portfolio_traffic_sign_model_classification.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yudahendriawan/google-colab-projects/blob/general/portfolio_traffic_sign_model_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1> Traffic Sign Classification Model"
      ],
      "metadata": {
        "id": "L3SLB00CTXUX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "German Traffic Sign Dataset is a benchmark dataset for multi-class image classification. There is one traffic sign in each image, so this dataset is also called a single image. This dataset was first launched in a challenge organized by the International Joint Conference on Neural Networks (IJCNN) in 2011.\n",
        "\n",
        "This dataset has the following components:\n",
        "\n",
        "- Data for single image classification problems with many classes.\n",
        "- Consists of 43 classes, meaning there are 43 types of traffic signs.\n",
        "- Has a total of more than 50,000 images.\n",
        "- Data taken from photos of actual signs (not synthetic).\n",
        "\n",
        "The data we will use is in the form of a pickle file created with a Python module called pickle. It is used to convert Python objects into byte representations to be stored in storage or transferred over the network. This allows objects to be stored or transmitted easily without converting the data to another format first."
      ],
      "metadata": {
        "id": "gxwKTOQinFe3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {
        "id": "JoxkTK9ATfo4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "id": "nmX3TzhJTSA_",
        "outputId": "a316fbca-dbcf-41e2-b11d-03246eae898d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import Conv2D, Dense, Flatten, Dropout, MaxPool2D\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pickle\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.style.use('ggplot')\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_file = \"/content/drive/MyDrive/google-colab-datasets/german-traffic-sign-datasets/train.p\"\n",
        "testing_file = \"/content/drive/MyDrive/google-colab-datasets/german-traffic-sign-datasets/test.p\"\n",
        "\n",
        "#open and load the training file\n",
        "with open(training_file, mode='rb') as f:\n",
        "    train = pickle.load(f)\n",
        "\n",
        "# open and load the testing file\n",
        "with open(testing_file, mode='rb') as f:\n",
        "    test = pickle.load(f)\n",
        "\n",
        "print('Data Loaded')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dg3vKoJuUDgC",
        "outputId": "8b3febe3-6b6b-4014-8748-7fe0b744ae42"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data Loaded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration"
      ],
      "metadata": {
        "id": "Ukl8YlfdadNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sign_name_df = pd.read_csv('/content/drive/MyDrive/google-colab-datasets/german-traffic-sign-datasets/signnames.csv')\n",
        "SIGN_NAMES = sign_name_df.SignName.values\n",
        "sign_name_df.set_index('ClassId', inplace=True)\n",
        "sign_name_df.head(10)"
      ],
      "metadata": {
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        "id": "Hta7QHKIW4Qp",
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      "execution_count": 3,
      "outputs": [
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              "                            SignName\n",
              "ClassId                             \n",
              "0               Speed limit (20km/h)\n",
              "1               Speed limit (30km/h)\n",
              "2               Speed limit (50km/h)\n",
              "3               Speed limit (60km/h)\n",
              "4               Speed limit (70km/h)\n",
              "5               Speed limit (80km/h)\n",
              "6        End of speed limit (80km/h)\n",
              "7              Speed limit (100km/h)\n",
              "8              Speed limit (120km/h)\n",
              "9                         No passing"
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          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "9rt8rtOxbqd4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will define features and labels on the loaded pickle data, then convert the lists to numpy arrays. We will do this on Training data and Testing data"
      ],
      "metadata": {
        "id": "p7awu1Gxa6vb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X,y = train['features'],train['labels']\n",
        "\n",
        "# convert lists to numpy arrays\n",
        "data = np.array(X)\n",
        "labels = np.array(y)\n",
        "print(data.shape, labels.shape)\n",
        "\n",
        "X_test, y_test = test['features'], test['labels']\n",
        "\n",
        "# convert lists to numpy arrays\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "print(X_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzEUzSrSauRE",
        "outputId": "8004ed7e-d4d0-4f8a-defe-9da1b182d872"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(34799, 32, 32, 3) (34799,)\n",
            "(12630, 32, 32, 3) (12630,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train-Test-Split"
      ],
      "metadata": {
        "id": "rWRQQbB2buLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n",
        "print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XIWulxobspR",
        "outputId": "245b57ed-3dc8-498c-e2aa-76ca3b4ea347"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(31319, 32, 32, 3) (3480, 32, 32, 3) (31319,) (3480,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, visualize the class distribution in the training, validation and testing data. We need to do this to see how the classes are distributed in each of the data above."
      ],
      "metadata": {
        "id": "lGhio-qFcAaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_labels = np.unique(y_train).size\n",
        "def hist_data(y_data, title=None, ax=None, **kwargs):\n",
        "    if not ax:\n",
        "        fig = plt.figure()\n",
        "        ax = fig.add_subplot(111)\n",
        "    ax.hist(y_data, np.arange(-0.5, n_labels+1.5), stacked=True, **kwargs)\n",
        "    ax.set_xlim(-0.5, n_labels-0.5)\n",
        "    if 'label' in kwargs : ax.legend()\n",
        "    if title : ax.set_title(title)\n",
        "\n",
        "fig, ax = plt.subplots(1,3,figsize=(20,5))\n",
        "hist_data(y_train, title='Distribusi kelas pada data training', ax=ax[0])\n",
        "hist_data(y_val, title='Distribusi kelas pada data validasi', ax=ax[1], color='black')\n",
        "hist_data(y_test, title='Distribusi kelas pada data test', ax=ax[2], color='grey')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "Xf8Si828b5JS",
        "outputId": "bb32ff4a-661b-429f-96d9-ff7a888b76ab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ],
            "image/png": 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upBjkRYYOeZFikBcZPPIiA8+OJ0PIjBkz8o53vCPJG4mEhoaGQ9rcddddXd/o733vezNypNqjI+mJJ55IR0fHYc+/9NJL+du//duuitP3vOc9Ryo0+ujSSy9NRcUbP0JXrVqVvXv3HnR+7969WbVqVZKksrIy73vf+454jMPdtm3buj4wHc6TTz7Z9UvEqFGjcuGFFx6J0IaM9vb23HTTTVm/fn2SN+bFlVde2ed+zKeB1R/jZD4NvLVr1x7yvf/H7rrrrjz99NNJ3qiu3/97334XXXRRjjnmmCTJbbfd1vUkkf06OzvzD//wD+ns7EySLF68uL/CHzbe6ji98sorefbZZ7u9fsOGDfkf/+N/JHnjw+yiRYveYtS8WdYnKB55kfInLzJ0WCfLn89xA09epDjkRopBbqQY5EaGD+sTFI+8SPmTFxlarJXlz+e4gSc3UgzyIsUgL1IM8iLDh7Xp8HyKHGI+9rGP5brrrsvevXtz44035gMf+EBOO+207N27N7/5zW+yZs2aJMnUqVNz2WWXDXK0w8+qVauycuXKnHPOOamrq8ukSZMyatSotLa2pr6+Pr/61a+6FvyZM2dKJAyQ559/Plu3bu36d2tra9fx1q1bs3bt2oPaz58//5A+pk2blsWLF+eOO+5IY2Njrrvuulx++eWZPHlympub87Of/azrF+7LLrssU6dOHZB7Gcre6jht3749N9xwQ+rq6nLWWWflbW97W8aNG5ckaW5uzrp16/Loo492VdpfffXVnhjTRzfffHOeeeaZJG9UwS9YsCB/+MMfDtt+5MiRmTZt2iGvm08Dqz/GyXwaeP/n//yf/M//+T9zzjnnZObMmZk8eXJGjx6d3bt35w9/+EP+6Z/+qSsRNHLkyHzyk5/s+oCz35gxY/Lnf/7nWblyZbZv357//J//cz74wQ/mpJNOys6dO3P33Xd3fYB917ve5Ulmb8JbHafXXnstN9xwQ972trdl7ty5OeWUUzJ+/PhUVFTkxRdfzFNPPZWHHnqo6z8qXXbZZTnllFMG5V6xPkFRyYuUN3mR8iAvUgzyIuVPXqQ45EaKQW6kGORGhg/rExSTvEh5kxcpH3IjxSA3Uv7kRopBXqQY5EWKQV5k+LA2Hd6Ifft/2jNkPPHEE/n2t7+dXbt2lTw/derUfPnLX86UKVOOcGRce+212b59e4/tzjnnnHzqU5/KscceewSiGn6+853vdG0h3Bu33357ydc7Ozvz93//93nggQcOe+2CBQtK/qJHz97qOD377LO54YYberyuqqoqH/3oR7Nw4cI+xzjcXXHFFX1qP3HixHznO98pec58Gjj9MU7m08Dr7e8INTU1WbZsWc4444zDtrn99tvz4x//OIf7NX/OnDn5T//pP2XUqFFvOt7h6q2O07Zt2/LpT3+6x+srKiryZ3/2Z/nQhz40bLbiPNJ62jp1P+sTFJO8SPmSFykP8iLFIC9S/uRFikNupBjkRopBbmRokBeBoU1epHzJi5QPuZFikBspf3IjxSAvUgzyIsUgLzI0yIu8NXY8GYLOPvvs3HTTTbnnnnvy1FNPpaWlJSNHjsyUKVPyzne+M5dcckmqqqoGO8xh6dprr019fX0aGhrS3Nyctra27Nq1K6NHj05NTU3q6uoyf/781NXVDXao9EJFRUWWLVuWc845J2vWrEljY2Pa2tpy3HHHZfr06bn44oszZ86cwQ5z2DrllFPymc98Jg0NDfn973+fnTt3pq2tLR0dHTn22GNz4oknZvbs2bnooou6qvAZPOZTeTOfBt5XvvKVPPXUU3n++efT3Nycl156Ka+88kpGjRqVsWPH5uSTT85ZZ52Vc889t8ff46644or823/7b/OLX/wizz33XF5++eUce+yxedvb3pb58+fnT/7kT47QXQ09b3Wcqqur84UvfCENDQ3ZsGFDWlpa0tbWlr179+aYY47JtGnTctppp2XBggWZNGnSINwhf8z6BMUkL1K+5EWGFutkefM5rljMp/JnTg08uZFikBsZXqxPUEzyIuVLXmTosVaWN5/jisV8Km/m08CTFykGeZHhxdpUmh1PAAAAAAAAAAAAAAAAKGn47O0CAAAAAAAAAAAAAABAnyg8AQAAAAAAAAAAAAAAoCSFJwAAAAAAAAAAAAAAAJSk8AQAAAAAAAAAAAAAAICSFJ4AAAAAAAAAAAAAAABQksITAAAAAAAAAAAAAAAASlJ4AgAAAAAAAAAAAAAAQEkKTwAAAAAAAAAAAAAAAChJ4QkAAAAAAAAAAAAAAAAlKTwBAAAAAAAAAAAAAACgJIUnAAAAAAAAAAAAAAAAlKTwBAAAAAAAAAAAAAAAgJIUngAAAAAAAAAAAAAAAFCSwhMAAAAAAAAAAAAAAABKUngCAAAAAAAAAAAAAABASQpPAAAAAAAAAAAAAAAAKEnhCQAAAAAAAAAAAAAAACUpPAEAAAAAAAAAAAAAAKAkhScAAAAAAAAAAAAAAACUpPAEAAAAAAAAAAAAAACAkhSeAAAAAAAAAAAAAAAAUJLCEwAAAAAAAAAAAAAAAEpSeAIAAAAAAAAAAAAAAEBJCk8AAAAAAAAAAAAAAAAoSeEJAAAAAAAAAAAAAAAAJf1/2TdUmvyN78AAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "image/png": {
              "width": 1615,
              "height": 452
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the picture, it can be seen that the class distribution of each piece of data looks similar. Therefore, we do not need to carry out a normalization process.\n",
        "\n",
        "The next stage is Data Preparation. At this stage we will convert the labels in the training and validation data using the one hot encoding technique. The goal is that the label, which was previously a string data type, becomes a categorical feature. We will use the to_categorical function from the tensorflow library."
      ],
      "metadata": {
        "id": "nEVGAn4QcuMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(y_train, 43)\n",
        "y_val = to_categorical(y_val, 43)"
      ],
      "metadata": {
        "id": "8HDPXfGtcluM"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training with Convolutional Neural Network"
      ],
      "metadata": {
        "id": "5piZqxmmdIpu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we will directly implement the callback on the model. We set it so that training stops when the model accuracy reaches 96%. Using callbacks will shorten training time because we have defined at the beginning what accuracy values ​​are needed. So, when the model has reached that accuracy, the training process will stop."
      ],
      "metadata": {
        "id": "-ljvzH_rdM5_"
      }
    },
    {
      "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 telah mencapai 96%. Stop training !')\n",
        "            self.model.stop_training = True\n",
        "\n",
        "callbacks = myCallback()"
      ],
      "metadata": {
        "id": "NzmieEJCc7yc"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, we use an architecture of 2 convolutional layers and 1 fully connected layer. For more details, let's discuss each layer.\n",
        "\n",
        "First, initialize the Sequential model and determine the input shape for the model. This set of layers uses a 5x5 kernel to learn features in traffic sign data. We adjust the input to the shape X_train, which is an RGB image measuring 32 pixels (32, 32, 3). The activation function that we will use is RELU, as explained previously. We also add a Max Pooling layer to reduce dimensions.\n",
        "\n",
        "Here, we also implement dropout. Dropout is a hyperparameter that aims to reduce overfitting. It works by disconnecting or dropping units during the neural network training process so that the layer or layers become inactive. According to documentation from Keras, the rate argument for dropout is a float data type between 0 and 1, which is the fraction of units to be dropped.\n",
        "\n",
        "If we set dropout rate = 0.25, that is, we will drop 25% of the layer and make it inactive during the training process.\n",
        "\n",
        "Here's our first set of layers. In this architecture, set the layer parameters as follows:"
      ],
      "metadata": {
        "id": "9jlSnfFpirDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    #first layer\n",
        "    Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]),\n",
        "    Conv2D(filters=32, kernel_size=(5,5), activation='relu'),\n",
        "    MaxPool2D(pool_size=(2,2)),\n",
        "    Dropout(rate=0.25),\n",
        "    #second layer\n",
        "    Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    MaxPool2D(pool_size=(2, 2)),\n",
        "    Dropout(0.25),\n",
        "    #fully connected layer\n",
        "    Flatten(),\n",
        "    Dense(256, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(43, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "pZTTrdQPiGys"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIWeKjT7kHUl",
        "outputId": "f78fc46f-89cb-4d22-e96e-acf03df441d9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 32)        2432      \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 24, 24, 32)        25632     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 12, 12, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 12, 12, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 10, 10, 64)        18496     \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 8, 8, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 4, 4, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 4, 4, 64)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               262400    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 43)                11051     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 356939 (1.36 MB)\n",
            "Trainable params: 356939 (1.36 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "epochs = 25\n",
        "history = model.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_val,y_val), callbacks=[callbacks])\n",
        "model.save('my_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CfXv1bAkbXm",
        "outputId": "253c5626-3116-45eb-9ffd-5256d4c42a6f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "979/979 [==============================] - 12s 7ms/step - loss: 1.6087 - accuracy: 0.5843 - val_loss: 0.4394 - val_accuracy: 0.8761\n",
            "Epoch 2/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.5843 - accuracy: 0.8237 - val_loss: 0.2169 - val_accuracy: 0.9319\n",
            "Epoch 3/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.4090 - accuracy: 0.8770 - val_loss: 0.1575 - val_accuracy: 0.9563\n",
            "Epoch 4/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.3216 - accuracy: 0.9030 - val_loss: 0.1394 - val_accuracy: 0.9601\n",
            "Epoch 5/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2711 - accuracy: 0.9226 - val_loss: 0.1354 - val_accuracy: 0.9626\n",
            "Epoch 6/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.2401 - accuracy: 0.9307 - val_loss: 0.0926 - val_accuracy: 0.9747\n",
            "Epoch 7/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2241 - accuracy: 0.9351 - val_loss: 0.0726 - val_accuracy: 0.9793\n",
            "Epoch 8/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1978 - accuracy: 0.9423 - val_loss: 0.0706 - val_accuracy: 0.9819\n",
            "Epoch 9/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2279 - accuracy: 0.9383 - val_loss: 0.0730 - val_accuracy: 0.9825\n",
            "Epoch 10/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2133 - accuracy: 0.9415 - val_loss: 0.0756 - val_accuracy: 0.9828\n",
            "Epoch 11/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1828 - accuracy: 0.9481 - val_loss: 0.0708 - val_accuracy: 0.9836\n",
            "Epoch 12/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2023 - accuracy: 0.9434 - val_loss: 0.0549 - val_accuracy: 0.9853\n",
            "Epoch 13/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1831 - accuracy: 0.9491 - val_loss: 0.0617 - val_accuracy: 0.9825\n",
            "Epoch 14/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2068 - accuracy: 0.9450 - val_loss: 0.0606 - val_accuracy: 0.9836\n",
            "Epoch 15/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.1771 - accuracy: 0.9514 - val_loss: 0.0663 - val_accuracy: 0.9830\n",
            "Epoch 16/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1614 - accuracy: 0.9561 - val_loss: 0.0723 - val_accuracy: 0.9874\n",
            "Epoch 17/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.2061 - accuracy: 0.9480 - val_loss: 0.0829 - val_accuracy: 0.9810\n",
            "Epoch 18/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1749 - accuracy: 0.9536 - val_loss: 0.0782 - val_accuracy: 0.9871\n",
            "Epoch 19/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.1869 - accuracy: 0.9510 - val_loss: 0.0525 - val_accuracy: 0.9874\n",
            "Epoch 20/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1840 - accuracy: 0.9533 - val_loss: 0.1088 - val_accuracy: 0.9693\n",
            "Epoch 21/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.1869 - accuracy: 0.9519 - val_loss: 0.0966 - val_accuracy: 0.9721\n",
            "Epoch 22/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.1872 - accuracy: 0.9514 - val_loss: 0.0836 - val_accuracy: 0.9767\n",
            "Epoch 23/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1819 - accuracy: 0.9518 - val_loss: 0.1030 - val_accuracy: 0.9759\n",
            "Epoch 24/25\n",
            "979/979 [==============================] - 5s 5ms/step - loss: 0.1749 - accuracy: 0.9554 - val_loss: 0.0953 - val_accuracy: 0.9750\n",
            "Epoch 25/25\n",
            "979/979 [==============================] - 6s 6ms/step - loss: 0.1948 - accuracy: 0.9527 - val_loss: 0.0817 - val_accuracy: 0.9828\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting graphs for accuracy\n",
        "plt.figure(0)\n",
        "plt.plot(history.history['accuracy'], label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plotting graphs for loss\n",
        "plt.figure(1)\n",
        "plt.plot(history.history['loss'], label='training loss')\n",
        "plt.plot(history.history['val_loss'], label='val loss')\n",
        "plt.title('Loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "id": "f27988zzl4Ik",
        "outputId": "61b26e14-4f76-4fbf-c592-96f01aa5d420"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 583,
              "height": 459
            }
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 552,
              "height": 437
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing Model"
      ],
      "metadata": {
        "id": "Yj00lL5Il__m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the data exploration stage, we divide the data into three parts, namely training, validation and testing data. After obtaining results on training and validation data, we will test the model on data that has never been seen before, namely testing data"
      ],
      "metadata": {
        "id": "MNmyTrXemQZ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this stage we use the accuracy_score function from the sklearn.metrics library. According to scikit-learn documentation, this function calculates subset accuracy on multiclass classification. Note that the set of predicted labels for a sample must exactly match the set of actual labels. In this case, the actual set of labels are the labels in the y_test data. Meanwhile, the prediction label is defined using the model.predict() function for the data in X_test and then entered into the pred variable."
      ],
      "metadata": {
        "id": "dmXHWlS3mYd4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# testing accuracy with the test data\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "pred = np.argmax(model.predict(X_test), axis=1)\n",
        "accuracy_score(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JwcUINwl4qO",
        "outputId": "f905861d-ff10-4d5a-b1df-ab6ceb6dc81b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "395/395 [==============================] - 3s 6ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9401425178147268"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similar to the accuracy results obtained in the training stage. This means that our model can adapt (fit) to new data well."
      ],
      "metadata": {
        "id": "ImzV6SO6mdsf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, to obtain other classification metrics such as precision, recall, and f1-score, let's apply the classification_report function from the sklearn library."
      ],
      "metadata": {
        "id": "pZFGhoTemo44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psMU63pJmLbi",
        "outputId": "029004dc-5635-4a48-8757-2cdd47615062"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95        60\n",
            "           1       0.92      0.97      0.94       720\n",
            "           2       0.94      0.97      0.95       750\n",
            "           3       0.93      0.90      0.91       450\n",
            "           4       1.00      0.94      0.96       660\n",
            "           5       0.87      0.93      0.90       630\n",
            "           6       0.97      0.87      0.92       150\n",
            "           7       0.95      0.96      0.96       450\n",
            "           8       0.97      0.92      0.94       450\n",
            "           9       0.95      1.00      0.97       480\n",
            "          10       0.99      0.98      0.99       660\n",
            "          11       0.94      0.89      0.92       420\n",
            "          12       0.97      0.96      0.97       690\n",
            "          13       0.98      0.99      0.99       720\n",
            "          14       1.00      0.92      0.96       270\n",
            "          15       1.00      0.88      0.93       210\n",
            "          16       0.99      0.99      0.99       150\n",
            "          17       1.00      0.99      1.00       360\n",
            "          18       0.93      0.84      0.88       390\n",
            "          19       0.92      0.40      0.56        60\n",
            "          20       0.85      0.97      0.91        90\n",
            "          21       0.85      0.88      0.86        90\n",
            "          22       0.96      0.99      0.98       120\n",
            "          23       0.73      1.00      0.85       150\n",
            "          24       0.81      0.61      0.70        90\n",
            "          25       0.97      0.99      0.98       480\n",
            "          26       0.99      0.81      0.89       180\n",
            "          27       0.62      0.48      0.54        60\n",
            "          28       0.88      0.95      0.91       150\n",
            "          29       0.88      0.77      0.82        90\n",
            "          30       0.63      0.83      0.71       150\n",
            "          31       0.95      0.96      0.95       270\n",
            "          32       0.97      1.00      0.98        60\n",
            "          33       1.00      0.98      0.99       210\n",
            "          34       0.93      0.99      0.96       120\n",
            "          35       0.97      1.00      0.99       390\n",
            "          36       0.99      0.97      0.98       120\n",
            "          37       1.00      1.00      1.00        60\n",
            "          38       0.91      0.98      0.95       690\n",
            "          39       0.99      0.98      0.98        90\n",
            "          40       0.88      0.84      0.86        90\n",
            "          41       0.71      0.67      0.69        60\n",
            "          42       0.99      0.77      0.86        90\n",
            "\n",
            "    accuracy                           0.94     12630\n",
            "   macro avg       0.92      0.90      0.91     12630\n",
            "weighted avg       0.94      0.94      0.94     12630\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we pay attention to almost all classes, the accuracy of precision, recall and f1-score have high numbers, on average in the upper 90s. However, in some classes, such as classes with indexes 27 and 42, the average accuracy is below 80%. This could be caused by the fairly complex feature representation of these two classes."
      ],
      "metadata": {
        "id": "GVimX0Mum1eZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MKX_zpY2mw2t"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}