4.Backpropagation-scratch-Regression.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "source": [
        "df = pd.DataFrame([[8,7,4],[8,4,6],[9, 7,4],[6,10,6]], columns =['cgpa','score','lpa'])\n",
        "df"
      ],
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              "   cgpa  score  lpa\n",
              "0     8      7    4\n",
              "1     8      4    6\n",
              "2     9      7    4\n",
              "3     6     10    6"
            ],
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 6,\n        \"max\": 9,\n        \"samples\": [\n          8,\n          9,\n          6\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 10,\n        \"samples\": [\n          7,\n          4,\n          10\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 6,\n        \"samples\": [\n          6,\n          4\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
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      "cell_type": "code",
      "source": [
        "#create initilize parameters\n",
        "def initialize_parameters(layers_dim):\n",
        "\n",
        "  np.random.seed(3)\n",
        "  parameters ={}\n",
        "  L = len(layers_dim)\n",
        "\n",
        "  for l in  range(1,L):\n",
        "     parameters['W' + str(l)] = np.ones((layers_dim[l-1], layers_dim[l]))*0.1\n",
        "     parameters['b' + str(l)] = np.zeros((layers_dim[l], 1))\n",
        "\n",
        "  return parameters"
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      "metadata": {
        "id": "pINeuDfoiHWF"
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      "execution_count": 21,
      "outputs": []
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      "source": [
        "initialize_parameters([2,2,1])"
      ],
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        },
        "id": "WyykkENKknYz",
        "outputId": "1eb0b7ec-df5e-4343-ebb9-6ee7019aef59"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.1, 0.1],\n",
              "        [0.1, 0.1]]),\n",
              " 'b1': array([[0.],\n",
              "        [0.]]),\n",
              " 'W2': array([[0.1],\n",
              "        [0.1]]),\n",
              " 'b2': array([[0.]])}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def linear_forward(A_prev, w,b):\n",
        "  z = np.dot(w.T, A_prev)+b\n",
        "  return z"
      ],
      "metadata": {
        "id": "7xzdhQT4lHM1"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#main forward propagation\n",
        "def L_layer_forward(X, parameters):\n",
        "\n",
        "  A = X\n",
        "  L = len(parameters) // 2                  # number of layers in the neural network\n",
        "\n",
        "  for l in range(1, L+1):\n",
        "    A_prev = A\n",
        "    Wl = parameters['W' + str(l)]\n",
        "    bl = parameters['b' + str(l)]\n",
        "    #print(\"A\"+str(l-1)+\": \", A_prev)\n",
        "    #print(\"W\"+str(l)+\": \", Wl)\n",
        "    #print(\"b\"+str(l)+\": \", bl)\n",
        "    #print(\"--\"*20)\n",
        "\n",
        "    A = linear_forward(A_prev, Wl, bl)\n",
        "    #print(\"A\"+str(l)+\": \", A)\n",
        "    #print(\"**\"*20)\n",
        "\n",
        "  return A,A_prev"
      ],
      "metadata": {
        "id": "HkfPXm10mWL6"
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      "execution_count": 24,
      "outputs": []
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      "source": [
        "X = df[['cgpa', 'score']].values[0].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['lpa']].values[0][0]\n",
        "\n",
        "# Parameter initialization\n",
        "parameters = initialize_parameters([2,2,1])\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X, parameters)"
      ],
      "metadata": {
        "id": "XFntfSDKmg-O"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat = y_hat[0][0]"
      ],
      "metadata": {
        "id": "6Zxo9LqWp_Cl"
      },
      "execution_count": 26,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "A1"
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        "id": "6cpo2adZqWnM",
        "outputId": "ae5670fc-ceb2-47b5-ae6b-cd12207794fa"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.5],\n",
              "       [1.5]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def update_parameters(parameters,y,y_hat,A1,X):\n",
        "  parameters['W2'][0][0] = parameters['W2'][0][0] + (0.001 * 2 * (y - y_hat)*A1[0][0])\n",
        "  parameters['W2'][1][0] = parameters['W2'][1][0] + (0.001 * 2 * (y - y_hat)*A1[1][0])\n",
        "  parameters['b2'][0][0] = parameters['W2'][1][0] + (0.001 * 2 * (y - y_hat))\n",
        "\n",
        "  parameters['W1'][0][0] = parameters['W1'][0][0] + (0.001 * 2 * (y - y_hat)*parameters['W2'][0][0]*X[0][0])\n",
        "  parameters['W1'][0][1] = parameters['W1'][0][1] + (0.001 * 2 * (y - y_hat)*parameters['W2'][0][0]*X[1][0])\n",
        "  parameters['b1'][0][0] = parameters['b1'][0][0] + (0.001 * 2 * (y - y_hat)*parameters['W2'][0][0])\n",
        "\n",
        "  parameters['W1'][1][0] = parameters['W1'][1][0] + (0.001 * 2 * (y - y_hat)*parameters['W2'][1][0]*X[0][0])\n",
        "  parameters['W1'][1][1] = parameters['W1'][1][1] + (0.001 * 2 * (y - y_hat)*parameters['W2'][1][0]*X[1][0])\n",
        "  parameters['b1'][1][0] = parameters['b1'][1][0] + (0.001 * 2 * (y - y_hat)*parameters['W2'][1][0])"
      ],
      "metadata": {
        "id": "lY0SV3YzqYxV"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "update_parameters(parameters,y,y_hat,A1,X)"
      ],
      "metadata": {
        "id": "TqLr8zFuqetm"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utzWimqEqy-U",
        "outputId": "d8e27e9e-f7cf-41b5-acd2-bfc8980ae444"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.10657712, 0.10575498],\n",
              "        [0.10657712, 0.10575498]]),\n",
              " 'b1': array([[0.00082214],\n",
              "        [0.00082214]]),\n",
              " 'W2': array([[0.1111],\n",
              "        [0.1111]]),\n",
              " 'b2': array([[0.1185]])}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'score']].values[3].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['lpa']].values[3][0]\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X, parameters)"
      ],
      "metadata": {
        "id": "tPUQf9Qqq3bi"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'score']].values[0].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['lpa']].values[0][0]\n",
        "\n",
        "# Parameter initialization\n",
        "parameters = initialize_parameters([2,2,1])\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X,parameters)\n",
        "y_hat = y_hat[0][0]\n",
        "\n",
        "update_parameters(parameters,y,y_hat,A1,X)\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaZxHt9jq-3L",
        "outputId": "31dace2f-7852-40b2-802c-4ef6f7a8c22b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.10657712, 0.10575498],\n",
              "        [0.10657712, 0.10575498]]),\n",
              " 'b1': array([[0.00082214],\n",
              "        [0.00082214]]),\n",
              " 'W2': array([[0.1111],\n",
              "        [0.1111]]),\n",
              " 'b2': array([[0.1185]])}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'score']].values[1].reshape(2,1) # Shape(no of features, no. of training exaplme)\n",
        "y = df[['lpa']].values[1][0]\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X,parameters)\n",
        "y_hat = y_hat[0][0]\n",
        "\n",
        "update_parameters(parameters,y,y_hat,A1,X)\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhtIqK8NrdKa",
        "outputId": "fd0bf926-09c0-4e8f-8b4e-e4684d10a1eb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.11781199, 0.11137241],\n",
              "        [0.11780209, 0.11136747]]),\n",
              " 'b1': array([[0.0022265 ],\n",
              "        [0.00222526]]),\n",
              " 'W2': array([[0.12542866],\n",
              "        [0.1253182 ]]),\n",
              " 'b2': array([[0.13651467]])}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'score']].values[2].reshape(2,1) # Shape(no of features, no. of training exaplme)\n",
        "y = df[['lpa']].values[2][0]\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X,parameters)\n",
        "y_hat = y_hat[0][0]\n",
        "\n",
        "update_parameters(parameters,y,y_hat,A1,X)\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRtssf4Zrma7",
        "outputId": "b5cbeea3-1479-43f0-b0d7-91947ac29123"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.12628227, 0.11796041],\n",
              "        [0.12622267, 0.1179168 ]]),\n",
              " 'b1': array([[0.00316764],\n",
              "        [0.00316088]]),\n",
              " 'W2': array([[0.13827334],\n",
              "        [0.13746182]]),\n",
              " 'b2': array([[0.14426821]])}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'score']].values[3].reshape(2,1) # Shape(no of features, no. of training exaplme)\n",
        "y = df[['lpa']].values[3][0]\n",
        "\n",
        "y_hat,A1 = L_layer_forward(X,parameters)\n",
        "y_hat = y_hat[0][0]\n",
        "\n",
        "update_parameters(parameters,y,y_hat,A1,X)\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5dPbgQ9rpk6",
        "outputId": "37484b36-5104-40b3-a320-bde88efb66b7"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.13647552, 0.13494916],\n",
              "        [0.13627394, 0.13466892]]),\n",
              " 'b1': array([[0.00486652],\n",
              "        [0.00483609]]),\n",
              " 'W2': array([[0.15978352],\n",
              "        [0.15755794]]),\n",
              " 'b2': array([[0.16819029]])}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# epochs implementation\n",
        "\n",
        "parameters = initialize_parameters([2,2,1])\n",
        "epochs = 5\n",
        "\n",
        "for i in range(epochs):\n",
        "\n",
        "  Loss = []\n",
        "\n",
        "  for j in range(df.shape[0]):\n",
        "\n",
        "    X = df[['cgpa', 'score']].values[j].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "    y = df[['lpa']].values[j][0]\n",
        "\n",
        "    # Parameter initialization\n",
        "\n",
        "\n",
        "    y_hat,A1 = L_layer_forward(X,parameters)\n",
        "    y_hat = y_hat[0][0]\n",
        "\n",
        "    update_parameters(parameters,y,y_hat,A1,X)\n",
        "\n",
        "    Loss.append((y-y_hat)**2)\n",
        "\n",
        "  print('Epoch - ',i+1,'Loss - ',np.array(Loss).mean())\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAKijRmiruLX",
        "outputId": "4d6c82b5-3677-4de8-fb91-085fbd3019b6"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch -  1 Loss -  21.218433661372927\n",
            "Epoch -  2 Loss -  16.95255036028114\n",
            "Epoch -  3 Loss -  11.54038350550595\n",
            "Epoch -  4 Loss -  6.431888438814288\n",
            "Epoch -  5 Loss -  3.397934893944273\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.31761759, 0.3011519 ],\n",
              "        [0.31241576, 0.29610706]]),\n",
              " 'b1': array([[0.02921005],\n",
              "        [0.02849786]]),\n",
              " 'W2': array([[0.41784733],\n",
              "        [0.40207219]]),\n",
              " 'b2': array([[0.40588761]])}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p-CtGbsirzow"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will also update weights and bias from backpropagation by using keras."
      ],
      "metadata": {
        "id": "QiAoDwTGsZPe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "hMxRKAt3svUo",
        "outputId": "228e7a35-c956-4650-e4ad-abd78a4967cb"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  score  lpa\n",
              "0     8      7    4\n",
              "1     8      4    6\n",
              "2     9      7    4\n",
              "3     6     10    6"
            ],
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-02686397-18ef-447b-8615-bdc48d11a8bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cc56b48c-1701-4b60-9e22-7d3a06e22803\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cc56b48c-1701-4b60-9e22-7d3a06e22803 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 6,\n        \"max\": 9,\n        \"samples\": [\n          8,\n          9,\n          6\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 4,\n        \"max\": 10,\n        \"samples\": [\n          7,\n          4,\n          10\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 6,\n        \"samples\": [\n          6,\n          4\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense\n"
      ],
      "metadata": {
        "id": "Se5Ko76us5JZ"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(2, activation= 'linear',input_dim =2))\n",
        "model.add(Dense(1,activation = 'linear'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIvifY9ftOYd",
        "outputId": "41d53f95-47cc-44ff-cda8-339c7b2c1055"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 2)                 6         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9\n",
            "Trainable params: 9\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0k-gS6euCvp",
        "outputId": "d2b52094-fcb8-4199-a443-a7d0e37b14ef"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 1.0049268, -0.3223561],\n",
              "        [-0.5392327,  0.7466271]], dtype=float32),\n",
              " array([0., 0.], dtype=float32),\n",
              " array([[-1.2031333],\n",
              "        [-0.6488251]], dtype=float32),\n",
              " array([0.], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "source": [
        "new_weights = [np.array([[0.1,0.1],[0.1,0.1]],dtype = np.float32),\n",
        "               np.array([.0,.0] ,dtype = np.float32),\n",
        "               np.array([[0.1],[0.1]],dtype = np.float32),\n",
        "               np.array([.0],dtype = np.float32)]"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "pqd5vy10yDUn"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.set_weights(new_weights)"
      ],
      "metadata": {
        "id": "cUmusLEBxp89"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0W2f22Wyd0V",
        "outputId": "f356e6ef-1e05-4b28-b101-aff20f3297bd"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.1, 0.1],\n",
              "        [0.1, 0.1]], dtype=float32),\n",
              " array([0., 0.], dtype=float32),\n",
              " array([[0.1],\n",
              "        [0.1]], dtype=float32),\n",
              " array([0.], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(learning_rate = 0.001)\n",
        "model.compile(loss = 'mean_squared_error', optimizer = optimizer)"
      ],
      "metadata": {
        "id": "2ynqQgykzcvA"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(df.iloc[:,0:-1].values, df['lpa'].values, epochs =75,verbose = 1, batch_size = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTrCXyUM0MOn",
        "outputId": "5ff96fde-55f0-46ce-90a1-f0394d25f39c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "4/4 [==============================] - 1s 5ms/step - loss: 23.0588\n",
            "Epoch 2/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 22.8022\n",
            "Epoch 3/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 22.5180\n",
            "Epoch 4/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 22.2554\n",
            "Epoch 5/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 21.9532\n",
            "Epoch 6/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 21.6547\n",
            "Epoch 7/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 21.3575\n",
            "Epoch 8/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 21.0422\n",
            "Epoch 9/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 20.7274\n",
            "Epoch 10/75\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 20.3873\n",
            "Epoch 11/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 20.0449\n",
            "Epoch 12/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 19.7120\n",
            "Epoch 13/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 19.3572\n",
            "Epoch 14/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 18.9777\n",
            "Epoch 15/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 18.5822\n",
            "Epoch 16/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 18.2133\n",
            "Epoch 17/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 17.8419\n",
            "Epoch 18/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 17.4605\n",
            "Epoch 19/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 17.0484\n",
            "Epoch 20/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 16.6351\n",
            "Epoch 21/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 16.2132\n",
            "Epoch 22/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 15.8361\n",
            "Epoch 23/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 15.4022\n",
            "Epoch 24/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 14.9824\n",
            "Epoch 25/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 14.5587\n",
            "Epoch 26/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 14.1448\n",
            "Epoch 27/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 13.7065\n",
            "Epoch 28/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 13.2695\n",
            "Epoch 29/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 12.8559\n",
            "Epoch 30/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 12.4502\n",
            "Epoch 31/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 12.0252\n",
            "Epoch 32/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 11.5960\n",
            "Epoch 33/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 11.1901\n",
            "Epoch 34/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 10.7643\n",
            "Epoch 35/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 10.3347\n",
            "Epoch 36/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 9.9978\n",
            "Epoch 37/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 9.5646\n",
            "Epoch 38/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 9.2111\n",
            "Epoch 39/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.7785\n",
            "Epoch 40/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 8.4285\n",
            "Epoch 41/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 8.0905\n",
            "Epoch 42/75\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 7.6875\n",
            "Epoch 43/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.4145\n",
            "Epoch 44/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 7.0682\n",
            "Epoch 45/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.7126\n",
            "Epoch 46/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.3883\n",
            "Epoch 47/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.1412\n",
            "Epoch 48/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.8281\n",
            "Epoch 49/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.5360\n",
            "Epoch 50/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.3043\n",
            "Epoch 51/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.0021\n",
            "Epoch 52/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 4.7820\n",
            "Epoch 53/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 4.5541\n",
            "Epoch 54/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 4.3250\n",
            "Epoch 55/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.1221\n",
            "Epoch 56/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.9317\n",
            "Epoch 57/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.7588\n",
            "Epoch 58/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.5741\n",
            "Epoch 59/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.4145\n",
            "Epoch 60/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.3075\n",
            "Epoch 61/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.1026\n",
            "Epoch 62/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 3.0060\n",
            "Epoch 63/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.8688\n",
            "Epoch 64/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.7493\n",
            "Epoch 65/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.6761\n",
            "Epoch 66/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.5527\n",
            "Epoch 67/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.4752\n",
            "Epoch 68/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.4335\n",
            "Epoch 69/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.3205\n",
            "Epoch 70/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.2884\n",
            "Epoch 71/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.2173\n",
            "Epoch 72/75\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.1507\n",
            "Epoch 73/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.0823\n",
            "Epoch 74/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.0584\n",
            "Epoch 75/75\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.0005\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x78818017e4a0>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42Yx9KgPBcXg",
        "outputId": "a7ba3b1e-9f28-4ae8-911a-02037f3086ec"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.37044755, 0.37044755],\n",
              "        [0.35304195, 0.35304195]], dtype=float32),\n",
              " array([0.27127632, 0.27127632], dtype=float32),\n",
              " array([[0.3711136],\n",
              "        [0.3711136]], dtype=float32),\n",
              " array([0.20475458], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uTcIp4kmDCra"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}