Recreer_Tensorflow.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPcYV/0en/IGWLYZKkFxOYv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MOISECHRIST/Tensorflow_DeepLearn_Course/blob/main/Recreer_Tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recreer Tensorflow"
      ],
      "metadata": {
        "id": "tKpejCmisnkO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DyW_TFPRLTSR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creation des boites pour les différentes opéralitions de chaque couche"
      ],
      "metadata": {
        "id": "329E0Bbysx4y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boite Générique"
      ],
      "metadata": {
        "id": "AvL_cXlfs8tT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BoxGeneric:\n",
        "  def __init__(self):\n",
        "    pass\n",
        "\n",
        "  def forward(self, imputs):\n",
        "    self.imputs = imputs\n",
        "    self.output = self.compute()\n",
        "    return self.output\n",
        "\n",
        "  def backward(self, derivee_output):\n",
        "    assert self.output.shape == derivee_output.shape, f\"La dérivee de l'output a un shape {derivee_output.shape} différent de ce celui de output {self.output.shape}\"\n",
        "    self.derivee_output = derivee_output\n",
        "\n",
        "    self.derivee_input = self.compute_gradient()\n",
        "    return self.derivee_input\n",
        "\n",
        "  def compute(self):\n",
        "    pass\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    pass"
      ],
      "metadata": {
        "id": "zF2u9bO-LiDo"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boite Avec Parametre"
      ],
      "metadata": {
        "id": "YG8QOWg-tCk2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BoxParams(BoxGeneric):\n",
        "  def __init__(self, params):\n",
        "    super().__init__()\n",
        "    self.params = params\n",
        "\n",
        "  def backward(self, derivee_output):\n",
        "    assert self.output.shape == derivee_output.shape, f\"La dérivee de l'output a un shape {derivee_output.shape} différent de ce celui de output {self.output.shape}\"\n",
        "    self.derivee_output = derivee_output\n",
        "\n",
        "    self.derivee_input = self.compute_gradient()\n",
        "    self.derivee_params = self.compute_gradient_params()\n",
        "\n",
        "    assert self.params.shape == self.derivee_params.shape, f\"La dérivee des parametres a un shape {self.derivee_params.shape} différent de ce celui des parametres {self.params.shape}\"\n",
        "    assert self.derivee_input.shape == self.imputs.shape, f\"La dérivee des imputs calculee a un shape {self.derivee_input.shape} différent de ce celui des parametres {self.imputs.shape}\"\n",
        "\n",
        "    return self.derivee_input\n",
        "\n",
        "  def update_params(self, lr = 0.001):\n",
        "    self.params=self.params - lr*self.derivee_params\n",
        "\n",
        "  def compute_gradient_params(self):\n",
        "    pass"
      ],
      "metadata": {
        "id": "zzNB1VV7NnaW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Opération Produit Matriciel W*X"
      ],
      "metadata": {
        "id": "osXCwJuEtKYP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BoxDotOperation(BoxParams):\n",
        "  def __init__(self, weight):\n",
        "    super().__init__(weight)\n",
        "\n",
        "  def compute(self):\n",
        "    return np.dot(self.imputs, self.params)\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return np.dot(self.derivee_output, self.params.T)\n",
        "\n",
        "  def compute_gradient_params(self):\n",
        "    return np.dot(self.imputs.T, self.derivee_output)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"DotOperation\""
      ],
      "metadata": {
        "id": "7TRi29aYbiWO"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On teste la classe `BoxDotOperation`"
      ],
      "metadata": {
        "id": "tQGl3ttMtVJy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=np.array([[2,3,-2],\n",
        "            [4,5,-1],\n",
        "            [5,2,3],\n",
        "            [0,5,4]])"
      ],
      "metadata": {
        "id": "GL4JklY4dM0D"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzOZy5zfdvNN",
        "outputId": "1933528a-3c84-4931-ba8e-e2cd3ae5d167"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "W=np.array([[0.49671415],\n",
        "            [-0.1382643],\n",
        "            [0.64768854]])"
      ],
      "metadata": {
        "id": "6-9bSPt-dwxJ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "W.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1XINkmDePa-",
        "outputId": "edf15aac-8036-4521-8071-ae5b7033d1a7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.dot(X,W)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYdKR4-TeRes",
        "outputId": "3b7b1936-c9b2-4cff-b527-1f0a45680375"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.71674168],\n",
              "       [ 0.64784656],\n",
              "       [ 4.15010777],\n",
              "       [ 1.89943266]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "M = BoxDotOperation(W)\n",
        "out_dot=M.forward(X)"
      ],
      "metadata": {
        "id": "ofcb5Er-eZt3"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d_output = np.random.randn(4,1)\n",
        "d_output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Nx8B0VZeiqc",
        "outputId": "5a085722-1d7a-4473-ee05-d65b1b8097d1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.32393699],\n",
              "       [ 0.19473151],\n",
              "       [ 2.18438485],\n",
              "       [-0.48897022]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "M.backward(d_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zraoAFqMfbwZ",
        "outputId": "deff1be1-ec02-4746-f369-3950c1592e1e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.65761824,  0.18305322, -0.85749882],\n",
              "       [ 0.0967259 , -0.02692442,  0.12612537],\n",
              "       [ 1.08501486, -0.30202244,  1.41480104],\n",
              "       [-0.24287843,  0.06760713, -0.31670041]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "M.derivee_params.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSPJMX9GgthK",
        "outputId": "20d87f8f-414a-4f35-8dac-362d0eaf704b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Opération Ajout du biais"
      ],
      "metadata": {
        "id": "evEs8Lo3ti3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BoxAddBiasOperation(BoxParams):\n",
        "  def __init__(self, bias):\n",
        "    super().__init__(bias)\n",
        "\n",
        "  def compute(self):\n",
        "    return self.imputs + self.params\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return np.ones_like(self.imputs) * self.derivee_output\n",
        "\n",
        "  def compute_gradient_params(self):\n",
        "    return (np.ones_like(self.imputs) * self.derivee_output).sum(axis=0).reshape(1,-1)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"AddBiasOperation\""
      ],
      "metadata": {
        "id": "W957pB24gc67"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On teste la classe `BoxAddBiasOperation`"
      ],
      "metadata": {
        "id": "5AauMQCVtqrI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "B = np.random.rand(1,1)\n",
        "B"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qz7FC7mVjocc",
        "outputId": "1b2627bf-1417-4b46-a3d7-cd536855158c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1834024]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b = BoxAddBiasOperation(B)\n",
        "b.forward(out_dot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khG-1HGQjyHg",
        "outputId": "c500152e-df87-40b9-9b13-1de41544d78f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.53333928],\n",
              "       [ 0.83124896],\n",
              "       [ 4.33351017],\n",
              "       [ 2.08283506]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "out_b=b.backward(d_output)"
      ],
      "metadata": {
        "id": "WhH8mOBLj6cA"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Opération Non paramétriques"
      ],
      "metadata": {
        "id": "8QFMbn4Rt1u8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sigmoid"
      ],
      "metadata": {
        "id": "PwhPVh3_t_aF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Sigmoid(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return 1/(1+np.exp(-self.imputs))\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return self.output * (1-self.output) * self.derivee_output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"Sigmoid\""
      ],
      "metadata": {
        "id": "pBd7P3FUk44P"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f=Sigmoid()\n",
        "f.forward(out_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3w3pVBq-k40r",
        "outputId": "e168306e-3f22-44a1-af85-83862ec80d97"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.21016403],\n",
              "       [0.54852962],\n",
              "       [0.89883848],\n",
              "       [0.38013619]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat = f.backward(d_output)"
      ],
      "metadata": {
        "id": "oCzNZP3kmMKH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tanh"
      ],
      "metadata": {
        "id": "LF2gVNQWuC1d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Tanh(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return np.tanh(self.imputs)\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return 1 - np.tanh(self.imputs)**2 * self.derivee_output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"Tanh\""
      ],
      "metadata": {
        "id": "R2wztlW5mokr"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g=Tanh()\n",
        "g.forward(out_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1zDu7ouohaw",
        "outputId": "5d9c2e83-971f-491c-ed0b-d2fdf352d334"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.86775967],\n",
              "       [ 0.19230685],\n",
              "       [ 0.97498325],\n",
              "       [-0.45339873]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g.backward(d_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgM27hBkokHz",
        "outputId": "93cc1aad-366b-47a4-b57c-39e1baec339c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.99693361],\n",
              "       [ 0.99279845],\n",
              "       [-1.07645951],\n",
              "       [ 1.10051781]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Softmax"
      ],
      "metadata": {
        "id": "K7Em92cSuHu_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Softmax(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    tmp = np.sum(np.exp(self.imputs))\n",
        "    return np.exp(self.imputs) / tmp\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return self.output * (1-self.output) * self.derivee_output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"Softmax\""
      ],
      "metadata": {
        "id": "a4ZLaaTPm42G"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h=Softmax()\n",
        "h.forward(out_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvo66Mtjooco",
        "outputId": "9f54cc17-1109-49d0-c76f-b08ef010b573"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.02423475],\n",
              "       [0.11065929],\n",
              "       [0.80925122],\n",
              "       [0.05585474]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h.backward(d_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tprs7rN4orZ2",
        "outputId": "2a783555-6fbf-4215-fb1f-f3301f6104e3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.0313077 ],\n",
              "       [ 0.01916427],\n",
              "       [ 0.33718969],\n",
              "       [-0.02578584]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Relu"
      ],
      "metadata": {
        "id": "5bM2RfafuMGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Relu(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return np.maximum(0, self.imputs)\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return (self.imputs > 0) * self.derivee_output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"Relu\""
      ],
      "metadata": {
        "id": "mQHIjmXRnKHJ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r = Relu()\n",
        "r.forward(out_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzPQi7NUpAtf",
        "outputId": "ddd1d97b-019a-4731-f9bc-3fe8bf96d4dc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        ],\n",
              "       [0.19473151],\n",
              "       [2.18438485],\n",
              "       [0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r.backward(d_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHR89fDepE33",
        "outputId": "a4ccb791-3884-4d42-d09e-a497fa23b00a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.        ],\n",
              "       [ 0.19473151],\n",
              "       [ 2.18438485],\n",
              "       [-0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linéaire"
      ],
      "metadata": {
        "id": "xMhc7ll4uOeN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Linear(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return self.imputs\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return np.ones_like(self.imputs)* self.derivee_output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"Linear\""
      ],
      "metadata": {
        "id": "D3yAVXRFqSnf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l=Linear()\n",
        "l.forward(out_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XY1L-HfSrslG",
        "outputId": "cee45bb5-1048-462e-d416-094aa41c3849"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.32393699],\n",
              "       [ 0.19473151],\n",
              "       [ 2.18438485],\n",
              "       [-0.48897022]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l.backward(d_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jr6dx-3br1dp",
        "outputId": "ba0ce292-5d52-4e7c-dd7d-d8844017e12d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.32393699],\n",
              "       [ 0.19473151],\n",
              "       [ 2.18438485],\n",
              "       [-0.48897022]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calcul des erreurs"
      ],
      "metadata": {
        "id": "2dfNLkm6z7Ii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class LossBox(BoxGeneric):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def forward(self, predic, target):\n",
        "    self.predic = predic\n",
        "    self.target = target\n",
        "    self.output = self.compute()\n",
        "    return self.output\n",
        "\n",
        "  def backward(self):\n",
        "    self.derivee_input = self.compute_gradient()\n",
        "    return self.derivee_input"
      ],
      "metadata": {
        "id": "81TPH_pV42U8"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### MSE"
      ],
      "metadata": {
        "id": "WlK31WoQ0HuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MSE(LossBox):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return np.mean((self.predic - self.target)**2)\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return 2 * (self.predic - self.target)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"MSE\""
      ],
      "metadata": {
        "id": "_WvMIkoxvWWN"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=np.random.rand(4,1)\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpF7p15a3r5V",
        "outputId": "0394d4e8-42bc-41c4-c9a0-7fdef5e74e5a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.81473189],\n",
              "       [0.38836847],\n",
              "       [0.72746554],\n",
              "       [0.9855786 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error = MSE()\n",
        "error.forward(y_hat, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avfsjF-U3wy1",
        "outputId": "7d323aa7-68f6-425a-cb8a-4adca36f3dff"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6693284957868754"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error.backward()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oF_XFi4W4GUE",
        "outputId": "92141a68-81d2-49f1-8412-0f07e097f55a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.06899791],\n",
              "       [-0.68028843],\n",
              "       [-1.05768815],\n",
              "       [-2.20159192]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### RMSE"
      ],
      "metadata": {
        "id": "5kBAGF4e6xhl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class RMSE(LossBox):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return np.sqrt(np.mean((self.predic - self.target)**2))\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return (self.predic - self.target) / self.output\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"RMSE\""
      ],
      "metadata": {
        "id": "N2irLQbh6yLD"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error = RMSE()\n",
        "error.forward(y_hat, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-c0nYyjY7NcN",
        "outputId": "70ef7d0d-9848-48af-99ce-94bcbcf2531f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8181249878758595"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error.backward()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58Tdp0xd8qKS",
        "outputId": "abe063df-a6f4-4be0-feb6-6dd8a28fbd70"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.26447544],\n",
              "       [-0.41576069],\n",
              "       [-0.64640988],\n",
              "       [-1.34551075]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class MAE(LossBox):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "  def compute(self):\n",
        "    return (np.mean(np.abs(self.predic - self.target)))\n",
        "\n",
        "  def compute_gradient(self):\n",
        "    return 1/len(self.predic) * np.sign(self.predic - self.target)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return \"MAE\""
      ],
      "metadata": {
        "id": "_ucvD9OpEnw5"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "err=MAE()\n",
        "err.forward(y_hat, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4W6beRWE9g7",
        "outputId": "85425f62-fe17-4fbf-b267-37eadeb8579a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7510708018815659"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "err.backward()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3u7pECCFLeC",
        "outputId": "2b3c1814-e3cd-4878-b88f-fa664142502a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.25],\n",
              "       [-0.25],\n",
              "       [-0.25],\n",
              "       [-0.25]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creation des Couches du réseau"
      ],
      "metadata": {
        "id": "3XVk5Kh7ICuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Dense:\n",
        "  def __init__(self, neurons : int, activation=Linear()):\n",
        "    self.neurons = neurons\n",
        "    self.activation = activation\n",
        "    self.operations=[]\n",
        "    self.initialisation=True\n",
        "    self.imputs_size=None\n",
        "    self.weights=None\n",
        "    self.bias=None\n",
        "\n",
        "  def builder(self, imputs_size : int, random_state=0):\n",
        "    np.random.seed(random_state)\n",
        "    self.imputs_size = imputs_size\n",
        "    self.weights = np.random.randn(self.imputs_size, self.neurons)\n",
        "    self.bias = np.random.randn(1, self.neurons)\n",
        "    self.operations.append(BoxDotOperation(self.weights))\n",
        "    self.operations.append(BoxAddBiasOperation(self.bias))\n",
        "    self.operations.append(self.activation)\n",
        "\n",
        "  def forward(self, imputs, random_state=0):\n",
        "\n",
        "    if self.initialisation :\n",
        "      self.builder(imputs.shape[1], random_state)\n",
        "      self.initialisation=False\n",
        "\n",
        "    tmp = imputs\n",
        "    for operation in self.operations:\n",
        "      tmp = operation.forward(tmp)\n",
        "\n",
        "    self.output=tmp\n",
        "    return self.output\n",
        "\n",
        "  def backward(self, derivee_output):\n",
        "    assert derivee_output.shape==self.output.shape\n",
        "\n",
        "    tmp = derivee_output\n",
        "    for operation in reversed(self.operations):\n",
        "      tmp = operation.backward(tmp)\n",
        "\n",
        "    self.derivee_input=tmp\n",
        "    return self.derivee_input\n",
        "\n",
        "  def update_params(self, lr=0.001):\n",
        "    for operation in self.operations:\n",
        "      if isinstance(operation, BoxParams):\n",
        "        operation.update_params(lr)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return f\"Dense(nerons={self.neurons}, activation={self.activation})\""
      ],
      "metadata": {
        "id": "fgAiOdwS31R-"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "couche = Dense(2, Sigmoid())"
      ],
      "metadata": {
        "id": "9aUsUPl0H_0H"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIgYS_wEIGoE",
        "outputId": "5f14f03e-3a64-4ee0-ef4c-8ee23df42915"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "couche.forward(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qFp4z3BIpr8",
        "outputId": "19a43829-8267-4b0e-a48c-fc3df0d323c3"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.97538055, 0.99991095],\n",
              "       [0.99998383, 0.9999988 ],\n",
              "       [0.99999997, 0.9676852 ],\n",
              "       [0.99999835, 0.99921103]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for params in couche.operations:\n",
        "  if isinstance(params, BoxParams):\n",
        "    print(params.params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAQRCaJUKDb5",
        "outputId": "96b7c7ae-6df2-433c-8779-198ebf5e4305"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.76405235  0.40015721]\n",
            " [ 0.97873798  2.2408932 ]\n",
            " [ 1.86755799 -0.97727788]]\n",
            "[[ 0.95008842 -0.15135721]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_out = np.random.randn(4,2)\n",
        "d_out"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ny5RtIr_JI1v",
        "outputId": "87ef2ae2-c84e-403f-f4ef-d62e2b96f4ab"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.10321885,  0.4105985 ],\n",
              "       [ 0.14404357,  1.45427351],\n",
              "       [ 0.76103773,  0.12167502],\n",
              "       [ 0.44386323,  0.33367433]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "couche.backward(d_out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGJJmaDFJPqd",
        "outputId": "64fa26c2-fec8-4def-d9f3-0b1f3f3d0f9a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.35779956e-03, -2.34399682e-03, -4.66471291e-03],\n",
              "       [ 4.80819828e-06,  6.19855804e-06,  2.64023033e-06],\n",
              "       [ 1.52257601e-03,  8.52627300e-03, -3.71834838e-03],\n",
              "       [ 1.06554315e-04,  5.90184734e-04, -2.55704407e-04]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "couche.update_params(lr=0.001)"
      ],
      "metadata": {
        "id": "xSSLqcDkJnWV"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for params in couche.operations:\n",
        "  if isinstance(params, BoxParams):\n",
        "    print(params.params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gX9Inc-J1Np",
        "outputId": "5236c1cc-81d9-48ad-b851-ac1e65da66a7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.76405729  0.4001381 ]\n",
            " [ 0.9787454   2.24088416]\n",
            " [ 1.86755303 -0.97729027]]\n",
            "[[ 0.95009089 -0.15136131]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classe pour le reseau de neurones"
      ],
      "metadata": {
        "id": "j4aALOLx6hzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from copy import deepcopy\n",
        "import pickle"
      ],
      "metadata": {
        "id": "0I0tgwQgQqDN"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NeuralNetwork:\n",
        "\n",
        "  def __init__(self, layers=[]):\n",
        "    self.layers = layers\n",
        "    self.loss = None\n",
        "    self.history=[]\n",
        "\n",
        "  def forward(self, inputs, random_state=0):\n",
        "    tmp = inputs\n",
        "    for layer in self.layers:\n",
        "      tmp = layer.forward(tmp, random_state)\n",
        "    self.output = tmp\n",
        "    return self.output\n",
        "\n",
        "  def backward(self, derivee_output):\n",
        "    tmp = derivee_output\n",
        "    for layer in reversed(self.layers):\n",
        "      tmp = layer.backward(tmp)\n",
        "\n",
        "    return tmp\n",
        "\n",
        "  def update_params(self, lr=0.001):\n",
        "    for layer in self.layers:\n",
        "      layer.update_params(lr)\n",
        "\n",
        "\n",
        "  def add(self, layer:Dense):\n",
        "    self.layers.append(layer)\n",
        "\n",
        "  def compile(self, loss=MSE()):\n",
        "    self.loss = loss\n",
        "\n",
        "  def print_train(self, loss_value, epoch, nb_epochs):\n",
        "    print(f\"Epoch {epoch}/{nb_epochs}\\n [==============================] - loss: {loss_value:.2f}\")\n",
        "    self.history.append(loss_value)\n",
        "\n",
        "  def fit(self, input_train, target_train, epochs : int, lr : float, random_state=0):\n",
        "    for epoch in range(epochs):\n",
        "      y_hat = self.forward(input_train, random_state)\n",
        "      loss_value = self.loss.forward(y_hat, target_train)\n",
        "      self.print_train(loss_value, epoch+1, epochs)\n",
        "      i=np.random.randint(input_train.shape[0])\n",
        "      #print(f\"- Targe {i} : {target_train[i]}\")\n",
        "      #print(f\"- Predict {i} : {y_hat[i]}\")\n",
        "      tmp = self.loss.backward()\n",
        "      self.backward(tmp)\n",
        "      self.update_params(lr)\n",
        "\n",
        "  def predict(self, input_test):\n",
        "    tmp = input_test\n",
        "    for layer in self.layers:\n",
        "      tmp = layer.forward(tmp)\n",
        "\n",
        "    return tmp\n",
        "\n",
        "  def save(self, path):\n",
        "    model_save = deepcopy(self)\n",
        "    with open(path, \"wb\") as f:\n",
        "      pickle.dump(model_save, f)\n",
        "\n",
        "  def __repr__(self):\n",
        "    r=\"-\"*40\n",
        "    r += \"\\n\"\n",
        "    for layer in self.layers:\n",
        "      r += f\"{layer} \\n\"\n",
        "    r += \"-\"*40\n",
        "    return r"
      ],
      "metadata": {
        "id": "gplS_3QV6gzX"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Application avec Boston Dataset"
      ],
      "metadata": {
        "id": "x53c-rORg5fU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "GS_mBSNjhRws"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_url = \"https://lib.stat.cmu.edu/datasets/boston\"\n",
        "raw_data = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
        "data = np.hstack([raw_data.values[::2, :], raw_data.values[1::2, :2]])\n",
        "target = raw_data.values[1::2, 2]\n",
        "target=target.reshape(-1,1)\n",
        "raw_data=pd.DataFrame(data)\n",
        "raw_data=raw_data.set_axis(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT'], axis=1)\n",
        "raw_data['MEDV']=target"
      ],
      "metadata": {
        "id": "yY8Wxv-8guPk"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tXq7cNI_haVy",
        "outputId": "4eb7bb53-3c83-42ba-de23-34ae57daf6cc"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
            ],
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              "      <th>CRIM</th>\n",
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              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
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              "      <td>5.33</td>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "raw_data",
              "summary": "{\n  \"name\": \"raw_data\",\n  \"rows\": 506,\n  \"fields\": [\n    {\n      \"column\": \"CRIM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.60154510533249,\n        \"min\": 0.00632,\n        \"max\": 88.9762,\n        \"num_unique_values\": 504,\n        \"samples\": [\n          0.09178,\n          0.05644,\n          0.10574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.32245299451514,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          25.0,\n          30.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.860352940897585,\n        \"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          8.14,\n          1.47,\n          1.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHAS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25399404134041037,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11587767566755595,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.401,\n          0.538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7026171434153233,\n        \"min\": 3.561,\n        \"max\": 8.78,\n        \"num_unique_values\": 446,\n        \"samples\": [\n          6.849,\n          4.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.148861406903617,\n        \"min\": 2.9,\n        \"max\": 100.0,\n        \"num_unique_values\": 356,\n        \"samples\": [\n          51.8,\n          33.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DIS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.105710126627611,\n        \"min\": 1.1296,\n        \"max\": 12.1265,\n        \"num_unique_values\": 412,\n        \"samples\": [\n          2.2955,\n          4.2515\n 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22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          19.6,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.29486438415783,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          396.24,\n          395.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LSTAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.141061511348571,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          6.15,\n          4.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MEDV\",\n      \"properties\": {\n        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            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=np.array(raw_data.drop('MEDV', axis=1))\n",
        "y=np.array(raw_data['MEDV'])"
      ],
      "metadata": {
        "id": "YU_yYW91kOYW"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=y.reshape(-1,1)"
      ],
      "metadata": {
        "id": "K8hZ3UOQkgDZ"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRPYqeMDkcqo",
        "outputId": "02497e54-8ab0-4fb9-a410-f4d196ea7fb4"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((506, 13), (506, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "5x5A2nZkhln8"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "nR6_lu1ChqYA"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scalar=StandardScaler()\n",
        "X_train = scalar.fit_transform(X_train)\n",
        "X_test = scalar.transform(X_test)"
      ],
      "metadata": {
        "id": "HWitfmqBhvZ0"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_8rd3rNklXI",
        "outputId": "e5ad8901-9b3c-4c39-f9bf-54cfbc05aa06"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((354, 13), (152, 13), (354, 1), (152, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_boston=NeuralNetwork()\n",
        "model_boston.add(Dense(20, Sigmoid()))\n",
        "model_boston.add(Dense(25, Sigmoid()))\n",
        "model_boston.add(Dense(1))\n",
        "model_boston.compile(MSE())"
      ],
      "metadata": {
        "id": "8Uzibcg9hxEh"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_boston"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YoPKqQBQG08",
        "outputId": "2c3b2b04-e62d-4883-f28f-b5a9fd587710"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "----------------------------------------\n",
              "Dense(nerons=20, activation=Sigmoid) \n",
              "Dense(nerons=25, activation=Sigmoid) \n",
              "Dense(nerons=1, activation=Linear) \n",
              "----------------------------------------"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_boston.fit(X_train, y_train, 100, 0.0001, random_state=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "CGZa3Dkih5gu",
        "outputId": "45409cd7-2ec4-4676-da3c-c0c9bff0c19d"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            " [==============================] - loss: 364.04\n",
            "Epoch 2/100\n",
            " [==============================] - loss: 75.79\n",
            "Epoch 3/100\n",
            " [==============================] - loss: 63.20\n",
            "Epoch 4/100\n",
            " [==============================] - loss: 56.22\n",
            "Epoch 5/100\n",
            " [==============================] - loss: 51.63\n",
            "Epoch 6/100\n",
            " [==============================] - loss: 48.22\n",
            "Epoch 7/100\n",
            " [==============================] - loss: 45.45\n",
            "Epoch 8/100\n",
            " [==============================] - loss: 43.09\n",
            "Epoch 9/100\n",
            " [==============================] - loss: 41.05\n",
            "Epoch 10/100\n",
            " [==============================] - loss: 39.27\n",
            "Epoch 11/100\n",
            " [==============================] - loss: 37.70\n",
            "Epoch 12/100\n",
            " [==============================] - loss: 36.29\n",
            "Epoch 13/100\n",
            " [==============================] - loss: 35.02\n",
            "Epoch 14/100\n",
            " [==============================] - loss: 33.86\n",
            "Epoch 15/100\n",
            " [==============================] - loss: 32.81\n",
            "Epoch 16/100\n",
            " [==============================] - loss: 31.84\n",
            "Epoch 17/100\n",
            " [==============================] - loss: 30.95\n",
            "Epoch 18/100\n",
            " [==============================] - loss: 30.12\n",
            "Epoch 19/100\n",
            " [==============================] - loss: 29.36\n",
            "Epoch 20/100\n",
            " [==============================] - loss: 28.65\n",
            "Epoch 21/100\n",
            " [==============================] - loss: 27.99\n",
            "Epoch 22/100\n",
            " [==============================] - loss: 27.38\n",
            "Epoch 23/100\n",
            " [==============================] - loss: 26.80\n",
            "Epoch 24/100\n",
            " [==============================] - loss: 26.26\n",
            "Epoch 25/100\n",
            " [==============================] - loss: 25.75\n",
            "Epoch 26/100\n",
            " [==============================] - loss: 25.26\n",
            "Epoch 27/100\n",
            " [==============================] - loss: 24.80\n",
            "Epoch 28/100\n",
            " [==============================] - loss: 24.36\n",
            "Epoch 29/100\n",
            " [==============================] - loss: 23.94\n",
            "Epoch 30/100\n",
            " [==============================] - loss: 23.54\n",
            "Epoch 31/100\n",
            " [==============================] - loss: 23.15\n",
            "Epoch 32/100\n",
            " [==============================] - loss: 22.78\n",
            "Epoch 33/100\n",
            " [==============================] - loss: 22.43\n",
            "Epoch 34/100\n",
            " [==============================] - loss: 22.08\n",
            "Epoch 35/100\n",
            " [==============================] - loss: 21.75\n",
            "Epoch 36/100\n",
            " [==============================] - loss: 21.43\n",
            "Epoch 37/100\n",
            " [==============================] - loss: 21.12\n",
            "Epoch 38/100\n",
            " [==============================] - loss: 20.82\n",
            "Epoch 39/100\n",
            " [==============================] - loss: 20.53\n",
            "Epoch 40/100\n",
            " [==============================] - loss: 20.25\n",
            "Epoch 41/100\n",
            " [==============================] - loss: 19.98\n",
            "Epoch 42/100\n",
            " [==============================] - loss: 19.71\n",
            "Epoch 43/100\n",
            " [==============================] - loss: 19.46\n",
            "Epoch 44/100\n",
            " [==============================] - loss: 19.21\n",
            "Epoch 45/100\n",
            " [==============================] - loss: 18.97\n",
            "Epoch 46/100\n",
            " [==============================] - loss: 18.74\n",
            "Epoch 47/100\n",
            " [==============================] - loss: 18.52\n",
            "Epoch 48/100\n",
            " [==============================] - loss: 18.30\n",
            "Epoch 49/100\n",
            " [==============================] - loss: 18.09\n",
            "Epoch 50/100\n",
            " [==============================] - loss: 17.88\n",
            "Epoch 51/100\n",
            " [==============================] - loss: 17.68\n",
            "Epoch 52/100\n",
            " [==============================] - loss: 17.48\n",
            "Epoch 53/100\n",
            " [==============================] - loss: 17.29\n",
            "Epoch 54/100\n",
            " [==============================] - loss: 17.11\n",
            "Epoch 55/100\n",
            " [==============================] - loss: 16.93\n",
            "Epoch 56/100\n",
            " [==============================] - loss: 16.75\n",
            "Epoch 57/100\n",
            " [==============================] - loss: 16.57\n",
            "Epoch 58/100\n",
            " [==============================] - loss: 16.40\n",
            "Epoch 59/100\n",
            " [==============================] - loss: 16.24\n",
            "Epoch 60/100\n",
            " [==============================] - loss: 16.07\n",
            "Epoch 61/100\n",
            " [==============================] - loss: 15.91\n",
            "Epoch 62/100\n",
            " [==============================] - loss: 15.75\n",
            "Epoch 63/100\n",
            " [==============================] - loss: 15.60\n",
            "Epoch 64/100\n",
            " [==============================] - loss: 15.44\n",
            "Epoch 65/100\n",
            " [==============================] - loss: 15.29\n",
            "Epoch 66/100\n",
            " [==============================] - loss: 15.15\n",
            "Epoch 67/100\n",
            " [==============================] - loss: 15.00\n",
            "Epoch 68/100\n",
            " [==============================] - loss: 14.86\n",
            "Epoch 69/100\n",
            " [==============================] - loss: 14.72\n",
            "Epoch 70/100\n",
            " [==============================] - loss: 14.58\n",
            "Epoch 71/100\n",
            " [==============================] - loss: 14.44\n",
            "Epoch 72/100\n",
            " [==============================] - loss: 14.31\n",
            "Epoch 73/100\n",
            " [==============================] - loss: 14.18\n",
            "Epoch 74/100\n",
            " [==============================] - loss: 14.05\n",
            "Epoch 75/100\n",
            " [==============================] - loss: 13.92\n",
            "Epoch 76/100\n",
            " [==============================] - loss: 13.80\n",
            "Epoch 77/100\n",
            " [==============================] - loss: 13.67\n",
            "Epoch 78/100\n",
            " [==============================] - loss: 13.55\n",
            "Epoch 79/100\n",
            " [==============================] - loss: 13.44\n",
            "Epoch 80/100\n",
            " [==============================] - loss: 13.32\n",
            "Epoch 81/100\n",
            " [==============================] - loss: 13.21\n",
            "Epoch 82/100\n",
            " [==============================] - loss: 13.10\n",
            "Epoch 83/100\n",
            " [==============================] - loss: 12.99\n",
            "Epoch 84/100\n",
            " [==============================] - loss: 12.88\n",
            "Epoch 85/100\n",
            " [==============================] - loss: 12.78\n",
            "Epoch 86/100\n",
            " [==============================] - loss: 12.67\n",
            "Epoch 87/100\n",
            " [==============================] - loss: 12.57\n",
            "Epoch 88/100\n",
            " [==============================] - loss: 12.48\n",
            "Epoch 89/100\n",
            " [==============================] - loss: 12.38\n",
            "Epoch 90/100\n",
            " [==============================] - loss: 12.29\n",
            "Epoch 91/100\n",
            " [==============================] - loss: 12.20\n",
            "Epoch 92/100\n",
            " [==============================] - loss: 12.11\n",
            "Epoch 93/100\n",
            " [==============================] - loss: 12.02\n",
            "Epoch 94/100\n",
            " [==============================] - loss: 11.93\n",
            "Epoch 95/100\n",
            " [==============================] - loss: 11.85\n",
            "Epoch 96/100\n",
            " [==============================] - loss: 11.76\n",
            "Epoch 97/100\n",
            " [==============================] - loss: 11.68\n",
            "Epoch 98/100\n",
            " [==============================] - loss: 11.60\n",
            "Epoch 99/100\n",
            " [==============================] - loss: 11.52\n",
            "Epoch 100/100\n",
            " [==============================] - loss: 11.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_boston.history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8SEWrbRjidEz",
        "outputId": "7ebb3d62-bbe1-4ce3-b681-21c26ad9a6a9"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7cef390c56f0>]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=model_boston.predict(X_test)"
      ],
      "metadata": {
        "id": "bepHSSN0jwl1"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error=RMSE()\n",
        "error.forward(y_predict, y_test), np.sqrt(model_boston.history[-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y08hG5Gbj-CY",
        "outputId": "90c08db1-9e3b-4bb7-bae0-49460be0358c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4.715871333132779, 3.3832890057423275)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error=MAE()\n",
        "error.forward(y_predict, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eugupe8ZqiQm",
        "outputId": "c4edc750-a116-4737-822d-cbc5d499dd85"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.8753352941687673"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i=np.random.randint(y_test.shape[0])\n",
        "i, y_test[i,0], y_predict[i,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWITBdFOqo8q",
        "outputId": "bb0c5d85-2d70-4aab-aa69-621c2984e643"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67, 15.0, 18.445956407174776)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist((y_predict-y_test))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "IwP41ktFzo1S",
        "outputId": "9a49d5c8-1010-40d8-8039-ebdba1105694"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_boston"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9kUM5Ex02tj",
        "outputId": "8d25089a-db20-4143-db6e-dd710cfb1409"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "----------------------------------------\n",
              "Dense(nerons=20, activation=Sigmoid) \n",
              "Dense(nerons=25, activation=Sigmoid) \n",
              "Dense(nerons=1, activation=Linear) \n",
              "----------------------------------------"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_boston.save(\"model_boston.pkl\")"
      ],
      "metadata": {
        "id": "SrIIvz7wP6zv"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(path):\n",
        "  with open(path, \"rb\") as f:\n",
        "    model = pickle.load(f)\n",
        "  return model"
      ],
      "metadata": {
        "id": "47JjYNszRRGK"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_loaded = load_model(\"model_boston.pkl\")"
      ],
      "metadata": {
        "id": "oMdlz3bgRbsO"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_loaded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlyu0cDWRhwp",
        "outputId": "3f2cab98-3c26-4404-b95c-fbf5f2ed1cb7"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "----------------------------------------\n",
              "Dense(nerons=20, activation=Sigmoid) \n",
              "Dense(nerons=25, activation=Sigmoid) \n",
              "Dense(nerons=1, activation=Linear) \n",
              "----------------------------------------"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creer un modèle en utilisant Tensorflow"
      ],
      "metadata": {
        "id": "_MRgHSPGSwYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import SGD"
      ],
      "metadata": {
        "id": "VvdLFR_vSrQv"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(20, activation='sigmoid', input_shape=(X_train.shape[1],)))\n",
        "model.add(Dense(25, activation='sigmoid'))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eouRueYKTTl7",
        "outputId": "cfd38557-e9c5-461c-c32a-e0ab35d1a48c"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "PG6KaCNlTb5T",
        "outputId": "ead96579-83a9-445f-af33-0e7b38a25146"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                  │             \u001b[38;5;34m280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m)                  │             \u001b[38;5;34m525\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m26\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">525</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m831\u001b[0m (3.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">831</span> (3.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m831\u001b[0m (3.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">831</span> (3.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=SGD(learning_rate=0.01), loss='mse')"
      ],
      "metadata": {
        "id": "_hT0k897TeyR"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "h = model.fit(X_train, y_train, epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2i0Z6C2XUe0O",
        "outputId": "3c27f749-f285-4a6c-8d10-8d085c929943"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 329.3225  \n",
            "Epoch 2/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 70.6455 \n",
            "Epoch 3/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 49.6914 \n",
            "Epoch 4/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 45.4052 \n",
            "Epoch 5/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 43.3398 \n",
            "Epoch 6/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 32.1367 \n",
            "Epoch 7/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 25.5523  \n",
            "Epoch 8/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 24.0534 \n",
            "Epoch 9/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 22.6242 \n",
            "Epoch 10/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 31.0864 \n",
            "Epoch 11/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 28.9140 \n",
            "Epoch 12/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 21.6790 \n",
            "Epoch 13/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 24.3181 \n",
            "Epoch 14/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 17.6541 \n",
            "Epoch 15/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 15.4304 \n",
            "Epoch 16/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 21.2253 \n",
            "Epoch 17/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 16.0719 \n",
            "Epoch 18/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 18.0119 \n",
            "Epoch 19/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 14.3988 \n",
            "Epoch 20/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 16.0901 \n",
            "Epoch 21/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 20.6199 \n",
            "Epoch 22/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 14.5848 \n",
            "Epoch 23/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 25.4978 \n",
            "Epoch 24/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 15.8886 \n",
            "Epoch 25/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 16.9697 \n",
            "Epoch 26/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 29.9415 \n",
            "Epoch 27/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 14.7533\n",
            "Epoch 28/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 15.8462  \n",
            "Epoch 29/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 13.1327 \n",
            "Epoch 30/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.4297  \n",
            "Epoch 31/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.2550 \n",
            "Epoch 32/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.8273 \n",
            "Epoch 33/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 12.8600 \n",
            "Epoch 34/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.4286 \n",
            "Epoch 35/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 17.0462  \n",
            "Epoch 36/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 11.0806 \n",
            "Epoch 37/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 15.6712 \n",
            "Epoch 38/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 12.7831 \n",
            "Epoch 39/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 8.8575 \n",
            "Epoch 40/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 13.6230 \n",
            "Epoch 41/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.4730  \n",
            "Epoch 42/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 13.9481\n",
            "Epoch 43/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.1198  \n",
            "Epoch 44/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 10.9818\n",
            "Epoch 45/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 14.4059  \n",
            "Epoch 46/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.2090 \n",
            "Epoch 47/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 11.2740 \n",
            "Epoch 48/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 17.0571 \n",
            "Epoch 49/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.5664   \n",
            "Epoch 50/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.9754 \n",
            "Epoch 51/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 10.9363  \n",
            "Epoch 52/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.9205  \n",
            "Epoch 53/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.9718   \n",
            "Epoch 54/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.0548 \n",
            "Epoch 55/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.6620   \n",
            "Epoch 56/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.1461 \n",
            "Epoch 57/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.2106   \n",
            "Epoch 58/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.6965 \n",
            "Epoch 59/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.4338 \n",
            "Epoch 60/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.9366 \n",
            "Epoch 61/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 10.2736  \n",
            "Epoch 62/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.1017  \n",
            "Epoch 63/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.6730  \n",
            "Epoch 64/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.9173 \n",
            "Epoch 65/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5117  \n",
            "Epoch 66/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 7.7946   \n",
            "Epoch 67/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.8833  \n",
            "Epoch 68/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.9280 \n",
            "Epoch 69/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.9005   \n",
            "Epoch 70/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.5947 \n",
            "Epoch 71/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.4056  \n",
            "Epoch 72/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.6658  \n",
            "Epoch 73/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.1712  \n",
            "Epoch 74/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.9157  \n",
            "Epoch 75/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 9.3682  \n",
            "Epoch 76/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.5723  \n",
            "Epoch 77/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.9705  \n",
            "Epoch 78/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.1042 \n",
            "Epoch 79/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.4397  \n",
            "Epoch 80/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5735 \n",
            "Epoch 81/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.5554 \n",
            "Epoch 82/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.2100 \n",
            "Epoch 83/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.1863 \n",
            "Epoch 84/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.9432 \n",
            "Epoch 85/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.3372 \n",
            "Epoch 86/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 6.9042  \n",
            "Epoch 87/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.4089   \n",
            "Epoch 88/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.4235  \n",
            "Epoch 89/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.4767  \n",
            "Epoch 90/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 13.7803  \n",
            "Epoch 91/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.2747  \n",
            "Epoch 92/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.8780  \n",
            "Epoch 93/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.2279   \n",
            "Epoch 94/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.0893  \n",
            "Epoch 95/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.2994  \n",
            "Epoch 96/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.8025 \n",
            "Epoch 97/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 10.0258  \n",
            "Epoch 98/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 5.7946 \n",
            "Epoch 99/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3289  \n",
            "Epoch 100/100\n",
            "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.8128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(h.history['loss'], label='Modele Tensorflow')\n",
        "plt.plot(model_boston.history, label = 'Mon Tensorflow')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "tr5RyVPvVqgz",
        "outputId": "a9590180-e8f9-45bc-8ff7-fed027b2d37e"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump, load"
      ],
      "metadata": {
        "id": "hD3PBHyrV0qF"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dump(scalar, 'scaler.joblib')\n",
        "dump(model, 'model_boston.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBJnOTeuX8Gb",
        "outputId": "e1e8496b-3630-4c1d-b0e7-0a977c68e481"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_boston.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_tf = load('model_boston.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBgIksooYEQ9",
        "outputId": "d9bd9abe-6883-479b-90d3-37d5d78b7efb"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:576: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 8 variables whereas the saved optimizer has 2 variables. \n",
            "  saveable.load_own_variables(weights_store.get(inner_path))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_tf.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "jHAkNc9RYIaa",
        "outputId": "38d69ef5-b6b2-45bd-fec8-730ef61156a7"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                  │             \u001b[38;5;34m280\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m)                  │             \u001b[38;5;34m525\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m26\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">280</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">525</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,664\u001b[0m (6.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> (6.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m831\u001b[0m (3.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">831</span> (3.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m833\u001b[0m (3.26 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">833</span> (3.26 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}