Athar_ML pemula.ipynb

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  "metadata": {
    "colab": {
      "name": "Athar_MLpemula.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Muhammad Athar Althariq Irawan\n",
        "\n",
        "Proyek akhir: Klasifikasi Gambar\n",
        "\n",
        "Dicoding: Belajar Machine Learning untuk Pemula"
      ],
      "metadata": {
        "id": "tDRN4rG9HROL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os,shutil,zipfile\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "#untuk menghitung waktu\n",
        "import time\n",
        "start_time = time.time()"
      ],
      "metadata": {
        "id": "XLeDw4KAv1nJ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0v6DSvRIoE6_",
        "outputId": "d8c1c7aa-09bd-425b-9115-29a7079677c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-17 13:42:38--  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220717%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220717T134238Z&X-Amz-Expires=300&X-Amz-Signature=5baf0869ac954ede27f8e88d481d9c2c53477bd18bbff5248fe3488e81450feb&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-07-17 13:42:38--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220717%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220717T134238Z&X-Amz-Expires=300&X-Amz-Signature=5baf0869ac954ede27f8e88d481d9c2c53477bd18bbff5248fe3488e81450feb&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 322873683 (308M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/rockpaperscissors.zip’\n",
            "\n",
            "/tmp/rockpapersciss 100%[===================>] 307.92M   146MB/s    in 2.1s    \n",
            "\n",
            "2022-07-17 13:42:41 (146 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip \\\n",
        "  -O /tmp/rockpaperscissors.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ekstraksi dataset zip file"
      ],
      "metadata": {
        "id": "vJNpDypdwSmR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "local_zip = '/tmp/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "zsiYxJ_4wNyp"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "membuat Direktori"
      ],
      "metadata": {
        "id": "XjOyZXc1w-q0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#nama Direktori\n",
        "base_dir = '/tmp/rockpaperscissors'\n",
        "train_dir = os.path.join(base_dir,'train')\n",
        "validation_dir = os.path.join(base_dir, 'val')\n",
        "roc_dir = os.path.join(base_dir,'rock')\n",
        "pap_dir = os.path.join(base_dir, 'paper')\n",
        "sci_dir = os.path.join(base_dir, 'scissors')\n",
        "\n",
        "#direktori train dan validasi\n",
        "os.mkdir(train_dir)\n",
        "os.mkdir(validation_dir)\n"
      ],
      "metadata": {
        "id": "YIEwNvapxA9U"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#untuk train\n",
        "train_roc = os.path.join(train_dir, 'rock')\n",
        "train_pap = os.path.join(train_dir, 'paper')\n",
        "train_sci = os.path.join(train_dir, 'scissor')\n",
        "\n",
        "#untuk validasi\n",
        "val_roc = os.path.join(validation_dir, 'rock')\n",
        "val_pap = os.path.join(validation_dir, 'paper')\n",
        "val_sci = os.path.join(validation_dir, 'scissor')"
      ],
      "metadata": {
        "id": "1gRRH7JEzK7T"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.mkdir(train_roc)\n",
        "os.mkdir(train_pap)\n",
        "os.mkdir(train_sci)\n",
        "\n",
        "os.mkdir(val_roc)\n",
        "os.mkdir(val_pap)\n",
        "os.mkdir(val_sci)"
      ],
      "metadata": {
        "id": "ipLgLE3L0bpm"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "test split dan train"
      ],
      "metadata": {
        "id": "_PB-808B0zvg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_roc_dir, val_roc_dir = train_test_split(os.listdir(roc_dir), test_size = 0.40)\n",
        "train_pap_dir, val_pap_dir = train_test_split(os.listdir(pap_dir), test_size = 0.40)\n",
        "train_sci_dir, val_sci_dir = train_test_split(os.listdir(sci_dir), test_size = 0.40)"
      ],
      "metadata": {
        "id": "NEYBf2wQ0y6l"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for file in train_roc_dir:\n",
        "  shutil.copy(os.path.join(roc_dir, file), os.path.join(train_roc, file))\n",
        "for file in train_pap_dir:\n",
        "  shutil.copy(os.path.join(pap_dir,file), os.path.join(train_pap,file))\n",
        "for file in train_sci_dir:\n",
        "  shutil.copy(os.path.join(sci_dir,file), os.path.join(train_sci,file))\n",
        "for file in val_roc_dir:\n",
        "  shutil.copy(os.path.join(roc_dir, file), os.path.join(val_roc,file))\n",
        "for file in val_pap_dir:\n",
        "  shutil.copy(os.path.join(pap_dir,file), os.path.join(val_pap,file))\n",
        "for file in val_sci_dir:\n",
        "  shutil.copy(os.path.join(sci_dir,file), os.path.join(val_sci,file))"
      ],
      "metadata": {
        "id": "mH_lDuqw1JY9"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "image data generator dan augmentasi gambar"
      ],
      "metadata": {
        "id": "XyRWXpLS1Ufy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale = 1./225,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    fill_mode = 'nearest'\n",
        "    )\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rescale = 1./225,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    fill_mode = 'nearest'\n",
        "    )"
      ],
      "metadata": {
        "id": "cF8KpOXU1ldI"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size = (150,150),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'categorical'\n",
        ")"
      ],
      "metadata": {
        "id": "58vhKrwh1skH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b10a154-b624-451d-d32f-5135932a184b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1312 images belonging to 3 classes.\n",
            "Found 876 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Membuat callback agar training berhenti setelah akurasi minimal 96%"
      ],
      "metadata": {
        "id": "tdnWElRxx4rk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy') > 0.96):\n",
        "      print(\"\\nAkurasi dihentikan!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()"
      ],
      "metadata": {
        "id": "niH0PpfKyTH7"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "model"
      ],
      "metadata": {
        "id": "TBoSTp-Q13lk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#dengan 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(256,(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')\n",
        "])\n"
      ],
      "metadata": {
        "id": "H1PxeMWU18Nq"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "NcoDmFkT2W7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ad017b5-83a9-47eb-d2d2-66f081cb721a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4 (Conv2D)           (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 74, 74, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 72, 72, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 36, 36, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 17, 17, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 15, 15, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 7, 7, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 12544)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 512)               6423040   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,812,995\n",
            "Trainable params: 6,812,995\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "mgKCz7Yq2ZE4"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch = 35,\n",
        "    epochs = 20,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = 25,\n",
        "    verbose =2,\n",
        "    \n",
        "      callbacks=[callbacks]\n",
        ")"
      ],
      "metadata": {
        "id": "qcXDK_gu22E3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8653e3a1-8733-43da-f35f-152d3209f95a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "35/35 - 90s - loss: 1.1002 - accuracy: 0.4420 - val_loss: 0.7819 - val_accuracy: 0.7450 - 90s/epoch - 3s/step\n",
            "Epoch 2/20\n",
            "35/35 - 81s - loss: 0.5903 - accuracy: 0.7741 - val_loss: 0.3231 - val_accuracy: 0.8788 - 81s/epoch - 2s/step\n",
            "Epoch 3/20\n",
            "35/35 - 82s - loss: 0.2829 - accuracy: 0.9062 - val_loss: 0.1956 - val_accuracy: 0.9413 - 82s/epoch - 2s/step\n",
            "Epoch 4/20\n",
            "35/35 - 81s - loss: 0.2349 - accuracy: 0.9170 - val_loss: 0.1795 - val_accuracy: 0.9425 - 81s/epoch - 2s/step\n",
            "Epoch 5/20\n",
            "35/35 - 82s - loss: 0.1796 - accuracy: 0.9393 - val_loss: 0.1278 - val_accuracy: 0.9513 - 82s/epoch - 2s/step\n",
            "Epoch 6/20\n",
            "35/35 - 80s - loss: 0.1747 - accuracy: 0.9482 - val_loss: 0.1605 - val_accuracy: 0.9425 - 80s/epoch - 2s/step\n",
            "Epoch 7/20\n",
            "35/35 - 84s - loss: 0.1430 - accuracy: 0.9536 - val_loss: 0.1365 - val_accuracy: 0.9613 - 84s/epoch - 2s/step\n",
            "Epoch 8/20\n",
            "35/35 - 81s - loss: 0.1250 - accuracy: 0.9589 - val_loss: 0.1434 - val_accuracy: 0.9513 - 81s/epoch - 2s/step\n",
            "Epoch 9/20\n",
            "\n",
            "Akurasi dihentikan!\n",
            "35/35 - 80s - loss: 0.1133 - accuracy: 0.9625 - val_loss: 0.0918 - val_accuracy: 0.9700 - 80s/epoch - 2s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "visualisasi"
      ],
      "metadata": {
        "id": "TlYMeGrZ3fIc"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "4-TniZ553hWS"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#akurasi train & validation\n",
        "plt.plot(history.history['accuracy'], label='Akurasi Training')\n",
        "plt.plot(history.history['val_accuracy'], label='Akurasi Validasi')\n",
        "plt.title('Plot akurasi')\n",
        "plt.ylabel('Value')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "-8E70yUmG9fx",
        "outputId": "fec52878-79be-4d0b-c794-a5e487d57633"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loss train & validation\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Loss Plot')\n",
        "plt.ylabel('Value')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "USUakxFMG4N3",
        "outputId": "f9610d2e-a2ce-468e-de58-63e6331ad915"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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)\n",
        "\n",
        "  print(fn)\n",
        "  if classes[0,0]!=0:\n",
        "    print('paper')\n",
        "  elif classes[0,1]!=0:\n",
        "    print('rock')\n",
        "  else:\n",
        "    print('scissors')"
      ],
      "metadata": {
        "id": "0o8cn7GH4FWK",
        "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": 360
        },
        "outputId": "dbfc881b-df42-444c-b2d2-2cf6b3d226bc"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8ce8e6dc-7d0c-4bbf-a4c1-260a19ae5d48\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8ce8e6dc-7d0c-4bbf-a4c1-260a19ae5d48\">\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> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 0cb6cVL8pkfi4wF6.png to 0cb6cVL8pkfi4wF6.png\n",
            "0cb6cVL8pkfi4wF6.png\n",
            "paper\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"total waktu yang dibutuhkan untuk mengeksekusi ialah %s detik\" % (time.time() - start_time))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjMSVEfPHj_l",
        "outputId": "9fe47f46-2073-447f-d829-e7c48060c2f9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total waktu yang dibutuhkan untuk mengeksekusi ialah 791.3852558135986 detik\n"
          ]
        }
      ]
    }
  ]
}