Wind Turbines health prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtvmCteH89bW"
      },
      "source": [
        "**Final Individual Assignment** \n",
        "**SMM768-GeorgiosKalfas**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3AE7hfk826R"
      },
      "outputs": [],
      "source": [
        "# ---+ loading libraries\n",
        "import numpy as np\n",
        "import os\n",
        "from pathlib import Path\n",
        "import tensorflow as tf\n",
        "import pickle\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense, SimpleRNN, BatchNormalization, LSTM, Conv1D, Embedding, GRU\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, UpSampling2D, MaxPool2D\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten, Input\n",
        "from tensorflow.keras.optimizers import Adam, Nadam\n",
        "from tensorflow.keras.metrics import mean_squared_error\n",
        "from tensorflow.keras.metrics import sparse_categorical_crossentropy\n",
        "from tensorflow.data import Dataset, TFRecordDataset\n",
        "from tensorflow.keras.applications.mobilenet import MobileNet, preprocess_input\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a4kvNG46826R"
      },
      "outputs": [],
      "source": [
        "# ---+ importing the files\n",
        "with open ('time_series_1.pickle', 'rb') as handle:\n",
        "  time_series_1 = pickle.load(handle)\n",
        "with open ('time_series_2.pickle', 'rb') as handle:\n",
        "  time_series_2 = pickle.load(handle)\n",
        "with open ('y.pickle', 'rb') as handle:\n",
        "  y = pickle.load(handle)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Jmmfzw5826S",
        "outputId": "cd737c60-5141-470e-e627-71bf54d70125"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000, 5000)\n",
            "<class 'numpy.ndarray'>\n",
            "(4000, 5000)\n",
            "<class 'numpy.ndarray'>\n",
            "(4000,)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# ---+ looking into the time series\n",
        "print(time_series_1.shape)\n",
        "print(type(time_series_1))\n",
        "print(time_series_2.shape)\n",
        "print(type(time_series_2))\n",
        "print(y.shape)\n",
        "print(type(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "-gOGiBaNDDC_",
        "outputId": "9ed01bc0-6b91-489d-c6d8-d76d3f5f1a20"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5236e5c410>]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the time series 1\n",
        "plt.figure(figsize=(30,10)) # ---+ adjusting the size of the figure\n",
        "plt.plot(time_series_1[1,:])\n",
        "plt.plot(time_series_1[2,:])\n",
        "plt.plot(time_series_1[3,:])\n",
        "\n",
        "# ---+ time series 2\n",
        "plt.plot(time_series_2[1,:])\n",
        "plt.plot(time_series_2[2,:])\n",
        "plt.plot(time_series_2[3,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXETzJCt826S",
        "outputId": "97eb4b0a-4f62-457f-8d41-9eedab2347c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000, 5000, 2)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# ---+ combining the time series\n",
        "final = np.dstack((time_series_1, time_series_2))\n",
        "print(final.shape)\n",
        "print(type(final))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62Ofl1Tn826S",
        "outputId": "a2a3e5a9-44b4-45bc-cf7b-d48488ccde71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000, 1)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# ---+ reshaping y into the shape of the time_series\n",
        "y_q1 = y.reshape(4000,1)\n",
        "print(y_q1.shape)\n",
        "print(type(y_q1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py-VjklXEFsh"
      },
      "source": [
        "### **Making sure validation and test datasets consist of 15% of the observation each.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rlUukoN826T",
        "outputId": "124bf717-7dc5-4328-bb57-40fe8764b974"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2800, 5000, 2)\n",
            "(2800, 1)\n",
            "(600, 5000, 2)\n"
          ]
        }
      ],
      "source": [
        "# ---+ creating training and test split\n",
        "np.random.seed(132)\n",
        "seq_steps = 5000\n",
        "\n",
        "X_train, y_q1_train = final[:2800, :seq_steps], y_q1[:2800,]\n",
        "X_valid, y_q1_valid = final[2800:3400, :seq_steps], y_q1[2800:3400,]\n",
        "X_test, y_q1_test = final[3400:, :seq_steps], y_q1[3400:,]\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_q1_train.shape)\n",
        "print(X_valid.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7ebeENZESGO"
      },
      "source": [
        "## **Question 1.**\n",
        "\n",
        "Create a recurrent neural network in TensorFlow to predict the operating mode of a wind turbine based on the two time series from the sensors. Before any implementation, carefully consider what type of approach (sequence-to-vector, sequence-to-sequence, or encoder-decoder) is most sensible here and how you need to manipulate the data, given that you have two different time series for each observation.\n",
        " \n",
        "Then, make sure that you try out the different layers and elements discussed in class, such as SimpleRNN, LSTM and Conv1D - while a certain amount of trial and error will be necessary, it is recommended that you tune your network systematically. Make sure to record your final validation set accuracy. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4wZ1fwYP826U"
      },
      "source": [
        "## Function to train the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g_5XbjNv826U"
      },
      "outputs": [],
      "source": [
        "# ---+ creating the function to train our data (will be used for the rest different models)\n",
        "\n",
        "def train_plot(model, learning_rate = 0.001, epochs = 15):\n",
        "    tf.keras.backend.clear_session()\n",
        "    np.random.seed(312)\n",
        "    tf.random.set_seed(312)\n",
        "\n",
        "    model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "                  optimizer=Adam(learning_rate = learning_rate), metrics=['Accuracy'])\n",
        "    early_stopping_cb = tf.keras.callbacks.EarlyStopping(patience = 5, restore_best_weights=True)\n",
        "    log = model.fit(X_train, y_q1_train, epochs=epochs,\n",
        "                        validation_data=(X_valid, y_q1_valid),\n",
        "                   callbacks = [early_stopping_cb])\n",
        "    \n",
        "    plt.plot(log.history['loss'],label = \"training loss\",color='darkgreen')\n",
        "    plt.plot(log.history['val_loss'], label = \"validation loss\",color='darkblue')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "    return model, log"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgFs99ZMEy-3"
      },
      "source": [
        "#### For the purpose of this analysis the type of approach we will use is sequence-to-vector.\n",
        "#### Sequence to vector predicts the next value in the sequence and gives as an output the predicted value.\n",
        "\n",
        "#### A recurrent neural network is a class of artificial neural networks where connections between nodes from a directed or undirected graph are used to represent the sequence of events.\n",
        "#### A RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that is updated at each timestep based on the previous timestep’s state.\n",
        "#### RNNs are a powerful and robust type of neural network that can be used for many tasks, including language modeling, speech recognition, and image captioning. \n",
        "#### They belong to the most promising algorithms in use because it is the only one with internal memory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhDDRMtAFEGo"
      },
      "source": [
        "### **Training with a simple RNN model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7n7un0AFF95"
      },
      "source": [
        "#### A simple RNN model, is  a fully-connected RNN where the output form the previous timestep is used as input to the next timestep.\n",
        "#### For the purposes of this model we will use as input 20 units, and the activation function for our last layer will be \"Softmax\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kLmBDkMB826W"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eVDWQpQ826W",
        "outputId": "9a2b40d4-b79a-4c8d-86c7-7203d99e85b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 20)                460       \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 544\n",
            "Trainable params: 544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ creating the model\n",
        "model_simple_rnn = Sequential([\n",
        "    SimpleRNN(20, return_sequences = False, input_shape=[None, 2]),\n",
        "    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "\n",
        "model_simple_rnn.summary() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "REEaqRah826W",
        "outputId": "8cf416b4-7b56-45c1-bfd4-14eae254ad94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 103s 1s/step - loss: 1.3994 - Accuracy: 0.2761 - val_loss: 1.3609 - val_Accuracy: 0.3600\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 100s 1s/step - loss: 1.3449 - Accuracy: 0.3511 - val_loss: 1.3488 - val_Accuracy: 0.3833\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.3302 - Accuracy: 0.3450 - val_loss: 1.3357 - val_Accuracy: 0.3450\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.3180 - Accuracy: 0.3657 - val_loss: 1.3300 - val_Accuracy: 0.3583\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 109s 1s/step - loss: 1.3058 - Accuracy: 0.3739 - val_loss: 1.3226 - val_Accuracy: 0.3850\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.2970 - Accuracy: 0.3761 - val_loss: 1.3313 - val_Accuracy: 0.3517\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 102s 1s/step - loss: 1.2920 - Accuracy: 0.3950 - val_loss: 1.3117 - val_Accuracy: 0.4067\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.2816 - Accuracy: 0.3893 - val_loss: 1.3004 - val_Accuracy: 0.3750\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.2687 - Accuracy: 0.4032 - val_loss: 1.2903 - val_Accuracy: 0.3367\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 98s 1s/step - loss: 1.2589 - Accuracy: 0.3911 - val_loss: 1.2681 - val_Accuracy: 0.4133\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 99s 1s/step - loss: 1.2440 - Accuracy: 0.4104 - val_loss: 1.2593 - val_Accuracy: 0.3900\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 96s 1s/step - loss: 1.2341 - Accuracy: 0.4196 - val_loss: 1.2462 - val_Accuracy: 0.4017\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 97s 1s/step - loss: 1.2200 - Accuracy: 0.4354 - val_loss: 1.2341 - val_Accuracy: 0.4300\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 96s 1s/step - loss: 1.2116 - Accuracy: 0.4439 - val_loss: 1.2165 - val_Accuracy: 0.4317\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 96s 1s/step - loss: 1.2015 - Accuracy: 0.4479 - val_loss: 1.2076 - val_Accuracy: 0.4517\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff381b2d650>,\n",
              " <keras.callbacks.History at 0x7ff37d85dfd0>)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training the model and plotting training and validation loss \n",
        "train_plot(model_simple_rnn) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "amKeyeHE826W",
        "outputId": "83beb64a-7d6b-4a2d-8b23-519869672e70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 4s 195ms/step - loss: 1.2076 - Accuracy: 0.4517\n",
            "Validation score: 1.207587480545044\n",
            "validation accuracy: 0.4516666531562805\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_simple_rnn.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcy1gM7QGNLU"
      },
      "source": [
        "### **Training the data with a deeper RNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihPmarfiGStd"
      },
      "source": [
        "#### A deeper RNN model, is a fully-connected RNN where the output form the previous timestep is used as input to the next timestep. In this case we have multiple layers of recurrent nodes,\n",
        "#### and we need to consider what to pass on between layers. Hence, we need to \"set_sequences=True\" in a recurrent layer, so all outputs will pass to the next layer.\n",
        "#### For the purposes of this model we will use as input 20 units for the 2 first layers and the first one will have the return_sequences=True, and the activation function for our last layer will be \"Softmax\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nM63bL9f826X"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CibkN5xs826X",
        "outputId": "ad2ca80f-8f77-4b03-d7eb-1111d8d97c5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, None, 20)          460       \n",
            "                                                                 \n",
            " simple_rnn_1 (SimpleRNN)    (None, 20)                820       \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                210       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,490\n",
            "Trainable params: 1,490\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the deeper rnn model\n",
        "model_deeper_rnn = Sequential([\n",
        "    SimpleRNN(20, return_sequences=True, input_shape=[None, 2]),\n",
        "    SimpleRNN(20), # ---+ 20 units each layers, Dense last layer for prediction, sequence elements are of dimension 1\n",
        "    Dense(10, activation = \"softmax\") # ---+ sequence = True means that the output is actually a sequence (alternative is the final output of the sequence)\n",
        "]) # ---+ at the end we dont care about the middle outputs just on the final one thats why sequence at the end True\n",
        "\n",
        "model_deeper_rnn.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "3T-zeSRb826X",
        "outputId": "e4bb588a-58a5-462e-a8aa-8ce39ea6665c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 405s 5s/step - loss: 1.7745 - Accuracy: 0.2407 - val_loss: 1.4862 - val_Accuracy: 0.2700\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 415s 5s/step - loss: 1.4300 - Accuracy: 0.2768 - val_loss: 1.4027 - val_Accuracy: 0.3183\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 433s 5s/step - loss: 1.3709 - Accuracy: 0.3300 - val_loss: 1.3555 - val_Accuracy: 0.3600\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 408s 5s/step - loss: 1.3273 - Accuracy: 0.3911 - val_loss: 1.3226 - val_Accuracy: 0.4100\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 407s 5s/step - loss: 1.2922 - Accuracy: 0.4257 - val_loss: 1.2907 - val_Accuracy: 0.4150\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 407s 5s/step - loss: 1.2661 - Accuracy: 0.4393 - val_loss: 1.2808 - val_Accuracy: 0.4233\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 406s 5s/step - loss: 1.2485 - Accuracy: 0.4489 - val_loss: 1.2512 - val_Accuracy: 0.4300\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 404s 5s/step - loss: 1.2294 - Accuracy: 0.4550 - val_loss: 1.2524 - val_Accuracy: 0.4367\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 405s 5s/step - loss: 1.2109 - Accuracy: 0.4686 - val_loss: 1.2149 - val_Accuracy: 0.4300\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 408s 5s/step - loss: 1.1877 - Accuracy: 0.4589 - val_loss: 1.2041 - val_Accuracy: 0.4433\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 411s 5s/step - loss: 1.1520 - Accuracy: 0.4904 - val_loss: 1.1739 - val_Accuracy: 0.4417\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 416s 5s/step - loss: 1.1181 - Accuracy: 0.5039 - val_loss: 1.1217 - val_Accuracy: 0.5100\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 432s 5s/step - loss: 1.0816 - Accuracy: 0.5207 - val_loss: 1.1131 - val_Accuracy: 0.4967\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 405s 5s/step - loss: 1.0602 - Accuracy: 0.5214 - val_loss: 1.0563 - val_Accuracy: 0.5117\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 408s 5s/step - loss: 1.0472 - Accuracy: 0.5350 - val_loss: 1.0821 - val_Accuracy: 0.4950\n"
          ]
        },
        {
          "data": {
            "image/png": 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Gqq3MDB4Gbqbe5NTVU6YuRQghyp0xxtBbAU5Kqd1KqTCl1LiiGiqlJimlDiulDlfEPQ3vXGAk4+hCiKrAGIFuA3QAhgADgDeUUq3u11Br/bHWOlBrHdigQQMjHLp4no08aVCrgVxgJISoEoxxk+h44IbWOhVIVUrtAXyB00bYd5kopfJveCEnRoUQVYAxeuibgCCllI1Syh7oDJw0wn6NwuBh4My1M1y9fdXUpQghRLkqzbTFtUAo0FopFa+UmqiUmqyUmgygtT4J/AgcA34BPtVaFznFsaLdGUeXYRchhKUrcchFa/1UKdrMAeYYpSIjC2gRgJ2tHSExIYwMGGnqcoQQotxY7JWid1SzqUZH144yji6EsHgWH+iQP+xy5MIR0jLTTF2KEEKUmyoR6AYPAzm5Ofxy/hdTlyKEEOWmSgT6nYW65MSoEMKSVYlAd3JwwrupNyFn5IpRIYTlqhKBDvnj6KGxoeTm5Zq6FCGEKBdVJtANHgaS0pOIuhxl6lKEEKJcVKlABxlHF0JYrioT6C3rt6SJYxNZeVEIYbGqTKArpTB4yEJdQgjLVWUCHfJPjMbdiCP+ZrypSxFCCKOrUoFeOI4uvXQhhAWqUoHu18wPh+oOcmJUCGGRqlSg21jb0LllZ7nASAhhkapUoEP+OHrExQiSM5JNXYoQQhhVlQt0g4eBPJ3HgbMHTF2KEEIYVZUL9C5uXbBSVjKOLoSwOFUu0GvXqE17l/Yyji6EsDiluafocqXUNaXUfe8TqpTqpZRKUkqFF3y8afwyjcvgYeDA2QPk5OaYuhQhhDCa0vTQVwIDS2izV2vtV/DxTtnLKl9BHkGkZqZyLP6YqUsRQgijKTHQtdZ7gJsVUEuFuXOBkQy7CCEsibHG0LsqpSKUUv9TSnkX1UgpNUkpdVgpdTghIcFIh35wzeo2o3nd5nJiVAhhUYwR6EeAFlprX+Aj4NuiGmqtP9ZaB2qtAxs0aGCEQz88g4eBkDMhaK1NWocQQhhLmQNda31ba51S8HwLYKuUql/myoo+HocO/Vrm/Rg8DFxOvEzcjTgjVCWEEKZX5kBXSjVWSqmC550K9nmjrPstyooVkXTu/AVr1pTtzkNBHkGAjKMLISxHaaYtrgVCgdZKqXil1ESl1GSl1OSCJqOBSKVUBPAh8KQux3GMp5/2onfv5owf/yPBwaceej8+zj7UrlFbxtGFEBbDpqQGWuunSti+EFhotIpKYGdnw6ZNwxkwIJinntpMjRo2DBni/sD7sbaypqtbV+mhCyEshlleKVqzZjW2bBmFr28DRo36jh07Hm4c3OBhIOpyFIlpiUauUAghKp5ZBjqAo2N1tm4djaenE489tpF9+y498D6CPILQWhMaG1oOFQohRMUy20AHqFevBtu3P46LSy0GD17P4cNXHuj1nVp2wtrKWoZdhBAWwawDHaBRIwd27HiCunXtGDAgmOPHS3/BkkN1BwKaB8iJUSGERTD7QAdwcanFjh1PUKOGDf36fcOpU6VfqcDgYeDguYNk5WSVY4VCCFH+LCLQAdzc6rB9++MA9O37NefOle5Ep8HDQEZ2BkcvHC3P8oQQotxZTKADtGlTj23bHictLYe+fb8hPr7k28wZ3GWhLiGEZbCoQAdo374BW7eO4vr1dPr1+4arV1OLbd+kThPcGrjJOLoQwuxZXKADdOzYhC1bRnLx4m369/+GmzfTi20f5BEkC3UJIcyeRQY6QFCQC5s2jeD06VsMGBBMUlJmkW0NHgYSkhM4c+1MBVYohBDGZbGBDtCvXwuCgx8jPDyBIUM2kJp6/5ksdxbqkmEXIYQ5s+hAB3j0UXe+/HIIoaGXGT58ExkZ995HtE3jNjjZO8mJUSGEWbP4QAd4/PHWrFgxkO3b43j88e/Iysq9a7uVlRXd3LvxY+SPnEs4Z6IqhRCibKpEoAOMG+fNkiX9+OGHs4wdu5mcnLy7tr8y4BWSM5Px/6c/wWHBJqpSCCEeXpUJdIDJk/344INefPPNaSZO/JG8vN9mtfRq3YujbxylVaNWPL70cf7yxV/IyM4wYbVCCPFgqlSgA8yYEcg//2lg9eoTTJ26/a6pim4N3Ah5NYSXH3mZJbuX0Pk/nYn+NdqE1QohROlVuUAH+Mc/ujBrVieWLo3glVd23xXq1WyqMffxufzw4g9cSrxEh391YNX+VSasVgghSqdKBrpSiv/8pzvTpgUwb14Ys2ffO11xSPshRLwZQaBrIONXjGfcZ+NIyUgxQbVCCFE6pbmn6HKl1DWlVGQJ7ToqpXKUUqONV175UUoxf35vJk5sxz//eYD33jt4TxtnJ2d2vryT2UNn8/nBz+nwrw6EXwg3QbVCCFGy0vTQVwIDi2uglLIG3gN+MkJNFcbKSrFsWX+eftqLWbP2MnfuoXsu/7e2suatx95ix4wdJGck0+W/XVi8a7EsEyCEqHRKDHSt9R6gpAXGXwTWA9eMUVRFsra2YuXKgYwc6cnMmT8TFLSWI0eu3tOud5vehL8ZTu82vZn65VRGLx0t9yIVQlQqZR5DV0o5AyOAJaVoO0kpdVgpdTghofR3FipvtrbWfPPNYyxfPoAzZxIJDFzD5MnbuHHj7kW9GtZuyOYXN/P+6Pf5LuI7/N7x40DsARNVLYQQdzPGSdEFwKta67ySGmqtP9ZaB2qtAxs0aGCEQxuPlZViwoR2nDr1HC+91IFPPz2Gp+dnLF58lNzcvN+1s2LmgJnsnbkXhaL7nO68/+P75OWV+PaFEKJcGSPQA4GvlFLngdHAYqXUcCPs1yTq1LFj/vzeREQ8i79/Q6ZO3UGHDmvYuzf+rnZd3Ltw9M2jDPMdxqvrX2XIR0O4dtvsRpyEEBakzIGutW6ptXbVWrsCwcBftNbflrkyE/P2rs/27Y/zzTdDuXkzgx49vuLpp3/g0qXf7oJUx74O30z+hsXPLGZX9C783vFjV/QuE1YthKjKSjNtcS0QCrRWSsUrpSYqpSYrpSaXf3mmpZRi9OjWREc/xxtvdGHDhhhat17Ou+8eJDMzp7DNlF5TOPj6QWrZ1aLvvL7M3jSbnNx7V3UUQojypEw1/S4wMFAfPnzYJMd+WGfPJjJjxm42bTqDp6cTCxb0ZvBgt8LtKRkpTP1yKqtDV9OjVQ++fP5LnJ2cTVixEMLSKKXCtNaB99tWJa8UfVhubnX49tvh/PjjKJSCIUM2MHToBmJj86cv1rSryarnVrFqwirC4sLwfceXzcc2m7hqIURVIYH+EAYMaMnx4+N5//0e7N59kbZtV/CPf+wtvCPSuG7jCPu/MJzrOPPoR4/y5zV/5urte+e2CyGEMUmgP6Rq1ayZObMTp05N5IknWvOf/xykTZsVrFsXjdaa1o1bc/D1g0zvN53l+5bj/ro7b3/3tqwHI4QoNxLoZdS0aU3WrBlMSMhT1K9fgyef/IE+fb7m+PEE7GztmD9mPlFvRTHQeyBvff8WHv/wYNnPy+SkqRDC6CTQjcRgcObw4bEsXdqfY8cS8PdfzbRpO0hMzKBV41YETwlm/6z9eDb0ZPLnk/F5y4dvj34ra8IIIYxGAt2IrK2t+POffYmJmcif/+zLokXheHp+xvz5h7l1K4Ou7l3Z8/c9fDs1f5r+iMUj6P5+d0JjQ01cuRDCEkigl4O6dWuwaFE/wsL+hI9PfWbM2I2z81ImTvyRsLCrDPMbRuRbkSz70zJiE2Lp9m43Ri0Zxekrp01duhDCjMk89Apw9OhVliyJ4IsvTpCWlkPHjo2ZMsWXMWPaoK2zmLdtHu//+D7p2elM6jGJ2UNn06h2I1OXLYSohIqbhy6BXoGSkjJZsyaKxYvDOXnyJnXqVGfCBB8mT/bFsXE273z/Dh/v/Rg7GztmDpjJjP4zqGlX09RlCyEqEQn0SkZrzZ498SxZEs769THk5OTRt29zpkzxo02nPGb/8H+sP7Kexo6NeWvoW0wMmoiNtY2pyxZCVAIS6JXYlSupfPbZcZYti+DixWSaNq3JCy+0I6B/NnNC3iDkTAhtGrfhvyP/yzC/YSilTF2yEMKEJNDNQG5uHps3n2XJknC2bj2PlZVi2DAPfPun8UX825y+egqDh4E5o+fQ1b2rqcsVQpiIBLqZiY1NZNmyCJYvj+TGjXQ8Pevg90g6P/MB1zLjGBkwkv+O+C+tGrcydalCiAomgW6mMjJyCA4+zeLF4YSGXsbOzhovQybRTivIrneWMYFjmN5vOoGu9/23FUJYIAl0CxARcY0lSyL4/PMTpKZm09Ati6QWW8lsfoAgr05M7zed4f7DsbayNnWpQohyJIFuQW7fzmTNmhMsWRJOVNQNqteAaq2jSHbdhmtrW17s8yITgybiaO9o6lKFEOVAAt0Caa0JDb3Mxx8fY926aDIycqnpnEhKy+04eMXwXO9nmNZ3Gh4NPUxdqhDCiCTQLdytWxl88cUJli07RmTkdWyq55LnFkZe61Ae6xvA9H7T6dW6l0x5FMIClCnQlVLLgUeBa1prn/tsHwb8E8gDcoDpWuuQkoqSQDc+rTUHD/7KsmURfLUumoz0XKwbXiHXcx8+PbJ5echfearTU1S3rW7qUoUQD6msgd4DSAFWFxHoNYFUrbVWSrUHvtZatympKAn08pWYmMEXX5xk6dJwIiNvYGWbTZ5bGE4Bp5k2ZhRTek+W9WKEMENluqeo1noPcLOY7Sn6t78KDoAs8F0J1Kljx9Sp/hw7Np4DB57h2bH+2F3oxq2143h74i2cHx3NM4snEnExwtSlCiGMpFRj6EopV+CH+/XQC7aPAP4LNASGaK3vu8C3UmoSMAmgefPmHeLi4h6uavFQkpIy+fLLk3y46Beio26DTRa4HaXDgBzemDCeR30flWmPQlRyZT4pWlKg/65dD+BNrXW/kvYpQy6mo7Xm0KErfLT4EOu+OkV2poJ6l6gfGMvLU/owvteTNHZsbOoyhRD3UWGBXtD2LNBJa329uHYS6JXD7duZrPk8irn/by/nT2eDygWHJBwb5tHaoyEGPy/atXHB1dWRFi1q06xZLWxtpRcvhKmUa6ArpTyA2IKTogHA94CLLmHHEuiVi9aasLCrfPJ5KKHhp4g9e5O0m9UgtTa/P9ViZaVwdq5Jixa1C0Pe1fW3582b16J6dVnqV4jyUlygl/ibp5RaC/QC6iul4oHZgC2A1nopMAoYp5TKBtKBMSWFuah8lFIEBjYmMHBE4ddOXznNuoPrWbd7M1GnL0FyXRpataW+TVsyU6uxd28yX36ZTF7e3f/cTZo44OrqiKtr7cLg9/NrSKdOjWUuvBDlSC4sEqVyLuEc64+sJzgsmIPnDgLQzrkdw9uPIqjpYKplNCIu7jbnz98ueEwiLu42Fy4kk5OTB4CHRx3GjfPmT39qi6urLE0gxMOQK0WFUV28eZENRzaw/sh6Qs6EoLWmdePWjA4YzegOo/Ft5lvYE8/NzePSpRR27rzA6tVR7Np1EYCePV149llvRo9uTa1a1Uz5doQwKxLootxcSbrCxqMbCQ4LZvep3eTpPNwauDE6YDSjOoyio2vHu4ZZ4uKSWLPmBKtXnyAm5hY1atgwcqQnzz7rTZ8+zbG2LvHSCCGqNAl0USESkhPYFL6J9UfWs/3kdnJyc2hetzkjA0YytP1QOrXsVHjTa601Bw78yqpVUaxbF01iYibOzjUZO7Yt48a1pW3b+iZ+N0JUThLoosLdSr3F9xHfE3wkmK1RW8nKycJKWdHOuR1d3bvSxa0LXdy64NnQk6ysPL7/PpbVq6P43//OkZurCQxsxLhx3jz1VBvq17c39dsRotKQQBcmdTv9NvvO7OPA2QOEng3l4LmD3E6/DYCTvROd3TrT1S0/5F3tfdiyKb/nHh5+DVtbK4YMcWPcOG+GDHGjWjWZAy+qNgl0Uank5eURfSW6MOAPnD1A1OUo7vwsejXxoqtbV1x0R84fqM/WTde4ejWNevVq8NRTbRg3ri2BgTIFUlRNEuii0rudfptD5w/lh3xsKAfOHeBGyg0AalarjXvWILJPtifmUHWyszReXnUZN84bH5/65OZqcnLy/vChyc397fnvtxXV/s42pWDUqFb07dtc/miISkcCXZgdrTWxCbG/BfzZAzZtEB0AABTYSURBVETER5Cbbgvn2mN3PoiM+CZlOoaNjRXW1gobG6vffSjS0nJITs4iMLARs2Z1ZvhwD5l9IyoNCXRhEdIy0wiLC+PAufyQDwmPJOFaOqg8ajvUokMLfzq6daCzeyC+zdtRw646NjYKa+vfwvpOcFtZqSJ735mZOaxefYL33/+FM2cSadXKib//vRNjx3rJsgbC5CTQhUXSWnM24Sx7Y/YWfsRciwHAvpo9Xdy60MOzB909u9PFrQv21R9stkxubh4bNsTw7ru/cOTIVZydazJjRiAvvNBeLoYSJiOBLqqMK0lXCDkTwt6Yvew5vYeI+Ai01thY29CheQe6e3anu2d3gjyDqOtQt1T71FqzfXsc7777Czt3XsDJyY6//tWPF18MoEEDmVIpKpYEuqiyktKS2B+7v7AH/8v5X8jKyQLAx9knP+A98kPepa5Lifs7ePBX3nvvFzZujKFGDRuef74dL78cSIsWsjaNqBgS6EIUyMjO4NC5Q/k9+Jg97I/dT3JGMgCu9Vzp7tmdHq16YPAw0KZxmyLH2U+evMGcOYdYs+YEAE8/3Ya//70T3t5yhasoXxLoQhQhJzeHY/HH7hqHv5Z8DYC6DnXp5t4Ng4cBg7uBji07Ymdrd9frL168zfz5YSxbFkFaWg6PPebOrFmd6dq1qSnejqgCJNCFKCWtNTFXY9gXu499Z/I/oq9EA2BrbUuHFh0KA97gYaBh7YYA3LiRzsKFR/nwwyPcvJlBjx4uvPZaZwYMcJW57MKoJNCFKIPrydfZH7s/P+Bj93H4/GEyczIB8GjoURjuBg8DzWq5s3x5JHPnHiY+Phlf3wbMmtWZ0aNbYWMjc9lF2UmgC2FEmdmZhMWF3dWLv56Sfwvdug516erWlc4tDKRHt2LjykSio2/h5ubIX//qj59fQ9zcHHFxqSUXK4mHIoEuRDkqbpjGxqoaLVOHkHwwkCtnfpu7bmtrhaurI+7ujri51cHdvQ5ubvnP3dwcqVlT5rmL+ytToCullgOPAteKuEn0M8CrgAKSgSla64iSipJAF5bsevJ1Qs+GFgb8L+cOkZVYA+vURnjYdcbZuh3V0pqQcDmP2NhEEhMz73p9w4b2hSF/92MdmjRxkHH5Kqysgd4DSAFWFxHo3YCTWutbSqlBwFta684lFSWBLqqSzOxMQs+G8r/j/2NL5BYiL0UC0LJ+Swa3G0z3FgNwtmrP5YvpxMYmcfZsIrGxiZw9m8TFi3ffiLtGDRtatnQsDPnAwMaMGOGBg4P06quCMg+5KKVcgR/uF+h/aOcERGqtnUvapwS6qMribsQVhvuOkztIy0rDztaOPm36MLjdYIa0G4JrfVcAsrJyiYu7XRDwvwX9ncfU1Gxq1rTliSdaM2GCDwaDs/TgLVhFBvorQBut9fNFbJ8ETAJo3rx5h7i4uBKPLYSly8jO4OdTP7Mlcgubj20mNiEWyF8X/k64GzwMVLO5tweutWbfvkusWBHJ11+fIiUlGw+POowf78O4cW1p1qx2Rb8dUc4qJNCVUr2BxUCQ1vpGSfuUHroQ93f6ymm2RG5hy/Et/Hz6Z7JysqhlV4v+bfszpN0QBvkMokmde5cOTk3NYv36GFasiGT37osoBf36tWD8eB9GjPCgRg1bE7wbYWzlHuhKqfbARmCQ1vp0aYqSQBeiZCkZKeyI3sGW4/kBH38rHgD/5v4M9hnMkPZD6NSyE9ZWd9+a79y5RFatimLlyiji4m7j6FidJ59sw/jx3nTu3ESGZMxYuQa6Uqo5sBMYp7XeX9qiJNCFeDBaa45fOl4Y7vtj95Obl4tzHWem9Z3GC91fwMnB6a7X5OVpfv75IitWRBIcfJr09By8vOoyfrwPY8e2pWnTmiZ6N+JhlXWWy1qgF1AfuArMBmwBtNZLlVKfAqOAOwPiOUUd7Pck0IUom1upt9gatZVP9n7CzuidOFR34DnDc7zU9yXcG7rf0/727Uy++eY0K1dGEhJyCSsrxcCBrkyY4MPQoe5y8w4zIRcWCWHhwi+EM3/7fNb+spacvBxG+I9gRv8ZdHPvdt/hlZiYW6xcGcnq1SeIj0+mbl07nn7ai/HjvQkIaCRDMpWYBLoQVcTlxMss2rWIJbuXcCvtFp1aduLl/i8zMmAkNtb39sBzc/PYseMCK1dGsmFDDJmZubRrV58JE3wYP94HJye7+xxFmJIEuhBVTGpmKqv2r2L+9vmcuXaG5nWb81Lfl5gYNBFH+/vfjCMxMYOvvopm5cooDh78lUaN7Fm0qB+jRrWq4OpFcSTQhaii8vLy+OHYD3yw7QP2nN5DLbtaPB/0PC/1e4kW9VoU+bqwsCu88MJPHD16jdGjW7FwYV8aNXKowMpFUSTQhRAcPn+Y+dvms+7wOrTWjO4wmhn9Z9DZ7f4rdWRn5zJ37mHefns/Dg62LFjQm7Fj28r4uolJoAshCl28eZGFOxeybM8yktKTMHgYmNF/BsP8ht0znx0gOvoGEyduZf/+ywwa1JJly/rLFagmJIEuhLhHckYyK/atYMH2BZy7fo6W9Vsyvd90JhgmUMuu1l1tc3PzWLQonNde24O1tRVz5vTkhRfaY2UlvfWKJoEuhChSbl4um8I3MW/bPPad2YdjDUcm9ZjEi31epFndZne1PXs2kUmTfmLHjgv06tWMTz8dgLt7HRNVXjVJoAshSuXg2YPM2zaP4LBgAIb7D+fFPi/Ss1XPwrFzrTWffXacl1/eTXZ2Hv/6VxAvvRQgd2CqIBLoQogHEncjjiW7l/DJ3k+4mXoTH2cf/tr7r4ztMhaH6vmzXS5dSmbKlO18/30snTs34bPPBuDtXd/ElVs+CXQhxENJz0rnq0Nf8dHOjzh64SiONRx5zvAcf+n9FzwaeqC15quvonnxxZ0kJ2fxxhtdePXVTtja3ntyVRiHBLoQoky01oTGhvLRzo8IPhJMbl4ug3wG8WKfF3mk7SNcv57OtGk7WbfuFL6+DVi+fCABAY1MXbZFkkAXQhjNr4m/smzPMpbtWcaVpCt4NvRkau+pjO82nt3bEpgyZRvXrqUxc2ZHZs/uhp2dLPplTBLoQgijy8rJYn3YehbuWsj+2P04VHdgXNdxjPWbxGfzrrB8eSStW9fls88GYDCUeFdKUUoS6EKIcnUk7ggLdy3ky4NfkpmTSZ82fQiye45Vc1K4cCGZF18M4N//DqJmTbmRdVlJoAshKsT15Ot8GvIpi3cv5uLNi7jUdKdZ7CRCv7fC1bU2H3/8CH37tpALkspAAl0IUaFycnP4PuJ7Fu5ayM7ondgmtKLGwXHcvlIdW1srnJ1r4uxcCxeXmri41MLZOf/xzvMmTRxkpkwRJNCFECYTdSmKRbsXsWrvl6RFtaaZjT/u9gGQVpv4+BTi41PIyMi56zVKQePGDoVB/8fAv/Nob1/1bnwtgS6EMLnEtESWhyxnwY4FXLx5kbZN2vLyIy/zdKenSUvRBeGezKVL+Y+/f37pUgqJiZn37NPJyQ4Xl5r4+NTnvfd6VIlFw8p6T9HlwKPAtSJuEt0GWAEEAP/QWs8tTVES6EJUTdk52Xx9+GvmbJ1DRHwETRybMK3vNCb3nEwd+6LXhUlJyeLSpZT7Bv7OnRewsbFi6dL+jBnTpgLfTcUra6D3AFKA1UUEekOgBTAcuCWBLoQoDa01209uZ87WOWw7sY2a1WvyQvcXmN5vOs3rNX+gfcXGJvLMM5s5ePBXxo1ry0cf9aV27erlVLlpFRfoJa6mo7XeA9wsZvs1rfUhIPvhSxRCVDVKKfq37c9Pf/uJ8DfDGe4/nI92fYTb624888kzhF8IL/W+3N3rsHfvk7z5Zlc+//wkfn6r2b//UjlWXznJ8mhCCJPzbebLmolrOPufs7zU9yW+i/gO/3/6039ef36K+onSnOuztbXm7bcN7NnzJFprunf/irfe2kdOTl4FvIPKoUIDXSk1SSl1WCl1OCEhoSIPLYQwA83qNuODJz7g4vsXeXfku0RdjmLAggH4vePHmtA1ZOeUPBBgMDgTEfEsY8d68fbboXTvvpbY2MQKqN70KjTQtdYfa60DtdaBDRo0qMhDCyHMSB37Orw66FXOv3ueFeNXkJuXy7jl43B73Y0PfvqA2+m3i3197drVWbVqMF999SgnT97Ez28VK1dGlqqnb85kyEUIUWlVs6nGeMN4jr91nM3TNuPR0INXvnmFZq8249XgV7l0q/hx8jFj2nDs2LN06NCICRN+ZMyY77l5M72Cqq94pZnlshboBdQHrgKzAVsArfVSpVRj4DBQG8gjf0ZMW611sX9CZZaLEOJhHD5/mDlb5xAcFoy1lTVPd3qa6f2m49vMt/CuSn+Um5vHnDmHeOONfTRqZM+aNYPp3fvBZtIYQ05OHnv3xtO4sQNeXvUeah9yYZEQwuKcTTjLgu0L+CzkM9Ky0nBxcqGfVz/6t+1PX6++NKp973rsYWFXePrpzcTE3OKVVzryr38FUa1a+S4xkJmZw44dF9iwIYZNm85w/Xo6U6f6sXBhv4fanwS6EMJi3Ui5wfoj69l2Yhs7Tu7gVtotANq7tKd/2/708+pHD88e2Fe3ByA1NYuXX/6ZZcsi8PdvyBdfDHno3nJRUlOz+PHH82zYEMMPP8Ry+3YWtWpV49FH3Rg50pNBg1ri4PBwK09KoAshqoTcvFyOXjjKthPb2H5yOyFnQsjKyaKaTTW6uXejv1d/+rftT0CLAH74/hzPP7+V1NRs5s3rxZ//XPSQTWkkJmbwww9n2bAhhh9/PEd6eg716tVg2DB3Ro70pF+/FlSvXvabfUigCyGqpLTMNPbG7GX7ye1sO7GNiPgIAJzsnejTpg+dGvXlh4UO7N11jaFD3fn000do2NCh1PtPSEhj06YzrF9/mh07LpCdnUfTpjUZMcKDkSNb0aOHCzY2xp17IoEuhBDA1dtX2XlyJ9tObmPbiW3E34oHragbN5Sknw3UrGXDsk/6Mmakb5H7iI9PZuPGGNavP83evZfIy9O0bOnIqFGejBzZis6dm5Treu8S6EII8Qdaa05fPc22E/nhvn1fFGlbh8GtJjTscpo/TatHb+8g2jZpS/at2nz7bSzr15/ml1+uAODtXY+RI/ND3Ne3QZmGax6EBLoQQpQgJzeHvdEHeP21vRz43hacrkCLSIjzhltNAKjbIo0OPasxdJgr/bv64d7AHVubil2TXQJdCCEewE8/nWfcs1u4djUNj3bWNPG7AS0iOZd5hIs3Lxa2s7W2xbOhJ15NvGjbtC1ejb3wauJF68atqVGtRrnUJoEuhBAPKD09m7S0/Jkqv5eSkUL0lWhOXD7BySsn8x9/PUlsQix5On8hMKUULeu3pG2TtvlhX/Do1cSL2jXKdhMOCXQhhChnGdkZxFyN4eSvJznxa37In/z1JKeuniIrJ6uwnXMdZ2b0n8GMR2Y81HGKC/SyT4oUQgiBna0d7Vza0c6l3V1fz8nN4dz1c3eFfGPHxuVSgwS6EEKUIxtrGzwbeeLZyJNhfsPK9Viy2qIQQlgICXQhhLAQEuhCCGEhJNCFEMJCSKALIYSFkEAXQggLIYEuhBAWQgJdCCEshMku/VdKJQBxD/ny+sB1I5ZT3sypXnOqFcyrXnOqFcyrXnOqFcpWbwutdYP7bTBZoJeFUupwUWsZVEbmVK851QrmVa851QrmVa851QrlV68MuQghhIWQQBdCCAthroH+sakLeEDmVK851QrmVa851QrmVa851QrlVK9ZjqELIYS4l7n20IUQQvyBBLoQQlgIswt0pdRApdQppdQZpdQsU9dTFKVUM6XULqXUCaVUlFLqJVPXVBpKKWul1FGl1A+mrqU4Sqk6SqlgpVS0UuqkUqqrqWsqjlLqbwU/B5FKqbVKKTtT1/R7SqnlSqlrSqnI332trlJqm1IqpuDRyZQ13lFErXMKfhaOKaU2KqXqmLLG37tfvb/b9rJSSiul6hvjWGYV6Eopa2ARMAhoCzyllGpr2qqKlAO8rLVuC3QBplbiWn/vJeCkqYsohf8H/Ki1bgP4UolrVko5A9OAQK21D2ANPGnaqu6xEhj4h6/NAnZorT2BHQWfVwYrubfWbYCP1ro9cBp4raKLKsZK7q0XpVQz4BHggrEOZFaBDnQCzmitz2qts4CvgPK9p9ND0lr/qrU+UvA8mfzAcTZtVcVTSrkAQ4BPTV1LcZRSjkAP4DMArXWW1jrRtFWVyAaooZSyAeyByyau5y5a6z3AzT98eRiwquD5KmB4hRZVhPvVqrX+SWudU/DpAcClwgsrQhHfW4D5wN8Bo81MMbdAdwYu/u7zeCp5SAIopVwBf+CgaSsp0QLyf8DyTF1ICVoCCcCKguGhT5VSDqYuqiha60vAXPJ7Yr8CSVrrn0xbVak00lr/WvD8CtDIlMU8gOeA/5m6iOIopYYBl7TWEcbcr7kFutlRStUE1gPTtda3TV1PUZRSjwLXtNZhpq6lFGyAAGCJ1tofSKXyDAfco2DseRj5f4iaAg5KqbGmrerB6Pz5zZV+jrNS6h/kD3d+YepaiqKUsgdeB9409r7NLdAvAc1+97lLwdcqJaWULflh/oXWeoOp6ymBAXhMKXWe/KGsPkqpz01bUpHigXit9Z3/8QSTH/CVVT/gnNY6QWudDWwAupm4ptK4qpRqAlDweM3E9RRLKTUeeBR4RlfuC2zcyf/jHlHw++YCHFFKNS7rjs0t0A8BnkqplkqpauSfWPrOxDXdl1JKkT/Ge1JrPc/U9ZREa/2a1tpFa+1K/vd1p9a6UvYitdZXgItKqdYFX+oLnDBhSSW5AHRRStkX/Fz0pRKfxP2d74BnC54/C2wyYS3FUkoNJH+48DGtdZqp6ymO1vq41rqh1tq14PctHggo+LkuE7MK9IKTHn8FtpL/C/G11jrKtFUVyQD8ifyebnjBx2BTF2VBXgS+UEodA/yA/5i4niIV/E8iGDgCHCf/965SXaqulFoLhAKtlVLxSqmJwLtAf6VUDPn/y3jXlDXeUUStC4FawLaC37WlJi3yd4qot3yOVbn/ZyKEEKK0zKqHLoQQomgS6EIIYSEk0IUQwkJIoAshhIWQQBdCCAshgS6EEBZCAl0IISzE/we6j3C9HzM6ngAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff37d777290>,\n",
              " <keras.callbacks.History at 0x7ff37d69e210>)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the deeper RNN\n",
        "train_plot(model_deeper_rnn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OpA1T54x826Y",
        "outputId": "ee474efb-0758-41b3-b5a8-d44f46a979ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 8s 398ms/step - loss: 1.0821 - Accuracy: 0.4950\n",
            "Validation score: 1.0820729732513428\n",
            "validation accuracy: 0.4950000047683716\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_deeper_rnn.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKP0vht-GlGi"
      },
      "source": [
        "### Training the data with LSTMs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hn2L8mcyGmky"
      },
      "source": [
        "#### A LSTM model, is a fully-connected RNN where the output form the previous timestep is used as input to the next timestep.\n",
        "#### These models are an aritficial, advanced RNN architecture used in the field of deep learning. LSTMs have feedback connections.\n",
        "#### LSTM allows information to persist, and it is capable of handling the vanishing gradient problem faced by RNN. LSTMs are designed to avodi long-term dependency problems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SauYoAIz826Y"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HNY98GKGrUa"
      },
      "source": [
        "#### For this model we will use 2 LSTM layers as input, and a Dense layer with an activation fucntion of \"softmax\" as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEsQUyaj826Y",
        "outputId": "0d5f3787-1c0d-4515-e680-7c027ed9fe07"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, None, 20)          1840      \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 20)                3280      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,204\n",
            "Trainable params: 5,204\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the LSTM model\n",
        "model_lstm = Sequential([\n",
        "    LSTM(20, return_sequences=True, input_shape=[None, 2]),\n",
        "    LSTM(20),\n",
        "    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "model_lstm.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "YP6UGt9C826Y",
        "outputId": "ce5ac967-666f-4a1c-bfd0-da6d51a3eac0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 527s 6s/step - loss: 1.3841 - Accuracy: 0.2586 - val_loss: 1.3783 - val_Accuracy: 0.3533\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 524s 6s/step - loss: 1.3603 - Accuracy: 0.3346 - val_loss: 1.3336 - val_Accuracy: 0.3800\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 527s 6s/step - loss: 1.2930 - Accuracy: 0.4004 - val_loss: 1.2741 - val_Accuracy: 0.4200\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 531s 6s/step - loss: 1.2330 - Accuracy: 0.4393 - val_loss: 1.1907 - val_Accuracy: 0.4450\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 534s 6s/step - loss: 1.1586 - Accuracy: 0.4711 - val_loss: 1.1448 - val_Accuracy: 0.4817\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 539s 6s/step - loss: 1.1156 - Accuracy: 0.4986 - val_loss: 1.1276 - val_Accuracy: 0.4750\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 537s 6s/step - loss: 1.0918 - Accuracy: 0.5075 - val_loss: 1.0546 - val_Accuracy: 0.5283\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 535s 6s/step - loss: 1.0538 - Accuracy: 0.5236 - val_loss: 1.0529 - val_Accuracy: 0.5183\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 532s 6s/step - loss: 1.0743 - Accuracy: 0.5268 - val_loss: 1.0428 - val_Accuracy: 0.5233\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 531s 6s/step - loss: 0.9971 - Accuracy: 0.5521 - val_loss: 0.9570 - val_Accuracy: 0.5683\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 540s 6s/step - loss: 0.9650 - Accuracy: 0.5779 - val_loss: 0.9593 - val_Accuracy: 0.5550\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 529s 6s/step - loss: 0.9339 - Accuracy: 0.5864 - val_loss: 1.0199 - val_Accuracy: 0.5233\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 539s 6s/step - loss: 0.9125 - Accuracy: 0.5914 - val_loss: 0.8776 - val_Accuracy: 0.6083\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 530s 6s/step - loss: 0.9370 - Accuracy: 0.5979 - val_loss: 0.8799 - val_Accuracy: 0.5950\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 524s 6s/step - loss: 0.8598 - Accuracy: 0.6450 - val_loss: 0.8627 - val_Accuracy: 0.6233\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff37d777350>,\n",
              " <keras.callbacks.History at 0x7ff37d0ce750>)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the LSTM\n",
        "train_plot(model_lstm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P62YmpAP826Z",
        "outputId": "c59ebacb-22f5-4027-a120-c98e9a4b4b88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 11s 599ms/step - loss: 0.8627 - Accuracy: 0.6233\n",
            "Validation score: 0.8627228736877441\n",
            "validation accuracy: 0.6233333349227905\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_lstm.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTdJbZOVG582"
      },
      "source": [
        "### **Training the data with GRUs**\n",
        "#### and 1 convolutional layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgCq-wloG87c"
      },
      "source": [
        "#### As the LSTMs the GRU give the solution for vanishing gradients, and they are capable of handling long-term dependency problems. They make use of memory cell to store the activation value of previous words \n",
        "#### in the long sequences.The so called gates(neural networks) are used to control the memory cell as long as the flow of information in the network. Gates are able to learn which inputs in the sequence are important\n",
        "#### and store the information in the memory cell. Subsequently, they pass the information in long sequences and ues them to make predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DwVRh8Zu826Z"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8ro7Y_r826Z",
        "outputId": "f7e21a0d-c53d-4232-a3ad-d99431bd19be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, None, 20)          180       \n",
            "                                                                 \n",
            " gru (GRU)                   (None, None, 20)          2520      \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 20)                2520      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,304\n",
            "Trainable params: 5,304\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the GRU model\n",
        "model_GRU = Sequential([\n",
        "    Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    GRU(20, return_sequences=True),\n",
        "    GRU(20),\n",
        "    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "model_GRU.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "p7WiZ3y1826Z",
        "outputId": "e8ffdc3f-73d3-4b82-d1a5-68d9a7d253c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 282s 3s/step - loss: 1.3021 - Accuracy: 0.3964 - val_loss: 1.2264 - val_Accuracy: 0.4517\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 281s 3s/step - loss: 1.1593 - Accuracy: 0.4904 - val_loss: 1.1537 - val_Accuracy: 0.4733\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 273s 3s/step - loss: 1.0801 - Accuracy: 0.5225 - val_loss: 1.0834 - val_Accuracy: 0.5000\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 283s 3s/step - loss: 0.9881 - Accuracy: 0.5675 - val_loss: 0.9821 - val_Accuracy: 0.5650\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 278s 3s/step - loss: 0.9308 - Accuracy: 0.6064 - val_loss: 0.8920 - val_Accuracy: 0.6167\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 318s 4s/step - loss: 0.8591 - Accuracy: 0.6368 - val_loss: 0.8578 - val_Accuracy: 0.6100\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 306s 3s/step - loss: 0.7760 - Accuracy: 0.6757 - val_loss: 0.7792 - val_Accuracy: 0.6683\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 302s 3s/step - loss: 0.7177 - Accuracy: 0.7089 - val_loss: 0.8132 - val_Accuracy: 0.6483\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 273s 3s/step - loss: 0.6934 - Accuracy: 0.7179 - val_loss: 0.7065 - val_Accuracy: 0.7083\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 274s 3s/step - loss: 0.6330 - Accuracy: 0.7443 - val_loss: 0.6884 - val_Accuracy: 0.7133\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 285s 3s/step - loss: 0.6044 - Accuracy: 0.7564 - val_loss: 0.6416 - val_Accuracy: 0.7433\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 287s 3s/step - loss: 0.5755 - Accuracy: 0.7704 - val_loss: 0.6369 - val_Accuracy: 0.7467\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 289s 3s/step - loss: 0.5570 - Accuracy: 0.7846 - val_loss: 0.6729 - val_Accuracy: 0.7317\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 286s 3s/step - loss: 0.5248 - Accuracy: 0.7950 - val_loss: 0.5970 - val_Accuracy: 0.7633\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 288s 3s/step - loss: 0.5014 - Accuracy: 0.8029 - val_loss: 0.5882 - val_Accuracy: 0.7650\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff37ac31c90>,\n",
              " <keras.callbacks.History at 0x7ff37bcb6c10>)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the GRU\n",
        "train_plot(model_GRU)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RKJ2Hoc826a",
        "outputId": "0470619f-d65e-4a51-9f9d-3d78e2202551"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 6s 298ms/step - loss: 0.5882 - Accuracy: 0.7650\n",
            "Validation score: 0.5881556272506714\n",
            "validation accuracy: 0.7649999856948853\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_GRU.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vB9y2j5SHU3G"
      },
      "source": [
        "#### Running GRU with more convolutional layers (optimising the model because of better performance)\n",
        "#### Convulational layers are used to extract features from the input data. By applying different parameters to the convolutional layers, we can get better results. Such as kernel size, strides, padding, filters, etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-FWndzOR826a"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKfUWhsr826a",
        "outputId": "9e5e33b8-1959-43ed-845b-09e7ddb23c3c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, None, 20)          180       \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, None, 20)          1620      \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, None, 20)          1620      \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, None, 20)          1620      \n",
            "                                                                 \n",
            " gru (GRU)                   (None, None, 20)          2520      \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 20)                2520      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,164\n",
            "Trainable params: 10,164\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the GRU improved model\n",
        "model_GRU_improved = Sequential([\n",
        "    Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    GRU(20, return_sequences=True),\n",
        "    GRU(20),\n",
        "    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "model_GRU_improved.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "QU7VQQti826a",
        "outputId": "bffe7011-ee0b-4a49-9967-3ad28a0d2b6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 35s 347ms/step - loss: 1.3266 - Accuracy: 0.3668 - val_loss: 1.1702 - val_Accuracy: 0.5167\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 1.0141 - Accuracy: 0.5643 - val_loss: 0.8921 - val_Accuracy: 0.6000\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 29s 330ms/step - loss: 0.8203 - Accuracy: 0.6700 - val_loss: 0.7477 - val_Accuracy: 0.6867\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 29s 333ms/step - loss: 0.6377 - Accuracy: 0.7546 - val_loss: 0.5885 - val_Accuracy: 0.7717\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 0.5514 - Accuracy: 0.7889 - val_loss: 0.6148 - val_Accuracy: 0.7550\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 0.4980 - Accuracy: 0.8118 - val_loss: 0.4365 - val_Accuracy: 0.8433\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 29s 333ms/step - loss: 0.4005 - Accuracy: 0.8496 - val_loss: 0.3786 - val_Accuracy: 0.8533\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 29s 331ms/step - loss: 0.3776 - Accuracy: 0.8589 - val_loss: 0.4802 - val_Accuracy: 0.8167\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 29s 331ms/step - loss: 0.4373 - Accuracy: 0.8314 - val_loss: 0.4120 - val_Accuracy: 0.8400\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 29s 331ms/step - loss: 0.3580 - Accuracy: 0.8575 - val_loss: 0.3259 - val_Accuracy: 0.8750\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 29s 330ms/step - loss: 0.3165 - Accuracy: 0.8775 - val_loss: 0.3703 - val_Accuracy: 0.8633\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 0.3210 - Accuracy: 0.8721 - val_loss: 0.3219 - val_Accuracy: 0.8750\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 29s 333ms/step - loss: 0.2989 - Accuracy: 0.8796 - val_loss: 0.3008 - val_Accuracy: 0.8833\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 0.2949 - Accuracy: 0.8800 - val_loss: 0.3154 - val_Accuracy: 0.8817\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 29s 333ms/step - loss: 0.3109 - Accuracy: 0.8743 - val_loss: 0.3183 - val_Accuracy: 0.8750\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff37c75e0d0>,\n",
              " <keras.callbacks.History at 0x7ff37c601f90>)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the GRU improved model\n",
        "train_plot(model_GRU_improved)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TGGrRQey826a",
        "outputId": "0366fcdf-416c-4cd2-913e-b64e8e879564"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 1s 61ms/step - loss: 0.3183 - Accuracy: 0.8750\n",
            "Validation score: 0.3183118402957916\n",
            "validation accuracy: 0.875\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_GRU_improved.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aS-ulM4y826a",
        "outputId": "d264ddd9-4385-4ad4-9b2b-7e162e4e9b6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[103,   1,   3,  47],\n",
              "       [  1, 136,   3,   1],\n",
              "       [  7,   2, 142,   1],\n",
              "       [  2,   6,   1, 144]])"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_GRU_improved = confusion_matrix(y_q1_valid, np.asarray(model_GRU_improved.predict(X_valid)).argmax(axis=1))\n",
        "\n",
        "cm_GRU_improved"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jc0qcDDJ826a",
        "outputId": "6291d434-d3e7-4e68-ef99-5ccf4e7ae4e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_GRU_improved, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAjO_e8VQad1"
      },
      "source": [
        "#### Different optimizers applying again for the GRU model, like number of units in the GRU layers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nrvr-klq826b"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPVporui826b",
        "outputId": "20746318-9848-4165-a1a1-0ec0e9048012"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, None, 20)          420       \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, None, 20)          4020      \n",
            "                                                                 \n",
            " gru (GRU)                   (None, None, 128)         57600     \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, None, 128)         99072     \n",
            "                                                                 \n",
            " gru_2 (GRU)                 (None, 128)               99072     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 260,700\n",
            "Trainable params: 260,700\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the model\n",
        "model_GRU_different = Sequential([\n",
        "    Conv1D(filters=20, kernel_size=10, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    Conv1D(filters=20, kernel_size=10, strides=2, padding=\"valid\", input_shape=[None, 2]),\n",
        "    GRU(128, return_sequences=True), # ---+ 128 units each layers, Dense last layer for prediction, sequence elements are of dimension 1\n",
        "    GRU(128, return_sequences=True),\n",
        "    GRU(128),\n",
        "    Dense(4, activation = \"softmax\")\n",
        "])\n",
        "model_GRU_different.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "id": "GUHoD2MO826b",
        "outputId": "42b2650a-9b90-41ae-d8f8-36980fb3da3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 373s 4s/step - loss: 1.2030 - Accuracy: 0.4479 - val_loss: 1.0653 - val_Accuracy: 0.5600\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 363s 4s/step - loss: 0.7137 - Accuracy: 0.7196 - val_loss: 0.5325 - val_Accuracy: 0.7750\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 362s 4s/step - loss: 0.4622 - Accuracy: 0.8161 - val_loss: 0.5414 - val_Accuracy: 0.7717\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 362s 4s/step - loss: 0.3450 - Accuracy: 0.8668 - val_loss: 0.4751 - val_Accuracy: 0.8017\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 363s 4s/step - loss: 0.2957 - Accuracy: 0.8761 - val_loss: 0.3368 - val_Accuracy: 0.8750\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 422s 5s/step - loss: 0.2541 - Accuracy: 0.8932 - val_loss: 0.3163 - val_Accuracy: 0.8783\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 366s 4s/step - loss: 0.2274 - Accuracy: 0.9021 - val_loss: 0.3077 - val_Accuracy: 0.8833\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 362s 4s/step - loss: 0.2282 - Accuracy: 0.9004 - val_loss: 0.3060 - val_Accuracy: 0.8883\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 373s 4s/step - loss: 0.2246 - Accuracy: 0.9061 - val_loss: 0.3246 - val_Accuracy: 0.8767\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 360s 4s/step - loss: 0.2007 - Accuracy: 0.9096 - val_loss: 0.3141 - val_Accuracy: 0.8900\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 361s 4s/step - loss: 0.2065 - Accuracy: 0.9082 - val_loss: 0.3180 - val_Accuracy: 0.8800\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 363s 4s/step - loss: 0.1839 - Accuracy: 0.9171 - val_loss: 0.3355 - val_Accuracy: 0.8400\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 361s 4s/step - loss: 0.1690 - Accuracy: 0.9221 - val_loss: 0.3186 - val_Accuracy: 0.8733\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(<keras.engine.sequential.Sequential at 0x7ff37d1ef810>,\n",
              " <keras.callbacks.History at 0x7ff37c714190>)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the GRU different approach model\n",
        "train_plot(model_GRU_different)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVQ-gwzi826b",
        "outputId": "af17de19-0025-4c12-e035-f02b44ac3b48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 15s 788ms/step - loss: 0.3060 - Accuracy: 0.8883\n",
            "Validation score: 0.30596116185188293\n",
            "validation accuracy: 0.8883333206176758\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_GRU_different.evaluate(X_valid, y_q1_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrlU9eNEQfoA"
      },
      "source": [
        "# Question 2\n",
        "\n",
        "## In your own words, explain why the approach outlined here can help analyze time-series data and why it might outperform RNNs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OenhdXXg826b"
      },
      "source": [
        "#### The main difference between CNN and RNN is the way to process temporal information in the input data. CNN can process temporal information in the input data, whereas RNN can only process the information in the sequence.\n",
        "#### As a result, CNNs and RNNs are used for completely different tasks. CNNs are mostly used for image processing (spatial data), whereas RNNs are usually used for text processing. \n",
        "#### CNNs are also more efficient than RNNs. CNNs are more efficient in terms of memory usage, and the number of parameters in the model.\n",
        "#### Moreover, in terms of training time, they are considered more powerful and faster as they are using more filters, and layers in the model.\n",
        "\n",
        "#### In this case, the goal is to achieve an approach to diagnose small anomalies in the output of the wind turbine system.\n",
        "#### The challenges addressed in this paper are dealing with time-series data, and these data are not easily processed by RNNs. \n",
        "#### Challenges include: \n",
        "\n",
        "\n",
        "*   the tricky nature of the time series, because of how  volatile the data is (e.g. the data is not always increasing/decreasing)\n",
        "*   the lack of a clear pattern in the data \n",
        "*   environmental disturbances\n",
        "\n",
        "#### One way to deal with time-series is to convert them into 2-D images. It is worth to mention that in CNN approaches, the information is extracted from the image,\n",
        "#### i.e. the information is extracted from the image by analysing the relationship between the adjacent pixels.\n",
        "#### Furtermore, another significant difference between CNN and RNN is that the first one is computationally cheaper than RNNs. CNNs learn by batch while RNNs train sequentially.\n",
        "#### Hence, unlike RNNs, CNNs learn patterns within the time window. \n",
        "\n",
        "#### Therefore, the CNN will outperfom RNN in terms of training time, the accuracy, and the sensitivity of the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HJTEVitR9aG"
      },
      "source": [
        "# Question 3\n",
        "\n",
        "#### Convert the data for use with a CNN. In particular, following the approach outlined in Scenario 2 (section 5.3 of the paper) and summarized in Figure 18, \n",
        "#### convert the two time series corresponding to one wind turbine run into a single (100,100,1) array (i.e., a gray- scale image)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C5xHxJ6rXCVl"
      },
      "outputs": [],
      "source": [
        "# ---+ creating the input_shape of the model\n",
        "input_shape = (100, 100, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ziv3tkg-XKML"
      },
      "outputs": [],
      "source": [
        "# ---+ combining the 2 time series\n",
        "final_q3 = [np.hstack((time_series_1[i].reshape(100, 50), time_series_2[i].reshape(100, 50))).reshape(100, 100, 1) for i in range(len(time_series_1))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DXmI6uC-X0lM"
      },
      "outputs": [],
      "source": [
        "# ---+ converting into array\n",
        "final_q3 = np.array(final_q3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljSJ_WJxXncO",
        "outputId": "fe349ba0-2bd8-4f18-e91a-b6965f2ebb1c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000, 100, 100, 1)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# ---+ looking into the shape of the data\n",
        "print(final_q3.shape)\n",
        "print(type(final_q3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G13YMcGiYfl0",
        "outputId": "044f3289-59cd-4306-be1d-b2f55163aa49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4000,)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "# ---+ reshaping y into the shape of the time_series\n",
        "y_q3 = y\n",
        "print(y_q3.shape)\n",
        "print(type(y_q3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CemkzryO826c",
        "outputId": "ceb75e53-9391-44cb-ec17-26a2b02e59e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2800, 100, 100, 1)\n",
            "(2800,)\n",
            "(600, 100, 100, 1)\n",
            "(600,)\n",
            "(600,)\n"
          ]
        }
      ],
      "source": [
        "# ---+ creating training and test split\n",
        "np.random.seed(132)\n",
        "seq_steps = 10000\n",
        "\n",
        "X_q3_train, y_q3_train = final_q3[:2800, :seq_steps], y_q3[:2800,]\n",
        "X_q3_valid, y_q3_valid = final_q3[2800:3400, :seq_steps], y_q3[2800:3400,]\n",
        "X_q3_test, y_q3_test = final_q3[3400:, :seq_steps], y_q3[3400:,]\n",
        "\n",
        "print(X_q3_train.shape)\n",
        "print(y_q3_train.shape)\n",
        "print(X_q3_valid.shape)\n",
        "print(y_q3_test.shape)\n",
        "print(y_q3_valid.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Km7hrjBK826c"
      },
      "outputs": [],
      "source": [
        "# ---+ transforming the data into floats\n",
        "X_q3_train = X_q3_train.astype('float32')\n",
        "X_q3_test = X_q3_test.astype('float32')\n",
        "X_q3_valid = X_q3_valid.astype('float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZjzBPIKF826c"
      },
      "outputs": [],
      "source": [
        "# ---+ transforming the data into a range of 0 to 1\n",
        "num_classes = len(np.unique(y_q3)) # ---+ number of classes in the data\n",
        "\n",
        "# ---+ convert class vectors to one-hot encoding\n",
        "y_q3_train = tf.keras.utils.to_categorical(y_q3_train, num_classes)\n",
        "y_q3_test = tf.keras.utils.to_categorical(y_q3_test, num_classes)\n",
        "y_q3_valid =  tf.keras.utils.to_categorical(y_q3_valid, num_classes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVNgwMKp826c",
        "outputId": "ab4dc684-a3bc-480e-dc4e-423c6e3d1d38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_q3_train shape: (2800, 100, 100, 1)\n",
            "y_q3_train shape: (2800, 4)\n",
            "X_q3_test shape: (600, 100, 100, 1)\n",
            "y_q3_test shape: (600, 4)\n",
            "X_q3_valid shape: (600, 100, 100, 1)\n",
            "y_q3_valid shape: (600, 4)\n"
          ]
        }
      ],
      "source": [
        "# ---+ printing out the shape of the train splits\n",
        "print('X_q3_train shape:', X_q3_train.shape)\n",
        "print('y_q3_train shape:', y_q3_train.shape)\n",
        "print('X_q3_test shape:', X_q3_test.shape)\n",
        "print('y_q3_test shape:', y_q3_test.shape)\n",
        "print('X_q3_valid shape:', X_q3_valid.shape)\n",
        "print('y_q3_valid shape:', y_q3_valid.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "En4fFJO6YlIE",
        "outputId": "0c8170dd-7b3f-481f-fe61-f442dd5009da"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the data\n",
        "plt.imshow(X_q3_train[1,:,:,0], cmap = 'gray')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3nIlwocSzjL"
      },
      "source": [
        "# Question 4\n",
        "\n",
        "#### In TensorFlow, replicate the CNN with two convolutional layers displayed in Figure 12 and train it on your data. Make sure to record your final validation set accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ssoQqghS53-"
      },
      "source": [
        "## Building a CNN for our data\n",
        "\n",
        "#### Convulotional neural network(CNN) is a type of deep learning model for processing data that has a grid pattern, such as images.\n",
        "#### The network of the CNNs leans to optimize the filters(kernerls) through automated learning, whereas in traditional deep learning models, the filters are manually tuned.\n",
        "#### A CNN has three layers: a convolutional layer, a pooling layer and a fully connected layer.\n",
        "\n",
        "#### Convulotional layer: convolve the input and pass its result to the next layer.\n",
        "#### Pooling layer: reduce the size of the input by taking the maximum value of the input.\n",
        "#### Fully connected layer: connect the pooling layer to the output layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eoH4E5ds826d"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xlg_J0jz826d",
        "outputId": "a69640f1-dfba-4e84-d51a-c9bebb56a4b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 98, 98, 32)        320       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 96, 96, 32)        9248      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 96, 96, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 94, 94, 16)        4624      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 31, 31, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 15376)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 61508     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75,700\n",
            "Trainable params: 75,700\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the model for the CNN\n",
        "model_CNN = Sequential([\n",
        "    Conv2D(32, kernel_size=(3, 3), input_shape = input_shape),\n",
        "    Conv2D(32, kernel_size=(3, 3)),\n",
        "    Dropout(0.2),\n",
        "    Conv2D(16, kernel_size=(3, 3), activation = \"relu\"),\n",
        "    MaxPooling2D(pool_size=(3, 3)),\n",
        "    Flatten(),\n",
        "    Dense(num_classes, activation='softmax')])\n",
        "\n",
        "model_CNN.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              metrics = ['accuracy'])\n",
        "model_CNN.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KumfzJzy826d",
        "outputId": "7f37d5a6-6f96-4191-845b-159ad683f019"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 110s 1s/step - loss: 1.2092 - accuracy: 0.4514 - val_loss: 0.9192 - val_accuracy: 0.6367\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.6127 - accuracy: 0.7629 - val_loss: 0.3838 - val_accuracy: 0.8017\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.3410 - accuracy: 0.8696 - val_loss: 0.2653 - val_accuracy: 0.8950\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.3094 - accuracy: 0.8818 - val_loss: 0.2996 - val_accuracy: 0.9017\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2952 - accuracy: 0.8807 - val_loss: 0.2663 - val_accuracy: 0.8900\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2672 - accuracy: 0.8807 - val_loss: 0.2604 - val_accuracy: 0.8983\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2861 - accuracy: 0.8821 - val_loss: 0.2501 - val_accuracy: 0.9033\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2686 - accuracy: 0.8857 - val_loss: 0.2641 - val_accuracy: 0.8533\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2521 - accuracy: 0.8900 - val_loss: 0.2847 - val_accuracy: 0.8033\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2634 - accuracy: 0.8875 - val_loss: 0.2679 - val_accuracy: 0.9083\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2582 - accuracy: 0.8811 - val_loss: 0.2309 - val_accuracy: 0.9067\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2487 - accuracy: 0.8829 - val_loss: 0.2464 - val_accuracy: 0.9033\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2465 - accuracy: 0.8911 - val_loss: 0.2453 - val_accuracy: 0.8850\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2487 - accuracy: 0.8886 - val_loss: 0.2299 - val_accuracy: 0.9050\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2386 - accuracy: 0.8918 - val_loss: 0.2497 - val_accuracy: 0.8967\n"
          ]
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the CNN model\n",
        "log_CNN = model_CNN.fit(X_q3_train, y_q3_train,\n",
        "          epochs = 15, validation_data = (X_q3_valid, y_q3_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mIIRqhYa826d",
        "outputId": "2e9bb023-f06b-43ef-95d3-a5afaabff02c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 4s 223ms/step - loss: 0.2497 - accuracy: 0.8967\n",
            "Validation score: 0.2496543675661087\n",
            "validation accuracy: 0.8966666460037231\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_CNN.evaluate(X_q3_valid, y_q3_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "ZYXFFdUFdKCn",
        "outputId": "9d58edfb-229e-41ac-cd35-d2c0f0e9be70"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the model\n",
        "def create_plot(log):\n",
        "    plt.plot(log.history['accuracy'], label = 'training accuracy', color = 'green')\n",
        "    plt.plot(log.history['val_accuracy'], label = 'validation accuracy', color = 'red')\n",
        "    plt.plot(log.history['loss'], label = 'training loss', color = 'blue')\n",
        "    plt.plot(log.history['val_loss'], label = 'validation loss', color = 'orange')\n",
        "    plt.title('Training and validation accuracy and loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "create_plot(log_CNN)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFYIoq1MXTmx",
        "outputId": "d626e31e-4e53-4182-8e64-f2a06927f11e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[110,   0,   0,  44],\n",
              "       [  2, 139,   0,   0],\n",
              "       [  2,   1, 149,   0],\n",
              "       [ 11,   0,   2, 140]])"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_Q4 = confusion_matrix(y_q1_valid, np.asarray(model_CNN.predict(X_q3_valid)).argmax(axis=1))\n",
        "\n",
        "cm_Q4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "y77zaSCnZhQq",
        "outputId": "f6b45fdd-6c87-4b18-f296-3cd8c573bca9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_Q4, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Atpt9kFsTOAX"
      },
      "source": [
        "# Question 5\n",
        "\n",
        "#### Can you do better by adjusting the CNN? Be creative in your design choices (you might also consider pre-trained CNN architectures) and record your final validation set accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twZrjlL5826d"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTag_hONTRrT"
      },
      "source": [
        "#### Use of a new callback, the 'ReduceLROnPlateau, which observes a certain metric and if this metric no longer improves (considering a certain patience), reduces the learning rate by a given factor.\n",
        "\n",
        "#### Very similar to the learning rate schedules, only some different mechanisms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x15CevEq826e"
      },
      "outputs": [],
      "source": [
        "# ---+ adding a callback to the model\n",
        "lr_red_callback = ReduceLROnPlateau(monitor = \"val_accuracy\", patience = 2, verbose = 1, factor = 0.3, min_lr = 1e-6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uq4EIur_UNxo"
      },
      "source": [
        "#### Building AlexNet model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGP8KBjV826e",
        "outputId": "fd5cb5e7-b2c2-49a2-c980-fa42dcff9a1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 25, 25, 96)        11712     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 25, 25, 96)       384       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 12, 12, 96)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 12, 12, 256)       614656    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 12, 12, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 5, 5, 384)         885120    \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 5, 5, 384)         1327488   \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 5, 5, 256)         884992    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 5, 5, 256)        1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              4198400   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4)                 16388     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 24,722,500\n",
            "Trainable params: 24,721,284\n",
            "Non-trainable params: 1,216\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the AlexNet model for the CNN\n",
        "model_AlexNet = Sequential([\n",
        "    Conv2D(96, 11, strides = 4, padding = 'same', activation = 'relu', input_shape = input_shape),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(3, strides=2),\n",
        "    Conv2D(256, 5, padding='same', activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(3, strides=2),\n",
        "    Conv2D(384, 3, strides=1, padding='same', activation='relu'),\n",
        "    Conv2D(384, 3, strides=1, padding='same', activation='relu'),\n",
        "    Conv2D(256, 3, strides=1, padding='same', activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(3, strides=2),\n",
        "    Flatten(),\n",
        "    Dense(4096, activation = 'relu'),\n",
        "    Dense(4096, activation = 'relu'),\n",
        "    Dense(num_classes, activation = 'softmax')])\n",
        "\n",
        "model_AlexNet.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              metrics = ['accuracy'])\n",
        "model_AlexNet.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mXGDQvhiBfb",
        "outputId": "3e2bac16-003c-4f82-fb7e-1ad31b812de9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 1.8810 - accuracy: 0.5314 - val_loss: 3.5884 - val_accuracy: 0.2533 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.5393 - accuracy: 0.7689 - val_loss: 3.6335 - val_accuracy: 0.3600 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.3242 - accuracy: 0.8689 - val_loss: 1.3543 - val_accuracy: 0.4833 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2802 - accuracy: 0.8786 - val_loss: 0.3444 - val_accuracy: 0.8683 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.7530 - accuracy: 0.6954 - val_loss: 1.3177 - val_accuracy: 0.5517 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.3550 - accuracy: 0.8504\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354.\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.3550 - accuracy: 0.8504 - val_loss: 0.8049 - val_accuracy: 0.6917 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2476 - accuracy: 0.8982 - val_loss: 0.2423 - val_accuracy: 0.9050 - lr: 3.0000e-04\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2297 - accuracy: 0.9018 - val_loss: 0.2410 - val_accuracy: 0.9083 - lr: 3.0000e-04\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2266 - accuracy: 0.9011 - val_loss: 0.2499 - val_accuracy: 0.8967 - lr: 3.0000e-04\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2228 - accuracy: 0.9004\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05.\n",
            "88/88 [==============================] - 107s 1s/step - loss: 0.2228 - accuracy: 0.9004 - val_loss: 0.2468 - val_accuracy: 0.9050 - lr: 3.0000e-04\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 108s 1s/step - loss: 0.2179 - accuracy: 0.9032 - val_loss: 0.2306 - val_accuracy: 0.9067 - lr: 9.0000e-05\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2167 - accuracy: 0.9032\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 2.700000040931627e-05.\n",
            "88/88 [==============================] - 107s 1s/step - loss: 0.2167 - accuracy: 0.9032 - val_loss: 0.2324 - val_accuracy: 0.9067 - lr: 9.0000e-05\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2143 - accuracy: 0.9032 - val_loss: 0.2320 - val_accuracy: 0.9067 - lr: 2.7000e-05\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2130 - accuracy: 0.9032\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 8.100000013655517e-06.\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2130 - accuracy: 0.9032 - val_loss: 0.2317 - val_accuracy: 0.9067 - lr: 2.7000e-05\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 106s 1s/step - loss: 0.2146 - accuracy: 0.9029 - val_loss: 0.2320 - val_accuracy: 0.9067 - lr: 8.1000e-06\n"
          ]
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the CNN model\n",
        "log_AlexNet = model_AlexNet.fit(X_q3_train, y_q3_train,\n",
        "          epochs = 15, callbacks = lr_red_callback, validation_data = (X_q3_valid, y_q3_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fTsm-pze826e",
        "outputId": "de86f9ee-0f6c-47f8-94c7-8ced60cde0e8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the model\n",
        "def create_plot(log):\n",
        "    plt.plot(log.history['accuracy'], label = 'training accuracy', color = 'green')\n",
        "    plt.plot(log.history['val_accuracy'], label = 'validation accuracy', color = 'red')\n",
        "    plt.plot(log.history['loss'], label = 'training loss', color = 'blue')\n",
        "    plt.plot(log.history['val_loss'], label = 'validation loss', color = 'orange')\n",
        "    plt.title('Training and validation accuracy and loss')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "create_plot(log_AlexNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k7jg8kHEZqJS",
        "outputId": "eaa697bf-b725-41cd-da31-5b09135a7d90"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[107,   0,   0,  47],\n",
              "       [  2, 139,   0,   0],\n",
              "       [  2,   1, 149,   0],\n",
              "       [  2,   0,   2, 149]])"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_AlexNet = confusion_matrix(y_q1_valid, np.asarray(model_AlexNet.predict(X_q3_valid)).argmax(axis=1))\n",
        "\n",
        "cm_AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-jjCIQtiZp9b",
        "outputId": "c3698ceb-c83a-499c-ad41-5be937326552"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_AlexNet, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxeUtoYwfHnp"
      },
      "source": [
        "#### Implementing MobileNet Architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bwc5lPSpPzO8"
      },
      "outputs": [],
      "source": [
        "# ---+ transforming the data into the shape for mobilenet\n",
        "X_q3_train = tf.constant(X_q3_train)\n",
        "X_q3_valid = tf.constant(X_q3_valid)\n",
        "\n",
        "# ---+ converting 1 channel into 3 channel image\n",
        "\n",
        "X_q3_train_image = tf.image.grayscale_to_rgb(X_q3_train)\n",
        "X_q3_valid_image = tf.image.grayscale_to_rgb(X_q3_valid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdVKFI25oz-Q",
        "outputId": "732be4cb-787e-4f76-c7d9-335bd522be75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(TensorShape([2800, 100, 100, 3]), TensorShape([600, 100, 100, 3]))"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ printing the shape of the preprocessed data\n",
        "x_preprocess_train = preprocess_input(X_q3_train_image)\n",
        "x_preprocess_valid = preprocess_input(X_q3_valid_image)\n",
        "x_preprocess_train.shape, x_preprocess_valid.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RT469441pD0t",
        "outputId": "6be43f75-21b1-45f6-f67a-89256fcda6bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5\n",
            "17227776/17225924 [==============================] - 0s 0us/step\n",
            "17235968/17225924 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# ---+ mobilenet with frozen layers\n",
        "mobilenet = MobileNet(weights='imagenet', include_top=False, input_shape = (100, 100, 3))\n",
        "mobilenet.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEoWEhkUpDyP",
        "outputId": "08aecd03-78b9-440d-d44c-a0e2483bb6fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " up_sampling2d (UpSampling2D  (None, 100, 100, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " mobilenet_1.00_224 (Functio  (None, 3, 3, 1024)       3228864   \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1024)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                65600     \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,294,724\n",
            "Trainable params: 65,860\n",
            "Non-trainable params: 3,228,864\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building MobileNet model\n",
        "model_MobileNet = Sequential([\n",
        "    UpSampling2D(size=(1, 1), input_shape = (100, 100, 3)),\n",
        "    mobilenet,\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(num_classes, activation='softmax')])\n",
        "    \n",
        "model_MobileNet.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              metrics=['accuracy'])\n",
        "model_MobileNet.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5I1zoKEpJ6v",
        "outputId": "7fb51096-ea2b-4d59-d34d-bb60c2aa600a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 26s 271ms/step - loss: 1.1962 - accuracy: 0.5146 - val_loss: 1.0037 - val_accuracy: 0.5850 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.8847 - accuracy: 0.6850 - val_loss: 0.7844 - val_accuracy: 0.7267 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.7632 - accuracy: 0.7254 - val_loss: 0.6910 - val_accuracy: 0.7400 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 23s 266ms/step - loss: 0.6414 - accuracy: 0.7818 - val_loss: 0.6074 - val_accuracy: 0.8117 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 23s 267ms/step - loss: 0.5806 - accuracy: 0.7986 - val_loss: 0.5216 - val_accuracy: 0.8150 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - 23s 266ms/step - loss: 0.5034 - accuracy: 0.8361 - val_loss: 0.4831 - val_accuracy: 0.8333 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 23s 267ms/step - loss: 0.4717 - accuracy: 0.8421 - val_loss: 0.4469 - val_accuracy: 0.8567 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.4465 - accuracy: 0.8364 - val_loss: 0.4327 - val_accuracy: 0.8533 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.3926 - accuracy: 0.8611\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354.\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.3926 - accuracy: 0.8611 - val_loss: 0.4048 - val_accuracy: 0.8333 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.3727 - accuracy: 0.8657 - val_loss: 0.3840 - val_accuracy: 0.8767 - lr: 3.0000e-04\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 23s 265ms/step - loss: 0.3545 - accuracy: 0.8750 - val_loss: 0.3693 - val_accuracy: 0.8667 - lr: 3.0000e-04\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.3510 - accuracy: 0.8718\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05.\n",
            "88/88 [==============================] - 23s 264ms/step - loss: 0.3510 - accuracy: 0.8718 - val_loss: 0.3658 - val_accuracy: 0.8683 - lr: 3.0000e-04\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 23s 267ms/step - loss: 0.3380 - accuracy: 0.8789 - val_loss: 0.3582 - val_accuracy: 0.8767 - lr: 9.0000e-05\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.3386 - accuracy: 0.8821\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 2.700000040931627e-05.\n",
            "88/88 [==============================] - 23s 267ms/step - loss: 0.3386 - accuracy: 0.8821 - val_loss: 0.3592 - val_accuracy: 0.8750 - lr: 9.0000e-05\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 23s 267ms/step - loss: 0.3335 - accuracy: 0.8825 - val_loss: 0.3566 - val_accuracy: 0.8750 - lr: 2.7000e-05\n"
          ]
        }
      ],
      "source": [
        "# add these to the final script\n",
        "log_MobileNet = model_MobileNet.fit(x_preprocess_train, y_q3_train,\n",
        "          batch_size=32,\n",
        "          epochs=15, callbacks = lr_red_callback,validation_data = (x_preprocess_valid, y_q3_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "NgOyoak5826e",
        "outputId": "64780b4c-d2a0-4ffe-93e2-b083520b9338"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the results\n",
        "create_plot(log_MobileNet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SW4JZnl826e",
        "outputId": "d2d6f3aa-2920-4e46-da62-cde941be85f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[109,   0,   5,  40],\n",
              "       [  2, 135,   2,   2],\n",
              "       [  1,   1, 147,   3],\n",
              "       [ 17,   0,   2, 134]])"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_MobileNet = confusion_matrix(y_q1_valid, np.asarray(model_MobileNet.predict(x_preprocess_valid)).argmax(axis=1))\n",
        "\n",
        "cm_MobileNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "GxmVI-bV826e",
        "outputId": "bb3b018f-0dd3-47e3-c1cb-6fcebb7fcccc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_MobileNet, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tm01V18cymty"
      },
      "source": [
        "#### Improvement of the papper's model by adjusting and adding extra parameters, like padding, strides, normalizaton,and multiple convolutional layers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BF4AH6NcVnie"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z05xl_9JVnbi",
        "outputId": "f8c84ee5-94c3-42e2-a0b1-553f46d35cf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 32)        3904      \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 25, 25, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 32)        25632     \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 25, 25, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 12, 12, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 12, 12, 96)        27744     \n",
            "                                                                 \n",
            " conv2d_18 (Conv2D)          (None, 10, 10, 96)        83040     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 10, 10, 96)        0         \n",
            "                                                                 \n",
            " conv2d_19 (Conv2D)          (None, 8, 8, 256)         221440    \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 2, 2, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 1024)              0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 64)                65600     \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 427,876\n",
            "Trainable params: 427,748\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the imporved model for the CNN (Q4)\n",
        "model_CNN_updated = Sequential([\n",
        "    Conv2D(32, 11, strides = 4, padding = 'same', activation = 'relu', input_shape = input_shape),\n",
        "    BatchNormalization(),\n",
        "    Conv2D(32, 5, padding='same', activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(3, strides=2),\n",
        "    Conv2D(96, kernel_size=(3, 3), padding = 'same'),\n",
        "    Conv2D(96, kernel_size=(3, 3)),\n",
        "    Dropout(0.2),\n",
        "    Conv2D(256, kernel_size=(3, 3), activation = \"relu\"),\n",
        "    MaxPooling2D(pool_size=(3, 3)),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(num_classes, activation='softmax')])\n",
        "\n",
        "model_CNN_updated.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              metrics = ['accuracy'])\n",
        "model_CNN_updated.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ou0wb39PasjX",
        "outputId": "ce7aa946-9277-421e-f790-5d730fd2ceda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "88/88 [==============================] - 32s 362ms/step - loss: 0.2467 - accuracy: 0.8914 - val_loss: 0.3035 - val_accuracy: 0.9000 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2345 - accuracy: 0.9004 - val_loss: 0.3144 - val_accuracy: 0.8917 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2339 - accuracy: 0.8964\n",
            "Epoch 3: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354.\n",
            "88/88 [==============================] - 28s 319ms/step - loss: 0.2339 - accuracy: 0.8964 - val_loss: 0.2849 - val_accuracy: 0.8900 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "88/88 [==============================] - 29s 324ms/step - loss: 0.2167 - accuracy: 0.9032 - val_loss: 0.2276 - val_accuracy: 0.9067 - lr: 3.0000e-04\n",
            "Epoch 5/15\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2122 - accuracy: 0.9029 - val_loss: 0.2490 - val_accuracy: 0.9067 - lr: 3.0000e-04\n",
            "Epoch 6/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2118 - accuracy: 0.9032\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05.\n",
            "88/88 [==============================] - 28s 319ms/step - loss: 0.2118 - accuracy: 0.9032 - val_loss: 0.2404 - val_accuracy: 0.9067 - lr: 3.0000e-04\n",
            "Epoch 7/15\n",
            "88/88 [==============================] - 29s 332ms/step - loss: 0.2101 - accuracy: 0.9032 - val_loss: 0.2352 - val_accuracy: 0.9067 - lr: 9.0000e-05\n",
            "Epoch 8/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2051 - accuracy: 0.9036\n",
            "Epoch 8: ReduceLROnPlateau reducing learning rate to 2.700000040931627e-05.\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2051 - accuracy: 0.9036 - val_loss: 0.2367 - val_accuracy: 0.9067 - lr: 9.0000e-05\n",
            "Epoch 9/15\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2045 - accuracy: 0.9032 - val_loss: 0.2340 - val_accuracy: 0.9067 - lr: 2.7000e-05\n",
            "Epoch 10/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2034 - accuracy: 0.9036\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 8.100000013655517e-06.\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2034 - accuracy: 0.9036 - val_loss: 0.2346 - val_accuracy: 0.9067 - lr: 2.7000e-05\n",
            "Epoch 11/15\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2027 - accuracy: 0.9036 - val_loss: 0.2350 - val_accuracy: 0.9067 - lr: 8.1000e-06\n",
            "Epoch 12/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2031 - accuracy: 0.9036\n",
            "Epoch 12: ReduceLROnPlateau reducing learning rate to 2.429999949526973e-06.\n",
            "88/88 [==============================] - 28s 319ms/step - loss: 0.2031 - accuracy: 0.9036 - val_loss: 0.2355 - val_accuracy: 0.9067 - lr: 8.1000e-06\n",
            "Epoch 13/15\n",
            "88/88 [==============================] - 28s 318ms/step - loss: 0.2021 - accuracy: 0.9032 - val_loss: 0.2358 - val_accuracy: 0.9067 - lr: 2.4300e-06\n",
            "Epoch 14/15\n",
            "88/88 [==============================] - ETA: 0s - loss: 0.2023 - accuracy: 0.9036\n",
            "Epoch 14: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "88/88 [==============================] - 28s 316ms/step - loss: 0.2023 - accuracy: 0.9036 - val_loss: 0.2360 - val_accuracy: 0.9067 - lr: 2.4300e-06\n",
            "Epoch 15/15\n",
            "88/88 [==============================] - 28s 316ms/step - loss: 0.2027 - accuracy: 0.9036 - val_loss: 0.2360 - val_accuracy: 0.9067 - lr: 1.0000e-06\n"
          ]
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the CNN model\n",
        "log_CNN_updated = model_CNN_updated.fit(X_q3_train, y_q3_train,\n",
        "          epochs = 15, callbacks = lr_red_callback, validation_data = (X_q3_valid, y_q3_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5qM7D2iativ",
        "outputId": "7e6b27ff-3b3b-4599-9383-6c430eed66b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 1s 72ms/step - loss: 0.2360 - accuracy: 0.9067\n",
            "Validation score: 0.23603080213069916\n",
            "validation accuracy: 0.9066666960716248\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_CNN_updated.evaluate(X_q3_valid, y_q3_valid)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "s2J5CE_batV_",
        "outputId": "577babc3-fa4b-48be-bffd-7c3d70eead1e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the results\n",
        "create_plot(log_CNN_updated)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4apInfzua4IH",
        "outputId": "84269030-616c-4778-fe14-72d97697a512"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[107,   0,   0,  47],\n",
              "       [  2, 139,   0,   0],\n",
              "       [  2,   1, 149,   0],\n",
              "       [  2,   0,   2, 149]])"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_Q4_updated = confusion_matrix(y_q1_valid, np.asarray(model_CNN_updated.predict(X_q3_valid)).argmax(axis=1))\n",
        "\n",
        "cm_Q4_updated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "_cC3ceAHa4AJ",
        "outputId": "225799c2-f3b9-448e-d32c-f6fc603cd925"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_Q4_updated, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KK7Rjj2AWErq"
      },
      "source": [
        "# Question 6\n",
        "\n",
        "#### Compare the models you have created so far (both RNNs and CNNs) and make a selection (making sure to justify this). Train that model on a combined training and validation set and evaluate it on your test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qrPHjDxyWJYb"
      },
      "source": [
        "#### By comparing the models we have created so far, we can see that the CNN model is better than the RNN model.\n",
        "\n",
        "\n",
        "#### The CNN model is better because it has a more complex architecture and it is able to learn more complex patterns.\n",
        "\n",
        "\n",
        "#### By training the CNN model on the combined training and validation set, we can see that the accuracy of the CNN model is higher than the accuracy of the RNN model.\n",
        "\n",
        "\n",
        "#### Lastly, we will choose the best CNN model and train it on the combined training and validation set, and evaluate it on the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yulNIcK3WDiK"
      },
      "source": [
        "##### Let's have a look on the compred models for our conclusion:\n",
        "\n",
        "\n",
        "| Type of CNN models      | Models      | Accuracy      |\n",
        "| :---        |    :----:   |          ---: |\n",
        "| CNN basic model      | Paper's CNN | 89.18%  |\n",
        "| Transfer learning model  | AlexNet        | 90.29%   |\n",
        "| Transfer learning model      | MobileNet | 88.25%  |\n",
        "| Paper's improved version   | Improving paper's CNN        | 90.36%     |\n",
        "\n",
        "#### It is shown that all models scored a really high accuracy. \n",
        "\n",
        "#### The paper's model, with only 3 convolutional layers, a dropout, a maxpooling(suitable spatial data), one flatten layer, and a Dense layer with an activation function 'softmax', scored an accuracy of 89.18% which is significantly impressive.\n",
        "\n",
        "#### The next model was the AlexNet. This model used multiple convolutional layers, with dropouts and maxpooling, as long as with the use of 'BatchNormalization' which standardizes the inputs to a layer for each mini-batch. More parameters were used to optimize the model like padding, strides, filters, and more activations for every layer.\n",
        "\n",
        "#### As already mentioned with AlexNet, MobileNet is a model of the same category as the first one. In order to implement the MobileNet architecture a preprocess of the data was followed. The data were transformed from 1 channel to 3 channel i.e converted the gray images into RGB ones. Moreover, upsampling layer was used (scaling up for the image), global average pooling, and some frozen layers in order to optimize the paper's model.\n",
        "\n",
        "#### Lastly, the improvement of the paper's model was tried to be optimized. Extra layers, filters, kernel size, padding, strides and activations were added to different layers. Dropouts were used to make the model more accurate, as long as BatchNormalization, however a high accuracy scored was noted but not with a big difference from the original one.\n",
        "\n",
        "#### The analysis was followed by implementing multiple models. The mentioned ones were the most suitable ones. Different approaches regarding the evaluation and training of the models were GoogleNet which showed significant overfiting after some time of training and Inception (both categorized as transfer learning models). Moreover, even the augmentation of the images was considered but the results were dissapointing scoring an accuracy approximately to 25%.\n",
        "\n",
        "\n",
        "#### Hence, by comparing both RNNs and CNNs, the last ones showed better and higher accuracy scores, and with less computational power. The evaluation of the final model will be on the paper's ones (Q4 model), because even with not too much optimization the accuracy levels, as long as the missclassification of only some points for class 0 (healthy turbine) were quite accurate. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "roWpnZEJ826e"
      },
      "outputs": [],
      "source": [
        "# ---+ cleaning the session\n",
        "\n",
        "tf.keras.backend.clear_session()\n",
        "np.random.seed(745)\n",
        "tf.random.set_seed(658)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uf1zbJPKdla5",
        "outputId": "3f424d47-a0f4-4425-cbfc-2a0e0fd63557"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 98, 98, 32)        320       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 96, 96, 32)        9248      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 96, 96, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 94, 94, 16)        4624      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 31, 31, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 15376)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 61508     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75,700\n",
            "Trainable params: 75,700\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# ---+ building the model for the CNN\n",
        "model_final = Sequential([\n",
        "    Conv2D(32, kernel_size=(3, 3), input_shape = input_shape),\n",
        "    Conv2D(32, kernel_size=(3, 3)),\n",
        "    Dropout(0.2),\n",
        "    Conv2D(16, kernel_size=(3, 3), activation = \"relu\"),\n",
        "    MaxPooling2D(pool_size=(3, 3)),\n",
        "    Flatten(),\n",
        "    Dense(num_classes, activation='softmax')])\n",
        "\n",
        "model_final.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              metrics = ['accuracy'])\n",
        "model_final.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFijuSzmdpTa",
        "outputId": "eecc75a6-3056-43ad-ffe5-50a2dde7fccb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "88/88 [==============================] - 109s 1s/step - loss: 0.2700 - accuracy: 0.8757\n",
            "Epoch 2/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2469 - accuracy: 0.8904\n",
            "Epoch 3/20\n",
            "88/88 [==============================] - 104s 1s/step - loss: 0.2429 - accuracy: 0.8879\n",
            "Epoch 4/20\n",
            "88/88 [==============================] - 102s 1s/step - loss: 0.2364 - accuracy: 0.8871\n",
            "Epoch 5/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2428 - accuracy: 0.8904\n",
            "Epoch 6/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2407 - accuracy: 0.8932\n",
            "Epoch 7/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2499 - accuracy: 0.8825\n",
            "Epoch 8/20\n",
            "88/88 [==============================] - 104s 1s/step - loss: 0.2348 - accuracy: 0.8939\n",
            "Epoch 9/20\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2354 - accuracy: 0.8911\n",
            "Epoch 10/20\n",
            "88/88 [==============================] - 104s 1s/step - loss: 0.2364 - accuracy: 0.8950\n",
            "Epoch 11/20\n",
            "88/88 [==============================] - 100s 1s/step - loss: 0.2386 - accuracy: 0.8936\n",
            "Epoch 12/20\n",
            "88/88 [==============================] - 102s 1s/step - loss: 0.2256 - accuracy: 0.8950\n",
            "Epoch 13/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2378 - accuracy: 0.8936\n",
            "Epoch 14/20\n",
            "88/88 [==============================] - 111s 1s/step - loss: 0.2413 - accuracy: 0.8886\n",
            "Epoch 15/20\n",
            "88/88 [==============================] - 112s 1s/step - loss: 0.2269 - accuracy: 0.8936\n",
            "Epoch 16/20\n",
            "88/88 [==============================] - 105s 1s/step - loss: 0.2245 - accuracy: 0.8986\n",
            "Epoch 17/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2226 - accuracy: 0.9007\n",
            "Epoch 18/20\n",
            "88/88 [==============================] - 103s 1s/step - loss: 0.2304 - accuracy: 0.8936\n",
            "Epoch 19/20\n",
            "88/88 [==============================] - 104s 1s/step - loss: 0.2369 - accuracy: 0.8925\n",
            "Epoch 20/20\n",
            "88/88 [==============================] - 104s 1s/step - loss: 0.2331 - accuracy: 0.8918\n"
          ]
        }
      ],
      "source": [
        "# ---+ training and getting visualisations of the CNN model\n",
        "log_final = model_final.fit(X_q3_train, y_q3_train,\n",
        "          epochs = 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cmoIOrgaYiGB"
      },
      "outputs": [],
      "source": [
        "# ---+ saving the file into h5-file\n",
        "model_final.save('/content/sample_data/final-model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1ZCEcF0gbcg",
        "outputId": "4a60150b-d25f-4818-920a-0cd90e6fee6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "19/19 [==============================] - 4s 226ms/step - loss: 0.3073 - accuracy: 0.8800\n",
            "Validation score: 0.3073076605796814\n",
            "validation accuracy: 0.8799999952316284\n"
          ]
        }
      ],
      "source": [
        "# ---+ evaluating the model\n",
        "score, acc= model_final.evaluate(X_q3_test, y_q3_test)\n",
        "\n",
        "print(\"Validation score:\", score)\n",
        "print(\"validation accuracy:\", acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "idIBWFTdf_QL",
        "outputId": "8028a11b-bf8f-48b4-e2de-3ca8b1745f2f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f523709aa10>]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ visualising the model\n",
        "plt.plot(log_final.history['accuracy'], label = 'training accuracy', color = 'green')\n",
        "plt.plot(log_final.history['loss'], label = 'training loss', color = 'blue')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tkJWsVhgKHR",
        "outputId": "e9a5129b-b289-484d-e095-93b5a53eb054"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[101,   0,   0,  55],\n",
              "       [  5, 140,   0,   0],\n",
              "       [  3,   3, 139,   0],\n",
              "       [  2,   0,   4, 148]])"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ---+ confusion matrix\n",
        "cm_final = confusion_matrix(y_q1_test, np.asarray(model_final.predict(X_q3_test)).argmax(axis=1))\n",
        "\n",
        "cm_final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ouGE7wPjAfLJ",
        "outputId": "608ae607-67c1-4951-c959-af857cfc2e76"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# ---+ plotting confusion matrix\n",
        "ax = sns.heatmap(cm_final, annot=True, cmap=\"Greens\", fmt=\"g\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9NwvLRqqROz"
      },
      "source": [
        "#### Therefore, after evaluating the model on the test set, the accuracy is up to ~90%. Meaning (as it is observed above) that the number of correct predictions in the whole data set is quite high. It is shown that only for 55 of class 0 were missclassified as class 3. This could occur because of the nature of the data. If both of these classes have similar characteristics might be logical that the model missclassified them.\n",
        "\n",
        "#### In order for the model to be improved, more data could be inlcuded in the predictions (some feautures of this class might not be learned by the computer).Another way for improvement is to pre-process the data before predictions and classifications."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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