Submission_Dicoding_ML_Pemula_Riyaldi.ipynb

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    "colab": {
      "name": "Submission_Dicoding_ML_Pemula_Riyaldi.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
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    "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/riyhs/ml-rock-paper-scissors/blob/main/Submission_Dicoding_ML_Pemula_Riyaldi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV-VhRvfqbyd"
      },
      "source": [
        "**Biodata**\n",
        "\n",
        "Nama : Riyaldi Hasan Setiawan\n",
        "\n",
        "E-mail : riyaldi.dev@gmail.com\n",
        "\n",
        "Alamat : Kab. Sukoharjo, Prov. Jawa Tengah"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4GNlRnUI40z"
      },
      "source": [
        "Mengimport library-library yang dibutuhkan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_2iviWQqSzp"
      },
      "source": [
        "import tensorflow as tf\n",
        "import zipfile,os\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\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",
        "%matplotlib inline\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-pB9_rLJEou"
      },
      "source": [
        "Mendownload dataset rockpaperscissors.zip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlWw4T3ArphZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5753052b-b948-4969-b786-63c40a6754a2"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-30 16:14:11--  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   855KB/s    in 5m 22s  \n",
            "\n",
            "2021-05-30 16:19:34 (979 KB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPZ1XZ0vJQ0N"
      },
      "source": [
        "Mengekstrak dataset yang telah didapatkan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuIcvJSBteXw"
      },
      "source": [
        "local_zip = '/tmp/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "base_dir = '/tmp/rockpaperscissors/rps-cv-images'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swVuG11MJtlE"
      },
      "source": [
        "melihat struktur data yang telah di ekstrak"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTCDzTStujNJ",
        "outputId": "c2c70518-431f-46db-f02e-447f9366f61b"
      },
      "source": [
        "os.listdir(base_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['scissors', 'paper', 'rock', 'README_rpc-cv-images.txt']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jD_C3tHjJ2-m"
      },
      "source": [
        "Augmentasi gambar menggunakan ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDVS3l7pv45Q"
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "                  rescale=1./255,\n",
        "                  rotation_range=20,\n",
        "                  horizontal_flip=True,\n",
        "                  shear_range=0.2,\n",
        "                  fill_mode='wrap',\n",
        "                  validation_split=0.4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mV9ATygKLqR"
      },
      "source": [
        "Mempersiapkan data untuk dipelajari model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jlm6QFhwepi",
        "outputId": "b9c7ab65-1339-4f0c-d227-264e249840cc"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "                    base_dir,\n",
        "                    target_size=(100,150),\n",
        "                    shuffle=True,\n",
        "                    subset='training')\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "                    base_dir,\n",
        "                    target_size=(100,150),\n",
        "                    subset='validation')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1314 images belonging to 3 classes.\n",
            "Found 874 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CqNQ967KdJP"
      },
      "source": [
        "Membangun arsitektur CNN menggunakan model Sequential"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yTjTc4txg9K"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "      tf.keras.layers.Conv2D(32, (3,3), activation= 'relu', input_shape = (100,150,3)),\n",
        "      tf.keras.layers.MaxPool2D(2,2),\n",
        "      tf.keras.layers.Conv2D(64, (3,3), activation= 'relu'),\n",
        "      tf.keras.layers.MaxPool2D(2,2),\n",
        "      tf.keras.layers.Conv2D(128, (3,3), activation= 'relu'),\n",
        "      tf.keras.layers.MaxPool2D(2,2),\n",
        "      tf.keras.layers.Flatten(),\n",
        "      tf.keras.layers.Dropout(0.2),\n",
        "      tf.keras.layers.Dense(512,activation= 'relu'),\n",
        "      tf.keras.layers.Dense(3, activation= 'softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJKbaGYYKtfE"
      },
      "source": [
        "Memanggil fungsi ``` compile()``` untuk menentukan loss function dan optimizer\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTV1FSr5zFfP"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1ZBWXf6K-E-"
      },
      "source": [
        "Melatih model menggunakan metode ```fit()```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfZbFpN7zRdf",
        "outputId": "9a051332-a3ac-4a8c-da53-73197b8ff4dd"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "25/25 - 29s - loss: 1.0276 - accuracy: 0.5025 - val_loss: 0.7401 - val_accuracy: 0.7937\n",
            "Epoch 2/20\n",
            "25/25 - 28s - loss: 0.4645 - accuracy: 0.8288 - val_loss: 0.4684 - val_accuracy: 0.8313\n",
            "Epoch 3/20\n",
            "25/25 - 28s - loss: 0.2420 - accuracy: 0.9200 - val_loss: 0.2980 - val_accuracy: 0.8938\n",
            "Epoch 4/20\n",
            "25/25 - 28s - loss: 0.2827 - accuracy: 0.8938 - val_loss: 0.2329 - val_accuracy: 0.9125\n",
            "Epoch 5/20\n",
            "25/25 - 28s - loss: 0.2083 - accuracy: 0.9337 - val_loss: 0.1515 - val_accuracy: 0.9563\n",
            "Epoch 6/20\n",
            "25/25 - 27s - loss: 0.1404 - accuracy: 0.9532 - val_loss: 0.1500 - val_accuracy: 0.9375\n",
            "Epoch 7/20\n",
            "25/25 - 28s - loss: 0.1025 - accuracy: 0.9663 - val_loss: 0.1118 - val_accuracy: 0.9500\n",
            "Epoch 8/20\n",
            "25/25 - 28s - loss: 0.1153 - accuracy: 0.9638 - val_loss: 0.2516 - val_accuracy: 0.9062\n",
            "Epoch 9/20\n",
            "25/25 - 28s - loss: 0.1470 - accuracy: 0.9463 - val_loss: 0.1675 - val_accuracy: 0.9312\n",
            "Epoch 10/20\n",
            "25/25 - 27s - loss: 0.0859 - accuracy: 0.9688 - val_loss: 0.1290 - val_accuracy: 0.9500\n",
            "Epoch 11/20\n",
            "25/25 - 28s - loss: 0.1631 - accuracy: 0.9375 - val_loss: 0.0832 - val_accuracy: 0.9688\n",
            "Epoch 12/20\n",
            "25/25 - 28s - loss: 0.0662 - accuracy: 0.9787 - val_loss: 0.1137 - val_accuracy: 0.9625\n",
            "Epoch 13/20\n",
            "25/25 - 28s - loss: 0.0495 - accuracy: 0.9837 - val_loss: 0.0877 - val_accuracy: 0.9750\n",
            "Epoch 14/20\n",
            "25/25 - 27s - loss: 0.0445 - accuracy: 0.9870 - val_loss: 0.0499 - val_accuracy: 0.9875\n",
            "Epoch 15/20\n",
            "25/25 - 28s - loss: 0.0601 - accuracy: 0.9850 - val_loss: 0.1855 - val_accuracy: 0.9625\n",
            "Epoch 16/20\n",
            "25/25 - 27s - loss: 0.0892 - accuracy: 0.9675 - val_loss: 0.1161 - val_accuracy: 0.9688\n",
            "Epoch 17/20\n",
            "25/25 - 27s - loss: 0.0467 - accuracy: 0.9844 - val_loss: 0.1665 - val_accuracy: 0.9312\n",
            "Epoch 18/20\n",
            "25/25 - 28s - loss: 0.0753 - accuracy: 0.9750 - val_loss: 0.0443 - val_accuracy: 0.9875\n",
            "Epoch 19/20\n",
            "25/25 - 28s - loss: 0.0479 - accuracy: 0.9883 - val_loss: 0.1119 - val_accuracy: 0.9688\n",
            "Epoch 20/20\n",
            "25/25 - 27s - loss: 0.0324 - accuracy: 0.9922 - val_loss: 0.0770 - val_accuracy: 0.9750\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1e98974350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHoUHUstLRZX"
      },
      "source": [
        "Membuat Google Colab lebih interaktif dengan menambahkan fungsi tambah gambar untuk di prediksi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "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": 358
        },
        "id": "CkjZ0A8gzl-3",
        "outputId": "43e0ab69-44dc-4dfd-a0fc-0fb9f5e39eaa"
      },
      "source": [
        "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=(100,150))\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",
        "  result = np.argmax(classes)\n",
        "  \n",
        "  print(fn)\n",
        "  if result==0:\n",
        "    print('paper')\n",
        "  elif result==1:\n",
        "    print('rock')\n",
        "  else:\n",
        "    print('scissors')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c7cba45b-8595-48ae-8e45-953eee0e4800\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c7cba45b-8595-48ae-8e45-953eee0e4800\">\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 0OEXfEooCXlljaEl.png to 0OEXfEooCXlljaEl (2).png\n",
            "0OEXfEooCXlljaEl.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"
          }
        }
      ]
    }
  ]
}