RGB2GrayscaleConverter.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "RGB2GrayscaleConverter.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "APISr7HAkhWw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d695d1cf-7169-48f9-9b79-b72d888ecda5"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import keras\n",
        "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D,Add,Flatten,Reshape\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing import image\n",
        "from tqdm import tqdm"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdzmARSVkly3",
        "colab_type": "code",
        "outputId": "52316fcf-66a2-43d1-ae50-c57517767a20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2utHILEIkhWz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "testPath = '/content/drive/My Drive/D1/test/'\n",
        "trainPath = '/content/drive/My Drive/D1/training/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ih6Rx4rUVho0",
        "colab_type": "text"
      },
      "source": [
        "Creating GroundTruths \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCpyMXPXkhW1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2 \n",
        "\n",
        "for i in range(1,21):\n",
        "    FILE_NAME = testPath +'images/'+ str(i)+'_test.tif'\n",
        "    img = cv2.imread(FILE_NAME)\n",
        "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    cv2.imwrite(testPath +'groundTruth/'+str(i)+'_test.tif', gray) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1C4aGOedVrW4",
        "colab_type": "text"
      },
      "source": [
        "Loading data and creating train set, validation set and test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oH-qqHXikhW4",
        "colab_type": "code",
        "outputId": "d212fd81-4da7-40bd-9b99-5ad7679ba3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_image = []\n",
        "for i in tqdm(range(21,41)):\n",
        "    img = image.load_img(trainPath+'images/'+str(i)+'_training.tif', target_size=(224,224,3) , grayscale=False)\n",
        "    img= image.img_to_array(img)\n",
        "    img = img/255\n",
        "    train_image.append(img)\n",
        "X_train = np.array(train_image)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [00:05<00:00,  3.94it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyPavZNpkhW7",
        "colab_type": "code",
        "outputId": "4a2b2c8a-dda5-4ba3-ac79-1b4e866fdab2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "GT_train_image = []\n",
        "for i in tqdm(range(21,41)):\n",
        "    img = image.load_img(trainPath+'groundTruth/'+str(i)+'_training.tif', target_size=(224,224,3) , grayscale=False)\n",
        "    img= image.img_to_array(img)\n",
        "    img = img/255\n",
        "    GT_train_image.append(img)\n",
        "X_train_GT = np.array(GT_train_image)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [00:06<00:00,  3.28it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4l5GiwVkhW-",
        "colab_type": "code",
        "outputId": "a09a4622-08dc-4695-a00c-f864af78289a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_image = []\n",
        "for i in tqdm(range(1,21)):\n",
        "    img = image.load_img(testPath+'images/'+str(i)+'_test.tif', target_size=(224,224,3) , grayscale=False)\n",
        "    img= image.img_to_array(img)\n",
        "    img = img/255\n",
        "    test_image.append(img)\n",
        "X = np.array(test_image)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [00:06<00:00,  3.70it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDSYq8AOkhXA",
        "colab_type": "code",
        "outputId": "66f4dd1e-85e6-4e2b-814b-2e8041d66b2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "GT_test_image = []\n",
        "for i in tqdm(range(1,21)):\n",
        "    img = image.load_img(testPath+'groundTruth/'+str(i)+'_test.tif', target_size=(224,224,3) , grayscale=False)\n",
        "    img= image.img_to_array(img)\n",
        "    img = img/255\n",
        "    GT_test_image.append(img)\n",
        "X_GT = np.array(GT_test_image)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 20/20 [00:05<00:00,  3.45it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7p4aeHEhkhXD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_valid=X[:10]\n",
        "X_test=X[10:20]\n",
        "X_GT_valid=X_GT[:10]\n",
        "X_GT_test=X_GT[10:20]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bOccrdX1WF8p",
        "colab_type": "text"
      },
      "source": [
        "Defining and Trainning model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtRUABxSkhXM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input1 = Input(shape=(224,224,3))\n",
        "\n",
        "x = Conv2D(64, kernel_size=(3, 3), activation='relu',padding='same')(input1)\n",
        "x = Conv2D(64, kernel_size=(3, 3), activation='relu',padding='same')(x)\n",
        "out = Conv2D(3, kernel_size=(3, 3), activation='sigmoid',padding='same')(x)\n",
        "model = Model(inputs=input1, outputs=out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7sRquMNkhXP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "4ee7c283-1ec5-4c9e-9725-659a1ccf5508"
      },
      "source": [
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0811 20:15:13.504961 140652601173888 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihjVZPq9khXR",
        "colab_type": "code",
        "outputId": "beb83a48-5c97-4b0f-d1e3-f98673bde1f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 224, 224, 3)       1731      \n",
            "=================================================================\n",
            "Total params: 40,451\n",
            "Trainable params: 40,451\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_EI5hU9khXU",
        "colab_type": "code",
        "outputId": "a40361b4-30ed-4751-a074-1c1b9c6a0054",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(X_train,X_train_GT,\n",
        "                epochs=100,\n",
        "                batch_size=10,\n",
        "                shuffle=True,\n",
        "                validation_data=(X_valid,X_GT_valid)\n",
        "                )"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0811 20:17:38.881507 140652601173888 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "W0811 20:17:38.977555 140652601173888 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 20 samples, validate on 10 samples\n",
            "Epoch 1/100\n",
            "20/20 [==============================] - 8s 390ms/step - loss: 0.0801 - val_loss: 0.0749\n",
            "Epoch 2/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0788 - val_loss: 0.0737\n",
            "Epoch 3/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0772 - val_loss: 0.0722\n",
            "Epoch 4/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0754 - val_loss: 0.0705\n",
            "Epoch 5/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0729 - val_loss: 0.0669\n",
            "Epoch 6/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0690 - val_loss: 0.0622\n",
            "Epoch 7/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0638 - val_loss: 0.0559\n",
            "Epoch 8/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0569 - val_loss: 0.0485\n",
            "Epoch 9/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0483 - val_loss: 0.0392\n",
            "Epoch 10/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0383 - val_loss: 0.0296\n",
            "Epoch 11/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0298 - val_loss: 0.0205\n",
            "Epoch 12/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0198 - val_loss: 0.0141\n",
            "Epoch 13/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0131 - val_loss: 0.0092\n",
            "Epoch 14/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0091 - val_loss: 0.0072\n",
            "Epoch 15/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0057 - val_loss: 0.0062\n",
            "Epoch 16/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0051 - val_loss: 0.0048\n",
            "Epoch 17/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0045 - val_loss: 0.0039\n",
            "Epoch 18/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0044 - val_loss: 0.0033\n",
            "Epoch 19/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0035 - val_loss: 0.0032\n",
            "Epoch 20/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0029 - val_loss: 0.0043\n",
            "Epoch 21/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0032 - val_loss: 0.0027\n",
            "Epoch 22/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0025 - val_loss: 0.0024\n",
            "Epoch 23/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0021 - val_loss: 0.0023\n",
            "Epoch 24/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 0.0020\n",
            "Epoch 25/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0018 - val_loss: 0.0018\n",
            "Epoch 26/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0017 - val_loss: 0.0017\n",
            "Epoch 27/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0016\n",
            "Epoch 28/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0014\n",
            "Epoch 29/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0014 - val_loss: 0.0013\n",
            "Epoch 30/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0013\n",
            "Epoch 31/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0012\n",
            "Epoch 32/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0012\n",
            "Epoch 33/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0011\n",
            "Epoch 34/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0012\n",
            "Epoch 35/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0012\n",
            "Epoch 36/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0011\n",
            "Epoch 37/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0010\n",
            "Epoch 38/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0010\n",
            "Epoch 39/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 9.3479e-04 - val_loss: 0.0011\n",
            "Epoch 40/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 9.7246e-04 - val_loss: 9.6373e-04\n",
            "Epoch 41/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 9.6046e-04 - val_loss: 9.1547e-04\n",
            "Epoch 42/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 9.0272e-04 - val_loss: 8.3999e-04\n",
            "Epoch 43/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 8.4909e-04 - val_loss: 8.0628e-04\n",
            "Epoch 44/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 7.9813e-04 - val_loss: 8.1130e-04\n",
            "Epoch 45/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 7.7880e-04 - val_loss: 7.6040e-04\n",
            "Epoch 46/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 7.5036e-04 - val_loss: 7.4078e-04\n",
            "Epoch 47/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 7.3955e-04 - val_loss: 6.8935e-04\n",
            "Epoch 48/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 6.9231e-04 - val_loss: 6.6899e-04\n",
            "Epoch 49/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 6.9180e-04 - val_loss: 6.3457e-04\n",
            "Epoch 50/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 6.3175e-04 - val_loss: 6.0574e-04\n",
            "Epoch 51/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 6.0101e-04 - val_loss: 5.7577e-04\n",
            "Epoch 52/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 5.7241e-04 - val_loss: 5.6564e-04\n",
            "Epoch 53/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 5.7028e-04 - val_loss: 5.2481e-04\n",
            "Epoch 54/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 5.2724e-04 - val_loss: 5.1242e-04\n",
            "Epoch 55/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 5.0869e-04 - val_loss: 4.8237e-04\n",
            "Epoch 56/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 5.0930e-04 - val_loss: 4.7368e-04\n",
            "Epoch 57/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.7880e-04 - val_loss: 4.5172e-04\n",
            "Epoch 58/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.5476e-04 - val_loss: 4.5666e-04\n",
            "Epoch 59/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.4872e-04 - val_loss: 4.5222e-04\n",
            "Epoch 60/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.7445e-04 - val_loss: 4.1038e-04\n",
            "Epoch 61/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.5244e-04 - val_loss: 4.0199e-04\n",
            "Epoch 62/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.1609e-04 - val_loss: 4.0037e-04\n",
            "Epoch 63/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.0279e-04 - val_loss: 4.1078e-04\n",
            "Epoch 64/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 4.0662e-04 - val_loss: 3.8800e-04\n",
            "Epoch 65/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.8527e-04 - val_loss: 3.6778e-04\n",
            "Epoch 66/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.7817e-04 - val_loss: 3.5972e-04\n",
            "Epoch 67/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.6568e-04 - val_loss: 3.6779e-04\n",
            "Epoch 68/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.6743e-04 - val_loss: 3.8287e-04\n",
            "Epoch 69/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.7105e-04 - val_loss: 4.3356e-04\n",
            "Epoch 70/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.7454e-04 - val_loss: 3.6465e-04\n",
            "Epoch 71/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.5208e-04 - val_loss: 3.7289e-04\n",
            "Epoch 72/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.5087e-04 - val_loss: 3.2772e-04\n",
            "Epoch 73/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.3710e-04 - val_loss: 3.2109e-04\n",
            "Epoch 74/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.3732e-04 - val_loss: 3.4742e-04\n",
            "Epoch 75/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.3239e-04 - val_loss: 3.4035e-04\n",
            "Epoch 76/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.4013e-04 - val_loss: 3.2439e-04\n",
            "Epoch 77/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.4172e-04 - val_loss: 3.0947e-04\n",
            "Epoch 78/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.2251e-04 - val_loss: 3.1593e-04\n",
            "Epoch 79/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.1501e-04 - val_loss: 3.1871e-04\n",
            "Epoch 80/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.1726e-04 - val_loss: 2.8907e-04\n",
            "Epoch 81/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 3.0148e-04 - val_loss: 2.8690e-04\n",
            "Epoch 82/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.9333e-04 - val_loss: 2.9161e-04\n",
            "Epoch 83/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.9179e-04 - val_loss: 2.7878e-04\n",
            "Epoch 84/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.8292e-04 - val_loss: 2.8016e-04\n",
            "Epoch 85/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.7872e-04 - val_loss: 2.7232e-04\n",
            "Epoch 86/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.7511e-04 - val_loss: 2.6808e-04\n",
            "Epoch 87/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.7667e-04 - val_loss: 2.7562e-04\n",
            "Epoch 88/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.7155e-04 - val_loss: 2.6461e-04\n",
            "Epoch 89/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.7439e-04 - val_loss: 2.5458e-04\n",
            "Epoch 90/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.6474e-04 - val_loss: 2.6858e-04\n",
            "Epoch 91/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.6289e-04 - val_loss: 2.5213e-04\n",
            "Epoch 92/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.5927e-04 - val_loss: 2.4292e-04\n",
            "Epoch 93/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.4992e-04 - val_loss: 2.4760e-04\n",
            "Epoch 94/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.4649e-04 - val_loss: 2.4353e-04\n",
            "Epoch 95/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.5036e-04 - val_loss: 2.3634e-04\n",
            "Epoch 96/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.4108e-04 - val_loss: 2.4901e-04\n",
            "Epoch 97/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.4303e-04 - val_loss: 2.2934e-04\n",
            "Epoch 98/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.3490e-04 - val_loss: 2.3199e-04\n",
            "Epoch 99/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.3066e-04 - val_loss: 2.4135e-04\n",
            "Epoch 100/100\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 2.3020e-04 - val_loss: 2.2371e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7febf0d5f208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M98mYSgokhXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('/content/drive/My Drive/D1/model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrbdQQy3WRRR",
        "colab_type": "text"
      },
      "source": [
        "Testing Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbFx3rWykhXY",
        "colab_type": "code",
        "outputId": "7578eb93-9538-4552-de9d-5eee303e2c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "img1=X_valid[0]\n",
        "plt.imshow(img1)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7febe019c860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TikVP5vmA980dFRsbUcxSoSy5BcEJqVT+15/8FX7mNx5TtpfUlG2BeaMcO4Va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elyYeBoHaooqh43HFSoudJqRwfUo3I0pUuwYJNRGV65HpmBe4mqyzOUNo9GG4OmCuKorXMcmW1Z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\nelyPzbg2CtfjelyPzfj/AQ98fxmASHqYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1tzKurskhXb",
        "colab_type": "code",
        "outputId": "5ab0166e-726f-4c42-976c-dc983b958101",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "out_img=model.predict(X_valid)\n",
        "plt.imshow(out_img[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7febe01b8470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mg2VguqyEC7nqpCgLN5k3o4mhGbk573A41FG5DV+QDIWTGc3HlCJ9l/CN5869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31uqc\nra26nh7w5u4Wiu7suGUIQe/e1QaklGH9T05K2j1nnHxkuWuIICuVcV2JBCnJGJstFLGNrVAjWol4\n1TdosPlYEX5BAAAgAElEQVSwwtA8JJnnQ3L9GJO6VdU20nWSBMk76NAF2hpifJ0LeCnmIXV6OVRy\nsGwPx6WbQhLrMHRPZlh8L+H2gzTn0fXnysW0RpqwFyphhMCSbKU74Aw4SWli1sfai5mzV4xuUSqT\nrupTqdUt8VD2Se255ZShTAWrxtTuIQ8YSl9Zoq4oufgUKXdFAJJwXXSbBubjCkvcDZqKLekQDGVE\n7MeNHRVtUom+D1MyQK27hckT9hAnfeLxFI7Gv4mvDoL5VQC/pKr/KIC/C+BXpt/9lqr+ef//CQrh\nHYea67bvG/atmuVoHWkZtN0UIoaNAIwTwVmVZ0Vp7qC19dbese/Gvwg4sYZb5FJyFEW1uuP29h63\nt3c4n0/Y9h2NG5JtxMmbmaa4kb0LgLu6bolcJ4HDcAEEQMeNYROMTNPXugdDdMSNU5yYcnYlwbh/\njFwjoGiC0a1pLOfwaJI3CuWcbaw8Pysj352cdyAn6xrNuURlZvYGNCPITf63ewTCgixM6TKEV5Sm\n7xJIrIXdo3NXEMTkmrqyab1ZibgCpSw2Tr6UMWtCSGFfHXxjqDHCJvYIJLF7z5KRE3kXverUCVtU\nQrzDY5kPEsRABJIHC9ZQagEEjPUBdYrEvVGouX4MU/y3l6EARcS9QK4t31G36rMpp+vEpWL7uuNr\nPQV9xyAYVf2fp3/+GoB/6cnf+O5v+cqNK2y68f3DCVeHgpSzlyhLPGSLsbmiRk0lSVFK9/HxfCY6\nua4d531HOtumL95jb5sgYV06+noAh6m8fvsW6+mE49URrR4HGCgyxon7tdisSfMmUk6otYam75Nr\nyRhUBJA+aNrQu3lBiVOdUmwkKgeL9YdZYREQmZI0YyoospJw4hQX+MYUniQd6VaSgsItOguwmEKz\nNmynwxcBa0eJczLzn/Ik8FFXwRSlC6qfu7Iozb/YwjQPi1xAunNeAILlYIo7FxnnEQFUrOoyJcCT\ne6kPBx2u0KBGsQ/JKIlRiyAq/9j4pcNTmvq243kP71YD/KNBo3BTcHkVIpf4wCy9lqodCsE/HvIg\n87+pbCTxcqHKlvohHz/O8aeBKfwbAP7b6d9/TkT+TwBvAPwHqvq/vetD8q65D5O7ozAU9dXrL7F8\n/AJlKZaJgFvgZhvbBoIMpLr3MdtwfJkPcYUXHPWO8/kMERsgG9Vo3WZILuuKo3actw3n8xknL4yp\na8W6OoN0yeGqgvEmiDdghANsrvGfo3EqhMc2acw+nJQO43Jh4ZVjKC5jiCyG90lb3El3dCjanLLz\nPMTaD7dZBDoVebk740q0g8soiUSzCOCTuftIOdK3nsM50IOSCAdMgZj6UG8Oas1mZhAcTmR+ElN8\nrVqMvTjWQxp/q6EwDyG5IqVXZdXiXug2KTpQIWOsM6AR1tCuit+fhYyjSYuhIJUhuxsTBM2H7ao3\nRkWYYJsjSsznuggCu9yr8Sz9aoZS5vXbDlCoZ8YIYWso7cf6YApgvvb4EykFEfn3AVQA/7W/9AcA\nvqeqn4vIPw7gvxeRf1hV3zz+rL5r7sOj97Ra8frL1/jpj1/icDwY0zIfXlJTI90EIKc0bUK4O94n\n7Qtob+5iWqfi5orhsK6gFreuxYz1cEBJYzhrbw2ns6G6JWWUtPpTH7F40+Yjy3x4io9lq20QiZQy\neQAu1Lx3P1243BqB5SVeEGYZgCSMjQb3HLyXX51QhQAgYBbeBIbek9cStI4GwzMoMDYG3tu56e4m\nkpB6rYQk46QIDMAvb0Ytw63G8NhYt58ylq7YfCo250JoIsHJJaBb1hIZg0E2OzURiadfm69RWFT/\n/kGjYlacYCJxDgK40MBw4Ep0wi0D66GwXQqdZ7ac64B+XdSayOOdHn4EIo3K7+AW4Kbm+mI8c/H9\nIH2UjF9iCe/nMfzYSkFE/jUYAPkX1U2Sqp4BnP3n/0NEfgvALwD4359yTn20Xr02vP7ySyzrP4gE\n4FxP4AgwYErPce286s1y7jo0s2+ufe+QpWDJCXtt2PaKlHYsuSBlI1CBckbjAikLUkrYPM3TWsXD\ng+XVl3X1WZWC7uQvvdrmbK1ZKMOYT8kNaPE75wyEYFEoHXPjGow43L2n4Cf0TRBBqM9ocI7FshQw\n7VfKCFTNwicX2rFfApScY2fxvHoSjLSgC6Bb6T4JQuToPfaf9knQ2fPGIgWqCoV5TuIzIzIySrms\nVG1OSb/kZeAo6qQynjYcf8iF0pqbnShQdPmpwNjP4JGQeVlcBx2nVqHo8qsopIqY7gWJgiLhAvvX\nt+m7bXE0PBdAApiMRxB35CGFYqRNeVqF54gFosmYzCPU9OsbuvFJx4+lFETkLwH4dwD8U6p6P73+\nTQBfqGoTkZ+DTZ7+f55yzotr9ptorePt7S0ECSULNrj1TIIkakLCOA1sVfUR6n2kkOyUAhGb/rwU\nL3/mMBPHANQLkdh1mXPCIR0N0Noy7u/vUdsZ9w8PSFnwkTxDWRajn29jdHvvFXVvWNYl2uNyKbb5\nJ0Uwx9DmynPD2zUzOwHgAriSIclu1VmH4BbVATAlBRq4d4aSERFjv1W1vD0k8unciobKj9bfS1p9\nK57hMMco5tJRQs6S7jxlWPj5+TssrAP2OipRS7Fp360iPpey3ffM2SnumxN7sBsl6AewRmE2DrzG\ni9oDHVbfFzysNxVqWH1lARgJbYDx1SM0YzNTeBM6GK5J9w6vwpWESIkSh4KIj4OjVItfdxr36uul\nSZC64v7+zlvcHwnXe2iFp6Qk3zUI5lcAHAD8qmthph7/SQD/sYjsML3711X1i6dcCLfJfPTWcPv2\nDXrvKMvKC4p4i04FPaucMpoYhfcCAyXVtXlKGUuBjYSHkVU03yx7rcb+DLrNtCTmiaAkrLqgHw5g\n6uv+4QSRhGfXV4DYrAjObNRmQmKs0tlaeZNEfEtLT/o06Uz2IeL93tXSfC5ozElHPQBG7tpy4jad\nKLzG6H5MsSmm7ofhkfn5EhQZOYQh1rfTSzFBirQmwT0gMA/Ah6Lwibo1i/qFNnwLWlGmMHPKQPHQ\nrjd0z/sHU1EfSPzMjD3qUUa1Zng6jjmpKNCZCp3wAr8Ohjr0fqLgyhWGyad6JoJC6YcartHDyqvf\n91gfqAPFUW07Pqvz5hdE6nU6PYrYxPBQFvyaeNaeru4NDw8PkQINp1vG/T7leEr24V2DYP6LH/Le\nvwXgbz352+MYAhHngrmht7dvsdWGq+fF3FbGzy40HLghqpCckX3ac+2WrVCF5Z8lQ6RAtg3nbcNh\nXVCWhH2rOD08DOsJ+GwACxNKtvTb8XDA4eqIjyE4bxvevn2D27tbPNzf43BYcTgckMUnRDtzkAGS\nBdlz6TELUy0ODFoyABxZbnX84qlLE1L2VdASZfZliBiZbTcXkQNfaI3JemxDc42MptVmeIwkz8kD\nJSXDE2iRuQ692QAZ2KyLZWXhU/Jz2m5upEDLi4G1eYQfpLzPIpBcLjYoaw6a1XljKQXoxG6A5bAg\n54wKxsri1yrOlOU0/q1Z1kYAjrOnnxCGfPLHmWnoHkYxzLQy7x6hiWDUVLBLk2Ap1Yh4CbgEmYvt\n3VHMJheTp8w5mJWjEQOTYxM6+jN6M/AUKaNgZKB4L/T+NAFZBV98/gX++PPPY1TgY6appx4fTkXj\nBAoBCM3J4R0lO19/bRNdFQCx0exRaw8LIVprA3X2OM9cax+q2hWLSJQRt70ajVsIplkH61ZULP79\nKZsgXe1XaM3YmXCGZTG8PNlGujm/JBStOWuxAiJGfgJJzqbj9z6BZtmzMa0NRcXCn6H+hy0gmDjc\nc9boJ2/cILjX0V15xHCUkBX1c414HABKIU/gRKjCRwZb/ySKFmbLC4DcKmbVKLCJQiPGuqBUwmjG\n1EMJMfJdDonhdfE5iitEBSyrE4xQUyUrpjJhlYvXmWnJEx5lV+PZIg/FTDEbNmO8ij34K6I82tdv\nVGpq4AyB+4iFrmyDh+9TJAXqENfkGZmUBLX64J3udSh5UK4lNuOoZ6+8tfL29hbn83lk3qgT+P8T\njw9EKeg7Yx5Vxb5v+PLNa/yZb/2UVRtuezxIos3m6nsNP0/HLjEXpDFc1XCZ2prXAlhjzbbvOOT1\nEtxRrx5kNZwMtH45rFj33TgXzDuNEeW0YmRDBiy9avKfwr3s3pTVu025gtcctJKxPBLSoH4DIkYW\nVRe2DGjzoiQTykEVNtxMVm3ahdBjsd8xNKBi5WeRCxQ1LCwzEKbXCLpZWbBAxsRvGdcPwJqs4Pqv\nj2pFC/tIom/WmylHzkTgaDpayTmFyOc0MiNOuNK6FRR5+NFai+uf3XMCd8PVNuWZpmc9hwrxeRlY\nh+EaHngyFJGh0Gm1Sap7eT6EcjGFxeKykb4EMBQOzw33DrMNFurnhi++eIXztnmPy3togUfHUyoa\n/387FKYUfv/3fx85eXck3TJ/T2QiJsHnb6ggOQFqrl3gDEHGcLVaOpGuYUxjDqFuwVEoYjTay7KE\nkJ23M87nE1rd7fzw/va6o9Xqlidb0Q3I2Myhpv53B9al4LAsECCqM22acYsBurU1qGMYnKVA8tEk\nyWso5GJbUNmxyy9JtuvxgjAAIQSSk7nXhOO7ZRzgisPmFU5WMLECkvV/9mdt7SIvHxV8nntNQCh4\nNlxFvYHM4nP5t4kfnzyiY5SktOLfZ89+mEnm+uHKhZ4GVyiJOHejIJXBBA2Ya69hiRANZoH0B2Zj\n38duzjBQ8RvaqREqR6iDcX4RY/hiiGQKcez5AU4bTrWfNxs5MPc9/JjHB+IpvOPwaKLVhj/4g9/H\ntlWLOUMpSMRV8EGoGgJiMWMMWp0sn7mYgt4c4MoFqTUnZ81Y0+JvHcCcUafRC7BNm9VCCSkJWnds\npw26LFiYZYC3I0PMchcbSiIKVDTkkimlhi+ohS5lWYagUtGJej27xZyt2/lLtsdnA1VMeeQyAYGw\nPc+OxpSIwiMqANWZmOjqWhbGZlW21oBuFYRZEpDIbuXu1uQ98IjQwN3qEKwhe0D3Nvhkz4nPmsKa\nvAXa6iPGoNlEpm1/b+vejAREWNK7eiu6xHMbe8Kvg41gkOiYtFDIQoDyFaVKJTIJm/8YvRZUNn7P\nUFtDZY2KeKEVBVt8uI1w4OzErKVUYl4B6u6QwoDQDID0USp2/fvpjLe3b2MdZqUT2vOJxwemFKhH\nx9F7w+tXX+LzV1/i5SfPHDMYlsU+5m6htABpVDWakhiLR55dLaXJ+DhJwt4q9lqRcwpryGP0FVzW\ntS9LwVIKtvMGoAHFPAf2PxRYCWrK2bW+zzD0a5csg9/B51sirK8zD7vLmb1dm+PQAJ9/6Kwf0gca\nDxAxd0pw785jqAIFmr8vlKsOX1bhNQ97s7mbkoblBMxNt2UfLr0a7jDGy3HdJR6pgccdzWs4Ssqo\n+i6rLoHPAAgQkFTykb5NPvgGI9yjxSYGMWjMwI1hIQ49FN8fHe7NTJWEQm+TnZEy9ZNM3wEZ3kBU\nmfo1GckuYu9EBaWOUJf7mLhgKIXIVOjY883vz9/T0ZG6ERHv2z6BoI+Pp2uFD0opcF8+fu308IA/\n/MEP8OLFs4ssQahrlyVz1zTcs4j5qEB0CLRn2ZGy5cQ5ir6VgpILRMz6zFhCxMc6XLll8clTXd31\nTQHI5eRgYo8kJydVIhVP/0kPHoUIMJWOqNkGU3rJWqz9d0H+mSyGhlt5+LVkFjyJAA6eQRW1duRs\nnZGAKSzejA3OMa+kbRWSDPBbVysaClyme6oWGiXlBBZDUECERy0dCBvQu513iK8bZEobdu9v6B2L\nEHvwlK0CaSmTIRigrFnksba2H9RTtXAXP3kJNNB5jZ5qFVthdHRoNYXBeoucZPBQ+LPsTPVwvybC\nM1Sq87URa/DLcrwCU6ftzAuCRl9kNHhBBLXVCesZA4cksUsVRhAU4fFXFcB7OAofllIARuwJICzY\ndj7h7/7mb+DPffc7KGXBntyVxqUiIbHrjJ5z44KMN6G3QfUPkq6wco7kGoBveD8TH6q6a22WxrIS\n6jXvAKJrMHq1aNkgHsebJSd9HOBCrmNUWA6v0cE7YbMLbIPmbDMc1CMqZX+NhjtaPMZvfRDXEiWP\noSENAdxx6tZ23pAkYck5FA2fDanW0WlRJazmmJAFdG3R0yCedaieDk5O+CBNvBIyQbSNkmUXpNb5\n+YTDYQUxhyhe4vcz/BAJIQv8wsWBBUKxTxjvc49UjXW20EdDYygw5klgkM+GDvd/RCk1Q4uUrPrQ\n32thhoQ3QZ4UCvkcYcV+U0sj55L9nMMTSTD8J1Xg9dvX1uQ1n+DHPD44pQBcBhEdZrk++6MfYDtX\nHJ9f43QyTICubsIACyGY5u+NM/EB0LqoW5sMDaUiAidi8RSciG0EPpzWTNBjM1lJtFVHOvDn+e2g\nWcseQojRyktOUO/gZEw++BbE8vkyYs/koJ4dzWjBtaO6AiCwEjMawq3VIfgEr+D5dlVos5hXoGh1\nd1QfKDDXuixGZtN6M4GRNIpvgOE1sW4JjIEdB2ksT1YkVbRtt2E59KRUsdeGw2JC3JqBrVzY1jr2\ns4G05bA6KQ5CISjvWdiq7aGQd6Ta2pnvsPsoOjSSv0yxva+PJANRu5cnm4K187K+wJ7L+M7wRL38\nmUBnD5ACYJl16DB/ZiPycA/Hs1KmLOxnds+mac0MDwFq7UAxTKydTvjyy9fDUL1DoN4HfPwglULE\nUIzP1Ia6dng+211LX0Mv7nGARVOUw1psaVY5eWwGt5IKB4GmOJWgT6tOOlIWy2W7FaXLxgiRbnxZ\nFsj5jN6tOvKwOCdDV5ScvTDFN7IHqAb8pZHlUEQajlYHsKjC0p2+Mm6awoUcLo/H3P5OkcFmDAQh\nCAucLH/fsO3NiWcWC5t8U+ZinId7a2ites2HAazMdlj44xwQzXgYc5fo+OS6tWpb9ep4PQA77+K8\nP5/Mu6kdda/oAmz7DvSO7bRBsmVI6N1ERR/hNHFuCW56VUiemsTM3cK+7QZKEnNh5qBbmrb4pDHG\n8AxRDN1n9Sup9+zb7QomIff9FrvYCX2ZiBxgOEKTEvsRlmqLxnVY4ZNGK7ztWwtfUk6QkpEl4e3t\nLd68eRMyEdpllqf3kL4PSynIAHdCMdhPqPuGV29e4+b50awaS1rtY2itG1mGjx3vFD4d8WmEA+4C\nW0mtZwhyDoKTJMBSFnTtyJhr9q0y0tx2XiVGKk5tI9fDAcUVgQmLMe1ao9TcIGTXFvXxHqTGvIpo\n9rEUF1OiBpQJRHIULdk5Mkjo2eiOioNSHkuzvsEcDeIsEgpJ0SNWhQhKEtRqyrB3RXcFoaroYs+h\n7htqrejaUVKKMEB7R4JZM8mstXCkxH+v2xnnbUevHn5lYD8jyGyXdRmhVeuBf/C1i8wHFaCHH+rP\ns+0VZPYWYTjX0aWj+f1KwqgmDSBPY1hPktESH8bIQ6PAHqhUPM73iwLck7Xw0IffCt+qA1NgsKMY\nIKO/Tg+GaXRJCbIkoHd89sef4e7+frS+v1u0nnx8WErBF3ToubF5z+czfuM3fwM/9eI5AHoRU1Qr\nsFHcYpshvGm4xqaC0UtarlYbUIxoJYm5mnt1GNI3EHHw1rpzBbrLnk3QSi4o64LTwwmn7Yzlvlj9\nQhKcq2+u1qyDTRUJFiOX7N2MCQFu0WJI4AQamy9BUVWdiNiVnhf1MPVohkRi80MH4GdZBSqabt2d\nTT0Fl2IzL85mZB183swlCSI1Zm0Oi6uAZByWBEkZIgrpxnKcSgEgyIsrA2VpMQXEXPrkWAvDpJIt\ntQsBwLH1xEVirwzrZ0q7X+AK0hRVG/bzBoj4Pc2pyOxt5u7heJNRI2YxC/hkFCL29z9U2SKNQZ5C\nd46hToSymIyBA5+OVIrOfA5zmMv0sRsQkg5nCzVPt/f4ve//Pk7nedbDu0TrJz58uDxUzVP4vd/9\ne3j4xX8I1y+eQ1TQ2j6q48DY0lwyEpkoNFpiAbrtMqjj3cU1w2jMPL11nFGxhFIaXoF2y0ubNTdh\nKuuKm6sr1NpwOj3g9v4eZSm4vrnGrhX7eUPrDXut0NqAJGi9YfFMR/Gp2bR+io7u06rj+9UzJtKj\n3Nawr9EFmLzNmYxHOReugO/T7hmVhvPJKt+WXGxeQkqoToG/Hiw92msPoeCg3AyJoSSqCiSgJ5bV\nmhJK0m2sm3sorH3gUJ+RxWlQCNZlHYxPAHIpngWY26Lj9GBqj+VprGyUEDarv1Cf7l3WJVq7CXJC\nh4Cyu9XKshM6uk0LV6YzmQYVzMrILPikCAITEMskhIFz4yU2obxNCoYhLWSEuuKvUaGwshNiVbMQ\n2MChDjy8vcOrV186BZ2GIaRnMKean3p8MEqBC8fswOOjd8XD/T22veGTw+Kc/1Y9KOCIboTARpyn\nBMUGTsGS2MhRu4vGcmKVjlp3q4L0TQElMOfKJye0Zh2Qy7LgcDjgWevotWLvDW/e3mFZV6B3r2NX\nSFWUZXXwEFGRCDUwqdJNQgfaaIGWxEIXhbZuHXPu/ZgbW72WPw08QiQYq1lGDHiVZK3uXSXkxbkk\nqmELqRTvuGODDqLuH0jGhC3Ji2S6W/nh3hHDCI8MY2JTcy8lOVAi3SeFz3yTgIc6GMDwhJtA1AhL\nAbtHSRcs1K5SbYzc3rEcD1idwZpj1hi70/qKUEAFgzJOoimOBLUm8Gmy+hkXtZV+rWFEupctM/wB\nezPmyltE2CySJgWCWHt7i/peNE+z+5rcv7230uZZbPQdfsF7xA8fjFLgEVlduXjSUFgW4s3dLb67\nfBtlydjOAu+OgTnXU7vqvAj0IDxlyXH10bQCiw+1d5+vkIJXULXjdH6I+NV61zO0N1TsSHvCoXcc\nDwdcH6+gqnhz+wZ3p1sc32Ysy8Fq1CuwHFbcXF8DMD5J9TRc692zGh2izozkCoKUbEbTrkHm2lpH\nyk5E0jtSyih5Afv5Le/e0BMAD4As5y8oecVhKWgAkOACVL0RKcd6A1SwGB5MSKCnUy8Ugqf6wDoO\nExYFrFTaN/LoDhUsywrKWVQ5Os6h/XJr52TKCGJdl129/FoZ63vdhHa0rXmXZQlBzh5TSlKjauOO\nE5lYkiiU5lW4kxYWP+jn6ZXMzVZx/wiaOHJYqLuwHGuv/h091gXmVfUJc9HLSVyRrhfveagNf/zF\n59Y3g4F1fFWmJrl6wvHBKAVWfL3TT/C7tT6I7+MXf/4fwLIUpFyApiC9GV05enPz+Tq8usxXbW6l\nVfVWaVhrroggHQylN+NkD75kK7POSZzduWLbNutFUGBdF6zLguN6RN12vL59wMuPErA1yGrt1WVZ\nzLqIoLd8EbMyS8H4u/m9M68u6oVFrblg2VyKw5JNKaxLuOcMk1rvFzUQxncgKEtGUkP899rQmqKs\nJbIaoAdgi+UWnq3hiM7ECKGB8BpaaxO2k2Izp2Rj7Mfcz+4hjlGTh7eoFvIFcJgm0hGm7jB1gqJj\n7925Mjp0b95KbpkoZmlYT2EW3zcFPDRLLH9nVyZiI7HSkgJH5TYLK5/ZSFOycUrCE0jC/Sh8eQi5\npy2pSHrrQLrEM4hDMBV1f/sWf/SDP/I5D+FLvcNLkEsj+zXH1zZEicjfEJEfiMivT6/9RyLyfRH5\nv/z/vzz97ldE5DdF5O+IyD/75Cvh53/I6wpFrTt+57d/Bz/47DMsKY+pSb7YOvuDDKPc7R/NTX7j\nkfs39694/p5Vidkr+RaP+VN2JiNU1F6haNFM03vHeTvh7u4Wp/2MnBKuD9doveLVqze487QbrY01\nuSSUxRukSjGmoWRkLdWJYrV3m/izbdjPG07nDXXfzTnShMPhgJurayzrCim+y52jEAmOqntmgZTu\nS8GysFPTN2VntyXrPD1DAsb+CLfagDWyTGnE0AIBnPCVPI62H1IU+1ghlLfD14rW/7/23jXWsis5\nD/tqrb33Obe72XwMH8MhZ0gOZzQPacackWILykQIgsCxlAAT54ej/LEQBEgMSIANOECUOD+MAAaS\nIHIAw4EAGRIiBYYUA7Zj/QmgB5REz3lw+Gz2NNlNdg+b7Hff7vs8Z++9VuVHVa219nnce043Z/oS\nuEvi9L3n7rP32utRq+qrqq/Me1AsY/2xLOZraDuQ34kUUGQw+ihZqKGXcQrMieDVkP0QSmBJg6ZI\nlX/ntCoXpdM+bUBkk9wUeWcJYM4SwArsidWcMNU/y1Vx9ToxE1K4tjOzqACH017IwKj1FyT8CdxF\nbN2+i+3tncwGjqLDMztqDethJU3hfwfwTwH89szn/ysz/y/lB0T0ZQC/AODHAXwKwB8S0Y8xZ2Xt\nXpuolwF3797B9995G48++khKjZUwVDnRKW09maQYtXp04EE9AwLskID43KPmBsgJ5pwTDMG5pD1E\ntixKgvO1qM4QX32vAJ64S4XYZFzXmExbTKY9oMQk3leoxl65EiVMWXIkVP0MAVGDr3qNEbCKSF4j\n5KpRJa66qtI8CsAFO4OyMLToQ6utGCMLy1OKqdegrBiVc7KBUX0xoHa0enacU6BMQVBoBKLiH1Gf\naYVbokqdgdahm6bvJc/EMJ2yiK1tCgu8SuAgBEuBzlsCiWNA33eYTqZwzsk7eK8aAsCUVfoYWUu1\nJxVxoAWlDZ3MHAc4FbIqXMyaMmFmAiMVqk2ClBMeZBhGGR5va9o0Bnsn0nulmBgVulZlW+bSoXIe\nu7t7aLsc3YuyB0nj42Rerdruqe7DAe2bAH6XhcD1PSI6D+CvAviLVTu0yHywz5gZbTvBxfcu4Se/\n8lcwPjHGXtcjaCyqJAuZa40ThsBqc3ufqcEN8DIwy4CkECO6EFCpQICeJN45OPYpYlFUQzk9HRG6\nvkPX9ehDJyzOzuOhUw+h8hNMuwm6vsPW9m7CMOpGy98hwVQIQYSG96xqv9JwQcwAzwR4wmg8Upen\njo6q1ig2YowM9pq4BLPh1dPCZkdLjIEEAJl2lROumKFMwfKcYXgxAI7og/jpwRJL4bKKZv+PJA8A\nyXuIxoEAACAASURBVIOI4j0xV2ekmMBQc6MylRWjpFmsRHInc0ToenTTXmnvGjhv4dZqgzsHF2eo\n6lCaatANhLRLRYPSdafJX+SsPgdU0JrHw9i/0p3T+mAV2Mn8AdLBkp5lOAYVQgH2OcGySINS9zEB\nTV2j35vg2o3r6PoOxasN2mz49KrtfjCFXyaivw1hav77zLwJ4BlIcRhrl/WzuUYL6j4AeRHMK0Ay\njjFE7Gxt4872Lj718AlM6gph2iGyVit2xmqc+RLIVVpRyZB5nRObSgdowSiJIAwRgaGJUKp+2Yml\n1OniKtKJdaRJVJSAKO88xuMNjJoR2nYDd3e20fYt7tzdAseIkziFE6ca2dMQJmjjXHSaiAQQal+j\n4RxRyWCNB5CRMT4Fr6dKtJdTkwlFeDCgJ1Zh9sQA1E2d0rATBblukmT0MAMI+T4GuLEwS6UJUg0k\nu0g5yywT1BpRWTdVjrgsg5GI4KBl5sVeKXAJJIHQTVsgsCZsSYyECJaBziTj413i5DSgUzutY5EZ\nmS2nJGEKKLQY3afRNrLiTVKLkxPfo0TeqrofRQOxKEoLvDPvEAzrIBrUrpTDDbmQMQNuo0HtPG7c\nuokbN24mz9ZhbR3BcK8kK78G4EUAL0FqPfzqujdg5l9n5p9i5p9yhSsmBW3MqDsGejMY03aKq1ev\ngKJD3XgwU/JVW/KPlVLzvlKOAVXr9Bl936vkJy0d5pL7yCL/Uj/UxPDeJar2sq+A2Mu1lTJTPj9f\nOfimxsmTJ3D61CmMmgZ96LG1vYvdyT7ayVQPd7NPLfmJkrAhL8+tmxp1qnlgEfzZBjX3JEFdpmar\nwja6nhx62scowUsgcQvaiSf4i6idkiXpUhyEaQrGJ2jmmQkN+1tpx4eYuR8lC1L4BaTKU51ISngw\n5+bKy6q4qcIxMroYENopQhvgmxpNM0JdV2qruyRMGPqeOvG6/ZA0KgyB7TJteqDqK7uXuaydd9kE\nsxO+wKhMgNlJnanoOe3OhD8QMq5gGlZa9KrddUHM37pC01To2ikuvncJ27u7RWjzzP7CeoKgbPck\nFJj5GjMHFmPmn0FMBAD4AMCni0uf1c9WuGn6n/I5w4/1EGy7Cc689RZu3LyBCoIRWDYbYgQiUq3E\nPFmF9pEWKQ/+YDH9xhZtJhnNXuMy6FYsY9lgilswONGlwXucPHECj55+BBujDcAJn97dnW1MJxOx\nCXVhhRiV7FTAo+Q21YNPgDd7RjFOdgqpiluCXIYJWN8AoOt6dG2XMAB5BwZMTdVXNk3ExiwJbdWk\nHZX5FkiZpmWatY15YMlxIEcYjZtBePdwYRuISIm3MMSIadej71t0+xN0bcRoY4SmbpIZJf2MyNgP\nFKjj5JliqECLsVhumbnJhGwC7Em8KhlctNPf6oQiC2c2t3f2cuU1h9SfxCdq4CGGvjfWkHYhpBWt\nylcVXC2Rpndv3sKVK9cVT5jdM8jvbusVtI7z4d6EAhE9Xfz6NwGYZ+L3APwCEY2I6AVI3Ydvr3JP\nVpVtDhAxlbm4LvQBt27dxBtn3kI/7YTk05iPA0sJeUO6o51oQ0ldPre0ldNGS5POSU3O4a9ZvRS1\ns/BRkwWoKIClGwreYWNjjEcffhgnN04CxNjZ2cX27g4mkwlC7NVEiGoGAKY5GOiY0p/90OucBByb\nOaS0bM6l07NEymOMCF0n/VPAkygxTKQTM8UqlIi4Ln6J+VdOh8L4d0l4Ff2LZv934BBRjxs4W3pq\nbpBqYBlspJSjIUWBp9jf28V00sI5j/HGBqq6BjT/IrvtspAyopqsgMi1CWviEljkQiAonmIbyjAK\nGxG252h6tyW1QYlwktaWny2vmT0JUgsjCyDTXgQY75OHh+ygqYVPI/Y9bly/ib39/QLczOtgoX6w\nhkAA7r3uw79LRC9pDy4C+K+0U2eI6F8AeAtSTu6X7tfzQLMiTlWqdjrBDy5ewk986ct49IlHEboe\nbduDEvU4Cu1DJTADibA0YQs5+02eh3QK2rMDR7io5CRReQeQbpsXZHk0M6MLAQ1JWKtQcXtsnNyQ\nSMctxu7+HnZ39wEAozCWwCFQom2zalhlRl9dC9YAC6VNaqi8t+UxBE1GsiPDOAa7vgf6ICZLXWls\nARIRjLnFSAUek8YDQLUhRECxDwPlbANktmL5Xa5mgANiGxC6iHpDqfCtCEqpfpuAAINDTB6d0PUI\nXUDlK4ybcWYwRhYIUanZDNeJHEGhgDkALUYLk7gAkCpmmZloQt/UBFt+iTgFdq3Y/8wBfdDAKMN3\n9EDilNWo6y4msYLSLWm4Sux78Vw5J+HkuqosI7KuK2zduo0PTUuw+UVegwvbAnP8oPaR1n3Q6/8R\ngH+0cg+0mQI18NMu0nn04A0x4O7WFq5fv4HHH38cfuThQ65KxBaSWOoYjFT9Kd8sX2Laiol3Aeqc\nAJdFX0r12r5nVV/tcyLCZDKF3yDA6cIT8ixUtcepkycRolDE72zvohv3gkk0daq1ACBlVMY+CPsQ\n5VLpaY8a8Jh03nzKJSyBpIp333aSkDWWcOscY2CLtpyR/L4AFOQzFyXSSSncFjluIYkJp3U4JeAO\nteVUcABLHpKGb0NMLV0DvZaQi6FH6MU9fOrEKUC1iajnjESh5tBqRCCze5eaWtZ6Uk1PxX3EdBCr\n0/COrHJns8pMoaDuXVuHxrlQkuVUtdQgDbEwUWPGW0BZ+woqwNiAxBkzxipi+6aBA/D+pYu4dv2G\n5usUQqrcIqnfyAJudZlwdCIaARwuEGDvJifDZLKH1954HU9/6imceugUWprCOaAPphrKtVmtYnDi\nAyjt6Gz32skbY0T0Tkqb60mSF32pkSmwBmNLsjgGj8l0iqaSqEPnvOZSRHgncQYbG2NM2ynaTvIs\nurpCHRqhPlMPSlM3aGpJH66JwN6DNdYCMBSckpnFkaUmJpDsX1GjLcSW0IwbqURd+WIj9snlVsYM\n5DGUjZPIXMxMgNbOsNHIf5IksGmLpqrhmzoHjJnMcw59P00nb1V5xChBan3bASCt8t2k9GZzNZsm\nlcKLQ0TUICQih9q7HHLsCBwdYuzkwZo7YVqKRZFCDwTWMTBz0wBUqOkFjikpTNaC5UZkvo3IESH0\nwq5MUOFhYyMJTqRmh2xwShR15R4gAtgRak/YvnkTFy5cwu7ebhLUB+71NbSDsh0doWArCcsFAoDB\ni4YQcOPGDbz99nl87aWXYLUlvQP6QGmTlJLUpL49L2kHAIz4wzoSo5ztUETbdFHzDpjwSECgdc8C\neGLE/rTFKEZUVaMoPxAgiL2xNnWt1I8gIpwce7iqQhukH0bQ0XYduiAVpkYMVFzBwMlYFHRNbEu6\n3T25VCeTmVE3NZqmSRpCOR6MfFpnD8ZAm0YKT0wnPCXmK2Nn6rVKVzdt0fUB3leoIRu6t2I+xPBM\nUkwHtgHkZA19gCOP8cZYcAtn5kxMc2o4gGE+vqqSvZ6suHItOdG6QqA0zyxGvAiVBDrTYP2Vno9y\nUzuba1tHZgLEgNBKtGuv4ejOhISZeWy4hZl+4iUTQa+5MMzwpKzSTQUG4eb1W9ja2p2PYJzbIjN/\n40OEx0w7OkKhlAoHXUV5UTIzJpN9vH3+PL78pS9qWKuGKQMax55t5VlToryPmABmHWfbFOSFdZkj\ngsUyOKVw1xPEMYOdEp/oTYkcmrpKZexHY8JIkecYsvejaRp0bYfQ9QA7jEYbGI9G6BshMa3qCg6M\n/ekE077H7v4e2r7DqG6SGw4ArIArkK0IjoxAUquSowT6VE2tZefEllLLOWdk2qagHI9Puunt2jxP\npM+S6M8YI7quQ9tNMZ1MVbVw2GsniJAN0ndBhBgRRs0Yp8ZiRkUIXkIMdE4EpMSWFHPvHEiLuujT\nNe0cCUxOrDraRYtSlbRoqbFnuRnJK1HY3KXqbSXl0t+cSxGNEocBxD4ngQERbdsBkVH7StLSvTA6\nRStkrKHsMub6jgW+pbJORpkZIIeNUY2t25u48N4lTNvp+hrAAWfsonZkhEK51MzPe9gXRKUN2Lp7\nFzdu3MYTzz6JbtKmBS1YWJE5CQvFKdB7ymHASYOALKYQWBJUYCpkTExH6YRlvYaQEGjoc8fNCF3X\nC/IeA7quk6y+GFPFJK+BSqzBL+bPHo0bXWZC8nKqqlBPp5i0U8QYsD+dog9BgpzY2Jt0IO39VD2G\nI3AfhdKOkLAGZovqFPdp34ck8CxNPJVKk5dCpcBc0pRYsv762GuxmhaIkt5bj8Zoao8uBt0UcsY2\nVQNfVRiPRjJHISQzyDmChzfrIp1y5DIAQYnrgeArrxqEqtOMZGtrFzWiEKkWKek76gvquHESLKXQ\nt7kcNk6bWE56VSIjlGpfa3yoG1iwMiD2Mk+VQ8rlMLIaWV9ZEbP3FO+Ow5XLl3H12nWt/5EzK8Uy\noUMFxTpy4cgIhdmWVKDCdpu7BqLiTyZ7eOvsW/jrz38KsdJajYjZNRhLl1U+ReXe+aQwBB8kvuno\nslvI+BcAZPW1XDRiqkKlDJhyrAQgqH9ERDeZihfBCtuo+ghIduHO3h6YGCfoRDo5yTl4qkBUoWlG\nopqHDl3osT/tRNgg8yakIjim1RhSHhnckvIvCJ27MEyJz5yj8V5mzkUHYBjPYWIVKVaCSLQyYkLj\nRxiNG1R1BXYOzAGkZCcAa7i5sBCTd0DfJ+EN5DBgCabKgkHmO7sELYQ5JU7BQbKozc1IScjPzVfx\nPNbCvsSmDeX/s2eqvp/YrmBmR+ESZIjHRCjkRvL+5tXKS1mEqEXWwgQKpb+b9gUV3uwIW7dv4O1z\nF7C7tzcQCNZIv7zUw7CmYnFkhUJqehIvl3SSTnzpB5cQuohm3CBMOvQUkq1oiywh0pANnIqJGrgE\nPXEpRwuaml/pRCVTQ02JwSnC+STzLAugrhup/1BVkuzUBQTTbmNA1wf0oZOTNvSKRQgj0Qkeo6nr\nnHvhSTgeqwp1rNFHOZkpRPTGbMSakOUI3lWoSCpZec39sOeAhazEVz4BfG3fiWZChHHVwHmJoDQV\nGw6aLxHgvUdTN6KJOVKOTBVKjtQ7QQjBAd4Enw0yMiYAU/EjMpGIajOZz0QTkvLpbPEbCRuwOY4s\nRXQNNyzS36FmYQr8UiJY1rmW1HBJ9HJ2els0aDHFVMRGRIbyN/QApKiP0LHndcWKIaRTXTUNZxyb\naubaKjf3LDsJl37/Bx/i1u276go/YIevoDGs0o6+UMBQ9ZHJzDYtq727t7eL3Z1dPP7kJ7AXIjAt\nor2olMSmYpr5Ma8lJIkNpDDZ6OS0dbBoRxEABgQS55PQ4LoQI2qlHCPnUIEwHo8ROKb0aAKnDRtC\nEDOCo/A09D1OjAVjcE2jWg4rgxDBo4IfVRjV4xSHIEEv8t65KI1G7FUe1MkGsUpYUMTeqNnEpHAK\nirqkBSSLITJirYxEGu5NRHCVjaEJYuM7ILArgAGbR4a4MoFCKKueEhlUSUanCCLzJplwktCnXoFX\nwwYs1Y2ZBl4QM4MGC6rQHA34k4hSmRM7vc0DgaJ/IURNRBJBHPogfBZVpeaY5M6YlpDAUOcz3ZqZ\nB8WhpAqJ6CmOwJVHM2rw7nvvCwdj7vzSfbLwL7q0V21HRijwkheVvxXvNCMJTTiG0OPqh1fw+BOP\nox7V6NsezpmtZslRJW5R3JSRSn3byNoplNxCZj9alJxmzeW5t5Wj5dNYMIYuBNRJgIvG4KLQmHlS\ne7gSwRBD1IQmwHFE1wfs7EW0XYeHTp1EVWmdS7awXXUbOo/a14BqJ1aUFrCTkIXTj8XG8Uoh55SP\nwGxXYrG5nScF+FQYqFqeADU96YPWrzCSGxOy1tIpa6o3m/Ylwm2gwbFoOzYxSaCzaHQW7mspxGw4\nEIz8yTIdpW82veZdskAwqceRuI/SKW6h7Ym3wAFcJnoBCNA8BuW4IBI6OGGPKrgVogobPYUcEfoY\n4R0pY5TdNuZrE9+bZnlWHr6pgL7DlavX0PXdPWsBqiOtfP2REQoHSUCbfLls5rjXb3FkvH7mDD75\nqSfxxBNPYVo7UHAQdmEguOyDB5AWBxwjwCViEHMHgcX1RZYoZUFReg9PmdHH1Fo7SeVSp+qzbAbv\nndCAqeooOAHJaahv33Y12q5DO+2xMR6jIacEIi129x02xhG1q5MdatiBjI7WsDSiFCLE6ISFQPvV\nU0j4CKugCyzsSLZh5zLzzWTS+ADmnP1o2lLkqAE35ZRp2lZxYpa3NRNH/mr+f13AjmBHvZhDcWAm\npDoZJNijZDFmVd9mKjInlirxJLmUxWhmkWlTYmKY9kCm8wtdPhR05IjQCWBa+yql46d+chzUrfTG\nkM2ZoYoszJQjOGZ1RmApSmYTvJhgm1evFyHNhwuF5OpcMI+rtnvNkvzI28I+F9J28BEKG02PhwjG\n1asf4k//9M+xeecuxvUIrvIJA3BUJL1oGnTKejMbLp1OnO1A1S9T/r0+j7UzpScjqduRwYjKI5AF\nh0iPdHEKyzUUvW5qcAyY9FPs7U3AzGiqCo48Qt9jf2+CLnbKnyAbKyqlWtv3SjgiNOxgcdfl9FxO\nG9/qQJoADCVgxpzUfxMmKcEpZVjq5lH2IbtHtIxImx9TnYv5Y+Yc/ambtkxOsmQzkBCohF44KUlx\nilI4WUp22nBqAlosg/U5JXXBgEwzh6yYsGgJbSfkL13fa1RlTJGVfdch9BGV9xg3jdayUI1DoyjN\nuoGuEPOomDZTsi5wHhD5uxNCXHIEeAlp7qZTvHv+0qFxCWVbfBWv+nUAR0goAJgTAoR1lB6gbVt8\n8OEVXL18GQSHqlH73jACykj87I0HLkYTCN4NrxiEAZf2ImeUX1epbCYMGHPizPdt43kn8e1C++YR\nQ4+9vT1MlQClqirleAjY35tIwVEVNma/RMuq4wi2fyEuT8kW1UhOp1qRajJgy+wTL0umYCsSjdgE\nnS4u2VGaPehS0lcocjHEhNFAJWYMRIPuYhnzmDd0ll0AWLMEOQGl2dQoU7f1luZNYEt4CupNIfXz\nqXA3anwNF4/M6Dmia6fYn+yh61uJMu1btK2Q48QY1UyoJUjKEVxloeYigMu0e9LlkbQphow/aGBe\nZQFreTmkrFEViAMu/+BdvHPhYjKpDmo54nZ+9xfH2EqNlroxfoStrmo2f+5sWxbXPd9kMpwjjMcb\n+NyLn8O//Y1vYLTRYG9fque4wvZNpkfMtGV2EjIYdSWhxaY2Wsn4VJZeJzHfSk4FgsT2S/0FzS1g\noWtz3qNSzSREJVUJUSMgpW/ttMXW9jam0ym883j44dM4cWIDIIcQAtq2BcjhxIkRRnUD40UsxyYU\nGzGpwmxEpl5VbFK1Xd8fEkNQgocyPrn2RFmMxTaAN25LS9pKOSf5u05xmHRvRylbkZkTHyW0P+RI\nQM8052ryRJaw4aRzIwnmkm2p61S70P7Z5oyAxFmQuG/70GF/d4LIjIpkrp13ulYo5R1YbIoJFTlg\nshArKzPJvrSaHC5XsY4xRa26QkixrlxSHGE0bhD7Fmdeex0vv/w6dnZ3iz2weMObIJiNxEw7wxFO\nP3ISN69tvszMP7VkA+W5PeyCH2UbhCPzvECYvWbmL2DIiTadTHD5ww9x6+o1IDqJ4qPsJgJmgBfR\nVmXCqLybCAQ74ZP/2lRiM2Hsaj3BYtAFq4soxMz2YyewCZp0aio6XtUVRqORaAwcMW079H0AacAL\nkcQZTCYdpl2Hvu/TyaxyKqUv64ukUwjpNLXTIyuyVji15GQAkAhfjaORCiEk413UMGBk1yMgAisF\nCuVTTJ7P6UQXXkoZYa9p35nxScYvxZoU2AUhg8DmSkz2eyziAdK72zxFtH2Hyd4EMQQFmE1b0/fz\nGq3pCn3V3JopziBrmCVDtR06JpDLXJtyzYEs7VyeXTcNvCd8cPkivn/2PPb2JwcKhHybmdDsRReu\ncfYfIaAR+fQ+5A2WK1PyzRAjdra38J1XXsHPjGs8+eTTmDZAaKXeIWVBnR+bAB8JgjGQ0KoGlw/V\ntZn+ZoEvom3EdHIwW41HOZmJOPmaxZ7lpC4za+0HEqr4elph2k6xt7+HSnPpnROGKLlHxGQyARMw\nqhphg44MqjUnwgOxN9Az9xlQt6W+jtGXk56UBNaQabmCUZpGSBiBVV1OhVxZ80QKdd45SVfubXqJ\nE3OVaQpEqhXYqasBQinlWDs/SI3mjBGkOBMGnLpDTdtIxKmRB0zeIQR0bYeu7VE1VcKUbDUIdZto\nW1I5nIDosluTUQCGEI0xllm5Rf+QMzVBUDDSJ63WwF32sh5uXr2Ml7/zOm7d3kQIPdbZzcz2xPtr\nR0pTSMEbB9lPs1hAYdenz6A1Iq58gD//i29h8+4dbDRj4SKgEqhUl14R82/g2fA05MzbB7VbC2Zn\npM2WAS4hPdGqSFGqOSVbO4QcT1C+DwMgCXuu61pcWX2Pvf2pqMQhn5YcJVkq9gGTdoqd3T3sTScI\nXZfVZc42vVkRQZOVQsg07QwL6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ibLKkHCFaIsTu9yRSSz6UF5STMsFiMZHvCe4Eh4GUPw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\ncRu0Y6Fw3I7bcRu0/x8/hxH6VTwUVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4e1W3IGfkhXd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}