DeepLearning_hompimpa.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Submission_Dicoding_DeepLearning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "12L1w0skIE5pHJ-0r7MB99BG4rLrw-4b-",
      "authorship_tag": "ABX9TyPia1GSzidJeL5P3sai+Bhc",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AndyAlyf/deepLearning_hompimpa/blob/master/DeepLearning_hompimpa.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B--HkgwMqihr",
        "colab_type": "text"
      },
      "source": [
        "## Import Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO-WbEE-qlzT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ad76b05-1b2c-4ff8-84b5-31cf7b363f7f"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop as optimizer\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator as imageGenerator\n",
        "import zipfile,os\n",
        "import numpy as np\n",
        "import datetime\n",
        "import time\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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HND8J84lpXUt",
        "colab_type": "text"
      },
      "source": [
        "## Import and preprocessing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbutEg36k5Y5",
        "colab_type": "text"
      },
      "source": [
        "### Proses mendownload dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNLDY4bQpceN",
        "colab_type": "code",
        "outputId": "c96b6725-7c41-4892-c26a-09198f1aabd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip\\\n",
        "  -O rockpaperscissors.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-10 09:19:32--  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: ‘rockpaperscissors.zip’\n",
            "\n",
            "rockpaperscissors.z 100%[===================>] 307.92M  26.5MB/s    in 11s     \n",
            "\n",
            "2020-05-10 09:19:43 (28.3 MB/s) - ‘rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtv7sUN3k9wz",
        "colab_type": "text"
      },
      "source": [
        "### Ekstraksi Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEJYggRYqM7u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "local_zip = 'rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnD6jC56qlit",
        "colab_type": "code",
        "outputId": "f82eb437-28c3-4bce-92a5-c34e6f5a56ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "os.listdir('rockpaperscissors/rps-cv-images')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['README_rpc-cv-images.txt', 'paper', 'scissors', 'rock']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXNj1HubWjjH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.remove('rockpaperscissors/rps-cv-images/README_rpc-cv-images.txt') #Menghapus file yang tidak dibutuhkan"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JplfWpuFWkGq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9911cad-be98-46d4-ed64-7d4698ffd63b"
      },
      "source": [
        "os.listdir('rockpaperscissors/rps-cv-images')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['paper', 'scissors', 'rock']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2kF06brlCOc",
        "colab_type": "text"
      },
      "source": [
        "## Preprocessing dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6i012roPtEo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "direktori = 'rockpaperscissors/rps-cv-images' #direktoro dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWbTOE1ROofr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Image generator untuk preprocessing image/gambar\n",
        "datagen = imageGenerator( \n",
        "                rescale=1./255, \n",
        "                rotation_range=20,\n",
        "                horizontal_flip=True,\n",
        "                shear_range = 0.2,\n",
        "                fill_mode = 'nearest',\n",
        "                validation_split = 0.2)  #Membagi dataset untuk train dan valuasi sebesar 4 : 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DChpR15IPi7W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1bcf2c1e-babe-4749-9244-7d311ba6e1d5"
      },
      "source": [
        "train_data = datagen.flow_from_directory(\n",
        "    direktori,\n",
        "    target_size = (100,150), #Disesuaikan dengan resolusi gambar dataset dari 300x200 pixel\n",
        "    batch_size=32,\n",
        "    color_mode=\"grayscale\", #mengubah gambar menjadi hitam putih agar dapat diimplementasikan pada gambar lain\n",
        "    class_mode = 'categorical', #Categorical karena lebih dari 2 kelas\n",
        "    shuffle=True, #Mengacak dataset\n",
        ")\n",
        "\n",
        "validasi_data = datagen.flow_from_directory(\n",
        "    direktori,\n",
        "    target_size=(100,150), #Disesuaikan dengan resolusi gambar dataset dari 300x200 pixel\n",
        "    batch_size=32,\n",
        "    color_mode=\"grayscale\", #mengubah gambar menjadi hitam putih agar dapat diimplementasikan pada gambar lain\n",
        "    class_mode='categorical', #Categorical karena lebih dari 2 kelas\n",
        "    shuffle=False,\n",
        "    subset = 'validation' #validasi_data berasal dari subset validation yang telah dibagi di train_data\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2188 images belonging to 3 classes.\n",
            "Found 437 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNpzwOVjRVZC",
        "colab_type": "text"
      },
      "source": [
        "## Modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBGxKa5mSHuZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(100, 150, 1)), #Input shape disesikan dari dataset\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(3, activation='softmax') #Menggunakan 3 percepteron sebagai output\n",
        "])\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', #Menggunaakn loss function categorical_crossentropy karena lebih dari 2 label\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XIxvzJS4gzZO",
        "colab_type": "text"
      },
      "source": [
        "## Training Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9emcZ-nRS8l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "9850f5b4-ab48-495a-cf55-a32a721b2ec8"
      },
      "source": [
        "print(\"waktu mulai training : \",datetime.datetime.now()) \n",
        "print()\n",
        "waktu = (time.localtime().tm_hour)*60+time.localtime().tm_min\n",
        "model.fit(\n",
        "      train_data,\n",
        "      steps_per_epoch=25,  \n",
        "      epochs=10,\n",
        "      validation_data=validasi_data, \n",
        "      validation_steps=50,  \n",
        "      verbose=2)\n",
        "print()\n",
        "waktu_now = (time.localtime().tm_hour)*60+time.localtime().tm_min\n",
        "print(\"waktu selesai training : \",datetime.datetime.now()) \n",
        "print(\"Lama training  : \",waktu_now - waktu,\"menit\") #Memprint lama waktu training"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "waktu mulai training :  2020-05-10 09:19:50.148320\n",
            "\n",
            "Epoch 1/10\n",
            "25/25 - 35s - loss: 1.0488 - accuracy: 0.4238 - val_loss: 0.8914 - val_accuracy: 0.6158\n",
            "Epoch 2/10\n",
            "25/25 - 35s - loss: 0.7525 - accuracy: 0.6538 - val_loss: 0.6359 - val_accuracy: 0.7892\n",
            "Epoch 3/10\n",
            "25/25 - 35s - loss: 0.5850 - accuracy: 0.7564 - val_loss: 0.4439 - val_accuracy: 0.8539\n",
            "Epoch 4/10\n",
            "25/25 - 35s - loss: 0.4148 - accuracy: 0.8338 - val_loss: 0.5228 - val_accuracy: 0.7866\n",
            "Epoch 5/10\n",
            "25/25 - 35s - loss: 0.4702 - accuracy: 0.8263 - val_loss: 0.2605 - val_accuracy: 0.9254\n",
            "Epoch 6/10\n",
            "25/25 - 34s - loss: 0.2848 - accuracy: 0.9103 - val_loss: 0.2608 - val_accuracy: 0.9183\n",
            "Epoch 7/10\n",
            "25/25 - 35s - loss: 0.2380 - accuracy: 0.9187 - val_loss: 0.2164 - val_accuracy: 0.9235\n",
            "Epoch 8/10\n",
            "25/25 - 34s - loss: 0.1681 - accuracy: 0.9463 - val_loss: 0.2301 - val_accuracy: 0.9145\n",
            "Epoch 9/10\n",
            "25/25 - 34s - loss: 0.2050 - accuracy: 0.9308 - val_loss: 0.1286 - val_accuracy: 0.9560\n",
            "Epoch 10/10\n",
            "25/25 - 35s - loss: 0.1148 - accuracy: 0.9638 - val_loss: 0.1066 - val_accuracy: 0.9589\n",
            "\n",
            "waktu selesai training :  2020-05-10 09:25:45.711214\n",
            "Lama training  :  6 menit\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CkH_19-rcYU",
        "colab_type": "text"
      },
      "source": [
        "AKURASI MODEL TRAIN = 96.38%\n",
        "\n",
        "AKURASI MODEL VALIDASI = 95.89%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-0QKB45g2O4",
        "colab_type": "text"
      },
      "source": [
        "## File Upload"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqFY9DiTbLOa",
        "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": 376
        },
        "outputId": "c478f300-1f5b-4733-e16b-54e96d5cb13d"
      },
      "source": [
        "uploaded = files.upload()\n",
        " \n",
        "for i in uploaded.keys():\n",
        "    img = image.load_img(i, target_size=(100,150), color_mode='grayscale')\n",
        "    x = image.img_to_array(img)\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    plt.imshow(img)\n",
        "    \n",
        "    images = np.vstack([x])\n",
        "    kelas = model.predict(images, batch_size=32)\n",
        "    \n",
        "    print(i)\n",
        "    print()\n",
        "    if (kelas[0][0]==0):\n",
        "        if (kelas[0][1]==0):\n",
        "            print(\"Gambar adalah GUNTING / SCISSORS\")\n",
        "        else:\n",
        "            print(\"Gambar adalah BATU / ROCK\")\n",
        "    else:\n",
        "        print(\"Gambar adalah KERTAS / PAPER\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1ceaa592-d235-422c-a1d3-b1f0e4c04741\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-1ceaa592-d235-422c-a1d3-b1f0e4c04741\">\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 2E8j595xseKRF60J.png to 2E8j595xseKRF60J.png\n",
            "2E8j595xseKRF60J.png\n",
            "\n",
            "Gambar adalah GUNTING / 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"
          }
        }
      ]
    }
  ]
}