ImageClassification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "XcmCAQ1J4RVJ"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import RMSprop, Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing import image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "from google.colab import files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fTVo9c8H405m",
    "outputId": "efe80c53-78bd-48d8-fcc2-221ffb5a8923"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-05-22 05:39:41--  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip\n",
      "Resolving dicodingacademy.blob.core.windows.net (dicodingacademy.blob.core.windows.net)... 52.239.197.36\n",
      "Connecting to dicodingacademy.blob.core.windows.net (dicodingacademy.blob.core.windows.net)|52.239.197.36|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 322873683 (308M) [application/zip]\n",
      "Saving to: ‘/tmp/rockpaperscissors.zip’\n",
      "\n",
      "/tmp/rockpapersciss 100%[===================>] 307.92M  7.85MB/s    in 30s     \n",
      "\n",
      "2021-05-22 05:40:12 (10.2 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget --no-check-certificate \\\n",
    "  https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip \\\n",
    "  -O /tmp/rockpaperscissors.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "jXQxXYxl6JvG"
   },
   "outputs": [],
   "source": [
    "import zipfile, os\n",
    "\n",
    "local ='/tmp/rockpaperscissors.zip'\n",
    "zip = zipfile.ZipFile(local, 'r')\n",
    "zip.extractall('/tmp')\n",
    "zip.close()\n",
    "\n",
    "base_dir = '/tmp/rockpaperscissors/rps-cv-images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xpr9gbyp7L3G",
    "outputId": "34c2852c-3938-48d1-977b-ce2e867393d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['paper', 'scissors', 'README_rpc-cv-images.txt', 'rock']"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/tmp/rockpaperscissors/rps-cv-images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H7l3mG4Z7wnB",
    "outputId": "139a183f-9b69-419f-bb4c-6a6c93dc2a52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['paper', 'scissors', 'rock']"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.remove('/tmp/rockpaperscissors/rps-cv-images/README_rpc-cv-images.txt')\n",
    "os.listdir('/tmp/rockpaperscissors/rps-cv-images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rj8N6o5-8-LG",
    "outputId": "fff273cd-db48-4be8-af8a-1369f1aa9ed7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1314 images belonging to 3 classes.\n",
      "Found 874 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "data_generator = ImageDataGenerator(rescale= 1./255,\n",
    "                                    horizontal_flip= True,\n",
    "                                    vertical_flip= True,\n",
    "                                    zoom_range= 0.3,\n",
    "                                    rotation_range= 90,\n",
    "                                    shear_range= 0.3,\n",
    "                                    validation_split= 0.4)\n",
    "\n",
    "train = data_generator.flow_from_directory(base_dir,\n",
    "                                           class_mode= 'categorical',\n",
    "                                           batch_size= 64,\n",
    "                                           target_size= (150, 150),\n",
    "                                           subset='training')\n",
    "\n",
    "validation = data_generator.flow_from_directory(base_dir,\n",
    "                                           class_mode= 'categorical',\n",
    "                                           batch_size= 64,\n",
    "                                           target_size= (150, 150),\n",
    "                                           subset='validation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create  Custom callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "t8B-KoY6P5Lq"
   },
   "outputs": [],
   "source": [
    "class Callbacks(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('accuracy')>0.95):\n",
    "      print(\"\\nAkurasi telah mencapai lebih dari 95%, Training dihentikan!\")\n",
    "      self.model.stop_training = True\n",
    "\n",
    "callback = Callbacks()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "Ux8nHXI2sQzo"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "\n",
    "                                    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    \n",
    "                                    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    \n",
    "                                    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
    "                                    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "                                    \n",
    "                                    tf.keras.layers.Flatten(),\n",
    "                                    tf.keras.layers.Dense(1024, activation='relu'),\n",
    "                                    tf.keras.layers.Dense(3, activation='softmax')\n",
    "                                    ])\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9uGpbB8ywJWR",
    "outputId": "723ae949-d0f4-4a74-9301-e09a692002be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "21/21 [==============================] - 49s 2s/step - loss: 1.1020 - accuracy: 0.3690 - val_loss: 1.0200 - val_accuracy: 0.4760\n",
      "Epoch 2/20\n",
      "21/21 [==============================] - 47s 2s/step - loss: 0.9367 - accuracy: 0.5436 - val_loss: 0.7850 - val_accuracy: 0.7185\n",
      "Epoch 3/20\n",
      "21/21 [==============================] - 52s 2s/step - loss: 0.7272 - accuracy: 0.6987 - val_loss: 0.6328 - val_accuracy: 0.7586\n",
      "Epoch 4/20\n",
      "21/21 [==============================] - 47s 2s/step - loss: 0.5906 - accuracy: 0.7515 - val_loss: 0.5775 - val_accuracy: 0.8192\n",
      "Epoch 5/20\n",
      "21/21 [==============================] - 47s 2s/step - loss: 0.5292 - accuracy: 0.7945 - val_loss: 0.4546 - val_accuracy: 0.8215\n",
      "Epoch 6/20\n",
      "21/21 [==============================] - 46s 2s/step - loss: 0.4634 - accuracy: 0.8091 - val_loss: 0.4161 - val_accuracy: 0.8387\n",
      "Epoch 7/20\n",
      "21/21 [==============================] - 46s 2s/step - loss: 0.4451 - accuracy: 0.8173 - val_loss: 0.3723 - val_accuracy: 0.8535\n",
      "Epoch 8/20\n",
      "21/21 [==============================] - 45s 2s/step - loss: 0.4073 - accuracy: 0.8304 - val_loss: 0.3434 - val_accuracy: 0.8673\n",
      "Epoch 9/20\n",
      "21/21 [==============================] - 46s 2s/step - loss: 0.3305 - accuracy: 0.8785 - val_loss: 0.2830 - val_accuracy: 0.8844\n",
      "Epoch 10/20\n",
      "21/21 [==============================] - 46s 2s/step - loss: 0.2457 - accuracy: 0.9020 - val_loss: 0.2024 - val_accuracy: 0.9279\n",
      "Epoch 11/20\n",
      "21/21 [==============================] - 48s 2s/step - loss: 0.2353 - accuracy: 0.9103 - val_loss: 0.1558 - val_accuracy: 0.9359\n",
      "Epoch 12/20\n",
      "21/21 [==============================] - 46s 2s/step - loss: 0.1213 - accuracy: 0.9601 - val_loss: 0.1532 - val_accuracy: 0.9371\n",
      "\n",
      "Akurasi telah mencapai lebih dari 95%, Training dihentikan!\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train,\n",
    "                    epochs= 20,\n",
    "                    verbose= 1,\n",
    "                    callbacks=[callback],\n",
    "                    validation_data=validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B9GeyMEAeJBH",
    "outputId": "69eb5f99-58c1-4fdb-e483-4b091c6f2653"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 12s 813ms/step - loss: 0.1356 - accuracy: 0.9554\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13557863235473633, 0.9553775787353516]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "id": "zM0i9inZNCOz",
    "outputId": "54539743-3dfc-46f2-8367-ba0b73c2ccb4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jwiy_Gt6olbs",
    "outputId": "1a3e3493-954f-48b3-abd8-40784d4e6904"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'paper': 0, 'rock': 1, 'scissors': 2}\n"
     ]
    }
   ],
   "source": [
    "class_names = train.class_indices\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "id": "9iEEjFVQYGEE",
    "outputId": "eb897ac2-241d-4573-df1b-540af1223677"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-6598d0ba-e342-4329-9928-70350351e2f7\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-6598d0ba-e342-4329-9928-70350351e2f7\">\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": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving paper (1).jpeg to paper (1).jpeg\n",
      "paper\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uploaded = files.upload()\n",
    " \n",
    "for fn in uploaded.keys():\n",
    "  path = fn\n",
    "  img = image.load_img(path, target_size=(150,150, 3))\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",
    "  predict = model.predict(images)\n",
    "  index = predict[0].argmax()\n",
    "\n",
    "  for key,value in class_names.items():\n",
    "    if value == index:\n",
    "        print(key)\n",
    "  "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Dicoding_Submission.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}