ml_sardi_dicoding.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ml_sardi_dicoding.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/Sardiirfan27/machine_learning/blob/master/ml_sardi_dicoding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFb2yPf1SV7g"
      },
      "source": [
        "Nama : Sardi Irfansyah\n",
        "\n",
        "**submission Dicoding**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SN2Z18anVcvG"
      },
      "source": [
        "## Import library dan fetch dataset dari server\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbrfwXKdVyH_"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import zipfile,os,shutil"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H5C81DyVbTt",
        "outputId": "86d57869-7427-4c49-d281-8bd757040ccb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "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": [
            "--2020-09-26 03:03:59--  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  18.9MB/s    in 15s     \n",
            "\n",
            "2020-09-26 03:04:14 (20.7 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUX-_OecQT5S"
      },
      "source": [
        "#ekstrak dataset\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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWm10e9ol0sE"
      },
      "source": [
        "# Penggunaan Callback mencegah overfitting dan menghentikan training setelah akurasi terpenuhi\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy') > 0.97):\n",
        "      print(\"\\nAkurasi di atas 97%, hentikan training!\")\n",
        "      self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qp34911gQ-d7"
      },
      "source": [
        "## split dataset , membuat direktori\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxnpmQEzVGhX"
      },
      "source": [
        "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')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nx-EwantZiX"
      },
      "source": [
        "os.mkdir(train_dir)\n",
        "os.mkdir(validation_dir)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoNMavVhtFNf"
      },
      "source": [
        "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, 'scissors')\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, 'scissors')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVCfRs-uXIAe"
      },
      "source": [
        "os.mkdir(train_roc)\n",
        "os.mkdir(train_pap)\n",
        "os.mkdir(train_sci)\n",
        "os.mkdir(val_roc)\n",
        "os.mkdir(val_pap)\n",
        "os.mkdir(val_sci)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yp-n0pCNcNDb"
      },
      "source": [
        "## Train & test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spiFYMNFcRfE"
      },
      "source": [
        "# memecah setiap direktori menjadi data train dan data validasi(validation 40% of dataset)\n",
        "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)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R87h3wSFeEgY"
      },
      "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))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFwYg436eqFY"
      },
      "source": [
        "## augmentation and generate image data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FUacYtpeyeW"
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    fill_mode = 'nearest',\n",
        ")\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rescale = 1./225,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    vertical_flip = True,\n",
        "    shear_range = 0.2,\n",
        "    fill_mode = 'nearest'\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuhfeNozf8su",
        "outputId": "8dd5a818-f74a-4c0b-e4e2-bbcf4c4e6376",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "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",
        ")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1312 images belonging to 3 classes.\n",
            "Found 876 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5V4DrXBhnVN",
        "outputId": "d343f670-51be-4ade-89f8-1fe9ee60c6a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "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.Flatten(),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(512, activation= 'relu'),\n",
        "  tf.keras.layers.Dense(3, activation= 'softmax')\n",
        "])\n",
        "\n",
        "model.summary()\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 148, 148, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 74, 74, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 72, 72, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 36, 36, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 34, 34, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 17, 17, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 36992)             0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 36992)             0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 512)               18940416  \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 3)                 1539      \n",
            "=================================================================\n",
            "Total params: 19,035,203\n",
            "Trainable params: 19,035,203\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cfjaGUIh2ah",
        "outputId": "4d4feee1-e669-4d3c-c962-d82bfe1938a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        }
      },
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch = 41, # 1312 images = batch_size * steps\n",
        "    epochs = 20,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = 27, # 876 images = batch_size * steps\n",
        "    verbose =2,\n",
        "      callbacks=[callbacks]\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "41/41 - 74s - loss: 0.9828 - accuracy: 0.4809 - val_loss: 0.7662 - val_accuracy: 0.6644\n",
            "Epoch 2/20\n",
            "41/41 - 74s - loss: 0.4799 - accuracy: 0.8194 - val_loss: 0.4252 - val_accuracy: 0.8229\n",
            "Epoch 3/20\n",
            "41/41 - 74s - loss: 0.3563 - accuracy: 0.8689 - val_loss: 0.2909 - val_accuracy: 0.9086\n",
            "Epoch 4/20\n",
            "41/41 - 74s - loss: 0.2528 - accuracy: 0.9062 - val_loss: 0.2623 - val_accuracy: 0.9178\n",
            "Epoch 5/20\n",
            "41/41 - 77s - loss: 0.2295 - accuracy: 0.9146 - val_loss: 0.2178 - val_accuracy: 0.9248\n",
            "Epoch 6/20\n",
            "41/41 - 74s - loss: 0.1915 - accuracy: 0.9284 - val_loss: 0.2476 - val_accuracy: 0.9259\n",
            "Epoch 7/20\n",
            "41/41 - 74s - loss: 0.1821 - accuracy: 0.9314 - val_loss: 0.1886 - val_accuracy: 0.9294\n",
            "Epoch 8/20\n",
            "41/41 - 74s - loss: 0.1653 - accuracy: 0.9444 - val_loss: 0.1958 - val_accuracy: 0.9259\n",
            "Epoch 9/20\n",
            "41/41 - 74s - loss: 0.1360 - accuracy: 0.9520 - val_loss: 0.1442 - val_accuracy: 0.9491\n",
            "Epoch 10/20\n",
            "41/41 - 75s - loss: 0.1292 - accuracy: 0.9573 - val_loss: 0.1793 - val_accuracy: 0.9387\n",
            "Epoch 11/20\n",
            "41/41 - 75s - loss: 0.1205 - accuracy: 0.9566 - val_loss: 0.2101 - val_accuracy: 0.9282\n",
            "Epoch 12/20\n",
            "41/41 - 74s - loss: 0.1354 - accuracy: 0.9497 - val_loss: 0.1460 - val_accuracy: 0.9583\n",
            "Epoch 13/20\n",
            "41/41 - 77s - loss: 0.0927 - accuracy: 0.9688 - val_loss: 0.1437 - val_accuracy: 0.9572\n",
            "Epoch 14/20\n",
            "\n",
            "Akurasi di atas 97%, hentikan training!\n",
            "41/41 - 75s - loss: 0.0793 - accuracy: 0.9726 - val_loss: 0.1240 - val_accuracy: 0.9606\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvUTymHHgfmE",
        "outputId": "2f14a6e2-dc72-47b4-d40d-0c94ac08f801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "#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=\"upper right\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "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": {
        "id": "0eYD9ATQnggA",
        "outputId": "cdfb4e4c-b8d8-41c5-81f8-eb6e47e097eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "#accuracy train & validation\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Accuracy Plot')\n",
        "plt.ylabel('Value')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "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"
          }
        },
        {
          "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": {
        "id": "zanp_kIXn9kO",
        "outputId": "19961051-8028-4cda-a192-ecc53a27d0ac",
        "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
        }
      },
      "source": [
        "#main driver\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",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "%matplotlib inline\n",
        "\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)\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')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-32ff7285-ba66-4d1c-ab72-00ebbedf6c35\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-32ff7285-ba66-4d1c-ab72-00ebbedf6c35\">\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 7UoLr02FuYuLrJxz.png to 7UoLr02FuYuLrJxz.png\n",
            "7UoLr02FuYuLrJxz.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"
          }
        }
      ]
    }
  ]
}