Copy_of_Proyek_Dicoding_Image_Classification_Model_Deployment_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Proyek-Dicoding_Image-Classification-Model-Deployment .ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-01_MSva-97"
      },
      "source": [
        "\n",
        "    Nama : Dini Ramadhani Salsabila\n",
        "    Umur = 21 tahun\n",
        "    E-mail = diniramadhani553@gmail.com\n",
        "    Asal kampus = Universitas Andalas\n",
        "    Jurusan = Teknik Komputer\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2jz5MTBr-eU"
      },
      "source": [
        "#import paket yg dbutuhkan\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import zipfile\n",
        "import shutil\n",
        "from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D\n",
        "from keras.models import Sequential\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",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvIUojGLsCe-",
        "outputId": "e8103153-88c0-4784-a229-a5c1164021d8"
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-07-14 14:32:06--  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  8.03MB/s    in 63s     \n",
            "\n",
            "2021-07-14 14:33:11 (4.85 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSxSMqxB5KHS"
      },
      "source": [
        "# melakukan ekstraksi file zip\n",
        "local_zip = '/tmp/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r') #baca file rockpaperscissors.zip \n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "#membuat nama pada direktori\n",
        "base_dir = '/tmp/rockpaperscissors'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'val')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhuTJYmnIhxz",
        "outputId": "571d4a01-e1dd-4062-a820-1b50c81376de"
      },
      "source": [
        "os.listdir(base_dir)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rock', 'scissors', 'rps-cv-images', 'README_rpc-cv-images.txt', 'paper']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8_2pt5MCrU4",
        "outputId": "9b07792e-4d15-41ed-bb30-991433408f4a"
      },
      "source": [
        "path = \"/tmp/rockpaperscissors/rps-cv-images/\"\n",
        "\n",
        "myList = os.listdir(path)\n",
        "myList.remove('README_rpc-cv-images.txt')\n",
        "\n",
        "noOfClasses = len(myList)\n",
        "\n",
        "print(\"Label Number:\", noOfClasses)\n",
        "print(myList)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Label Number: 3\n",
            "['rock', 'scissors', 'paper']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptOJH5T3FcL_"
      },
      "source": [
        "os.mkdir(train_dir)\n",
        "os.mkdir(validation_dir)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Dx8gFbdgHmj",
        "outputId": "ad70c782-e39d-41e2-8b65-7059a73ea8e3"
      },
      "source": [
        "os.listdir('/tmp/rockpaperscissors')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rock',\n",
              " 'train',\n",
              " 'scissors',\n",
              " 'val',\n",
              " 'rps-cv-images',\n",
              " 'README_rpc-cv-images.txt',\n",
              " 'paper']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcsfB26mgLcC"
      },
      "source": [
        "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')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OS9bfVhagjGq"
      },
      "source": [
        "# membagi dataset menjadi training 80% dan testing 20% \n",
        "# membuat direktori rock pada direktori data training dan validation\n",
        "train_rock_dir, val_rock_dir = train_test_split(os.listdir(rock_dir), test_size = 0.2) \n",
        "\n",
        "# membuat direktori paper pada direktori data training dan validation\n",
        "train_paper_dir, val_paper_dir = train_test_split(os.listdir(paper_dir), test_size = 0.2)\n",
        "\n",
        "# membuat direktori scissors pada direktori data validasi dan training\n",
        "train_scissors_dir, val_scissors_dir = train_test_split(os.listdir(scissors_dir), test_size = 0.2)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reCxeuKT2Bn3"
      },
      "source": [
        "#membuat directory train\n",
        "os.mkdir(train_rock)\n",
        "os.mkdir(train_paper)\n",
        "os.mkdir(train_scissors)\n",
        "\n",
        "#membuat direktory val\n",
        "os.mkdir(val_rock)\n",
        "os.mkdir(val_paper)\n",
        "os.mkdir(val_scissors)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArE3PHds2jU7"
      },
      "source": [
        "#memasukkan data ke directory baru\n",
        "\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",
        "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": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "masSsWFOeBsl",
        "outputId": "ebaed04e-081e-4d7c-e0e6-eaf5ec254409"
      },
      "source": [
        "!pip install split_folders\n",
        "\n",
        "import splitfolders\n",
        "\n",
        "splitfolders.ratio('/tmp/rockpaperscissors/rps-cv-images', '/tmp/rockpaperscissors/data', seed=1, ratio=(0.8, 0.2))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting split_folders\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/5f/3c2b2f7ea5e047c8cdc3bb00ae582c5438fcdbbedcc23b3cc1c2c7aae642/split_folders-0.4.3-py3-none-any.whl\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.4.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Copying files: 2188 files [00:00, 3632.20 files/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_JE2fSOeJcF",
        "outputId": "212fc69b-40eb-4b7f-860a-2b980c9e07f5"
      },
      "source": [
        "base_dir = '/tmp/rockpaperscissors/data'\n",
        "train_dir = os.path.join(base_dir, 'train') ## Train Dataset\n",
        "validation_dir = os.path.join(base_dir, 'val') ## Validation Dataset\n",
        "os.listdir('/tmp/rockpaperscissors/data/train')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rock', 'scissors', 'paper']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tKADIN-eS9p",
        "outputId": "9e7ba95a-983e-45d0-eac2-43982086e673"
      },
      "source": [
        "os.listdir('/tmp/rockpaperscissors/data/val')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rock', 'scissors', 'paper']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_kIWgWEeg6I"
      },
      "source": [
        "train_rock_dir = os.path.join(train_dir, 'rock')\n",
        "train_paper_dir = os.path.join(train_dir, 'paper')\n",
        "train_scissors_dir = os.path.join(train_dir, 'scissors')\n",
        "\n",
        "validation_rock_dir = os.path.join(validation_dir, 'rock')\n",
        "validation_paper_dir = os.path.join(validation_dir, 'paper')\n",
        "validation_scissors_dir = os.path.join(validation_dir, 'scissors')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "varYgf6FepSp",
        "outputId": "d9dd51b1-58a6-4807-b9df-f2720bb27433"
      },
      "source": [
        "#train 80% ini\n",
        "total_size_train = (\n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/train/rock')) + \n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/train/paper')) +\n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/train/scissors'))\n",
        ")\n",
        "print('Total Training Data is : {}'.format(total_size_train))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Training Data is : 1749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJF_39RBewlH",
        "outputId": "e709e8f6-d7a6-4139-e031-bca2408926af"
      },
      "source": [
        "#test 20% nih\n",
        "total_size_validation = (\n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/val/rock')) +\n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/val/paper')) +\n",
        "    len(os.listdir('/tmp/rockpaperscissors/data/val/scissors'))\n",
        ")\n",
        "print('Total Test Data is : {}'.format(total_size_validation))\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Test Data is : 439\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmvyCAfVbxuD"
      },
      "source": [
        "\n",
        "*   Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ih-zSpDqfYoS",
        "outputId": "9022a246-e5bc-4cfb-c1d6-9ef81f8297c3"
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range = 20,\n",
        "    width_shift_range = 0.1,\n",
        "    height_shift_range = 0.2,\n",
        "    horizontal_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,\n",
        "    fill_mode = 'nearest'\n",
        ")\n",
        "\n",
        "validation_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        ")\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size = (150, 150),\n",
        "    batch_size = 128,\n",
        "    class_mode = 'categorical'\n",
        ")\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size = (150, 150),\n",
        "    batch_size = 128,\n",
        "    class_mode = 'categorical'\n",
        ")"
      ],
      "execution_count": 29,
      "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": "05BO2A0I9PIT"
      },
      "source": [
        "model = Sequential() #menggunakan model sequential\n",
        "model.add(Conv2D(64, kernel_size = (3,3), strides = (1,1),activation = 'relu', padding = 'same', input_shape=(150,150,3)))\n",
        "model.add(MaxPooling2D(pool_size = (3,3))) # Menggunakan stride untuk mengurangi beban komputasi\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (3,3), strides = (1,1),activation = 'relu', padding = 'same'))\n",
        "model.add(MaxPooling2D(pool_size = (3,3))) # Menggunakan stride untuk mengurangi beban komputasi\n",
        "\n",
        "model.add(Conv2D(128, kernel_size = (3,3),strides = (1,1),activation = 'relu', padding = 'same'))\n",
        "model.add(MaxPooling2D(pool_size = (3,3))) # Menggunakan stride untuk mengurangi beban komputasi\n",
        "\n",
        "model.add(Conv2D(128, kernel_size = (3,3),strides = (1,1),activation = 'relu', padding = 'same'))\n",
        "model.add(MaxPooling2D(pool_size = (3,3))) # Menggunakan stride untuk mengurangi beban komputasi\n",
        "\n",
        "#Flatten feature map\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "#Fully connected layer\n",
        "model.add(Dense(512, activation = 'relu'))\n",
        "model.add(Dense(3, activation = 'softmax'))"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uVPnkjsRv7W"
      },
      "source": [
        "class testCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.88 and logs.get('val_accuracy') > 0.88):\n",
        "      print(\"\\nAchive 88% Accuracy\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = testCallback()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3yC9hluUaJ1"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vb6AOKSxZdN3"
      },
      "source": [
        "!rm -rf /tmp/rockpaperscissors/rps-cv-images/.ipynb_checkpoints"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJIVjZrwU_sn",
        "outputId": "7fe8ffb0-5aa9-40a2-810f-9402347b4e26"
      },
      "source": [
        "#latih dengan image data generator\n",
        "history = model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=25,  # berapa batch yang akan dieksekusi pada setiap epoch\n",
        "      epochs=20,\n",
        "      validation_data=validation_generator, # menampilkan akurasi pengujian data validasi\n",
        "      validation_steps=5, \n",
        "      verbose=2,\n",
        "      callbacks=[callbacks])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "25/25 - 151s - loss: 1.0806 - accuracy: 0.3944 - val_loss: 0.8602 - val_accuracy: 0.8254\n",
            "Epoch 2/20\n",
            "25/25 - 131s - loss: 0.7348 - accuracy: 0.6737 - val_loss: 0.2718 - val_accuracy: 0.9083\n",
            "Epoch 3/20\n",
            "25/25 - 132s - loss: 0.4355 - accuracy: 0.8385 - val_loss: 0.1611 - val_accuracy: 0.9577\n",
            "Epoch 4/20\n",
            "25/25 - 135s - loss: 0.3213 - accuracy: 0.8770 - val_loss: 0.0535 - val_accuracy: 0.9877\n",
            "Epoch 5/20\n",
            "25/25 - 136s - loss: 0.2137 - accuracy: 0.9303 - val_loss: 0.0498 - val_accuracy: 0.9894\n",
            "\n",
            "Achive 88% Accuracy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wrToTs24M4v8",
        "outputId": "1378ebc5-fd1c-4e59-fdcf-ff155c0b57f2"
      },
      "source": [
        "#loss \n",
        "history_df = pd.DataFrame(history.history)\n",
        "history_df.loc[1:, ['loss', 'val_loss']].plot()\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "t-4GNECjO5Ac",
        "outputId": "48b3f22c-1774-46bc-80d6-485fd44b2e01"
      },
      "source": [
        "#accuracy \n",
        "history_df.loc[1:, ['accuracy', 'val_accuracy']].plot()\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTAhGlfXPBKb",
        "outputId": "428563fb-fc16-4e16-e64e-9132778e19c1"
      },
      "source": [
        "#Best validation loss and accuracy score\n",
        "print((\"Best Validation Loss: {:0.2f}\" +\"\\nBest Validation Accuracy: {:0.2f}\").format(history_df['val_loss'].min(), history_df['val_accuracy'].max()))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Validation Loss: 0.05\n",
            "Best Validation Accuracy: 0.99\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30wlGVEJPGWr"
      },
      "source": [
        "Convert Model to tflite"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GTu9c2OPHIb",
        "outputId": "1c3ac6e1-55e3-47b9-9055-adac35b4021e"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with tf.io.gfile.GFile('model.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpm2guwl0f/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "vkTl1xA4PN4w",
        "outputId": "0c74b0ff-e6bd-41b8-a427-a82540aa6b53"
      },
      "source": [
        "#Download converted model\n",
        "files.download('model.tflite')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_b49bb64e-a563-4c48-aaa1-f92e2d102a7e\", \"model.tflite\", 1315720)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0atYc9skPYmq"
      },
      "source": [
        "klasifikasi dengan input gambar"
      ]
    },
    {
      "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": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "D6fnjuFffNmn",
        "outputId": "657fa3bc-13c4-4c0c-8e7e-b91be624c951"
      },
      "source": [
        "%matplotlib inline\n",
        " \n",
        "uploaded = files.upload()\n",
        " \n",
        "for fn in uploaded.keys():\n",
        "# predicting images\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]==1:\n",
        "    print('paper')\n",
        "  elif classes[0][1]==1:\n",
        "    print('rock')\n",
        "  elif classes[0][2]==1:\n",
        "    print('scissors')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ef87cde8-f402-4273-930d-c5fd747919e5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ef87cde8-f402-4273-930d-c5fd747919e5\">\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 3ONDEuB8iwAYO1Uz.png to 3ONDEuB8iwAYO1Uz.png\n",
            "3ONDEuB8iwAYO1Uz.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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Om1iIU4wGBcy"
      },
      "source": [
        "# Bagian Baru"
      ]
    }
  ]
}