#015 Latihan Convolutional Neural Network TensorFlow.ipynb

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  "metadata": {
    "colab": {
      "name": "Latihan Convolutional Neural Network - TensorFlow.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyb4zWxuw8uI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Mengimport semua library yang di butuhkan\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LetPnOfkSx7N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6e739fdd-912c-4821-da2d-837cb42b08d2"
      },
      "source": [
        "#Mencek Versi Tensorflow\n",
        "print(tf.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4WU9weixCXR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "1f10197f-22af-4ba4-f095-42ce080360db"
      },
      "source": [
        "#Mempersiapkan Data\n",
        "!wget --no-check-certificate \\\n",
        "  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/messy-vs-clean-room.zip \\\n",
        "  -O /tmp/messy_vs_clean_room.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-05 12:02:54--  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/messy-vs-clean-room.zip\n",
            "Resolving dicodingacademy.blob.core.windows.net (dicodingacademy.blob.core.windows.net)... 52.239.197.36\n",
            "Connecting to dicodingacademy.blob.core.windows.net (dicodingacademy.blob.core.windows.net)|52.239.197.36|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 70392746 (67M) [application/zip]\n",
            "Saving to: ‘/tmp/messy_vs_clean_room.zip’\n",
            "\n",
            "/tmp/messy_vs_clean 100%[===================>]  67.13M  8.36MB/s    in 11s     \n",
            "\n",
            "2020-07-05 12:03:06 (5.88 MB/s) - ‘/tmp/messy_vs_clean_room.zip’ saved [70392746/70392746]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5T6Ahl9pxqhc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Melakukan ekstraksi pada file zip\n",
        "import zipfile,os\n",
        "local_zip = '/tmp/messy_vs_clean_room.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "#Mendefinisikan nama direktori untuk data latih dan data validasi.\n",
        "base_dir = '/tmp/images'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'val')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vc4WpGADysk6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e118bcfe-700c-4e2e-8887-cc210d8e44d9"
      },
      "source": [
        "#Terdapat Sub Direktori\n",
        "#sub-direktori ‘clean’ terdapat gambar-gambar ruangan yang rapi dan pada sub-direktori ‘messy’ terdapat gambar-gambar ruangan yang berantakan\n",
        "os.listdir('/tmp/images/train')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['clean', 'messy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JT6AgnrSLsXc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e9e56f5d-7192-4a1c-e034-49b0e7c31a2c"
      },
      "source": [
        "os.listdir('/tmp/images/val')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['clean', 'messy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMQT00NwyZ4B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Membuat direktori ruangan rapi pada direktori data training\n",
        "train_clean_dir = os.path.join(train_dir, 'clean')\n",
        "\n",
        "#Membuat direktori ruangan berantakan pada direktori data training\n",
        "train_messy_dir = os.path.join(train_dir, 'messy')\n",
        "\n",
        "#Membuat direktori ruangan rapi pada direktori data validasi\n",
        "validation_clean_dir = os.path.join(validation_dir, 'clean')\n",
        "\n",
        "#Membuat direktori ruangan berantakan pada direktori data validasi\n",
        "validation_messy_dir = os.path.join(validation_dir, 'messy')"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bN5EPlHKzqZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Membuat sebuah objek ImageDataGenerator untuk data training dan data testing\n",
        "#Image data generator adalah sebuah fungsi yang sangat berguna untuk mempersiapkan data latih dan data testing yang akan diberikan ke model\n",
        "#Kemudahan image data generator adalah, preprocessing data, pelabelan sampel otomatis, dan augmentasi gambar\n",
        "#Kode di bawah menunjukkan proses augmentasi gambar pada setiap sampel di dataset\n",
        "#Augmentasi gambar adalah teknik untuk menciptakan data-data baru dari data yang telah ada\n",
        "#Contoh augmentasi gambar adalah horizontal flip di mana gambar akan dibalikkan secara horizontal\n",
        "train_datagen = ImageDataGenerator(\n",
        "                    rescale=1./255,\n",
        "                    rotation_range=20,\n",
        "                    horizontal_flip=True,\n",
        "                    shear_range = 0.2,\n",
        "                    fill_mode = 'nearest')\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "                    rescale=1./255,\n",
        "                    rotation_range=20,\n",
        "                    horizontal_flip=True,\n",
        "                    shear_range = 0.2,\n",
        "                    fill_mode = 'nearest')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68RFaVYFzwIx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e9b15eb3-8d57-4a42-f10b-8d2a04849fea"
      },
      "source": [
        "#Menggunakan objek image data generator sebelumnya untuk mempersiapkan data latih yang akan dipelajari oleh model\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,  # direktori data latih\n",
        "        target_size=(150, 150),  # mengubah resolusi seluruh gambar menjadi 150x150 piksel\n",
        "        batch_size=4,\n",
        "        # karena kita merupakan masalah klasifikasi 2 kelas maka menggunakan class_mode = 'binary'\n",
        "        class_mode='binary')\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir, # direktori data validasi\n",
        "        target_size=(150, 150), # mengubah resolusi seluruh gambar menjadi 150x150 piksel\n",
        "        batch_size=4, # karena kita merupakan masalah klasifikasi 2 kelas maka menggunakan class_mode = 'binary'\n",
        "        class_mode='binary')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 192 images belonging to 2 classes.\n",
            "Found 20 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56TDjdW2zp7i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Ketika data siap, selanjutnya membangun arsitektur sebuah CNN\n",
        "#Terdapatnya 2 lapis layer konvolusi dan max pooling\n",
        "#Fungsi dari layer konvolusi adalah untuk mengekstraksi atribut pada gambar\n",
        "#Sedangkan layer max pooling berguna untuk mereduksi resolusi gambar sehingga proses pelatihan MLP lebih cepat.\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOasoOyL1WeH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Memanggil fungsi compile pada objek model, dan tentukan loss function serta optimizer\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRreG_Cy1vQr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "defd939f-bf3e-4864-97b8-57bccf141517"
      },
      "source": [
        "#Melatih model kita menggunakan metode fit\n",
        "#Menggunakan image data generator, kita tidak perlu memasukkan parameter gambar dan labelnya\n",
        "#Image data generator secara otomatis melabeli sebuah gambar sesuai dengan direktori di mana ia disimpan\n",
        "#Contohnya sebuah gambar yang terdapat di direktori clean, secara otomatis akan diberi label “clean” oleh image data generator \n",
        "model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=25,  # berapa batch yang akan dieksekusi pada setiap epoch\n",
        "      epochs=20,\n",
        "      validation_data=validation_generator, # menampilkan akurasi pengujian data validasi\n",
        "      validation_steps=5,  # erapa batch yang akan dieksekusi pada setiap epoch\n",
        "      verbose=2)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "25/25 - 1s - loss: 0.4895 - accuracy: 0.7800 - val_loss: 0.3633 - val_accuracy: 0.9500\n",
            "Epoch 2/20\n",
            "25/25 - 1s - loss: 0.4517 - accuracy: 0.8400 - val_loss: 0.4119 - val_accuracy: 0.8000\n",
            "Epoch 3/20\n",
            "25/25 - 1s - loss: 0.4967 - accuracy: 0.7700 - val_loss: 0.3450 - val_accuracy: 0.8000\n",
            "Epoch 4/20\n",
            "25/25 - 1s - loss: 0.4619 - accuracy: 0.8000 - val_loss: 0.3864 - val_accuracy: 0.8000\n",
            "Epoch 5/20\n",
            "25/25 - 1s - loss: 0.5532 - accuracy: 0.7500 - val_loss: 0.5462 - val_accuracy: 0.9000\n",
            "Epoch 6/20\n",
            "25/25 - 1s - loss: 0.6090 - accuracy: 0.5900 - val_loss: 0.4866 - val_accuracy: 0.7500\n",
            "Epoch 7/20\n",
            "25/25 - 1s - loss: 0.5231 - accuracy: 0.7600 - val_loss: 0.4068 - val_accuracy: 0.8000\n",
            "Epoch 8/20\n",
            "25/25 - 1s - loss: 0.3796 - accuracy: 0.8500 - val_loss: 0.2958 - val_accuracy: 0.9000\n",
            "Epoch 9/20\n",
            "25/25 - 1s - loss: 0.5079 - accuracy: 0.7900 - val_loss: 0.4622 - val_accuracy: 0.8500\n",
            "Epoch 10/20\n",
            "25/25 - 1s - loss: 0.5001 - accuracy: 0.7400 - val_loss: 0.4296 - val_accuracy: 0.8500\n",
            "Epoch 11/20\n",
            "25/25 - 1s - loss: 0.4961 - accuracy: 0.7800 - val_loss: 0.3467 - val_accuracy: 0.9000\n",
            "Epoch 12/20\n",
            "25/25 - 1s - loss: 0.3211 - accuracy: 0.8700 - val_loss: 0.2698 - val_accuracy: 0.9000\n",
            "Epoch 13/20\n",
            "25/25 - 1s - loss: 0.4263 - accuracy: 0.8600 - val_loss: 0.3601 - val_accuracy: 0.8000\n",
            "Epoch 14/20\n",
            "25/25 - 1s - loss: 0.3924 - accuracy: 0.8100 - val_loss: 0.4218 - val_accuracy: 0.8500\n",
            "Epoch 15/20\n",
            "25/25 - 1s - loss: 0.4015 - accuracy: 0.8200 - val_loss: 0.4672 - val_accuracy: 0.7000\n",
            "Epoch 16/20\n",
            "25/25 - 1s - loss: 0.3645 - accuracy: 0.8400 - val_loss: 0.4392 - val_accuracy: 0.7000\n",
            "Epoch 17/20\n",
            "25/25 - 1s - loss: 0.5123 - accuracy: 0.7600 - val_loss: 0.4672 - val_accuracy: 0.7500\n",
            "Epoch 18/20\n",
            "25/25 - 1s - loss: 0.4483 - accuracy: 0.7700 - val_loss: 0.4837 - val_accuracy: 0.7000\n",
            "Epoch 19/20\n",
            "25/25 - 1s - loss: 0.4343 - accuracy: 0.7900 - val_loss: 0.4653 - val_accuracy: 0.8000\n",
            "Epoch 20/20\n",
            "25/25 - 1s - loss: 0.3618 - accuracy: 0.8600 - val_loss: 0.4555 - val_accuracy: 0.8500\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fde20afef98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
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            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "3c42ed05-e7da-40a1-f7f7-c18dba37832a"
      },
      "source": [
        "#Kode di bawah memungkinkan kita untuk secara interaktif memilih sebuah berkas gambar\n",
        "#Kemudian melakukan resize gambar dan mengubahnya menjadi larik numpy\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "%matplotlib inline\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(150,150))\n",
        "  imgplot = plt.imshow(img)\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "\n",
        "  images = np.vstack([x])\n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  \n",
        "  print(fn)\n",
        "  if classes==0:\n",
        "    print('clean')\n",
        "  else:\n",
        "    print('messy')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c600f73a-7ff6-4f95-bb39-d65083765444\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c600f73a-7ff6-4f95-bb39-d65083765444\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving iStock-873941944.jpg to iStock-873941944.jpg\n",
            "iStock-873941944.jpg\n",
            "clean\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2h4SLqGHlgG6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}