Linear_Classification_part_3.ipynb

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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/tusharbansal22/Tensorflow-Notebooks/blob/main/Linear_Classification_part_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "xKoBVfffGUuQ",
        "outputId": "b290a9cf-d08f-430e-c1e9-2ad824ad4d69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_breast_cancer"
      ],
      "metadata": {
        "id": "e_UPkrPRG0v2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=load_breast_cancer()"
      ],
      "metadata": {
        "id": "bKzIwLENHeX4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "data"
      ],
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        "id": "3xqkb1qYH4fN",
        "outputId": "f3502b18-348c-49b4-c0f9-c196c522db86"
      },
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      "outputs": [
        {
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              "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "         7.039e-02]]),\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
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              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),\n",
              " 'frame': None,\n",
              " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
              " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
              " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " 'filename': 'breast_cancer.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
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        "type(data)"
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        "outputId": "cccbea99-11bc-4ab4-ff48-22e87a624038"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYWJDJiTIBsz",
        "outputId": "240cab64-0044-49c4-ac67-f06fbbe449b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data.shape"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
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          "metadata": {},
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      "source": [
        "data.target"
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kj4Gtn7IrOD",
        "outputId": "7bf1e360-3fcb-4da8-88b8-307a7ce47ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VaCUs3qJw1u",
        "outputId": "a2e7c2a5-8732-40d3-a5a0-4ee502faadfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569,)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgWqZCZ_J6PF",
        "outputId": "1a8f4e65-78e2-4d61-eb23-e49cf1cf702b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "        1.189e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "        8.902e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "        8.758e-02],\n",
              "       ...,\n",
              "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "        7.820e-02],\n",
              "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "        1.240e-01],\n",
              "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "        7.039e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpdXMompJ98d",
        "outputId": "3e68ad7c-211b-4c14-da1e-a299ec8a16fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error',\n",
              "       'fractal dimension error', 'worst radius', 'worst texture',\n",
              "       'worst perimeter', 'worst area', 'worst smoothness',\n",
              "       'worst compactness', 'worst concavity', 'worst concave points',\n",
              "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test , y_train , y_test = train_test_split(data.data,data.target,test_size=0.33)"
      ],
      "metadata": {
        "id": "gt4u3Bh-KEQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N,D = X_train.shape"
      ],
      "metadata": {
        "id": "kukDUcF6Lnsq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N, D"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsjA2fpkLdo3",
        "outputId": "e41040cf-d99a-404b-f869-3f387bd21720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(381, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train=scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "r-318Aa1LtTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input(shape=(D,)),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "V2S6NTZsMSzR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "z3nYLjQ2NEPM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r=model.fit(X_train,y_train,validation_data=(X_test,y_test), epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3jwW2DJNqAl",
        "outputId": "9bf52f51-482b-42dc-dc7d-c7b27294f660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 3s 115ms/step - loss: 0.6559 - accuracy: 0.6247 - val_loss: 0.6223 - val_accuracy: 0.6330\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 26ms/step - loss: 0.5827 - accuracy: 0.6982 - val_loss: 0.5570 - val_accuracy: 0.7181\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.5230 - accuracy: 0.7585 - val_loss: 0.5009 - val_accuracy: 0.7766\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.4726 - accuracy: 0.8346 - val_loss: 0.4543 - val_accuracy: 0.8404\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 29ms/step - loss: 0.4311 - accuracy: 0.8793 - val_loss: 0.4160 - val_accuracy: 0.8617\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 25ms/step - loss: 0.3986 - accuracy: 0.8976 - val_loss: 0.3832 - val_accuracy: 0.8989\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.3699 - accuracy: 0.9081 - val_loss: 0.3565 - val_accuracy: 0.9149\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.3473 - accuracy: 0.9213 - val_loss: 0.3330 - val_accuracy: 0.9149\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.3268 - accuracy: 0.9265 - val_loss: 0.3135 - val_accuracy: 0.9309\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 28ms/step - loss: 0.3097 - accuracy: 0.9239 - val_loss: 0.2967 - val_accuracy: 0.9415\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 36ms/step - loss: 0.2950 - accuracy: 0.9265 - val_loss: 0.2816 - val_accuracy: 0.9468\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.2818 - accuracy: 0.9239 - val_loss: 0.2685 - val_accuracy: 0.9468\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.2701 - accuracy: 0.9318 - val_loss: 0.2570 - val_accuracy: 0.9468\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 23ms/step - loss: 0.2599 - accuracy: 0.9344 - val_loss: 0.2463 - val_accuracy: 0.9468\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 38ms/step - loss: 0.2505 - accuracy: 0.9344 - val_loss: 0.2368 - val_accuracy: 0.9521\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 23ms/step - loss: 0.2418 - accuracy: 0.9370 - val_loss: 0.2286 - val_accuracy: 0.9521\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 32ms/step - loss: 0.2342 - accuracy: 0.9396 - val_loss: 0.2207 - val_accuracy: 0.9468\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.2270 - accuracy: 0.9423 - val_loss: 0.2135 - val_accuracy: 0.9468\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 1s 44ms/step - loss: 0.2204 - accuracy: 0.9423 - val_loss: 0.2071 - val_accuracy: 0.9521\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.2145 - accuracy: 0.9423 - val_loss: 0.2009 - val_accuracy: 0.9574\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 42ms/step - loss: 0.2087 - accuracy: 0.9475 - val_loss: 0.1957 - val_accuracy: 0.9574\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.2035 - accuracy: 0.9528 - val_loss: 0.1905 - val_accuracy: 0.9574\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 22ms/step - loss: 0.1985 - accuracy: 0.9528 - val_loss: 0.1857 - val_accuracy: 0.9574\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 1s 76ms/step - loss: 0.1941 - accuracy: 0.9554 - val_loss: 0.1814 - val_accuracy: 0.9574\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 1s 48ms/step - loss: 0.1897 - accuracy: 0.9580 - val_loss: 0.1772 - val_accuracy: 0.9574\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 1s 55ms/step - loss: 0.1857 - accuracy: 0.9580 - val_loss: 0.1731 - val_accuracy: 0.9628\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 1s 71ms/step - loss: 0.1819 - accuracy: 0.9606 - val_loss: 0.1693 - val_accuracy: 0.9628\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 28ms/step - loss: 0.1782 - accuracy: 0.9633 - val_loss: 0.1659 - val_accuracy: 0.9628\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 29ms/step - loss: 0.1749 - accuracy: 0.9633 - val_loss: 0.1625 - val_accuracy: 0.9628\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 14ms/step - loss: 0.1716 - accuracy: 0.9633 - val_loss: 0.1595 - val_accuracy: 0.9681\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 24ms/step - loss: 0.1685 - accuracy: 0.9633 - val_loss: 0.1565 - val_accuracy: 0.9681\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1656 - accuracy: 0.9659 - val_loss: 0.1539 - val_accuracy: 0.9681\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1629 - accuracy: 0.9659 - val_loss: 0.1512 - val_accuracy: 0.9681\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1603 - accuracy: 0.9659 - val_loss: 0.1487 - val_accuracy: 0.9681\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1577 - accuracy: 0.9685 - val_loss: 0.1463 - val_accuracy: 0.9681\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1554 - accuracy: 0.9685 - val_loss: 0.1440 - val_accuracy: 0.9681\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 28ms/step - loss: 0.1531 - accuracy: 0.9685 - val_loss: 0.1419 - val_accuracy: 0.9681\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1509 - accuracy: 0.9685 - val_loss: 0.1398 - val_accuracy: 0.9681\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 25ms/step - loss: 0.1488 - accuracy: 0.9685 - val_loss: 0.1378 - val_accuracy: 0.9681\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.1468 - accuracy: 0.9685 - val_loss: 0.1359 - val_accuracy: 0.9681\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1449 - accuracy: 0.9711 - val_loss: 0.1341 - val_accuracy: 0.9681\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 12ms/step - loss: 0.1430 - accuracy: 0.9711 - val_loss: 0.1322 - val_accuracy: 0.9681\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 25ms/step - loss: 0.1412 - accuracy: 0.9711 - val_loss: 0.1305 - val_accuracy: 0.9681\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1396 - accuracy: 0.9711 - val_loss: 0.1289 - val_accuracy: 0.9681\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1379 - accuracy: 0.9711 - val_loss: 0.1274 - val_accuracy: 0.9681\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1364 - accuracy: 0.9711 - val_loss: 0.1259 - val_accuracy: 0.9734\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1348 - accuracy: 0.9711 - val_loss: 0.1244 - val_accuracy: 0.9734\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 27ms/step - loss: 0.1333 - accuracy: 0.9711 - val_loss: 0.1230 - val_accuracy: 0.9734\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1320 - accuracy: 0.9711 - val_loss: 0.1216 - val_accuracy: 0.9734\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1306 - accuracy: 0.9711 - val_loss: 0.1203 - val_accuracy: 0.9734\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 23ms/step - loss: 0.1292 - accuracy: 0.9711 - val_loss: 0.1191 - val_accuracy: 0.9734\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.1280 - accuracy: 0.9711 - val_loss: 0.1179 - val_accuracy: 0.9734\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 30ms/step - loss: 0.1268 - accuracy: 0.9738 - val_loss: 0.1167 - val_accuracy: 0.9734\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.1256 - accuracy: 0.9764 - val_loss: 0.1155 - val_accuracy: 0.9734\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.1244 - accuracy: 0.9764 - val_loss: 0.1144 - val_accuracy: 0.9734\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1233 - accuracy: 0.9764 - val_loss: 0.1133 - val_accuracy: 0.9734\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 32ms/step - loss: 0.1222 - accuracy: 0.9764 - val_loss: 0.1122 - val_accuracy: 0.9734\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1212 - accuracy: 0.9790 - val_loss: 0.1112 - val_accuracy: 0.9734\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 35ms/step - loss: 0.1202 - accuracy: 0.9790 - val_loss: 0.1102 - val_accuracy: 0.9734\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.1192 - accuracy: 0.9790 - val_loss: 0.1093 - val_accuracy: 0.9734\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1182 - accuracy: 0.9790 - val_loss: 0.1084 - val_accuracy: 0.9734\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.1173 - accuracy: 0.9790 - val_loss: 0.1075 - val_accuracy: 0.9734\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1164 - accuracy: 0.9790 - val_loss: 0.1066 - val_accuracy: 0.9734\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 28ms/step - loss: 0.1155 - accuracy: 0.9790 - val_loss: 0.1057 - val_accuracy: 0.9734\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1147 - accuracy: 0.9790 - val_loss: 0.1048 - val_accuracy: 0.9734\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1138 - accuracy: 0.9790 - val_loss: 0.1040 - val_accuracy: 0.9734\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1130 - accuracy: 0.9790 - val_loss: 0.1032 - val_accuracy: 0.9734\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.1122 - accuracy: 0.9790 - val_loss: 0.1024 - val_accuracy: 0.9734\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 23ms/step - loss: 0.1115 - accuracy: 0.9816 - val_loss: 0.1017 - val_accuracy: 0.9734\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 24ms/step - loss: 0.1107 - accuracy: 0.9843 - val_loss: 0.1010 - val_accuracy: 0.9734\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1100 - accuracy: 0.9843 - val_loss: 0.1003 - val_accuracy: 0.9734\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 24ms/step - loss: 0.1093 - accuracy: 0.9843 - val_loss: 0.0996 - val_accuracy: 0.9734\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.1086 - accuracy: 0.9843 - val_loss: 0.0989 - val_accuracy: 0.9734\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.1080 - accuracy: 0.9843 - val_loss: 0.0982 - val_accuracy: 0.9734\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1072 - accuracy: 0.9843 - val_loss: 0.0975 - val_accuracy: 0.9734\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1066 - accuracy: 0.9843 - val_loss: 0.0969 - val_accuracy: 0.9734\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 24ms/step - loss: 0.1060 - accuracy: 0.9843 - val_loss: 0.0962 - val_accuracy: 0.9734\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1054 - accuracy: 0.9843 - val_loss: 0.0957 - val_accuracy: 0.9734\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.1048 - accuracy: 0.9843 - val_loss: 0.0951 - val_accuracy: 0.9734\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1042 - accuracy: 0.9843 - val_loss: 0.0945 - val_accuracy: 0.9734\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.1036 - accuracy: 0.9843 - val_loss: 0.0939 - val_accuracy: 0.9734\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1031 - accuracy: 0.9843 - val_loss: 0.0934 - val_accuracy: 0.9787\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1025 - accuracy: 0.9843 - val_loss: 0.0928 - val_accuracy: 0.9787\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.1020 - accuracy: 0.9843 - val_loss: 0.0922 - val_accuracy: 0.9787\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.1015 - accuracy: 0.9843 - val_loss: 0.0918 - val_accuracy: 0.9787\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 25ms/step - loss: 0.1009 - accuracy: 0.9843 - val_loss: 0.0912 - val_accuracy: 0.9787\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 19ms/step - loss: 0.1004 - accuracy: 0.9843 - val_loss: 0.0907 - val_accuracy: 0.9787\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 20ms/step - loss: 0.0999 - accuracy: 0.9843 - val_loss: 0.0903 - val_accuracy: 0.9787\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 17ms/step - loss: 0.0995 - accuracy: 0.9843 - val_loss: 0.0898 - val_accuracy: 0.9787\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.0990 - accuracy: 0.9843 - val_loss: 0.0893 - val_accuracy: 0.9787\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.0985 - accuracy: 0.9843 - val_loss: 0.0888 - val_accuracy: 0.9787\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 24ms/step - loss: 0.0981 - accuracy: 0.9843 - val_loss: 0.0884 - val_accuracy: 0.9787\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.0977 - accuracy: 0.9843 - val_loss: 0.0878 - val_accuracy: 0.9787\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 15ms/step - loss: 0.0972 - accuracy: 0.9843 - val_loss: 0.0875 - val_accuracy: 0.9787\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.0968 - accuracy: 0.9843 - val_loss: 0.0869 - val_accuracy: 0.9787\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 25ms/step - loss: 0.0964 - accuracy: 0.9843 - val_loss: 0.0866 - val_accuracy: 0.9787\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 22ms/step - loss: 0.0959 - accuracy: 0.9843 - val_loss: 0.0862 - val_accuracy: 0.9787\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 16ms/step - loss: 0.0955 - accuracy: 0.9843 - val_loss: 0.0858 - val_accuracy: 0.9787\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 18ms/step - loss: 0.0951 - accuracy: 0.9843 - val_loss: 0.0854 - val_accuracy: 0.9787\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 21ms/step - loss: 0.0947 - accuracy: 0.9843 - val_loss: 0.0849 - val_accuracy: 0.9787\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Train Score :\",model.evaluate(X_train,y_train))\n",
        "print(\"Test Score :\",model.evaluate(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpW_NxSNOTsr",
        "outputId": "7f98f450-6386-43cc-de46-b4abd76abf12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 8ms/step - loss: 0.0945 - accuracy: 0.9843\n",
            "Train Score : [0.0944911539554596, 0.9842519760131836]\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.0849 - accuracy: 0.9787\n",
            "Test Score : [0.08494724333286285, 0.978723406791687]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(r.history['loss'],label='loss')\n",
        "plt.plot(r.history['val_loss'],label='val_loss')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CmCjge5NOwn9",
        "outputId": "6e4b990a-64dc-42e0-eb78-5c4df3ea01fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(r.history['accuracy'],label='acc')\n",
        "plt.plot(r.history['val_accuracy'],label='val_acc')\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "rvIJN9rNPR1t",
        "outputId": "2d67d789-4fa2-4660-a50e-ead059f4ab05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 2"
      ],
      "metadata": {
        "id": "z7KPa0psTTb4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "P=model.predict(X_test)\n",
        "print(P)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7aHWPxdTVpc",
        "outputId": "dab92ac0-8ff4-4272-bcae-449f13de57ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6/6 [==============================] - 0s 5ms/step\n",
            "[[9.84337032e-01]\n",
            " [2.06469795e-05]\n",
            " [7.48177767e-01]\n",
            " [9.93638992e-01]\n",
            " [7.68954515e-01]\n",
            " [9.21082973e-01]\n",
            " [4.70145680e-02]\n",
            " [9.26789582e-01]\n",
            " [9.02896896e-02]\n",
            " [9.89297628e-01]\n",
            " [2.95602367e-04]\n",
            " [9.96683896e-01]\n",
            " [9.11616147e-01]\n",
            " [9.99490023e-01]\n",
            " [9.76702332e-01]\n",
            " [9.98231888e-01]\n",
            " [8.11814010e-01]\n",
            " [9.83821094e-01]\n",
            " [9.62767482e-01]\n",
            " [9.89981711e-01]\n",
            " [8.97639632e-01]\n",
            " [8.05785298e-01]\n",
            " [9.91781890e-01]\n",
            " [9.08862174e-01]\n",
            " [9.41375474e-05]\n",
            " [9.48637486e-01]\n",
            " [9.90540087e-01]\n",
            " [9.82847452e-01]\n",
            " [9.98576939e-01]\n",
            " [9.98588622e-01]\n",
            " [2.91879434e-04]\n",
            " [9.86514628e-01]\n",
            " [9.77842748e-01]\n",
            " [9.89182770e-01]\n",
            " [9.95842159e-01]\n",
            " [9.97750700e-01]\n",
            " [9.45198099e-06]\n",
            " [9.87379014e-01]\n",
            " [3.43667604e-02]\n",
            " [9.90666807e-01]\n",
            " [8.17419775e-03]\n",
            " [9.38415706e-01]\n",
            " [8.01926490e-06]\n",
            " [9.96679425e-01]\n",
            " [6.60319477e-02]\n",
            " [8.23946655e-01]\n",
            " [3.36017199e-02]\n",
            " [9.85607028e-01]\n",
            " [9.99840915e-01]\n",
            " [2.71470381e-05]\n",
            " [9.72681522e-01]\n",
            " [9.95410025e-01]\n",
            " [9.96903062e-01]\n",
            " [7.43085206e-01]\n",
            " [9.98231292e-01]\n",
            " [2.43609771e-04]\n",
            " [9.90825653e-01]\n",
            " [1.12764792e-04]\n",
            " [9.75828409e-01]\n",
            " [6.55682385e-02]\n",
            " [9.58899021e-01]\n",
            " [9.31064963e-01]\n",
            " [9.98692036e-01]\n",
            " [9.37683165e-01]\n",
            " [9.74090815e-01]\n",
            " [9.94621694e-01]\n",
            " [4.04481471e-01]\n",
            " [6.46795332e-03]\n",
            " [1.89355651e-05]\n",
            " [9.76975977e-01]\n",
            " [1.71866670e-01]\n",
            " [9.95529354e-01]\n",
            " [9.60449159e-01]\n",
            " [9.87359643e-01]\n",
            " [7.62108147e-01]\n",
            " [9.95203197e-01]\n",
            " [1.41083542e-13]\n",
            " [9.81004059e-01]\n",
            " [9.99074101e-01]\n",
            " [1.26155317e-02]\n",
            " [9.98788834e-01]\n",
            " [9.99031484e-01]\n",
            " [9.90121603e-01]\n",
            " [9.71946955e-01]\n",
            " [7.69948363e-01]\n",
            " [4.77690902e-03]\n",
            " [2.51714624e-02]\n",
            " [8.01987946e-01]\n",
            " [9.99027014e-01]\n",
            " [9.91736710e-01]\n",
            " [2.39310026e-01]\n",
            " [4.35531512e-03]\n",
            " [9.98410583e-01]\n",
            " [3.45318869e-04]\n",
            " [5.35679270e-08]\n",
            " [5.39596425e-03]\n",
            " [9.39674079e-01]\n",
            " [6.68280125e-02]\n",
            " [1.62370861e-01]\n",
            " [9.31248963e-01]\n",
            " [6.23382986e-01]\n",
            " [1.47897089e-02]\n",
            " [8.89718115e-01]\n",
            " [9.91287529e-01]\n",
            " [9.81394231e-01]\n",
            " [4.73394036e-01]\n",
            " [4.40553049e-05]\n",
            " [9.93132114e-01]\n",
            " [9.90686893e-01]\n",
            " [8.49077165e-01]\n",
            " [9.84929442e-01]\n",
            " [2.52995902e-04]\n",
            " [1.37710189e-02]\n",
            " [3.26790325e-02]\n",
            " [9.93787706e-01]\n",
            " [7.00828314e-01]\n",
            " [9.98727977e-01]\n",
            " [9.96713817e-01]\n",
            " [5.93345646e-07]\n",
            " [8.65630150e-01]\n",
            " [9.89935815e-01]\n",
            " [9.23079193e-01]\n",
            " [9.94622707e-01]\n",
            " [3.01759522e-02]\n",
            " [9.44060206e-01]\n",
            " [9.98135030e-01]\n",
            " [4.96090837e-02]\n",
            " [8.47754069e-03]\n",
            " [1.47649795e-02]\n",
            " [7.77554214e-06]\n",
            " [1.09899370e-02]\n",
            " [9.53171313e-01]\n",
            " [7.51963973e-01]\n",
            " [7.65726507e-01]\n",
            " [2.19425070e-03]\n",
            " [9.98883367e-01]\n",
            " [9.98352230e-01]\n",
            " [2.81164736e-01]\n",
            " [5.79110980e-02]\n",
            " [2.64254143e-03]\n",
            " [9.99779582e-01]\n",
            " [9.72040892e-01]\n",
            " [2.59559184e-01]\n",
            " [9.92225647e-01]\n",
            " [8.45145404e-01]\n",
            " [9.25076008e-01]\n",
            " [6.21765435e-01]\n",
            " [9.48477209e-01]\n",
            " [8.62060547e-01]\n",
            " [5.86670940e-04]\n",
            " [4.94946689e-05]\n",
            " [7.57378221e-01]\n",
            " [9.90760028e-01]\n",
            " [9.99913216e-01]\n",
            " [1.12707421e-01]\n",
            " [7.72238135e-01]\n",
            " [1.21849373e-01]\n",
            " [1.07189237e-07]\n",
            " [9.96019363e-01]\n",
            " [9.75220919e-01]\n",
            " [9.54204559e-01]\n",
            " [9.98908043e-01]\n",
            " [3.35659310e-02]\n",
            " [2.73132864e-05]\n",
            " [1.82173010e-02]\n",
            " [4.58644867e-01]\n",
            " [9.96164262e-01]\n",
            " [1.23229079e-01]\n",
            " [1.28135679e-03]\n",
            " [2.46864860e-03]\n",
            " [9.88249421e-01]\n",
            " [2.35289941e-03]\n",
            " [4.89506777e-03]\n",
            " [9.46579754e-01]\n",
            " [9.18139338e-01]\n",
            " [9.58056450e-01]\n",
            " [8.97686541e-01]\n",
            " [5.68618634e-06]\n",
            " [1.24105973e-05]\n",
            " [9.95368659e-01]\n",
            " [9.92106199e-01]\n",
            " [9.95704591e-01]\n",
            " [6.14940980e-03]\n",
            " [9.99314308e-01]\n",
            " [9.86679018e-01]\n",
            " [9.94099319e-01]\n",
            " [9.99439240e-01]\n",
            " [4.08174694e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "P=np.round(P).flatten()\n",
        "print(P)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3POOBveT4PH",
        "outputId": "59d6c328-350b-4160-d2df-f0f8dad14aa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 0. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 0. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1.\n",
            " 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1.\n",
            " 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 0.\n",
            " 1. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0. 1.\n",
            " 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1.\n",
            " 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0.\n",
            " 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Manually calculated accuracy ',np.mean(P==y_test))\n",
        "print('Evaluated accuracy ',model.evaluate(X_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYKzJaMyUAXZ",
        "outputId": "056c7296-8959-4495-ba02-448d343fb6af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Manually calculated accuracy  0.9787234042553191\n",
            "6/6 [==============================] - 0s 7ms/step - loss: 0.0849 - accuracy: 0.9787\n",
            "Evaluated accuracy  [0.08494724333286285, 0.978723406791687]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3"
      ],
      "metadata": {
        "id": "_kSP890-j1Za"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('linearclassifier.h5')"
      ],
      "metadata": {
        "id": "fOfqz_Lwj2ti"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -lh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRyBLv9pj_kx",
        "outputId": "2de2372d-f878-48c9-c46b-1f02f7b69343"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 24K\n",
            "-rw-r--r-- 1 root root  19K Jan 23 16:58 linearclassifier.h5\n",
            "drwxr-xr-x 1 root root 4.0K Jan 19 14:35 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.models.load_model('linearclassifier.h5')\n",
        "print(model.layers)\n",
        "model.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LV2FkJ3kBzx",
        "outputId": "b218f5fe-18e4-42ec-bf39-a3f16ec56724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<keras.layers.core.dense.Dense object at 0x7fd3bd81c790>]\n",
            "6/6 [==============================] - 0s 3ms/step - loss: 0.0849 - accuracy: 0.9787\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08494724333286285, 0.978723406791687]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}