{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "classificationFCNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Dataset - Breast Cancer"
      ],
      "metadata": {
        "id": "MDfw3VTykAm8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "f2CQaXKPjGwq"
      },
      "outputs": [],
      "source": [
        "from sklearn import datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "4Hy86U_XjO9p"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCnT365mjO6u",
        "outputId": "3776c3de-94a4-4878-8a29-7b9310c76e3b"
      },
      "execution_count": 3,
      "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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXPHkptgjO4b",
        "outputId": "a277fb4a-c86e-402e-ae90-e303c34383e5"
      },
      "execution_count": 4,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53bpRNwijO2M",
        "outputId": "c381ac0f-c5aa-4511-e03f-b825958be773"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(data.feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTxes9kZjOz6",
        "outputId": "9be7b161-a4d2-496b-d752-be21df20bbc6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNxyf8vEjOxu",
        "outputId": "08889df8-e11b-4d9f-8adf-69e97f0dd528"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDkRm9LTjOus",
        "outputId": "ff8fbb76-c1da-4373-9abb-f73aae730d4a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569,)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.data"
      ],
      "metadata": {
        "id": "jh9xnFMpjOsi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = data.target"
      ],
      "metadata": {
        "id": "7A1vI92IjOqZ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "-4a_vt_3jOoL"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=456, stratify=y)"
      ],
      "metadata": {
        "id": "VUlV9t2GjOl1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiTqB9fyjOjh",
        "outputId": "17e67972-9c23-4d05-ca6c-6a7ffeadb254"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(398, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SDafnC6jOgz",
        "outputId": "02ff0cc0-ad64-4aa2-d30e-af3eba319e8a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(171, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hziBgpkPjOeM",
        "outputId": "e67a0810-9f03-4c14-fb61-e9c05947a42d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.613e+01, 1.788e+01, 1.070e+02, 8.072e+02, 1.040e-01, 1.559e-01,\n",
              "       1.354e-01, 7.752e-02, 1.998e-01, 6.515e-02, 3.340e-01, 6.857e-01,\n",
              "       2.183e+00, 3.503e+01, 4.185e-03, 2.868e-02, 2.664e-02, 9.067e-03,\n",
              "       1.703e-02, 3.817e-03, 2.021e+01, 2.726e+01, 1.327e+02, 1.261e+03,\n",
              "       1.446e-01, 5.804e-01, 5.274e-01, 1.864e-01, 4.270e-01, 1.233e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "tk64ph2VjObZ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "uZjD8KKCjOYj"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESDQ_bqujOT5",
        "outputId": "4205c1f2-4847-4424-8b27-c18aa11eb7dc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.53569949, -0.32144709,  0.58344649,  0.40671395,  0.53185623,\n",
              "        0.87858605,  0.52298087,  0.68280034,  0.67148343,  0.26905974,\n",
              "       -0.26699658, -0.95260858, -0.35390298, -0.11690941, -0.92389033,\n",
              "        0.12784915, -0.20007137, -0.47113407, -0.42159193, -0.03806135,\n",
              "        0.80695644,  0.27222786,  0.73958519,  0.67132174,  0.53767841,\n",
              "        2.01780444,  1.17757981,  1.06284667,  2.20643957,  2.08111864])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "VgPyh0tmjlx0"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(input_dim=X_train.shape[1], units=100, activation='relu'))\n",
        "model.add(Dense(units=40, activation='relu'))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WLXRER0jlvJ",
        "outputId": "fdbee282-4e75-43cd-ea35-97fe6645e348"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 100)               3100      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 40)                4040      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 41        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,181\n",
            "Trainable params: 7,181\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "tKVEZIc4jlrp"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, batch_size=64, epochs=20, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-t53nj1YkUsN",
        "outputId": "cb058b99-7ec4-4b6c-d12f-429c20524329"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 6s 64ms/step - loss: 0.5247 - accuracy: 0.7767 - val_loss: 0.4173 - val_accuracy: 0.9125\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.3352 - accuracy: 0.9182 - val_loss: 0.3011 - val_accuracy: 0.9125\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.2399 - accuracy: 0.9308 - val_loss: 0.2249 - val_accuracy: 0.9625\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.1867 - accuracy: 0.9434 - val_loss: 0.1765 - val_accuracy: 0.9625\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1526 - accuracy: 0.9434 - val_loss: 0.1470 - val_accuracy: 0.9750\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.1290 - accuracy: 0.9591 - val_loss: 0.1270 - val_accuracy: 0.9875\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.1136 - accuracy: 0.9686 - val_loss: 0.1137 - val_accuracy: 0.9875\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.1028 - accuracy: 0.9717 - val_loss: 0.1044 - val_accuracy: 0.9875\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0939 - accuracy: 0.9748 - val_loss: 0.0978 - val_accuracy: 0.9875\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0875 - accuracy: 0.9811 - val_loss: 0.0929 - val_accuracy: 0.9875\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0826 - accuracy: 0.9811 - val_loss: 0.0902 - val_accuracy: 0.9875\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0786 - accuracy: 0.9811 - val_loss: 0.0884 - val_accuracy: 0.9875\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0743 - accuracy: 0.9811 - val_loss: 0.0866 - val_accuracy: 0.9875\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0708 - accuracy: 0.9780 - val_loss: 0.0849 - val_accuracy: 0.9875\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0679 - accuracy: 0.9780 - val_loss: 0.0836 - val_accuracy: 0.9875\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0654 - accuracy: 0.9780 - val_loss: 0.0830 - val_accuracy: 0.9875\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0629 - accuracy: 0.9780 - val_loss: 0.0824 - val_accuracy: 0.9875\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0607 - accuracy: 0.9780 - val_loss: 0.0830 - val_accuracy: 0.9875\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0583 - accuracy: 0.9811 - val_loss: 0.0840 - val_accuracy: 0.9875\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0565 - accuracy: 0.9843 - val_loss: 0.0854 - val_accuracy: 0.9875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.epoch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqibdjMNjvJA",
        "outputId": "6cb16230-6a15-4445-893d-5b544be5a75b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "rkC3nOHPjlo-"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = history.epoch\n",
        "plt.plot(epochs, history.history['loss'])\n",
        "plt.plot(epochs, history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "H1eJqmMyj4Yr",
        "outputId": "8d611dd0-f0d7-49f2-8c37-834f7d667fa7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6720060410>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epochs, history.history['accuracy'])\n",
        "plt.plot(epochs, history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "kZxs-3kyj4UC",
        "outputId": "5af03c64-2d39-466a-c526-88de3e862ef8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f67200d00d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test, batch_size=64)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_3CjReGj4K5",
        "outputId": "4408ca17-bb23-4454-a0e4-67210a16fe09"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 8ms/step - loss: 0.0708 - accuracy: 0.9766\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07082781940698624, 0.9766082167625427]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}