Model Klasifikasi Gambar.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ixvan_BGK.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jlp_bUw0VR_u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f9d230fa-2827-4ee8-ca49-6529cfcd6e69"
      },
      "source": [
        "#import library\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "print(tf.__version__)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYyVgszcVVZx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "1b66dda2-8e45-47e6-bfd6-0e14561eaf75"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip \\\n",
        "-O /tmp/rockpaperscissors.zip"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-16 13:38:45--  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.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: 322873683 (308M) [application/zip]\n",
            "Saving to: ‘/tmp/rockpaperscissors.zip’\n",
            "\n",
            "/tmp/rockpapersciss 100%[===================>] 307.92M  8.91MB/s    in 47s     \n",
            "\n",
            "2020-07-16 13:39:34 (6.49 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a78yi3yuVo5N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#extrak file\n",
        "import zipfile,os\n",
        "local_zip ='/tmp/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Fs4FIGspeHY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#pengelolaan file\n",
        "import shutil\n",
        "shutil.rmtree('/tmp/__MACOSX')\n",
        "shutil.rmtree('/tmp/rockpaperscissors/rps-cv-images')\n",
        "os.remove('/tmp/rockpaperscissors.zip')\n",
        "# buat direktori\n",
        "base_dir = '/tmp/rockpaperscissors'\n",
        "train_dir = os.path.join(base_dir,'train')\n",
        "validation_dir = os.path.join(base_dir,'val')\n",
        "os.mkdir(train_dir)\n",
        "os.mkdir(validation_dir)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIgvNF2gp6j6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#path direktori\n",
        "rock_dir = os.path.join(base_dir,'rock')\n",
        "paper_dir = os.path.join(base_dir,'paper')\n",
        "scissors_dir = os.path.join(base_dir,'scissors')\n",
        "\n",
        "# bagi direktori kelas data jadi data train dan data val\n",
        "train_rock_dir, val_rock_dir = train_test_split(os.listdir(rock_dir), test_size=0.2)\n",
        "train_paper_dir, val_paper_dir = train_test_split(os.listdir(paper_dir), test_size=0.2)\n",
        "train_scissors_dir, val_scissors_dir = train_test_split(os.listdir(scissors_dir), test_size=0.2)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LndSy14anozz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# path untuk direktori training & validation pada kelas data\n",
        "train_rock = os.path.join(train_dir,'rock')\n",
        "train_paper = os.path.join(train_dir,'paper')\n",
        "train_scissors = os.path.join(train_dir,'scissors')\n",
        "val_rock = os.path.join(validation_dir,'rock')\n",
        "val_paper = os.path.join(validation_dir,'paper')\n",
        "val_scissors = os.path.join(validation_dir,'scissors')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNmtVxuknRFS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# buat direktori di dalam direktori train\n",
        "if not os.path.exists(train_rock):\n",
        "  os.mkdir(train_rock)\n",
        "if not os.path.exists(train_paper):\n",
        "  os.mkdir(train_paper)\n",
        "if not os.path.exists(train_scissors):\n",
        "  os.mkdir(train_scissors)\n",
        "\n",
        "# membuat direktori di dalam direktori validation\n",
        "if not os.path.exists(valid_rock):\n",
        "  os.mkdir(val_rock)\n",
        "if not os.path.exists(valid_paper):\n",
        "  os.mkdir(val_paper)\n",
        "if not os.path.exists(valid_scissors):\n",
        "  os.mkdir(val_scissors)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWXyaJMOZ7Fl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#paste data train ke direktori baru\n",
        "for i in train_rock_dir:\n",
        "  shutil.copy(os.path.join(rock_dir, i), os.path.join(train_rock, i))\n",
        "for i in train_paper_dir:\n",
        "  shutil.copy(os.path.join(paper_dir,i), os.path.join(train_paper,i))\n",
        "for i in train_scissors_dir:\n",
        "  shutil.copy(os.path.join(scissors_dir,i), os.path.join(train_scissors,i))\n",
        "\n",
        "#paste data validation ke direktori baru\n",
        "for i in val_rock_dir:\n",
        "  shutil.copy(os.path.join(rock_dir, i), os.path.join(val_rock,i))\n",
        "for i in val_paper_dir:\n",
        "  shutil.copy(os.path.join(paper_dir,i), os.path.join(val_paper,i))\n",
        "for i in val_scissors_dir:\n",
        "  shutil.copy(os.path.join(scissors_dir,i), os.path.join(val_scissors,i))"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RUnuhApU193",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#proses augmentasi gambar\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')\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGrEeSaZa_Vg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c0c49299-7d91-4ae7-8d00-1d691fe69c47"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,  # direktori train\n",
        "        target_size=(150, 150), \n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        " \n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir, # direktori validasi\n",
        "        target_size=(150, 150),\n",
        "        batch_size=32, \n",
        "        class_mode='categorical')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1749 images belonging to 3 classes.\n",
            "Found 439 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4pnwUQ933b_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if (logs.get('loss') < 0.1):\n",
        "      print()\n",
        "      print(\"akurasi mencapai lebih dari 95%!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()\n"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6e76DQdbF1B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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(256, (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(3, activation='softmax')\n",
        "])"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjcEwkvgbIJA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mR-NQt8bbKhT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "846a9957-8551-4b39-debf-2b1cb948245b"
      },
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch = 25,\n",
        "    epochs = 20,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = 5,\n",
        "    verbose =2,\n",
        "    callbacks=[callbacks]\n",
        ")"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "25/25 - 7s - loss: 0.6323 - accuracy: 0.3967 - val_loss: 0.5305 - val_accuracy: 0.6938\n",
            "Epoch 2/20\n",
            "25/25 - 7s - loss: 0.3702 - accuracy: 0.7613 - val_loss: 0.2604 - val_accuracy: 0.8500\n",
            "Epoch 3/20\n",
            "25/25 - 7s - loss: 0.2179 - accuracy: 0.8834 - val_loss: 0.1837 - val_accuracy: 0.9187\n",
            "Epoch 4/20\n",
            "25/25 - 7s - loss: 0.1772 - accuracy: 0.9050 - val_loss: 0.1530 - val_accuracy: 0.9375\n",
            "Epoch 5/20\n",
            "25/25 - 7s - loss: 0.1489 - accuracy: 0.9150 - val_loss: 0.1367 - val_accuracy: 0.9125\n",
            "Epoch 6/20\n",
            "25/25 - 7s - loss: 0.1244 - accuracy: 0.9337 - val_loss: 0.1742 - val_accuracy: 0.9250\n",
            "Epoch 7/20\n",
            "25/25 - 7s - loss: 0.1677 - accuracy: 0.8973 - val_loss: 0.1668 - val_accuracy: 0.9250\n",
            "Epoch 8/20\n",
            "25/25 - 7s - loss: 0.1151 - accuracy: 0.9312 - val_loss: 0.1043 - val_accuracy: 0.9500\n",
            "Epoch 9/20\n",
            "\n",
            "akurasi mencapai lebih dari 95%!\n",
            "25/25 - 7s - loss: 0.0995 - accuracy: 0.9438 - val_loss: 0.1457 - val_accuracy: 0.9312\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOwXb2b_VAQF",
        "colab_type": "code",
        "colab": {
          "resources": {
            "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": 360
        },
        "outputId": "46a3b614-f9a9-462f-867d-8efacb4b4f29"
      },
      "source": [
        "#penentuan gambar dari gambar yg diupload\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",
        "  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[0,0]!=0:\n",
        "    print(\"Paper\")\n",
        "  elif classes[0,1]!=0:\n",
        "    print(\"Rock\")\n",
        "  else:\n",
        "    print(\"Scissors\")\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-54b76d9d-449d-47d5-9c91-b832160fde72\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-54b76d9d-449d-47d5-9c91-b832160fde72\">\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 1MOm36DHK0R8OfIC.png to 1MOm36DHK0R8OfIC.png\n",
            "1MOm36DHK0R8OfIC.png\n",
            "Rock\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"
          }
        }
      ]
    }
  ]
}