Assn1DL.ipynb

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  "metadata": {
    "colab": {
      "name": "Assn1DL.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBJuhbf4Dp1t",
        "colab_type": "text"
      },
      "source": [
        "**Assignment 1 Deep Learning COSC2779**\n",
        "\n",
        "The goal of the assignment is to develop a deep convolutional neural network (CNN) architechture to predict the head pose of given image of a person.\n",
        "The head pose is quantified by two values:\n",
        "\n",
        "1.   Tilt : vertical angle of head.\n",
        "2.   Pan : Horizontal angle of head.\n",
        "\n",
        "we have a original data from head Pose Image Database published with N. Gourier,\n",
        "D. Hall, J. L. Crowley, “Estimating Face Orientation from Robust Detection of Salient Facial Features”, Proceedings of ICPR International Workshop on Visual Observation of Deictic Gestures 2004.\n",
        "\n",
        "The data set contains 2 main files 'train_data.csv' and 'test_data.csv' in which the image filenames are mentioned along with their person id and sequence id. All the images are present in 'modified_data.zip' folder.\n",
        "\n",
        "The dataset contains two class lables namely tilt and pan. \n",
        "Tilt values varies in between +90 to -90 where negative values represent bottom and positive values represent top direction and this label have 9 different classes.\n",
        "\n",
        "Pan values varies in between +90 to -90 and have 13 different classes. Here, negative values represent left direction while positive values represent right direction of a face.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tjUAueunKqXD",
        "colab_type": "text"
      },
      "source": [
        "# Mounting Drive and Loading required packages and libraries\n",
        "\n",
        "In this section, before starting the experiement, we have to mount the google drive with our .ipynb file to read the data from the drive. \n",
        "We are also loading the required packages in this section so that we can use them in below coding cells.  \n",
        "The next important step is to set up a tensorboard platform to check the performance realted matrices of the CNN model we are building for this experiment. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLO8IdxHjNys",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ddf71fa-287a-419e-bfab-176f7a67c523"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4LNS9U8jY1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "import keras.preprocessing.image as image \n",
        "import os\n",
        "\n",
        "import pathlib\n",
        "import shutil\n",
        "import tempfile\n",
        "\n",
        "from  IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "logdir = pathlib.Path(tempfile.mkdtemp())/\"tensorboard_logs\"\n",
        "shutil.rmtree(logdir, ignore_errors=True)\n",
        "\n",
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard\n",
        "\n",
        "# Open an embedded TensorBoard viewer\n",
        "%tensorboard --logdir {logdir}/models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h14waVAiPBci",
        "colab_type": "text"
      },
      "source": [
        "# Setting up a basic plotter function to plot the performance graphs\n",
        "\n",
        "the below code is to set up a plotter function which is used to plot graphs of the given model as per the provided performance metric. It will generate basic line graph for both training and validation data with solid and dotted line respectively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgKLUVZZjY3_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from itertools import cycle\n",
        "def plotter(history_hold, metric = 'binary_crossentropy', ylim=[0.0, 1.0]):\n",
        "  cycol = cycle('bgrcmk')\n",
        "  for name, item in history_hold.items():\n",
        "    y_train = item.history[metric]\n",
        "    y_val = item.history['val_' + metric]\n",
        "    x_train = np.arange(0,len(y_val))\n",
        "\n",
        "    c=next(cycol)\n",
        "\n",
        "    plt.plot(x_train, y_train, c+'-', label=name+'_train')\n",
        "    plt.plot(x_train, y_val, c+'--', label=name+'_val')\n",
        "\n",
        "  plt.legend()\n",
        "  plt.xlim([1, max(plt.xlim())])\n",
        "  plt.ylim(ylim)\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel(metric)\n",
        "  plt.grid(True)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bFO_ONfQEjl",
        "colab_type": "text"
      },
      "source": [
        "# Reading data from the cloud splitting it into Training and Validation datasets.\n",
        "\n",
        "Below code cell contains code to read the files 'train_data.csv' and 'test_data.csv' into google colab environment and splitting the train_data into training and validation datasets so that, we can perform the model training on the training data while try to generalize the performance of trained model on the validation data. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHxaeTkajY7N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "4002f762-ee91-4065-8bca-7ad0cade2fcf"
      },
      "source": [
        "# Reading the train and test data files from google cloud to colab environment.\n",
        "DF = pd.read_csv(\"/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/train_data.csv\")\n",
        "DF_Test = pd.read_csv(\"/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/test_data.csv\")\n",
        "\n",
        "print(DF.shape)\n",
        "print(DF.dtypes)\n",
        "print(DF['tilt'].value_counts())\n",
        "print(DF['pan'].value_counts())"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2325, 5)\n",
            "filename     object\n",
            "person_id     int64\n",
            "series        int64\n",
            "tilt          int64\n",
            "pan           int64\n",
            "dtype: object\n",
            " 15    325\n",
            "-15    325\n",
            " 60    325\n",
            "-60    325\n",
            " 30    325\n",
            "-30    325\n",
            " 0     325\n",
            " 90     25\n",
            "-90     25\n",
            "Name: tilt, dtype: int64\n",
            " 0     225\n",
            " 75    175\n",
            "-75    175\n",
            " 45    175\n",
            "-45    175\n",
            " 15    175\n",
            "-15    175\n",
            " 90    175\n",
            "-90    175\n",
            " 60    175\n",
            "-60    175\n",
            " 30    175\n",
            "-30    175\n",
            "Name: pan, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgQmdLy1UGFq",
        "colab_type": "text"
      },
      "source": [
        "1. From the output of above cells, we can see that, the train_data.csv contains \n",
        "2325 image data and 5 feature columns namely, 'filename', 'person_id', 'series', 'tilt' and 'pan'.\n",
        "2. We can see that, the data type of of our experiment labels 'tilt' and 'pan' are int64 which is numeric data. \n",
        "3. furthermore, we can see from above output that, the class label 'tilt' contain 9 unique values out of which +90 and -90 tilt values have minimum samples in the data. \n",
        "4. +90 and -90 represents highest and lowest vertical angles of head position.\n",
        "5. As we can see the tilt and pan values varies in between fixed set of class values, we can use classification approch to predict the head position angles of unseen test data images.\n",
        "6. We need to convert the class labels into str to make it as a classification problem.\n",
        "7. For the classification purpose, we are encoding these class variable values as below to predict them for test data images.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r38yId6LRsbX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8b9b68da-4fd3-47ea-a20d-9f91feed93c9"
      },
      "source": [
        "DF['pan'] = DF['pan'].astype(str)\n",
        "DF['tilt'] = DF['tilt'].astype(str)\n",
        "DF['tilt'] = DF['tilt'].replace({\"-90\":\"0\" , \"-60\":\"1\", \"-30\":\"2\", \"-15\":\"3\", \"0\":\"4\", \"15\":\"5\", \"30\":\"6\", \"60\":\"7\", \"90\":\"8\"})\n",
        "DF['pan'] = DF['pan'].replace({\"-90\":\"0\", \"-75\":\"1\", \"-60\":\"2\", \"-45\":\"3\", \"-30\":\"4\", \"-15\":\"5\", \"0\":\"6\", \"15\":\"7\", \"30\":\"8\", \"45\":\"9\", \"60\":\"10\", \"75\":\"11\", \"90\":\"12\"})\n",
        "\n",
        "# We split the Training datatset into Training and Validation dataset by using 80:20 splitting (80% for Training and 20% for validation)\n",
        "X_train,X_val = train_test_split(DF,test_size=0.2, random_state=999)\n",
        "\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_val.shape)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1860, 5)\n",
            "(465, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjsF3L6TWeG5",
        "colab_type": "text"
      },
      "source": [
        "We are splitting the training data in dataframe DF into training and validation data. Training data is used to build and train our convolutional neural network model and validation data is used to check how our model is generalizing on the unseen data so that we can perform hyperparameter tuning to squeeze out some more performance from the model.\n",
        "Here, we have used '**train_test_split()**' function to split the 80% of the total data into training dataset and remaing 20% data as a validation dataset.\n",
        "We are setting random_state to 999 due to which we can get the same records into each training and validation dataset every time we run this code cell.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbrO2a8hsxAF",
        "colab_type": "text"
      },
      "source": [
        "# Problem Approach and Basic Exploration\n",
        "\n",
        "As we can state from the above read data that, this problem can be solved using classification as well as regression method. But first we have do some basic exploration of the target label classes and check if there are enough data available for training and validation. because lack of data may generate  high variance in the developed model which may result in overfitting and bad performance.\n",
        "\n",
        "In this assignment we are goin to predict the tilt and pan seperately so that we can see their respective performances during training and validation. This assignment is divided into two parts :\n",
        "\n",
        "1. Tilt prediction \n",
        "2. Pan prediction\n",
        "\n",
        "We are using branching technique to create a model in which the partial model is same for both the above predictions so that we can use power of trained model in both the predictions and then branch the model seperately for further prediction of tilt and pan.\n",
        "\n",
        "\n",
        "After building the model, we are going to check the performance of the model and then try to improve that performance by some hyper-parameter tuning methodologies.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPCPVQmJciqr",
        "colab_type": "text"
      },
      "source": [
        "# **tilt prediction**\n",
        "\n",
        "First let check the number of images in the training and validation dataset for each of the tilt class values. \n",
        "we can see from the below result that both in training and validation data set, the images with tilt +90 and -90 that is upper top and lower bottom angle of vertical head position have very few records. \n",
        "in the next step we explore  the percentage wise class label proportions in the tilt class. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Q50mp8UK5H4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "8bf9c9b6-be9c-4c9a-d362-3bc7eac4293c"
      },
      "source": [
        "print(X_train['tilt'].value_counts())"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3    275\n",
            "6    269\n",
            "4    263\n",
            "5    259\n",
            "7    254\n",
            "1    253\n",
            "2    251\n",
            "0     21\n",
            "8     15\n",
            "Name: tilt, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7ctHQNXLA40",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "1cc6e8d4-263a-4471-c846-3b63570ebf82"
      },
      "source": [
        "print(X_val['tilt'].value_counts())"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2    74\n",
            "1    72\n",
            "7    71\n",
            "5    66\n",
            "4    62\n",
            "6    56\n",
            "3    50\n",
            "8    10\n",
            "0     4\n",
            "Name: tilt, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hi5urKCgjY-1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "79e01fa3-c153-4d8a-8ed2-ffbd7130e20d"
      },
      "source": [
        "# Some exploration about tilt and pan which are labels \n",
        "a = X_train['tilt'].value_counts()\n",
        "x1 = a.index\n",
        "fig1 = plt.figure()\n",
        "ax = fig1.add_axes([0,0,0.5,0.5])\n",
        "ax.pie(a,autopct ='%.2f%%',labels = x1,shadow = True, radius= 2.0)\n",
        "ax.legend(loc = \"right\", bbox_to_anchor=(2,1.3,0,0))\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mqjTrAHllyQa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "7b8d699e-0964-450e-c342-563f0d51d5c3"
      },
      "source": [
        "a = X_val['tilt'].value_counts()\n",
        "x1 = a.index\n",
        "fig1 = plt.figure()\n",
        "ax = fig1.add_axes([0,0,0.5,0.5])\n",
        "ax.pie(a,autopct ='%.2f%%',labels = x1,shadow = True, radius= 2.0)\n",
        "ax.legend(loc = \"right\", bbox_to_anchor=(2,1.3,0,0))\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTx2HHhs8m9U",
        "colab_type": "text"
      },
      "source": [
        "# Reading Images with ImageDataGenerator with basic Augmentation\n",
        "\n",
        "The next step is to read images corresponding to respective datasets for tilt prediction in training and validation data. \n",
        "For this purpose, we are using ImageDataGenerator() class in keras.preprocessing.image package.\n",
        "We can perform various kinds of image augmentations while importing the images like rotation, zoom, horizontal and vertical flip, height-shift, width-shift, adjust brightness, rescaling etc.\n",
        "\n",
        "As we have a data with labels as directions/angles, its hard to perform many of the augmentation operations like flip, rotation etc because it will change the direction and orientation of image which then is out of synch with labels associated with them.\n",
        "\n",
        "Hence we are doing some basic Augmentation operations first to check the performance of the model on tilt prediction. If required, we can change those Augmentations to improve performance of model in model tuning phase.\n",
        "\n",
        "by using featurewise_center to True it will set featurewise mean to zerro for the dataset. then we are rescaling the data by using basic normalization (dividing by 255) and then by choosing data_format as 'channels_last' we are converting image into format [batch_size,image Height, image Width, No of channels].\n",
        "\n",
        "We are using method named 'flow_from_dataframe()' to read the images and labels from the dataframe. Here, we are converting all the images into 'grayscale' format so that we can extract most features from them and  to size (32x32) from their original size. As we are using classification approach for solving this problem our class mode should be 'categorical' and we are using standard batch size as 32. If required we can change the batch size to check the impact of it on the performance of a model.\n",
        "\n",
        "\n",
        "\n",
        "We are creating 3 ImageDataGenerators to read images from training dataset, validation dataset and test dataset and save them to corresponding generators.\n",
        "\n",
        "After that, we are creating a sample dataframe to check the training images and validation images after augmentation that happened during importing images.\n",
        "and tried to print some of the images with their encoded tilt label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlyLH6nsjZB3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "def make_train_generator():\n",
        "  train_datagen = ImageDataGenerator(rescale=1./255,data_format='channels_last')\n",
        "  train_generator = train_datagen.flow_from_dataframe(\n",
        "          dataframe=X_train,\n",
        "          directory='/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/modified_data/modified_data/',\n",
        "          x_col=\"filename\",\n",
        "          y_col=\"tilt\",\n",
        "          target_size=(32, 32),\n",
        "          batch_size=32,\n",
        "          color_mode=\"grayscale\",\n",
        "          class_mode='categorical',\n",
        "          shuffle = False)\n",
        "  return train_generator\n",
        "\n",
        "def make_val_generator():\n",
        "  val_datagen = ImageDataGenerator(rescale=1./255, data_format='channels_last')\n",
        "  val_generator = val_datagen.flow_from_dataframe(\n",
        "          dataframe=X_val,\n",
        "          directory='/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/modified_data/modified_data/',\n",
        "          x_col=\"filename\",\n",
        "          y_col=\"tilt\",\n",
        "          target_size=(32, 32),\n",
        "          batch_size=32,\n",
        "          color_mode=\"grayscale\",\n",
        "          class_mode='categorical',\n",
        "          shuffle= False)\n",
        "  return val_generator\n",
        "\n",
        "def make_test_predict_generator():\n",
        "  test_datagen = ImageDataGenerator(rescale=1./255,data_format = 'channels_last')\n",
        "  test_generator = test_datagen.flow_from_dataframe(\n",
        "          dataframe= DF_Test,\n",
        "          directory='/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/modified_data/modified_data/',\n",
        "          x_col=\"filename\",\n",
        "          target_size=(32, 32),\n",
        "          batch_size=32,\n",
        "          color_mode=\"grayscale\",\n",
        "          class_mode= None,\n",
        "          shuffle= False)\n",
        "  return test_generator\n",
        "\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_generator(make_train_generator,output_types=(tf.float32, tf.float32), output_shapes=([32,32,32,1], [32,9]))\n",
        "validation_dataset = tf.data.Dataset.from_generator(make_val_generator,output_types=(tf.float32, tf.float32), output_shapes=([32,32,32,1], [32,9]))"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZgLMgfxqJmlJ",
        "colab_type": "text"
      },
      "source": [
        "We are printing random 6 of the augmented images to see if the augmentation takes place and data is generated from ImageDataGenerator. We can also see from below output that tilt labels are onne hot encoded."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6Vx0_OHMcgq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "367902fe-83af-411f-ab57-a37973e419b6"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "i=1\n",
        "for image,label in train_dataset.take(6):\n",
        "  plt.subplot(2,3,i)\n",
        "  plt.imshow(image[0,:,:,0])\n",
        "  plt.title(label[0,:].numpy())\n",
        "  i=i+1"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1860 validated image filenames belonging to 9 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/text.py:1165: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if s != self._text:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBbtQIRLKBPB",
        "colab_type": "text"
      },
      "source": [
        "# Building a Deep Convolutional Network.\n",
        "\n",
        "* Before building a CNN model, the first step is to decide an error metric and performance metric for our project. As we know our problem is to predict tilt class for the images. As we see above that tilt label have 9 different class values, we will simply consider Categorical Crossentropy as our error metric and Accuracy as our performance metric.\n",
        "\n",
        "*  We First consider the traditional LeNet5 Architechture to build our model with some changes in the number of layers and number of trainable parameters.\n",
        "**LeNet5** is CNN classic architechture that existed for so lonag and is used for prediction of hand written notes. The below developed model takes inspiration from it but with some advancements in case of number of layers, Kernel size, Stride size and Activation functions ('Relu' is used instead of  'Sigmoid' as we are using Stochastic Gradient Descent optimization for our model).\n",
        "\n",
        "* We will first go with a simple CNN architechture consisting of 3 Convolutional layers and 2 dense layers. we are using non-linear activation function as 'Relu' (Rectified Linear unit) which fits good with the optimizer 'Adam()' which is the advanced version of stochastic Gradient Descent.\n",
        "\n",
        "* The next step is while disigning the model, we are using extraction power of partially build model to disign both tilt_model and pan_model. That means, we are designing a models which uses same convolutional layers and use same power to extract features for tilt and pan prediction both. \n",
        "\n",
        "* To build this architachture, we are defining 3 functions. First function will return partially buid model till Convolutional layers. Second function is specific to generate tilt prediction model and thirst function is specific to the pan prediction model.\n",
        "\n",
        "* We are using maxpooling layers with stride (2, 2) to achieve maximum from the feature extraction and to avoid overfiiting the model has some Kernel L2 regularizers and dropout layers.\n",
        "\n",
        "* In the end we are using 'softmax' activation function to predict class labels for tilt and pan.\n",
        "\n",
        "* we will built and run this simple basic model on train and validation data to check if this model is overfitting and then we will try some hyper parameter tuning to achieve some more from the model if the model is giving low performance.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZHvDg68jZFb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Lambda, Input\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.metrics import categorical_accuracy\n",
        "from tensorflow.keras import regularizers, optimizers\n",
        "\n",
        "def get_conv_model():\n",
        "  model_cnn = Sequential()\n",
        "\n",
        "  # input\n",
        "  model_cnn.add(Input(shape=(32,32,1)))\n",
        "\n",
        "  # Conv Layer 1\n",
        "  model_cnn.add(Conv2D(64, (3, 3),kernel_regularizer=regularizers.l2(0.0001)))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  model_cnn.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "  # Conv Layer 2 \n",
        "  model_cnn.add(Conv2D(128, (3, 3),kernel_regularizer=regularizers.l2(0.001)))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  \n",
        "  # Conv Layer 3\n",
        "  model_cnn.add(Conv2D(256, (3, 3),kernel_regularizer=regularizers.l2(0.001)))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  model_cnn.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "  return model_cnn\n",
        "\n",
        "def tilt_model():\n",
        "\n",
        "  # to get basic Convolutional model\n",
        "  model_cnn = get_conv_model()\n",
        "\n",
        "  # MLP\n",
        "  model_cnn.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
        "  model_cnn.add(Dropout(0.5))\n",
        "  model_cnn.add(Dense(128))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  model_cnn.add(Dropout(0.5))\n",
        "  model_cnn.add(Dense(9))\n",
        "  model_cnn.add(Activation('softmax'))\n",
        "\n",
        "  return model_cnn\n",
        "\n",
        "def pan_model():\n",
        "\n",
        "  # to get basic covolutional model\n",
        "  model_cnn = get_conv_model()\n",
        "\n",
        "  #Conv Layer 4\n",
        "  #model_cnn.add(Conv2D(256, (3, 3),kernel_regularizer=regularizers.l2(0.01)))\n",
        "  #model_cnn.add(Activation('relu'))\n",
        "  #model_cnn.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "  # MLP\n",
        "  model_cnn.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
        "  model_cnn.add(Dropout(0.5))\n",
        "  model_cnn.add(Dense(128))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  model_cnn.add(Dense(64))\n",
        "  model_cnn.add(Activation('relu'))\n",
        "  model_cnn.add(Dropout(0.5))\n",
        "  model_cnn.add(Dense(13))\n",
        "  model_cnn.add(Activation('softmax'))\n",
        "\n",
        "  return model_cnn"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UQaIOcGjZHx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_histories = {}\n",
        "\n",
        "def get_callbacks(name):\n",
        "  return [\n",
        "    tf.keras.callbacks.TensorBoard(logdir/name),\n",
        "  ]\n",
        "\n",
        "model_tilt = tilt_model()  \n",
        "model_tilt.compile(optimizer = tf.keras.optimizers.Adam(),\n",
        "              loss=tf.losses.CategoricalCrossentropy(),\n",
        "              metrics=[tf.losses.CategoricalCrossentropy(name='CategoricalCrossentropy'), 'accuracy'])\n"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AObN9sHirEQw",
        "colab_type": "text"
      },
      "source": [
        "In the next code cell we will show the model summary with output shape after each layer and number of trainable parameters after each layer in the above model.\n",
        "This will help us to understand model working more appropriately and help us in tuning the model if the performance is low (or overfitting occurs)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwgETLQFrEXM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "outputId": "7eeb8f4b-1a0e-4062-d2e8-7b329ba87f32"
      },
      "source": [
        "model_tilt.summary()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 30, 30, 64)        640       \n",
            "_________________________________________________________________\n",
            "activation_11 (Activation)   (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 13, 13, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_12 (Activation)   (None, 13, 13, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 11, 11, 256)       295168    \n",
            "_________________________________________________________________\n",
            "activation_13 (Activation)   (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 5, 5, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 128)               819328    \n",
            "_________________________________________________________________\n",
            "activation_14 (Activation)   (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 9)                 1161      \n",
            "_________________________________________________________________\n",
            "activation_15 (Activation)   (None, 9)                 0         \n",
            "=================================================================\n",
            "Total params: 1,190,153\n",
            "Trainable params: 1,190,153\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8JZXyzfor0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b7d24977-833f-48e7-81f1-f27335083e9b"
      },
      "source": [
        "train_generator = make_train_generator()\n",
        "val_generator = make_val_generator()\n",
        "m_histories['Tilt_Pred'] = model_tilt.fit_generator(train_generator, \n",
        "                                                    epochs=100, \n",
        "                                                    validation_data=val_generator, \n",
        "                                                    verbose=2, \n",
        "                                                    callbacks=get_callbacks('models/Tilt_Pred')\n",
        "                                                    )"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1860 validated image filenames belonging to 9 classes.\n",
            "Found 465 validated image filenames belonging to 9 classes.\n",
            "Epoch 1/100\n",
            "59/59 - 4s - loss: 2.1873 - CategoricalCrossentropy: 2.0659 - accuracy: 0.1677 - val_loss: 2.0422 - val_CategoricalCrossentropy: 1.9798 - val_accuracy: 0.2452\n",
            "Epoch 2/100\n",
            "59/59 - 4s - loss: 1.8841 - CategoricalCrossentropy: 1.8268 - accuracy: 0.2769 - val_loss: 1.6841 - val_CategoricalCrossentropy: 1.6304 - val_accuracy: 0.3634\n",
            "Epoch 3/100\n",
            "59/59 - 4s - loss: 1.6426 - CategoricalCrossentropy: 1.5909 - accuracy: 0.3704 - val_loss: 1.5543 - val_CategoricalCrossentropy: 1.5019 - val_accuracy: 0.4409\n",
            "Epoch 4/100\n",
            "59/59 - 4s - loss: 1.5097 - CategoricalCrossentropy: 1.4604 - accuracy: 0.4102 - val_loss: 1.4310 - val_CategoricalCrossentropy: 1.3789 - val_accuracy: 0.4839\n",
            "Epoch 5/100\n",
            "59/59 - 4s - loss: 1.4070 - CategoricalCrossentropy: 1.3568 - accuracy: 0.4624 - val_loss: 1.2937 - val_CategoricalCrossentropy: 1.2417 - val_accuracy: 0.5247\n",
            "Epoch 6/100\n",
            "59/59 - 4s - loss: 1.3130 - CategoricalCrossentropy: 1.2549 - accuracy: 0.4946 - val_loss: 1.2597 - val_CategoricalCrossentropy: 1.2047 - val_accuracy: 0.5634\n",
            "Epoch 7/100\n",
            "59/59 - 4s - loss: 1.2438 - CategoricalCrossentropy: 1.1842 - accuracy: 0.5263 - val_loss: 1.2069 - val_CategoricalCrossentropy: 1.1464 - val_accuracy: 0.5548\n",
            "Epoch 8/100\n",
            "59/59 - 4s - loss: 1.1922 - CategoricalCrossentropy: 1.1301 - accuracy: 0.5554 - val_loss: 1.1285 - val_CategoricalCrossentropy: 1.0693 - val_accuracy: 0.5914\n",
            "Epoch 9/100\n",
            "59/59 - 4s - loss: 1.1053 - CategoricalCrossentropy: 1.0391 - accuracy: 0.5806 - val_loss: 1.0556 - val_CategoricalCrossentropy: 0.9899 - val_accuracy: 0.6409\n",
            "Epoch 10/100\n",
            "59/59 - 4s - loss: 1.0819 - CategoricalCrossentropy: 1.0131 - accuracy: 0.5742 - val_loss: 1.0055 - val_CategoricalCrossentropy: 0.9431 - val_accuracy: 0.6581\n",
            "Epoch 11/100\n",
            "59/59 - 4s - loss: 1.0070 - CategoricalCrossentropy: 0.9362 - accuracy: 0.6156 - val_loss: 0.9446 - val_CategoricalCrossentropy: 0.8775 - val_accuracy: 0.6817\n",
            "Epoch 12/100\n",
            "59/59 - 4s - loss: 0.9873 - CategoricalCrossentropy: 0.9151 - accuracy: 0.6177 - val_loss: 0.9307 - val_CategoricalCrossentropy: 0.8604 - val_accuracy: 0.6645\n",
            "Epoch 13/100\n",
            "59/59 - 4s - loss: 0.9597 - CategoricalCrossentropy: 0.8805 - accuracy: 0.6398 - val_loss: 0.9081 - val_CategoricalCrossentropy: 0.8350 - val_accuracy: 0.6882\n",
            "Epoch 14/100\n",
            "59/59 - 4s - loss: 0.9086 - CategoricalCrossentropy: 0.8309 - accuracy: 0.6441 - val_loss: 0.8927 - val_CategoricalCrossentropy: 0.8211 - val_accuracy: 0.6882\n",
            "Epoch 15/100\n",
            "59/59 - 4s - loss: 0.8756 - CategoricalCrossentropy: 0.7920 - accuracy: 0.6817 - val_loss: 0.8981 - val_CategoricalCrossentropy: 0.8211 - val_accuracy: 0.6860\n",
            "Epoch 16/100\n",
            "59/59 - 4s - loss: 0.9068 - CategoricalCrossentropy: 0.8211 - accuracy: 0.6645 - val_loss: 0.8822 - val_CategoricalCrossentropy: 0.8095 - val_accuracy: 0.6946\n",
            "Epoch 17/100\n",
            "59/59 - 4s - loss: 0.8564 - CategoricalCrossentropy: 0.7685 - accuracy: 0.6747 - val_loss: 0.7892 - val_CategoricalCrossentropy: 0.7144 - val_accuracy: 0.7290\n",
            "Epoch 18/100\n",
            "59/59 - 4s - loss: 0.8563 - CategoricalCrossentropy: 0.7751 - accuracy: 0.6651 - val_loss: 0.8088 - val_CategoricalCrossentropy: 0.7298 - val_accuracy: 0.7204\n",
            "Epoch 19/100\n",
            "59/59 - 4s - loss: 0.8229 - CategoricalCrossentropy: 0.7354 - accuracy: 0.6952 - val_loss: 0.8238 - val_CategoricalCrossentropy: 0.7448 - val_accuracy: 0.7161\n",
            "Epoch 20/100\n",
            "59/59 - 4s - loss: 0.8114 - CategoricalCrossentropy: 0.7232 - accuracy: 0.7016 - val_loss: 0.8094 - val_CategoricalCrossentropy: 0.7302 - val_accuracy: 0.7183\n",
            "Epoch 21/100\n",
            "59/59 - 4s - loss: 0.7775 - CategoricalCrossentropy: 0.6868 - accuracy: 0.7108 - val_loss: 0.7620 - val_CategoricalCrossentropy: 0.6786 - val_accuracy: 0.7806\n",
            "Epoch 22/100\n",
            "59/59 - 4s - loss: 0.7713 - CategoricalCrossentropy: 0.6792 - accuracy: 0.7215 - val_loss: 0.7381 - val_CategoricalCrossentropy: 0.6580 - val_accuracy: 0.7462\n",
            "Epoch 23/100\n",
            "59/59 - 4s - loss: 0.7946 - CategoricalCrossentropy: 0.6994 - accuracy: 0.7172 - val_loss: 0.7640 - val_CategoricalCrossentropy: 0.6804 - val_accuracy: 0.7269\n",
            "Epoch 24/100\n",
            "59/59 - 4s - loss: 0.7574 - CategoricalCrossentropy: 0.6658 - accuracy: 0.7226 - val_loss: 0.7254 - val_CategoricalCrossentropy: 0.6428 - val_accuracy: 0.7462\n",
            "Epoch 25/100\n",
            "59/59 - 4s - loss: 0.7387 - CategoricalCrossentropy: 0.6428 - accuracy: 0.7376 - val_loss: 0.7641 - val_CategoricalCrossentropy: 0.6811 - val_accuracy: 0.7312\n",
            "Epoch 26/100\n",
            "59/59 - 4s - loss: 0.7492 - CategoricalCrossentropy: 0.6558 - accuracy: 0.7172 - val_loss: 0.7350 - val_CategoricalCrossentropy: 0.6505 - val_accuracy: 0.7484\n",
            "Epoch 27/100\n",
            "59/59 - 4s - loss: 0.6974 - CategoricalCrossentropy: 0.6002 - accuracy: 0.7468 - val_loss: 0.6923 - val_CategoricalCrossentropy: 0.6099 - val_accuracy: 0.7806\n",
            "Epoch 28/100\n",
            "59/59 - 4s - loss: 0.6864 - CategoricalCrossentropy: 0.5939 - accuracy: 0.7484 - val_loss: 0.6940 - val_CategoricalCrossentropy: 0.6083 - val_accuracy: 0.7720\n",
            "Epoch 29/100\n",
            "59/59 - 4s - loss: 0.7003 - CategoricalCrossentropy: 0.6119 - accuracy: 0.7565 - val_loss: 0.6934 - val_CategoricalCrossentropy: 0.6090 - val_accuracy: 0.7849\n",
            "Epoch 30/100\n",
            "59/59 - 4s - loss: 0.6779 - CategoricalCrossentropy: 0.5790 - accuracy: 0.7629 - val_loss: 0.7413 - val_CategoricalCrossentropy: 0.6523 - val_accuracy: 0.7548\n",
            "Epoch 31/100\n",
            "59/59 - 4s - loss: 0.6933 - CategoricalCrossentropy: 0.5939 - accuracy: 0.7452 - val_loss: 0.6666 - val_CategoricalCrossentropy: 0.5777 - val_accuracy: 0.8065\n",
            "Epoch 32/100\n",
            "59/59 - 4s - loss: 0.6721 - CategoricalCrossentropy: 0.5712 - accuracy: 0.7656 - val_loss: 0.6527 - val_CategoricalCrossentropy: 0.5652 - val_accuracy: 0.7914\n",
            "Epoch 33/100\n",
            "59/59 - 4s - loss: 0.6585 - CategoricalCrossentropy: 0.5585 - accuracy: 0.7742 - val_loss: 0.6517 - val_CategoricalCrossentropy: 0.5604 - val_accuracy: 0.8065\n",
            "Epoch 34/100\n",
            "59/59 - 4s - loss: 0.6164 - CategoricalCrossentropy: 0.5147 - accuracy: 0.7812 - val_loss: 0.6829 - val_CategoricalCrossentropy: 0.5896 - val_accuracy: 0.7699\n",
            "Epoch 35/100\n",
            "59/59 - 4s - loss: 0.6381 - CategoricalCrossentropy: 0.5409 - accuracy: 0.7715 - val_loss: 0.6528 - val_CategoricalCrossentropy: 0.5594 - val_accuracy: 0.8086\n",
            "Epoch 36/100\n",
            "59/59 - 4s - loss: 0.6309 - CategoricalCrossentropy: 0.5287 - accuracy: 0.7769 - val_loss: 0.6343 - val_CategoricalCrossentropy: 0.5433 - val_accuracy: 0.7935\n",
            "Epoch 37/100\n",
            "59/59 - 4s - loss: 0.5972 - CategoricalCrossentropy: 0.4980 - accuracy: 0.7892 - val_loss: 0.6309 - val_CategoricalCrossentropy: 0.5399 - val_accuracy: 0.8065\n",
            "Epoch 38/100\n",
            "59/59 - 4s - loss: 0.6437 - CategoricalCrossentropy: 0.5402 - accuracy: 0.7726 - val_loss: 0.6321 - val_CategoricalCrossentropy: 0.5409 - val_accuracy: 0.8086\n",
            "Epoch 39/100\n",
            "59/59 - 4s - loss: 0.6031 - CategoricalCrossentropy: 0.5048 - accuracy: 0.7984 - val_loss: 0.6164 - val_CategoricalCrossentropy: 0.5255 - val_accuracy: 0.7935\n",
            "Epoch 40/100\n",
            "59/59 - 4s - loss: 0.5728 - CategoricalCrossentropy: 0.4695 - accuracy: 0.8075 - val_loss: 0.6303 - val_CategoricalCrossentropy: 0.5429 - val_accuracy: 0.7935\n",
            "Epoch 41/100\n",
            "59/59 - 4s - loss: 0.5774 - CategoricalCrossentropy: 0.4766 - accuracy: 0.7962 - val_loss: 0.6311 - val_CategoricalCrossentropy: 0.5407 - val_accuracy: 0.7914\n",
            "Epoch 42/100\n",
            "59/59 - 4s - loss: 0.5544 - CategoricalCrossentropy: 0.4529 - accuracy: 0.8140 - val_loss: 0.5929 - val_CategoricalCrossentropy: 0.5009 - val_accuracy: 0.8086\n",
            "Epoch 43/100\n",
            "59/59 - 4s - loss: 0.5897 - CategoricalCrossentropy: 0.4881 - accuracy: 0.7989 - val_loss: 0.5960 - val_CategoricalCrossentropy: 0.5082 - val_accuracy: 0.8172\n",
            "Epoch 44/100\n",
            "59/59 - 4s - loss: 0.5527 - CategoricalCrossentropy: 0.4515 - accuracy: 0.8231 - val_loss: 0.6083 - val_CategoricalCrossentropy: 0.5162 - val_accuracy: 0.8108\n",
            "Epoch 45/100\n",
            "59/59 - 4s - loss: 0.5721 - CategoricalCrossentropy: 0.4709 - accuracy: 0.8065 - val_loss: 0.6020 - val_CategoricalCrossentropy: 0.5131 - val_accuracy: 0.8151\n",
            "Epoch 46/100\n",
            "59/59 - 4s - loss: 0.5560 - CategoricalCrossentropy: 0.4526 - accuracy: 0.8113 - val_loss: 0.6182 - val_CategoricalCrossentropy: 0.5233 - val_accuracy: 0.8258\n",
            "Epoch 47/100\n",
            "59/59 - 4s - loss: 0.5847 - CategoricalCrossentropy: 0.4825 - accuracy: 0.8022 - val_loss: 0.6368 - val_CategoricalCrossentropy: 0.5409 - val_accuracy: 0.7978\n",
            "Epoch 48/100\n",
            "59/59 - 4s - loss: 0.5603 - CategoricalCrossentropy: 0.4588 - accuracy: 0.8097 - val_loss: 0.6168 - val_CategoricalCrossentropy: 0.5226 - val_accuracy: 0.8043\n",
            "Epoch 49/100\n",
            "59/59 - 4s - loss: 0.5321 - CategoricalCrossentropy: 0.4432 - accuracy: 0.8274 - val_loss: 0.5941 - val_CategoricalCrossentropy: 0.5000 - val_accuracy: 0.8108\n",
            "Epoch 50/100\n",
            "59/59 - 4s - loss: 0.5360 - CategoricalCrossentropy: 0.4387 - accuracy: 0.8183 - val_loss: 0.5987 - val_CategoricalCrossentropy: 0.5063 - val_accuracy: 0.8065\n",
            "Epoch 51/100\n",
            "59/59 - 4s - loss: 0.5296 - CategoricalCrossentropy: 0.4340 - accuracy: 0.8242 - val_loss: 0.6252 - val_CategoricalCrossentropy: 0.5299 - val_accuracy: 0.8000\n",
            "Epoch 52/100\n",
            "59/59 - 4s - loss: 0.5134 - CategoricalCrossentropy: 0.4118 - accuracy: 0.8317 - val_loss: 0.6176 - val_CategoricalCrossentropy: 0.5272 - val_accuracy: 0.8065\n",
            "Epoch 53/100\n",
            "59/59 - 4s - loss: 0.5004 - CategoricalCrossentropy: 0.4009 - accuracy: 0.8328 - val_loss: 0.5576 - val_CategoricalCrossentropy: 0.4653 - val_accuracy: 0.8301\n",
            "Epoch 54/100\n",
            "59/59 - 4s - loss: 0.5018 - CategoricalCrossentropy: 0.4036 - accuracy: 0.8296 - val_loss: 0.6063 - val_CategoricalCrossentropy: 0.5122 - val_accuracy: 0.8151\n",
            "Epoch 55/100\n",
            "59/59 - 4s - loss: 0.5178 - CategoricalCrossentropy: 0.4187 - accuracy: 0.8312 - val_loss: 0.6189 - val_CategoricalCrossentropy: 0.5243 - val_accuracy: 0.8086\n",
            "Epoch 56/100\n",
            "59/59 - 4s - loss: 0.5040 - CategoricalCrossentropy: 0.4046 - accuracy: 0.8392 - val_loss: 0.6226 - val_CategoricalCrossentropy: 0.5257 - val_accuracy: 0.8194\n",
            "Epoch 57/100\n",
            "59/59 - 4s - loss: 0.5105 - CategoricalCrossentropy: 0.4107 - accuracy: 0.8387 - val_loss: 0.5865 - val_CategoricalCrossentropy: 0.4969 - val_accuracy: 0.8237\n",
            "Epoch 58/100\n",
            "59/59 - 4s - loss: 0.4927 - CategoricalCrossentropy: 0.3905 - accuracy: 0.8430 - val_loss: 0.5685 - val_CategoricalCrossentropy: 0.4783 - val_accuracy: 0.8430\n",
            "Epoch 59/100\n",
            "59/59 - 4s - loss: 0.5015 - CategoricalCrossentropy: 0.3995 - accuracy: 0.8280 - val_loss: 0.5542 - val_CategoricalCrossentropy: 0.4610 - val_accuracy: 0.8452\n",
            "Epoch 60/100\n",
            "59/59 - 4s - loss: 0.4771 - CategoricalCrossentropy: 0.3761 - accuracy: 0.8446 - val_loss: 0.5695 - val_CategoricalCrossentropy: 0.4795 - val_accuracy: 0.8280\n",
            "Epoch 61/100\n",
            "59/59 - 4s - loss: 0.5048 - CategoricalCrossentropy: 0.4026 - accuracy: 0.8355 - val_loss: 0.5661 - val_CategoricalCrossentropy: 0.4710 - val_accuracy: 0.8194\n",
            "Epoch 62/100\n",
            "59/59 - 4s - loss: 0.4865 - CategoricalCrossentropy: 0.3854 - accuracy: 0.8312 - val_loss: 0.5830 - val_CategoricalCrossentropy: 0.4903 - val_accuracy: 0.8301\n",
            "Epoch 63/100\n",
            "59/59 - 4s - loss: 0.4954 - CategoricalCrossentropy: 0.3932 - accuracy: 0.8317 - val_loss: 0.5775 - val_CategoricalCrossentropy: 0.4845 - val_accuracy: 0.8280\n",
            "Epoch 64/100\n",
            "59/59 - 4s - loss: 0.4952 - CategoricalCrossentropy: 0.3956 - accuracy: 0.8263 - val_loss: 0.5524 - val_CategoricalCrossentropy: 0.4647 - val_accuracy: 0.8387\n",
            "Epoch 65/100\n",
            "59/59 - 4s - loss: 0.4781 - CategoricalCrossentropy: 0.3824 - accuracy: 0.8430 - val_loss: 0.5905 - val_CategoricalCrossentropy: 0.4968 - val_accuracy: 0.8387\n",
            "Epoch 66/100\n",
            "59/59 - 4s - loss: 0.4693 - CategoricalCrossentropy: 0.3705 - accuracy: 0.8500 - val_loss: 0.5769 - val_CategoricalCrossentropy: 0.4842 - val_accuracy: 0.8323\n",
            "Epoch 67/100\n",
            "59/59 - 4s - loss: 0.4827 - CategoricalCrossentropy: 0.3809 - accuracy: 0.8435 - val_loss: 0.5699 - val_CategoricalCrossentropy: 0.4783 - val_accuracy: 0.8172\n",
            "Epoch 68/100\n",
            "59/59 - 4s - loss: 0.4627 - CategoricalCrossentropy: 0.3618 - accuracy: 0.8511 - val_loss: 0.5640 - val_CategoricalCrossentropy: 0.4711 - val_accuracy: 0.8495\n",
            "Epoch 69/100\n",
            "59/59 - 4s - loss: 0.4761 - CategoricalCrossentropy: 0.3753 - accuracy: 0.8403 - val_loss: 0.5537 - val_CategoricalCrossentropy: 0.4650 - val_accuracy: 0.8366\n",
            "Epoch 70/100\n",
            "59/59 - 4s - loss: 0.4645 - CategoricalCrossentropy: 0.3656 - accuracy: 0.8441 - val_loss: 0.5707 - val_CategoricalCrossentropy: 0.4786 - val_accuracy: 0.8473\n",
            "Epoch 71/100\n",
            "59/59 - 4s - loss: 0.4269 - CategoricalCrossentropy: 0.3268 - accuracy: 0.8602 - val_loss: 0.5333 - val_CategoricalCrossentropy: 0.4464 - val_accuracy: 0.8688\n",
            "Epoch 72/100\n",
            "59/59 - 4s - loss: 0.4304 - CategoricalCrossentropy: 0.3290 - accuracy: 0.8677 - val_loss: 0.5387 - val_CategoricalCrossentropy: 0.4488 - val_accuracy: 0.8495\n",
            "Epoch 73/100\n",
            "59/59 - 4s - loss: 0.4365 - CategoricalCrossentropy: 0.3365 - accuracy: 0.8634 - val_loss: 0.5171 - val_CategoricalCrossentropy: 0.4331 - val_accuracy: 0.8473\n",
            "Epoch 74/100\n",
            "59/59 - 4s - loss: 0.4513 - CategoricalCrossentropy: 0.3537 - accuracy: 0.8602 - val_loss: 0.5975 - val_CategoricalCrossentropy: 0.5046 - val_accuracy: 0.8108\n",
            "Epoch 75/100\n",
            "59/59 - 4s - loss: 0.4829 - CategoricalCrossentropy: 0.3876 - accuracy: 0.8355 - val_loss: 0.5207 - val_CategoricalCrossentropy: 0.4327 - val_accuracy: 0.8559\n",
            "Epoch 76/100\n",
            "59/59 - 4s - loss: 0.4454 - CategoricalCrossentropy: 0.3454 - accuracy: 0.8742 - val_loss: 0.5572 - val_CategoricalCrossentropy: 0.4625 - val_accuracy: 0.8452\n",
            "Epoch 77/100\n",
            "59/59 - 4s - loss: 0.4411 - CategoricalCrossentropy: 0.3427 - accuracy: 0.8634 - val_loss: 0.5311 - val_CategoricalCrossentropy: 0.4378 - val_accuracy: 0.8538\n",
            "Epoch 78/100\n",
            "59/59 - 4s - loss: 0.4433 - CategoricalCrossentropy: 0.3421 - accuracy: 0.8559 - val_loss: 0.5330 - val_CategoricalCrossentropy: 0.4401 - val_accuracy: 0.8624\n",
            "Epoch 79/100\n",
            "59/59 - 4s - loss: 0.4340 - CategoricalCrossentropy: 0.3335 - accuracy: 0.8683 - val_loss: 0.5301 - val_CategoricalCrossentropy: 0.4385 - val_accuracy: 0.8452\n",
            "Epoch 80/100\n",
            "59/59 - 4s - loss: 0.4199 - CategoricalCrossentropy: 0.3189 - accuracy: 0.8742 - val_loss: 0.5849 - val_CategoricalCrossentropy: 0.4912 - val_accuracy: 0.8301\n",
            "Epoch 81/100\n",
            "59/59 - 4s - loss: 0.4310 - CategoricalCrossentropy: 0.3308 - accuracy: 0.8613 - val_loss: 0.5125 - val_CategoricalCrossentropy: 0.4231 - val_accuracy: 0.8645\n",
            "Epoch 82/100\n",
            "59/59 - 4s - loss: 0.4227 - CategoricalCrossentropy: 0.3228 - accuracy: 0.8624 - val_loss: 0.5362 - val_CategoricalCrossentropy: 0.4494 - val_accuracy: 0.8516\n",
            "Epoch 83/100\n",
            "59/59 - 4s - loss: 0.4315 - CategoricalCrossentropy: 0.3333 - accuracy: 0.8559 - val_loss: 0.5052 - val_CategoricalCrossentropy: 0.4151 - val_accuracy: 0.8624\n",
            "Epoch 84/100\n",
            "59/59 - 4s - loss: 0.3938 - CategoricalCrossentropy: 0.2943 - accuracy: 0.8812 - val_loss: 0.4970 - val_CategoricalCrossentropy: 0.4069 - val_accuracy: 0.8731\n",
            "Epoch 85/100\n",
            "59/59 - 4s - loss: 0.4143 - CategoricalCrossentropy: 0.3166 - accuracy: 0.8699 - val_loss: 0.5296 - val_CategoricalCrossentropy: 0.4368 - val_accuracy: 0.8538\n",
            "Epoch 86/100\n",
            "59/59 - 4s - loss: 0.4145 - CategoricalCrossentropy: 0.3152 - accuracy: 0.8651 - val_loss: 0.5178 - val_CategoricalCrossentropy: 0.4263 - val_accuracy: 0.8473\n",
            "Epoch 87/100\n",
            "59/59 - 4s - loss: 0.4107 - CategoricalCrossentropy: 0.3124 - accuracy: 0.8737 - val_loss: 0.5722 - val_CategoricalCrossentropy: 0.4828 - val_accuracy: 0.8473\n",
            "Epoch 88/100\n",
            "59/59 - 4s - loss: 0.4202 - CategoricalCrossentropy: 0.3208 - accuracy: 0.8699 - val_loss: 0.5747 - val_CategoricalCrossentropy: 0.4877 - val_accuracy: 0.8495\n",
            "Epoch 89/100\n",
            "59/59 - 4s - loss: 0.4024 - CategoricalCrossentropy: 0.3052 - accuracy: 0.8849 - val_loss: 0.5323 - val_CategoricalCrossentropy: 0.4475 - val_accuracy: 0.8602\n",
            "Epoch 90/100\n",
            "59/59 - 4s - loss: 0.3997 - CategoricalCrossentropy: 0.3003 - accuracy: 0.8828 - val_loss: 0.5162 - val_CategoricalCrossentropy: 0.4254 - val_accuracy: 0.8688\n",
            "Epoch 91/100\n",
            "59/59 - 4s - loss: 0.4006 - CategoricalCrossentropy: 0.3011 - accuracy: 0.8790 - val_loss: 0.5329 - val_CategoricalCrossentropy: 0.4479 - val_accuracy: 0.8688\n",
            "Epoch 92/100\n",
            "59/59 - 4s - loss: 0.4026 - CategoricalCrossentropy: 0.3026 - accuracy: 0.8774 - val_loss: 0.5416 - val_CategoricalCrossentropy: 0.4525 - val_accuracy: 0.8710\n",
            "Epoch 93/100\n",
            "59/59 - 4s - loss: 0.3870 - CategoricalCrossentropy: 0.2894 - accuracy: 0.8941 - val_loss: 0.5554 - val_CategoricalCrossentropy: 0.4656 - val_accuracy: 0.8538\n",
            "Epoch 94/100\n",
            "59/59 - 4s - loss: 0.4102 - CategoricalCrossentropy: 0.3118 - accuracy: 0.8726 - val_loss: 0.5174 - val_CategoricalCrossentropy: 0.4309 - val_accuracy: 0.8581\n",
            "Epoch 95/100\n",
            "59/59 - 4s - loss: 0.4065 - CategoricalCrossentropy: 0.3085 - accuracy: 0.8737 - val_loss: 0.5262 - val_CategoricalCrossentropy: 0.4389 - val_accuracy: 0.8731\n",
            "Epoch 96/100\n",
            "59/59 - 4s - loss: 0.3886 - CategoricalCrossentropy: 0.2908 - accuracy: 0.8833 - val_loss: 0.5435 - val_CategoricalCrossentropy: 0.4547 - val_accuracy: 0.8452\n",
            "Epoch 97/100\n",
            "59/59 - 4s - loss: 0.3909 - CategoricalCrossentropy: 0.2945 - accuracy: 0.8785 - val_loss: 0.5452 - val_CategoricalCrossentropy: 0.4570 - val_accuracy: 0.8559\n",
            "Epoch 98/100\n",
            "59/59 - 4s - loss: 0.3704 - CategoricalCrossentropy: 0.2777 - accuracy: 0.8925 - val_loss: 0.5038 - val_CategoricalCrossentropy: 0.4165 - val_accuracy: 0.8581\n",
            "Epoch 99/100\n",
            "59/59 - 4s - loss: 0.3970 - CategoricalCrossentropy: 0.3030 - accuracy: 0.8780 - val_loss: 0.4937 - val_CategoricalCrossentropy: 0.4051 - val_accuracy: 0.8688\n",
            "Epoch 100/100\n",
            "59/59 - 4s - loss: 0.3934 - CategoricalCrossentropy: 0.2971 - accuracy: 0.8780 - val_loss: 0.5431 - val_CategoricalCrossentropy: 0.4605 - val_accuracy: 0.8473\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3hGQflk4rts7",
        "colab_type": "text"
      },
      "source": [
        "We can see from the above training of a model that model is working fine with average accuracy of 85% which is good for tilt prediction. but the performance of a model is best visualised with error plot and accuracy plot which is also called learning curves.\n",
        "\n",
        "In the below cells, we are plotting learning curves of a model for error metric Categorical Crossextropy and performance metric Accuracy. We can see from these learning curves that our model is not overfitting and showing steady decrease in Categorical Crossentropy values which represents our model is working fine with predicting the tilt values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmKr6HiFosHz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "fdb9fadd-d78e-46ef-e535-31275e86f698"
      },
      "source": [
        "plotter(m_histories, ylim=[0.0, 5.0], metric = 'CategoricalCrossentropy')"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHeCxuvAosXk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "c4c3036c-9c25-4c17-84c5-bb2c6336d165"
      },
      "source": [
        "plotter(m_histories, ylim=[0.0, 1.0], metric = 'accuracy')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNBCWoCg736D",
        "colab_type": "text"
      },
      "source": [
        "The next step is to predict the tilt labels for the images in 'test_data.csv'. For this we have imported thos images with test_generator which is ImageDataGenerator and then we will apply .predict function of our trained model to predict the tilt angles for unseen images.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohD3g9uMbRA3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "80cab6df-42ec-455f-8dd2-7062f68f44ba"
      },
      "source": [
        "test_generator = make_test_predict_generator()\n",
        "predictions = model_tilt.predict_generator(test_generator, verbose= 1)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 465 validated image filenames.\n",
            "15/15 [==============================] - 1s 49ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgAs670B8VE2",
        "colab_type": "text"
      },
      "source": [
        "We will use argmax function to return the predicted class values in readable format and then we are going to replace them with tilt labels as mentioned above while encoding them.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JA5loi-osgO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "e85da887-9ac4-44a2-e8ec-9d10a98d7dd1"
      },
      "source": [
        "tilt_result = pd.Series (np.argmax(predictions, axis=1).tolist()).replace({0:\"-90\" , 1:\"-60\", 2:\"-30\", 3:\"-15\", 4:\"0\", 5:\"15\", 6:\"30\", 7:\"60\", 8:\"90\"})\n",
        "tilt_result.head(10)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    60\n",
              "1    60\n",
              "2     0\n",
              "3    15\n",
              "4    15\n",
              "5    30\n",
              "6    60\n",
              "7    30\n",
              "8    60\n",
              "9    30\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8GpynddcFR8",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "# **Pan Prediction**\n",
        "\n",
        "We will do the similar functionality to check the distribution of pan class labels as we did it for tilt. We can see from below cells that, class labels for pan are almost equally distributed.\n",
        "we can see it more clearly with the help of small visualization of pie charts as drawn below.\n",
        "We can see that there are 13 different values for pan class label which we have to predict in this problem. \n",
        "In the starting cells awe divide the training data into training and validation datasets using train_test-split functionality. \n",
        "we encoded the values of pan class (-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90)  as (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oilJDtg6c3m5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "3c7d49bc-76af-42ac-ad78-cf31437f59f0"
      },
      "source": [
        "print(X_train['pan'].value_counts())"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6     174\n",
            "12    147\n",
            "1     146\n",
            "11    144\n",
            "8     143\n",
            "2     143\n",
            "10    142\n",
            "9     142\n",
            "7     142\n",
            "5     137\n",
            "0     135\n",
            "3     133\n",
            "4     132\n",
            "Name: pan, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJiriY_rc30H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "577534f8-128c-42df-9017-283e988f39cd"
      },
      "source": [
        "print(X_val['pan'].value_counts())"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6     51\n",
            "4     43\n",
            "3     42\n",
            "0     40\n",
            "5     38\n",
            "7     33\n",
            "10    33\n",
            "9     33\n",
            "2     32\n",
            "8     32\n",
            "11    31\n",
            "1     29\n",
            "12    28\n",
            "Name: pan, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7bDuaZVdMg5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "a2523123-8805-49a4-d2e4-b41868a79837"
      },
      "source": [
        "# Some exploration about tilt and pan which are labels \n",
        "a = X_train['pan'].value_counts()\n",
        "x1 = a.index\n",
        "fig1 = plt.figure()\n",
        "ax = fig1.add_axes([0,0,0.5,0.5])\n",
        "ax.pie(a,autopct ='%.2f%%',labels = x1,shadow = True, radius= 2.0)\n",
        "ax.legend(loc = \"right\", bbox_to_anchor=(2,1.3,0,0))\n",
        "plt.show()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLg-9z-2dMmq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "0dd2e79c-32ff-4f94-ae08-1b2978573a5b"
      },
      "source": [
        "# Some exploration about tilt and pan which are labels \n",
        "a = X_val['pan'].value_counts()\n",
        "x1 = a.index\n",
        "fig1 = plt.figure()\n",
        "ax = fig1.add_axes([0,0,0.5,0.5])\n",
        "ax.pie(a,autopct ='%.2f%%',labels = x1,shadow = True, radius= 2.0)\n",
        "ax.legend(loc = \"right\", bbox_to_anchor=(2,1.3,0,0))\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nZQAQQITyLVJzuNuNWwFZOov6wtUrGQfgdQCbUWO6GDUm9l5gBmTTpk2a0aNHuwwGg6xQKDB58uTOV199NSkcbbMXY5i9lJuX+/6wghUVas2POUrlWUIEEvKxxv8gByPZfjzJbvM61FzY/z9HAHgJwDrUmE4NV6OU0o/ZHOahbdSoUd5vvvnG0NjYyHd2dnIffvihad++fcq+H9m3k+YSONJqrFbdKLXmwQq1epmS48Ly5PTHyMaT5+7/fJ8nkgWOxgD4FDWmFQB+gxpHXQTPxQwBY8aM8f3yl79snDlzZrFGo5HLyso8PB+efgPrMQ/SdIOBvJyXd/0cg3HvcLX62mgmZQBIE3mLqV1yR/OcMSFReiH1RmNq4HmU0m2oMf0RNSZ9FM7HJLAbb7yxdcuWLVu//fbb7WazWSouLg5L54El5kF4NCt73P+kpW8co9E+ZuJ5S6ziGLtBbIvVuaPF0u5zJpPozEI5VLa0+tA0uytQY4pMfVQm4TU0NAgAsGPHDuXKlSuTrrrqqvZwtMuGMkLwy9RU/VSd/olpev2FijAVlx+M8TtAVs+IdRSRNd3tdQEwRfOchBArgGcBXI4a05WoceyI5vmZ+Ldw4cICu90uCIJAH3744b0pKSlSONqNeVJJJNMNBjLfYDzzPFPS31IEIT3W8RxW0kGSqSyDcEP3AuhSyWOM4fXdqZTSjaTGdAeAB1HjCMubjwmf/kxvi4TvvvtueyTaHbrv5DBbYDLprjRb/jHbYHgznpIyAOgop83fIyd0bdoT0dgD7gJOjulNTkKIGsB9AL5EjWl4LGNhhj6WmPvhbmvGlF+lpG4Zq9VeHOnpb6Eav1EasnvoTXJ4nLGO4RjjKaXfo8b0Czb2zEQKS8wnMN1gUD2bk/vwAqNxVaZCkRfreE5kzF6oYx1DpFwseuJq6TkhRAPgLwA+QI3JGut4mKGHJeZeXJucUvzrlNRvq3S6X6qiPAUuFHldy7OH3GITwSX6JnJizGa89OF0AN9FujgSc/JhifknphsM5Lfp6edfajZ/WaBSlcc6nv4SQPjyzeKQ2w+v3O4Z8A7DUZZJKV2DGtMNsQ6EGTpYYj7GdINBOc9guG+JKenFZEGI115aryYMweXZSwKeWIfQJ0KIAsAjqDG9jBqTNtbxMImPJeZDphsMlsvN5nfmGYw3qxNg6KInI5u4IbWVEfFL4hz442oGTB8uAvAVakyFsQ6EibydO3cqJk6cWFxQUFBWWFhYdvfdd7Oyn+F0ZXJy5c0pqSuGqVQJ/YZKFTmzuVVydaTwQ2IpcX6b164gZNB1q6OsglL6LakxXYIax7uxDuZksWp1QVjLfs6csavPedEKhQIPPvjg/ilTpng6Ojq40aNHj5g3b55z7Nixg16WfVL3mKcbDOTnKamLLjdbVid6Uj5s7AYxLEtC48FCnzchh2YIISZK6VuoMV0d61iYyMnLywtOmTLFAwBms1kuKCjw7t27NyxX2ydtYp5uMPCVavUvLjWbX0hJwPHk3ozbSYfG3FpRlpfAm2i95SMO7T34NGpMt8c6Fibytm/frvzhhx+006ZNC8t6gpMyMU83GBSTtNo7LjVb7jPyQ+Oy/7ASO5cyFHbPTmv3OwwEiljHEQZ3ocb0KCvEP3Q5HA5u0aJFBffee+8+i8USljffSfdimW4waKbr9A9dYEqq1nLckFuUoaWcZtiuxF+efYbH4411DGH0cwCvoMaUkDeVmd75/X4yf/78gsWLF7cvXbo0bFM7T6rEPN1gMM41GJ5dZDItV3LcUOiN9WjCpsRenk1likskT1i26IkjSwD8BzWmk2Zjg6FOlmVccMEFecXFxb6ampqmcLZ90iTm6QZDytlG48vzDcYLIrH/XjwZneDLs40dgc5sjg7F+cAzAaxGjSmq5UuZyPjwww/1b731VvLatWsNJSUlI0pKSka89tprYXluT4rpctMNhtQzDcYXTtcb5nBdW9kPaXleLlXplYMBTWJeFUx2eTqBIbtl1jh09ZxnocYx9HeeiZL+TG8Lt9mzZ7sopRE575DvMU83GFLmGQzPzTGcHEkZAHgQvmJT4i7PvlT0DMXe8rGqALyNGlNCX9kwkTOkE/NUU3JKUenZT6cXzht9siTlwyZsTczl2Upn0DuSk4ba+HJPZgBYgRpTQl7VMJE1ZBNzha3KlFpyzsvllZee7Sy9MPObzJn1sY4pmioSdHn2aLvHEesYomg+gJdRYxrS9zyYgRuSibnCVqUrzBj54NSRi6fzvEAIIXAVL8r7MmdeXaxji5YUiTNbWqTOWMcxUBcEPSdbkloM4O+s6D5zrCGXmCtsVWqrOe+OqWULLxZ4RbfLRG/BfNtn+YvqYhRa1I3bIHbEOoaB4LxScAYJpMY6jhi4DMDDsQ6CiR9DKjFX2Ko4gybp2tNHLr5aKah7vLESzJtp+7jwwpNiWGPcTppQz29xu9fOnVy3Ao51A2pM18c6CCY+JNQbty88JyycNerC3+g1SSe8eSRnT8lbPfzyepnSaIUWE8PtXDJkOWF+yUV+jxjrGGKJUvoIakyzYx0H03933nlnWmFhYVlRUVHZggUL8j0eT1h6FkNmHnOFrWr8rFEX/DHVlJXVrwdkjM9bzSn3Tv/h6RyeYEh20zTgNAU75ZZdxVyPwwOtH7Si45MOgALmaWakzO5eM4hSioMvH4RrowtESZB9VTY0Ng0CrQHsfXQvIANUokg+PRmWGRbIQRl7H9mLYEcQlhkWJM9MBgA0PNcAy3QLNDZN78EGZels6k3DydtjBiGElyl55vTf/u301X+8enus40kk1jUbwlr2s3H6qD7nJ+/Zs0fx9NNPp2/fvn2zXq+n8+bNG/bMM89YbrjhhrbBnn9I9JgrbFXDJhbPeniYtbxkII/j0itzV1dc3yCCJH7Vn16M3yT1uIjBt9+Hjk86UPD7AhTeXYjO2k74m/zdjnFtdCHQFEDRfUXIujwLB148AAAQkgQMu20YCu8uxLDfD0PLyhYEO4JwbXZBW6xF4d2FsH/RVTbAu9cLKtMTJ2UAWW0+h4Yb2isy+9Lk5Xxz3XcIu2nm27bqlWx1YAKQJIm43W4uGAzC6/Vy2dnZYZmmmvCJucJWlVKcOfqByvwpk0J5PJ8yInv1yF8dDIKXwh1bPBizFz1mRP8BPzTDNOBUHAhPoBuug/M7Z7djnN87kTQ5CYQQaAu1kDwSgvYgOIEDp+h66VCRAocGSwhPIAdkUOno95rfaEb6or43IZnnHVJFiwas1qFzzxb/LGwXiq0AigG8ZKteefJePiSA/Pz84PXXX9+Yn58/Mi0trdJgMEiLFi1y9v3IviV0Yq6wVamSdCm3Ti6dP5sjXMi/i2ApzFo9+tfNfqJIyEUZJ5Lj41JVXjnw0++rslXw/OiB6BIh+2V0buxEsK37ry92iFBYjk5sUZgVEDu6hoEDbQHsuG0Htt+0HSnzUqAwK6Av0yPYGsTuu3cj+YxkOL93Qp2nhsJ84jUUVKb0QtmbHI7fNxGtaMlzLVI8rrUr0o4dWjwTwJ2xionpW0tLC79y5cqknTt3bmpsbNzo8Xi4J554Iiy13RM2MVfYqggBWTJz5JILVQr1oJfwKkx5GR+P+U27l1P5+z46cfAg3MiNxy/PVmeqkTIvBXX316HuwTpocjUgXP87aMpkJYr+UITi+4ph/9wO0SGC8AQ5y3NQeFchTONNaPugDSlzUnDwlYPY+9heOL/vuTNhbvc7Uzl60i1PlmQq39001XOz4Y96iVMe959PKb3NVr3yzFjExvTtnXfeMebm5vozMzNFlUpFzz77bPsXX3wRlvruCZuYAYypKp33q1RTZv9u9vWDwpCV/umYW51uTjOkLqvHb5V7nO1gmWZB4Z2FGPa7YeB0HJTW7uWCBbOAYPvRXnSwIwjB3P1+scKsgCpbBfeP3Yey21a3IakqCd5dXvAaHjk/y0Hrez2XiZ7u8px0xXxcQQQua78y8Kxpea+dCtJVRuBZW/XKk3Fud9yz2WyB9evX6zs7OzlZlrF69WpDaWnpoPf7AxI0MVfYqtLy00bcVpY7cVS421bo01PXjqv2OAW9J9xtx0pFM9fjjSTReXRYwvmtE0mTus8yNI4ywv65HZRSeHZ6wGt4KJIUCLYHIQe67pdKbgmeHz1QWVVHHie5JXTWdiJpclLXcYf6gjTQ88y9SyXPkNpFpi/73ArPPM+d+Fx/en+uEtIA/DXSMTEDN2PGDPeCBQs6Ro4cWTp8+PAyWZbJTTfdFJbiYYQm2FzeCluVQq823XFu1c9u0Ch1ESsNGfR2dExYf79gDjqGRPnJ664gzrZ0vlv9jN337IbkkkB4AuuFVuhH6NG+umsvV8sMS9d0uX8cROemTnAqDtlXZkOTr4FrswsHXz0IQggoPTRd7rSjQ2sH/+8gDKMN0JfqIQdk1D9SD7FDhGW6BclndB9K1tgD7m86GnVR+C+IC190JLmuUvxR6xEGvNXU0rp7578YkaASUG1tbV1lZWVC79RTW1ubUllZaevpZ1FJzISQOgCdACQAIqV0XKhtVdiqzl044coHMi35tvBE1zvR73SOXX8/kv3tCVkQ6FjPjJX2fjBLlRvrOH5q6l77wcclZ0as44iG55pLXHcabtODDPxClVLqIISMrLt3/t4IhJZwhnpijuZQxnRK6ahBJuXSstyJ10cjKQOAoDIavxtbzTWrUsO2l1esjN8Vn8uzLw56hvw+eEEJ0m+a53rvNP4+pKQMAIQQE4Dn2BS6k0Ncvll7UmGr0mhVhusmFJ0+IZrnFZQ6/YZxtyoPaDMHvZrnhfZ2LNizGwv37MbNBxrg72E36/86nThzz24s2LMbvzlw4Mj333I4MGf3LszZvQtvOboqYwZkGdfs24eFe3bjlY6j9YruaDyIH3zd70EU27mUeFuezbtFfxUnDulpcnY/8Z9v/7n4L+OlJ15h0z8zANwQhnaYOBetxEwBfEAI+Y4Qck2IbZw5rezsWSqFJurjkYJCo90y5te6fbrckAf2m4JBvGTvwL/ybHg7fxgkAP/p7D59rC4QwN/a2/Bybh7eyR+G6rQ0AIBdkvBEWytezbPhtTwbnmhrhUOSsNbjxhitBm/Z8vG2sytZb/P5IAEY8ZMaThpw6sId8bV7dlmHd0jXXt7RqfLM9t/LrddVqfo+un8opX+0Va8c0ApXJvFEKzFPoZSOATAXwPWEkKkDeXCFrSq/wFqxLDe1eHhkwusbL6jV28bcaNxjKAh5N1yJUvgohUgpfLKMNKH7wosVdjsuSjLDxHetTE4Wuqamfe524xStDkk8DxPP4xStDmvdbggg8Mpd7R3uCj/a2oobUrrXvDhs/CYprmaaLA64e+zBP/KVH+VPuFD2hAsPf3X8tPKXNwYx8kkXKp50oepZN2objy7atD3ciYonXRj1VxfGPX10s/BbP/Rh5JMuXPbm0ZmQL20M9Nh+OHzYluY6k3tM3aTMCesOJYQQDYAXbdUrE+Zqlxm4qDy5lNKGQ382A3gTQL+HIypsVQqloLp6cum8U2K9OxTPK1W7Rt1g2WEqPTjQx6YrFFhmsWDmrp2Ytmsn9ByPybrunf+6YAB1gQAurq/HBfV1+MzdlViaxCAyFEfnD1sFAU1iEFU6HQ4Eg7hgbz0uMZux2tWJUrXquIR/2Oh9cbR7tl8W58F/3Pzczc0S/rY+iG+u1qF2uQ7v/ihiZ3v3IZ98M8Enl+uw6To9bp+qxDXvdp92vmapFhuW6/HtNV2z8Bw+ivWNEjZep4eSBzY1SfAGKZ7bEMT148M7xC1TSh9pHuO+Wvew3s/rIvX+Gg/gigi1zcSBiCdmQoiOEGI4/HcAswBsHkATZ0wpPXO+VmWIi33gOF5Q1Fdel7bVMrJhII9zSBJWu1z4cFgBPi4ohJfKeNvR/UpeohT1wQCez83FAxmZuKOxEU6p9xIeAiG4PzMTb9jyMdtgxD86OrDMYsF9zU34VUMDVru6b2CS4+NSVe7jl2fHgq3d61CS4++EbW2RMTGLh1ZBIHAE0/IEvLG1+1LxqhwBZk3Xh/SkbAH7nSceOucIEJS6quV5ghQKHnjgiwB+MUEJBR++D3ufCPFnrUt8DxlvjvhwG5tV5yAAACAASURBVKX0D7bqlUNiKmciW7x4sc1isVQWFRWVHf7e3//+d3NhYWEZx3FjP/3005BWJUej7Gc6gDcP9XYFAP9HKX2vPw+ssFVZU41ZlxdmVJb1fXT0cBzPHyi/KkP84cV9Fa3f5vTnMV963MhSKGA5NDxxht6ADT4vFpqOrv1IFxQYqVFDQQiylUrkKZWoDwSQLijwjefoKESjKGKCtvvz/aq9AwuNRtR6fdBzPB7MTMOyfXsxQ3/0vcuDcCM3iS3rJinDtloyVAu9bl9PxVbL0zj8z2oJbR4ZGgXBf3aKGJfRe//h2e8DmFt49GVMCDDrHx4QAlw7VolrxiphUBHMKxIw+ik3ZuYLMKkIvm6QcPu0sA39otnL+S713cxtN4wKx02+PhFC0gH8DsBvo3G+eGerXhnWsp91987vs+wnAFxxxRWtv/zlL5uXLVuWf/h7o0aN8r7++us7r776aluo5494j5lSuptSWnnoq4xS+r/9eVyFrYoAOH9y6fxxHMfFXTlIwvFcc9nS7O/Tqvo1rzRDUKDW64VXlkEpxVceN4Ypu19GzzTose5QAu4QRdQHAshRKjFZp8MXHjcckgSHJOELj7vbMIhDkvCxy4WzjCZ4ZRkcuhbb+XuYoz5hW8/Ls6NKlOkS6utxmXFpKo9bJysx6yUP5rzkwah0DnwvNTzW7BHx7PdB3Hf60QS7dpkO66/V478Xa/H4ugA+re/6dW+ZrMKG5Xo8OFuN29f4cdd0FZ5ZH8CSf3nwh08HN8680aFzzxL/LGzXjIrq1D9K6Y226pW2aJ6T6W7u3Lmu1NTUbu+pMWPG+CorKwf1oornGwglBdaKuVZzbn7fh8YGIRxpL70od13G9D63qqrUaDDLYMB59XU4q24PZABLTEl4tLXlyJDDlEM3+M7csxuX79uHm1PTkMTzSOJ5LE9OxpL6Oiypr8N1yclI4o9+Vj3Z1oprk5PBEYIpOh2+83pwVt0eLDAevy6mt+XZ0ZTa7neYOPSaxK4co8R31+jx6TIdzBqC4uTjX6YbmyRc9Y4X/75Ag2Tt0Z9nGbv+nqbjcE6JgG8aug8FfX9QAqXA8GQO//ohiH8u1mJXh4wdbaFVfX2jJdd1zvGV4aKCEKIC8Kdon5eJvLjcwaTCViUAuGRi8RmjYx1LXwgh6Bx+Xt5Xgqpu0r73bCc69hcpqfhFSupx3zu2rVvT0nFrD48915SEc009D7NXpx2td6ziODyT0/sCP4vEJSU3ic62dCFmqxlP93jcAHq9Z9DslpGm47DXIeONrSK+uqr7kO1eh4xFr3nwj3M0KE4++gHlDlDIFDCoCNwBig92Sfj9T4Yrbl/jx9ML1AjKgHToniIHwDPAgq+STOV7Wqb6njVdF+s6H4tt1Sun1N07f22M42DCKC4TM4DxI22TJxu1lrRYB9JfnoIFtrW8um5K3Vu2WMfSl/EbJPt7s2OTmKlMcZnkMZ/oWu3cf3rR5um6Sff4PDWS1AR//bbrnuXycUrc9YkfbV6Kn63sWkQjcMC31+jR5KY457WuoSBRBi4qV2DOMePPb20LYlwmh0xD18lHWXlUPOnCyHQOldb+j5a5gwhc67hCXms6Y9DlZsPkIVv1ygl1986PqwVETOjiLjFX2KrUPCdcNHrY1MpYxzJQAdsZtk94Vd20Xa/ZYh3LiYzbBa5fd18jwGAPdGZz9ISzCT5bdvykhuXjjo58PLNQg2cWHn+PbZiZQ+3y3juwZ5cocHbJ0amED8xS44F+RX3UPrfguSR4m1CvL46fqYfAOACXAXgh1oEw4RGPY8zTxhScNkaj1MXF9LiBknKm2tYUXxbXO3AXO7gUIsVmeXZVp6ez76Pi01cdSa459C+qenVxPNb3+IOtemVYF7MwfVuwYEH+lClTSvbs2aNKT08f+dBDD6W8+OKLSenp6SM3bNigO+ecc4qmTJlSNNB246rHXGGr0nKEO2dEzvjSWMcyGDRzYt4aTrl3+tZncrg43IFbDaIu3CG37CjpeffsSLpE9GrjsjvQh+ebh7tqDLeHXIQoCrIBXISTtNfc3+lt4fbOO+/s6en7l1122aAKn8Xbq6xqpG1yiUapM8c6kMEi1tG5q8p/tj9ed+Aev0mM+vJsRWfQO5oTE+pKKChDuqV5rqfGeEc8J+XDbo51AEx4xM0rrcJWpQJwVnnepBGxjiVc+NSynNUjfxmXO3CP3k+ishDiWKPsiVW0yO4n/vM7fi7+03hpvNzk60u5rXrlvFgHwQxe3CRmABNKs8eV6NWmIbW/mWApyloz6sZmPxHiagfubB+XonbLUd149vygO55ebye0s1Plme3/Y1grw0XJb2IdADN4cfFGOTRv+exR+acOmd7ysYSk/IyPR/+m3csp46JOBdC1PLtyoxi1MqDEKwVnIpAQH7oftaW65nOPqZuUuYl4M+00W/XK8bEOghmcuEjMAEYNs5aXmnTJmbEOJFIUxuz0T8fc6nDz6rDsohsOE7bRqC3PLmr32oVYlwfsg0wpfbRptPsq3SORrAwXDYPqNRNCkgghKwgh2wghWwkhp4QrMKZ/Yv7iq7BVcQAWVdomF8Y6lkhT6K2pa8dWuzt5bVzURS5vJlG7EXeOf6Br66LLL0K8vnWJ70HTbxJ+Y1hK6SJb9cqCQTTxCID3KKUlACoBbA1PZEx/xTwxAyg0aMz5qaaswbyQEoZCm5r8xbjf+e0Ko6vvoyPLLHOm1INi5G/IBWX5HHjjdhVns5fzLey8Rf6v4Zyo3xCNBEIID+DXIT7WBGAqgGcBgFIaoJQm/J6XkdJT2c9wiId5zKeNyp9i4wgXDx8SUaHQmM1fj6t2jPvuAWdyILY7cI+vlRz/yRAiWtgoo81n1xFiieQ5QrXJoXVfyt2jsmuiX4Qokiill9uqV/627t75A/3gzQfQAuA5QkglgO8A/JJS6g57kOFUYwpr2U/UOEIu+xkOMU2GFbYqI4AJw6zlxbGMIxYUKpPpu3G3kmZVakffR0fO2F2IeEnVuV6Pt++jou/NlhzX2YonYlIZLtIObUF1fggPFQCMAfAkpXQ0ADeA6nDGNpT0VPYzHGLdSx1XlFGZpVHq4rI3FWmCUm/YMO4W1UFNxqB34A5VsTOyy7OpTOnFsjeunl9JpvR/m6e4bzTcp5c4ZVzfkByky0N4zH4A+ymlXx/69wp0JWomimKWmA/d9JtTljsxbustR4Og0Go3j71Zu1+bE5MdrFUgquLtcsi7f/clqcPfmcbRuBm7dQcRuLxtmf9vxp8l/E2+fjjFVr1yQFejlNJGAPsIIYc3Pp4J4IewR8acUCx7zAU6tSk7LSl7yM/G6AsvqDVbx9xkqNMPC3kH7sEYv1mK2FDDtE5v3BQt2u8WvPPdd+Azw6x4qgwXaZeH8JhfAHiZELIRwCgA94Q1IqZPsUzMU0fkjM/iSPxtGxULvKBU7Rx9g2WncfiAd+AerNH7EbEe7aWSJy42DP2qw+SaTf+irFMPj8fKcBFDKb0ohMdsoJSOo5SOpJSeTSmN6X2Qk1FMbnocqosxMT+tNOabgh62euMKfLHtPyAgyLTk45LTboFCOPoe3nlgI1Z8+TgOtO3GstNvw+hh0wAAPzZ8j9e/fPLIcU32vVg28zZU5k/B86vuwYH23SjPnYSFE68CALy3/iVkmG2ozJ9yXAwcr1DUjfpZqrjpmQMlHZuittgmy8+lalyy36vnwrr8WO0IeEo4KaazTgDgxeZi1x2G2/SUDLl7fH0ihOTZqleeUnfv/C9jHctQtGDBgvyvvvrK0NHRIaSnp4+srq4+cOONNw56WDJWr9QirUqvTdKnxsX4st3dgk82v4n/WfJ3KAUVnv3wLny3azUmDZ9z5BizIQ2XnnYLVtX+q9tji7NG47fnPQ0AcPucuPPVy1CaPQ4NbbugEJT43eJn8Oi7v4HX70JA9KOuaSvmjLmk11g4ThAaKq62ilte2F/e9l12ZH7jn5wThIzaKLZ+WRXe3bPHObwOADErABSUId3eMtv/qmlprLd/irULAAztxNzP6W3h1lvZz8GK1VDGhNLs8ZnxNHdZkiUERT8kWUJA9MGkTen282SDFVnJBTjRquLvd3+KETkToFSowXMCgmIAMpUhyRI4jsfKb5/H/HFL+4yFcDzXVH551oa0U/YN+hfrp/Hb5LBXwLtIdMes1oTdT/wXdPxMfNW0NFEqw0UMpXSxrXpl3LzXmL5FvcdcYatSAJiQnz4iKr3B/kjSpWJm5WLc/vKFUAoqlGSPQ2nOuAG3s37XGkwfeR4AwGrOg15twn2vL8eEotPR4mgApTJyUvt3k5wQjrSVXpzzLa+sH3fwk7wBBzNA5S1cWJdn827RfyoRU/o+Mvx2dqo8l8h3Khp1CVmEKOwIIRkATgXwSaxjYfonFkMZhSqFRmvRp8XFMAYAePyd2FT3Be686GVolXo8+9Gd+ObHDzGh+Ix+t+Fwt+FA+x6MyD5a2Ou8ydcf+ftf//s/uGDqjXhv/ctoaNuFkuyxmFw6/4RtEkLgHL4k72teVT9x/wcRTc5JMmdMOyA6mjPDswqwtMPrABD1Zdir2lJdP1Pfo/UrEroIUSTMBkvMCSMWL95xJVljMzmOj5s7Mdv2r0eywQqDJgk8L6Ay/1TsaRrY1M31uz/GSNsU8D38WhvrPkdOajH8QS9anQdw5Rm/x/e7P0Ug2L9Cc+7Cs/I+z1tYN6CAQjC+VgpbTYTzAp6o7txCKcVjzZXuKxO/MlyknB7rAJj+i+oL+FDd5VPy0kriqi6vRZ+GPc1bEQj6QCnF9ob1SDfnDqiN73auwbjC6cd9X5JErNn0Os6oPB9BKXBkA0BKZYhy/1dy+vNn2z4dtrhuQEEN0LjdYbqCCsjSAvii1lv2ixB/3nKe9wHjrSfDopGQUErH2qpXJtS2XiezaPcssgGokg1WW5TPe0K29FKMzp+K+95Yjnv+dRUopZhcOh/vrnsOG+u+AADUN2/DbS+dj+93f4pXPn0If/jnFUce39bZiA5XMwozK49r+9Mt/8bE4llQKtTIsgxDQPTjf/91FXJSiqBVDWyygJh7mu3joksitgN3oZNLJaI86J5ubpvXriTR2SCvxcf5znbeLK80nhs3qwvjEel6Po7vOTBxidAIvcl7UmGrOt2alHvd2ZOuWRS1kw5BtHH93ulbn43IDty/X0ibt5UpBtXbvX5Py4Hl8EZ8HvaRynBDsAhRhDxRd+/86/s+LP7V1tbWVVZWxqSMwbEWL15sW7VqlSk5OVncsWPHFgC49tprsz/44AOTQqGgeXl5/ldeeaUuJSXluFlPtbW1KZWVlbae2o3qC5pSOj7T4DO0tr5Vp9aU6rSaghSOE4ZyEZmIINYxuat5xb7TtjyVJYCGtWc6YbPs3TaYyrISpefL3pRIX4u91ZLtull3t07kVOz100+U0iE7zlzxQkVYy35uWrop5LKfs2fPdj722GP7FQoFrrvuuqzbb7/d+uSTTzYM5PxRS8xlWVlqENXcJLWqTM+LFgR2w+ujAZ+obYGQLao0I3RadX4KFz9Tm+Mal1qRs7riFw3TNz1mVUAO27L2UQ3QvjiIxye3+xxmDhEby5RkSu9rmex52vTzAS8aaf3Pw/DuWgdea0LmlU90teftROu/74PobIJgTEfK2dXg1cc33fHxc/DuWgcAMFVdAF3pVABA48u3QA50lRqRPQ4oM4qRtug2uLd/DsdnL4PT6JG66DbwGiOCHQdh//RFpJ51a8i//2AQQopt1Suz6+6dvz8mAQxBc+fOdW3fvr3bMv9FixY5D//9lFNOca9YscI80HajmAWlbFDP7jSD+siyX54jSp3Sm6XjduQJ/n+neNof8rU2PbW/re0/9R5vXXv0YktMQvLwrDWVNzYFiBC2erCZfi5F0ymFvC/h6W5PxAqqe4IIXtG+1P+06ech3eTTV5yOtMV3dvue86t/QW2rRNY1f4PaVgnnV/867nGeXesQaNyFjGWPwnrpn+H85k3I/q7dwawX/wmZyx5F5rJHocosgba4a3u8zu/egXXpn6EfNRfuH7pmqdk/+weSTu191WeUDNleczx6/vnnU+bMmTPgXYKi2T3Nt+g0WrVC6PVNJfBErVe6s3XctjzO94als/XPntbmp/e1tb1f7/U1sO1teiCYh2WuGX1zqz9MO3BzIGT0RimksTtKKS6VvBHpLTd4BO889x30E/2ckCvDqXPKwWu611Ty7PwauvKZAABd+Ux4dnx13OOCrXuhyikD4XhwSjUUqTZ4d3e/0pX9Hvjqa6EtOrRvKeFAJRE06AfhePj2bQavM0NhiXl5GJaYo+TWW2+18jxPly9fPuBOZjQTc2WuJWlAixcUPLR6hStHx23JI97Xkjpb/+xqbX5mX3v7R3t9/iZn3y2cHBTGHOvHo2+xe8K0A/f4baHNzNDbA648Tg77lLWvO0yuWXJkKsNJbjsEfVcdf15nhuQ+/vNfmZYP3571kIM+SB4H/Hs3QursXsLas+NLqPMqwam6VoCbJi1G86v/A+/Or6EbMQ2OL16DqeqCcIc/YJTS46tnMWH3l7/8Jfn9999PeuONN/aEMjwblTHmsiyrAKDIajIMaomugodewTv1wEbAsxFOJ5wBmmTnFcOIVltuVqlSTtpiNQpDRtpnY25tnbz+flkveQZVH6K8NbTl2ac4PU4AYX0O/tFc5Pq94faoVIYjpOdpLpr8MQgc3IHGl34DXmOCMqsEIN2H9d0/fAp95axjHjMamvzRAADX5lXQDBsHsb0B7d+8AU6th/n0a8Apol8W+lC1OUPdvfPjpk72ULNixQrjI488Yv3ss8+2GwyGkDo50eoxZwAQkjTqsF7HKQUY9Qp7rgbrc6jnRb2j5SF7a/Pzezvsn+0LBDo84TxXIlDo0lI+H/dbv0MwDGqc1yRzRmuDOOCho4tFb9gyTVCGVN08y3O78c6IJmVelwTR1XWlKbrawel6/kwyVZ2PzGWPIv2CPwAUUFiOzgaUPA4EDv4IbcH44x4nB31wbVoFw5j5sK99Gcnzb4IquwzuLR9H5Pfpp/JYnnwoWbBgQf6UKVNK9uzZo0pPTx/50EMPpdx00025brebnzFjRnFJScmIiy66aGCr1RC9WRm5BODUSkVEi9qoBJqkQnsSaDtk9zrYHVy7SC2dgrKQ1+krUhSCYcjvXKHQWMxfjat2TFh/v9McsIdcC3l8reR4J0vod89Z0Rn0jePEsOzt5wgQ/xWd1+E745SIV4bTFk6Ee/MqmCYthnvzKmgLJx53DJUlyH43eI0RgeY9CLbsgTr/piM/92z/HJrC8SDC8SMtzq/fgHHsAhBeABUDAAFACKjoj+Sv1ZcKDLEyoP2d3hZuPZX9TKR6zDlJWo2CIySqu5WoBdkCtFpAWyE6v6QukW+VkOoWVEUKna4sWSHowloYPl4o1EmmdWOrO0evv9+e6m8LaVhi7G4I7wzg+JF2rx2ANZRzHWtXp9JziVyjOKizhb0yXMvbf4J/7yZIXif2P74UpikXwzjpPLT++164Nn4AwZiGlLO6NoT2H9wB14b/InnuDYAsoenlriluRKlFypk349iNd9xbP4Vp0uLjzid2tiFw8EckTenaRMQwdgEaX7gJnFqH1EW3hfvXGwjWY45zUVn5V5ZlvbkwLXnS1OL8cyN+sn6ilFKfKLRKJN2tUBUpdbqyVIFXD6kykWLA7R65/kG/1dc04J5sADRw2c28ICv6d+fivl2NTfO4QPrAozxqdVuq6zr1PVpWhCjiPq67d35CL8+Ol5V/gxEPK/8yzDpNXBWYIYQQjUJKBQ6kQjqAgONjySkqm2SS7lOqh6t02pJUnlcl9H6EglKn2zT2Fk78/s+t2Z6GAQ0jKUGUw7dLTVvLuT6TLfFJ4iziT0OIK8QppXiieZT7ftOtJ+3N22iilLIec5yLeM+kLMuqAGA2qdVxsSlnbzhCeK0imK4X9ucpxVVWv/0x2t78WGNry+t1na4tTZIcjGoZy3DhFWrN1jG/NtTrbc0DfeyEzXK/pt8VtHs7hBNt7XICfgniL1rO9d5vYpXhooUQkmKrXjnoYacYk2VZTtjl+Idi7zWnROOS0QyAalWKsNwYihaOI4JWEbDqhXqbIvh+uq/jL1Jb8+MHWlvfqnO5t7fIshi96k+DxAsq1Y7Rv0raZSxqHMjjRjX0b7++s32ekFYetvo43yLHr6V3jeexynDRl+i95s0tLS2mREzOsiyTlpYWE4DNvR0TjaEMCwCqUSRWYv4pniMKHefPBHYDgd3w+KjfL2lbwSdGnQ+OVyj3jPp5irTx6QPF9i39qvyWEeBStE7J6zHyvSdOUZbPhW/A9bU3O7TuS7k/qDq0VlYZLjYqAHwU6yBCJYriVY2Njc80NjaWI3Z7l4ZKBrBZFMWrejsgWomZUyuEARfyiGcCR1QC580CdgD+HfB4qM8n6VuJkCNptCMMWo0t7j6IOE4Q9o68Nj24+bn9Ze3f97nnIgdCRtdKbZ+fyvd6rLXN59ATDOi5/XdrtuvXWlYZLsZssQ5gMMaOHdsMYGGs44iUaCRmq0GtUvIcF/bltPFE4Ilaz7uzgW2Abxs63fD4ZX0b4fNkra7cpFFnxcXuERzH840VV2SKP7y0r7Ll65y+jp+wXZY/P7X3n8/2eDxA/xKzJFP6p9bJnqeMA68Mx4TdoGbQMJEVjcSck6LXDsn5wiei4KFV8C4tsAXwboHTBVdANrZzgg1aXUWSWpUe8uKPwSKE41pHXJK9fruqfkzjpyfc5LWsjes16VKZ4hLZa+nPhaQniODPHJdJHxvnspt8cYBSGvWNcpn+i0ZizrTotBFfwRXvlDz0yjiq80EIRzqGL8n7hlfVT2j4sNfkbJQ5Q8Y+0X4w5/hVgKYOv9PK0T4/YA54BO/FgWp+j37EkF95mTgo6zHHsYgm5rIsKw8gxagZ4OZ2JwGlAKMSdiOwHtSzHg4nsQep2ckrC4hOW56sVJoj/mFGCIGr6Oy8L3hVXdXed229HTe+VnK83UNiPrXT6wJwwsS8rsPoWqb4o8alNif0nPChh7AecxyL9N3MJADQKhQxu2xPFCqBJukV7bkaui5Hcv1da295uL21+cV6u/3L/UGxMyzlPHvjGzbX9ln+eXW9/XzsHvS4IvIyyXPCYYmXmgtdS9SP6V0CS8pxyGKrXplosxlOGpEeykgCQAWeO+nGmAeDEPKTOh9fdKvzodeVpwiCNqw3U4N5020f86r603a+fNywRoGLS+WDsiQpjhaIUDkCnhGc1GN9bVGG/PuWM3z/Z1rGrpTi1KFds1MADHjhERN5fSZmQshwAK8d861hAH5PKX24H+0rAYAb4jMyIq1r+bicAjSlQG5C0PkZ7RSFlnDX+ZCzq/JW88q9p217rtsO3EoQRck2qWlLxdHl2WMcXgdw/AIUZ4D4r+hcjm9Np5709xUSQDpYYo5LfSZmSul2AKMAgHRVh2sA8GY/21cCIEN9qly0RbTOR8a43NW8ct/0LU9l88ck5wmbZd+WiqOHXRT0CD8dCNvtUnouliJTGY6JCDbOHKcGOpQxE8AuSml9P4/vSsyExCwxNztdeOmr74/8u83lwezyYkwtzu92zGvrarG/w4m55cU4raTgyM+2HWzGvzf8AJlSTMzPwYzSQgDAy199j0ZHJ0oz0jBvZAkA4KMfdsBqMqA8K7plCA7X+QD2A+J++O0fiT5J1SiTDJ9KU6LRaotTeU7R7/FELm1kzmruF/unb34sUwDlAKDyAI6MJ3MeMTCVBFKPLVq0pi3ZtVx9r9avYpXhEghLzHFqoIn5AgCvDOD4Qz1mErMeVJpRj5tmda2QkGWKu99dhfKs7jOFNEoFzhpdhi0N3UtJyDLFm+u34JppE2HSqPHIR2sxIjMdMqVQ8Dx+PXsqnvrka3gDQQQlCfVtdpw+oihqv1tvOI4IWi5gBeqBYD18He8FfZK6kXJZAbWmVKfVFKRwnHDCVXd8Skn26sobD5y28ZE0JZUEa4BL1jkkj9vEa0vavXaOdN3Vp5TiyZZK95+M1Ww8OfGw4aY41e/eDenq9S4EcPz+7r3rGmMm8TGUsaO5Fck6LSy67q9Hg1qFXEsSONL9v2Nvux3Jei2S9VoIPIdRuZnYcqAJPEcQlCTIlEKSZXCE4P3NP2J2eXE0f51+4zmi0Cn8mXp+t00IrEz1dDwSaGt+sqGt7Z16l2dXq9zL3quCuSBzzahft3qJIHMgZEyt1AEA5wY8EtBVGe6G1kXePxmr2aKRxMRmy8SpgfSY5wJYTyltGsBjtABkQuKjyMiGvQcwKrdf9XsAAA6vD0nao/V7kjRq1LfbkW40QK9S4uEP12JMXhZaXW5QUGSbB7QJeMwMpM6HwpRnXT3qJsfU2of1o7a7VZ9NkqWF1Jfe6ud9Sz03ki2GsawyXOJiBaTi1ECemAsxsGGMw+3LJNQK6mEkSjK2HGg6Mh48WGeNLjvy92c/W4fzxlXgox924IC9E8XpKZhUMOD9F2OmtzoflGQr1ZoSi8E0zLRm1K8CI7c8os1pczp2ubSqy8j/qtpYZbhEx56/ONWvJ4YQogNwBoBrB9g+d6iBmPeYtzU2I9tsgkHd/ynVJo0ado/3yL/tXh9Mmu6rijc3NCLbbEJAFNHm8uCyqjF4+pOvMSYvC0ohMa8Uj9b52AYEt8HeLIsev1L+LLeS5m2vpeckL0NA4XAAjliHyoSCUg6EiJxKF4x1KEzP+pWYKaVuAMkhtE8A0HjoMQ90GAMAciwmtLrcaHN5YNKosWHvAVw8afSRn0uyjM9+3IMrT52AVpcbh/fwoIfGnhN6CE8WgiqS7NPzaZzFaBWMGrNylWKVvFt1inQR/5z/A7NK4UzljYSL+VPLhE418L4WEw2RvpQ5nJli+u71iyJ+bGrFuWOPTsT9YmfXPvqREgAAIABJREFUjL+qwjw4vT488tHn8AVFEAJ8tqMOv5kzFWqFAueMKcffPv0GlFKMz8+G1XR0h6zPd9ZjnC0bSoFHhsmAgCjjgfc/RYk1FRpl4kzlJVD49Vxah1mRGTCrrKpUjdVg1pi1HOmaTbPVXSf+R7WRZNm+5bdpbtW4N+Xj9fY/0jYn8d+vNLjXp+l00PBsdWfiCWnnGSbyIrpLdlmW9XwAMy6vGnMFx3FsPCsOcFTp0/FpHRZlZtCstKpTtVZDkjpJw/WyZd+7zi87G5I79YRvkE6d8omwzP20pFzzo99KAop/KmtotkZWBiil/+C0B180GmmbWZlJQtz/j4m66zYt3fTXWAfBHC/SyZIDAJlC5NiNhqjjqMKr5612szIzaFFa1anadINZY9YAyOjrsW7JF3zJuyrIp6kNPHhk5X0d4DgIolILzmYmBxoNisXBP3r/5fmdL1srqa+k3swrHV7stHOu+1XG1i+TdRaq5lnxqvjGesxxKtLJUgRAgrLkFniO1eKNII4qPQbBajcrMkWzyqpJ06YbktRJGgADns72o3ef+0PlRqUqRa0FAI7b4RuW59MCAFWqeH9FsUZbtzV4UJ2jWSTd73nHd0tHulo0A0AhkfVPBex68UAHfZloDjxvMtKWJFUG+ekkcSYesMQcpyKdmDsACEFJ9mgUId08ZHrAQ+UxcFZ7kjJTsqis6jRtusGkNmkRhpVc7zi/6DyQ7NKrePWR4YjConUeAOpOUSmRQxXm/GahSelFdjNv1c4TH8RK381tVnXwyHMsEEKWwpe51OHDng7O/YDa0LI2WW+W1XxiTPY+OXhiHQDTs0gn5k4ACIqSO8LnGbJ4qnYbBKvD3JWENamadINJbQxLEj6WW/IGX/KtCvJpGgN/zGwSQdjsTk+XLADgDGqDUHT9UBqVY6Gft8mE47g2IVU7R3qIW+m9qSVLEzhux+x8TtY9HnDopAN2vEY0B581GqUmsyqT9aJjrrHvQ5hYiHRi9gCgAVFkibkfeKp1GYV0h1mZKVtUVk2qNt1gVBl0ACK65Hmbt961SrVZpUrWHJfsi4dvCBw+v0vWHbn0JUatVsTe/QoYsgHAzlvUc6SHybuem5rztL4ei+PwhOAi+DIucvqw10E8D6iMLZ9Y9EmyhvWiY4Ql5jgV6cTsBgA/6zEfR4C20yhYnUmKDNmiytCmadMNBpVeDyCqxYDecq7tbEr2dBu6OEypWu9KTqZHNmN1SppuY5JSgYlX7DlaZ6OTT1LNkx5JettzY2OB1nPCEnu5hGr/EnDkSQfteJ2oG582GsVGszqDcCQmk78lt4SG5xrg2+8DIQRZV2ZBW3j0c4pSioMvH4RrowtESZB9VTY0Ng289V4cePEAZK8McEDagjSYJnZ9zuz76z749vtgGGWA9byu/47mt5uhzlLDODYu7ouyxBynopGYqS94cveYBeg6jbzVYVZmUIu6KwnrlToDAEOfD46QTtETfNm/Oij8ZOjiMEollJRskY793rE9ZgCghekZ8o97HJxCfaTH6+YNyvnyX1L+7f7VweE6V5+zP3hCsAR+6xJnC/Y7iOdBpaFlTbLBKGn4XnfnjoSD/3cQ+go9cn+eC1mUQf3dp5G6NroQaAqg6L4ieHd1JeOC3xeAU3HIvjobKqsKwY4gdtXsgr5cj2B7EJySQ9EfirDn/j2QPBLkgAzvLi/SFsa+2iaVqW/zss3OWMfB9CwaiZnzBoMnTWJWQN9pFNIdSYpMJKsztGnaNIMuxkn4p37w1rnWqLb0OHRxmEb7jdNkQrchhs6fJGbCcQgmyR0qd/fjfJxWWCD/Je1N168ayvTOrP7GlU2o9qGgMw+NTrwOVePTBmOwwaLOIByJ6OtU8khwb3cj66quUDnh+Mmdzu+dSJqcBEIItIVaSB4JQXsQKuvRdTUKswKCUYDYKQI8IAdkUJmCihTggOY3mpF2TuyTMgCAoiXWITC9i8YYM/EEAkPy7q8SBqdBsDrNykwkq6zaNG2aQavUxlUS/qk3nWs7W3oZujiM0iAtLd153M/d0B03vUquzP7/9s47PKoybeP3e2bO9EmbtEkCCRAIkWIB/STY1rLLsrZVcV3L2rCtu7rrum53+6669goorq6oFGUXlBrpBAid0DKEQHqfZPrMqe/3xwSSACGTZFrg/K4rF/HMOe95Eif3eeZ5n5JON7dJhGF6uN08o1PdQt6wLnL/ou5ic3tOf+28HVzm7e5WNDqJ/xWNuf4biylBMqgj4kXzrTzUZjXqP6hHoDYAfZ4e1nusYLRde5Nihwg2pauak01mg8eSuo75jvlARQpNugaEIVCb1aj8YyWSipLAN/OglEKfFx/N+PrZJVIhykRUmA/WN8njsjM9Ho4f0uW6lFJoSaLTrM50pWiyiEWXYUw3ZJj1rD4BQFwEC/vCLfqEedxagU3Xm5k+eniYzFucJlNwwnl3vNR4WuNmYtYbJHhr1TAPO/U1kWiZO9SvZX3ufrb2MnPraa+HgpWh+pdFVy6aXVgCTfMsUyJXm6LLIqowetEy4K/2w3qvFYZRBjR+2ojWr1uRcXtG39d2IjgE1M2pQ87MHJzoH2K9pyuSU/1aNbIeyELL0hYEagMwjTMh5ZqU3paLOASkMWY3V+iTaFTjOd1+LqrxwsFAKYWOSXSYVVZ3ssZKLLrMThHWJQIYktkDB/3HPeu1h84aujgBpQF6QWG15kztTbwwnbGjvpifyKqPnbnZvkRY5gfqV3I+cT1Xc0VC06B6od4CPuMWTyua3STwKmuuX51iMolG9aDz49XJarDJLAyjgr+ehMkJaF3Weto5QntXMzahQ4A6OfjnI/klVL9WjYzbM3psGJ7AtdsFXZ4OMieDb+Ux/MnhqHq5CklTknp45VGFQX1sbqwQCtEQZoc7wJkopTTeeihQSqmOJDkTWKs7mbUyFl2mMd2YYdaptUnA6R7jUGSxa5O7LTVg0jK9hy66k5S0yaHTkzM+SH29pE7TURmZsu24g2F1Z/ydUaIm97IvD/+369fV30qoyw3Z+F7IIFT3oujKfbHFheXQtL5jSvBXp+itRDWwEWZsEgvWwoJr5KC1auE55IEuq2ehasJFCbCvsSPx/xLhr/RDpVeBTWIhizJq3qxBUlESEi89/blNRQr7ajtyf54LrpnrOn4i9hyjz5KEIYowxzHREOYOCowQZdnPqlQxmzEWFOFkRyJrdSdrrKqgJ5xu1p5DItwdl+jlP+XXScHQRWheGaVeeWxho763ZoD+Xqq7CcNASJYdWs9Zfo+EwYPsC7mznL+vnpZYNWhxPsF08GnTPW1oc4N7lTVXr0wxm4QBeNHWe6yonV0bjBGnaZAzMwfta9sBACnXpsB0oQnuMjeOPHckmInxcDBs7trugveIF5JHgmOzAwCQPTMb+tzg78q+xo6kqUHPWDdMB8pTVPy+AuaJZqiMMW0Lq6TKxTER7S4HAOOyM28D8N0ZkydMM+u0/d4EGhAUVMskdySqrZ5kjVWVqss0pRvSTRq1Zgg3SA6dA75jng36w1qtXtcvDzLFsqJ93Li2XgOfz7X/pqbeMvmM4Qjq9vt0m9u0p24Cnok3uT9W3ZxYkdcf2/rDaqppfcuU4D9u0WcSVewmtMc51++/f/+aWBuhcGai4TG3AFB7AlxzRISZQtYxKY7uIpxmTDdrVGwKgNjtrsSIL10b3fZULuTQxQkodUoFBa2ms7XO5oi+1xeJWW8Q4a1lz7AJeCpPaf+cF3D8rerOpEN5/bExVL5N+LRve9vQ7gb/usZcvSzZZORNbGok7jWE2R9rAxR6JxrC3AxAdvgDzdakQSYwUMg6ktKRxGZ5kzVWtUWfaUozpJnOVxHujlP08J/x6/sVuuhORuYGh1pNzhoC4BndWReWRidp2ErpbKec5Dnd7/MCjpeqfpS0Ny90K/tHCgPNX0R37l9a3VjTwra9ZUrwHU0xZBL1+e1FU4k6Djx0oCXWdij0TrSEmbR5fP3Km6SUyAYmpT2RzfKlaKxqiy4owqxKbcHAxlyds5T5Kj2b9DadNkU/IMGhtFXIz3ck9jVoRmB0Zw9T5GdmyOVHOxhWH1IWzvO65/I4x2tVjyTtyAvZ2AFyHRFSr/Pa4fDYhTfVppqlyWY9Z2ZPa7h0PkAleri/1xBCPgRwI4AWSun4zmP/AnATAB5AJYAHKaWOcNp6vhINYXYD8NZ3OPlez6BE0jOpHUms1ZessbLBcESqSc2olY+fZ0GmMr50b3J3pPImLaMdcMZLds4ml0p1dm8ZAERG0+f7RUiGU+tByOmRf9f9PM/veLf6p4mbcqORtJNEwD4veYY/3+bBhlbW/oYxwXPEos8kamZI59r3k30DuOYjAG8D+E+3Y8UAfkMpFQkhLwL4DYBfDd48hYgL88H6JjouO7PKxwvDOVF0alUak4GxtCexWf5kjZVN1WeaUg0WRYT7iUPw8J8J6yRNumFAoYsuGoQRIzzJoYxllBhtn+8X+cLsDLrp9ErAs/Gq7se5nFNT/WzimqiI8wmuJoLlap/d4vRCeEdtqv1vslkbMLNxUjMdOQhLdvT3GkrpRkJI3inHVnf7z20A7hicZQoniNa4pwoVYYtGaq9zXJ5zaYKaUZ2XHyHDxV7fUfcWwxG9xmwYdKx0eG6Ji2H69pYBQFaxfWZ5EJNeL5LQNgG7845uZi7n1NT8NnHFMCbK+e6JBOxvJc+w37Z5sKVV3f6aMdF92KLPIOpzc+oOIWRnBJZ9CMCCCKx7XhIVYU5gLW6W0Y1w+nyymlHFVZHJUEKmMha5N7qdqYJJM4jQxQkIqeKGD/dbQh1iTlWsJpQzpfwkNtRNwO58oPvRcM7J1v45cWk2Q2LTRL+IiClFPnuKx0vFd1Wmmi+TzVpfgib02uw4h8qUIww5GM41CSG/Q3BM1afhXPd8JipvfhObdFCr0u1t8zk3RuN+oVBpr8F3/v3Qya/C16bhgx0Le5xz1F6NWz55AqNevg6zSj/v8dq6Y6W4+v17cMXsH+KdbfNOHv/pV3/BDR8+gBc2zDl57I0tH2PlkU2DstchuLlZ/mV+d7pkZhgmLA+3kaO2eUJ1TkWZgKjVoeVF52dmyoK/YyA2faL74bBfOe9okGR65hrvKGEiRP2c7B1eam/KmHusoWNck6caguyPpU3hgAr00P779/f/qdkLhJAHENwUvIdGuijiPCJaoYw2ANyuhgPuH118a1yUZo+yDMeqBz8EAEiyhEvfvR3TxlzV45wkXQL+fP1TWFWxucdxSZbw++LX8NkPXoXVnIYbP34UN+RfAUmWoFNrUfzQR7h7/jNwcR74hQD2NhzG00X3D9jWPd4K91ZjhV5jNoRtg4pR2fxZWULI2S0eUSdAhZALVsQUODXu0DcBu7NId3sO79LUv5rwWaYqRo3zu3MZEZPn+9uTfTV2aZbaVLswycx6EzVnHQQQx2wL10KEkGkAngNwNaX0nOwgGSui4jEXV5TIAGxuzqtzBtxx1wd2c/Uu5CZlISex599aqjEZF1kLwZ6yj7W38TDykrKRm5QFjYrFzYXXYXXFZqgZFQIiB5nKEGQRKsLglU0f4pkrHhyQXTKVscC1zr0jucqk0fW98dYfRo/e1S/vzyUZ+jVRWbowJ4PK0oCnMC/R3ZT9E9f9zaIcP5OcDQxRPSN7h21rb8r86Fh9x4SgFz2kBIloyIA+uhFCPgewFUABIaSOEPIwglkaZgDFhJC9hJBZYTT1vCaacbwDAExNnra6KN4zJJYeXotbCq8L+fwmdxuyEro2763mNDR5WjE6NQ8WfRK++9FMXJ9fhKqOeshUxoTMgn7b1C66uFmBZQF3uhy20MUJ1Ooyb3q61K+CHJd0ei/ms0GMOr1IfINqLblCNy3rMffDbYIEoe+zo8skIiV/5m/P3V5Tq51Z3VZndvJN8f5JvrOR2IDKsCmlP6SUWimlLKU0h1I6l1KaTykdRim9qPPr8XDbfL4SrVAGANQCoMfba2vGpo28JIr3PSu8JKD4aAl+ffWjYVnvT9c/dfL7B7/4Nf75nWfx5pb/4HBLJa7Mm4y7L7qpzzV2+Y54Sg1HdRqTISL/fwrGlvVb6Nz99JgBQBqTrGErBufwrtFel/mQR9s81/RukkYVq15svaNniOpp2ZfzdLsP++wq58v6hI69FmM6NEzMGnb1BuWo7cBjSsXfUCCaHnMdALL++PYKmcpx41qsO7YN4zNGI80YugOZaU5Fg6vr/d3obkWmqWcG4KqKTZiQOQY+wY9qRwPeu/XPWGZbD78Q6HVdicr0c9da966k6rCHLk6g1e1wp6TQfnfTO3XeX0iMzMgY6CZgdzZpr8i4z/uUk5PA9X127LiQSImfBDryttfV6h6vaqtLcHCN8eRFU4kui7UNCqERNWEurijxAqhs8do1rd72mmjdty+WHFqDWwqv79c1F1rHoqqjDjWOBvCSgKWH1+CG/KknXxckEXN3foEn/u9uBETuZDKaTGXw0pmdVbvoDMwKLOO86dTMMJH53xIcsFo+IKXwwDCgnXzBgrAM/CzVXJ5+l/cXbr+IuM+M0BPCPEl9OSUdzdYFxxtckxrc1eDlmM+9ZPSMkmc8RIh2rugmAAnlrcf6XasfCXy8H5uqduK7BV3ZGJ/sWYJP9iwBALR47Lj0ndvx/o6FeGvrJ7j0ndvh5rxQM2r89Yaf4d6Fz+JbH9yHG8d+CwVpI06u8fHuxbhj/DToWR0K00bBL3K4fu79mJA5Bom608cB7vCWuxewW9TaZENECxoMhm3OhISBjcLyUeOA4rzyxMFtAnZnj2ZS6gzfb3w+EUNmw20ckRI+4jpyd9bV6p+saq1P6uAaYpFWJouykzARKSxRiAAR78fcnRtGT00D8OKI5Bzn89f+5GdRu3GcIlGZLnCv83hTZTOJkJd8Akp5OmnyfK/RSEwDuX6O/fbqDal3D6jBvWpleQ1LzIMaK9Wdsfyhji/0f2dNLB3QzxJrDsuM62V9QvsOizGValVR+Rkkr/TfQz8+dFs07qUweKLqMRdXlLQCqD/eUSe3+xzn9TDINsEZmMUt43zpiLgoA0BCwhbnQEUZALwwDvgJLo1JCeumXbnmguRbAn+SXDxx93WuI0Bxx0Ifxr7tQeE7Hmyt7em8f1omYOJ7Hkx4z4OiuV7sa+oZsZFkiotne3DjZ11O+j2LfZj4nge/XdO1X/C3jRz+Vx7ah4pCRk6YyznydtbXGZ+uaq1P7uDqI+1FEzX5IpLrK4SXWJS9bgaQfKStKi7CGbFgu/ewe6Fmi1qbFNnQxQkoDdCxhTWD6qvR27y/kBiZniELvvbB3P9UKtnRiTdxf6cOnnGe7bynVwYwLV+N8p+YsO9xIwrTeuakj0gm2PCAEfufMOEPV2nw6Nc9Q9hvlPIoTO36MylrlqBXE5Q9YcKOBgnOAEWjW0ZpvYRbx/Zv5KCGEDKT+rM3Opqz/3e83julwVVNOKnPh01/oZTKjJZRNv6GELEQ5gMAUFKzuzwG944pEpXpPNc3nn0p9WaNtu8WmuEiKXljh05HBpW+FUDv00tCQbAwYRecajYv4Xv8P5l2TnXGHsDOAMXGahEPXxwUTI2KIOmUwS5Fw9RI7vzRLs9Ro87V5bjWuWQsqxAx85KuZxrLAH6RQqYUggSoGOD5dRz+fM3gPhTkE9k0h3Pk7q6vM/3ieEuDpT1QT8NUli4H5L37799/1geYQnwRC2FuANB+oPmI1xXw2GNw/5jQKjj8s7hlXCCdmKJZkU6pWx47tsk42HX8xDAoo+WJ2WHbBOxOvXqY+bvCi2xrQH1aWt5xh4w0A8GDSwK4eLYHM5f64eV7jxjM3cPju/ldz8ufrQzgpet16F7eU5imQpqBwSWzvbhpjBpH22XIFLjEGp7KcTUh5AEEstY7W7KXHq/3X1HvqiIBaXCZLRRfhcU4hagRdWEuriihCIYzUo62V58X4YxtnkPuLzRbNdEKXXQnNW2jQ6Mhg47x8kQ/qPcKMep0IuNrGKwdZ6JZnWX8rvSytjnA9njQizKwu1HGE5NZ7HnMBCNL8MLmM6dCrzsuYu4eAS9eH/xVfX1EQLqRYFLW6YL7+jQd9j5uwi+KtPjDOg5/vVaLv2/kcOciH97f1fs8iP4ykpGN7/GOvD0NdQm/Ot7SmNYeqBuIF81oGaXr2xAjJq0VEZygwGyvLTunwxkilegnrm88+y0NZlYb/QndFB3imDH20/PzBkBf8/5CQRqTErEHU5sq3TBNfNXQ4Ne0nTiWk0CQk0DwfzlBL/iOC9TY3XS6rpU1S5j5lR9L7tLDYgj+mCU1EpbaROS97sZdX/ix9riIexf3jD8vKRcwycrAw1NUdshYOMOALw4L8Anh3cdTEYJ7EbCudbbkLKuqC1xd56xiAlJIoQnJJx0+8NCBirAapBBxYiXM1QA82+vK2nyCPywFCPFGs9ARmM0t57gohy66k5m50alWk/7tSPWCwGgH/2AZkZ4uCb6Iha861Bb9NPl1c61f2wIAmSYGwxIZ2NqCmRZrjou4ILXnW77GKeO2BT588n09xli6fsR/Xq9D3TNmVP3MjPl36HHtCDXm3aY/+bogUbxeyuO5qVr4ha6O1pIM8GFrqnk6uYQa3hacebsb6hJ/V9XcmGEP1FGZ9npHmZM/iJw1CpEiJsLc2W1uCwW1HGuvPee85i2eg+7F2m1sLEIXXbQI+fnOfpde94YYwlipkNZJZTzhWKc3XKok7TT5zaRjPn0zALz1XR3uWezHxPc82Nsk47dXajFrJ49ZO4Mhh79s4GD3U/x4WQAXzfJg8pzQzHtnB4/7L2RhYAkmZjDwiRQT3vNgklV12gZjJFARgrsoZ/3G1ZKzoqqOu67OWc34pR6boFSiPKNj5vS2hkL8EtUCk+7cMHpqAYDnLrIW8k8X3X9OdKUSqUQ/c6/1cmmx85JPkJ3zpX3kSF/YponfF/jIKevNiYNdh/o4TrehmSGMKiyefG/oZK/wFf1Z22ij1xrJ+8QbX0DbPMecwDck67Ilr7Tq8E8PT4+1TQr9J1ahDCA47ty9t/Gwu8Vjj5veGQOlmW8PzOaX83w6E3NRBur4vDxvv9p69kUo8/5CgRi0WpEZXDvQUAgwRvZG8kbaIU9CRDYc45U7wGWsdrcOW1lVJ9zlcL0ca3sUBkbMhLm4okQEsBxA6paa3aWxsiMclHgOuBfrtrPaxPBNGBkMeSO2uBkmvE8HqmIHPfj1BFKBRd/3WYOHYwzqW5g3Mso8SfXRuF88oeFk5x//UL821nYoDIxYeswAUApAXmbbcMQnBMJegBBpRCrKH7tXew5Zmsysho35CCQAIMyxQE5OIGwhDADgZYYSlSp8BTF5aWmR3ATsjsBoVd9XvWHd6bbE3YCGSOLn6NuxtkFh4MRUmIsrSpwASkRZTNvfZBtSna8aebt/Fr9CENJUcRC66GLUqO3ecNvjEgxhnyAipqoiugnYHYmwzJ3q17K3ujJqo3XPWMJLlEvWk9dibYfCwIm1xwwA6wBolh5es0uSpQgmGoWPTZ4y1/90OzS6OAldnEClOuy3WkMfsBoq/Z33FwryhdmZVO6lOXUEkIma3M3+K2ejK2vI72f0RZuPfpXwT1fUHnwK4WdAwkwI+ZAQ0kIIOdDtWAohpJgQUtH5b6gTkqsBVDW4WzQV9up9A7EnWohUlD9yr/aUW1oS4iV00Z0xY3ZHpIm8JwLCTPTR2QTsDiVq8iP2peHFruHV0bxvNJEppZKMv8XaDoXBMaB0OULIVQA8AP5DKR3feewlAO2U0hcIIb8GkEwp/VUo690weurFAJ4amzrS/dxVj/yExFNsoJMGvs2/GFsZXUJPL7mtrQ1ffNHVUbGjowPf+ta3cPnll/c4Z8mSJWhsbMS1116LoqKik68dPXoUK1euhCzLuOSSS3DFFVcAABYvXozm5maMGTMG110XHBS7ceNGpKenY+zYsafZx7J7PZdP2R+R3r4lznHt7yb9JaxZHgCA6tZWXTmf1veJ4ecd7g/V30usHFB/6XimziVvyXnVPbXvMxXimQF5zJTSjQBObeN4C4CPO7//GMCt/ViyDEBbedsxvtpRH3f9MzZ49rmX6HdqThVlAEhNTcXjjz+Oxx9/HI8++ihYlj1NOPV6PaZNm4YpU6b0OC7LMpYvX4577rkHTz75JA4cOIDW1lY0NzdDrVbjiSeeQENDAwKBANxuN+rr688oypTKGFt4IOxe7Qnccvg9ZgBAblqaJPja+j4x/Dyp/Wvul46xVbG4d6SQKaV1LvmZWNuhMHjCGWPOoJSe+GjaBCAj1AuLK0okAP8FYFlm27A5jDYNCkEW5X+7V3krUtvMLNt36OL48eNISUlBUlLPgjuj0Yjs7GyoVD2XqK+vR0pKCpKTk6FSqTBu3DiUl5eDYRiIoghKKSRJAsMwWLduHa655poz3len3+lOSur/gNVQ8VJTxERfTFPFbBbeL3TP533qmFgVq/uHm1on3Xj5B94hnXqqECQim3+d0xj6GyPZBcCzs36/o9HdejwCZvWLer7VP1tcIUhp6pBbZh44cADjx48P+R5utxsJCV0j+BISEuB2u5GWlgaDwYDZs2djzJgxaG9vB6UUVuvpRWyyLKKw0BbR8k0PNUZsU1aemG2lshi+lmz95He6X+d96JhUFav7hwtJpnKDW34q1nYohIdwCnMzIcQKAJ3/tvTn4uKKEg7AEgBpK49s3BBGu/rNevde91f6XWcMXfSGJEmw2Wy44IILwmLDtGnT8Pjjj6OoqAjr1q3Dtddei40bN2LRokXYtWvXyfNM5q1Os3lgA1ZDxQdDWBq2nwmi12pElT+mY8b+ovtF3ruOKVWxtGGwVDtp8ZS53rJY26EQHsI5RWMpgPsBvND575IBrLEVwB0bq3Y4rh01pTw3Kev0gGoE4WVBnucA7QEtAAAf90lEQVRd45PTWLMa/atArqiogNVqhckU+v6b2WyGy9XVXM/lcsFs7tmls7y8HFarFTzPo6OjAzNmzMC8efMwceJEqNSUFhYeV3f1NosMg5n3FwrS2FQje7irT7Ic8MC+4k3wbcHMttTpT0ObXdjjmkBNGdrXvA9IEhhDAjLvfgGCvQ6tS188eY7oaELSFfci4dJb0LH+3/Af2wVN+gik3vgLAIDn4DrIPhcSLr0FL+l+msc5NNU/S1yfG4d7z2dFlKnU6JafGhlrQxTCxkDT5T5HUEQLCCF1hJCHERTkGwghFQCu7/zvflFcUeIFsAhA5id7/rdKjMDEi96o41t8c8SVgpzGDiizob9hDADIzs6G3W5HR0cHJEnCwYMHUVBQcPJ1SZJQWlqKqVOnQhC6Un5lWYYkSUhM3Ow0GMigp5P0hX8w8/5CYXhqqiR4T24Ctq+ZA93ISch+ZBayHnoLrGVYj9PlgAftq99D+u1/QNbMd5F2y68BAKwlB1kPvoWsB9+C9f7XQVgtDGOmQOa84JsqkfXQ2yAqFnxrFWSBg3d/McyXfO/kum/oHst9wfntGjlWnb0GSLWDfj31Q++RWNuhED4G5DFTSn/Yy0vXDcKWE2wEcENle41uT8OhkktzJlwdhjXPylr3bteRpFaTjjUM6EHF8zyOHTuGG2+88eSxnTuDhYyTJ0+Gx+PBnDlzwHEcCCHYtm0bnnzySWi1WkyfPh3z5s0DpRQXXXQR0tPTT66xY8cOXHjhhWBZFhkZGRBFEe+99x7y8/Oh1cpyYWGdNtLeMjD4sVKhIKarfaoOQOa8CNQehGX6zwEARMWCnNKIzntoA/RjiqBOCP6uVMbT9z0D1fvAJlmhTkyHzPlA5eBmqixwIIwKru2LYb7kJpBTKs1n6x4czjk1tc8nfp3DDAHXmZeoWO+WfzYq1oYohJWYtf08GzeMnnoBgF8ZWH3dS9Oee9Ko0Q+63eSZ4GRBmuddE6BpbMS9znCTnLyyffyE1vDnFp+BZ9qfr2m2XDg8kvegfo7XrWuC0Fajsa96C6xlOPiW49Bm5iP5ukfBaLpaW7d/MwdUliC0VYPyfpgn3wzT+J4+Qdvy16HJGIWESTcBAJylX8B7aAN0uRci4bLb0L7yLaTf8cde7bkrsKju7wmLs1QMiYfq2F6psEsLRr/luSvWdiiEl3h90x0GsMMn+FOLj25eHYkb1HLNvvelVdJQFGVKXVLB2Jao2R2OsVJ9QfRajaj2N1JZAt9UCfPF05H14JsgrBaubYt6nkwl8E1HkX7Hn5B+51/g3DIfQnt9t5cF+I9uh3HsFSePJf7fHch68C2kXDsTzk3zkHjFPXDvW4XW/70Ax5b5p9kzXzcj51nXXY3SWaaDxJqASLl6N1Xyls9B4lKYOwe2LgSgXnJ4TUWTu7UqnOt/497lXmbYq9OZ9WFrZRlN0tI3Olh28ANWQ4VndFEpP5cKU41qcypU5lRos4KxdkPBVPDNlT3OU5lToR9xCRiNDipDIrQ548G3dGVY+o/tgiZjFFTG07sC8M2VoJSCTcmBr3wz0m79NcSOph7CfoL/6m7Jftp1X7MoI2p7Hf3hQIv8zjUfec+rftPnC3EpzABQXFHSgmCmh/Xzsq9XyFQedMyFkwVprnuFtyrNYVaz6rj92c8GpXZx9Oj2iKbHnYoYjnl/oTAsNRVaTas6IRWCPdilM1C9D2xqzyiKIf9ycPUHQWUJshAA32gDa8k5+br30AYYC6864y0cm+Yh6cp7AVkETgycJgRUPPP07K9107OecD/UKsiIWsOlUGhwy3WzdvK/jbUdCpEh3sVpNQBXWZPNd6ilcsdgFqrimrzB0IVmyIUuumPN2hS2AauhEq55fyHdK0PtT7n+cbR9/TIaPvwJ+JbjSJhyJ9x7lsO9ZzkAgE0dBt2ISWj88Cdo+s8zME38DjRpeQAAmQ8gULUXhoKi09b2HdkKTWY+1GYLGJ0JmvSRaJj7JKjEQ5Pee7JZsfZ660z3Y+28hJgVwnRHkqm8s0F67IPd/JmfJgpDnrjc/OvODaOnTgLwlMWQ1Pj3G575qVat6XfuVrFrp7syxW5Uq4eml9xFkzD1itUqJsobUvdy/3FTndHc95mDh3K8oFvTKBOVOq5aqgLAFG5Ly0emtxO1KsTUtrJmadnE9zw39n2mwlAlap7QINgD4LDd5xi+sWrH2hvyp4b8hgzIvDTPuyaAdI1ZPSR+1LMzbNhml88nW155uQ1VVTwIAZ59Ng0XjOvKWPB4ZLzwzxa0tIiQJIoZdyZh2rSgps6ZbUdpqQ+UApdM0uPJJy0QBOD555vQ1irippsTcMstwQSYV19txU03JmD0GC1omOb9hQLRalhR7a9mqTnuOr9t1Ral3+PRtM0zvkp1asRkAnqHnzo3VYsPTYzFzRWiRtx7kMUVJTKATwHo55ct22v3dYS02XE80Oj9QF4tYYiHLk5ASA2Xm+dLeedtOy69VI9/fzQMs+fkYHhuT81cusSJ3FwWc97PwSuvZmH2LDsEgeLgwQAOHgxgzvs5eP+DHNjKOezbF8DOnT6MH6/DnPdz8E1xsLd6ZSUHWQJGjwk6hlSljuomqTguLSLtS8PBTu3k1Dt9v/L6RfiifW+ZUlpSKz735PJAv9odKAw94l6YAaC4oqQWwGqZylkf7Fj0pSiLZ92IWeXa7lplKtPrTEMz6+JM5I3Y6vF6Kdm/P4DvTg96wCxLYDKdsi9HAJ+fglIKv1+G2cxApQqWofA8hShSCAKFJFEkJ6ugVhFwAQpR7Oo69dG/O/DAg8GMBq/IyoRhGNnjhuNPz6Lt/u+j7YHbwB/sOdNA9rjR8dunYZ95J9oevB3+FV0V+e7Zr6PtwdvR9sBtcL31IiiloDyPjl89ibaH7oBvycKT57pe+StEb4tFErytYf8lhokyzYWW23y/57wCiWpnvLJmeeOcXcIH/bnmTEMtFOKfISHMnSwF0FHedoyuObp1xZlOCMic9L5nubc23Z0w9OPJXTDM0UBODm9pahKQmKjCv15qxWOP1eGVl1vh9/fsL3TrrYmoqebxgztr8MjMOvz4yVQwDMEF43S46CI97pxRgzvvrMbkyQbk5mowabIeTc0CfvrTenz/+4nYssWL/NEapKYGQz8uMTjvz/32S9BcWoTUj/8Ly/sLoM7tuVnmX7IQ6ryRsHywECmvvQ/3rFdBBQH8gb0QDuyF5YOFsMxdBNF2EMK+XeB2bAE7/iJYPliIQPHXAACh0gYqS2DHFELMZCMyjSVcHNZckHxr4HnBLZCojHBq91PHyqPiPUttQn8bSn0EYFoETFKIIENGvDr7aLwHIGH+/mUHjnfUHez++rFAg+8DuVgiqdpzInTRnfz8HT4AkCSgooLDTTcnYPbsHOh0BPPnO3qcu3OHD6PyNViwcDhmz8nB22+1weuVUV8voLqGx/wFw7FgQS727PFjf5kfKhXB736Xgdmzc3D11UYs/tKJGTOS8N67dvz5T83YtNknyx43+LLd0E//PgCAsCwY0yl7gQSgPm/QG/b7wZgTEXTVCSjPA6IACDyoKIJJTgFRq0G5ALq76p4P34XpwR8DAOQJOVYq9ZLDFidUsAVJNwf+Kjt5xtX32QNHppRuqhZ/9etvAqcnW/dBL0MtFOKcISPMAFBcUVIJ4AsA2W9vnfe1l/c7AWCFu9RVbDqgO5dCFydQqQ74MjLFFABIS1MhLU2NwsLgvtNVVxlRUdFTu1aucuPKK4wghCA7m0Vmphq1tTw2b/bigkId9HoGej2Dyy4z4NChntcuXeLCDTeYcfhQAEYTg9//IR2r/tvMSk0NYBKT4Xrpj7A/ehecL/8Z1N/TodXfehfEmuNom/Ft2B+eAfNPfgnCMNCMuxCaiyaj9Y4b0Drj29BeWgR17khoJl8OuakB7T/5EfS3/RCBkvVgRxdClRrsf0G0LCuy/qaI/WLDxHF2ZML3uH+QDo5xRuoe2+ulFXP3CO9Han2F+GNICXMnKwGUt/sdSXN3f7FhjnuZtz7Nk6BSq4biz9InBWP3nVTPlBQ10tKCQgsAu/f4kZvb81mUnq7G7j1B0exoF1FbK8BqZZGersa+sgAkKRhnLivzY/jwro1Dt1vCtm0+3PBtEwIcBUMAQgCOp4AkQqwoh+HmGbDMmQ+i08P7+Yc97svv2AJ2VAFSF61Gyvvz4XrzBcheD8T6Gog1x5G6cBVSF64Cv2c7+LLdICo1En//T1jmzIfu6uvh+/IzGO68D+53X4bjT88iULIe4gXxuwnYnTp2uHm68KLKzqk6wr32Ebt0/LWt/H1LbUJ857UqhJUhJ2adY6g+8INPXtlQkl/ZXnPOtjvUaHZ7LBa5R13xT35qwT//0YJHZtah8iiPu+9OwldfufDVV8FP0/fem4xDBznMnFmLX/6yEY88koLERBWuusqILKsaj8ysw6OP1GHkSC2mFHVFfT75pAN335MEhiG49FI99u8P4JGZdRh9xSg/k5YBJi0dbOEEAIDuqushVJT3sNW/cim0V14LQgjU2cOhysyGVFMFbtM6sBdMAKM3gNEboLlsKoRDPfu5+5csgv7bN0I4tB/EaEbiH16Eb9EnINkWiyR4h0QGQqM62zRN/Je2JaC2h2tNu092zisTZiw4KCihiPOMIZncW1xR0nbhmPHvchAf27l7V2Vqepo+OTk5qk31I01wwOrB0xro5Odr8e57OT2O3XRTV4V2aqoaL750+ggqlYrg58/0PpD6xz9OPfm9RsOcXGNB+2TXkZRUsyo9E2JNFdTD88Dv3n7a5p8qPRP87u3QTLwEUrsdUm0VVFnZUDXVw79sMejdIkAphH27Ybj97pPXyW4XuG0bkfTiu+C2bsAJV51ywQ8KopUNqGIyrrX/tKoyDdPEV8iywLNtVp2Q2vcVvcOJVFhwUHzmLxu4XX2frXCuMeQ85hPsO3JgAwi+BDB8w4YN/+M47pzyKvSG7a7ERESk3Wl/8HbO+zP/9Fdw/uO3sM+8E2KlDcZ7HoZv6SL4lgY7vxnvewTCwX2wPzwDHc8+BtOjT4NJTIb2quuhyhoG+8N3wv7ID6AeNRraoq4W297/zIHxnpkgDAPtpUUQyvbA/vAM6G8INrCXx+dkxfsmYHfa1Wn6adJrpjq/dsDpfpRSfHVEfH/lUfHfg7Wnl6EWCnFO3Jdkn42CggI1gGcA5GdlZYlXXnnlTIZhotpHIhLIVMSkSZ+7TSZEpQz6bLxlv7tqW+rtebG0QVVcXs3K8VcJeDZMkov/mvy8I8/gD3la/AlKasSNL5bw31lqEwKRsE0h/hmyHjMA2Gw2EcAcAL6GhgauvLz861jbFA7M5i2OeBBlAPBFeqxUCIjj0ofEJmB3PKoEzXS8nnLUa+hXZsnRdrn6vZ38nYoon98MaWEGAJvN5gDwNoCkffv22aqqqtbG2qbBQClHCwur4sbrj/i8vxAgWSlDZhOwOz7GzN5I3kwt95pDaiPQ7qeu/+zj75xXJjRH2jaF+GbICzMA2Gy2owA+BJC9devWbfX19VtibdNASUza5NTrIz9gNVT8RB8Xc+8Eq2ZIepABxqC+mbyRsd+TeNbiEF6i4sKDwi//soHbHi3bFOKXc0KYAcBms21CsNnRsI0bN65tbm7eHWub+gulXrmwsCEmXct6gyMDG1AbbuiEnCwqDc2P9zyjU31f9bp1tzul7kyvizKVPt8vvLu8QuxXHwyFc5e4+KMLI6sB/BdA7rp165bZ7faDfV0QT1hSNzo0GhJXwsyTyM/7CwWiUatFTWDIfsQXiZa5Q/16dqk7rbb7cZlS+vl+YdGXh8VfD6APhsI5Slz80YULm81GAfwPwEpKae6aNWv+63A4KmJtVyhQ6pQKClrjbpNLiNZYqRAQx6XHxYboQJGJmtylfiVnk8taAwTT4r48JH696JD42FKbENdNmxSiyzklzMBJcV4AYKMkScPWrFmzyO12V8farr7IyNzoUKtJ3PX6iNq8vxAg1pQUSfAMWa8ZAChRk/vYfw0vduQ0LKsQ135SJjyw1CZEtAmSwtDjnBNmALDZbBKAjwHs5Hk+65tvvvnM643nacJtYn5+R8yLSc6EqIrevL9QELK0Q6bYpFcIg+carj0+Z5dw91KbUm6tcDrnpDADJ3Oc3wdwMBAIZKxdu3ae3++Py+brWdmbHCoViSsBPEE0x0qFAh0/dDcBT3Jk7X7D3k/vX2pT0uIUzsw5K8wAYLPZOADvADju8XiS169f/wnHcY6+rosuDfyIEe6UWFvRG1TFxlV4hWjUakHDDVlB81XuKGnZv/l7JUfaK2Nti0L8ck4LMwDYbDY/gDcANDscDuOGDRs+DgQCYesANliG55a4oz31OlRkGv15f6EgjU9P6Pus+MNXuX2zZ++Ku3wVpbV9n61wPhOXghBubDabG8ArADrsdruhuLh4rsfjOWNOaTQhpIobPtxvibUdveEWdSIhcVFf0gOSmZwsiUNnE5BSWfbaStZ59q78oa+iNObvO4X457wQZuBk6fYLAGo8Hk/aqlWr/tPR0VHe13WRZMTIbZ54FL4TuCXDWYfexhIhS8vH2oZQoCLPuXd9vcR7YM19iigrhMp5I8wAYLPZnABeBrCH5/mcVatWLWpqatoRC1sYxubPzhbi1lsGALdoOK0fdLzQuQkY17m/kt/tdGyZ/0Ggeu+jvorSfs/rUzh/Oa+EGQBsNlsAwaGuxZTSvHXr1hUfO3asmEa5/+noMbviWlQAwCUbxVjb0BuEVasELRe3jY0ER1OjY8PHfxdaq37pqygdIq3+FeKF806YgZOpdJ8CmA9gWGlp6Z69e/d+LklSVHJk1ex+X3q6FLeZGCfwyIa4FWYAkMZlxGXuN9dgO+JY/++nJW/7q76K0rh/ACvEH+elMAPBCkGbzbYcwJsALOXl5fZNmzbN5Tgu7AM1T6WgYN+QKJLwUGPcxpgBgGQmJcXTJiClMvXatmxzbl3wAJWEL3wVpXEbClKIb85bYT6BzWbbCeCvAKTGxkZ21apV77tcruORup9Gu9OTkkKT+z4z9ng6x0rFM2K2Li42AanI8+7dy77yHvjmPl9F6VZfRenQHQ2kEHPOe2EGAJvNVg3gzwCqvF5vxooVKz5taGjYGu6wM6USCgsPD5kOYl7EvzDL47JjvgkoBTwux5b5cwNVe2b6KkqPxtIWhXODc1KYCSFPE0IOEEIOEkJ+Fso1nel0LwNYL8ty7oYNGzbt2LHj43BWChqMpc6EBAyZ4ggfNca91xfrTUC++dixjnUf/k1orXrWV1EalyX/CkOPc06YCSHjATwC4DIAFwK4kRCSH8q1NpuNR7D50bsAEisrK7lly5a919zcPOgR8pTytLDwaNx0aguFeJj3FwrShOhvAlKRD7j3rdrg2Dzvl7LP8aqvotQXbRsUzl3OOWEGUAiglFLqo5SKADYAuC3Uizs3BbcB+B0AG8dxOWvXrv1m165d83ieH3B7RnPCFqfRSOKu3/LZiId5f6FA0pOSRNHTr6Gng0FwNNW3r5v7sf9o6aO+itLFyiafQrg5F4X5AIArCSEWQogBwHQAw/q7iM1mswN4HcEOdSlHjhzxLl++/N3W1tZ9/V2L0gAtLKyJu54TfREg+iHz/pBydBHPIKGSKHhtm7d3rHn/T5Kr9Re+itIjkb6nwvlJXLaaHAyU0sOEkBcRHDPlBbAXwIA8GpvNJgPYVFBQYAPwgN/vH/fNN9+sKCwsPDRu3LibWJYNyQNOStrk0OnIkMjE6A4XJ4NYQ0G+IDuL1tf5iIqNiJsvOBrr3DuXrhOdza8B2KtkXShEEhLlgreoQwj5B4A6Sum7g1mnoKBABeBqAHcDEEwmk6uoqGi6xWIZf7brZNkjTylaLGg0RDuY+8eCx53/anAnjcyKtR2hwqy1VWkEU14416QiH/CWb97ps23+DMB8X0VpxPPcFRTOSWEmhKRTSlsIIcMR9Jwvp5SGJbuioKAgE8DDAMYAqB83blx+YWHhdJZljWc635K6vP2CC+xxX+V3Jh52v9UcSMjKiLUdoUJbnQ7dLndSuBpD8a1Vle5dX62TvB3vANineMkK0eJcFeZNACwABADPUErXhHP9Tu/5OgA/AMBrtdqOyZMnF2VnZxepVKqTEz8o7ZCKpn4lq9UkrqaAhMoD3jl2wWSJ60ZLp6JeZmtUq03WwawhOptrPQfWlPFNRxcB+NJXUeoJk3kKCiFxTgpztCgoKMhCUJwvAuBMTEzkJ02a9K309PSLCSEkI3OJfcwY15AStu7cF/i3U9YnxGU/it4g+2rqtE2qnIFcK3k6mjyH1u/javdvBfC5srmnECsUYR4kBQUFBEABgrHnXACtmZkpwyZMyP72zbfsNqtUZEjlLnfnHuFTHzS6oZEz1wkVJVm7qibAqDUh2y353XafbXOZv3JHKYKNrfb7KkoHVKFJCPkQwI0AWiil4zuPzQDwJwRTOS+jlO4cyNoK5w+KMIeJzvDGJQDu1uudUzVab+PFF2tarrnGdE16ujqkApd44255gUBU6iEXhmHW2qo1gim3r/Nk3u/yVWzb57Nt3gVKFwDY6asoHVRHPULIVQA8AP7TTZgLAcgAZgN4VhFmhb5QhDnMFBQUaA1G+/e1Wv+VhMAAoGXiRF3KUBNoUWbo/apFQyZdrju01enU7XIn9rYJKAucL3B8937PofW7IQmLAJT4KkrD1gyJEJIH4OsTwtzt+HoowqwQAudcHnOs6ZzMPX9KkfF/AKYC+H5ZWUBdVhb48sILdalXXGG8IiNDPYZh4nimFACXqBOhwpDzlgGApCUmSlLTaZuAMu93BmoPVHgPrS+jvH8xgHVKKbVCPKIIc4TYusUbALBmSpFxC4ICfeu+fQHVvn2BFVaretmVVxovzs/XXmwwMEkxNvWMuCSDCAxNYQYAaZheUjcGvxddrdX+yh1H/cd3VYHSlQBW+ypKB1xer6AQaRRhjjBbt3j9AL6ZUmQsQTAG/Z3GRjFn4ULnUQBbL7/cMPzii/WXWK3qsQxD4qYE2i0aBQD6WNsxUOT8tJTA7i17/LYtVYK9rgbAcgClvopSZ6xtU1DoC0WYo0SnQJd0etDDEfSir9q2zSdu2+b7JjVVteyqq4wXjRmjvcRkUsU8xS7ex0qdCUop5NbmKm79OknYtKWCcPx6ACsBHBzspp6CQjRRNv9iyJQiow7ARADfBjAKwZ4eLZdcos+cPFk/KTubvUClIjF5eK7umNzyccpv0mNx7/4i+7wOofzAEa5kfbXc1NAKiZYQj7Ddf3Br1JvWE0I+B3ANgFQAzQD+CKAdwFsA0gA4AOyllH4n2rYpDB0UYY4DphQZCYAsAFMAXItgCMGTmMh4L7vMMGbUKM3ojAw2n2WJLlo2LW6/uuFLy1Nx2SeDSpIkt7dVi8eO1gn7d9vF40cdAPYBWAfgcGDLhrieVaig0BeKMMcZU4qMGgDjESz5HguAAOAZBh0XXaTPKCzUjs7OZsckJKgi6s1+0j69dqXl4X63S40UstfTLtXXHhcO72/ly3a1IBCQABwDsBXA7sCWDUpzIYVzBkWY45gpRUYTgHwAFwOYDOCEx+xKT1dLEybo8nJz2byMDHaE0ciEtVHSrPYZ1Zssd/VZpBEpqCjycmtzlVB5pF4o22WX6mvdCBZubAdQBuBYYMsGpYeFwjmJIsxDhClFRhWCDf8LAUwCMKLzJQrAZbWqMWGCLi8zk7UmJanSzGYmVacjvRZZ9MWr9vurdqXenBcO2/uCyrJMvR673NHeJLU02sVjFV7hUFkrBEEGUA5gB4AjABoDWzYob1iFcx5FmIcoU4qMegR7c4xGMA0vF8GyXwZBsfYZjQyfl8easrLYtLQ0dWpSkiotIYFJNRiYlL5S8/5mf6LqcOr1eeG0mcqSRH2+Dup22WVnh11qbnKL1ZUe8dhRDwReBKACYEfQKz6AoFccCKcNCgpDAUWYzxGmFBkNANIRzAbIApCHoFgnIyjUQDBe7VerEcjL0+hzclhLSooqWadj9Dod0Ws0jE6lZhIEwqbOkR6rOmaZmgOVigWjUoNh1N29byqJAgSRo6IQgCBwVBA4KvAB8BxHeZ6jPBeggQBH/T5RdrQLUl1NQGqsC0CWpU47AKABwNHOr2YATUp4QkFBEeZznilFRi2CYp0KIAPBEEhu5/dAl2gDAAlAl1yFvJF+mHYhmL4nn/zSaAhhtRoa8FFIkgpd3jk9ZR10vkYAuAHUA6jt/LJ3frUr2RMKCmdGEebzlClFRjUAE4IbivoTXyJUpiqMzHYj0QVA0/m6tvN7NQA/grMU3Z3/cgACp/x78vvAlg1KYYeCQj9RhFlBQUEhzoib3gwKXRBChhFC1hFCDhFCDhJCno61TQoKCtFD8ZjjEEKIFYCVUrqbEGIGsAvArZTSQzE2TUFBIQooHnMcQiltpJTu7vzeDeAwgOzYWhV5CCE/7/yEcIAQ8jkh0StBV1CIJxRhjnM6p2FcDKA0tpZEFkJINoCnAEzunPyhAnBXbK1SUIgNijDHMYQQE4AvAfyMUno+NHZXA9ATQtQADAjmOSsonHcowhynEEJYBEX5U0rp4ljbE2kopfUAXgZQA6ARgJNSujq2VikoxAZFmOMQEiyxmwvgMKX01VjbEw0IIckAbkGwACYLgJEQcm9srVJQiA2KMMcnUwHcB+BaQsjezq/psTYqwlwP4DiltJVSKgBYDKAoxjYpKMQEZbRUHEIp3YyufhLnCzUALieEGBCsLrwOwM7YmqSgEBsUj1khLqCUlgL4AsBuAPsRfG/OialRCgoxQikwUVBQUIgzFI9ZQUFBIc5QhFlBQUEhzlCEWUFBQSHOUIRZQUFBIc5QhFlBQUEhzlCEWUFBQSHOUIRZQUFBIc5QhFlBQUEhzvh/TaSySmjITmoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBlJI56T-b3Z",
        "colab_type": "text"
      },
      "source": [
        "# Reading Images with ImageDataGenerator with basic Augmentation\n",
        "\n",
        "The next step is to read images corresponding to respective datasets for pan prediction in training and validation data. \n",
        "We can perform various kinds of image augmentations while importing the images like rotation, zoom, horizontal and vertical flip, height-shift, width-shift, adjust brightness, rescaling etc.\n",
        "\n",
        "As we have a data with labels as directions/angles, its hard to perform many of the augmentation operations like flip, rotation etc because it will change the direction and orientation of image which then is out of synch with labels associated with them.\n",
        "\n",
        "Hence we are doing some basic Augmentation operations first to check the performance of the model on pan prediction. If required, we can change those Augmentations to improve performance of model in model tuning phase.\n",
        "\n",
        "we shift the Height range by 0.1 just to fit the whole image in (32,32) format. then we are rescaling the data by using basic normalization (dividing by 255) and then by choosing data_format as 'channels_last' we are converting image into format [batch_size,image Height, image Width, No of channels]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsW19Hxwc39t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_pan_train_generator():\n",
        "  train_datagen = ImageDataGenerator(rescale=1./255,data_format='channels_last')\n",
        "  train_generator = train_datagen.flow_from_dataframe(\n",
        "          dataframe=X_train,\n",
        "          directory='/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/modified_data/modified_data/',\n",
        "          x_col=\"filename\",\n",
        "          y_col=\"pan\",\n",
        "          target_size=(32, 32),\n",
        "          batch_size=32,\n",
        "          color_mode=\"grayscale\",\n",
        "          class_mode='categorical',\n",
        "          shuffle = False)\n",
        "  return train_generator\n",
        "\n",
        "\n",
        "def make_pan_val_generator():\n",
        "  val_datagen = ImageDataGenerator(rescale=1./255, data_format='channels_last')\n",
        "  val_generator = val_datagen.flow_from_dataframe(\n",
        "          dataframe=X_val,\n",
        "          directory='/content/drive/My Drive/ColabNotebooks/DeepLearningLabs/Assignment1/Assign1/A1_data/modified_data/modified_data/',\n",
        "          x_col=\"filename\",\n",
        "          y_col=\"pan\",\n",
        "          target_size=(32, 32),\n",
        "          batch_size=32,\n",
        "          color_mode=\"grayscale\",\n",
        "          class_mode='categorical',\n",
        "          shuffle= False)\n",
        "  return val_generator\n",
        "\n",
        "train_dataset = tf.data.Dataset.from_generator(make_pan_train_generator,output_types=(tf.float32, tf.float32), output_shapes=([32,32,32,1], [32,13]))\n",
        "validation_dataset = tf.data.Dataset.from_generator(make_pan_val_generator,output_types=(tf.float32, tf.float32), output_shapes=([32,32,32,1], [32,13]))"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFBqPhBH_GKF",
        "colab_type": "text"
      },
      "source": [
        "We can see some sample images from the created datasets to check if the augmentation and importing part is done correctly.\n",
        "We can see from the output that,  the images are imported and augmented correctly and are in grayscale format as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntDq4Ef8dvPr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "3321a581-91e4-4d5b-fda2-3a3397cd96e1"
      },
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "i=1\n",
        "for image,label in train_dataset.take(6):\n",
        "  plt.subplot(2,3,i)\n",
        "  plt.imshow(image[0,:,:,0])\n",
        "  plt.title(label[0,:].numpy())\n",
        "  i=i+1"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1860 validated image filenames belonging to 13 classes.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/text.py:1165: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if s != self._text:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGf-GPMD_j5i",
        "colab_type": "text"
      },
      "source": [
        "# Build and Compilation of pan prediction model\n",
        "\n",
        "As stated above we are using power of partially built model for tilt prediction for pan prediction as well. That means wee are using same model till convolutional layers for pan prediction and hence we structured our pan model using functions stated above.\n",
        "\n",
        "We are using same error and performance metrics as that in tilt prediction which are CategoricalCrossentropy and Accuracy respectively.\n",
        "\n",
        "In the Pan model we are first build a model same as that of tilt but it gave us a lower performance and hence we came up with increasing some depth in the model by adding an extra dense layer to make it step-up and then step-down kind of model.\n",
        "\n",
        "Hence we are using 3 Dense layers in the architechture of pan model. Further, we have chosen 'Adam()' optimizer which works good with our non-linear activation function 'Relu' and which is an advanced version of Stochastic Gradient Descent algorithm used for the loss optimization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyGlhYR7dvcD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_histories = {}\n",
        "\n",
        "def get_callbacks(name):\n",
        "  return [\n",
        "    tf.keras.callbacks.TensorBoard(logdir/name),\n",
        "  ]\n",
        "\n",
        "model_pan = pan_model()  \n",
        "model_pan.compile(optimizer = tf.keras.optimizers.Adam(),\n",
        "              loss=tf.losses.CategoricalCrossentropy(),\n",
        "              metrics=[tf.losses.CategoricalCrossentropy(name='CategoricalCrossentropy'), 'accuracy'])"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5APfEQoxBXdv",
        "colab_type": "text"
      },
      "source": [
        "We can see the network architechture more closely with the help of model summary. It will give us brief undersatanding of the output shape after each layer of a model and number of trainable parameters after each layer. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuxJNbbvdvmd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "outputId": "1b3366d4-accd-4dad-9372-72ff4f8af8ab"
      },
      "source": [
        "model_pan.summary()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 30, 30, 64)        640       \n",
            "_________________________________________________________________\n",
            "activation_34 (Activation)   (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 15, 15, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 13, 13, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_35 (Activation)   (None, 13, 13, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 11, 11, 256)       295168    \n",
            "_________________________________________________________________\n",
            "activation_36 (Activation)   (None, 11, 11, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 5, 5, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 128)               819328    \n",
            "_________________________________________________________________\n",
            "activation_37 (Activation)   (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "activation_38 (Activation)   (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 13)                845       \n",
            "_________________________________________________________________\n",
            "activation_39 (Activation)   (None, 13)                0         \n",
            "=================================================================\n",
            "Total params: 1,198,093\n",
            "Trainable params: 1,198,093\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_1lUK3XBzMR",
        "colab_type": "text"
      },
      "source": [
        "After compiling the model we can train our model for 100 epochs and store it to see the learning curves for our model. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t06Zkno2dvxs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1ec132dc-15b2-4c9c-921e-bf04eba2b29a"
      },
      "source": [
        "train_generator = make_pan_train_generator()\n",
        "val_generator = make_pan_val_generator()\n",
        "m_histories['Pan_Pred'] = model_pan.fit_generator(train_generator, \n",
        "                                                    epochs=100, \n",
        "                                                    validation_data=val_generator, \n",
        "                                                    verbose=2, \n",
        "                                                    callbacks=get_callbacks('models/Pan_Pred')\n",
        "                                                    )"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1860 validated image filenames belonging to 13 classes.\n",
            "Found 465 validated image filenames belonging to 13 classes.\n",
            "Epoch 1/100\n",
            "59/59 - 4s - loss: 2.6869 - CategoricalCrossentropy: 2.5700 - accuracy: 0.0763 - val_loss: 2.6159 - val_CategoricalCrossentropy: 2.5650 - val_accuracy: 0.1097\n",
            "Epoch 2/100\n",
            "59/59 - 4s - loss: 2.5982 - CategoricalCrossentropy: 2.5648 - accuracy: 0.0801 - val_loss: 2.5859 - val_CategoricalCrossentropy: 2.5652 - val_accuracy: 0.1097\n",
            "Epoch 3/100\n",
            "59/59 - 4s - loss: 2.5794 - CategoricalCrossentropy: 2.5649 - accuracy: 0.0909 - val_loss: 2.5738 - val_CategoricalCrossentropy: 2.5642 - val_accuracy: 0.1097\n",
            "Epoch 4/100\n",
            "59/59 - 4s - loss: 2.5710 - CategoricalCrossentropy: 2.5641 - accuracy: 0.0935 - val_loss: 2.5685 - val_CategoricalCrossentropy: 2.5637 - val_accuracy: 0.1097\n",
            "Epoch 5/100\n",
            "59/59 - 4s - loss: 2.5684 - CategoricalCrossentropy: 2.5645 - accuracy: 0.0946 - val_loss: 2.5658 - val_CategoricalCrossentropy: 2.5630 - val_accuracy: 0.1097\n",
            "Epoch 6/100\n",
            "59/59 - 4s - loss: 2.5368 - CategoricalCrossentropy: 2.5329 - accuracy: 0.1011 - val_loss: 2.4970 - val_CategoricalCrossentropy: 2.4918 - val_accuracy: 0.1419\n",
            "Epoch 7/100\n",
            "59/59 - 4s - loss: 2.3130 - CategoricalCrossentropy: 2.3022 - accuracy: 0.1586 - val_loss: 1.9011 - val_CategoricalCrossentropy: 1.9011 - val_accuracy: 0.2731\n",
            "Epoch 8/100\n",
            "59/59 - 4s - loss: 1.9780 - CategoricalCrossentropy: 1.9633 - accuracy: 0.2328 - val_loss: 1.7157 - val_CategoricalCrossentropy: 1.7093 - val_accuracy: 0.3355\n",
            "Epoch 9/100\n",
            "59/59 - 4s - loss: 1.8368 - CategoricalCrossentropy: 1.8297 - accuracy: 0.2613 - val_loss: 1.6877 - val_CategoricalCrossentropy: 1.6793 - val_accuracy: 0.3032\n",
            "Epoch 10/100\n",
            "59/59 - 4s - loss: 1.7304 - CategoricalCrossentropy: 1.7233 - accuracy: 0.2962 - val_loss: 1.7157 - val_CategoricalCrossentropy: 1.7021 - val_accuracy: 0.2882\n",
            "Epoch 11/100\n",
            "59/59 - 4s - loss: 1.6858 - CategoricalCrossentropy: 1.6652 - accuracy: 0.3022 - val_loss: 1.5486 - val_CategoricalCrossentropy: 1.5356 - val_accuracy: 0.3484\n",
            "Epoch 12/100\n",
            "59/59 - 4s - loss: 1.6104 - CategoricalCrossentropy: 1.5928 - accuracy: 0.3183 - val_loss: 1.5158 - val_CategoricalCrossentropy: 1.5037 - val_accuracy: 0.3849\n",
            "Epoch 13/100\n",
            "59/59 - 4s - loss: 1.5838 - CategoricalCrossentropy: 1.5648 - accuracy: 0.3565 - val_loss: 1.4844 - val_CategoricalCrossentropy: 1.4722 - val_accuracy: 0.3742\n",
            "Epoch 14/100\n",
            "59/59 - 4s - loss: 1.5171 - CategoricalCrossentropy: 1.5007 - accuracy: 0.3462 - val_loss: 1.4359 - val_CategoricalCrossentropy: 1.4239 - val_accuracy: 0.4172\n",
            "Epoch 15/100\n",
            "59/59 - 4s - loss: 1.4904 - CategoricalCrossentropy: 1.4668 - accuracy: 0.3704 - val_loss: 1.3523 - val_CategoricalCrossentropy: 1.3363 - val_accuracy: 0.4323\n",
            "Epoch 16/100\n",
            "59/59 - 4s - loss: 1.4346 - CategoricalCrossentropy: 1.4060 - accuracy: 0.3688 - val_loss: 1.3533 - val_CategoricalCrossentropy: 1.3366 - val_accuracy: 0.4108\n",
            "Epoch 17/100\n",
            "59/59 - 4s - loss: 1.4123 - CategoricalCrossentropy: 1.3827 - accuracy: 0.3753 - val_loss: 1.3040 - val_CategoricalCrossentropy: 1.2857 - val_accuracy: 0.4387\n",
            "Epoch 18/100\n",
            "59/59 - 4s - loss: 1.3855 - CategoricalCrossentropy: 1.3618 - accuracy: 0.4043 - val_loss: 1.3590 - val_CategoricalCrossentropy: 1.3375 - val_accuracy: 0.4237\n",
            "Epoch 19/100\n",
            "59/59 - 4s - loss: 1.3513 - CategoricalCrossentropy: 1.3212 - accuracy: 0.4247 - val_loss: 1.2464 - val_CategoricalCrossentropy: 1.2207 - val_accuracy: 0.4817\n",
            "Epoch 20/100\n",
            "59/59 - 4s - loss: 1.3641 - CategoricalCrossentropy: 1.3359 - accuracy: 0.4247 - val_loss: 1.2879 - val_CategoricalCrossentropy: 1.2630 - val_accuracy: 0.4301\n",
            "Epoch 21/100\n",
            "59/59 - 4s - loss: 1.3105 - CategoricalCrossentropy: 1.2797 - accuracy: 0.4360 - val_loss: 1.2188 - val_CategoricalCrossentropy: 1.1947 - val_accuracy: 0.4903\n",
            "Epoch 22/100\n",
            "59/59 - 4s - loss: 1.2922 - CategoricalCrossentropy: 1.2570 - accuracy: 0.4532 - val_loss: 1.2205 - val_CategoricalCrossentropy: 1.1930 - val_accuracy: 0.4710\n",
            "Epoch 23/100\n",
            "59/59 - 4s - loss: 1.2680 - CategoricalCrossentropy: 1.2340 - accuracy: 0.4516 - val_loss: 1.1790 - val_CategoricalCrossentropy: 1.1490 - val_accuracy: 0.5075\n",
            "Epoch 24/100\n",
            "59/59 - 4s - loss: 1.2650 - CategoricalCrossentropy: 1.2249 - accuracy: 0.4581 - val_loss: 1.2077 - val_CategoricalCrossentropy: 1.1805 - val_accuracy: 0.5054\n",
            "Epoch 25/100\n",
            "59/59 - 4s - loss: 1.2257 - CategoricalCrossentropy: 1.1851 - accuracy: 0.4645 - val_loss: 1.2374 - val_CategoricalCrossentropy: 1.2096 - val_accuracy: 0.4538\n",
            "Epoch 26/100\n",
            "59/59 - 4s - loss: 1.2202 - CategoricalCrossentropy: 1.1791 - accuracy: 0.4699 - val_loss: 1.1903 - val_CategoricalCrossentropy: 1.1623 - val_accuracy: 0.5247\n",
            "Epoch 27/100\n",
            "59/59 - 4s - loss: 1.2238 - CategoricalCrossentropy: 1.1840 - accuracy: 0.4812 - val_loss: 1.1503 - val_CategoricalCrossentropy: 1.1201 - val_accuracy: 0.5398\n",
            "Epoch 28/100\n",
            "59/59 - 4s - loss: 1.1878 - CategoricalCrossentropy: 1.1458 - accuracy: 0.4946 - val_loss: 1.2605 - val_CategoricalCrossentropy: 1.2268 - val_accuracy: 0.4624\n",
            "Epoch 29/100\n",
            "59/59 - 4s - loss: 1.2252 - CategoricalCrossentropy: 1.1841 - accuracy: 0.4828 - val_loss: 1.1319 - val_CategoricalCrossentropy: 1.0930 - val_accuracy: 0.4839\n",
            "Epoch 30/100\n",
            "59/59 - 4s - loss: 1.1989 - CategoricalCrossentropy: 1.1517 - accuracy: 0.4855 - val_loss: 1.2117 - val_CategoricalCrossentropy: 1.1774 - val_accuracy: 0.4968\n",
            "Epoch 31/100\n",
            "59/59 - 4s - loss: 1.1411 - CategoricalCrossentropy: 1.0979 - accuracy: 0.5086 - val_loss: 1.1329 - val_CategoricalCrossentropy: 1.1024 - val_accuracy: 0.5441\n",
            "Epoch 32/100\n",
            "59/59 - 4s - loss: 1.1479 - CategoricalCrossentropy: 1.1118 - accuracy: 0.5156 - val_loss: 1.1067 - val_CategoricalCrossentropy: 1.0760 - val_accuracy: 0.5548\n",
            "Epoch 33/100\n",
            "59/59 - 4s - loss: 1.0981 - CategoricalCrossentropy: 1.0532 - accuracy: 0.5473 - val_loss: 1.0775 - val_CategoricalCrossentropy: 1.0422 - val_accuracy: 0.5570\n",
            "Epoch 34/100\n",
            "59/59 - 4s - loss: 1.0884 - CategoricalCrossentropy: 1.0485 - accuracy: 0.5398 - val_loss: 1.0537 - val_CategoricalCrossentropy: 1.0172 - val_accuracy: 0.5677\n",
            "Epoch 35/100\n",
            "59/59 - 4s - loss: 1.1312 - CategoricalCrossentropy: 1.0890 - accuracy: 0.5414 - val_loss: 1.0925 - val_CategoricalCrossentropy: 1.0556 - val_accuracy: 0.5720\n",
            "Epoch 36/100\n",
            "59/59 - 4s - loss: 1.1282 - CategoricalCrossentropy: 1.0840 - accuracy: 0.5280 - val_loss: 1.0685 - val_CategoricalCrossentropy: 1.0325 - val_accuracy: 0.5548\n",
            "Epoch 37/100\n",
            "59/59 - 4s - loss: 1.0574 - CategoricalCrossentropy: 1.0120 - accuracy: 0.5478 - val_loss: 1.0507 - val_CategoricalCrossentropy: 1.0164 - val_accuracy: 0.5677\n",
            "Epoch 38/100\n",
            "59/59 - 4s - loss: 1.0672 - CategoricalCrossentropy: 1.0209 - accuracy: 0.5462 - val_loss: 1.0878 - val_CategoricalCrossentropy: 1.0549 - val_accuracy: 0.5355\n",
            "Epoch 39/100\n",
            "59/59 - 4s - loss: 1.0489 - CategoricalCrossentropy: 1.0022 - accuracy: 0.5495 - val_loss: 1.0155 - val_CategoricalCrossentropy: 0.9783 - val_accuracy: 0.6000\n",
            "Epoch 40/100\n",
            "59/59 - 4s - loss: 1.0319 - CategoricalCrossentropy: 0.9853 - accuracy: 0.5656 - val_loss: 1.0365 - val_CategoricalCrossentropy: 0.9973 - val_accuracy: 0.5935\n",
            "Epoch 41/100\n",
            "59/59 - 4s - loss: 1.0191 - CategoricalCrossentropy: 0.9713 - accuracy: 0.5586 - val_loss: 1.0726 - val_CategoricalCrossentropy: 1.0392 - val_accuracy: 0.5398\n",
            "Epoch 42/100\n",
            "59/59 - 4s - loss: 1.0186 - CategoricalCrossentropy: 0.9731 - accuracy: 0.5661 - val_loss: 1.1019 - val_CategoricalCrossentropy: 1.0631 - val_accuracy: 0.5419\n",
            "Epoch 43/100\n",
            "59/59 - 4s - loss: 1.0501 - CategoricalCrossentropy: 1.0048 - accuracy: 0.5656 - val_loss: 1.0559 - val_CategoricalCrossentropy: 1.0174 - val_accuracy: 0.5806\n",
            "Epoch 44/100\n",
            "59/59 - 4s - loss: 0.9981 - CategoricalCrossentropy: 0.9524 - accuracy: 0.6000 - val_loss: 1.0299 - val_CategoricalCrossentropy: 0.9857 - val_accuracy: 0.5548\n",
            "Epoch 45/100\n",
            "59/59 - 4s - loss: 1.0085 - CategoricalCrossentropy: 0.9621 - accuracy: 0.5753 - val_loss: 1.0113 - val_CategoricalCrossentropy: 0.9701 - val_accuracy: 0.5892\n",
            "Epoch 46/100\n",
            "59/59 - 4s - loss: 1.0130 - CategoricalCrossentropy: 0.9657 - accuracy: 0.5796 - val_loss: 0.9941 - val_CategoricalCrossentropy: 0.9501 - val_accuracy: 0.5871\n",
            "Epoch 47/100\n",
            "59/59 - 4s - loss: 1.0050 - CategoricalCrossentropy: 0.9546 - accuracy: 0.5876 - val_loss: 1.1150 - val_CategoricalCrossentropy: 1.0735 - val_accuracy: 0.5505\n",
            "Epoch 48/100\n",
            "59/59 - 4s - loss: 1.0033 - CategoricalCrossentropy: 0.9561 - accuracy: 0.5790 - val_loss: 1.0147 - val_CategoricalCrossentropy: 0.9717 - val_accuracy: 0.5978\n",
            "Epoch 49/100\n",
            "59/59 - 4s - loss: 0.9791 - CategoricalCrossentropy: 0.9332 - accuracy: 0.6000 - val_loss: 1.0464 - val_CategoricalCrossentropy: 1.0005 - val_accuracy: 0.5462\n",
            "Epoch 50/100\n",
            "59/59 - 4s - loss: 0.9665 - CategoricalCrossentropy: 0.9252 - accuracy: 0.5914 - val_loss: 1.0625 - val_CategoricalCrossentropy: 1.0167 - val_accuracy: 0.5677\n",
            "Epoch 51/100\n",
            "59/59 - 4s - loss: 0.9543 - CategoricalCrossentropy: 0.9022 - accuracy: 0.5898 - val_loss: 1.0048 - val_CategoricalCrossentropy: 0.9668 - val_accuracy: 0.6043\n",
            "Epoch 52/100\n",
            "59/59 - 4s - loss: 0.9465 - CategoricalCrossentropy: 0.8947 - accuracy: 0.6129 - val_loss: 0.9718 - val_CategoricalCrossentropy: 0.9288 - val_accuracy: 0.6000\n",
            "Epoch 53/100\n",
            "59/59 - 4s - loss: 0.9651 - CategoricalCrossentropy: 0.9145 - accuracy: 0.5952 - val_loss: 0.9934 - val_CategoricalCrossentropy: 0.9499 - val_accuracy: 0.6022\n",
            "Epoch 54/100\n",
            "59/59 - 4s - loss: 0.9182 - CategoricalCrossentropy: 0.8699 - accuracy: 0.6177 - val_loss: 0.9672 - val_CategoricalCrossentropy: 0.9227 - val_accuracy: 0.5957\n",
            "Epoch 55/100\n",
            "59/59 - 4s - loss: 0.9502 - CategoricalCrossentropy: 0.9002 - accuracy: 0.6011 - val_loss: 0.9865 - val_CategoricalCrossentropy: 0.9362 - val_accuracy: 0.5935\n",
            "Epoch 56/100\n",
            "59/59 - 4s - loss: 0.9239 - CategoricalCrossentropy: 0.8777 - accuracy: 0.6242 - val_loss: 0.9649 - val_CategoricalCrossentropy: 0.9248 - val_accuracy: 0.6280\n",
            "Epoch 57/100\n",
            "59/59 - 4s - loss: 0.9158 - CategoricalCrossentropy: 0.8633 - accuracy: 0.6129 - val_loss: 0.9803 - val_CategoricalCrossentropy: 0.9328 - val_accuracy: 0.6086\n",
            "Epoch 58/100\n",
            "59/59 - 4s - loss: 0.9314 - CategoricalCrossentropy: 0.8833 - accuracy: 0.6032 - val_loss: 0.9682 - val_CategoricalCrossentropy: 0.9248 - val_accuracy: 0.6237\n",
            "Epoch 59/100\n",
            "59/59 - 4s - loss: 0.9361 - CategoricalCrossentropy: 0.8860 - accuracy: 0.6145 - val_loss: 1.0112 - val_CategoricalCrossentropy: 0.9635 - val_accuracy: 0.5849\n",
            "Epoch 60/100\n",
            "59/59 - 4s - loss: 0.9241 - CategoricalCrossentropy: 0.8691 - accuracy: 0.6183 - val_loss: 1.0109 - val_CategoricalCrossentropy: 0.9603 - val_accuracy: 0.6065\n",
            "Epoch 61/100\n",
            "59/59 - 4s - loss: 0.8953 - CategoricalCrossentropy: 0.8407 - accuracy: 0.6237 - val_loss: 0.9943 - val_CategoricalCrossentropy: 0.9472 - val_accuracy: 0.6151\n",
            "Epoch 62/100\n",
            "59/59 - 4s - loss: 0.9125 - CategoricalCrossentropy: 0.8583 - accuracy: 0.6172 - val_loss: 0.9830 - val_CategoricalCrossentropy: 0.9388 - val_accuracy: 0.6344\n",
            "Epoch 63/100\n",
            "59/59 - 4s - loss: 0.8868 - CategoricalCrossentropy: 0.8354 - accuracy: 0.6323 - val_loss: 1.0591 - val_CategoricalCrossentropy: 1.0103 - val_accuracy: 0.5634\n",
            "Epoch 64/100\n",
            "59/59 - 4s - loss: 0.8842 - CategoricalCrossentropy: 0.8338 - accuracy: 0.6237 - val_loss: 0.9944 - val_CategoricalCrossentropy: 0.9506 - val_accuracy: 0.5849\n",
            "Epoch 65/100\n",
            "59/59 - 4s - loss: 0.8711 - CategoricalCrossentropy: 0.8169 - accuracy: 0.6306 - val_loss: 1.0056 - val_CategoricalCrossentropy: 0.9642 - val_accuracy: 0.6086\n",
            "Epoch 66/100\n",
            "59/59 - 4s - loss: 0.8575 - CategoricalCrossentropy: 0.8085 - accuracy: 0.6457 - val_loss: 0.9897 - val_CategoricalCrossentropy: 0.9393 - val_accuracy: 0.5978\n",
            "Epoch 67/100\n",
            "59/59 - 4s - loss: 0.8941 - CategoricalCrossentropy: 0.8449 - accuracy: 0.6210 - val_loss: 1.0367 - val_CategoricalCrossentropy: 0.9934 - val_accuracy: 0.6000\n",
            "Epoch 68/100\n",
            "59/59 - 4s - loss: 0.8918 - CategoricalCrossentropy: 0.8327 - accuracy: 0.6392 - val_loss: 0.9700 - val_CategoricalCrossentropy: 0.9236 - val_accuracy: 0.6172\n",
            "Epoch 69/100\n",
            "59/59 - 4s - loss: 0.8374 - CategoricalCrossentropy: 0.7825 - accuracy: 0.6527 - val_loss: 0.9747 - val_CategoricalCrossentropy: 0.9245 - val_accuracy: 0.6129\n",
            "Epoch 70/100\n",
            "59/59 - 4s - loss: 0.8486 - CategoricalCrossentropy: 0.7956 - accuracy: 0.6441 - val_loss: 0.9921 - val_CategoricalCrossentropy: 0.9431 - val_accuracy: 0.6237\n",
            "Epoch 71/100\n",
            "59/59 - 4s - loss: 0.8549 - CategoricalCrossentropy: 0.8048 - accuracy: 0.6543 - val_loss: 0.9827 - val_CategoricalCrossentropy: 0.9369 - val_accuracy: 0.6237\n",
            "Epoch 72/100\n",
            "59/59 - 4s - loss: 0.8566 - CategoricalCrossentropy: 0.8058 - accuracy: 0.6457 - val_loss: 0.9794 - val_CategoricalCrossentropy: 0.9339 - val_accuracy: 0.6237\n",
            "Epoch 73/100\n",
            "59/59 - 4s - loss: 0.8480 - CategoricalCrossentropy: 0.7884 - accuracy: 0.6425 - val_loss: 0.9852 - val_CategoricalCrossentropy: 0.9353 - val_accuracy: 0.6108\n",
            "Epoch 74/100\n",
            "59/59 - 4s - loss: 0.8154 - CategoricalCrossentropy: 0.7602 - accuracy: 0.6554 - val_loss: 1.0045 - val_CategoricalCrossentropy: 0.9558 - val_accuracy: 0.6000\n",
            "Epoch 75/100\n",
            "59/59 - 4s - loss: 0.8617 - CategoricalCrossentropy: 0.8149 - accuracy: 0.6435 - val_loss: 0.9697 - val_CategoricalCrossentropy: 0.9233 - val_accuracy: 0.6301\n",
            "Epoch 76/100\n",
            "59/59 - 4s - loss: 0.8212 - CategoricalCrossentropy: 0.7653 - accuracy: 0.6780 - val_loss: 0.9730 - val_CategoricalCrossentropy: 0.9281 - val_accuracy: 0.6301\n",
            "Epoch 77/100\n",
            "59/59 - 4s - loss: 0.8009 - CategoricalCrossentropy: 0.7440 - accuracy: 0.6806 - val_loss: 0.9641 - val_CategoricalCrossentropy: 0.9138 - val_accuracy: 0.6301\n",
            "Epoch 78/100\n",
            "59/59 - 4s - loss: 0.8154 - CategoricalCrossentropy: 0.7601 - accuracy: 0.6554 - val_loss: 1.0590 - val_CategoricalCrossentropy: 1.0094 - val_accuracy: 0.5935\n",
            "Epoch 79/100\n",
            "59/59 - 4s - loss: 0.7869 - CategoricalCrossentropy: 0.7316 - accuracy: 0.6790 - val_loss: 0.9776 - val_CategoricalCrossentropy: 0.9306 - val_accuracy: 0.6086\n",
            "Epoch 80/100\n",
            "59/59 - 4s - loss: 0.8201 - CategoricalCrossentropy: 0.7677 - accuracy: 0.6661 - val_loss: 0.9746 - val_CategoricalCrossentropy: 0.9250 - val_accuracy: 0.6344\n",
            "Epoch 81/100\n",
            "59/59 - 4s - loss: 0.7896 - CategoricalCrossentropy: 0.7311 - accuracy: 0.6801 - val_loss: 0.9553 - val_CategoricalCrossentropy: 0.9127 - val_accuracy: 0.6430\n",
            "Epoch 82/100\n",
            "59/59 - 4s - loss: 0.7829 - CategoricalCrossentropy: 0.7251 - accuracy: 0.6828 - val_loss: 0.9997 - val_CategoricalCrossentropy: 0.9509 - val_accuracy: 0.6237\n",
            "Epoch 83/100\n",
            "59/59 - 4s - loss: 0.7497 - CategoricalCrossentropy: 0.6890 - accuracy: 0.6989 - val_loss: 1.0124 - val_CategoricalCrossentropy: 0.9610 - val_accuracy: 0.6172\n",
            "Epoch 84/100\n",
            "59/59 - 4s - loss: 0.7871 - CategoricalCrossentropy: 0.7320 - accuracy: 0.6672 - val_loss: 1.0048 - val_CategoricalCrossentropy: 0.9554 - val_accuracy: 0.6280\n",
            "Epoch 85/100\n",
            "59/59 - 4s - loss: 0.8000 - CategoricalCrossentropy: 0.7427 - accuracy: 0.6796 - val_loss: 0.9627 - val_CategoricalCrossentropy: 0.9124 - val_accuracy: 0.6301\n",
            "Epoch 86/100\n",
            "59/59 - 4s - loss: 0.7840 - CategoricalCrossentropy: 0.7283 - accuracy: 0.6844 - val_loss: 0.9864 - val_CategoricalCrossentropy: 0.9333 - val_accuracy: 0.6086\n",
            "Epoch 87/100\n",
            "59/59 - 4s - loss: 0.7652 - CategoricalCrossentropy: 0.7076 - accuracy: 0.7032 - val_loss: 0.9812 - val_CategoricalCrossentropy: 0.9293 - val_accuracy: 0.6430\n",
            "Epoch 88/100\n",
            "59/59 - 4s - loss: 0.7611 - CategoricalCrossentropy: 0.7050 - accuracy: 0.6957 - val_loss: 0.9880 - val_CategoricalCrossentropy: 0.9426 - val_accuracy: 0.6237\n",
            "Epoch 89/100\n",
            "59/59 - 4s - loss: 0.7629 - CategoricalCrossentropy: 0.7013 - accuracy: 0.6995 - val_loss: 1.0262 - val_CategoricalCrossentropy: 0.9777 - val_accuracy: 0.6065\n",
            "Epoch 90/100\n",
            "59/59 - 4s - loss: 0.8082 - CategoricalCrossentropy: 0.7560 - accuracy: 0.6763 - val_loss: 1.0103 - val_CategoricalCrossentropy: 0.9551 - val_accuracy: 0.5871\n",
            "Epoch 91/100\n",
            "59/59 - 4s - loss: 0.7758 - CategoricalCrossentropy: 0.7177 - accuracy: 0.6855 - val_loss: 0.9943 - val_CategoricalCrossentropy: 0.9411 - val_accuracy: 0.6043\n",
            "Epoch 92/100\n",
            "59/59 - 4s - loss: 0.7696 - CategoricalCrossentropy: 0.7089 - accuracy: 0.7054 - val_loss: 1.0286 - val_CategoricalCrossentropy: 0.9765 - val_accuracy: 0.6258\n",
            "Epoch 93/100\n",
            "59/59 - 4s - loss: 0.7687 - CategoricalCrossentropy: 0.7081 - accuracy: 0.6941 - val_loss: 0.9299 - val_CategoricalCrossentropy: 0.8750 - val_accuracy: 0.6409\n",
            "Epoch 94/100\n",
            "59/59 - 4s - loss: 0.7423 - CategoricalCrossentropy: 0.6827 - accuracy: 0.6957 - val_loss: 1.0131 - val_CategoricalCrossentropy: 0.9565 - val_accuracy: 0.6086\n",
            "Epoch 95/100\n",
            "59/59 - 4s - loss: 0.7142 - CategoricalCrossentropy: 0.6530 - accuracy: 0.7220 - val_loss: 0.9804 - val_CategoricalCrossentropy: 0.9292 - val_accuracy: 0.6301\n",
            "Epoch 96/100\n",
            "59/59 - 4s - loss: 0.7194 - CategoricalCrossentropy: 0.6597 - accuracy: 0.7177 - val_loss: 1.0349 - val_CategoricalCrossentropy: 0.9811 - val_accuracy: 0.6280\n",
            "Epoch 97/100\n",
            "59/59 - 4s - loss: 0.7510 - CategoricalCrossentropy: 0.6885 - accuracy: 0.6919 - val_loss: 1.0356 - val_CategoricalCrossentropy: 0.9800 - val_accuracy: 0.6258\n",
            "Epoch 98/100\n",
            "59/59 - 4s - loss: 0.7393 - CategoricalCrossentropy: 0.6777 - accuracy: 0.7124 - val_loss: 1.0180 - val_CategoricalCrossentropy: 0.9646 - val_accuracy: 0.6237\n",
            "Epoch 99/100\n",
            "59/59 - 4s - loss: 0.7452 - CategoricalCrossentropy: 0.6877 - accuracy: 0.7091 - val_loss: 0.9416 - val_CategoricalCrossentropy: 0.8861 - val_accuracy: 0.6151\n",
            "Epoch 100/100\n",
            "59/59 - 4s - loss: 0.7551 - CategoricalCrossentropy: 0.6974 - accuracy: 0.6995 - val_loss: 1.0501 - val_CategoricalCrossentropy: 1.0012 - val_accuracy: 0.6409\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CE5mygibd3GG",
        "colab_type": "text"
      },
      "source": [
        "We can similarly check the learning curve for pan prediction of error metric as well as for performance metric.\n",
        "We can see that, the pan prediction model is performing with almost 70% validation data accuracy. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szIjkN-khDPY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "7fea9468-33e9-498b-ce3e-050e5a69f368"
      },
      "source": [
        "plotter(m_histories, ylim=[0.0, 5.0], metric = 'CategoricalCrossentropy')"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgP1jRL_hDaQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2e497086-a5a4-45ab-f04a-a4ef38325c4f"
      },
      "source": [
        "plotter(m_histories, ylim=[0.0, 1.0], metric = 'accuracy')"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVRnssa9eZ6t",
        "colab_type": "text"
      },
      "source": [
        "The next step is to predict the pan labels for 'test_data.csv' file images. For this we are using 'mode.predict_generator' function which takes in the test generator created above and predict the pan labels for them.\n",
        "\n",
        "Then next step is to convert them into readable format an decode them to the class values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eDKBl2tI3jL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "209e7002-17d3-496a-c84f-ee3e4beca5b7"
      },
      "source": [
        "test_generator = make_test_predict_generator()\n",
        "predictions = model_pan.predict_generator(test_generator, verbose= 1)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 465 validated image filenames.\n",
            "15/15 [==============================] - 1s 48ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K99tjEZMJFao",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "3f703ff6-93e8-45a2-d58f-2d44d8b42617"
      },
      "source": [
        "pan_result = pd.Series (np.argmax(predictions, axis=1).tolist()).replace({0:\"-90\" , 1: \"-75\", 2:\"-60\", 3:\"-45\", 4:\"-30\", 5:\"-15\", 6:\"0\", 7:\"15\", 8:\"30\", 9:\"45\", 10:\"60\", 11:\"75\", 12:\"90\"})\n",
        "pan_result.head(10)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     30\n",
              "1     75\n",
              "2     75\n",
              "3     30\n",
              "4      0\n",
              "5    -45\n",
              "6     60\n",
              "7    -15\n",
              "8    -60\n",
              "9    -45\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rzMZqydxk8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['pan_result'] = pan_result\n",
        "df['tilt_result'] = tilt_result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4gUkY-RyEkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.shape\n",
        "df.to_excel(\"Output.xlsx\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}