Rock_Paper_Scissors.ipynb

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  "metadata": {
    "colab": {
      "name": "Rock_Paper_Scissors.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Farah-Amalia/Rock-Paper-Scissors-Image-Classification/blob/main/Rock_Paper_Scissors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sk1Tms4KkTun"
      },
      "source": [
        "# Rock Paper Scissors Image Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqtDuQvesXRJ"
      },
      "source": [
        "Student name: Farah Fajrina Amalia\n",
        "\n",
        "Profile link: https://www.dicoding.com/users/farah_-_l3j1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FY_gb4xbrwpq"
      },
      "source": [
        "## Loading data and packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hik09nn5-aUq",
        "outputId": "963ef906-f715-497d-d501-be22e1ad8dc4"
      },
      "source": [
        "!wget https://dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-14 08:27:18--  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  1.98MB/s    in 95s     \n",
            "\n",
            "2021-05-14 08:28:54 (3.23 MB/s) - ‘rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slFQPaGg-_Hh"
      },
      "source": [
        "import tensorflow as tf\n",
        "import zipfile\n",
        "import os\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "import matplotlib.image as mpimg\n",
        "from keras.callbacks import ReduceLROnPlateau"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jjJP2XRtECp"
      },
      "source": [
        "## Data preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EttqqvnM--82"
      },
      "source": [
        "# file extraction\n",
        "\n",
        "local_zip = '/content/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "base_dir = '/tmp/rockpaperscissors'\n",
        "train_dir = os.path.join(base_dir, 'rps-cv-images')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F82RWcjh_kQM",
        "outputId": "da01851f-1f3b-44d0-b612-b3e83c57c430"
      },
      "source": [
        "# generate data (train and validation)\n",
        "\n",
        "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",
        "                validation_split=0.4)\n",
        "\n",
        "validation_datagen = ImageDataGenerator(rescale = 1.0/255,\n",
        "                                        validation_split=0.4)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,  \n",
        "        target_size=(100,150),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical',\n",
        "        subset='training')\n",
        " \n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        train_dir, \n",
        "        target_size=(100,150), \n",
        "        batch_size=32,\n",
        "        class_mode='categorical',\n",
        "        subset='validation')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1314 images belonging to 3 classes.\n",
            "Found 874 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdDfdP72tYeU"
      },
      "source": [
        "## Model construction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8wy0wHqFzqH"
      },
      "source": [
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',\n",
        "                                            patience=2,\n",
        "                                            verbose=1,\n",
        "                                            factor=0.5,\n",
        "                                            min_lr=0.000003)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ggfrcb-g_qO0"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(100, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wq2n1OIw_tQO"
      },
      "source": [
        "# compile model with 'adam' optimizer and 'categorical_crossentropy' loss function \n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhyT_4gzJUr2",
        "outputId": "39075693-759f-4cbe-8ae9-2433188e1510"
      },
      "source": [
        "history = model.fit(\n",
        "    train_generator,  \n",
        "    epochs=10,\n",
        "    validation_data=validation_generator,\n",
        "    callbacks=[learning_rate_reduction]\n",
        ")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "42/42 [==============================] - 23s 533ms/step - loss: 0.2091 - accuracy: 0.9330 - val_loss: 0.3506 - val_accuracy: 0.8467\n",
            "Epoch 2/10\n",
            "42/42 [==============================] - 22s 533ms/step - loss: 0.1935 - accuracy: 0.9384 - val_loss: 0.2060 - val_accuracy: 0.9256\n",
            "Epoch 3/10\n",
            "42/42 [==============================] - 23s 534ms/step - loss: 0.1658 - accuracy: 0.9414 - val_loss: 0.2431 - val_accuracy: 0.9062\n",
            "Epoch 4/10\n",
            "42/42 [==============================] - 22s 532ms/step - loss: 0.1415 - accuracy: 0.9444 - val_loss: 0.1492 - val_accuracy: 0.9634\n",
            "Epoch 5/10\n",
            "42/42 [==============================] - 23s 534ms/step - loss: 0.1431 - accuracy: 0.9551 - val_loss: 0.1489 - val_accuracy: 0.9588\n",
            "Epoch 6/10\n",
            "42/42 [==============================] - 23s 535ms/step - loss: 0.1357 - accuracy: 0.9612 - val_loss: 0.1515 - val_accuracy: 0.9668\n",
            "Epoch 7/10\n",
            "42/42 [==============================] - 23s 536ms/step - loss: 0.1168 - accuracy: 0.9642 - val_loss: 0.1440 - val_accuracy: 0.9622\n",
            "Epoch 8/10\n",
            "42/42 [==============================] - 23s 535ms/step - loss: 0.1163 - accuracy: 0.9635 - val_loss: 0.1308 - val_accuracy: 0.9691\n",
            "Epoch 9/10\n",
            "42/42 [==============================] - 23s 534ms/step - loss: 0.1086 - accuracy: 0.9597 - val_loss: 0.1562 - val_accuracy: 0.9531\n",
            "Epoch 10/10\n",
            "42/42 [==============================] - 23s 538ms/step - loss: 0.1016 - accuracy: 0.9680 - val_loss: 0.1181 - val_accuracy: 0.9634\n",
            "\n",
            "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HuBHBNtyu34"
      },
      "source": [
        "## Model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHKlUgn3MR5J"
      },
      "source": [
        "# function for plotting accuracy and loss\n",
        "\n",
        "def eval_plot(history):\n",
        "\n",
        "  plt.figure(figsize=(14, 5))\n",
        "\n",
        "  # Accuracy plot\n",
        "  plt.subplot(1, 2, 1)\n",
        "  acc = history.history['accuracy']\n",
        "  val_acc = history.history['val_accuracy']\n",
        "  epochs = range(len(acc))\n",
        "  acc_plot, = plt.plot(epochs, acc, 'r')\n",
        "  val_acc_plot, = plt.plot(epochs, val_acc, 'b')\n",
        "  plt.title('Training and Validation Accuracy')\n",
        "  plt.legend([acc_plot, val_acc_plot], ['Training Accuracy', 'Validation Accuracy'])\n",
        "\n",
        "  # Loss plot\n",
        "  plt.subplot(1, 2, 2)\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "  epochs = range(len(loss))\n",
        "  loss_plot, = plt.plot(epochs, loss, 'r')\n",
        "  val_loss_plot, = plt.plot(epochs, val_loss, 'b')\n",
        "  plt.title('Training and Validation Loss')\n",
        "  plt.legend([loss_plot, val_loss_plot], ['Training Loss', 'Validation Loss'])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-q158R4RHB3"
      },
      "source": [
        "# function for model evaluation \n",
        "\n",
        "def evaluate(model):\n",
        "\n",
        "  validation_generator = train_datagen.flow_from_directory(\n",
        "          train_dir, \n",
        "          target_size=(100,150), \n",
        "          batch_size=32, \n",
        "          class_mode='categorical',\n",
        "          shuffle = False,\n",
        "          subset='validation')\n",
        "\n",
        "  batch_size = 32\n",
        "  num_of_train_samples = 1314\n",
        "  num_of_test_samples = 874\n",
        "\n",
        "  Y_pred = model.predict_generator(validation_generator, num_of_test_samples // batch_size+1)\n",
        "  y_pred = np.argmax(Y_pred, axis=1)\n",
        "\n",
        "  print('\\nConfusion Matrix\\n')\n",
        "  print(confusion_matrix(validation_generator.classes, y_pred))\n",
        "\n",
        "\n",
        "  print('\\n\\nClassification Report\\n')\n",
        "  target_names = ['Rock', 'Paper', 'Scissors']\n",
        "  print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "HPhiVDwJ9Cks",
        "outputId": "bf829eb4-98fd-45ef-9d7f-dbbf4d7ef718"
      },
      "source": [
        "eval_plot(history)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMFJsdjf20TX",
        "outputId": "12f6dca1-cc67-4892-9d15-110e872dbeea"
      },
      "source": [
        "evaluate(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 874 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Confusion Matrix\n",
            "\n",
            "[[264  15   5]\n",
            " [  5 285   0]\n",
            " [  4   4 292]]\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Rock       0.97      0.93      0.95       284\n",
            "       Paper       0.94      0.98      0.96       290\n",
            "    Scissors       0.98      0.97      0.98       300\n",
            "\n",
            "    accuracy                           0.96       874\n",
            "   macro avg       0.96      0.96      0.96       874\n",
            "weighted avg       0.96      0.96      0.96       874\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iahQPrQcQhDi"
      },
      "source": [
        "Using Convulutional Neural Network, we achieved 96% accuracy with 132s training execution time. Next, we will use transfer learning methods to increase the model accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvxndcANzhb0"
      },
      "source": [
        "## Transfer learning (using ResNet50)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Uxsxj1EkYpx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0791e4a9-05b5-4d2f-8757-0a54367f3b8f"
      },
      "source": [
        "base_model_resnet = tf.keras.applications.ResNet50V2(input_shape=(100, 150, 3),\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94674944/94668760 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj-Swdb4khZX"
      },
      "source": [
        "base_model_resnet.trainable = False"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0W2XAr9khXD"
      },
      "source": [
        "model_resnet = tf.keras.models.Sequential()\n",
        "model_resnet.add(base_model_resnet)\n",
        "model_resnet.add(tf.keras.layers.Flatten())\n",
        "model_resnet.add(tf.keras.layers.Dropout(0.2))\n",
        "model_resnet.add(tf.keras.layers.Dense(3, activation='softmax'))"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5cWLk-YkhT9"
      },
      "source": [
        "model_resnet.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YV52IVS4kYiT",
        "outputId": "3083ee29-e63f-416e-fece-b2ff6dccda19"
      },
      "source": [
        "history_resnet = model_resnet.fit(train_generator,\n",
        "      epochs=10,\n",
        "      validation_data = validation_generator,\n",
        "      callbacks=[learning_rate_reduction]\n",
        "      ) "
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "42/42 [==============================] - 101s 2s/step - loss: 1.0727 - accuracy: 0.8059 - val_loss: 0.0808 - val_accuracy: 0.9817\n",
            "Epoch 2/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0675 - accuracy: 0.9829 - val_loss: 0.0310 - val_accuracy: 0.9943\n",
            "Epoch 3/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0561 - accuracy: 0.9915 - val_loss: 0.0343 - val_accuracy: 0.9897\n",
            "Epoch 4/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0543 - accuracy: 0.9888 - val_loss: 0.0378 - val_accuracy: 0.9943\n",
            "\n",
            "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "Epoch 5/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0681 - accuracy: 0.9885 - val_loss: 0.0197 - val_accuracy: 0.9954\n",
            "Epoch 6/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0099 - accuracy: 0.9963 - val_loss: 0.0348 - val_accuracy: 0.9931\n",
            "Epoch 7/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0230 - accuracy: 0.9969 - val_loss: 0.0812 - val_accuracy: 0.9863\n",
            "\n",
            "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "Epoch 8/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0161 - accuracy: 0.9983 - val_loss: 0.0405 - val_accuracy: 0.9943\n",
            "Epoch 9/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 2.5620e-04 - accuracy: 0.9999 - val_loss: 0.0284 - val_accuracy: 0.9954\n",
            "\n",
            "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "Epoch 10/10\n",
            "42/42 [==============================] - 97s 2s/step - loss: 0.0190 - accuracy: 0.9961 - val_loss: 0.0227 - val_accuracy: 0.9954\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-yRLtS8z5oU"
      },
      "source": [
        "## ResNet50V2 model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "EpCAxXT19GDQ",
        "outputId": "cd82651d-188d-45be-ca11-761a75236a37"
      },
      "source": [
        "eval_plot(history_resnet)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSdumGke24Ko",
        "outputId": "9ca3e4b8-234b-4eff-9a88-5d84b08b4ccb"
      },
      "source": [
        "evaluate(model_resnet)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 874 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Confusion Matrix\n",
            "\n",
            "[[283   1   0]\n",
            " [  0 290   0]\n",
            " [  2   0 298]]\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Rock       0.99      1.00      0.99       284\n",
            "       Paper       1.00      1.00      1.00       290\n",
            "    Scissors       1.00      0.99      1.00       300\n",
            "\n",
            "    accuracy                           1.00       874\n",
            "   macro avg       1.00      1.00      1.00       874\n",
            "weighted avg       1.00      1.00      1.00       874\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7Rg89664tsi"
      },
      "source": [
        "## Transfer learning (using InceptionV3)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCnKmh2P40DG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7d310a4-d447-45e5-8819-37eb3bc394ef"
      },
      "source": [
        "base_model_inception = tf.keras.applications.inception_v3.InceptionV3(input_shape = (100, 150, 3), include_top = False, weights = 'imagenet')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rv9RAAv24-pM"
      },
      "source": [
        "for layer in base_model_inception.layers:\n",
        "    layer.trainable = False"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrHti52B5KDq"
      },
      "source": [
        "model_inception = tf.keras.models.Sequential()\n",
        "model_inception.add(base_model_inception)\n",
        "model_inception.add(tf.keras.layers.Flatten())\n",
        "model_inception.add(tf.keras.layers.Dense(32, activation='relu'))\n",
        "model_inception.add(tf.keras.layers.Dropout(0.2))\n",
        "model_inception.add(tf.keras.layers.Dense(3, activation='softmax'))"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUNhCuFS4-ie"
      },
      "source": [
        "model_inception.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpVa8uEp4-bo",
        "outputId": "55f5c76f-ed65-474e-b7c6-4bab2dd0d933"
      },
      "source": [
        "history_inception = model_inception.fit(train_generator,\n",
        "      epochs=10,\n",
        "      validation_data = validation_generator,\n",
        "      callbacks=[learning_rate_reduction]\n",
        "      ) "
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "42/42 [==============================] - 56s 1s/step - loss: 0.6140 - accuracy: 0.8204 - val_loss: 0.1507 - val_accuracy: 0.9554\n",
            "Epoch 2/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.1831 - accuracy: 0.9437 - val_loss: 0.0989 - val_accuracy: 0.9771\n",
            "Epoch 3/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.1192 - accuracy: 0.9642 - val_loss: 0.0852 - val_accuracy: 0.9725\n",
            "Epoch 4/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.1187 - accuracy: 0.9635 - val_loss: 0.0801 - val_accuracy: 0.9805\n",
            "Epoch 5/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0905 - accuracy: 0.9680 - val_loss: 0.0684 - val_accuracy: 0.9805\n",
            "Epoch 6/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0859 - accuracy: 0.9764 - val_loss: 0.0759 - val_accuracy: 0.9794\n",
            "\n",
            "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "Epoch 7/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0681 - accuracy: 0.9810 - val_loss: 0.0665 - val_accuracy: 0.9805\n",
            "Epoch 8/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0605 - accuracy: 0.9840 - val_loss: 0.0721 - val_accuracy: 0.9805\n",
            "\n",
            "Epoch 00008: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "Epoch 9/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0654 - accuracy: 0.9772 - val_loss: 0.0612 - val_accuracy: 0.9863\n",
            "Epoch 10/10\n",
            "42/42 [==============================] - 52s 1s/step - loss: 0.0625 - accuracy: 0.9772 - val_loss: 0.0519 - val_accuracy: 0.9840\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EnJTACJ7G0A"
      },
      "source": [
        "## InceptionV3 model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "AOojHoDg7E3c",
        "outputId": "c63f7a2d-91e1-488e-cb92-8e6c5d64aede"
      },
      "source": [
        "eval_plot(history_inception)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWo9rozh9Koh",
        "outputId": "cee06700-d460-4865-e568-4eedb65e621b"
      },
      "source": [
        "evaluate(model_inception)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 874 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1905: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Confusion Matrix\n",
            "\n",
            "[[278   2   4]\n",
            " [  5 285   0]\n",
            " [  2   0 298]]\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Rock       0.98      0.98      0.98       284\n",
            "       Paper       0.99      0.98      0.99       290\n",
            "    Scissors       0.99      0.99      0.99       300\n",
            "\n",
            "    accuracy                           0.99       874\n",
            "   macro avg       0.99      0.98      0.99       874\n",
            "weighted avg       0.99      0.99      0.99       874\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfCX0q5uBJYc"
      },
      "source": [
        "## Predicting new image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xm9OzEVqR80Y"
      },
      "source": [
        "Based on model accuracy, we will use ResNet50V2 model to predict new image as it has the highest accuracy compared to CNN and InceptionV3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMwbsWFPBIjZ"
      },
      "source": [
        "def predict_image(image_upload, model = model_resnet):\n",
        "  im = image_upload\n",
        "  im_array = np.asarray(im)\n",
        "  im_array = im_array*(1/225)\n",
        "  im_input = tf.reshape(im_array, shape = [1, 100, 150, 3])\n",
        "\n",
        "  predict_proba = sorted(model.predict(im_input)[0])[2]\n",
        "  predict_class = np.argmax(model.predict(im_input))\n",
        "\n",
        "  if predict_class == 0:\n",
        "      predict_label = 'Paper'\n",
        "  elif predict_class == 1:\n",
        "      predict_label = 'Rock'\n",
        "  else:\n",
        "      predict_label = 'Scissor'\n",
        "\n",
        "  print('\\n')\n",
        "  plt.show()\n",
        "  print(\"\\nImage prediction result: \", predict_label)\n",
        "  print(\"Probability: \", round(predict_proba*100,2), \"%\")\n",
        "  print('\\n')"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "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": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "09Iy7cNcH8WE",
        "outputId": "81888637-bf34-4445-9022-424ad64e6fc1"
      },
      "source": [
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(100,150))\n",
        "  imgplot = plt.imshow(img)\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  img = np.vstack([x])\n",
        "\n",
        "predict_image(img)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6c8e4ec1-c446-40e8-aba1-4debe06edf96\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6c8e4ec1-c446-40e8-aba1-4debe06edf96\">\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 male-hand-clenched-fist-white_99433-2771.jpg to male-hand-clenched-fist-white_99433-2771.jpg\n",
            "\n",
            "\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Image prediction result:  Rock\n",
            "Probability:  100.0 %\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Do7e-0gdYQLP",
        "outputId": "11aa59c6-084b-4d73-ded7-a89cb679a4aa"
      },
      "source": [
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(100,150))\n",
        "  imgplot = plt.imshow(img)\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  img = np.vstack([x])\n",
        "\n",
        "predict_image(img)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-602b5aa0-73ff-4cde-b772-2646be94f9dc\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-602b5aa0-73ff-4cde-b772-2646be94f9dc\">\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 download.jpg to download.jpg\n",
            "\n",
            "\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Image prediction result:  Scissor\n",
            "Probability:  100.0 %\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "hc3kBFzYaYA-",
        "outputId": "19aae880-8b45-4bb1-d6dd-5f63f8ca5a5c"
      },
      "source": [
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(100,150))\n",
        "  imgplot = plt.imshow(img)\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  img = np.vstack([x])\n",
        "\n",
        "predict_image(img)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-acbb6684-0b03-4955-834f-9a6a26654b4e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-acbb6684-0b03-4955-834f-9a6a26654b4e\">\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 depositphotos_39788169-stock-photo-empty-open-woman-hand-on.jpg to depositphotos_39788169-stock-photo-empty-open-woman-hand-on.jpg\n",
            "\n",
            "\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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Image prediction result:  Paper\n",
            "Probability:  100.0 %\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQGuDTn3OWYI"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZcHjTBkXOY-a"
      },
      "source": [
        "Best model: Resnet50V2\n",
        "\n",
        "Accuracy: 99%"
      ]
    }
  ]
}