ML_RPS_Submission.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML RPS Submission.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZy6hfCTY7cq"
      },
      "source": [
        "Nama  : Wahyu Septiadi\n",
        "\n",
        "Email : wahyusptd@gmail.com"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znl11urNodJU"
      },
      "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"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ok6iSpQxo1X2",
        "outputId": "21938aa8-a3d0-4299-b04d-c5430bcdeaac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip -O /tmp/rockpaperscissors.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-02 12:26:17--  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  7.13MB/s    in 44s     \n",
            "\n",
            "2020-11-02 12:27:02 (6.93 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PpJypwfpK-p"
      },
      "source": [
        "# melakukan ekstraksi pada file zip\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": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SrrGSYJ7RGm"
      },
      "source": [
        "# base direktori\n",
        "base_dir = '/tmp/rockpaperscissors/rps-cv-images'"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2789VKA8PBd"
      },
      "source": [
        "# image generator (preprocessing data, pelabelan sampel otomatis, dan 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 = 'wrap',\n",
        "                    zoom_range=0.2,\n",
        "                    validation_split=0.4) # data validasi 40 % dari data training\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "                    rescale=1./255,\n",
        "                    rotation_range=20,                    \n",
        "                    horizontal_flip=True,\n",
        "                    shear_range = 0.2,\n",
        "                    fill_mode = 'wrap',\n",
        "                    zoom_range=0.2,)"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQZeXuoA_rth",
        "outputId": "3f01b303-4def-4687-de33-259e6b23a5c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# pembagian class (class index 0 = paper, class index 1 = rock, class index 2 = scissors)\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        base_dir,  # direktori data latih\n",
        "        target_size=(150, 150),\n",
        "        shuffle = True,\n",
        "        class_mode='categorical',\n",
        "        subset = 'training')\n",
        " \n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "        base_dir, # direktori data validasi\n",
        "        target_size=(150, 150),\n",
        "        shuffle = True,\n",
        "        class_mode='categorical',\n",
        "        subset = 'validation')"
      ],
      "execution_count": 132,
      "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": "code",
      "metadata": {
        "id": "qwsLebN7AaKc"
      },
      "source": [
        "# melakukan model sequential\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(130, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax') # 3 layer\n",
        "])"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0-dfZ_FAiTT"
      },
      "source": [
        "# menjalankan model dengan metrics accuracy\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8JqUMuqAmhB",
        "outputId": "e259e450-0946-4410-fd8a-483936c6888b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# melatih model dengan metode fit (selama [+-] 12 menit)\n",
        "model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=25,  \n",
        "      epochs=22, \n",
        "      validation_data=validation_generator, \n",
        "      validation_steps=5, \n",
        "      verbose=2)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/22\n",
            "25/25 - 38s - loss: 0.0506 - accuracy: 0.9688 - val_loss: 0.0604 - val_accuracy: 0.9563\n",
            "Epoch 2/22\n",
            "25/25 - 38s - loss: 0.0201 - accuracy: 0.9909 - val_loss: 0.0314 - val_accuracy: 0.9812\n",
            "Epoch 3/22\n",
            "25/25 - 39s - loss: 0.0482 - accuracy: 0.9700 - val_loss: 0.0417 - val_accuracy: 0.9750\n",
            "Epoch 4/22\n",
            "25/25 - 37s - loss: 0.0428 - accuracy: 0.9831 - val_loss: 0.0473 - val_accuracy: 0.9875\n",
            "Epoch 5/22\n",
            "25/25 - 39s - loss: 0.0269 - accuracy: 0.9875 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 6/22\n",
            "25/25 - 37s - loss: 0.0353 - accuracy: 0.9818 - val_loss: 0.0619 - val_accuracy: 0.9688\n",
            "Epoch 7/22\n",
            "25/25 - 37s - loss: 0.0272 - accuracy: 0.9844 - val_loss: 0.0509 - val_accuracy: 0.9875\n",
            "Epoch 8/22\n",
            "25/25 - 39s - loss: 0.0557 - accuracy: 0.9700 - val_loss: 0.0786 - val_accuracy: 0.9375\n",
            "Epoch 9/22\n",
            "25/25 - 39s - loss: 0.0601 - accuracy: 0.9688 - val_loss: 0.0403 - val_accuracy: 0.9750\n",
            "Epoch 10/22\n",
            "25/25 - 38s - loss: 0.0386 - accuracy: 0.9818 - val_loss: 0.0360 - val_accuracy: 0.9812\n",
            "Epoch 11/22\n",
            "25/25 - 38s - loss: 0.0432 - accuracy: 0.9779 - val_loss: 0.0192 - val_accuracy: 0.9937\n",
            "Epoch 12/22\n",
            "25/25 - 39s - loss: 0.0386 - accuracy: 0.9812 - val_loss: 0.0139 - val_accuracy: 0.9875\n",
            "Epoch 13/22\n",
            "25/25 - 39s - loss: 0.0376 - accuracy: 0.9812 - val_loss: 0.0296 - val_accuracy: 0.9812\n",
            "Epoch 14/22\n",
            "25/25 - 38s - loss: 0.0293 - accuracy: 0.9883 - val_loss: 0.0459 - val_accuracy: 0.9750\n",
            "Epoch 15/22\n",
            "25/25 - 42s - loss: 0.0215 - accuracy: 0.9862 - val_loss: 0.0248 - val_accuracy: 0.9812\n",
            "Epoch 16/22\n",
            "25/25 - 39s - loss: 0.0110 - accuracy: 0.9912 - val_loss: 0.0252 - val_accuracy: 0.9875\n",
            "Epoch 17/22\n",
            "25/25 - 38s - loss: 0.0374 - accuracy: 0.9831 - val_loss: 0.0384 - val_accuracy: 0.9750\n",
            "Epoch 18/22\n",
            "25/25 - 38s - loss: 0.0172 - accuracy: 0.9909 - val_loss: 0.0322 - val_accuracy: 0.9812\n",
            "Epoch 19/22\n",
            "25/25 - 39s - loss: 0.0213 - accuracy: 0.9812 - val_loss: 0.0186 - val_accuracy: 0.9875\n",
            "Epoch 20/22\n",
            "25/25 - 38s - loss: 0.0290 - accuracy: 0.9896 - val_loss: 0.0735 - val_accuracy: 0.9688\n",
            "Epoch 21/22\n",
            "25/25 - 39s - loss: 0.0138 - accuracy: 0.9925 - val_loss: 0.0092 - val_accuracy: 1.0000\n",
            "Epoch 22/22\n",
            "25/25 - 38s - loss: 0.0294 - accuracy: 0.9792 - val_loss: 0.0647 - val_accuracy: 0.9688\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f806b448588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQH3YRvJBSFY",
        "outputId": "a10d026a-f66c-4b82-89d0-4f76d29fce33",
        "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": 361
        }
      },
      "source": [
        "# pengujian gambar\n",
        "uploaded = files.upload()\n",
        " \n",
        "for fn in uploaded.keys():\n",
        " \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) # semisal nilai classes [[1. 0. 0.]] di analogikan [[paper. rock. scissors.]]\n",
        "  check = np.argmax(classes) # output = PAPER\n",
        "  print(fn)\n",
        "\n",
        "  if check==0:\n",
        "    print('paper')\n",
        "  elif check==1:\n",
        "    print('rock')\n",
        "  else:\n",
        "    print('scissors')"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-cfb651fd-6767-4ace-83d9-d773949a9ebe\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-cfb651fd-6767-4ace-83d9-d773949a9ebe\">\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 2PAcPusQ59xIMfiw.png to 2PAcPusQ59xIMfiw.png\n",
            "2PAcPusQ59xIMfiw.png\n",
            "paper\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"
          }
        }
      ]
    }
  ]
}