Linear_Classification.ipynb

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    "colab": {
      "name": "Linear_Classification.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "QbV0a61010Vw"
      },
      "outputs": [],
      "source": [
        "# Import tensorflow\n",
        "import tensorflow as tf\n",
        "# Load breast cancer data from sklearn\n",
        "from sklearn.datasets import load_breast_cancer\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data\n",
        "data = load_breast_cancer()\n",
        "type(data) # A Bunch data type works like a dictionary and the attributes are the keys"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-u8lncFmdCoD",
        "outputId": "0f5bf678-a499-44ca-b84f-1c65cb2baaff"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys() # 'data' and 'target' refer to the input data X and the targets Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TsCry0wQdDwR",
        "outputId": "0db0076f-d2f4-4177-d481-40ee2e9f63cd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data.shape # We see that we have 569 observations and 30 features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwwE5S8GdD_x",
        "outputId": "0fee5382-838c-4186-b06e-9c8e6b0b856d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.target.shape)\n",
        "data.target # A bunch of 0 and 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWTtrn0jtRv7",
        "outputId": "eff02b1d-55ce-4521-921f-286e430ef53f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569,)\n"
          ]
        },
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ownwR3cFdEfv",
        "outputId": "9a1c97c6-aa66-455b-92f3-1a785eba8dbf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxsPpxzythtR",
        "outputId": "0a9255bf-c968-48c1-8828-43009cfeff02"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we will split the data into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train_full,X_test,y_train_full,y_test = train_test_split(data.data,data.target,test_size = 0.2)\n",
        "X_valid,X_train = X_train_full[:100],X_train_full[100:]\n",
        "y_valid,y_train = y_train_full[:100],y_train_full[100:]\n",
        "N, D = X_train.shape"
      ],
      "metadata": {
        "id": "TUhsgznBttHw"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing\n",
        "## Scale the data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "### Scaling the data is very important in deeplearning, we want the inputs to have the same range\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train) # We only fit on the training set\n",
        "X_valid = scaler.transform(X_valid) # We don't fit on the valid set\n",
        "X_test = scaler.transform(X_test) # We don't fit on the test set"
      ],
      "metadata": {
        "id": "EGoIFZk0uSPz"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build model\n",
        "model = tf.keras.models.Sequential([\n",
        "                                    tf.keras.layers.Input(shape=(D,)),\n",
        "                                    tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "                                "
      ],
      "metadata": {
        "id": "-oHN1zbDuSdr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile model\n",
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "DvSIp1vkuSmx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train model\n",
        "r = model.fit(X_train,y_train,validation_data = (X_valid,y_valid), epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFSQKhn1uSqI",
        "outputId": "33ab8497-e1cb-4550-a3ac-0cf9f192cf58"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 1s 32ms/step - loss: 0.5225 - accuracy: 0.7493 - val_loss: 0.4582 - val_accuracy: 0.8100\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.4780 - accuracy: 0.7775 - val_loss: 0.4208 - val_accuracy: 0.8300\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.4401 - accuracy: 0.8028 - val_loss: 0.3893 - val_accuracy: 0.8500\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.4080 - accuracy: 0.8197 - val_loss: 0.3618 - val_accuracy: 0.8700\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.3800 - accuracy: 0.8563 - val_loss: 0.3390 - val_accuracy: 0.8800\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.3547 - accuracy: 0.8732 - val_loss: 0.3177 - val_accuracy: 0.9000\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.3330 - accuracy: 0.8873 - val_loss: 0.2992 - val_accuracy: 0.9000\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.3138 - accuracy: 0.8958 - val_loss: 0.2836 - val_accuracy: 0.9000\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2971 - accuracy: 0.8958 - val_loss: 0.2688 - val_accuracy: 0.9100\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2818 - accuracy: 0.9099 - val_loss: 0.2559 - val_accuracy: 0.9200\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.2684 - accuracy: 0.9099 - val_loss: 0.2442 - val_accuracy: 0.9200\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2562 - accuracy: 0.9155 - val_loss: 0.2340 - val_accuracy: 0.9300\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2456 - accuracy: 0.9239 - val_loss: 0.2242 - val_accuracy: 0.9300\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2355 - accuracy: 0.9324 - val_loss: 0.2157 - val_accuracy: 0.9300\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2269 - accuracy: 0.9352 - val_loss: 0.2076 - val_accuracy: 0.9300\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2188 - accuracy: 0.9380 - val_loss: 0.2005 - val_accuracy: 0.9300\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.2112 - accuracy: 0.9380 - val_loss: 0.1939 - val_accuracy: 0.9400\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.2043 - accuracy: 0.9408 - val_loss: 0.1876 - val_accuracy: 0.9400\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1980 - accuracy: 0.9408 - val_loss: 0.1819 - val_accuracy: 0.9400\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1920 - accuracy: 0.9465 - val_loss: 0.1758 - val_accuracy: 0.9400\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1864 - accuracy: 0.9465 - val_loss: 0.1709 - val_accuracy: 0.9400\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1816 - accuracy: 0.9465 - val_loss: 0.1663 - val_accuracy: 0.9400\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1769 - accuracy: 0.9465 - val_loss: 0.1620 - val_accuracy: 0.9400\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1728 - accuracy: 0.9493 - val_loss: 0.1580 - val_accuracy: 0.9400\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1687 - accuracy: 0.9521 - val_loss: 0.1544 - val_accuracy: 0.9400\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1650 - accuracy: 0.9577 - val_loss: 0.1509 - val_accuracy: 0.9400\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1617 - accuracy: 0.9577 - val_loss: 0.1481 - val_accuracy: 0.9400\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1586 - accuracy: 0.9577 - val_loss: 0.1451 - val_accuracy: 0.9400\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1556 - accuracy: 0.9577 - val_loss: 0.1425 - val_accuracy: 0.9400\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1526 - accuracy: 0.9606 - val_loss: 0.1392 - val_accuracy: 0.9500\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1496 - accuracy: 0.9634 - val_loss: 0.1366 - val_accuracy: 0.9500\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1470 - accuracy: 0.9634 - val_loss: 0.1341 - val_accuracy: 0.9500\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1446 - accuracy: 0.9634 - val_loss: 0.1316 - val_accuracy: 0.9500\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1422 - accuracy: 0.9634 - val_loss: 0.1293 - val_accuracy: 0.9500\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1398 - accuracy: 0.9662 - val_loss: 0.1272 - val_accuracy: 0.9600\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1377 - accuracy: 0.9662 - val_loss: 0.1251 - val_accuracy: 0.9600\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1355 - accuracy: 0.9662 - val_loss: 0.1232 - val_accuracy: 0.9600\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1336 - accuracy: 0.9662 - val_loss: 0.1214 - val_accuracy: 0.9600\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1317 - accuracy: 0.9662 - val_loss: 0.1196 - val_accuracy: 0.9600\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1299 - accuracy: 0.9690 - val_loss: 0.1180 - val_accuracy: 0.9600\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1283 - accuracy: 0.9746 - val_loss: 0.1166 - val_accuracy: 0.9600\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1264 - accuracy: 0.9746 - val_loss: 0.1147 - val_accuracy: 0.9600\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1249 - accuracy: 0.9746 - val_loss: 0.1131 - val_accuracy: 0.9600\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1232 - accuracy: 0.9746 - val_loss: 0.1116 - val_accuracy: 0.9600\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1218 - accuracy: 0.9746 - val_loss: 0.1099 - val_accuracy: 0.9600\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1204 - accuracy: 0.9746 - val_loss: 0.1086 - val_accuracy: 0.9600\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1190 - accuracy: 0.9746 - val_loss: 0.1072 - val_accuracy: 0.9600\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1177 - accuracy: 0.9718 - val_loss: 0.1060 - val_accuracy: 0.9600\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1165 - accuracy: 0.9718 - val_loss: 0.1048 - val_accuracy: 0.9600\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1153 - accuracy: 0.9718 - val_loss: 0.1037 - val_accuracy: 0.9600\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1142 - accuracy: 0.9718 - val_loss: 0.1025 - val_accuracy: 0.9600\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1129 - accuracy: 0.9718 - val_loss: 0.1015 - val_accuracy: 0.9600\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1119 - accuracy: 0.9718 - val_loss: 0.1005 - val_accuracy: 0.9600\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1108 - accuracy: 0.9718 - val_loss: 0.0995 - val_accuracy: 0.9600\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.1098 - accuracy: 0.9718 - val_loss: 0.0986 - val_accuracy: 0.9600\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1087 - accuracy: 0.9718 - val_loss: 0.0977 - val_accuracy: 0.9600\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1078 - accuracy: 0.9718 - val_loss: 0.0968 - val_accuracy: 0.9600\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 0s 8ms/step - loss: 0.1067 - accuracy: 0.9718 - val_loss: 0.0961 - val_accuracy: 0.9600\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1059 - accuracy: 0.9746 - val_loss: 0.0954 - val_accuracy: 0.9600\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.1050 - accuracy: 0.9775 - val_loss: 0.0946 - val_accuracy: 0.9600\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1042 - accuracy: 0.9775 - val_loss: 0.0938 - val_accuracy: 0.9600\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1033 - accuracy: 0.9803 - val_loss: 0.0929 - val_accuracy: 0.9600\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1025 - accuracy: 0.9831 - val_loss: 0.0921 - val_accuracy: 0.9600\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1017 - accuracy: 0.9831 - val_loss: 0.0914 - val_accuracy: 0.9600\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1010 - accuracy: 0.9831 - val_loss: 0.0908 - val_accuracy: 0.9600\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.1002 - accuracy: 0.9831 - val_loss: 0.0902 - val_accuracy: 0.9600\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.0996 - accuracy: 0.9831 - val_loss: 0.0896 - val_accuracy: 0.9600\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.0988 - accuracy: 0.9831 - val_loss: 0.0889 - val_accuracy: 0.9600\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 0s 7ms/step - loss: 0.0981 - accuracy: 0.9831 - val_loss: 0.0883 - val_accuracy: 0.9600\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.0975 - accuracy: 0.9831 - val_loss: 0.0877 - val_accuracy: 0.9600\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 0s 13ms/step - loss: 0.0969 - accuracy: 0.9831 - val_loss: 0.0870 - val_accuracy: 0.9600\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 0s 11ms/step - loss: 0.0962 - accuracy: 0.9831 - val_loss: 0.0864 - val_accuracy: 0.9600\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.0955 - accuracy: 0.9831 - val_loss: 0.0858 - val_accuracy: 0.9600\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 0s 10ms/step - loss: 0.0949 - accuracy: 0.9831 - val_loss: 0.0851 - val_accuracy: 0.9600\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0943 - accuracy: 0.9831 - val_loss: 0.0845 - val_accuracy: 0.9600\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0936 - accuracy: 0.9831 - val_loss: 0.0839 - val_accuracy: 0.9600\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0930 - accuracy: 0.9831 - val_loss: 0.0834 - val_accuracy: 0.9600\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0924 - accuracy: 0.9831 - val_loss: 0.0828 - val_accuracy: 0.9600\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0919 - accuracy: 0.9831 - val_loss: 0.0823 - val_accuracy: 0.9600\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 0s 6ms/step - loss: 0.0913 - accuracy: 0.9831 - val_loss: 0.0818 - val_accuracy: 0.9600\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.9831 - val_loss: 0.0814 - val_accuracy: 0.9600\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0903 - accuracy: 0.9831 - val_loss: 0.0809 - val_accuracy: 0.9600\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0898 - accuracy: 0.9831 - val_loss: 0.0804 - val_accuracy: 0.9600\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0893 - accuracy: 0.9831 - val_loss: 0.0800 - val_accuracy: 0.9600\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0887 - accuracy: 0.9831 - val_loss: 0.0796 - val_accuracy: 0.9600\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0882 - accuracy: 0.9831 - val_loss: 0.0791 - val_accuracy: 0.9600\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0877 - accuracy: 0.9831 - val_loss: 0.0788 - val_accuracy: 0.9600\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0873 - accuracy: 0.9831 - val_loss: 0.0784 - val_accuracy: 0.9600\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0868 - accuracy: 0.9831 - val_loss: 0.0779 - val_accuracy: 0.9600\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0864 - accuracy: 0.9831 - val_loss: 0.0775 - val_accuracy: 0.9600\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0859 - accuracy: 0.9831 - val_loss: 0.0769 - val_accuracy: 0.9600\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0854 - accuracy: 0.9859 - val_loss: 0.0764 - val_accuracy: 0.9600\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0849 - accuracy: 0.9859 - val_loss: 0.0760 - val_accuracy: 0.9600\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0845 - accuracy: 0.9859 - val_loss: 0.0756 - val_accuracy: 0.9600\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0840 - accuracy: 0.9859 - val_loss: 0.0752 - val_accuracy: 0.9600\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0836 - accuracy: 0.9859 - val_loss: 0.0748 - val_accuracy: 0.9600\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 0s 5ms/step - loss: 0.0833 - accuracy: 0.9859 - val_loss: 0.0744 - val_accuracy: 0.9600\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0829 - accuracy: 0.9859 - val_loss: 0.0741 - val_accuracy: 0.9600\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0825 - accuracy: 0.9859 - val_loss: 0.0737 - val_accuracy: 0.9600\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0821 - accuracy: 0.9859 - val_loss: 0.0735 - val_accuracy: 0.9600\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model - evaluate() returns loss and accuracy\n",
        "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": "wo3dBhAuuStW",
        "outputId": "d8dba119-1ccd-4d56-d233-28dfde25f566"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 2ms/step - loss: 0.0819 - accuracy: 0.9887\n",
            "Train score: [0.08187694847583771, 0.9887323975563049]\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1138 - accuracy: 0.9561\n",
            "Test score: [0.11379444599151611, 0.9561403393745422]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot what's returned by model.fit\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(r.history['loss'],label = 'loss')\n",
        "plt.plot(r.history['val_loss'],label = 'val_loss') # validation set\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CUM2tJZLuSwv",
        "outputId": "6054d0c1-a0a1-4b22-ba7a-db7b510593d1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yE7FZTHy2pcKPhQkhxMAI3kBPn2ycvri1+77VEVYzp4xOYemmctxuOX1RCBFcgjfQlTJ66cWfQ7uzq/nsiWlUNLhYWyKzLwohgkvwBjrAmHOhvcXrqtFTx6ZgMSs+2ljux8KEEKL/BXeg584BazRs6757XmyEhRNHJPHRxnK5alQIEVSCO9DDwmHk6VD0EbjdXc3nTExjb00LW/Y3+rE4IYToX8Ed6GAMuzTuh/3fdzWdMT4Vk4KPNsmwixAieAR/oI86C0xhsOXdrqak6HBm5CSwVMbRhRBBJPgDPTLBuNfo5ne8Jus6e2Ia2yoa2emQybqEEMEh+AMdYPwFULMTKjZ2Nf1wQhoAH0ovXQgRJEIj0MfOA2WCTW93NWXERTAjJ563vi+Vs12EEEEhNAI9Ksm41+jmt72GXS6cmsWOyiY2ljb4sTghhOgfoRHoAON/ZNxntHJLV9N5+elYzSbe/L7Ej4UJIUT/CJ1AH3c+oIyDox6xkRZOH5fCkrVltHe6+/5dIYQIAKET6NEpMPwkr0AHuGhaFtXNbXy5XW58IYQIbKET6GCc7eLYAo5tXU0nj04mPtLCm2tK/ViYEEIcvxAL9PmAgg2Lu5qsYSbOn5zBJ5sraHC2+682IYQ4TqEV6PY0436j6/7lNbfLhVMzcXW4+XDDfj8WJ4QQxye0Ah1gylVQvw92d0+pOyU7jhHJUbyycp8fCxNCiOMTeoE+dh7YYuH7l7ualFJcPWs46/bVsV5ufCGECFChF+gWG0y8BLYsAWd9V/NF07OItJp58ds9fixOCCGOnU+BrpQ6Wym1TSm1Qyl1Ty/Lf6qU2qCUWquU+kopNb7/S+1HU66CDidsequrKcZm4UdTM1myroza5jY/FieEEMfmiIGulDIDjwPnAOOBK3oJ7Fe01vla6ynAw8Cj/V5pf8qcBsljvYZdAK6eNRxXh5vXV8tYuhAi8PjSQ58J7NBa79RatwGvAhf0XEFr3XMylChgaM92pZTRSy9ZCY6iruZx6THMyInnpe/24nYP7U0QQoiD+RLomUDPLmuJp82LUuoWpVQxRg/9tt7eSCl1k1KqUClV6HD4+crMSQtAmWHN817NV8/OYW9NC1/IlaNCiADTbwdFtdaPa61HAL8Cft3HOk9prQu01gXJycn99dHHxp4KE34Ea14AZ/cfGGdPSCPZHs5zX+/2X21CCHEMfAn0UiC7x+ssT1tfXgV+dDxFDZrZt4KrAb5/qavJGmbi2tnD+aLIwZb9Mq2uECJw+BLoq4BRSqlcpZQVuBxY0nMFpdSoHi/PA7b3X4kDKHMaDDsRVjwBnR1dzQtnDSfSauZ/l+/0Y3FCCHF0jhjoWusO4FZgKbAFWKS13qSUekApNd+z2q1KqU1KqbXAncC1A1Zxf5t9C9Ttha3vdTXFRVpZMCObJevKKKtr9WNxQgjhO+Wv268VFBTowsJCv3y2F3cn/M90iEqGn3zS1VxS28LJj/yb60/M4dfzhvZp9UKI0KGUWq21LuhtWehdKXowkxlm/cw4hXHfyq7mrPhI5k1K518r91LfKrMwCiGGPgl0gClXGvO7fP2YV/NNc/Nobuvk5RUyHYAQYuiTQAcIj4aZNxnj6D1ufjEhI5a5o5N5+stdNLk6DvMGQgjhfxLoB5zwUwiLgK/+4tV855mjqWlu47mvd/mpMCGE8I0E+gFRSTD9WtiwCOq6L4ydkh3HGeNS+cfyndS3yFi6EGLokkDvafatxvM3/+PVfNdZo2lydfDUl8V+KEoIIXwjgd5TXLYxx8uaF6C5qqt5XHoM8yZl8OzXu6lqcvmxQCGE6JsE+sFO+oUxV/q3j3s133HGKFwdbh5ftsNPhQkhxOFJoB8seTRMuBBWPAmN5V3NecnRXDo9i5e+28OuqmY/FiiEEL2TQO/Nab+Gzjb44o9ezXeeNRqr2cT/+2CLnwoTQoi+SaD3JnEETL8eVj8PVd1DLCl2G7ecNpJPNlfw9Y6qw7yBEEIMPgn0vpz8K7BEwGe/82r+8Um5ZCdE8MC7m+nodPupOCGEOJQEel+ik+HEn8OWJbBvVVezzWLm3nPGsa2ikVdXyb1HhRBDhwT64cy+xZiF8eNfQ49ZKc+emMYJuQn86eNtVDY6/VigEEJ0k0A/nHA7nP5b2PcdrF/U1ayU4qELJ9LS1slv396Ev6YgFkKIniTQj2TKQsiYBp/8xuveoyNT7PzijFF8tKmc9zfs92OBQghhkEA/EpMJzvsTNFUechrjTXPymJQVy2/f2US1XEEqhPAzCXRfZE6HadcYFxtVbu1qDjObeOSSyTQ62/ntkk1+LFAIISTQfXf6f4E1Gt6/C9zdpyuOSbNz++mjeH/9fpasK/NjgUKIUCeB7quoRDjrQdjzFaz8h9ein548ginZcfzm7Y2U18tZL0II/5BAPxpTF8Los+HT+8FR1NUcZjbx6GWTcXV08ss31stZL0IIv5BAPxpKwfl/BUskvPUf0Nl9W7q85GjuO3ccy4scvLRirx+LFEKEKgn0o2VPhXmPQtka+PK/vRYtnDWcuaOTeej9zWwqq/dTgUKIUCWBfiwmXAj5lxqnMZas7mpWSvHfl04mPtLKTS+slpthCCEGlQT6sTr3TxCTAW/cAK7GruZkezhPXV1AVZOLn720hrYOmcBLCDE4JNCPVUQcXPQU1O2BD+/xWpSfFcvDl0xi5e4a/muJTA0ghBgcEujHY/iJMOcuWPsSbHrLa9EFUzK5+ZQR/GvlXp75erd/6hNChBQJ9ON18q8gswCW3O51MwyAu88awzkT03jw/c18vKm8jzcQQoj+IYF+vMwWuPRZ4/lfC6C1rmuRyaR49LIpTMqK4/ZX17K+pO4wbySEEMdHAr0/xA2DBS9C7R5Y/GOv89MjrGaevqaAhCgrNzxfyL6aFj8WKoQIZhLo/WX4iXDef0PxZ/DJb70WJdvDefb6GbR1uLn6nytwNMrpjEKI/ieB3p+mXwsn/BS+exxWPe21aHSqnWeum0FFg4trn1lJg7PdT0UKIYKVBHp/O+shY76XD+6Goo+9Fk0fHs8TC6dRVNHIT54vpLWt009FCiGCkQR6fzOHwcX/hNSJ8Pp1sH+d1+JTxqTw6IIprNpdw09eWCWhLoToNxLoAyE8Gq5cBBHx8PJlULPLa/H8yRn86ZLJfFNczY+fW0VLW0cfbySEEL7zKdCVUmcrpbYppXYope7pZfmdSqnNSqn1SqnPlFLD+7/UABOTDgsXQ6cLnp8Pdd4zMF48PYtHL5vMil1GqDe7JNSFEMfniIGulDIDjwPnAOOBK5RS4w9a7XugQGs9CVgMPNzfhQaklHFw9dvgqofnz4cG7zsaXTg1i0cvm8LKXTVc+fQKaprb/FSoECIY+NJDnwns0Frv1Fq3Aa8CF/RcQWu9TGt94ATr74Cs/i0zgGVMgYVvQXN1r6H+o6mZPLlwOlv3N3DJk99QUivnqQshjo0vgZ4J7OvxusTT1pcbgA97W6CUukkpVaiUKnQ4HL5XGeiyphvDL40V8MzZh4ypnzUhjRdvOAFHo4tLnviWreUNfipUCBHI+vWgqFJqIVAAPNLbcq31U1rrAq11QXJycn9+9NA3bBZc+w64GoxQr9zitXhmbgKv/3Q2Gs0lT3zLF0Uh9IUnhOgXvgR6KZDd43WWp82LUuoM4D5gvtZaLoXsTeZ0uO4DQMOz53rdHANgbFoMb99yElnxEfz4uVW8IreyE0IcBV8CfRUwSimVq5SyApcDS3quoJSaCvwDI8wr+7/MIJI6Hn78EYTb4fl5sP0Tr8XpsREsvvlEfjAyiXvf2sAD726mo1NukiGEOLIjBrrWugO4FVgKbAEWaa03KaUeUErN96z2CBANvK6UWquUWtLH2wmAhDy44RNIHAmvLIC1r3gtjg4P45/XFnDdiTk88/UurnlmpZwBI4Q4IuWvu+kUFBTowsJCv3z2kOFqhNcWws5/w9xfwin/CSbv79jFq0u4960NJEeH8/erpjE5O84/tQohhgSl1GqtdUFvy+RKUX8Kt8OVr8PUhbD8YXjtKnB6n+FyyfQsFv90NlprLnriG/78SRHtMgQjhOiFBLq/hVlh/t/gnEegaCk8fQbU7PRaZVJWHB/+Yi4XTM7gsc+2c9Hfv6GoorGPNxRChCoJ9KFAKTjhJrj6LWiuhKfPhBLv4ajYCAuPLpjCkwunUVrXyry/fsXf/71DDpgKIbpIoA8leSfDDZ8ak3s9Nw+2vn/IKmdPTOfjO+Zy+rgUHv5oGxc/+a301oUQgAT60JM00gj1lHHw6lWw/BFwe0+xmxQdzhMLp/O3K6eyr6aFcx/7kgff20yj3DRDiJAmgT4URSfDde/BxIvg8wfhufOM+5UeZN6kDD6982QuLcjmn1/v4tQ/fcEbq0twu/1z5pIQwr8k0Icqa5Rxo4wLn4KKTfDESbDmRTjoNNOEKCu/vyifdzxXmN71+joufvIb1u2r81PhQgh/kfPQA0HtHnj7ZtjzNeSdCuc/BvGHTjnvdmve/L6UP3y4lepmFxdPy+KOM0eTGRfhh6KFEAPhcOehS6AHCrcbCv8Jn95v9NJPuw9m/odxy7uDNDrb+Z/Pd/DcN7tBw1WzhnHLqSNJig4f9LKFEP1LAj2Y1O2D9+6AHZ9AWj7M+wtk9bpvKatr5bFPt/P66n1Yw0xcPmMYN87Nkx67EAFMAj3YaA2b34GP7oHGcph2DZz+W4hK6nX1YkcTf19WzDtrjUkyL5iSyU1z8xiTZh/MqoUQ/UACPVi5GmHZ72HlP8ASBSf/EmbeZFx92ovSulb+d/lOXlu1j9b2Tk4encyNc/I4aWQiSqlBLl4IcSwk0IOdowiW3msMw8Tnwqn3wcSLD5no64Da5jZeXrGH577ZQ1WTi9Gp0VwzO4eLpmUSaT10TF4IMXRIoIeK7Z/Ap7+Dig2QMgFOvRfGnmdMLdALZ3sn764r47lvdrOprAG7LYzLZ2RzzewcshMiB7l4IYQvJNBDidsNm9+Czx+CmmJIzTeGYsbO67PHrrVm9Z5anv1mNx9tLEdrzZnjU7l2dg6zR8hwjBBDiQR6KOrsgA2vG1MH1BRD8lgouAEmLwBbbJ+/VlbXykvf7eFfK/dS29LOyJRorpk9nAumZBIbYRnEDRBC9EYCPZR1dsDGN2DFE1D2PVgijfH1E/7DOO2xDweGY174dg8bSuuxhpk4c3wql0zPYs7IJMLMcpGxEP4ggS4MZd9D4TOwYTG0t8DwH8DMG2HMuX2eGaO1ZkNpPW+sLuGddWXUtbSTGGXl3Px05k/JYPqweEwmGZIRYrBIoAtvrbXw/Uuw8imo2wsRCZB/CUy5CjKm9Plrro5Olm118O66Mj7dUoGrw016rI15k9KZNymDSVmxMt4uxACTQBe9c3dC8efGTaq3vg+dLsiZAz+4A0ac1ufZMQBNrg4+3VzBe+vL+KLIQXunZlhCJPMmpXP+5AzGptkl3IUYABLo4shaa+H7l+Hbv0HjfkibBNOvhQkXQWTCYX+1vqWdpZvKeXd9Gd8UV9Pp1uQmRXHm+FTOHJ/KtGHxmGVYRoh+IYEufNfhgvWvwXdPQOVmMFth9A9h0uUw6kwIO/wEX9VNLj7cWM7Hmyv4triK9k5NYpSV08amcMb4VOaMSpKLl4Q4DhLo4uhpDeXrYd1rsGERNDvAFgcTLoRJC2DYrMMOyQA0ONv5YpuDT7dUsGxrJQ3ODqxmEzNy45k7Kpm5o5NlaEaIoySBLo5PZwfs/LfRc9/6nnGGTHyOEewTLoKUsUd8i/ZON6t21fDvIgfLixxsLTfug5oUbeWkkUmcNDKJE0ckkhUvV6gKcTgS6KL/uJpgy7uw/lXY+QWgIWkMjJ9vTDOQPuWIPXeA8nonX2538NWOKr7eUUVVUxsAwxIimZWXQEFOAgXD48lNipIevBA9SKCLgdGw3+ixb37HuJuSdoM9wxhzH3MO5M4Fy5HnXne7Ndsrm/imuIpviqtZuauG+lbjhtdJ0VZOHJHED0YmcdKoJJnLXYQ8CXQx8JqrYPvHsO0D2PE5tDdDWATknQKjzjBunZeQ51Pv3e3WFDuaWL2nlu92VvN1cTWORhcAaTE2pg+PZ9rweG1LIHIAAA8jSURBVAqGxzMhI0auWhUhRQJdDK52J+z5CrZ9BEVLoX6v0R6bDXknG+GeezJEJ/v0dlpriiqMHvyavXWs2VNLaV0rAJFWM1OHxTE5K45JWbFMyoojPdYmwzQiaEmgC//RGmp2ws5lULwMdn8JznpjWcoEyJ1jXMw0/MQjnu/eU3m9k8I9NazaVUPhnlq2lTfS4Tb+LSdFhzMpK5b8zFgmZsYyMTOGtBgJeREcJNDF0OHuhP1rjbNmdi2HvSugw+htkzQGsmdC9gmQOR2Sx4DJ7NPbOts72bK/gfUl9awvqWdDaR3bK5s48M87IcrKhIwYJmTEMiEjhnHpMeQkRspwjQg4Euhi6OpwQelq2PMN7FsJ+1aAs85YZomCzGlGDz7vZCPkzb5P4dvs6mBreQObyhrYWFrPprIGiioaae80/s1bzSbykqMYnWpndGo0o1LtjE61MywhUq5sFUOWBLoIHG63MX976Roj6Pd9B/vXA9oI+KwCGDYbhp0AGdMgIu6o3r6tw01RRSNFFY1sq2ikqLyRooqmrjF5AGuYibykKEal2hmbZmdMqp1RqdFkxkVIj174nQS6CGwtNcbY++6vYO+3ULHJOEUSIGm00XMfNhuGnwSJI3w6k+Zgza4OdlQ2sb2yie2VjWyvaKKoopGS2u6gt5gV2fGR5CVHMSI52nikRJGTGEVClFXG6MWgkEAXwcXZAKWFRg++ZDWUrIKWKmNZVIoxTJM2CdInG4/YrGMKeYBGZztFFU0UVzaxq7qZ3VXNFDua2F3VQlunu2u9GFsYucnRjEiOYmRKNHlJ0QxLiCQ7IQK7Te70JPqPBLoIblpD9Q7j4qY938L+dVC1rbsXH5FgBHvqBEgZB8njjAOu4dHH/JGdbk1JbQvFjiZ2VbWwu6qZnVVNFFc2U97g9Fo3LtJCTmJUV88+Kz6CzLgIMuMjSLHbZLxeHJXjDnSl1NnAY4AZeFpr/YeDls8F/gJMAi7XWi8+0ntKoIsB1dZiDM2UrzMCfv86cGyDjh5hGzccUsYbc9EkjzVCPmk0WKOO66Mbne3sqmpmX00r+2pb2FvTwi5HM7uqDg17i1mRGRdBVnykV9BnxkWQERdBWqwNi4zbix6OK9CVUmagCDgTKAFWAVdorTf3WCcHiAH+D7BEAl0MSe5OqN1tTAtcudXzvNno3bs7uteLG2b04pNGGWPyCXmQONKY1sB0fOHa0tZBWV0rJbU9Hy3sq22ltLaVqiaX1/omZVwdmxlvhH56rI30WBupMTbSY43AT4yyym0AQ8jhAt2XialnAju01js9b/YqcAHQFeha692eZe7e3kCIIcFkNgI6cQSMO7+7vbMdanaBYws4ioznyq2w6wvvHr0lEhJGQGKeEfLxuZCQa/T0YzLBfOT/nSKtYYxMsTMyxd7rcmd7JyW1reyvb6Wszgj5Es/zyl01VDQ4uy6g6irLrEixe4I+1kaq3UZKTDjJ0eEk28NJ83wBxNjC5MBtkPMl0DOBfT1elwAnHMuHKaVuAm4CGDZs2LG8hRD9z2yB5NHGoye3GxrLoLrY6MUfeC7fCFs/AHd797qmMOPga3yO55EL8cONsI/PgYh4nw7M2ixmRqZEMzKl9/F9t1tT1eyivN5pPBqclNU5qWhwsr++lc1lDSxrqKSlrfOQ342wmEmJCSfVbiPZE/hJ0VaSPMF/4DnZHi7DPAFqUG8do7V+CngKjCGXwfxsIY6ayWSEdGyWcWFTT+5OqC8xhnDq9hjPBx5b3oWWau/1zVaITjUe9jSwpxvPMZkQm+l5zjriHaFMJqM3nmK3MSmr7/WaXR04Gl1UNDipaHRR4Qn/ykYXlQ1OtpQ1sLzJRaOzo9ffT4q2kmy3kWLvDvkkzxdAYlQ4SXbjiyAhUoZ7hhJfAr0UyO7xOsvTJkToMpmNHnj88N6XOxugbq8n8PdCUzk0VRr3a60u9p7TpqfoVGMSs7hhEOd5js2GmAwj9H3s6UeFhxEVHkZO0uEP8DrbO6lqclHV1EZVowtHk4vKBhcVjU4q6p04mlwUVTRS1eTqusK2J7NJkRhl9Qr9xGgriVFWEqLCSYyyEh9lJSHSSkK0lSirWYZ9BpAvgb4KGKWUysUI8suBKwe0KiECnS0G0iYaj760tRgBX18CDaXGc91e47F/rdHT7zmsA2AO9/TsMyA6xTjvPioZopIgMtF4RCUZXww+hL/NYvacYXP4O0VprWlo7cDR5KKqyUV1UxuORqfxRdDkwtHoorLRRVF5I1VNbV7n6PdkDTN5wr77ERdhITbCQmyklfhIC/GRVmI9z/GRFuw2i5za6SNfT1s8F+O0RDPwjNb6IaXUA0Ch1nqJUmoG8BYQDziBcq31hMO9p5zlIsQRuN3QVGGEfUMpNJQZj8Zy44ugqcK412trbe+/b7IYoR+d4hnuORD+PR4HlvnY8/eF1ppGVwe1zW3UHPSobm6juqmN2hbj59rmNupb22lwttNXFCkFsREWI/gjrSREWozef7SVuEgLCZFW4iKNn2MjLMRFWoiLsGKzmILyrwG5sEiIYNbRBq01xrh9S7UxtNPsMAK/qdLzXAGNFcZyfegBUyP8U8Ge6un1J3kC39MW7WmPTobwmH4L/wPcbk2Ds53alnZqW9qoa2mjrqXd82ijrtX4ubbF+GKo9Xw5uDr6PrHOajYR6wn5GFuY8Rxx4PWBvwqMvwSMLwHjdVyEFWvY0D0ofLynLQohhrIwq+dAa9qR13W7jdksmyqhudIT+D1Dv9wY+in73vhS6C38w2yH9vQPfAFEJhrz2kckQFQiRCZBuP2IXwAmk/L0sq3k4vuFXa1tndS0GAHf0NreFfz1rQcenr8AWjuoamqj2NFMg7OdhtZ23Ifpy0ZazUbge8LfbgsjxvPFYLdZiIkIIzrcQrQtDLstDHu40W63GccuosPD/DJMJIEuRCgxmYzAjUwAxh5+Xbfb6PkfCPpmR48vAofxurEMyjcYPx883n+AOdwI+og4Y2gnIh5scZ7XPdq62uON9vDYI17IFWE1k2mNOOp7zbrdmqa2Duo9vf56zxdBXWs7dZ5hoHrPF0Sjs53yBifbKhppdHbQ6Dz8l8EBkVYz0eFhntC3YPcEvd0WxkXTspg9IvGoavaFBLoQoncmk6fnnWTMg3M4WoOrwTPs02P4p7nKM85fA611xqNml/FXQmsttLcc5k2VcXDZFntQ0NuNsA+3G8si4ozl4dHGtA3WaM86McbrXv46MJkUMTaj952dcPgDwoduqqa5rZMmZwdNrnYanB00OTu6wr7J1UGzq7Pr50ZnBw2enysanDS5OgYkzEECXQjRH5TyBG+scRWtr9qd3eHeWusJ/VqjzVlvvHbWdT9XbjW+OFyN0NbkQ10mI9gPhH5EXHedEQndB4ojE4zlXV8ecX3eTEUpZfS8w8MAm+/bOggk0IUQ/mOxgcXH8f+DdXYY4e6sN8Le1QRtzUbQHwh9Z4/lrZ4viYb9xuuWmr6HicC4oYot1tPrj+ru9dtijOdwe4+H57UtxrvNGm0c4xgkEuhCiMBkDutxPOAYaO05QOw59dNZ7x3+rbXgqjeuF2hrNr4g6vYYXxKueuO19mH6KrPV+EIIsxk/h4XDKffAxIuPre7DkEAXQoQmpboPxh4LrY1jAE7PXwOuxu6g73o0GX8xtDUZ98/tbIdO17F/5hFIoAshxLFQqns4hnR/VwPA0D17XgghxFGRQBdCiCAhgS6EEEFCAl0IIYKEBLoQQgQJCXQhhAgSEuhCCBEkJNCFECJI+O0GF0opB7DnGH89Cajqx3ICRShudyhuM4TmdofiNsPRb/dwrXVybwv8FujHQylV2NcdO4JZKG53KG4zhOZ2h+I2Q/9utwy5CCFEkJBAF0KIIBGogf6Uvwvwk1Dc7lDcZgjN7Q7FbYZ+3O6AHEMXQghxqEDtoQshhDiIBLoQQgSJgAt0pdTZSqltSqkdSql7/F3PQFBKZSullimlNiulNimlbve0JyilPlFKbfc8D8xtT/xIKWVWSn2vlHrP8zpXKbXCs79fU0oN3g0aB4lSKk4ptVgptVUptUUpNTtE9vUdnn/fG5VS/1JK2YJtfyulnlFKVSqlNvZo63XfKsNfPdu+Xik17Wg/L6ACXSllBh4HzgHGA1copcb7t6oB0QHcpbUeD8wCbvFs5z3AZ1rrUcBnntfB5nZgS4/XfwT+rLUeCdQCN/ilqoH1GPCR1nosMBlj+4N6XyulMoHbgAKt9UTADFxO8O3v54CzD2rra9+eA4zyPG4CnjjaDwuoQAdmAju01ju11m3Aq8AFfq6p32mt92ut13h+bsT4HzwTY1uf96z2PPAj/1Q4MJRSWcB5wNOe1wo4DVjsWSUYtzkWmAv8E0Br3aa1riPI97VHGBChlAoDIoH9BNn+1lovB2oOau5r314AvKAN3wFxSqmjurddoAV6JrCvx+sST1vQUkrlAFOBFUCq1nq/Z1E5kOqnsgbKX4BfAgdupZ4I1GmtOzyvg3F/5wIO4FnPUNPTSqkognxfa61LgT8BezGCvB5YTfDvb+h73x53vgVaoIcUpVQ08AbwC611Q89l2jjfNGjOOVVKzQMqtdar/V3LIAsDpgFPaK2nAs0cNLwSbPsawDNufAHGF1oGEMWhQxNBr7/3baAFeimQ3eN1lqct6CilLBhh/rLW+k1Pc8WBP8E8z5X+qm8AnATMV0rtxhhKOw1jbDnO8yc5BOf+LgFKtNYrPK8XYwR8MO9rgDOAXVprh9a6HXgT499AsO9v6HvfHne+BVqgrwJGeY6EWzEOoizxc039zjN2/E9gi9b60R6LlgDXen6+FnhnsGsbKFrr/9RaZ2mtczD26+da66uAZcAlntWCapsBtNblwD6l1BhP0+nAZoJ4X3vsBWYppSI9/94PbHdQ72+PvvbtEuAaz9kus4D6HkMzvtFaB9QDOBcoAoqB+/xdzwBt4w8w/gxbD6z1PM7FGFP+DNgOfAok+LvWAdr+U4D3PD/nASuBHcDrQLi/6xuA7Z0CFHr299tAfCjsa+B3wFZgI/AiEB5s+xv4F8YxgnaMv8Zu6GvfAgrjLL5iYAPGGUBH9Xly6b8QQgSJQBtyEUII0QcJdCGECBIS6EIIESQk0IUQIkhIoAshRJCQQBdCiCAhgS6EEEHi/wMuzjVF2nLqWwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot accuracy\n",
        "plt.plot(r.history['accuracy'],label = 'acc')\n",
        "plt.plot(r.history['val_accuracy'],label = 'val_acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "NjwS0AGZz2Oc",
        "outputId": "c2eff596-9f1e-4d22-874b-331aa3b10239"
      },
      "execution_count": 18,
      "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": [
        "# Make predictions\n",
        "X_new = X_test[:5]\n",
        "y_pred = model.predict(X_new)"
      ],
      "metadata": {
        "id": "6xuNgX2NeH44"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(y_pred)):\n",
        "  if y_pred[i] >=0.5:\n",
        "    print(f'We predict person number {i+1} has a benign tumor.')\n",
        "  else:\n",
        "    print(f'We predict person number {i+1} has a malignant tumor.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN_Rk4k0ekHZ",
        "outputId": "ea152241-cd67-442f-aeb6-f58c6177551a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We predict person number 1 has a malignant tumor.\n",
            "We predict person number 2 has a benign tumor.\n",
            "We predict person number 3 has a malignant tumor.\n",
            "We predict person number 4 has a benign tumor.\n",
            "We predict person number 5 has a malignant tumor.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(y_pred)):\n",
        "  if y_test[i]>=0.5:\n",
        "    print(f'In reality person number {i+1} has a benign tumor.')\n",
        "  else:\n",
        "    print(f'In reality person number {i+1} has a malignant tumor.')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sq3_lzjagaih",
        "outputId": "ebb280a9-565b-4095-ede7-42e5df44a788"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In reality person number 1 has a malignant tumor.\n",
            "In reality person number 2 has a benign tumor.\n",
            "In reality person number 3 has a malignant tumor.\n",
            "In reality person number 4 has a benign tumor.\n",
            "In reality person number 5 has a benign tumor.\n"
          ]
        }
      ]
    }
  ]
}