Submission_Belajar_Machine_Learning_untuk_Pemula.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Submission Belajar Machine Learning untuk Pemula.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSfATVbKuWZ-"
      },
      "source": [
        "Nama : Robertus<br>\n",
        "Domisili : Tana Toraja, Sulawesi Selatan<br>\n",
        "No. HP/WA : 082290009830<br>\n",
        "Alamat Email : robertus988@gmail.com"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErePVMdMuP1u"
      },
      "source": [
        "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",
        "import shutil"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYIbpTyevjmI",
        "outputId": "5e5a9f2a-1b76-4e81-cb81-fb79c04bcd9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip \\\n",
        "  -O /tmp/rockpaperscissor.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-30 04:26:20--  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/rockpaperscissor.zip’\n",
            "\n",
            "/tmp/rockpapersciss 100%[===================>] 307.92M  12.0MB/s    in 35s     \n",
            "\n",
            "2020-10-30 04:26:55 (8.91 MB/s) - ‘/tmp/rockpaperscissor.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZdDkI6jwpja"
      },
      "source": [
        "# eksrak file zip\n",
        "import zipfile, os\n",
        "local_zip = '/tmp/rockpaperscissor.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyTkSIfj01Rg"
      },
      "source": [
        "# split dataset\n",
        "base_dir = '/tmp/rockpaperscissors'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'val')\n",
        "rock_dir = os.path.join(base_dir, 'rock')\n",
        "scissors_dir = os.path.join(base_dir, 'scissors')\n",
        "paper_dir = os.path.join(base_dir, 'paper')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKkBajUa2Dlc"
      },
      "source": [
        "os.mkdir(train_dir)\n",
        "os.mkdir(validation_dir)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkJ6YOwi2RIo"
      },
      "source": [
        "train_rock = os.path.join(train_dir, 'rock')\n",
        "train_scissors = os.path.join(train_dir, 'scissors')\n",
        "train_paper = os.path.join(train_dir, 'paper')\n",
        "val_rock = os.path.join(validation_dir, 'rock')\n",
        "val_scissors = os.path.join(validation_dir, 'scissors')\n",
        "val_paper = os.path.join(validation_dir, 'paper')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEmRxg6F204B"
      },
      "source": [
        "os.mkdir(train_rock)\n",
        "os.mkdir(train_scissors)\n",
        "os.mkdir(train_paper)\n",
        "os.mkdir(val_rock)\n",
        "os.mkdir(val_scissors)\n",
        "os.mkdir(val_paper)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9Qz4S0A3D8B"
      },
      "source": [
        "# data train dan data validasi\n",
        "train_rock_dir, val_rock_dir = train_test_split(os.listdir(rock_dir), test_size = 0.4)\n",
        "train_scissors_dir, val_scissors_dir = train_test_split(os.listdir(scissors_dir), test_size = 0.4)\n",
        "train_paper_dir, val_paper_dir = train_test_split(os.listdir(paper_dir), test_size = 0.4)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4FJ1F8a37MM"
      },
      "source": [
        "for file in train_rock_dir:\n",
        "  shutil.copy(os.path.join(rock_dir, file), os.path.join(train_rock, file))\n",
        "for file in train_scissors_dir:\n",
        "  shutil.copy(os.path.join(scissors_dir, file), os.path.join(train_scissors, file))\n",
        "for file in train_paper_dir:\n",
        "  shutil.copy(os.path.join(paper_dir, file), os.path.join(train_paper, file))\n",
        "for file in val_rock_dir:\n",
        "  shutil.copy(os.path.join(rock_dir, file), os.path.join(val_rock, file))\n",
        "for file in val_scissors_dir:\n",
        "  shutil.copy(os.path.join(scissors_dir, file), os.path.join(val_scissors, file))\n",
        "for file in val_paper_dir:\n",
        "  shutil.copy(os.path.join(paper_dir, file), os.path.join(val_paper, file))"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULEFiImr5CWC"
      },
      "source": [
        "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",
        "    vertical_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    fill_mode = 'nearest')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlA01uzc57pp",
        "outputId": "98f23f71-e5b7-4b5a-dc46-abeb4031c19b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\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,\n",
        "    target_size = (150,150),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'categorical')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1312 images belonging to 3 classes.\n",
            "Found 876 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_mIfF3x7uIl",
        "outputId": "c6b9498f-9758-4e15-d7aa-64e15c54a291",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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(32, (3,3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(2,2),\n",
        "  tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(2,2),\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(512, activation='relu'),\n",
        "  tf.keras.layers.Dense(3, activation='softmax')                                  \n",
        "])\n",
        "\n",
        "model.summary()\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 148, 148, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 72, 72, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 34, 34, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9248)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 9248)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               4735488   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 1539      \n",
            "=================================================================\n",
            "Total params: 4,756,419\n",
            "Trainable params: 4,756,419\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWKJVEYn9bz2",
        "outputId": "08bfe87d-dfa7-4f5f-99ea-417d2e4619ce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "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)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "25/25 - 26s - loss: 1.1933 - accuracy: 0.4137 - val_loss: 0.9688 - val_accuracy: 0.7125\n",
            "Epoch 2/20\n",
            "25/25 - 26s - loss: 0.8042 - accuracy: 0.6762 - val_loss: 0.7825 - val_accuracy: 0.6687\n",
            "Epoch 3/20\n",
            "25/25 - 26s - loss: 0.6174 - accuracy: 0.7563 - val_loss: 0.6091 - val_accuracy: 0.7250\n",
            "Epoch 4/20\n",
            "25/25 - 26s - loss: 0.4471 - accuracy: 0.8275 - val_loss: 0.4005 - val_accuracy: 0.8313\n",
            "Epoch 5/20\n",
            "25/25 - 26s - loss: 0.3255 - accuracy: 0.8712 - val_loss: 0.2569 - val_accuracy: 0.9187\n",
            "Epoch 6/20\n",
            "25/25 - 26s - loss: 0.2744 - accuracy: 0.8950 - val_loss: 0.3048 - val_accuracy: 0.8875\n",
            "Epoch 7/20\n",
            "25/25 - 26s - loss: 0.2525 - accuracy: 0.8925 - val_loss: 0.4446 - val_accuracy: 0.8188\n",
            "Epoch 8/20\n",
            "25/25 - 26s - loss: 0.2472 - accuracy: 0.9125 - val_loss: 0.2563 - val_accuracy: 0.9062\n",
            "Epoch 9/20\n",
            "25/25 - 26s - loss: 0.1606 - accuracy: 0.9475 - val_loss: 0.1672 - val_accuracy: 0.9250\n",
            "Epoch 10/20\n",
            "25/25 - 26s - loss: 0.1296 - accuracy: 0.9550 - val_loss: 0.1644 - val_accuracy: 0.9375\n",
            "Epoch 11/20\n",
            "25/25 - 26s - loss: 0.1337 - accuracy: 0.9588 - val_loss: 0.0700 - val_accuracy: 0.9812\n",
            "Epoch 12/20\n",
            "25/25 - 26s - loss: 0.1133 - accuracy: 0.9575 - val_loss: 0.2962 - val_accuracy: 0.8813\n",
            "Epoch 13/20\n",
            "25/25 - 30s - loss: 0.1291 - accuracy: 0.9513 - val_loss: 0.1095 - val_accuracy: 0.9625\n",
            "Epoch 14/20\n",
            "25/25 - 26s - loss: 0.1842 - accuracy: 0.9375 - val_loss: 0.1857 - val_accuracy: 0.9438\n",
            "Epoch 15/20\n",
            "25/25 - 26s - loss: 0.1301 - accuracy: 0.9500 - val_loss: 0.1555 - val_accuracy: 0.9375\n",
            "Epoch 16/20\n",
            "25/25 - 26s - loss: 0.1049 - accuracy: 0.9663 - val_loss: 0.2393 - val_accuracy: 0.9312\n",
            "Epoch 17/20\n",
            "25/25 - 26s - loss: 0.1172 - accuracy: 0.9588 - val_loss: 0.0872 - val_accuracy: 0.9750\n",
            "Epoch 18/20\n",
            "25/25 - 26s - loss: 0.0846 - accuracy: 0.9712 - val_loss: 0.1030 - val_accuracy: 0.9688\n",
            "Epoch 19/20\n",
            "25/25 - 26s - loss: 0.0614 - accuracy: 0.9800 - val_loss: 0.1300 - val_accuracy: 0.9625\n",
            "Epoch 20/20\n",
            "25/25 - 26s - loss: 0.0633 - accuracy: 0.9787 - val_loss: 0.0524 - val_accuracy: 0.9812\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8cc3936860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20AofOUVAc-Q",
        "outputId": "dfbcdb6b-a850-45b0-94e0-b22669052bcd",
        "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": 453
        }
      },
      "source": [
        "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",
        "%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",
        "  print(classes[0,0])\n",
        "  print(classes[0,1])\n",
        "  print(classes[0,2])\n",
        "\n",
        "  if classes[0,0] == 1:\n",
        "    print('Paper')\n",
        "  elif classes[0,1] == 1:\n",
        "    print('Rock')\n",
        "  elif classes[0,2] == 1:\n",
        "    print('Scissors')\n",
        "  \n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5eeb3d7e-7b86-442f-916f-e7f1836c75e7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5eeb3d7e-7b86-442f-916f-e7f1836c75e7\">\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 beb656e8-37a1-41c5-bac0-ed030e59cd4b.png to beb656e8-37a1-41c5-bac0-ed030e59cd4b (3).png\n",
            "beb656e8-37a1-41c5-bac0-ed030e59cd4b.png\n",
            "0.0\n",
            "0.0\n",
            "1.0\n",
            "Scissors\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"
          }
        }
      ]
    }
  ]
}