n",
            "One Relu Hidden 29: 0.23689456284046173\n",
            "One Relu Hidden 30: 0.2393845170736313\n",
            "Two ReLU Hidden Layers     : 0.2821284234523773\n"
          ]
        }
      ],
      "source": [
        "print(f\"Simple No Hidden Layer: {simple_losses[-1]}\")\n",
        "\n",
        "print(f\"One Linear Hidden Layer : {linear_hidden_losses[-1]}\")\n",
        "\n",
        "for i, instance in enumerate(relu_hidden):\n",
        "  print(f\"One Relu Hidden {i+1}: {relu_hidden_losses[i][-1]}\")\n",
        "\n",
        "print(f\"Two ReLU Hidden Layers     : {two_hidden_losses[-1]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "4M92ibF_ysAX",
        "outputId": "765c36c6-5b36-4a42-d290-dffc99d525ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plots for the losses:\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "\n",
        "plt.plot(simple_losses, label='Simple NN', color='b')\n",
        "plt.plot(linear_hidden_losses, label='One Linear Hidden', color='g')\n",
        "\n",
        "for i, instance in enumerate(relu_hidden):\n",
        "  if i == 15: color = 'r'\n",
        "  else: color = colors[i]\n",
        "  plt.plot(relu_hidden_losses[i], label=f'One ReLU Hidden {i+1}', color=color)\n",
        "\n",
        "plt.plot(two_hidden_losses, label='Two ReLU Hidden', color='m')\n",
        "\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Training Loss')\n",
        "plt.title('Training Loss for Different Neural Networks')\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "Kntq5W-C8nFj",
        "outputId": "44fc884b-c778-447b-d047-c6edbb476fd2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plots for the losses:\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "\n",
        "plt.plot(simple_losses, label='Simple NN', color='b')\n",
        "plt.plot(linear_hidden_losses, label='Linear Hidden', color='g')\n",
        "plt.plot(relu_hidden_losses[0], label=f'One ReLU Hidden {1}', color=colors[0])\n",
        "plt.plot(relu_hidden_losses[1], label=f'One ReLU Hidden {2}', color=colors[1])\n",
        "plt.plot(relu_hidden_losses[2], label=f'One ReLU Hidden {3}', color=colors[2])\n",
        "plt.plot(relu_hidden_losses[15], label=f'One ReLU Hidden {16}', color='r')\n",
        "plt.plot(relu_hidden_losses[29], label=f'One ReLU Hidden {30}', color=colors[29])\n",
        "plt.plot(two_hidden_losses, label='Two ReLU Hidden', color='m')\n",
        "\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Training Loss')\n",
        "plt.title('Training Loss for Selected Neural Networks')\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "id": "GCIoB2Biu7iK",
        "outputId": "2be6f384-edbe-4ff2-cd13-fba5a5fcc120"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot for final losses for the linear hidden layer\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "plt.axhline(y=simple_losses[-1], color='g', linestyle='--', label='Simple NN')\n",
        "plt.axhline(y=linear_hidden_losses[-1], color='g', linestyle='--', label='Linear Hidden')\n",
        "plt.plot(range(1, MAX_NEURONS + 1), [relu_hidden_losses[i][-1] for i in range(MAX_NEURONS)], marker='o') #, color='k'\n",
        "plt.axhline(y=two_hidden_losses[-1], color='m', linestyle='--', label='Two Hidden (8,8)')\n",
        "\n",
        "plt.xticks(range(1, MAX_NEURONS + 1))\n",
        "plt.xlabel('Number of Neurons in the ReLU Hidden Layer')\n",
        "plt.ylabel('Final Loss')\n",
        "plt.title('Final Loss vs. Number of Neurons in the ReLU Hidden Layer')\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "id": "tlrRGZ1chgPw",
        "outputId": "ca7b9412-1963-4b9f-a766-b66a707c4467"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.evaluate_model(y_true, y_pred, message)>"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>evaluate_model</b><br/>def evaluate_model(y_true, y_pred, message)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/content/&lt;ipython-input-19-75223e0d8410&gt;</a>&lt;no docstring&gt;</pre></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Function to Evaluate the Neural Network Models:\n",
        "\n",
        "def evaluate_model(y_true, y_pred, message):\n",
        "    accuracies = []\n",
        "    print(f\"\\n--- {message} Results ---\")\n",
        "\n",
        "    for i in range(n_outputs):\n",
        "        acc = (y_true[:, i] == y_pred[:, i]).mean()\n",
        "        accuracies.append(acc)\n",
        "        print(f\"Output y{i+1} Accuracy: {acc:.4f}\")\n",
        "\n",
        "    # print(f\"Average Accuracy: {np.mean(accuracies):.4f}\")\n",
        "    return accuracies\n",
        "\n",
        "evaluate_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJtfgcoDhcrT",
        "outputId": "2aade7b6-c6de-43df-b6a8-6ba5f4e1d815"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Multivariate Logistic Regression Results ---\n",
            "Output y1 Accuracy: 0.9015\n",
            "Output y2 Accuracy: 0.7810\n",
            "Output y3 Accuracy: 0.7090\n",
            "\n",
            "--- Neural Network with No Hidden Layer Results ---\n",
            "Output y1 Accuracy: 0.9010\n",
            "Output y2 Accuracy: 0.7810\n",
            "Output y3 Accuracy: 0.7090\n",
            "\n",
            "--- NN with One Linear Hidden Layer) Results ---\n",
            "Output y1 Accuracy: 0.9010\n",
            "Output y2 Accuracy: 0.7815\n",
            "Output y3 Accuracy: 0.7080\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (1N)) Results ---\n",
            "Output y1 Accuracy: 0.8975\n",
            "Output y2 Accuracy: 0.5680\n",
            "Output y3 Accuracy: 0.6395\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (2N)) Results ---\n",
            "Output y1 Accuracy: 0.9065\n",
            "Output y2 Accuracy: 0.7750\n",
            "Output y3 Accuracy: 0.6765\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (3N)) Results ---\n",
            "Output y1 Accuracy: 0.9065\n",
            "Output y2 Accuracy: 0.8425\n",
            "Output y3 Accuracy: 0.7135\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (4N)) Results ---\n",
            "Output y1 Accuracy: 0.9015\n",
            "Output y2 Accuracy: 0.8425\n",
            "Output y3 Accuracy: 0.7720\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (5N)) Results ---\n",
            "Output y1 Accuracy: 0.8920\n",
            "Output y2 Accuracy: 0.8585\n",
            "Output y3 Accuracy: 0.7540\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (6N)) Results ---\n",
            "Output y1 Accuracy: 0.9020\n",
            "Output y2 Accuracy: 0.8710\n",
            "Output y3 Accuracy: 0.8190\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (7N)) Results ---\n",
            "Output y1 Accuracy: 0.9020\n",
            "Output y2 Accuracy: 0.8785\n",
            "Output y3 Accuracy: 0.8215\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (8N)) Results ---\n",
            "Output y1 Accuracy: 0.8985\n",
            "Output y2 Accuracy: 0.8750\n",
            "Output y3 Accuracy: 0.8090\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (9N)) Results ---\n",
            "Output y1 Accuracy: 0.9020\n",
            "Output y2 Accuracy: 0.8740\n",
            "Output y3 Accuracy: 0.8315\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (10N)) Results ---\n",
            "Output y1 Accuracy: 0.9035\n",
            "Output y2 Accuracy: 0.8745\n",
            "Output y3 Accuracy: 0.8675\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (11N)) Results ---\n",
            "Output y1 Accuracy: 0.9035\n",
            "Output y2 Accuracy: 0.8785\n",
            "Output y3 Accuracy: 0.8650\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (12N)) Results ---\n",
            "Output y1 Accuracy: 0.9015\n",
            "Output y2 Accuracy: 0.8770\n",
            "Output y3 Accuracy: 0.8680\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (13N)) Results ---\n",
            "Output y1 Accuracy: 0.9045\n",
            "Output y2 Accuracy: 0.8835\n",
            "Output y3 Accuracy: 0.8740\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (14N)) Results ---\n",
            "Output y1 Accuracy: 0.9010\n",
            "Output y2 Accuracy: 0.8820\n",
            "Output y3 Accuracy: 0.8695\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (15N)) Results ---\n",
            "Output y1 Accuracy: 0.9025\n",
            "Output y2 Accuracy: 0.8805\n",
            "Output y3 Accuracy: 0.8690\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (16N)) Results ---\n",
            "Output y1 Accuracy: 0.9005\n",
            "Output y2 Accuracy: 0.8810\n",
            "Output y3 Accuracy: 0.8655\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (17N)) Results ---\n",
            "Output y1 Accuracy: 0.9010\n",
            "Output y2 Accuracy: 0.8805\n",
            "Output y3 Accuracy: 0.8740\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (18N)) Results ---\n",
            "Output y1 Accuracy: 0.9040\n",
            "Output y2 Accuracy: 0.8805\n",
            "Output y3 Accuracy: 0.8765\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (19N)) Results ---\n",
            "Output y1 Accuracy: 0.9035\n",
            "Output y2 Accuracy: 0.8845\n",
            "Output y3 Accuracy: 0.8770\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (20N)) Results ---\n",
            "Output y1 Accuracy: 0.9010\n",
            "Output y2 Accuracy: 0.8865\n",
            "Output y3 Accuracy: 0.8835\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (21N)) Results ---\n",
            "Output y1 Accuracy: 0.9040\n",
            "Output y2 Accuracy: 0.8885\n",
            "Output y3 Accuracy: 0.8715\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (22N)) Results ---\n",
            "Output y1 Accuracy: 0.8985\n",
            "Output y2 Accuracy: 0.8795\n",
            "Output y3 Accuracy: 0.8785\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (23N)) Results ---\n",
            "Output y1 Accuracy: 0.9005\n",
            "Output y2 Accuracy: 0.8815\n",
            "Output y3 Accuracy: 0.8715\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (24N)) Results ---\n",
            "Output y1 Accuracy: 0.9040\n",
            "Output y2 Accuracy: 0.8820\n",
            "Output y3 Accuracy: 0.8720\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (25N)) Results ---\n",
            "Output y1 Accuracy: 0.9020\n",
            "Output y2 Accuracy: 0.8875\n",
            "Output y3 Accuracy: 0.8800\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (26N)) Results ---\n",
            "Output y1 Accuracy: 0.9000\n",
            "Output y2 Accuracy: 0.8845\n",
            "Output y3 Accuracy: 0.8765\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (27N)) Results ---\n",
            "Output y1 Accuracy: 0.9030\n",
            "Output y2 Accuracy: 0.8815\n",
            "Output y3 Accuracy: 0.8835\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (28N)) Results ---\n",
            "Output y1 Accuracy: 0.9060\n",
            "Output y2 Accuracy: 0.8870\n",
            "Output y3 Accuracy: 0.8765\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (29N)) Results ---\n",
            "Output y1 Accuracy: 0.9055\n",
            "Output y2 Accuracy: 0.8845\n",
            "Output y3 Accuracy: 0.8890\n",
            "\n",
            "--- NN with One ReLU Hidden Layer (30N)) Results ---\n",
            "Output y1 Accuracy: 0.9015\n",
            "Output y2 Accuracy: 0.8830\n",
            "Output y3 Accuracy: 0.8900\n",
            "\n",
            "--- NN with Two ReLU Hidden Layers) Results ---\n",
            "Output y1 Accuracy: 0.9040\n",
            "Output y2 Accuracy: 0.8795\n",
            "Output y3 Accuracy: 0.8585\n"
          ]
        }
      ],
      "source": [
        "# Prediction and Evaluations:\n",
        "\n",
        "simple_nn.eval()\n",
        "linear_hidden.eval()\n",
        "\n",
        "for i,instance in enumerate(relu_hidden):\n",
        "  instance.eval()\n",
        "relu_hidden_pred = []\n",
        "\n",
        "two_hidden.eval()\n",
        "\n",
        "X_test_tensor = X_test_tensor.to(device)\n",
        "Y_test_tensor = Y_test_tensor.to(device)\n",
        "\n",
        "with torch.no_grad():\n",
        "    simple_nn_pred = (simple_nn(X_test_tensor) > 0.5).float().cpu().numpy()\n",
        "    linear_hidden_pred = (linear_hidden(X_test_tensor) > 0.5).float().cpu().numpy()\n",
        "\n",
        "    for y in relu_hidden:\n",
        "        relu_hidden_pred.append((y(X_test_tensor) > 0.5).float().cpu().numpy())\n",
        "\n",
        "    two_nn_pred = (two_hidden(X_test_tensor) > 0.5).float().cpu().numpy()\n",
        "\n",
        "regression_accuracies = evaluate_model(Y_test, lr_predictions, \"Multivariate Logistic Regression\")\n",
        "simple_nn_accuracies = evaluate_model(Y_test, simple_nn_pred, \"Neural Network with No Hidden Layer\")\n",
        "linear_nn_accuracies = evaluate_model(Y_test, linear_hidden_pred, \"NN with One Linear Hidden Layer)\")\n",
        "\n",
        "relu_nn_accuracies = [[] for _ in range(len(relu_hidden))]\n",
        "\n",
        "for i in range(len(relu_hidden_pred)):\n",
        "  relu_nn_accuracies[i] = evaluate_model(Y_test, relu_hidden_pred[i], f\"NN with One ReLU Hidden Layer ({i+1}N))\")\n",
        "\n",
        "two_nn_accuracies = evaluate_model(Y_test, two_nn_pred, \"NN with Two ReLU Hidden Layers)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YA-yqbso8V_V",
        "outputId": "4f6efd36-dcb2-4b92-a82c-96f916cd7031"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Barcharts to Compare the Accuracies across Models:\n",
        "\n",
        "labels = ['Output y1', 'Output y2', 'Output y3']\n",
        "x = np.arange(len(labels))\n",
        "width = 0.04\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 15))\n",
        "rects1 = ax.bar(x - 3*width, regression_accuracies, width, label='Logistic Regression', color='c')\n",
        "rects2 = ax.bar(x - 2*width, simple_nn_accuracies, width, label='Simple NN', color='b')\n",
        "rects3 = ax.bar(x - width, linear_nn_accuracies, width, label='Linear Hidden NN', color='g')\n",
        "\n",
        "for i in range(len(relu_nn_accuracies)):\n",
        "  if i == 15: color = 'r'\n",
        "  else: color = colors[i]\n",
        "  rects4 = ax.bar(x + i*width/2, relu_nn_accuracies[i], width/2, label=f'Relu Hidden ({i+1}N))', color=color)\n",
        "\n",
        "# rects4 = ax.bar(x + 15*width/2, relu_nn_accuracies[15], width/2, label=f'Relu Hidden ({16}N))', color='r')\n",
        "\n",
        "gap = (len(relu_hidden)+1)*width/2\n",
        "\n",
        "rects5 = ax.bar(x + gap, two_nn_accuracies, width, label='Two ReLU Hidden', color='m')\n",
        "\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_title('Comparison of Model Accuracies on Different Outputs')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend(bbox_to_anchor=(1, 1))\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL74O0dK2pDF",
        "outputId": "c623642f-721e-4d8f-a4f9-902cda77a99e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Comparison: Logistic Regression vs Simple Neural Network ---\n",
            "\n",
            "Output y1:\n",
            "Logistic Regression Coefficients: [ 6.1746 -1.4161  0.3815 -0.0338  0.0152 -0.0186]\n",
            "Simple NN weights: [ 5.7833 -1.3152  0.3595 -0.0308  0.0152 -0.019 ]\n",
            "Mean absolute difference: 0.086268\n",
            "\n",
            "Output y2:\n",
            "Logistic Regression Coefficients: [-0.233   1.7507  0.9906  0.071   0.0163 -0.0107]\n",
            "Simple NN weights: [-0.233   1.7507  0.9906  0.071   0.0163 -0.0107]\n",
            "Mean absolute difference: 0.000000\n",
            "\n",
            "Output y3:\n",
            "Logistic Regression Coefficients: [ 0.3402 -0.0834  1.0905  0.3627  0.0783 -0.0058]\n",
            "Simple NN weights: [ 0.3402 -0.0834  1.0905  0.3627  0.0783 -0.0058]\n",
            "Mean absolute difference: 0.000000\n"
          ]
        }
      ],
      "source": [
        "# Compare weights and coefficients:\n",
        "\n",
        "print(\"\\n--- Comparison: Logistic Regression vs Simple Neural Network ---\")\n",
        "with torch.no_grad():\n",
        "    for i in range(n_outputs):\n",
        "        print(f\"\\nOutput y{i+1}:\")\n",
        "        # Extract sklearn logistic regression weights (coefficients and intercept)\n",
        "        lr_weights = np.concatenate([lr_models[i].coef_[0], [lr_models[i].intercept_[0]]])\n",
        "\n",
        "        # Extract PyTorch weights (weights and bias)\n",
        "        nn_weights = np.concatenate([\n",
        "            simple_nn.linear.weight.data[i].cpu().numpy(),\n",
        "            [simple_nn.linear.bias.data[i].cpu().numpy()]\n",
        "        ])\n",
        "\n",
        "        print(f\"Logistic Regression Coefficients: {lr_weights.round(4)}\")\n",
        "        print(f\"Simple NN weights: {nn_weights.round(4)}\")\n",
        "        print(f\"Mean absolute difference: {np.mean(np.abs(lr_weights - nn_weights)):.6f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "d_26QOuodk6G"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}