5.backpropagation_classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/singhsourav0/DeepLearningOdyssey/blob/main/backpropagation_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
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      "cell_type": "code",
      "source": [
        "df = pd.DataFrame([[8,8,1],[7,9,1],[6,10,0],[5,5,0]], columns=['cgpa', 'profile_score', 'placed'])"
      ],
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        "id": "qsjhE0IG5qy5"
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      "outputs": []
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      "source": [
        "df.head()"
      ],
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      "execution_count": 4,
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        {
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          "data": {
            "text/plain": [
              "   cgpa  profile_score  placed\n",
              "0     8              8       1\n",
              "1     7              9       1\n",
              "2     6             10       0\n",
              "3     5              5       0"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>profile_score</th>\n",
              "      <th>placed</th>\n",
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              "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\": 5,\n        \"max\": 8,\n        \"samples\": [\n          7,\n          5,\n          8\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profile_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 10,\n        \"samples\": [\n          9,\n          5,\n          8\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "def initialize_parameters(layer_dims):\n",
        "\n",
        "  np.random.seed(3)\n",
        "  parameters = {}\n",
        "  L = len(layer_dims)\n",
        "\n",
        "  for l in range(1, L):\n",
        "\n",
        "    parameters['W' + str(l)] = np.ones((layer_dims[l-1], layer_dims[l]))*0.1\n",
        "    parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\n",
        "\n",
        "\n",
        "  return parameters"
      ],
      "metadata": {
        "id": "BSAs5N7L5z9f"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Utility Functions\n",
        "def sigmoid(Z):\n",
        "\n",
        "  A = 1/(1+np.exp(-Z))\n",
        "\n",
        "  return A"
      ],
      "metadata": {
        "id": "VDwatKWP543L"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def linear_forward(A_prev, W, b):\n",
        "\n",
        "  Z = np.dot(W.T, A_prev) + b\n",
        "\n",
        "  A = sigmoid(Z)\n",
        "\n",
        "  return A"
      ],
      "metadata": {
        "id": "Q7Pqn3Le6W88"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# L-layer feed forward\n",
        "\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\n"
      ],
      "metadata": {
        "id": "VkUO9wdf6gXj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def update_parameters(parameters,y,y_hat,A1,X):\n",
        "  parameters['W2'][0][0] = parameters['W2'][0][0] + (0.0001 * (y - y_hat)*A1[0][0])\n",
        "  parameters['W2'][1][0] = parameters['W2'][1][0] + (0.0001 * (y - y_hat)*A1[1][0])\n",
        "  parameters['b2'][0][0] = parameters['W2'][1][0] + (0.0001 * (y - y_hat))\n",
        "\n",
        "  parameters['W1'][0][0] = parameters['W1'][0][0] + (0.0001 * (y - y_hat)*parameters['W2'][0][0]*A1[0][0]*(1-A1[0][0])*X[0][0])\n",
        "  parameters['W1'][0][1] = parameters['W1'][0][1] + (0.0001 * (y - y_hat)*parameters['W2'][0][0]*A1[0][0]*(1-A1[0][0])*X[1][0])\n",
        "  parameters['b1'][0][0] = parameters['b1'][0][0] + (0.0001 * (y - y_hat)*parameters['W2'][0][0]*A1[0][0]*(1-A1[0][0]))\n",
        "\n",
        "  parameters['W1'][1][0] = parameters['W1'][1][0] + (0.0001 * (y - y_hat)*parameters['W2'][1][0]*A1[1][0]*(1-A1[1][0])*X[0][0])\n",
        "  parameters['W1'][1][1] = parameters['W1'][1][1] + (0.0001 * (y - y_hat)*parameters['W2'][1][0]*A1[1][0]*(1-A1[1][0])*X[1][0])\n",
        "  parameters['b1'][1][0] = parameters['b1'][1][0] + (0.0001 * (y - y_hat)*parameters['W2'][1][0]*A1[1][0]*(1-A1[1][0]))"
      ],
      "metadata": {
        "id": "NKBtskel6jsf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'profile_score']].values[0].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['placed']].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",
        "print('Loss for this student - ',-y*np.log(y_hat) - (1-y)*np.log(1-y_hat))\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRuFdbwf7uDz",
        "outputId": "0d0972f1-a3cd-4956-b50b-218b6a73afdb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss for this student -  0.613402628898913\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.10000513, 0.10000513],\n",
              "        [0.10000513, 0.10000513]]),\n",
              " 'b1': array([[6.41054186e-07],\n",
              "        [6.41054186e-07]]),\n",
              " 'W2': array([[0.10003815],\n",
              "        [0.10003815]]),\n",
              " 'b2': array([[0.100084]])}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'profile_score']].values[1].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['placed']].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",
        "print('Loss for this student - ',-y*np.log(y_hat) - (1-y)*np.log(1-y_hat))\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHTFMij_8zJc",
        "outputId": "56ca978e-664b-4f23-e227-ed5461454370"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss for this student -  0.568725622654268\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.10000937, 0.10001059],\n",
              "        [0.10000937, 0.10001059]]),\n",
              " 'b1': array([[1.24770113e-06],\n",
              "        [1.24770113e-06]]),\n",
              " 'W2': array([[0.10007424],\n",
              "        [0.10007424]]),\n",
              " 'b2': array([[0.10011761]])}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'profile_score']].values[2].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['placed']].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",
        "print('Loss for this student - ',-y*np.log(y_hat) - (1-y)*np.log(1-y_hat))\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZCtTGoI90Jb",
        "outputId": "ac030026-0ba0-4cf9-f482-60fae3ee6f85"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss for this student -  0.8353333695154365\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.10000463, 0.10000267],\n",
              "        [0.10000463, 0.10000267]]),\n",
              " 'b1': array([[4.56125378e-07],\n",
              "        [4.56135584e-07]]),\n",
              " 'W2': array([[0.10002712],\n",
              "        [0.10002712]]),\n",
              " 'b2': array([[0.09997049]])}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['cgpa', 'profile_score']].values[3].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "y = df[['placed']].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",
        "print('Loss for this student - ',-y*np.log(y_hat) - (1-y)*np.log(1-y_hat))\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvIOVb8j94zz",
        "outputId": "5a5467d8-ad97-4864-ae57-cfb11f75d531"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss for this student -  0.8238180133031612\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.09999911, 0.09999716],\n",
              "        [0.09999911, 0.09999716]]),\n",
              " 'b1': array([[-6.47175329e-07],\n",
              "        [-6.47175081e-07]]),\n",
              " 'W2': array([[0.09998609],\n",
              "        [0.09998609]]),\n",
              " 'b2': array([[0.09992997]])}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# epochs implementation\n",
        "\n",
        "parameters = initialize_parameters([2,2,1])\n",
        "epochs = 50\n",
        "\n",
        "for i in range(epochs):\n",
        "\n",
        "  Loss = []\n",
        "\n",
        "  for j in range(df.shape[0]):\n",
        "\n",
        "    X = df[['cgpa', 'profile_score']].values[j].reshape(2,1) # Shape(no of features, no. of training example)\n",
        "    y = df[['placed']].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*np.log(y_hat) - (1-y)*np.log(1-y_hat))\n",
        "\n",
        "  print('Epoch - ',i+1,'Loss - ',np.array(Loss).mean())\n",
        "\n",
        "parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RTy2Hsy9-M7",
        "outputId": "3ef77fec-809b-4695-91cd-e77c4f003f72"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch -  1 Loss -  0.7103199085929446\n",
            "Epoch -  2 Loss -  0.6991702892802629\n",
            "Epoch -  3 Loss -  0.6991679314811485\n",
            "Epoch -  4 Loss -  0.6991655746710999\n",
            "Epoch -  5 Loss -  0.6991632188496667\n",
            "Epoch -  6 Loss -  0.699160864016399\n",
            "Epoch -  7 Loss -  0.6991585101708473\n",
            "Epoch -  8 Loss -  0.6991561573125619\n",
            "Epoch -  9 Loss -  0.6991538054410936\n",
            "Epoch -  10 Loss -  0.6991514545559935\n",
            "Epoch -  11 Loss -  0.6991491046568126\n",
            "Epoch -  12 Loss -  0.6991467557431024\n",
            "Epoch -  13 Loss -  0.6991444078144144\n",
            "Epoch -  14 Loss -  0.6991420608703007\n",
            "Epoch -  15 Loss -  0.6991397149103132\n",
            "Epoch -  16 Loss -  0.6991373699340042\n",
            "Epoch -  17 Loss -  0.6991350259409265\n",
            "Epoch -  18 Loss -  0.6991326829306324\n",
            "Epoch -  19 Loss -  0.6991303409026751\n",
            "Epoch -  20 Loss -  0.699127999856608\n",
            "Epoch -  21 Loss -  0.6991256597919842\n",
            "Epoch -  22 Loss -  0.6991233207083575\n",
            "Epoch -  23 Loss -  0.6991209826052818\n",
            "Epoch -  24 Loss -  0.699118645482311\n",
            "Epoch -  25 Loss -  0.6991163093389996\n",
            "Epoch -  26 Loss -  0.699113974174902\n",
            "Epoch -  27 Loss -  0.6991116399895729\n",
            "Epoch -  28 Loss -  0.6991093067825676\n",
            "Epoch -  29 Loss -  0.699106974553441\n",
            "Epoch -  30 Loss -  0.6991046433017485\n",
            "Epoch -  31 Loss -  0.6991023130270458\n",
            "Epoch -  32 Loss -  0.699099983728889\n",
            "Epoch -  33 Loss -  0.6990976554068338\n",
            "Epoch -  34 Loss -  0.6990953280604367\n",
            "Epoch -  35 Loss -  0.6990930016892543\n",
            "Epoch -  36 Loss -  0.6990906762928432\n",
            "Epoch -  37 Loss -  0.6990883518707602\n",
            "Epoch -  38 Loss -  0.6990860284225631\n",
            "Epoch -  39 Loss -  0.6990837059478086\n",
            "Epoch -  40 Loss -  0.6990813844460546\n",
            "Epoch -  41 Loss -  0.699079063916859\n",
            "Epoch -  42 Loss -  0.6990767443597797\n",
            "Epoch -  43 Loss -  0.6990744257743753\n",
            "Epoch -  44 Loss -  0.699072108160204\n",
            "Epoch -  45 Loss -  0.6990697915168249\n",
            "Epoch -  46 Loss -  0.6990674758437966\n",
            "Epoch -  47 Loss -  0.6990651611406782\n",
            "Epoch -  48 Loss -  0.6990628474070294\n",
            "Epoch -  49 Loss -  0.6990605346424095\n",
            "Epoch -  50 Loss -  0.6990582228463785\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'W1': array([[0.09994267, 0.09984548],\n",
              "        [0.09994272, 0.09984548]]),\n",
              " 'b1': array([[-3.38405750e-05],\n",
              "        [-3.38419977e-05]]),\n",
              " 'W2': array([[0.09920806],\n",
              "        [0.09920816]]),\n",
              " 'b2': array([[0.09915209]])}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nkBltTUj-M_g"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets check from keras to find the issue with not reducing the loss."
      ],
      "metadata": {
        "id": "CXD8WvXDG8i-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "6S0BY15SHK9o",
        "outputId": "7ee58a5c-34e5-4894-f39e-121c23c082d5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  profile_score  placed\n",
              "0     8              8       1\n",
              "1     7              9       1\n",
              "2     6             10       0\n",
              "3     5              5       0"
            ],
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>profile_score</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-2899f7d8-0458-4702-a85f-52f3ca52972e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2899f7d8-0458-4702-a85f-52f3ca52972e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-4dbf890a-aa57-4d52-833a-5265787cfe7b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_da5959cc-33af-48e2-8072-74effcaad405\">\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",
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              "        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",
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              "      }\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",
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              "    <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_da5959cc-33af-48e2-8072-74effcaad405 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\": 5,\n        \"max\": 8,\n        \"samples\": [\n          7,\n          5,\n          8\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"profile_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 10,\n        \"samples\": [\n          9,\n          5,\n          8\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "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": "S7e8-SyKHZ_-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(2, activation= 'sigmoid',input_dim =2))\n",
        "model.add(Dense(1,activation = 'sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mc9DSFbSHmwF",
        "outputId": "9013d015-3f13-45a8-def4-2b318985da6b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 2)                 6         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 9 (36.00 Byte)\n",
            "Trainable params: 9 (36.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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)]\n",
        ""
      ],
      "metadata": {
        "id": "KoMA8akZJCeJ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.set_weights(new_weights)"
      ],
      "metadata": {
        "id": "pdgy-miIJIZX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bbOO7TmJLo8",
        "outputId": "87a04492-e6d2-4e58-d09c-b94dae1d448b"
      },
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = keras.optimizers.Adam(learning_rate = 0.001)\n",
        "model.compile(loss = 'binary_crossentropy', optimizer = optimizer)"
      ],
      "metadata": {
        "id": "XOQDbauyJPJe"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(df.iloc[:,0:-1].values, df['placed'].values, epochs =50,verbose = 1, batch_size = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWZmzcTTJinh",
        "outputId": "ce2f5f11-ddd4-43ab-a6af-0040c26c7a7f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "4/4 [==============================] - 1s 4ms/step - loss: 0.6925\n",
            "Epoch 2/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6923\n",
            "Epoch 3/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6923\n",
            "Epoch 4/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6922\n",
            "Epoch 5/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 6/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.6923\n",
            "Epoch 7/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6921\n",
            "Epoch 8/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6921\n",
            "Epoch 9/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 10/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 11/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 12/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 13/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 14/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 15/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6921\n",
            "Epoch 16/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.6923\n",
            "Epoch 17/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 18/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6922\n",
            "Epoch 19/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 20/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 21/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 22/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 23/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 24/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 25/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 26/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 27/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6920\n",
            "Epoch 28/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 29/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 30/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 31/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 32/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 33/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 34/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6919\n",
            "Epoch 35/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 36/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 37/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 38/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 39/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6918\n",
            "Epoch 40/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6918\n",
            "Epoch 41/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 42/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6921\n",
            "Epoch 43/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6920\n",
            "Epoch 44/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 45/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 46/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6918\n",
            "Epoch 47/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 48/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n",
            "Epoch 49/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6918\n",
            "Epoch 50/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6919\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7dfb59f04400>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9v_Rc8R-JsBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "this data set have some issue from that the loss function of this dataset  are not reducing."
      ],
      "metadata": {
        "id": "sby-BcfTLTP9"
      }
    }
  ]
}