KM_06_01_01_Deep_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KM-06-01-01 Deep Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/KM_06_01_01_Deep_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMQNmzNDRqq-"
      },
      "source": [
        "### Define Model dengan cara sequential"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4HgqFiuRyO_"
      },
      "source": [
        "Menerima 8 input\n",
        "\n",
        "Memiliki 1 hidden layer dengan 10 neuron\n",
        "\n",
        "memiliki 1 output layer dengan 1 neuron\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMLC1ieBK-ER"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "# define the model\n",
        "model = Sequential()\n",
        "model.add(Dense(10, input_shape=(8,)))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KVRHCYTR4s2"
      },
      "source": [
        "Menerima 10 input\n",
        "\n",
        "Memiliki 6 hidden layer\n",
        " \n",
        "memiliki 1 output layer dengan 1 neuron\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAUv06l0J8yp"
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "# define the model\n",
        "model = Sequential()\n",
        "model.add(Dense(100, input_shape=(10,)))\n",
        "model.add(Dense(80))\n",
        "model.add(Dense(30))\n",
        "model.add(Dense(10))\n",
        "model.add(Dense(5))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "340B0E2PSCp9"
      },
      "source": [
        "### Define Model dengan cara functional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qeyyEGnSG1x"
      },
      "source": [
        "Menerima 8 input\n",
        "\n",
        "Memiliki 1 hidden layer dengan 10 neuron\n",
        "\n",
        "memiliki 1 output layer dengan 1 neuron"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwyI8ma7Llv7"
      },
      "source": [
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras import Input\n",
        "from tensorflow.keras.layers import Dense\n",
        "# define the layers\n",
        "x_in = Input(shape=(8,))\n",
        "x = Dense(10)(x_in)\n",
        "x_out = Dense(1)(x)\n",
        "# define the model\n",
        "model = Model(inputs=x_in, outputs=x_out)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5NlfR_-t4yT"
      },
      "source": [
        "# Membangun model Neural Network dengan Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25XptbUbqhZ9"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exjOMaHBlNa5"
      },
      "source": [
        "Run dari sini untuk latihan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmEev4AgPOcH"
      },
      "source": [
        "from numpy import argmax\n",
        "from pandas import read_csv\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EG0I4qU8PSo6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "3044eb57-da2c-4bb7-ba72-900567a49905"
      },
      "source": [
        "# load dataset\n",
        "path = 'https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/iris.csv'\n",
        "df = read_csv(path)\n",
        "\n",
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm           Label\n",
              "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
              "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
              "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
              "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
              "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
              "..             ...           ...            ...           ...             ...\n",
              "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
              "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
              "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
              "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
              "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isuFigKlPVFB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f5d4216-e621-418f-f852-0b1009b0d035"
      },
      "source": [
        "# split data \n",
        "X, y = df.values[:, :-1], df.values[:, -1]\n",
        "# ubah tipe data menjadi float32\n",
        "X = X.astype('float32')\n",
        "\n",
        "# encode string menjadi integer\n",
        "y = LabelEncoder().fit_transform(y)\n",
        "\n",
        "# split dataset menjadi 80% data training dan 20% data testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
        "\n",
        "# memperoleh banyaknya jumlah fitur pada input\n",
        "n_features = X_train.shape[1]\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 4) (30, 4) (120,) (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtHknn_zuXwS"
      },
      "source": [
        "## Latihan 1 \n",
        "Membangun model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd_R96Gwu0O8"
      },
      "source": [
        "![perbaikan.PNG](data:image/png;base64,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)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOZaEhEynPwh"
      },
      "source": [
        "# TODO 1 : Ketik kode di atas pada cell ini\n",
        "# define the model\n",
        "model = Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(n_features,)))\n",
        "model.add(Dense(8, activation='relu', ))\n",
        "model.add(Dense(3, activation='sigmoid'))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Xsa3ZcVvhmv"
      },
      "source": [
        "![2.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5mpxjE7nRWS"
      },
      "source": [
        "# TODO 2 : Ketik kode di atas pada cell ini\n",
        "\n",
        "# compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hVOZ1B3vl9r"
      },
      "source": [
        "![3.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gMDQcawnrs7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47fbbad4-5b8f-4a40-d00f-3d56e7460d4c"
      },
      "source": [
        "# TODO 3 : Ketik kode di atas pada cell ini\n",
        "\n",
        "# fit the model\n",
        "model.fit(X_train, y_train, epochs=150, batch_size=32, verbose=0)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff2d8371a50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30x9pKBFvod6"
      },
      "source": [
        "![4.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-kYQcaInuGM"
      },
      "source": [
        "# TODO 4 : Ketik kode di atas pada cell ini\n",
        "\n",
        "# evaluate the model\n",
        "loss = model.evaluate(X_test, y_test, verbose=0)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jeNZZNYvqwX"
      },
      "source": [
        "![5.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1HW8eCknul5"
      },
      "source": [
        "# TODO 5 : Ketik kode di atas pada cell ini\n",
        "# make a prediction\n",
        "row = [5.1, 3.5, 1.4, 0.2]\n",
        "yhat = model.predict([row])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OhlbDqewc95"
      },
      "source": [
        "# Visualize Deep Learning Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NkMeZjewmOR"
      },
      "source": [
        "### Model Text Description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37stvA1Vwogp"
      },
      "source": [
        "# Contoh \n",
        "from tensorflow.keras.layers import Dense\n",
        "# define model1\n",
        "model1 = Sequential()\n",
        "model1.add(Dense(100, input_shape=(10,)))\n",
        "model1.add(Dense(80))\n",
        "model1.add(Dense(30))\n",
        "model1.add(Dense(10))\n",
        "model1.add(Dense(5))\n",
        "model1.add(Dense(1))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSCjgS4d0Izk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7829c79e-28e1-4ccd-e18f-77a5fb4b4beb"
      },
      "source": [
        "# Model text description model 1\n",
        "model1.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_14 (Dense)             (None, 100)               1100      \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 80)                8080      \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 30)                2430      \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                310       \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 5)                 55        \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 1)                 6         \n",
            "=================================================================\n",
            "Total params: 11,981\n",
            "Trainable params: 11,981\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXuvOtGwxfcU"
      },
      "source": [
        "### Model Architecture Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ftNrut00REr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "d54cacc4-a52f-4419-95d3-26e147f79f50"
      },
      "source": [
        "# Contoh model architecture plot\n",
        "from tensorflow.keras.utils import plot_model\n",
        "# summarize model 1\n",
        "plot_model(model1, 'model_1_summary.png', show_shapes=True)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHCURZsDH1Me"
      },
      "source": [
        "### Plotting Learning curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQlHk9FKyKee"
      },
      "source": [
        "# Contoh plotting learning curves\n",
        "# define model 2 \n",
        "model2 = Sequential()\n",
        "model2.add(Dense(10, activation='relu', input_shape=(n_features,)))\n",
        "model2.add(Dense(8, activation='relu', ))\n",
        "model2.add(Dense(3, activation='softmax'))\n",
        "\n",
        "# compile model 2\n",
        "model2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# fit model 2\n",
        "history = model2.fit(X_train, y_train, epochs=150, batch_size=32, verbose=0,validation_split=0.3)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqCy-TTWyhpQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "49c7ca17-2680-4df1-b0d4-bcb5644ef1f6"
      },
      "source": [
        "from matplotlib import pyplot\n",
        "# plot learning curves\n",
        "pyplot.title('Learning Curves')\n",
        "pyplot.xlabel('Epoch')\n",
        "pyplot.ylabel('Loss function')\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='val')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "De6g0TPey5Ek"
      },
      "source": [
        "### Save and Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "US2PFoguzCwO"
      },
      "source": [
        "# save model 2 ke dalam file dengan nama \"model_2.h5\"\n",
        "model2.save('model_2.h5')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKW_wuvqzLSG"
      },
      "source": [
        "Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ED5neCBdzNAW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8955669a-a94f-466b-9f03-36f952d7e755"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "# load model 2 untuk digunakan lagi dari file\n",
        "model3 = load_model('model_2.h5')\n",
        "\n",
        "loss, acc = model3.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test Accuracy_model_3: %.3f' % acc)\n",
        "\n",
        "# Buat prediksi\n",
        "row = [0.1,0.5,7.4,7.2]\n",
        "yhat = model3.predict([row])\n",
        "print('Predicted: %s (class=%d)' % (yhat, argmax(yhat)))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy_model_3: 0.833\n",
            "Predicted: [[0.30440453 0.32262725 0.37296823]] (class=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKURV_KFqMob"
      },
      "source": [
        "# Latihan 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBcluqtnqPv8"
      },
      "source": [
        "1. Tampilkan deskripsi text dari model pada latihan 1\n",
        "2. Plot arsitektur dari model pada latihan 1\n",
        "3. Save model pada latihan 1 dengan nama \"nama_kelas.h5\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04UG9lzizjaI"
      },
      "source": [
        "# Get Better Model Performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_YPNvZwzrQn"
      },
      "source": [
        "### Menggunakan Dropout untuk mengurangi overfitting\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXbGa62Lzt_q"
      },
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(100, input_shape=(10,)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(80))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(30))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(5))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqlNDEcWz4yH"
      },
      "source": [
        "### Mempercepat Training dengan Batch Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbDqWFrbz545"
      },
      "source": [
        "from tensorflow.keras.layers import BatchNormalization\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(100, input_shape=(10,)))\n",
        "model.add(Dense(80))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(30))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(5))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7-rZWpp0N5Z"
      },
      "source": [
        "# Activity "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYTJ3xPT0cMC"
      },
      "source": [
        "### Fashion-MNIST adalah kumpulan dataset terdiri dari 28x28 gambar grayscale dari 70.000 produk fashion dari 10 kategori, dengan 7.000 gambar per kategori.Training set memiliki 60.000 gambar dan testing set memiliki 10.000 gambar. \n",
        "\n",
        "\n",
        "<table>\n",
        "  <tr><td>\n",
        "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
        "         alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
        "  </td></tr>\n",
        "  <tr><td align=\"center\">\n",
        "    <b>Figure</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
        "  </td></tr>\n",
        "</table>\n",
        "\n",
        "### Buat model neural network untuk menyelesaikan permasalahan klasifikasi pada dataset Fashion-MNIST\n",
        "\n",
        "### Silahkan buka link berikut, kemudian save as copy \n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LAkpMgD0IRfgUQP9CcGpkgMDt-QXY_uK?usp=sharing)\n"
      ]
    }
  ]
}