From ed29a6b5940d57c12072f0c3a059557b65e83cb9 Mon Sep 17 00:00:00 2001
From: "Fathelbab, Ahmed Elhaytham Sayed E (PG/T - Comp Sci & Elec Eng)"
 <af01075@surrey.ac.uk>
Date: Wed, 24 May 2023 03:22:49 +0000
Subject: [PATCH] Replace Untitled0.ipynb

---
 Untitled0.ipynb | 7922 ++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 7921 insertions(+), 1 deletion(-)

diff --git a/Untitled0.ipynb b/Untitled0.ipynb
index ffabbed..4e5c5bb 100644
--- a/Untitled0.ipynb
+++ b/Untitled0.ipynb
@@ -1 +1,7921 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1BgebwSGs3PQdJeX6_sZD67SS_1YLtIut","authorship_tag":"ABX9TyNhBPau4qwFfDOf66Vs+H2a"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["!cd /content/drive/MyDrive/CourseWork"],"metadata":{"id":"U70S5cdH3nDN","executionInfo":{"status":"ok","timestamp":1684883303532,"user_tz":-60,"elapsed":409,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["!unzip /content/drive/MyDrive/archive.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hGUGy9uu3vzF","executionInfo":{"status":"ok","timestamp":1684883554756,"user_tz":-60,"elapsed":39503,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"8873cce0-7482-4a63-907c-49602bc536b1"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/drive/MyDrive/archive.zip\n","  inflating: Data/features_30_sec.csv  \n","  inflating: Data/features_3_sec.csv  \n","  inflating: Data/genres_original/blues/blues.00000.wav  \n","  inflating: Data/genres_original/blues/blues.00001.wav  \n","  inflating: Data/genres_original/blues/blues.00002.wav  \n","  inflating: Data/genres_original/blues/blues.00003.wav  \n","  inflating: Data/genres_original/blues/blues.00004.wav  \n","  inflating: Data/genres_original/blues/blues.00005.wav  \n","  inflating: Data/genres_original/blues/blues.00006.wav  \n","  inflating: Data/genres_original/blues/blues.00007.wav  \n","  inflating: Data/genres_original/blues/blues.00008.wav  \n","  inflating: Data/genres_original/blues/blues.00009.wav  \n","  inflating: Data/genres_original/blues/blues.00010.wav  \n","  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Haythem","userId":"12900721674845837314"}},"outputId":"6158a33d-2c65-4271-f927-d16a0fe10ffd"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","/content/Data/genres_original/blues/blues.00064.wav, segment:6\n","/content/Data/genres_original/blues/blues.00064.wav, segment:7\n","/content/Data/genres_original/blues/blues.00064.wav, segment:8\n","/content/Data/genres_original/blues/blues.00064.wav, segment:9\n","/content/Data/genres_original/blues/blues.00064.wav, segment:10\n","/content/Data/genres_original/blues/blues.00029.wav, segment:1\n","/content/Data/genres_original/blues/blues.00029.wav, segment:2\n","/content/Data/genres_original/blues/blues.00029.wav, segment:3\n","/content/Data/genres_original/blues/blues.00029.wav, segment:4\n","/content/Data/genres_original/blues/blues.00029.wav, segment:5\n","/content/Data/genres_original/blues/blues.00029.wav, 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segment:9\n","/content/Data/genres_original/rock/rock.00073.wav, segment:10\n"]}],"source":["import json\n","import os\n","import math\n","import numpy as np\n","import librosa, librosa.display\n","import matplotlib.pyplot as plt\n","     \n","\n","DATASET_PATH = \"/content/Data/genres_original\"\n","JSON_PATH = \"data_10.json\"\n","SAMPLE_RATE = 22050\n","TRACK_DURATION = 30 # measured in seconds\n","SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION\n","     \n","\n","def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):\n","    \"\"\"Extracts MFCCs from music dataset and saves them into a json file along with genre labels.\n","        \"\"\"\n","\n","    # dictionary to store mapping, labels, and MFCCs\n","    data = {\n","        \"mapping\": [],\n","        \"labels\": [],\n","        \"mfcc\": []\n","    }\n","\n","    samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)\n","    num_mfcc_vectors_per_segment = math.ceil(samples_per_segment / hop_length)\n","\n","    # loop through all genre sub-folder\n","    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):\n","\n","        # ensure we're processing a genre sub-folder level\n","        if dirpath is not dataset_path:\n","\n","            # save genre label (i.e., sub-folder name) in the mapping\n","            semantic_label = dirpath.split(\"/\")[-1]\n","            data[\"mapping\"].append(semantic_label)\n","            print(\"\\nProcessing: {}\".format(semantic_label))\n","\n","            # process all audio files in genre sub-dir\n","            for f in filenames:\n","\n","\t\t# load audio file\n","                file_path = os.path.join(dirpath, f)\n","                signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)\n","\n","                # process all segments of audio file\n","                for d in range(num_segments):\n","\n","                    # calculate start and finish sample for current segment\n","                    start = samples_per_segment * d\n","                    finish = start + samples_per_segment\n","\n","                    # extract mfcc\n","                    mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)\n","                    mfcc = mfcc.T\n","\n","                    # store only mfcc feature with expected number of vectors\n","                    if len(mfcc) == num_mfcc_vectors_per_segment:\n","                        data[\"mfcc\"].append(mfcc.tolist())\n","                        data[\"labels\"].append(i-1)\n","                        print(\"{}, segment:{}\".format(file_path, d+1))\n","\n","    # save MFCCs to json file\n","    with open(json_path, \"w\") as fp:\n","        json.dump(data, fp, indent=4)\n","     \n","\n","save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)"]},{"cell_type":"code","source":["import json\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","import tensorflow.keras as keras\n","\n","import matplotlib.pyplot as plt\n","import random\n","\n","import librosa\n","import math\n","     \n","\n","\n","# path to json\n","DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n","     \n","\n","def load_data(data_path):\n","\n","    with open(data_path, \"r\") as f:\n","        data = json.load(f)\n","\n","    # convert lists to numpy arrays\n","    X = np.array(data[\"mfcc\"])\n","    y = np.array(data[\"labels\"])\n","\n","    print(\"Data succesfully loaded!\")\n","\n","    return  X, y\n","     \n","\n","# load data\n","X, y = load_data(DATA_PATH)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vlb2BpDgBaTY","executionInfo":{"status":"ok","timestamp":1684890541186,"user_tz":-60,"elapsed":16348,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"7643f328-52ae-4991-a7f9-56029efb94de"},"execution_count":86,"outputs":[{"output_type":"stream","name":"stdout","text":["Data succesfully loaded!\n"]}]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n","X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2)\n","\n","# add an axis to input sets\n","X_train = X_train[..., np.newaxis]\n","X_validation = X_validation[..., np.newaxis]\n","X_test = X_test[..., np.newaxis]\n","     \n","y_train_categorical = to_categorical(y_train)\n","y_validation_categorical = to_categorical(y_validation)\n","\n","# Update the number of classes for the output layer\n","num_classes = y_train_categorical.shape[1]\n","\n","\n","\n"],"metadata":{"id":"xCDKGt_9Boro","executionInfo":{"status":"ok","timestamp":1684890694156,"user_tz":-60,"elapsed":457,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":90,"outputs":[]},{"cell_type":"code","source":["input_shape = (X_train.shape[1], X_train.shape[2], 1)\n","     \n","\n","# build the CNN\n","model_cnn = keras.Sequential()\n","\n","# 1st conv layer\n","model_cnn.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n","model_cnn.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n","model_cnn.add(keras.layers.BatchNormalization())\n","\n","# 2nd conv layer\n","model_cnn.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))\n","model_cnn.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n","model_cnn.add(keras.layers.BatchNormalization())\n","\n","# 3rd conv layer\n","model_cnn.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))\n","model_cnn.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))\n","model_cnn.add(keras.layers.BatchNormalization())\n","\n","# flatten output and feed it into dense layer\n","model_cnn.add(keras.layers.Flatten())\n","model_cnn.add(keras.layers.Dense(64, activation='relu'))\n","model_cnn.add(keras.layers.Dropout(0.3))\n","\n","# output layer\n","model_cnn.add(keras.layers.Dense(num_classes, activation='softmax'))\n","\n","# compile model\n","optimiser = keras.optimizers.Adam(learning_rate=0.0001)\n","model_cnn.compile(optimizer=optimiser,\n","                  loss='categorical_crossentropy',\n","                  metrics=['accuracy'])\n","\n","model_cnn.summary()\n","\n","\n","     "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9OvXNYF_BomU","executionInfo":{"status":"ok","timestamp":1684890718880,"user_tz":-60,"elapsed":739,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"bb6e2a37-0701-4f84-c2e2-c4ca61e9babc"},"execution_count":91,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_7\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d_15 (Conv2D)          (None, 128, 11, 32)       320       \n","                                                                 \n"," max_pooling2d_15 (MaxPoolin  (None, 64, 6, 32)        0         \n"," g2D)                                                            \n","                                                                 \n"," batch_normalization_15 (Bat  (None, 64, 6, 32)        128       \n"," chNormalization)                                                \n","                                                                 \n"," conv2d_16 (Conv2D)          (None, 62, 4, 32)         9248      \n","                                                                 \n"," max_pooling2d_16 (MaxPoolin  (None, 31, 2, 32)        0         \n"," g2D)                                                            \n","                                                                 \n"," batch_normalization_16 (Bat  (None, 31, 2, 32)        128       \n"," chNormalization)                                                \n","                                                                 \n"," conv2d_17 (Conv2D)          (None, 30, 1, 32)         4128      \n","                                                                 \n"," max_pooling2d_17 (MaxPoolin  (None, 15, 1, 32)        0         \n"," g2D)                                                            \n","                                                                 \n"," batch_normalization_17 (Bat  (None, 15, 1, 32)        128       \n"," chNormalization)                                                \n","                                                                 \n"," flatten_6 (Flatten)         (None, 480)               0         \n","                                                                 \n"," dense_15 (Dense)            (None, 64)                30784     \n","                                                                 \n"," dropout_5 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_16 (Dense)            (None, 11)                715       \n","                                                                 \n","=================================================================\n","Total params: 45,579\n","Trainable params: 45,387\n","Non-trainable params: 192\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["history = model_cnn.fit(X_train, y_train_categorical, validation_data=(X_validation, y_validation_categorical), batch_size=32, epochs=50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c4iSXlJkGu9R","executionInfo":{"status":"ok","timestamp":1684891528069,"user_tz":-60,"elapsed":804341,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"c388e343-dd76-46cb-c3d1-3910dd15a51a"},"execution_count":92,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","188/188 [==============================] - 16s 79ms/step - loss: 2.5528 - accuracy: 0.1931 - val_loss: 2.0436 - val_accuracy: 0.2757\n","Epoch 2/50\n","188/188 [==============================] - 15s 77ms/step - loss: 1.9746 - accuracy: 0.3338 - val_loss: 1.6655 - val_accuracy: 0.3992\n","Epoch 3/50\n","188/188 [==============================] - 15s 77ms/step - loss: 1.7252 - accuracy: 0.4068 - val_loss: 1.5075 - val_accuracy: 0.4593\n","Epoch 4/50\n","188/188 [==============================] - 15s 77ms/step - loss: 1.5961 - accuracy: 0.4412 - val_loss: 1.3999 - val_accuracy: 0.4973\n","Epoch 5/50\n","188/188 [==============================] - 14s 77ms/step - loss: 1.4898 - accuracy: 0.4744 - val_loss: 1.3334 - val_accuracy: 0.5247\n","Epoch 6/50\n","188/188 [==============================] - 14s 77ms/step - loss: 1.3946 - accuracy: 0.5086 - val_loss: 1.2795 - val_accuracy: 0.5427\n","Epoch 7/50\n","188/188 [==============================] - 14s 77ms/step - loss: 1.3532 - accuracy: 0.5163 - val_loss: 1.2274 - val_accuracy: 0.5681\n","Epoch 8/50\n","188/188 [==============================] - 15s 81ms/step - loss: 1.2838 - accuracy: 0.5457 - val_loss: 1.2039 - val_accuracy: 0.5761\n","Epoch 9/50\n","188/188 [==============================] - 17s 90ms/step - loss: 1.2175 - accuracy: 0.5618 - val_loss: 1.1564 - val_accuracy: 0.6021\n","Epoch 10/50\n","188/188 [==============================] - 19s 101ms/step - loss: 1.1871 - accuracy: 0.5815 - val_loss: 1.1293 - val_accuracy: 0.6048\n","Epoch 11/50\n","188/188 [==============================] - 15s 81ms/step - loss: 1.1424 - accuracy: 0.6046 - val_loss: 1.1127 - val_accuracy: 0.6128\n","Epoch 12/50\n","188/188 [==============================] - 15s 82ms/step - loss: 1.1018 - accuracy: 0.6129 - val_loss: 1.0772 - val_accuracy: 0.6315\n","Epoch 13/50\n","188/188 [==============================] - 15s 78ms/step - loss: 1.0609 - accuracy: 0.6299 - val_loss: 1.0686 - val_accuracy: 0.6355\n","Epoch 14/50\n","188/188 [==============================] - 15s 77ms/step - loss: 1.0485 - accuracy: 0.6308 - val_loss: 1.0343 - val_accuracy: 0.6489\n","Epoch 15/50\n","188/188 [==============================] - 15s 77ms/step - loss: 0.9868 - accuracy: 0.6526 - val_loss: 1.0012 - val_accuracy: 0.6589\n","Epoch 16/50\n","188/188 [==============================] - 14s 75ms/step - loss: 0.9765 - accuracy: 0.6643 - val_loss: 1.0050 - val_accuracy: 0.6575\n","Epoch 17/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.9431 - accuracy: 0.6730 - val_loss: 0.9551 - val_accuracy: 0.6776\n","Epoch 18/50\n","188/188 [==============================] - 15s 78ms/step - loss: 0.9268 - accuracy: 0.6822 - val_loss: 0.9502 - val_accuracy: 0.6809\n","Epoch 19/50\n","188/188 [==============================] - 15s 77ms/step - loss: 0.8954 - accuracy: 0.6969 - val_loss: 0.9651 - val_accuracy: 0.6749\n","Epoch 20/50\n","188/188 [==============================] - 15s 78ms/step - loss: 0.8921 - accuracy: 0.6965 - val_loss: 0.9298 - val_accuracy: 0.6729\n","Epoch 21/50\n","188/188 [==============================] - 14s 77ms/step - loss: 0.8512 - accuracy: 0.7131 - val_loss: 0.9224 - val_accuracy: 0.6889\n","Epoch 22/50\n","188/188 [==============================] - 15s 77ms/step - loss: 0.8285 - accuracy: 0.7134 - val_loss: 0.8977 - val_accuracy: 0.6896\n","Epoch 23/50\n","188/188 [==============================] - 18s 95ms/step - loss: 0.8339 - accuracy: 0.7079 - val_loss: 0.9043 - val_accuracy: 0.6956\n","Epoch 24/50\n","188/188 [==============================] - 14s 76ms/step - loss: 0.7983 - accuracy: 0.7238 - val_loss: 0.8965 - val_accuracy: 0.7023\n","Epoch 25/50\n","188/188 [==============================] - 14s 76ms/step - loss: 0.7782 - accuracy: 0.7361 - val_loss: 0.8812 - val_accuracy: 0.6996\n","Epoch 26/50\n","188/188 [==============================] - 14s 76ms/step - loss: 0.7639 - accuracy: 0.7393 - val_loss: 0.8699 - val_accuracy: 0.7056\n","Epoch 27/50\n","188/188 [==============================] - 14s 75ms/step - loss: 0.7538 - accuracy: 0.7441 - val_loss: 0.8644 - val_accuracy: 0.7049\n","Epoch 28/50\n","188/188 [==============================] - 14s 76ms/step - loss: 0.7291 - accuracy: 0.7500 - val_loss: 0.8570 - val_accuracy: 0.7123\n","Epoch 29/50\n","188/188 [==============================] - 15s 78ms/step - loss: 0.7109 - accuracy: 0.7588 - val_loss: 0.8483 - val_accuracy: 0.7116\n","Epoch 30/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.7138 - accuracy: 0.7640 - val_loss: 0.8621 - val_accuracy: 0.7049\n","Epoch 31/50\n","188/188 [==============================] - 15s 78ms/step - loss: 0.6782 - accuracy: 0.7682 - val_loss: 0.8389 - val_accuracy: 0.7236\n","Epoch 32/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.6865 - accuracy: 0.7628 - val_loss: 0.8252 - val_accuracy: 0.7303\n","Epoch 33/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.6692 - accuracy: 0.7730 - val_loss: 0.8165 - val_accuracy: 0.7290\n","Epoch 34/50\n","188/188 [==============================] - 15s 81ms/step - loss: 0.6608 - accuracy: 0.7753 - val_loss: 0.8294 - val_accuracy: 0.7203\n","Epoch 35/50\n","188/188 [==============================] - 15s 81ms/step - loss: 0.6530 - accuracy: 0.7803 - val_loss: 0.8306 - val_accuracy: 0.7109\n","Epoch 36/50\n","188/188 [==============================] - 17s 91ms/step - loss: 0.6164 - accuracy: 0.7887 - val_loss: 0.8161 - val_accuracy: 0.7176\n","Epoch 37/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.6045 - accuracy: 0.7962 - val_loss: 0.8000 - val_accuracy: 0.7176\n","Epoch 38/50\n","188/188 [==============================] - 15s 81ms/step - loss: 0.5976 - accuracy: 0.7982 - val_loss: 0.8176 - val_accuracy: 0.7143\n","Epoch 39/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5964 - accuracy: 0.7975 - val_loss: 0.8169 - val_accuracy: 0.7136\n","Epoch 40/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.5850 - accuracy: 0.7960 - val_loss: 0.8232 - val_accuracy: 0.7316\n","Epoch 41/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5793 - accuracy: 0.8010 - val_loss: 0.8249 - val_accuracy: 0.7156\n","Epoch 42/50\n","188/188 [==============================] - 15s 81ms/step - loss: 0.5571 - accuracy: 0.8092 - val_loss: 0.7856 - val_accuracy: 0.7296\n","Epoch 43/50\n","188/188 [==============================] - 15s 79ms/step - loss: 0.5578 - accuracy: 0.8099 - val_loss: 0.7732 - val_accuracy: 0.7323\n","Epoch 44/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5236 - accuracy: 0.8239 - val_loss: 0.7671 - val_accuracy: 0.7417\n","Epoch 45/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5393 - accuracy: 0.8169 - val_loss: 0.7892 - val_accuracy: 0.7290\n","Epoch 46/50\n","188/188 [==============================] - 15s 81ms/step - loss: 0.5361 - accuracy: 0.8146 - val_loss: 0.7751 - val_accuracy: 0.7390\n","Epoch 47/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5178 - accuracy: 0.8251 - val_loss: 0.7783 - val_accuracy: 0.7303\n","Epoch 48/50\n","188/188 [==============================] - 15s 80ms/step - loss: 0.5052 - accuracy: 0.8261 - val_loss: 0.7833 - val_accuracy: 0.7363\n","Epoch 49/50\n","188/188 [==============================] - 18s 94ms/step - loss: 0.4929 - accuracy: 0.8322 - val_loss: 0.7725 - val_accuracy: 0.7356\n","Epoch 50/50\n","188/188 [==============================] - 16s 85ms/step - loss: 0.4884 - accuracy: 0.8346 - val_loss: 0.7723 - val_accuracy: 0.7417\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","def plot_history(history):\n","\n","    fig, axs = plt.subplots(2)\n","\n","    # create accuracy sublpot\n","    axs[0].plot(history.history[\"accuracy\"], label=\"train accuracy\")\n","    axs[0].plot(history.history[\"val_accuracy\"], label=\"test accuracy\")\n","    axs[0].set_ylabel(\"Accuracy\")\n","    axs[0].legend(loc=\"lower right\")\n","    axs[0].set_title(\"Accuracy eval\")\n","\n","    # create error sublpot\n","    axs[1].plot(history.history[\"loss\"], label=\"train error\")\n","    axs[1].plot(history.history[\"val_loss\"], label=\"test error\")\n","    axs[1].set_ylabel(\"Error\")\n","    axs[1].set_xlabel(\"Epoch\")\n","    axs[1].legend(loc=\"upper right\")\n","    axs[1].set_title(\"Error eval\")\n","    \n","    plt.show()\n","     "],"metadata":{"id":"6AUH6850XwEJ","executionInfo":{"status":"ok","timestamp":1684891848967,"user_tz":-60,"elapsed":442,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":96,"outputs":[]},{"cell_type":"code","source":["plot_history(history)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"9j5pr_VyJ5b7","executionInfo":{"status":"ok","timestamp":1684891852669,"user_tz":-60,"elapsed":1169,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"4eda1b40-1483-433e-f4ff-36140a11ae5f"},"execution_count":97,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACeT0lEQVR4nOzdd3gU5drA4d/uJrvpjfSQRu+hI02ULsgREEEsEFBsoCLyHcUCCueABRUEFY8FUEGwABYUpaOI9N4TEgIpkEJ6353vjyELawpJSLIhee7rmmuzM+/MPDtE98lbNYqiKAghhBBC1BFaawcghBBCCFGVJLkRQgghRJ0iyY0QQggh6hRJboQQQghRp0hyI4QQQog6RZIbIYQQQtQpktwIIYQQok6R5EYIIYQQdYokN0IIIYSoUyS5EUKIW4RGo+G1116zdhhC1HqS3AhRT3344YdoNBq6detm7VCEEKJKSXIjRD21YsUKQkJC2LNnDxEREdYORwghqowkN0LUQ1FRUfz111+8++67eHl5sWLFCmuHVKqsrCxrhyCEuMVIciNEPbRixQrc3d0ZOnQoo0aNKjW5SU1N5bnnniMkJASDwUDDhg0ZN24cSUlJ5jK5ubm89tprNGvWDDs7O/z8/Bg5ciSRkZEAbNu2DY1Gw7Zt2yyuHR0djUajYdmyZeZ94eHhODk5ERkZyZAhQ3B2dubBBx8E4I8//uC+++4jKCgIg8FAYGAgzz33HDk5OcXiPnXqFKNHj8bLywt7e3uaN2/Oyy+/DMDWrVvRaDSsXbu22HkrV65Eo9Gwa9euMp9famoqU6dOJTAwEIPBQJMmTXjzzTcxmUwAFBQU4OHhwYQJE4qdm56ejp2dHdOnTwcgPz+fmTNn0qlTJ1xdXXF0dKR3795s3bq1zBiEEKWzsXYAQoiat2LFCkaOHIler2fs2LF89NFH7N27ly5dupjLZGZm0rt3b06ePMnEiRPp2LEjSUlJ/Pjjj1y8eBFPT0+MRiN33303mzdv5v777+fZZ58lIyODjRs3cuzYMRo3blzh2AoLCxk0aBC9evVi/vz5ODg4APDtt9+SnZ3Nk08+SYMGDdizZw+LFi3i4sWLfPvtt+bzjxw5Qu/evbG1teWxxx4jJCSEyMhIfvrpJ/773/9yxx13EBgYyIoVKxgxYkSx59K4cWO6d+9eanzZ2dn06dOH2NhYHn/8cYKCgvjrr7+YMWMG8fHxLFiwAFtbW0aMGMGaNWv4+OOP0ev15vPXrVtHXl4e999/P6AmO59++iljx45l0qRJZGRk8NlnnzFo0CD27NlD+/btK/wMhaj3FCFEvbJv3z4FUDZu3KgoiqKYTCalYcOGyrPPPmtRbubMmQqgrFmzptg1TCaToiiK8vnnnyuA8u6775ZaZuvWrQqgbN261eJ4VFSUAihLly417xs/frwCKC+++GKx62VnZxfbN2/ePEWj0Sjnz58377v99tsVZ2dni33Xx6MoijJjxgzFYDAoqamp5n2XL19WbGxslFmzZhW7z/XmzJmjODo6KmfOnLHY/+KLLyo6nU6JiYlRFEVRfvvtNwVQfvrpJ4tyQ4YMURo1amR+X1hYqOTl5VmUuXLliuLj46NMnDjRYj9ww/iEEIoizVJC1DMrVqzAx8eHO++8E1CHF48ZM4ZVq1ZhNBrN5b7//nvCwsKK1W4UnVNUxtPTk6effrrUMpXx5JNPFttnb29v/jkrK4ukpCR69OiBoigcPHgQgMTERHbs2MHEiRMJCgoqNZ5x48aRl5fHd999Z963evVqCgsLeeihh8qM7dtvv6V37964u7uTlJRk3vr374/RaGTHjh0A9O3bF09PT1avXm0+98qVK2zcuJExY8aY9+l0OnPNjslkIiUlhcLCQjp37syBAwdu+KyEEMVJciNEPWI0Glm1ahV33nknUVFRREREEBERQbdu3bh06RKbN282l42MjKRNmzZlXi8yMpLmzZtjY1N1Ldw2NjY0bNiw2P6YmBjCw8Px8PDAyckJLy8v+vTpA0BaWhoA586dA7hh3C1atKBLly4WfY1WrFjBbbfdRpMmTco89+zZs2zYsAEvLy+LrX///gBcvnzZ/DnuvfdefvjhB/Ly8gBYs2YNBQUFFskNwPLly2nXrh12dnY0aNAALy8v1q9fb/5cQoiKkT43QtQjW7ZsIT4+nlWrVrFq1apix1esWMHAgQOr9J6l1eBcX0t0PYPBgFarLVZ2wIABpKSk8MILL9CiRQscHR2JjY0lPDzc3JG3IsaNG8ezzz7LxYsXycvL4++//2bx4sU3PM9kMjFgwAD+/e9/l3i8WbNm5p/vv/9+Pv74Y3799VeGDx/ON998Q4sWLQgLCzOX+eqrrwgPD2f48OH83//9H97e3uh0OubNm2fulC2EqBhJboSoR1asWIG3tzcffPBBsWNr1qxh7dq1LFmyBHt7exo3bsyxY8fKvF7jxo3ZvXs3BQUF2NralljG3d0dUEcYXe/8+fPljvvo0aOcOXOG5cuXM27cOPP+jRs3WpRr1KgRwA3jBjXxmDZtGl9//TU5OTnY2toWq1EpSePGjcnMzDTX1JTl9ttvx8/Pj9WrV9OrVy+2bNliHrVV5LvvvqNRo0asWbPGIhGcNWvWDa8vhCiZNEsJUU/k5OSwZs0a7r77bkaNGlVsmzJlChkZGfz4448A3HvvvRw+fLjEIdOKopjLJCUllVjjUVQmODgYnU5n7otS5MMPPyx37DqdzuKaRT8vXLjQopyXlxe33347n3/+OTExMSXGU8TT05O77rqLr776ihUrVjB48GA8PT1vGMvo0aPZtWsXv/32W7FjqampFBYWmt9rtVpGjRrFTz/9xJdffklhYWGxBKqkz7Z79+4bDkcXQpROam6EqCd+/PFHMjIy+Ne//lXi8dtuu808od+YMWP4v//7P7777jvuu+8+Jk6cSKdOnUhJSeHHH39kyZIlhIWFMW7cOL744gumTZvGnj176N27N1lZWWzatImnnnqKe+65B1dXV+677z4WLVqERqOhcePG/Pzzz+a+KeXRokULGjduzPTp04mNjcXFxYXvv/+eK1euFCv7/vvv06tXLzp27Mhjjz1GaGgo0dHRrF+/nkOHDlmUHTduHKNGjQJgzpw55Yrl//7v//jxxx+5++67CQ8Pp1OnTmRlZXH06FG+++47oqOjLZKkMWPGsGjRImbNmkXbtm1p2bKlxfXuvvtu1qxZw4gRIxg6dChRUVEsWbKEVq1akZmZWe5nJIS4jhVHagkhatCwYcMUOzs7JSsrq9Qy4eHhiq2trZKUlKQoiqIkJycrU6ZMUQICAhS9Xq80bNhQGT9+vPm4oqhDtF9++WUlNDRUsbW1VXx9fZVRo0YpkZGR5jKJiYnKvffeqzg4OCju7u7K448/rhw7dqzEoeCOjo4lxnbixAmlf//+ipOTk+Lp6alMmjRJOXz4cLFrKIqiHDt2TBkxYoTi5uam2NnZKc2bN1deffXVYtfMy8tT3N3dFVdXVyUnJ6c8j1FRFEXJyMhQZsyYoTRp0kTR6/WKp6en0qNHD2X+/PlKfn6+RVmTyaQEBgYqgPKf//yn2LVMJpMyd+5cJTg4WDEYDEqHDh2Un3/+WRk/frwSHBxsURYZCi5EuWgU5R91tUIIUU8UFhbi7+/PsGHD+Oyzz6wdjhCiikifGyFEvbVu3ToSExMtOikLIW59UnMjhKh3du/ezZEjR5gzZw6enp4yWZ4QdYzU3Agh6p2PPvqIJ598Em9vb7744gtrhyOEqGJScyOEEEKIOkVqboQQQghRp0hyI4QQQog6pd5N4mcymYiLi8PZ2fmmVi0WQgghRM1RFIWMjAz8/f2LrT/3T/UuuYmLiyMwMNDaYQghhBCiEi5cuEDDhg3LLFPvkhtnZ2dAfTguLi5WjkYIIYQQ5ZGenk5gYKD5e7ws9S65KWqKcnFxkeRGCCGEuMWUp0uJdCgWQgghRJ0iyY0QQggh6hRJboQQQghRp9S7PjdCCCGEqDq5BUbScwvIyC0kPUd9tdfr6BLiYbWYJLkRQgghRDGKonA5I4+Iy5lEXM4kMjGTqKQs0nIsE5l8o6nYuV1C3Pn2iR5WiFolyY0QQghRhymKQl6hiQKjifxCE/lGEwWFCvlG49X9CvmFJtJyCohMzLRIZjJyC8t1D40GnA02ONvZ4mJvS0gDx2r+VGWT5EYIIYSoIZczcvnpcDy7IpPxc7WjpZ8LLfycae7jjKOh6r6SU7Ly+TMiiT/OJPJnRBLxabmVuo5WA8ENHGns5UhjbycaezrRwEl/NYm5mszY2eCot0GrrT2z/ktyI4QQQlSjnHwjv59IYM2BWP6MSMJoUoqV0Wgg2MOBFr4u5oSnpa8L3i4G7Gx1N7xHXqGR/dFX+CMiiT/OJnI8Lh2l+G0AsNVpsNVp0dto1VedFoONFgeDjlBPJ5p4OdHEW91CPB0w2Nz4/rWNJDdCCCFEFTOaFHZFJrP2YCwbjsWTlW80H2sf6MbgNr5cycrnZEIGJ+PTSczIIzo5m+jkbDYcT7C4lt5Gi4udDS52tjjb2eBif/XVzhYngw0RiZnsPpdCToHR4rwWvs7c3syLXk08CWvohsFWTWRqUw1LdbF6cvPBBx/w9ttvk5CQQFhYGIsWLaJr166lll+wYAEfffQRMTExeHp6MmrUKObNm4ednV0NRi2EEKI+URSFmJRs4lJzKTSZKDQpFBoVjCa1z4rRpFBgNGE0KUQlZfHDoTgS0q81BQV62DOifQDDOwTQyMup2PWTMvM4FZ/BqYR0TsarCU/E5Uzyr/aTScrMJykzv8wYvZwN9G7iSe9mnvRs4om3c/39XrRqcrN69WqmTZvGkiVL6NatGwsWLGDQoEGcPn0ab2/vYuVXrlzJiy++yOeff06PHj04c+YM4eHhaDQa3n33XSt8AiGEEHVRcmYehy+mcuhCGocvpHL4Yiqp2QUVuoaLnQ13h/kzskMAnYLdy1w2wNPJQK+mBno19TTvM5oUMvMKycgtID1Hfc3ILbQcdp1XiLezel5zH+dyLU1QH2gUpbRWuerXrVs3unTpwuLFiwEwmUwEBgby9NNP8+KLLxYrP2XKFE6ePMnmzZvN+55//nl2797Nn3/+Wa57pqen4+rqSlpamqwtJYQQ9Vyh0UR8Wi4XUrI5EZ/OoauJzIWUnGJl9TotgR722Oq02Og02Gi12Gg16LRqHxb1VYOznS2DWvtwZwvvW7K/Sm1Vke9vq9Xc5Ofns3//fmbMmGHep9Vq6d+/P7t27SrxnB49evDVV1+xZ88eunbtyrlz5/jll194+OGHS71PXl4eeXl55vfp6elV9yGEEEJUWEZuAfFpuWTmFZKVV0hmbuG1n/MKycwzkpVXSG6BERudmjyYEwmdBlvttUTCRqfFzkaLvV6Hna0Oe1sd9nr11e7qz3qdlsTMPC6kZHPxSg4XUrK5cCWbmJRs4lNzKSyhgy9AYy9H2ge60z7QlbBAN1r4uqC3kYn9bwVWS26SkpIwGo34+PhY7Pfx8eHUqVMlnvPAAw+QlJREr169UBSFwsJCnnjiCV566aVS7zNv3jxef/31Ko1dCCFE+aRk5XM8Lo1jsekci0vjeGwa0cnZ1g7Lgl6npaG7PY29nWgf6Eb7QDfaNnTFxc7W2qGJSrJ6h+KK2LZtG3PnzuXDDz+kW7duRERE8OyzzzJnzhxeffXVEs+ZMWMG06ZNM79PT08nMDCwpkIWQoh6IyUrn0MXrnD04rVEJq6U+VVcr474cTKom6Phnz/rMNjqMJkUtfPu1U68RuO190aTQn6hQm6hkdx8IzkFV7d8I7nX/1xoooGjnkB3Bxp62BPk4UCguwOBHg4Eetjj42xXL0YQ1SdWS248PT3R6XRcunTJYv+lS5fw9fUt8ZxXX32Vhx9+mEcffRSAtm3bkpWVxWOPPcbLL7+MVlu8utBgMGAwGKr+AwghRD2WV2jkRJzaR+XQhVQOxqQSk1JyjUyopyOt/F1o4+9KmwAXWvu74uGor+GIRX1iteRGr9fTqVMnNm/ezPDhwwG1Q/HmzZuZMmVKiedkZ2cXS2B0OrWzlhX7RQshRJ2lKMrV/io5xKRkcfhCGocupHIiLr3ENYUaezkS1tCN1gGutPF3oZW/C87SvCNqmFWbpaZNm8b48ePp3LkzXbt2ZcGCBWRlZTFhwgQAxo0bR0BAAPPmzQNg2LBhvPvuu3To0MHcLPXqq68ybNgwc5IjhBCifEwmhcx8dUhxanaBRWdb9TWHi1eyyS0onsQAeDjqaR/oRodAN9oHudGuoRuu9pLICOuzanIzZswYEhMTmTlzJgkJCbRv354NGzaYOxnHxMRY1NS88soraDQaXnnlFWJjY/Hy8mLYsGH897//tdZHEEKIWslkUjiZkM5fEcmcjE8n/epcKenXzZWSmVdY6hT919NowM/FjoYeDrTyc6FDkBsdAt0J9LCXeVVErWTVeW6sQea5EULURoVGE4mZecSn5XIpLZeE9FzsbHUEezgQ7OmIn8uNO71eSMnmz4gk/oxIYldkMilZZc9oW0Sv0+Jib0uAm5rAXOtwa0+guwP+bvYyBFpY3S0xz40QQtQn6bkFxCSrzT0xKdnEp+WSkJZLfHouCWk5JGbkUcp0K8C1CeSCGzgS3MDBnPRk5hbyV6Sa0Pxz4jkHvY6uoR50CfHAw1FfbG2iovWJyrMwoxC3EkluhBCiiqTnFnA8Np2YlCxiUrI5f10yc6UcU/fbaDX4uNjh42LA19WO7HyjmhBdySbfaCIyMYvIxKwyz+8Q5EaPxp70aqoulig1LqI+kuRGCCEqQVEULl7JYd/5FPZFX2H/+SucvpRRZh+WBo56gho4mJt6/Fzt8HW1U19d7GjgZEBXQtOT0aQQl5rD+eRszqdkEZOcTXRyFueTs9FpNdzWqAG9mnjSJdQDJ4P8b10I+a9ACCHKISffyKmEdPafVxOZfeevkJiRV6xcgJs9TbydCLradyWowdU+LB4OlU48dFrN1QnnHOiF541PEKKek+RGCCGuMpkUYlNzOJeURVRiJueSsjiXmEVUUhaxqcUXUrTVaWjt70qnYHc6B7vTKdgdbxc7K0QuhLieJDdCiHolI1edz+Xi1TlciuZ2OX+1qSevsOQ5XQDcHWzpGOROpxB3Ogd70K6hq3TGFRVnMkJGAjj7QQkz69/STEZIPQ+F+eDdwmphSHIjhLjlmUwKaTkFJGflk5KVT0pWnvpzZj7JWfnEp+WYE5q0nLI79trqNAQ3cKSRpyOhXo409nSikZcjoZ6OeDjqZV4XUXHGAog7BOd3wvm/IOZvyEsDlwBo+S9odQ8EdqtYomMywaWjEL0TdLbQsAv4tAFdDX2t52VA0tmr2xlIvvpzciQY86DRnTBuXc3EUgJJboQQtxRFUTgel85vxxPYdjqR+LQcrmQXYCxrHPU/uDvY0tDdgYbu9lc3tV9MIy9HAtzssdHVsb+mRc0qyIGL+9RE5vxOuLgXCkpYdys9FnZ/pG5OvtBymJroBPcA7T9qBBUFkiPg3DaI2gHRf0DOFcsytg4Q0ElNdAK7qa+ODYrf11gAV6KvJSZFr+lxaoKltQWtjZo0aXWW7xUTpERBRlzpn19nKB5/DZNJ/IQQtZ7RpHAg5gobjiWw4VhCif1fAJztbGjgqMfDUY+Ho0H92UmPr4udOYkJcLe/9UcU5WdD0mm4fFL9grMxgI092NqprzYGsLUHGzv11c4NnH3VqYbrE5MJTv0Mx74D9xDoPFF9razkSEg4Crmp6nPPuQI51/2cm6q+z0gA0z9qCO091KSlaPNspiYpJ36AU7+oNTlFHL2gxd3QYihkXoao7WrZjHjLa+qd1GspJjWByk2jmAZNoGFXcPJS4086AynnwFRY+edgjtNb/RyeTa97bQqugdWS3FTk+1uSGyFErZRfaGLXuWQ2HEtg44kEkjKvzbZrb6vjjuZeDGztQ3MfFxo46XF30Ne9OV2MBeoX0uUTaiJz+YS6pUQBFfxft7Pftb/oA7uCX5iaBNVFhXlwZDXsXKjWdphpoOkA6DIJmvQr3xdwVjIc+169Xuy+8sfg7AfBPSG4u/rq2bz0ZqfCfDWBObEOTq0vXiNTRGdQ/+0a9YHQPuDfQa1NATWRSzoDF/fAhd1wYa+aAJfG1kFNfDybXUtM3ILUGiJT4dWtQO1DYypUfxdNhWoi5R6inmvvVv7nUQUkuSmDJDdC1G6nEzL46u/zrDsUS0butb8uXexs6N/Sh0FtfLm9qRf2+jrakddkgtO/wK7FELsfjKUsoWDvAT6twckHCnPVL/TCXLVJxPyaB4U5am2CYrQ8X6cHv/bql2VgVzXpcfat7k9XvXLTYf8y+PvDa7Ucdq7Q4WE1KYzccq2sW7Bak9Ph4eJNNwW5cOZXOLwaIjZeq+XQ6CCgo1qzYu+u1ojZu6tf8te/OnqDa8PK1ZQZC9QmpxM/QMQWcPaB0NvVZCawq1oTV17ZKerv0IXd6u9AUc2KZzNw9r/lOjNLclMGSW6EqH3yCo1sOJbAV3+fZ2/0tb9aPZ0MDGrtw+A2vtzWqAG2dbkvjMkIx9fCH++oX8RF9E7g1QJ8WoF3K/Buqb46epX/yzM/G+IOXv2r/uqWnVS8nHvo1dqGq00n7iHlv0deJiSeUmuY8jNvXF6nVz+DefNUE5HKJASZl+Hvj2DvZ9ead5z9oftT0CkcDM7qvuRItcyhr6414egM0HoEdHlETSyOrILjP1g2E/mFQbv7oe0ocPKueHyiSkhyUwZJboSoOJNJ4e+oZC6l52IygUlRUBQwKgomRcGkqB19TSYFB70NwQ0cCPV0xMvZUOboogsp2azYHcO3+y6QfHWRR51Ww8BWPjzYLZjujRuUOGOvVeRlXu1wqbPsZKm1UUeoaG2u21eBJMxYoDZ5/PEupESq+wwu0HWSWqvgFlz1f2Eritrv4uLea00Yl4+rTQ7Xc/a7rp/I1aYVU6E6MqaomezS1aay1PM3H5fWVk10nLyu1Y5obYs/3+vfp8fCkW/UEToADZpCr6nQdjTY6Eu+T3622tS09xOIP1xyGZeG0G40tBtj1SHN4hpJbsogyY0Q5ZeeW8B3+y7yxa5oopNLGO1xAw56HcENHAlp4ECI59XXBo6k5xayYvd5tp9JNC9X4Otix9iuQdzfNRAfa0+El5kICYfVzqPxRyDhiPpXf3n6uWi04NHoai3LdTUtHo0sh+kW5Ko1CH8uhLQYdZ+9O9w2WU1sarg/A7npao1O0XDl2P3FO8UaXKEgq/TOqE4+ai2To9eN71eQo9YeZSWqzzs/4+biD+gMvZ6D5kPKnwwqCsQegL2fqsmOTg+t71FraYJ73nLNNnWdJDdlkORGiBuLuJzJF7ui+X7/RbLy1b4aznY2hDV0Q6fVoNWAVqNBo9Gg06o/q+8hLaeA6OQsYq/klLnKdZHeTT156LZg+rXwrroh2DlXICni2vwbBTnXalkshrheVxuQlagmMfFHIDOh5OsaXAHlug6WN14M00xnAK9maqLj7Kv25yi6j6M39Hha7QNicLrpj18l/jmc+cIetf8OqDVLRUmbOYFrqTYt3cz9sq4mO0WvOVcsO7YWdWq9ftNorw6f7nlzo8EKctVrlVbbI6xOkpsySHIjRMlMJoWtpy+z7K9o/jh7rT9GU28nxvcIYUSHABwrMIQ6v9DEhSvZnE/OIjpJnf03Ojmb6KQs8gtNDAvz44FuwYR6Oqp/QafHXjci6CRcOQ96h6sdNa/vvOl+rfOmwVltKko6YzlfR1biTT4NjToaxLct+LVTX33bldzfwmS6+uV7NeHJz7o2TNs8yulkyfOcuDRUm1A6PFSxjqLWUJiv9qlx8FAnn6tvw8qF1UlyUwZJbkR9oSgKRy6m8WdEEkaTglYDmqs1LEW1Lep7yMwt5LsDFzl/telJo4H+LX0I7xFCj8YNqm5WXkVR/yov+vK/dPzal//1HTirgrP/tdEhdq5X/+o3XlcTUGhZG2BwvpbE+LSu2hoUk0ntk1KU8FyJUkcntbtfagqEKKeKfH/f4jNZCSH+KTEjj3UHY/l2/wXOXCrHqJXruNjZcH/XIB6+LZhAD4fKB2GeAbWoRiXi2s+5qSWfo7VRO4MWNXc0aKQ2FZgnR7tSfOK03DS106vnP+braNDk2giZ2kCrBY9QdWsxxNrRCFHnSXIjRB1QYDSx7XQi3+y7wNZTlym82tnFYKOlbwtv3Bz06mimqyObTFdHNhX9DNCjsSfDO/jjoC/hfwspUXBopdoEVNYEX6ZCtUmozBlQNepkYd6tLIc3N2hSdyeVE0LUKEluhLiFnbmUwbf7LrD2YKzFDL5hgW7c16khw8L8cbW3rdzFTUY4u1EdSRKxiQrPiGvrcG3CsAbXTR7WoHHt718ihLilSXIjRG1WkHt19EgiSlYiyZdjibt4gdTEi+SlXSY3LxcXxZdeJn+SHYMIa9+Ff3VtTjOfm2iSyUqCg1/Cvs8hNeba/sb91PlOdPrS53fR2aj9W27RGVCFEHWDJDdC1DYxu2H7GygX96HJSzfv1gCeVzez61cgMAL7gTP+ln1QPBpfG1mkd1I7yuqdLNfVURR1Qre9n6qz5BZN+W/npo7k6TxRrXERQohbgCQ3QtQSWdH7yPt9Nh5x2wE1mQHIV3Qk40qy4kIKrigOnti7++DhHUCAhxP26dHXDYG+DBlx6ha1o+wb2jpeS3QUkzqCp4h/B3VxwTYjpQlJCHHLkeRGCCtQFIWopCwOxKRy8cx+Op1bQu+Cv3AEChUt3xr7sNw4iDS9D82CAugS6kGXEA+6BrphZ1vGgpE5qeoqyNfP+5ISBXkZkJeurvlT1NG3IEvduKS+t7GDNveqa+wEdKrmJyCEENVHkhshasjljFy2nLzMllOX2Xf+Ck7ZF5hq8z3PaHei1SiYFA2bbHqzO/gxgpu2ZX6QOy39XCq2tpK9GzTsrG4lURR1pej8TDXZyctUfy7IUWtrHDyq5LMKIYQ1SXIjRFUwmdSFBy/sVkcZ2dih2NgRmwkH4nLYczGb45fzyUWPBoXpuo2M1m/HRqMuVJgQMADbfi8zsFEHBlZnnBoN2Nqp281MlS+EELWYJDdCVIaiqAspRm1T+7ZE/QE5KRZFNEDDq9u/AEqawqVJf+j7Cr7+Hao7YiGEqDckuRGivNIuqonMue3qa0acxeF8nQN7jE1JNdphRz525OOgLaCBwYSbrREnXSE6Y57aLOTfHu6YAcHdrfNZhBCiDpPkRoiypF2EY2vg2PcQf8jymE6PEtiV0/YdeS/Sj83pARRig5ezgX4tvOnf0odOTTyx15fRAVgIIUSVk+RGiH/KTIQT69SEJmbXtf0aLfh3hNDboVEfDmtaMHvDOfafugKAn6sd/x7cnHvCAtBWpBOwEEKIKiXJjRCgDqE+9TMc/Q6itqvzvgCgUWflbXMvtLoHHD2JS83hzQ2n+OHQfgDsbXU80acxj93eSGpphBCiFpDkRtR9JhNkJ6mLPmYkXJ3kLuG69/HqnDDGa2sz4d9RTWhajwDXAACy8gpZ8vtp/rfjHHmFavJzb8eG/N+g5vi62lnjkwkhhChBhZObkJAQJk6cSHh4OEFBQdURkxA3T1HgzAb48z2I3V/GCtXXJNg14pBLP/Y730m8zo/CSIWCM3EUmGIpKDRx9nImSZl5AHQN9eDVoa1o29C1uj+JEEKICqpwcjN16lSWLVvG7NmzufPOO3nkkUcYMWIEBkNJ41yFqGEmI5z4Af54By4du+6ABpx8wNkXXPzJs/fmaJoDv1/QcCrbiRjFm+hcP0gtKh9f4uWDPBx4aUgLBrX2RaORfjVCCFEbaRRFUSpz4oEDB1i2bBlff/01RqORBx54gIkTJ9KxY8eqjrFKpaen4+rqSlpaGi4uLtYOR1QVY4HaX+aPdyD5rLpP7wxdH4VO4eDSEHQ2nE/OYunOaL7dd4GsfCMAHo56/hXmj5uDLbY6LbY6DbY6LTY6LXqdBhutFlsbLc4GG3o0aYDBRvrVCCFETavI93elk5siBQUFfPjhh7zwwgsUFBTQtm1bnnnmGSZMmFAr/7KV5KaOKcyDQyvgzwWQel7dZ+cGtz0F3R4De3cURWF3VAqf/RnFppOXKPqNb+bjxMSeoQzvEFD2ek1CCCGsriLf35XuUFxQUMDatWtZunQpGzdu5LbbbuORRx7h4sWLvPTSS2zatImVK1fe8DoffPABb7/9NgkJCYSFhbFo0SK6du1aavnU1FRefvll1qxZQ0pKCsHBwSxYsIAhQ4ZU9qOIW01+NiQcgfN/wZ5Prk2m5+gF3adAl0cosHHkVHwG+89H8e3+ixyPSzeffkdzLyb2DKV3U89amYALIYS4ORVObg4cOMDSpUv5+uuv0Wq1jBs3jvfee48WLVqYy4wYMYIuXbrc8FqrV69m2rRpLFmyhG7durFgwQIGDRrE6dOn8fb2LlY+Pz+fAQMG4O3tzXfffUdAQADnz5/Hzc2toh9D3CqMBXD5BMQegLgDEHtQfa8YzUUUZ3/SOj7JX65DORCXx8HPj3IsNs08ognAzlbLyI4NmdgzhCbeztb4JEIIIWpIhZuldDodAwYM4JFHHmH48OHY2toWK5OVlcWUKVNYunRpmdfq1q0bXbp0YfHixQCYTCYCAwN5+umnefHFF4uVX7JkCW+//TanTp0q8b7lIc1StYSxQJ1bJueKuuUW/Xz1NStRrZ1JOAqFucVOVxx9uGDfgm104qMrXYnPMhUr42JnQ/sgd3o2bsDozoG4O+qr/WMJIYSoHtXa5+b8+fMEBwffVICg1sI4ODjw3XffMXz4cPP+8ePHk5qayg8//FDsnCFDhuDh4YGDgwM//PADXl5ePPDAA7zwwgvodOXrMyHJjRUV5sH2N2HvZ2oyU152ruDfQZ17JqAjf2YH8dKmZGKu5JiL2Gg1tPRzoX2gm7oFuRHawFFmChZCiDqiWvvcXL58mYSEBLp162axf/fu3eh0Ojp37lyu6yQlJWE0GvHx8bHY7+Pjw6lTp0o859y5c2zZsoUHH3yQX375hYiICJ566ikKCgqYNWtWiefk5eWRl5dnfp+enl5iOVHN4g7BuifVJqXr2bmCvbvaCdjeHeyLXt3Bq4Wa0Hg0Aq2W88lZzP7pBJtPXQTAx8XAxJ6hdAp2p02Aq3QKFkIIAVQiuZk8eTL//ve/iyU3sbGxvPnmm+zevbvKgvsnk8mEt7c3//vf/9DpdHTq1InY2FjefvvtUpObefPm8frrr1dbTOIGCvPhj/mwY77aT8bBE4bOh9A+amKjvXFCkpNv5KNtp1my4xz5hSZsdRom9grlmb5NcTTIJNtCCCEsVfib4cSJEyXOZdOhQwdOnDhRwhkl8/T0RKfTcenSJYv9ly5dwtfXt8Rz/Pz8sLW1tWiCatmyJQkJCeTn56PXF+9TMWPGDKZNm2Z+n56eTmBgYLnjFDch4Rise0LtNwPQajgMfQccPct1uqIo/Hb8EnN+PkFsqtoE1bupJ7OGtaaJt1M1BS2EEOJWp63oCQaDoVhCAhAfH4+NTflzJb1eT6dOndi8ebN5n8lkYvPmzXTv3r3Ec3r27ElERAQm07XOo2fOnMHPz6/ExKYoXhcXF4tNVDNjIex4G/53h5rY2HvAqKUwenm5E5tziZmM+3wPT3y1n9jUHALc7FnyUEe+mNhVEhshhBBlqnByM3DgQGbMmEFaWpp5X2pqKi+99BIDBgyo0LWmTZvGJ598wvLlyzl58iRPPvkkWVlZTJgwAYBx48YxY8YMc/knn3ySlJQUnn32Wc6cOcP69euZO3cukydPrujHENXl8in4rD9s+Q+YCqDF3TB5N7QZWa7TE9JyeXXdMQYt2MEfZ5PQ67RMubMJm6b1YXAbP5mXRgghxA1VuFlq/vz53H777QQHB9OhQwcADh06hI+PD19++WWFrjVmzBgSExOZOXMmCQkJtG/fng0bNpg7GcfExKDVXsu/AgMD+e2333juuedo164dAQEBPPvss7zwwgsV/RiiqhTmw+Xj6jw0sQfg6LdgzFP70wyZD23vg3IkJJczcvlwayQr98SQf3V+mjubezFrWGtCPB2r+1MIIYSoQyq1/EJWVhYrVqzg8OHD2Nvb065dO8aOHVvpuWdqkgwFvwkmIySdvTqZ3tVJ9RKOqcnM9ZoOhGHvg4vfDS+ZnJnHxzvO8cWuaHIL1KSma4gH0wY247ZGDarjUwghhLgFVfvyC46Ojjz22GOVCk7cgjIS4LeX4cwGyM8sftzODQI6qsO2g3tA4743rK25kpXPJ3+cY9lf0WRfXcCyQ5Abzw9oTs8mDaT5SQghRKVVehztiRMniImJIT8/32L/v/71r5sOStQSigKHVsJvMyD3ah8rWwfwCzNPqEdAR3APLVfTE6hJzdKdUXy+M5rMvEIA2jV05bkBzbijmZckNUIIIW5ahZObc+fOMWLECI4ePYpGo6GoVavoS8loNJZ1urhVpF6An56FyKuj2fw7wF1vq6+6iufEhy+k8sWu8/x0JM7cp6alnwvTBjSjf0tvSWqEEEJUmQp/Sz377LOEhoayefNmQkND2bNnD8nJyTz//PPMnz+/OmIUNclkgv1LYeNMtQlKZ4A7X1JX265gUpNbYGT9kXi+2BXN4YvXRte1CXBh8h1NGNTaV5ZHEEIIUeUqnNzs2rWLLVu24OnpiVarRavV0qtXL+bNm8czzzzDwYMHqyNOURNSzsGPz0D0H+r7wG5wzwfg2bRCl7mQks2K3TF8s+8CKVlqs6Vep2VoOz8e7h5Mh0A3qakRQghRbSqc3BiNRpydnQF1luG4uDiaN29OcHAwp0+frvIARQ0wGWH3x7B5NhTmqP1q+s2CrpPKtTxCkSMXU3l/cwRbTl3CdHUMnr+rHQ/eFsyYLoF4Ohmq6QMIIYQQ11Q4uWnTpg2HDx8mNDSUbt268dZbb6HX6/nf//5Ho0aNqiNGUV0UBSI2wbY3IHafui+kN/xrEXiEVuAyCp/vjGbeLycpvJrV9GriycPdg+nXwhsbXYXnihRCCCEqrcLJzSuvvEJWVhYAs2fP5u6776Z37940aNCA1atXV3mAohoYC+HEOvhzAVy6uu6T3hkGzoaO4aAtfzKSnlvAv789wobjCQAMaevLtAHNZYkEIYQQVlOpSfz+KSUlBXd391uiH0W9nsQvPxsOrYC/FkHqeXWfrSN0nqB2GC7HpHvXOxabxlMrDhCTko2tTsOrd7fi4duCb4nfAyGEELeWapvEr6CgAHt7ew4dOkSbNm3M+z08PCoXqagZOVdgz6ewewlkJ6n7HDyh2xPQ5RFwqNi/n6IorNwTw+s/nSC/0ESAmz0fPtiRsEC3qo9dCCGEqKAKJTe2trYEBQXJXDa3ipxUdXXu/cuuzSzsFgQ9noH2D4LeocKXzMor5OW1R1l3KA6Afi28eWd0GG4OJa/KLoQQQtS0Cve5efnll3nppZf48ssvpcamNoveCWsfh7QL6nufNtBzKrQeUalJ+ADOXsrgyRUHiLiciU6r4d+DmjOpdyOZq0YIIUStUuFvucWLFxMREYG/vz/BwcE4Olqu2HzgwIEqC05UgrEAts2DP94FFHAPUWcWbjqg3EskXC+3wMjJ+HT2RKWwYNNZcgqM+LgYWDS2I11DJbkVQghR+1Q4uRk+fHg1hCGqRFIErHkU4q5OpNj+IbjrDTA4l+v0nHwjJ+LTORabxtHYNI7FpnH2ciZG07U+5z2bNGDh/R1kzhohhBC1VpWMlrqV1MnRUooCB5bDhhlQkK2u0j1sIbQefsNTs/IKmf/7af6KSObs5QxMJfw2eDrpaRPgyh3NvHi4ewg6aYYSQghRw6pttJSohbKS4cen4fR69X3o7TB8CbgG3PDUxIw8Ji7by9HYa+s+eToZaBvgQtsAV9oEuNK2oSu+LnYyvFsIIcQto8LJjVarLfOLTkZS1aCITbDuKci8BDo99JsJt00u1yR85xIzGb90DxdScvBw1DP7ntZ0CfHAx8WuBgIXQgghqk+Fk5u1a9davC8oKODgwYMsX76c119/vcoCEzew/W3Y+h/1Z8/mcO+n4NeuXKfuP3+FR5fv5Up2AcENHFg2oSuhno43PlEIIYS4BVRZn5uVK1eyevVqfvjhh6q4XLW55fvcKApsnQs73lLfd5kEA+eArX25Tv/9eAJPf32QvEITYQ1d+Sy8i3QOFkIIUetV5Pu7ylY0vO2229i8eXNVXU6URFHUlbuLEpsBc2Do/HInNl/uiuaJr/aTV2iiXwtvvn7sNklshBBC1DlV0qE4JyeH999/n4CAG3diFZWkKLBxJvz1vvp+0Dzo/lS5TjWZFN767TRLtkcCMLZrEHPuaS2rdQshhKiTKpzc/HOBTEVRyMjIwMHBga+++qpKgxNXKQr89jL8/YH6/q63odtj5To1v9DEv787bF4u4fkBzZjSt4mMfhJCCFFnVTi5ee+99yy+GLVaLV5eXnTr1g13d/cqDU6gJja/vgB7PlbfD30HujxarlPTcwt48qv97IxIxkarYd7IttzXObAagxVCCCGsr8LJTXh4eDWEIUpkMsEv02HfZ+r7YQuhU3i5Tr14JZuJy/Zy5lImjnodHz7UiT7NvKovViGEEKKWqHBys3TpUpycnLjvvvss9n/77bdkZ2czfvz4KguuXjOZYP1z6oreaOBfi6Djw+U69fCFVB5Zvo+kzDx8XAx8Nr4LbQJcqzVcIYQQoraocI/SefPm4enpWWy/t7c3c+fOrZKg6j2TCX56+lpiM/yjcic2G44lMOZ/u0jKzKOFrzPrJveUxEYIIUS9UuGam5iYGEJDQ4vtDw4OJiYmpkqCqvd+/Tcc/Ao0WhjxMbQbfcNTFEXhsz+j+O8vJ1EUuKO5F4sf6IiTQVbYEEIIUb9UuObG29ubI0eOFNt/+PBhGjRoUCVB1WuRW2HvJ4AGRn5SrsSm0Gji1R+O8Z/1amLz0G1BfDqusyQ2Qggh6qUKf/uNHTuWZ555BmdnZ26//XYAtm/fzrPPPsv9999f5QHWK/lZ8NOz6s9dHoW2o254SkZuAVNWHmT7mUQ0Gnh5SEse6RUqQ72FEELUWxVObubMmUN0dDT9+vXDxkY93WQyMW7cOOlzc7O2zoXU8+DSEPrPumHxuNQcJi7by6mEDOxstSwY04HBbXxrIFAhhBCi9qr02lJnz57l0KFD2Nvb07ZtW4KDg6s6tmpRa9eWit0Pn/YHxQQPfAPNBpVZ/FhsGhOX7eVyRh6eTgY+G9+ZsEC3molVCCGEqGEV+f6udKeMpk2b0rRp08qeLq5nLIAfn1ETmzajbpjY7DiTyJNf7Scr30gzHyc+D+9CQ3eHGgpWCCGEqN0q3KH43nvv5c033yy2/6233io2940op50L4NIxsPeAu4o/2+t9v/8iE5ftJSvfSI/GDfjuyR6S2AghhBDXqXBys2PHDoYMGVJs/1133cWOHTuqJKh6JfEMbL+6yvfgN8Cx+BxCoA71/mBrBM9/e5hCk8K/wvxZNqErLna2NRisEEIIUftVuFkqMzMTvV5fbL+trS3p6elVElS9YTLBT8+AMR+a9C912LfRpPDaj8f58u/zADx+eyNeGNwCrVZGRAkhhBD/VOGam7Zt27J69epi+1etWkWrVq2qJKh6Y//nELMLbB3h7veghOHbuQVGnvxqP1/+fR6NBmbe3YoZQ1pKYiOEEEKUosI1N6+++iojR44kMjKSvn37ArB582ZWrlzJd999V+UB1llpsbDxNfXnfjPBLahYkStZ+Tz6xT72n7+C3kbLgjHtGdLWr2bjFEIIIW4xFa65GTZsGOvWrSMiIoKnnnqK559/ntjYWLZs2UKTJk0qFcQHH3xASEgIdnZ2dOvWjT179pTrvFWrVqHRaBg+fHil7ms1igLrn4f8DGjYBbpOKlbkQko29y75i/3nr+BiZ8OXE7tKYiOEEEKUQ4WTG4ChQ4eyc+dOsrKyOHfuHKNHj2b69OmEhYVV+FqrV69m2rRpzJo1iwMHDhAWFsagQYO4fPlymedFR0czffp0evfuXZmPYF3H18CZX0Frq672rdVZHD4Rl87Ij/7iXGIW/q52fPdkD7o1kqUthBBCiPKoVHID6qip8ePH4+/vzzvvvEPfvn35+++/K3ydd999l0mTJjFhwgRatWrFkiVLcHBw4PPPPy/1HKPRyIMPPsjrr79Oo0aNKvsRrCM7BX75t/pz7+fBu6XF4fxCE49/tY/EDHVV7zVP9aSZj7MVAhVCCCFuTRXqc5OQkMCyZcv47LPPSE9PZ/To0eTl5bFu3bpKdSbOz89n//79zJgxw7xPq9XSv39/du3aVep5s2fPxtvbm0ceeYQ//vijzHvk5eWRl5dnfm/1EV2/vQzZSeDVAnpPK3b46z0xXEjJwdvZwOrHu+NqL0O9hRBCiIood83NsGHDaN68OUeOHGHBggXExcWxaNGim7p5UlISRqMRHx8fi/0+Pj4kJCSUeM6ff/7JZ599xieffFKue8ybNw9XV1fzFhgYeFMx35T0eDi8Uv35X4vAxmBxOCuvkEVbIgB4pl9TSWyEEEKISih3cvPrr7/yyCOP8PrrrzN06FB0Ot2NT6piGRkZPPzww3zyySd4epY82d0/zZgxg7S0NPN24cKFao6yDKd/UV8bdoHArsUOL90ZRVJmHsENHBjTxYpJmBBCCHELK3ezVFGNSadOnWjZsiUPP/ww999//03d3NPTE51Ox6VLlyz2X7p0CV/f4qtbR0ZGEh0dzbBhw8z7TCYTADY2Npw+fZrGjRtbnGMwGDAYLGtIrObUevW1xdBih65k5fPx9nMATBvQDFtdpbtDCSGEEPVauZOb2267jdtuu40FCxawevVqPv/8c6ZNm4bJZGLjxo0EBgbi7Fyxjq96vZ5OnTqxefNm83Buk8nE5s2bmTJlSrHyLVq04OjRoxb7XnnlFTIyMli4cKF1m5xuJDcNoq4uT9Hi7mKHl2yPJCOvkJZ+Lgxr51/DwQkhROWZTCby8/OtHYaoA/R6PVrtzf9xX+FJ/BwdHZk4cSITJ07k9OnTfPbZZ7zxxhu8+OKLDBgwgB9//LFC15s2bRrjx4+nc+fOdO3alQULFpCVlcWECRMAGDduHAEBAcybNw87OzvatGljcb6bmxtAsf21ztmNYCoAz2bgabmaenxaDsv+igbg34Oby+zDQohbRn5+PlFRUeZadCFuhlarJTQ0tMRlniqiwsnN9Zo3b85bb73FvHnz+Omnn8ocvl2aMWPGkJiYyMyZM0lISKB9+/Zs2LDB3Mk4JiamSrI4qzM3SRWvtXl/81nyCk10DfHgjmZeNRyYEEJUjqIoxMfHo9PpCAwMrBv/rxZWYzKZiIuLIz4+nqCgIDQlLElUXhpFUZQqjK3WS09Px9XVlbS0NFxcXGrmpoV58FZjdUbiR7dAw07mQ+cSMxnw3g6MJoXvnuhO5xCPmolJCCFuUkFBAREREfj7++Pq6mrtcEQdkJaWRlxcHE2aNMHW1nLEcEW+vyXNrglRO9TExtkP/DtYHHpn4xmMJoV+LbwlsRFC3FKMRiPATTchCFGk6Hep6HersiS5qQmnflZfmw+B66ptj8Wmsf5IPBoNTB/U3ErBCSHEzbmZ5gMhrldVv0uS3FQ3kwlOXZ3f5h9DwN/67TQAw9sH0NKvhprIhBBCiDpOkpvqFrsPsi6DwQVCri3y+VdkEjvOJGKj1fBc/2ZWDFAIIcTNCAkJYcGCBdYOQ1znpkZLiXIoapJqOhBs1LZERVF4a4Naa/NAtyCCGjhYKzohhKh37rjjDtq3b19lCcnevXtxdHSskmuJqiHJTXVSFDh5Nbm5rklq44lLHLqQir2tjil9m1gpOCGEEKVRFAWj0YiNzY2/Jr286t4UHhX5/LWRNEtVp6QzkBIJOj006Q+A0aTw9tW+NhN7heDtbGfNCIUQol4JDw9n+/btLFy4EI1Gg0ajITo6mm3btqHRaPj111/p1KkTBoOBP//8k8jISO655x58fHxwcnKiS5cubNq0yeKa/2yW0mg0fPrpp4wYMQIHBweaNm16wwluv/zySzp37oyzszO+vr488MADXL582aLM8ePHufvuu3FxccHZ2ZnevXsTGRlpPv7555/TunVrDAYDfn5+5pn+o6Oj0Wg0HDp0yFw2NTUVjUbDtm3bAG7q8+fl5fHCCy8QGBiIwWCgSZMmfPbZZyiKQpMmTZg/f75F+UOHDqHRaIiIiCjzmdwMSW6qU1GTVGgfsFM7DK89GMvZy5m42tvy2O2NyzhZCCFuLYqikJ1faJWtvFO2LVy4kO7duzNp0iTi4+OJj4+3WLrnxRdf5I033uDkyZO0a9eOzMxMhgwZwubNmzl48CCDBw9m2LBhxMTElHmf119/ndGjR3PkyBGGDBnCgw8+SEpKSqnlCwoKmDNnDocPH2bdunVER0cTHh5uPh4bG8vtt9+OwWBgy5Yt7N+/n4kTJ1JYWAjARx99xOTJk3nsscc4evQoP/74I02aVLxloDKff9y4cXz99de8//77nDx5ko8//hgnJyc0Gg0TJ05k6dKlFvdYunQpt99+e6XiK69bs77pVlE0K3FLdVZik0lhwaYzADx5R2Nc7W1LO1MIIW45OQVGWs38zSr3PjF7EA76G3+lubq6otfrcXBwKHGB5tmzZzNgwADzew8PD8LCwszv58yZw9q1a/nxxx9LXAOxSHh4OGPHjgVg7ty5vP/+++zZs4fBgweXWH7ixInmnxs1asT7779Ply5dyMzMxMnJiQ8++ABXV1dWrVplntyuWbNrg1H+85//8Pzzz/Pss8+a93Xp0uVGj6OYin7+M2fO8M0337Bx40b69+9vjv/65zBz5kz27NlD165dKSgoYOXKlcVqc6qa1NxUl/Q4iN0PaKDZXQAcj0vn4pUcnAw2jO8eYtXwhBBCFNe5c2eL95mZmUyfPp2WLVvi5uaGk5MTJ0+evGHNTbt27cw/Ozo64uLiUqyZ6Xr79+9n2LBhBAUF4ezsTJ8+fQDM9zl06BC9e/cuNmsvwOXLl4mLi6Nfv37l/pylqejnP3ToEDqdzhzvP/n7+zN06FDz8kw//fQTeXl53HfffTcda1mk5qa6FNXaBHYFZ3WdrB1nEwHo3rgB9nqdtSITQohqYW+r48TsQVa7d1X456in6dOns3HjRubPn0+TJk2wt7dn1KhRN1wF/Z9JiEajKXVx0aysLAYNGsSgQYNYsWIFXl5exMTEMGjQIPN97O3tS71XWccA85pf1zfdFRQUlFi2op//RvcGePTRR3n44Yd57733WLp0KWPGjMHBoXpHCUtyU13MC2VeGyW144ya3Nze1NMaEQkhRLXSaDTlahqyNr1eX+7p/Xfu3El4eDgjRowA1JqM6OjoKo3n1KlTJCcn88Ybb5j7/+zbt8+iTLt27Vi+fDkFBQXFEidnZ2dCQkLYvHkzd955Z7HrF43mio+Pp0MHdQmg6zsXl+VGn79t27aYTCa2b99ubpb6pyFDhuDo6MhHH33Ehg0b2LFjR7nufTOkWao65KRC9B/qz1dXAc/MK+RAzBUAejete8MGhRDiVhESEsLu3buJjo4mKSmp1BoVgKZNm7JmzRoOHTrE4cOHeeCBB8osXxlBQUHo9XoWLVrEuXPn+PHHH5kzZ45FmSlTppCens7999/Pvn37OHv2LF9++SWnT6ujb1977TXeeecd3n//fc6ePcuBAwdYtGgRoNau3HbbbeaOwtu3b+eVV14pV2w3+vwhISGMHz+eiRMnsm7dOqKioti2bRvffPONuYxOpyM8PJwZM2bQtGlTunfvfrOP7IYkuakOZzeCqRC8WkADdUTU35HJFBgVgjwcCPGUyZ6EEMJapk+fjk6no1WrVuYmoNK8++67uLu706NHD4YNG8agQYPo2LFjlcbj5eXFsmXL+Pbbb2nVqhVvvPFGsQ63DRo0YMuWLWRmZtKnTx86derEJ598Yq7FGT9+PAsWLODDDz+kdevW3H333Zw9e9Z8/ueff05hYSGdOnVi6tSp/Oc//ylXbOX5/B999BGjRo3iqaeeokWLFkyaNImsrCyLMo888gj5+flMmDChMo+owjRKecfP1REVWTK90r4ZDyfWQe/nod9MAGb9cIzlu87zYLcg/juibfXcVwghalBubi5RUVGEhoZiZydzdonS/fHHH/Tr148LFy7g4+NTarmyfqcq8v1d+xtHbzUFuRBxdYKj6/rb/HE2CZAmKSGEEPVHXl4eiYmJvPbaa9x3331lJjZVSZqlqlrUDsjPBGd/8FM7bl1IyeZcUhY6rYYeTRpYOUAhhBCiZnz99dcEBweTmprKW2+9VWP3leSmqhXNStxiCFwdfldUa9Mh0A0XO5m4TwghRP0QHh6O0Whk//79BAQE1Nh9JbmpSiYjnP5F/fnqKCmAP67ObyNNUkIIIUT1k+SmKl3cC1mJYHCFkF4AFBpN7IxQa25ubybz2wghhBDVTZKbqlTUJNVsEOjU5qfDF9NIzy3Exc6Gdg3drBebEEIIUU9IclNVFAVOFvW3uX6UlNok1aupJzqtxhqRCSGEEPWKJDdVJfEUXIkCnQGaXFu8rKgz8e3S30YIIYSoETLPTVXJTgGftuDiDwZnANJyCjh0IRVQa26EEEIIUf0kuakqIT3hyT/VSfyu2hWZhNGk0MjLkYbu1bsCqhBCCCFU0ixV1WyvTRe9Q5qkhBCi1rnjjjuYOnVqlV4zPDyc4cOHV+k1ReVJclNNFEVhxxm1M7EMARdCCFFbFRQUWDuEKifJTTWJTs7m4pUcbHUauoXKkgtCCFEbhIeHs337dhYuXIhGo0Gj0RAdHQ3AsWPHuOuuu3BycsLHx4eHH36YpKQk87nfffcdbdu2xd7engYNGtC/f3+ysrJ47bXXWL58OT/88IP5mtu2bSvx/hs2bKBXr164ubnRoEED7r77biIjIy3KXLx4kbFjx+Lh4YGjoyOdO3dm9+7d5uM//fQTXbp0wc7ODk9PT0aMGGE+ptFoWLduncX13NzcWLZsGQDR0dFoNBpWr15Nnz59sLOzY8WKFSQnJzN27FgCAgJwcHCgbdu2fP311xbXMZlMvPXWWzRp0gSDwUBQUBD//e9/Aejbty9TpkyxKJ+YmIher2fz5s03/HepapLcVJOiIeCdgz1wNEjXJiFEPaAokJ9lnU1RyhXiwoUL6d69O5MmTSI+Pp74+HgCAwNJTU2lb9++dOjQgX379rFhwwYuXbrE6NGjAYiPj2fs2LFMnDiRkydPsm3bNkaOHImiKEyfPp3Ro0czePBg8zV79OhR4v2zsrKYNm0a+/btY/PmzWi1WkaMGIHJZAIgMzOTPn36EBsby48//sjhw4f597//bT6+fv16RowYwZAhQzh48CCbN2+ma9euFf6nevHFF3n22Wc5efIkgwYNIjc3l06dOrF+/XqOHTvGY489xsMPP8yePXvM58yYMYM33niDV199lRMnTrBy5UrzQpiPPvooK1euJC8vz1z+q6++IiAggL59+1Y4vpsl37rVZMeZq6uAS5OUEKK+KMiGuf7WufdLcaB3vGExV1dX9Ho9Dg4O+Pr6mvcvXryYDh06MHfuXPO+zz//nMDAQM6cOUNmZiaFhYWMHDmS4OBgANq2bWsua29vT15ensU1S3LvvfdavP/888/x8vLixIkTtGnThpUrV5KYmMjevXvx8PAAoEmTJuby//3vf7n//vt5/fXXzfvCwsJu+Ln/aerUqYwcOdJi3/Tp080/P/300/z222988803dO3alYyMDBYuXMjixYsZP348AI0bN6ZXL3U2/pEjRzJlyhR++OEHc0K4bNkywsPD0Whqfo43qbmpBvmFJnZFSmdiIYS4VRw+fJitW7fi5ORk3lq0aAFAZGQkYWFh9OvXj7Zt23LffffxySefcOXKlQrf5+zZs4wdO5ZGjRrh4uJCSEgIADExMQAcOnSIDh06mBObfzp06BD9+vUr8VhFdO7c2eK90Whkzpw5tG3bFg8PD5ycnPjtt9/McZ08eZK8vLxS721nZ8fDDz/M559/DsCBAwc4duwY4eHhNx1rZUjNTTU4GHOFrHwjDRz1tPJzsXY4QghRM2wd1BoUa937JmRmZjJs2DDefPPNYsf8/PzQ6XRs3LiRv/76i99//51Fixbx8ssvs3v3bkJDQ8t9n2HDhhEcHMwnn3yCv78/JpOJNm3akJ+fD6g1QGW50XGNRoPyjya6kjoMOzpa1nK9/fbbLFy4kAULFtC2bVscHR2ZOnVqueMCtWmqffv2XLx4kaVLl9K3b19zLVdNk5qbalA0K3Gvpp5oZckFIUR9odGoTUPW2CrQ9KHX6zEajRb7OnbsyPHjxwkJCaFJkyYWW1EioNFo6NmzJ6+//joHDx5Er9ezdu3aUq/5T8nJyZw+fZpXXnmFfv360bJly2K1P+3atePQoUOkpKSUeI127dqV2UHXy8uL+Ph48/uzZ8+SnZ1dZlwAO3fu5J577uGhhx4iLCyMRo0acebMGfPxpk2bYm9vX+a927ZtS+fOnfnkk09YuXIlEydOvOF9q4skN9Vgx9XOxL2lSUoIIWqdkJAQdu/eTXR0NElJSZhMJiZPnkxKSgpjx45l7969REZG8ttvvzFhwgSMRiO7d+9m7ty57Nu3j5iYGNasWUNiYiItW7Y0X/PIkSOcPn2apKSkEmtL3N3dadCgAf/73/+IiIhgy5YtTJs2zaLM2LFj8fX1Zfjw4ezcuZNz587x/fffs2vXLgBmzZrF119/zaxZszh58iRHjx61qG3q27cvixcv5uDBg+zbt48nnngCW1vbGz6Tpk2bmmumTp48yeOPP86lS5fMx+3s7HjhhRf497//zRdffEFkZCR///03n332mcV1Hn30Ud544w0URbEYxVXjlHomLS1NAZS0tLRquX5yZp4S8uLPSvALPyuX0nKq5R5CCFEb5OTkKCdOnFBycm6t/9edPn1aue222xR7e3sFUKKiohRFUZQzZ84oI0aMUNzc3BR7e3ulRYsWytSpUxWTyaScOHFCGTRokOLl5aUYDAalWbNmyqJFi8zXvHz5sjJgwADFyclJAZStW7eWeO+NGzcqLVu2VAwGg9KuXTtl27ZtCqCsXbvWXCY6Olq59957FRcXF8XBwUHp3Lmzsnv3bvPx77//Xmnfvr2i1+sVT09PZeTIkeZjsbGxysCBAxVHR0eladOmyi+//KK4uroqS5cuVRRFUaKiohRAOXjwoEVcycnJyj333KM4OTkp3t7eyiuvvKKMGzdOueeee8xljEaj8p///EcJDg5WbG1tlaCgIGXu3LkW18nIyFAcHByUp556qvz/INcp63eqIt/fGkUp5/i5OiI9PR1XV1fS0tJwcan6/jA/HY7j6a8P0sLXmQ1Tb6/y6wshRG2Rm5tLVFQUoaGh2NnZ3fgEUedFR0fTuHFj9u7dS8eOHSt8flm/UxX5/pYOxVWsaFbi3rJQphBCiHqioKCA5ORkXnnlFW677bZKJTZVqVb0ufnggw8ICQnBzs6Obt26WUwa9E+ffPIJvXv3xt3dHXd3d/r3719m+ZqkKIq5M/HtzaS/jRBCiPph586d+Pn5sXfvXpYsWWLtcKyf3KxevZpp06Yxa9YsDhw4QFhYGIMGDeLy5csllt+2bRtjx45l69at7Nq1i8DAQAYOHEhsbGwNR15cxOVMEtJzMdho6RJS8hwFQgghRF1zxx13oCgKp0+ftpjc0Fqsnty8++67TJo0iQkTJtCqVSuWLFmCg4ODeSKgf1qxYgVPPfUU7du3p0WLFnz66aeYTCarrF3xT0WrgHcN9cDOVmflaIQQQoj6yarJTX5+Pvv376d///7mfVqtlv79+5uHvd1IdnY2BQUFpc7mmJeXR3p6usVWXYr62/SRJikhRD1Sz8aliGpUVb9LVk1ukpKSMBqN5oW3ivj4+JCQkFCua7zwwgv4+/tbJEjXmzdvHq6uruYtMDDwpuMuSW6Bkd1RyYDMbyOEqB90OrWGumgWWyFuVtHvUtHvVmXd0qOl3njjDVatWsW2bdtKHYY4Y8YMi0mS0tPTqyXB2X/+CrkFJnxcDDTzcary6wshRG1jY2ODg4MDiYmJ2NraotVavaeDuIWZTCYSExNxcHDAxubm0hOrJjeenp7odDqLWRABLl26dMOVVefPn88bb7zBpk2baNeuXanlDAYDBoOhSuItS3ADB/5vUHN0Wo1VVkAVQoiaptFo8PPzIyoqivPnz1s7HFEHaLVagoKCbvp71KrJjV6vp1OnTmzevJnhw4cDmDsHT5kypdTz3nrrLf773//y22+/FVvZ1Foaujsw+c4mNy4ohBB1iF6vp2nTptI0JaqEXq+vkhpAqzdLTZs2jfHjx9O5c2e6du3KggULyMrKYsKECQCMGzeOgIAA5s2bB8Cbb77JzJkzWblyJSEhIea+OUVL1AshhKhZWq1WZigWtYrVk5sxY8aQmJjIzJkzSUhIoH379mzYsMHcyTgmJsYii/voo4/Iz89n1KhRFteZNWsWr732Wk2GLoQQQohaSNaWEkIIIUStV5Hvb+naLoQQQog6xerNUjWtqKKqOifzE0IIIUTVKvreLk+DU71LbjIyMgCqbTI/IYQQQlSfjIwMXF1dyyxT7/rcmEwm4uLicHZ2rvL5aIomCLxw4YL056kB8rxrljzvmiXPu2bJ865ZlXneiqKQkZGBv7//DYeL17uaG61WS8OGDav1Hi4uLvIfRw2S512z5HnXLHneNUued82q6PO+UY1NEelQLIQQQog6RZIbIYQQQtQpktxUIYPBwKxZs2pkLSshz7umyfOuWfK8a5Y875pV3c+73nUoFkIIIUTdJjU3QgghhKhTJLkRQgghRJ0iyY0QQggh6hRJboQQQghRp0hyU0U++OADQkJCsLOzo1u3buzZs8faIdUZO3bsYNiwYfj7+6PRaFi3bp3FcUVRmDlzJn5+ftjb29O/f3/Onj1rnWBvcfPmzaNLly44Ozvj7e3N8OHDOX36tEWZ3NxcJk+eTIMGDXBycuLee+/l0qVLVor41vbRRx/Rrl0780Rm3bt359dffzUfl2ddvd544w00Gg1Tp04175NnXnVee+01NBqNxdaiRQvz8ep81pLcVIHVq1czbdo0Zs2axYEDBwgLC2PQoEFcvnzZ2qHVCVlZWYSFhfHBBx+UePytt97i/fffZ8mSJezevRtHR0cGDRpEbm5uDUd669u+fTuTJ0/m77//ZuPGjRQUFDBw4ECysrLMZZ577jl++uknvv32W7Zv305cXBwjR460YtS137Jly9BoNERHR1vsb9iwIW+88Qb79+9n37599O3bl3vuuYfjx48D8qyr0969e/n4449p166dxX555lWrdevWxMfHm7c///zTfKxan7UiblrXrl2VyZMnm98bjUbF399fmTdvnhWjqpsAZe3ateb3JpNJ8fX1Vd5++23zvtTUVMVgMChff/21FSKsWy5fvqwAyvbt2xVFUZ+tra2t8tRTTylAqduuXbusHHntsnTpUgVQoqKibljW3d1d+fTTT83P+ttvvzUfO3nypDzfKpCRkaE0bdpU2bhxo9KnTx/l2WefVRRFkWdexWbNmqWEhYWVeKy6n7XU3Nyk/Px89u/fT//+/c37tFot/fv3Z9euXVaMrH6IiooiISHB4vm7urrSrVs3ef5VIC0tDQAPDw8A9u/fT0FBAa1btwZg9uzZfPnllzRo0IAHHniAL7/8ki+//JImTZpYLeZbldFoZNWqVWRlZdG9e3fzs77+d7tFixYEBQXJ7/ZNmjx5MkOHDrV4toA882pw9uxZ/P39adSoEQ8++CAxMTFA9T/rerdwZlVLSkrCaDTi4+Njsd/Hx4dTp05ZKar6IyEhAaDE5190TFSOyWRi6tSp9OzZkzZt2gDq89br9Tg4OABw11130blzZ95//30aNmzIQw89dMPrFhYWYjKZ0Ov1xY5lZWXh6OhY6ZgVRSE3Nxd7e/tKX6OmHT16lO7du5Obm4uTkxNr166lVatWHDp0CL1ej5ubm0V5+d2+OatWreLAgQPs3bu32LGi32955lWjW7duLFu2jObNmxMfH8/rr79O7969OXbsWLU/a6m5EUKUaPLkyRw7doxVq1ZV+hrR0dFoNBrmz5/PggULaNy4MQaDgRMnTpg7G544cYIHHngAd3d3evXqBagJ0Jw5c8zlQ0JCeOmll8jLy7O4fkhICHfffTe//fYbnTt3xt7eno8//rjMmHbv3s3gwYNxdXXFwcGBPn36sHPnTvPx7777Do1Gw/bt24ud+/HHH6PRaDh27BgAR44cITw8nEaNGmFnZ4evry8TJ04kOTm53M+oefPmHDp0iN27d/Pkk08yfvx4Tpw4Ue7zRflduHCBZ599lhUrVmBnZ2ftcOq8u+66i/vuu4927doxaNAgfvnlF1JTU/nmm2+q/d5Sc3OTPD090el0xXp4X7p0CV9fXytFVX8UPeNLly7h5+dn3n/p0iXat29vpahufVOmTOHnn39mx44dNGzY0Lzf19eX/Px8srOzAbXZKikpibi4OJycnEhKSkKj0dCgQQOL6y1dupTc3Fwee+wxDAaDuZkL4L777qNp06bMnTsX5epqMI8++ijLly9n1KhRPP/88+zevZt58+Zx8uRJ1q5da3Ht06dPM3bsWB5//HEmTZpE8+bNS/1cW7Zs4a677qJTp07MmjULrVbL0qVL6du3L3/88Qddu3Zl6NChODk58c0339CnTx+L81evXk3r1q3NNVkbN27k3LlzTJgwAV9fX44fP87//vc/jh8/zt9//41Go7nhs9br9eZmvE6dOrF3714WLlzImDFjyM/PJzU11eKvW/l/S+Xt37+fy5cv07FjR/M+o9HIjh07WLx4Mb/99ps882rk5uZGs2bNiIiIYMCAAdX7rG+6145QunbtqkyZMsX83mg0KgEBAdKhuBpQSofi+fPnm/elpaVJh+JKMplMyuTJkxV/f3/lzJkzxY4XdQKcPHlyqZ2JDQaDuXxUVJQCKC4uLsrly5ctrjVr1iwFUMaOHWux/9ChQwqgPProoxb7p0+frgDKli1bzPuCg4MVQNmwYUO5PlvTpk2VQYMGKSaTybw/OztbCQ0NVQYMGGDeN3bsWMXb21spLCw074uPj1e0Wq0ye/Zsi3P/6euvv1YAZceOHeZ9FelQfOeddyrjx483P+vvvvvOfOzUqVPSufUmpKenK0ePHrXYOnfurDz00EPK0aNH5ZlXs4yMDMXd3V1ZuHBhtT9rSW6qwKpVqxSDwaAsW7ZMOXHihPLYY48pbm5uSkJCgrVDqxMyMjKUgwcPKgcPHlQA5d1331UOHjyonD9/XlEURXnjjTcUNzc35YcfflCOHDmi3HPPPUpoaKiSk5Nj5chvPU8++aTi6uqqbNu2TYmPjzdv13+JP/HEE4qHh4cCKC+88ILSsmVLpWXLlsrGjRuVjRs3WiQfRcnNhAkTit2rKLkpGolVZO7cuQqgnDhxwmJ/fHy8AijPP/+8eV9wcLASGhpars924MABBVCWL1+uJCYmWmyPPvqoYjAYFKPRqCiKoqxbt04BlE2bNpnPX7RokQIop0+fLvH6OTk5SmJiovkzL1iwwHystOTmxRdfVLZv365ERUUpR44cUV588UVFo9Eov//+u6Io6rMOCgpStmzZouzbt0/p3r270r1793J9XlE+14+WUhR55lXp+eefV7Zt26ZERUUpO3fuVPr37694enqa/9CpzmctyU0VWbRokRIUFKTo9Xqla9euyt9//23tkOqMrVu3llhDMH78eEVR1L/IX331VcXHx0cxGAxKv379Sv0CEmUrrTZm6dKl5jI5OTlK3759zbU0I0aMUOLj40u8XtEX/fW1HUWKkpuYmBiL/Y8//rii1WqV/Pz8Yue4ubkpo0aNMr8PDg5W+vbtW67Ptnr16jKHrwNKSkqKoiiKkpubq7i6uiqTJk0yn9+rVy+lffv2FtdMTk5WnnnmGcXb27vYtV5//XVzudKSm4kTJyrBwcGKXq9XvLy8lH79+pkTG0VRn/VTTz2luLu7Kw4ODmU+a1E5/0xu5JlXnTFjxih+fn6KXq9XAgIClDFjxigRERHm49X5rKXPTRWZMmUKU6ZMsXYYddIdd9xh7otREo1Gw+zZs5k9e3YNRlU3lfWci9jZ2fHwww+zZcsW/vzzTzp37nzDc8oavVTasfL0V7nRta9nMpkAePvtt0vtj+Xk5ASAwWBg+PDhrF27lg8//JBLly6xc+dO5s6da1F+9OjR/PXXX/zf//0f7du3x8nJCZPJxODBg833K8tnn31W5nE7Ozs++OCDUiewFDdv27ZtFu/lmVedGw1GqM5nLcmNEKJWCQ4OxmQycfbsWVq2bGnef+nSJVJTUwkODq7UdRs3bgyAi4tLsflNSjJmzBiWL1/O5s2bOXnyJIqiMGbMGPPxK1eusHnzZl5//XVmzpxp3i9LfwhhfTIUXAhRqwwZMgSABQsWWOx/9913ARg6dGilrtupUycaN27M/PnzyczMLHY8MTHR4n3//v3x8PBg9erVrF69mq5duxIaGmo+rtPpgOK1Xf+MWwhR86TmRghRab/++muJk1X26NGDRo0aVeqaYWFhjB8/nv/973+kpqbSp08f9uzZw/Llyxk+fDh33nlnpa6r1Wr59NNPueuuu2jdujUTJkwgICCA2NhYtm7diouLCz/99JO5vK2tLSNHjjTPGjx//nyL67m4uHD77bfz1ltvUVBQQEBAAL///jtRUVGVik8IUXUkuRFCVNr1zTHXW7p0aaWTG4BPP/2URo0asWzZMtauXYuvry8zZsxg1qxZlb4mqP23du3axZw5c1i8eDGZmZn4+vrSrVs3Hn/88WLlx4wZw6effopGo2H06NHFjq9cuZKnn36aDz74AEVRGDhwIL/++iv+/v43FacQ4uZolPL0IBRCCCGEuEVInxshhBBC1CmS3AghhBCiTpHkRgghhBB1iiQ3QgghhKhTJLkRQgghRJ0iyY0QQggh6pR6N8+NyWQiLi4OZ2fncq9dI4QQQgjrUhSFjIwM/P390WrLrpupd8lNXFwcgYGB1g5DCCGEEJVw4cIFGjZsWGaZepfcODs7A+rDcXFxsXI0QgghhCiP9PR0AgMDzd/jZal3yU1RU5SLi4skN0IIIcQtpjxdSqRDsRBCCCHqFEluhBBCCFGnSHIjhBBCiDql3vW5qS4n4tL5345IHA02/HdEW2uHI4QQdZrRaKSgoMDaYYgqZmtri06nu+nrWDW5mTdvHmvWrOHUqVPY29vTo0cP3nzzTZo3b17qOcuWLWPChAkW+wwGA7m5udUdbpnyCo2sOxSHh6Oe/wxvI3PoCCFENcnMzOTixYsoimLtUEQV02g0NGzYECcnp5u6jlWTm+3btzN58mS6dOlCYWEhL730EgMHDuTEiRM4OjqWep6LiwunT582v68NiUQrfxf0Oi0pWflcSMkhqIGDtUMSQog6x2g0cvHiRRwcHPDy8qoV//8XVUNRFBITE7l48SJNmza9qRocqyY3GzZssHi/bNkyvL292b9/P7fffnup52k0Gnx9fas7vAox2Oho6e/C4QupHLxwRZIbIYSoBgUFBSiKgpeXF/b29tYOR1QxLy8voqOjKSgouKnkplZ1KE5LSwPAw8OjzHKZmZkEBwcTGBjIPffcw/Hjx0stm5eXR3p6usVWXToEugFw6EJqtd1DCCFE7aixF1Wvqv5da01yYzKZmDp1Kj179qRNmzallmvevDmff/45P/zwA1999RUmk4kePXpw8eLFEsvPmzcPV1dX81adSy+0l+RGCCGEsLpak9xMnjyZY8eOsWrVqjLLde/enXHjxtG+fXv69OnDmjVr8PLy4uOPPy6x/IwZM0hLSzNvFy5cqI7wgWvJzfG4dPILTdV2HyGEEPVbSEgICxYssHYYtVatGAo+ZcoUfv75Z3bs2HHDxbD+ydbWlg4dOhAREVHicYPBgMFgqIowbyi4gQPuDrZcyS7gZHw6YVeTHSGEEPXbHXfcQfv27assIdm7d2+ZA2/qO6vW3CiKwpQpU1i7di1btmwhNDS0wtcwGo0cPXoUPz+/aoiwYjQajTmhkaYpIYQQFaEoCoWFheUq6+XlhYNDzQ5cMRqNmEzFWyXy8/Mrdb3KnlceVk1uJk+ezFdffcXKlStxdnYmISGBhIQEcnJyzGXGjRvHjBkzzO9nz57N77//zrlz5zhw4AAPPfQQ58+f59FHH7XGRyimQ6A7AAdjrlg5EiGEELVBeHg427dvZ+HChWg0GjQaDdHR0Wzbtg2NRsOvv/5Kp06dMBgM/Pnnn0RGRnLPPffg4+ODk5MTXbp0YdOmTRbX/GezlEaj4dNPP2XEiBE4ODjQtGlTfvzxxzLjysvLY/r06QQEBODo6Ei3bt3Ytm2b+fiyZctwc3Pjxx9/pFWrVhgMBmJiYggJCWHOnDmMGzcOFxcXHnvsMQC+//57WrdujcFgICQkhHfeeadYzCWdVx2smtx89NFHpKWlcccdd+Dn52feVq9ebS4TExNDfHy8+f2VK1eYNGkSLVu2ZMiQIaSnp/PXX3/RqlUra3yEYtoHuQFScyOEEDVBURSy8wutspV3EsGFCxfSvXt3Jk2aRHx8PPHx8RaDW1588UXeeOMNTp48Sbt27cjMzGTIkCFs3ryZgwcPMnjwYIYNG0ZMTEyZ93n99dcZPXo0R44cYciQITz44IOkpKSUWn7KlCns2rWLVatWceTIEe677z4GDx7M2bNnzWWys7N58803+fTTTzl+/Dje3t4AzJ8/n7CwMA4ePMirr77K/v37GT16NPfffz9Hjx7ltdde49VXX2XZsmUW9/znedXFqn1uyvOLcX0WCfDee+/x3nvvVVNEN699QzcAopOzuZKVj7uj3roBCSFEHZZTYKTVzN+scu8TswfhoL/x16irqyt6vR4HB4cS52ibPXs2AwYMML/38PAgLCzM/H7OnDmsXbuWH3/8kSlTppR6n/DwcMaOHQvA3Llzef/999mzZw+DBw8uVjYmJoalS5cSExODv78/ANOnT2fDhg0sXbqUuXPnAuq8Qh9++KFFPAB9+/bl+eefN79/8MEH6devnzlhadasGSdOnODtt98mPDy81POqS60ZLVVXuDrY0shT7eR16GKqdYMRQghR63Xu3NnifWZmJtOnT6dly5a4ubnh5OTEyZMnb1hz065dO/PPjo6OuLi4cPny5RLLHj16FKPRSLNmzXBycjJv27dvJzIy0lxOr9dbXLe0mE+ePEnPnj0t9vXs2ZOzZ89iNBpLPa+61IrRUnVN+0A3ziVlcSgmlTube1s7HCGEqLPsbXWcmD3IaveuCv8c9TR9+nQ2btzI/PnzadKkCfb29owaNeqGHXBtbW0t3ms0mhI7AIOaQOl0Ovbv319sJuDr13Wyt7cvcWK9yo7UqqkRXpLcVIP2QW6sORgr/W6EEKKaaTSacjUNWZter7eowSjLzp07CQ8PZ8SIEYCaiERHR1dpPB06dMBoNHL58mV69+5909dr2bIlO3futNi3c+dOmjVrViWrfFeUNEtVg6LJ/A5fTJVVa4UQQhASEsLu3buJjo4mKSmp1BoVgKZNm7JmzRoOHTrE4cOHeeCBB8osXxnNmjXjwQcfZNy4caxZs4aoqCj27NnDvHnzWL9+fYWv9/zzz7N582bmzJnDmTNnWL58OYsXL2b69OlVGnd5SXJTDVr4uqC30ZKaXUB0cra1wxFCCGFl06dPR6fT0apVK7y8vMrsP/Puu+/i7u5Ojx49GDZsGIMGDaJjx45VHtPSpUsZN24czz//PM2bN2f48OHs3buXoKCgCl+rY8eOfPPNN6xatYo2bdowc+ZMZs+ebdGZuCZplHpWtZCeno6rqytpaWm4uLhU231GfriTAzGpvDcmjBEdKjbrshBCiJLl5uYSFRVFaGgodnZ21g5HVLGy/n0r8v0tNTfVpP3VyfwOxaRaNxAhhBCinpHkpprIZH5CCCGEdUhyU006XO1UfCI+ndyC8vWQF0IIIcTNk+SmmjR0t6eBo54Co8LxuHRrhyOEEELUG5LcVBONRkMHaZoSQgghapwkN9WoaL4bSW6EEEKImiPJTTUyj5i6cMXKkQghhBD1hyQ31ahdoCsaDVxIySE5M8/a4QghhBD1giQ31cjFzpbGXuoCZNI0JYQQQtQMSW6qmfS7EUIIIWqWJDfVTJIbIYQQd9xxB1OnTq3Sa4aHhzN8+PAqvWZdIclNNbs+uTGZ6tUyXkIIIW5BiqJQWFhYbH9+fn6lrlfZ826GJDfVrIWvM3a2WjJyCzmXlGXtcIQQQtSw8PBwtm/fzsKFC9FoNGg0GqKjowE4duwYd911F05OTvj4+PDwww+TlJRkPve7776jbdu22Nvb06BBA/r3709WVhavvfYay5cv54cffjBfc9u2bSXe32QyMW/ePEJDQ7G3tycsLIzvvvvOfHzbtm1oNBp+/fVXOnXqhMFg4M8//+SOO+5gypQpTJ06FU9PTwYNGgTA9u3b6dq1KwaDAT8/P1588UWLZKi082qSTY3fsZ6x0WlpG+DK3ugrHLqQShNvJ2uHJIQQdYeiQEG2de5t6wAazQ2LLVy4kDNnztCmTRtmz54NgJeXF6mpqfTt25dHH32U9957j5ycHF544QVGjx7Nli1biI+PZ+zYsbz11luMGDGCjIwM/vjjDxRFYfr06Zw8eZL09HSWLl0KgIeHR4n3nzdvHl999RVLliyhadOm7Nixg4ceeggvLy/69OljLvfiiy8yf/58GjVqhLu7OpXJ8uXLefLJJ9m5cycAsbGxDBkyhPDwcL744gtOnTrFpEmTsLOz47XXXjNf65/n1TRJbmpA+0C3q8nNFUZ1amjtcIQQou4oyIa5/ta590txoHe8YTFXV1f0ej0ODg74+vqa9y9evJgOHTowd+5c877PP/+cwMBAzpw5Q2ZmJoWFhYwcOZLg4GAA2rZtay5rb29PXl6exTX/KS8vj7lz57Jp0ya6d+8OQKNGjfjzzz/5+OOPLZKb2bNnM2DAAIvzmzZtyltvvWV+//LLLxMYGMjixYvRaDS0aNGCuLg4XnjhBWbOnIlWqy3xvJomyU0NUCfzi5JOxUIIIcwOHz7M1q1bcXIqXqMfGRnJwIED6devH23btmXQoEEMHDiQUaNGmWtVyiMiIoLs7OxiSUt+fj4dOnSw2Ne5c+di53fq1Mni/cmTJ+nevTua62qsevbsSWZmJhcvXiQoKKjE82qaJDc1oP3VNaZOxmeQk2/EXq+zbkBCCFFX2DqoNSjWuvdNyMzMZNiwYbz55pvFjvn5+aHT6di4cSN//fUXv//+O4sWLeLll19m9+7dhIaGlvseAOvXrycgIMDimMFgsHjv6Fi8FqqkfeVR2fOqiiQ3NcDf1Q5vZwOXM/I4FpdGl5CS20WFEEJUkEZTrqYha9Pr9RiNRot9HTt25PvvvyckJAQbm5K/jjUaDT179qRnz57MnDmT4OBg1q5dy7Rp00q85j+1atUKg8FATEyMRRNUZbVs2ZLvv/8eRVHMtTc7d+7E2dmZhg1rT7cLGS1VAzQazbUh4TGpVo1FCCFEzQsJCWH37t1ER0eTlJSEyWRi8uTJpKSkMHbsWPbu3UtkZCS//fYbEyZMwGg0snv3bubOncu+ffuIiYlhzZo1JCYm0rJlS/M1jxw5wunTp0lKSqKgoKDYfZ2dnZk+fTrPPfccy5cvJzIykgMHDrBo0SKWL19e4c/x1FNPceHCBZ5++mlOnTrFDz/8wKxZs5g2bZq5v01tUHsiqQsK8yD1QomHipqmpN+NEELUP9OnT0en09GqVSu8vLyIiYnB39+fnTt3YjQaGThwIG3btmXq1Km4ubmh1WpxcXFhx44dDBkyhGbNmvHKK6/wzjvvcNdddwEwadIkmjdvTufOnfHy8ip1ZNKcOXN49dVXmTdvHi1btmTw4MGsX7++3E1b1wsICOCXX35hz549hIWF8cQTT/DII4/wyiuv3NTzqWoaRVGsNrPcvHnzWLNmDadOncLe3p4ePXrw5ptv0rx58zLP+/bbb3n11VeJjo6madOmvPnmmwwZMqRc90xPT8fV1ZW0tDRcXFyq4mOozvwO3z8Kfu0g/Odih/+KTOKBT3YT4GbPzhf7Vt19hRCiHsnNzSUqKorQ0FDs7OysHY6oYmX9+1bk+9uqNTfbt29n8uTJ/P3332zcuJGCggIGDhxIVlbpk9399ddfjB07lkceeYSDBw8yfPhwhg8fzrFjx2ow8hJ4NYe8NDi/EzIvFzvcrqEbGg3EpuZwOSPXCgEKIYQQ9YNVk5sNGzYQHh5O69atCQsLY9myZcTExLB///5Sz1m4cCGDBw/m//7v/2jZsiVz5syhY8eOLF68uAYjL4F7MPh3AMUEp4rX3DgZbGjm7QxIvxshhBCiOtWqPjdpaWlA6bMsAuzatYv+/ftb7Bs0aBC7du0qsXxeXh7p6ekWW7VpdY/6euKHEg/LIppCCCFE9as1yY3JZGLq1Kn07NmTNm3alFouISEBHx8fi30+Pj4kJCSUWH7evHm4urqat8DAwCqN20JRchP1B2QlFzssnYqFEEKI6ldrkpvJkydz7NgxVq1aVaXXnTFjBmlpaebtwoWSRzNVCY9G4NsOFGOJTVNFNTdHLqZRaDRVXxxCCCFEPVYrkpspU6bw888/s3Xr1htOAuTr68ulS5cs9l26dKnUtTUMBgMuLi4WW7Uqo2mqmY8znk56MvMKWXfISjNqCiFEHWDFgb6iGlXVv6tVkxtFUZgyZQpr165ly5Yt5Rpz3717dzZv3myxb+PGjeYFwayu1XD1NWo7ZKdYHNJpNUzq3QiARVvOSu2NEEJUkE6nLl+Tn59v5UhEdSj6dy36d64sqy6/MHnyZFauXMkPP/yAs7Ozud+Mq6sr9vb2AIwbN46AgADmzZsHwLPPPkufPn145513GDp0KKtWrWLfvn3873//s9rnsODZBLxbw+XjcPpX6PCgxeGHuwfz8Y5znE/O5odDcdwrq4QLIUS52djY4ODgQGJiIra2trVqVlxxc0wmE4mJiTg4OJS6HEV5WTW5+eijjwC44447LPYvXbqU8PBwAGJiYix+eXv06MHKlSt55ZVXeOmll2jatCnr1q0rsxNyjWt1j5rcnPihWHLjoLfhsdsb8cavp1i05Sz3tPfHRif/cQohRHloNBr8/PyIiori/Pnz1g5HVDGtVktQUJDFquOVYdUZiq2h2mYovl7iafigK2ht4f8iwN7N4nBWXiG939pKSlY+79wXJrU3QghRQSaTSZqm6iC9Xl9qbVxFvr9lVfDq4NUcvFpA4ik4swHC7rc47GiwYVLvRry54RSLt0ZI7Y0QQlSQVquV5RdEqeQbtbrcYEK/cd2DcXewJSopix8Py8gpIYQQoqpIclNdipKbiM2QW3xWZEeDDY/d3hiARVsiZOSUEEIIUUUkuaku3q2gQRMw5sHZ30ssIrU3QgghRNWT5Ka6aDTX5rw5vrbEIo4GGybdrs57s1hqb4QQQogqIclNdTI3TW2CvMwSi4zrHoK7gy3nkrL46YjU3gghhBA3S5Kb6uTbFtxDoTC31KYpp+tqbxZtjsBoqlcj84UQQogqJ8lNddJobjhqCtTaG7ei2hvpeyOEEELcFEluqltRcnP2d8jPLrGI09V5bwDe33xWam+EEEKImyDJTXXz7wBuQVCQrfa9KcX4HlJ7I4QQQlQFSW6qm0XT1LpSi1nU3myR2hshhBCisiS5qQlFQ8LP/AYFOaUWG9c9WK29ScziZxk5JYQQQlSKJDc1IaATuDSE/EyI3FJqMWc7W3PtzULpeyOEEEJUiiQ3NaGco6bAsvZm5g/HqGeLtgshhBA3TZKbmlKU3Jz+FQrzSi3mbGfLf4a3QaOBFbtjmPPzSUlwhBBCiAqocHJTUFCAjY0Nx44dq4546q6GXcDZD/LSIXJrmUXvbufPmyPbAfD5zijm/366JiIUQggh6oQKJze2trYEBQVhNBqrI566S6uFlv9Sf75B0xTA6C6BzL6nNQAfbI1k8Zaz1RmdEEIIUWdUqlnq5Zdf5qWXXiIlJaWq46nbipqmTq2HrOQbFh/XPYSXh7QEYP7vZ/j0j3PVGZ0QQghRJ9hU5qTFixcTERGBv78/wcHBODo6Whw/cOBAlQRX5wTdBp7NIOkMfD8RHloDWl2Zp0y6vRE5BUbe3XiG/6w/icFWx8O3BddQwEIIIcStp1LJzfDhw6s4jHpCq4P7lsOn/eDcNtjyH+g/64anPd23CbkFRj7cFsmr645hZ6Plvs6B1R+vEEIIcQvSKPVsKE56ejqurq6kpaXh4uJinSCOfgffP6L+POYraDnshqcoisLsn0+wdGc0Wg0suL8D/wrzr+ZAhRBCiNqhIt/flaq5KbJ//35OnjwJQOvWrenQocPNXK7+aDsKYg/A3x/A2ifBszl4NSvzFI1Gw8y7W5FXaGLl7hieW30IvU7L4Da+NRS0EEIIcWuoVIfiy5cv07dvX7p06cIzzzzDM888Q6dOnejXrx+JiYlVHWPdNOB1CO4F+Rmw+kHIy7jhKRqNhv/c04aRHQMwmhSe/voAvx6Nr4FghRBCiFtHpZKbp59+moyMDI4fP05KSgopKSkcO3aM9PR0nnnmmaqOsW7S2cJ9S8HZX+1gvO4pKEcLoVar4a172zG0nR8FRoUnVxxgyfZImehPCCGEuKpSfW5cXV3ZtGkTXbp0sdi/Z88eBg4cSGpqalXFV+VqRZ+b613YC0vvAlMB9H8dek0t12mFRhNzfj7B8l3nARjTOZD/jGiDrU4mnRZCCFH3VOT7u1LfhCaTCVtb22L7bW1tMZlMlblk/RXYBYa8pf68+XV1FFU52Oi0vH5PG14b1gqtBlbvu8D4z/eQllNQfbEKIYQQt4BKJTd9+/bl2WefJS4uzrwvNjaW5557jn79+pX7Ojt27GDYsGH4+/uj0WhYt25dmeW3bduGRqMptiUkJFTmY9QenSZA+4dAMcG3EyA1ptynhvcM5dPxnXHU6/grMpmRH+4kJjm7GoMVQgghardKJTeLFy8mPT2dkJAQGjduTOPGjQkNDSU9PZ1FixaV+zpZWVmEhYXxwQcfVOj+p0+fJj4+3rx5e3tX9CPULhoNDJ0Pfu0hJwVWPwwFueU+vW8LH759ogd+rnZEJmYx4sOd7D8vs0cLIYSonyo9z42iKGzatIlTp04B0LJlS/r371/5QDQa1q5dW+YEgdu2bePOO+/kypUruLm5Veo+ta7PzfVSY+DjPmqC0+Eh+NdiNfEpp0vpuTyyfC/HYtPR22iZf1+YzIUjhBCiTqjWPjdFq4IfP36cAQMG8PTTT/P000/fVGJTUe3bt8fPz48BAwawc+fOMsvm5eWRnp5usdVabkEw6jPQaOHgV7D+eTAWlvt0Hxc7vnm8O/1b+pBfaOKZrw+yaPNZGUklhBCiXrmlVgX38/NjyZIlfP/993z//fcEBgZyxx13lLmW1bx583B1dTVvgYG1fNmCxn1hyNuABvZ9BqvGQl5muU930Nvw8cOdeLRXKADvbDzD018fJCuv/EmSEEIIcSurVLPUZ599xpo1a/jyyy/x8PComkDK0SxVkj59+hAUFMSXX35Z4vG8vDzy8vLM79PT0wkMDKydzVLXO/kTfD8JCnPAty088A24VKyJ6au/z/Paj8cpNCk09nJkyUOdaOrjXE0BCyGEENWn2oeCL168mB07duDv70/z5s3p2LGjxVaTunbtSkRERKnHDQYDLi4uFtstoeUwCF8Pjl6QcBQ+7Q8Jxyp0iYduC2bVY7fh42IgMjGLfy3eybqDsdUUsBBCCFE73PKrgh86dAg/Pz9rh1E9GnaCRzfBivvUWYw/Hwyjl0GT8vdv6hziwfpnejN11SH+jEhi6upD7I1O4dW7W2Fnq6u+2IUQQggrqXByU1hYiEajYeLEiTRs2PCmbp6ZmWlR6xIVFcWhQ4fw8PAgKCiIGTNmEBsbyxdffAHAggULCA0NpXXr1uTm5vLpp5+yZcsWfv/995uKo1ZzD4FHfleHh0f/AStGw9B3oPOEcl/C08nA8oldWbj5LIu2nGXF7hiOXEzjwwc7EujhUH2xCyGEEFZQ4WYpGxsb3n77bQoLb76D6r59++jQoYN5NfFp06bRoUMHZs6cCUB8fDwxMdcmtMvPz+f555+nbdu29OnTh8OHD7Np06YKTRx4S7J3h4fWQLv7QTHCz1Nh4yyowGzQOq2GaQOasTS8C+4OthyNTePuRX+y+eSl6otbCCGEsIJKdSi+5557GDlyJOPHj6+OmKpVrZ7n5kYUBba/Cdvmqe9bj1DnwjE4Vegycak5TF55gIMxqQA8eUdjnh/QDBtZl0oIIUQtVZHv70olN0uWLOH111/nwQcfpFOnTjg6Oloc/9e//lXRS9aYWzq5KXLoa/jxaXWxTQdP6PNvdQkHG325L5FfaGLerydZujMagEaejtwd5s/Qtn4083FCU4HJA4UQQojqVu3JjVZb+l/4Go3GKnPglFedSG4Aov9UE5yUc+p7t2Do+wq0GQVl/Pv80/oj8bz4/REyrpsHp7GXI0PbSaIjhBCi9qj25OZWVmeSGwBjARxYDtvfgsyrfWd82kL/WeqIqnImJem5BWw6cYlfjsaz40wS+cZrfXkaezkytK0fQ9v5S6IjhBDCaqotuRkyZAhff/01rq6uALzxxhs88cQT5nWekpOT6d27NydOnKh89NWsTiU3RfKz4O+PYOdCyLu6vERwL+j/GgR2qdCl0nML2HzyEuuPFE90Wvu78MLgFtzezKsKgxdCCCFurNqSG51OZ7EKt4uLC4cOHaJRo0YAXLp0CX9/f2mWspbsFPjjHdjzCRivzsrc4m61T45fWIUvdy3RSWDHmURzotOnmRcvD21JM5ntWAghRA2ptuRGq9WSkJBgTm6cnZ05fPiwJDe1TeoF2PYGHF4JytWal6AecNsT0Hwo6Co+d+OVrHwWbYngy7+jKTAqaDUwpksQ0wY0w8vZUMUfQAghhLBU7csviFrOLRCGfwBP7oI294LWBmL+gm/Gwfvt1ear7JQKXdLdUc/MYa3Y+FwfBrf2xaTA13tiuOPtrSzecpbcgtqb0AohhKhfKtwslZCQgJeX2ufC2dmZI0eOEBqqrkAtNTe1VHoc7P0M9i+F7GR1n60DtBsD3Z4A7xYVvuSeqBT+u/4Ehy+mAeDnasf/DWrO8PYBaLXS6VgIIUTVqtZmqbvuuguDQW2G+Omnn+jbt695npu8vDw2bNggyU1tVZADR7+D3Uvg0nWLcDa6U01ymg4AbfnXmzKZFH46EsdbG04Tm5oDQNsAV14Y3IJeTT2rOnohhBD1WLUlNxMmlG89o6VLl5b3kjWuXic3RRRFnSdn9xI4/cu1fjluwdDlEejwMDh4lPtyuQVGPt8ZxYdbI8m8Ol9Oryae/Htwc9o1dKuGDyCEEKK+kXluyiDJzT9ciVZHVx38CnJT1X02dmpfnS6PQkDHcl8qKTOPxVsiWLH7PAVG9ddqaFs/pg1sRmOvii0RIYQQQlxPkpsySHJTivxsOPadmugkHLm2P6AzdJ0ErYaDrV25LnUhJZv3Np1h7cFYFEVdtHN054Y8268Zvq7lu4YQQghxPUluyiDJzQ0oClzcC3v+B8fXqetXATg0gNYjwb8D+LUDz+Y3XMvqVEI68387zaaTlwEw2GgJ7xnCk30a4+ZQ/nWwhBBCCEluyiDJTQVkXlaXd9i3FNJjLY/p9ODVAnzbgW9bNeHxaQN2xZ/pvugU3txwir3RVwBwNtjQu5knXUM86NaoAc19nGWElRBCiDJJclMGSW4qwVgIZzaonZATjqpbXlrJZT2bQ7v7IOwBcA0w71YUha2nL/PWhtOcSsiwOMXV3pYuIR50C/Wga6gHrf1dsNHJFExCCCGukeSmDJLcVAFFgdTzapITf+RawpN+8bpCGmjcFzo8BC2Ggo06fYDJpLA/5gp7olL4+1wy+89fITvfcuoAR72OTiEeDGjlw91t/XB3lCYsIYSo7yS5KYMkN9UoKxnO/q6OvDr/57X99u7QdrSa6Pi1szil0GjiWFw6e6KS2ROVwp6oFNJzC83HbXUa+jTzZkSHAPq19MbOtvzz8AghhKg7JLkpgyQ3NSTlHBxaqW7X99fxbQvtH4RGd6hNWFrL5iejSeF0QgZ/RiTyw6E4jselm485G2y4q60vwzsEcFtoA+mnI4QQ9YgkN2WQ5KaGmYxwbqtam3NqPRjzrx3TO6vz6DTsDA27qMPOnbwsTj9zKYN1B2P54VCceRZkUJd7uKd9AP8K86elnzMajSQ6QghRl0lyUwZJbqwoO0Vd/uHkjxB7AAqyipdxC1YTnYadwas5uIeAayAmjQ17olNYdzCW9Ufjybiu6SqkgQOD2/gxpK0vbQNcJdERQog6SJKbMkhyU0uYjHD5pDqnTuw+uLgPEk+VXFajBdeG4B4K7iEUuAZzLNud9Rft+Oa8I+mFNuaiAW723NXGl7va+tIh0F2aroQQoo6Q5KYMktzUYrlpao3OxX0Qd0Dtt3MlGgpzSz1FsbEjsUEXtpvC+CyhCacKvM3HfFwM3NXGj8FtfOkS4oFOEh0hhLhlSXJTBklubjEmE2ReUpMc8xalviZHQnaSRfEsp2D22Xbi65TmbM1rTh7qMHJPJwOD2/gwpI0fXUM9ZB4dIYS4xUhyUwZJbuoQRVGbss5uhIiNcH7XteUiAKPOwBm79mzJCiahwIE0xZE0nDDZuRHWNJgebZrSpUUotra2VvwQQgghykOSmzJIclOH5WXAue1qonN20z8mFSxdjtaJQidf8hv2RNe0H84t7kRnL78bQghRm0hyUwZJbuqJolqdiE2QdBZyrkBuKkr2FfIyUyDnCnam7BJPzVd0HNW24Jh9Z2LcupHr2QYvF3sC3OwZ2NoXV3up6RFCiJp2yyQ3O3bs4O2332b//v3Ex8ezdu1ahg8fXuY527ZtY9q0aRw/fpzAwEBeeeUVwsPDy31PSW5EEWNBPgfORPPX0bNkxB6nRdY+uhgPEqy5bFEuSXHhT1Mb/jC245C+PXf37MTEnqG4OkiSI4QQNaUi3982ZR6tZllZWYSFhTFx4kRGjhx5w/JRUVEMHTqUJ554ghUrVrB582YeffRR/Pz8GDRoUA1ELOoSna2eLq2b0aV1M2AooC4HkRx7htxTG9FHb8Xt0i48jekM1/3FcN1fAJz5I4Cf/myHY8v+3DloBG7uDaz4KYQQQvxTrWmW0mg0N6y5eeGFF1i/fj3Hjh0z77v//vtJTU1lw4YN5bqP1NyICinMV+fiidyMErkF4g6h4dp/MoWKlksubfBoOwj75v3UyQd119XomIzqUPbCvKtbrjpLs2tD0Dta4QMJIcSt6ZapuamoXbt20b9/f4t9gwYNYurUqaWek5eXR15envl9enp6qWWFKMZGDyE9IaQnmn4zITsFU9QOLu7/FV30dgJM8QRkHIG/jsBfb6PYOqCxtb+WyJgKS76u3gnajoJOE8C/fY1+JCGEqOtuqeQmISEBHx8fi30+Pj6kp6eTk5ODvb19sXPmzZvH66+/XlMhirrOwQNt6+EEtR6OyaSwfd9+Dm77gUYZe+mpPUaDggwoKLmjsqLRYdIZ0Gi1aPMzYf8ydfPvAJ3Coc0oMDjV5KcRQog66ZZKbipjxowZTJs2zfw+PT2dwMBAK0Yk6gqtVkOfrp25vUsnNp28TPimU2TFn0GLiTxsyVdsyUPd8rHFiO7qmQr97c/yuOMOOmb/gS7uIMQdhN9ehrb3QecJ4Bd28wEaCyEjXh0p5tVCrYUSQoh64JZKbnx9fbl06ZLFvkuXLuHi4lJirQ2AwWDAYDDURHiintJoNAxo5UP/lt4cvhhGXGoOyVn5pGTmk5KVR3JWPsmZ+aRk5ZOclc+V7Hw25TRjU04zPBjNvbodjNNvIzA/DvYvVTf/DtB8KBicwdYebB2uvl7/swMoJki7CGkx6mvqhavvL0B6HChGNUi9E4T2gSb9oOkAcAuy7kMTQohqdEslN927d+eXX36x2Ldx40a6d+9upYiEuEaj0dA+0I32gW5llis0mjh8MZU/ziaxMyKJpTHD+CRnKN21Jxir28Jg7R70RbU5N0trC3oHdd2u0+vVDcCzOTTpD037Q3BPsJE/AIQQdYdVR0tlZmYSEREBQIcOHXj33Xe588478fDwICgoiBkzZhAbG8sXX3wBqEPB27Rpw+TJk5k4cSJbtmzhmWeeYf369eUeCi6jpURtk5FbwO5zKfwZkcQfZxO5khjPCN0fNNdcxF6Thx352JOHvSYfZ10BTtoCHDT52JGHVgMa1wBsPYLBNVAdheUWCK5B6s9OV/uoJRxRJzSM2AQX9lyr0QG1BiikN3iEgsEF7Fz+8ep67dXRE7S6kj+IEEJUo1tmEr9t27Zx5513Fts/fvx4li1bRnh4ONHR0Wzbts3inOeee44TJ07QsGFDXn31VZnET9Qp8Wk5/Hk2iVMJGVxIySYmJZsLKdlk5RtLPSe4gQO3hTage2N183GxK/0GOalwbqua6JzdBJkJ5Q/O1hF826ojvPzaq6+ezSThEUJUu1smubEGSW7ErUhRFK5kF1xLdq6oCc+J+AyOXkzF9I//ikM9HbmtkZrodAlxx9XeFr1OW3w1dEWBS8cg6g/ISlSbr/LSITe95FdK+N+FrQP4tLmW8Lj4Q36mutZXXqZ6nsX7DHWB06LaoFI3N3ANUPsXCSHqPUluyiDJjahrMnIL2Bd9hV3nktkVmczxuLRiyU4RrQb0NlpsdVoMNlr0Oi16Gy12tjo6BLlxVxs/ujdugO0/kyBQJyRMOgvxhyDukPoafwQKsqrx0wGOXmoHaNdA9fX6zTVQhs8LUU9IclMGSW5EXZeWU8DeqBT+PpfMrnPJnIhPpyL/lbs52DKwlQ93tfWjZ2NP9DYlJDpFTEZIjoD4w9cSnuwUdZSXwVlNPAzOoP/He62tWoOTm1b6lpOi1vjciN4ZnLzA0Vt9dfK59rOjt/rezlWdEVrvoDatybB4IW45ktyUQZIbUd8UGk3kG03kF6pbXqH6vuC6fanZBWw9fZnfjieQlJlvPtfZzoYBrXwY0saPXk09sbOtwb41igK5qZAaow5xT41Rh7inxkDqeXVfbmrlrl00iszW8VrSo3cuuTP19fvs3MDeXd3sXMvX1yg/Wx2Wnx57bcu4BC5+4NtO7cPk5AMaTeU+ixD1hCQ3ZZDkRojSGU0Ke6NT+PVoPL8eS+ByxrWlS5wMNnQN9aClnzMt/Vxo4etCqKcjOq0Vv5Rz0yHzMmRdvvqaCJmXrvv56v68NDXJMBVU7f3tXK8mOm7Xkh69A2QmXktkcq78f3v3HtvUdfgB/Hv9fsROnKfzgAAFEqAjXXlkabc+AI2yh9qOaUxCW7ZpQ7QBwdj+oNpaYNMGGlPXtUO009byz1Q6KtF1j3ZjKaS/Mig0hRLaJAWaAiVxHOflR/y+5/fHdRxMQgjg2Inz/UhX92n7+CjIX84599wbv4+5QAk59s8NBZ682RyoTXQVhptRMNwQjY0sCzRe6sW/mjrwRpMDDndg2DUGrQoVRRZU2q2YV2xBZbEVRVYDBkIR+ENRDMQWfziirGP7WrUKNXfkYWFpNlSpDEeRkDJGKDSgPCYj5FOW8EBswLM71i3mTtweHGjt71Nai8bSXXY1rVkZHG0tAaxlSpdZ32XA0QR0n1MmY7yWxgDkzoq9pjS2lFy1X6K0Jt0uWQYGXEoIlCPKNAFCKGWSo8payEPHzfnK5xttbG2ilGK4GQXDDdHNk2WBM1f6ceazPjR3uNHc4UGrwwN/+Pq3p49FrlmH++cW4IGKAtw3pwA28yQZCxMJxcYF9Q4tgT5lHfQq4eXqQGLIvn4QCA0Azmags0kJO44mwHF2bAO1dRbAUnRVV1nO0PbVi1qrtGi52wGPA/C0A+4OZdvruP4DXkczGNiyy5TFWja0nTNNGeyt1t78+wLKuK2eT5TySRIACZBUyrakiu1D2RYiFk69Q3flhbzX7PuAaGgosA2GODm2DG7byoHKryqzg1uKblBISjWGm1Ew3BAlR1QWuNjtQ4vDEw88zR1u9PvDMOrUMOnUMOk0sbUaRm1srdOg1xfC0fMueIJDP6qSBNw1LQcPzC3EAxUF+FyqW3UmElkGetuUsUXudmXp/2xo233l1scbjUgCTHnKTNXxEKFWtlWxtaQGIJTuPl/XGN5SpYS7nHLANkMJDjnlQ2u1Vgkwg0v3haHtpH63WyEB05YClV9Twk7eHWkuDwEMN6NiuCGaGMJRGY0Xe3GktQtHWp1ocXgSzueadZhXbMHsgizMLoqtC7OQn6WDxO4QpTXC3a50Jw22GsWXa/YjQaUlwlICWOxKa5LFPrSfVXhzrSxhfyxwXQb6rww9z6z/s6HtyPBuzJtiiXXBSapYt12sq0yIq7ahbOsGB4YP3p2XpdyZpzMrrVs681BwU2mUwKbSKIFNFTsGAJ+dBJr/AbS/n1iWwvlK0Jn3NWVMVCr+/iJBJfBpjUpI5B1+DDejYbghmpg6+v1oaO3CkdYuvHPeBW9w5K6SbKMWswuzMKdQCTtlNhNKc4woyTEg18zgMyHIsjLIu/ei0vrUexHo+3Rov/+K0hVkLVMe+5E7S2kdyZ2lLLYZSiBJl/4rQOu/gOa/A5++k/i4EqNNCRsWeywY2oe2LcXKnW9ZRWMPI0IodwA6P1Im1Oz8SNl2nUv8XHNhbNxWrCvQWhrbL1MGsUdCQDSodL8NbkeCQDQ8tB0eUIJpfB3bHhyDJmTAlKsMcB9xyVe6PlWjTA8xjhhuRsFwQzTxhSIyPmzvxzmnFxecXpxzenHe6cXl3oFR5+zRa1QoyTGiONuAkhwjSmLrQqseuWY9ck065GbpYNapGYLSKRpWxrhoR3lMyEQx0AOc+48SdM7XAxH/2F6n1iktSLpYC1K8JSl2TKVRBpN3fgSEPCO/h94aCyjBkc+nQ7yLcnB/8N+RlHisdDHw/X8m9aMZbkbBcEM0eQXCUVzoUoLOBacXF7p8+KzPj/Y+P7o8Y/8B0GlUStAx65CXpYPNpENJjhF3llrxudJsTM81MfzQcGG/MmmlpxPwdCiDsT3XLLcyQFulBQoqlO6vogXKUjhf6ZYDAJ8LcMfGXPVfUbb7Y1MN9F9Rwo9ar3QtavRKsFLrhrYH1zqz0s2lNSoDwrVG5fEpg8ckFTDQrXyeLzadgs8Vm1ahS5lSYazKlgI/PHRz9XADDDejYLghykzBSBSd/UG09/vR0e9He18A7bHg4/KG0OMLodsXRCA8wm3X17AaNLizNBufK83Ggti6PNc0dQc409jJcmy6AJ9yt1bIN3THVsg7dCdXNKR0wRXOB/Ln3PqdZakUCSotWfGpC66JD1fHCbUu6XecMdyMguGGaGobCEXQ4xsMOyH0xILPJy4fPmzvR0uHB6Ho8ABk0Wswr8SKOwrMmJlvxow8M2YVmDEt1wS9hpPtEY23m/n91qSoTEREE4Jye7oGZTbTiOdDERkfd3pw9ko/mq704+yVfjQ7PPAEIzjR1oMTbT0J16skoNRmVMJOvhJ2ACAcFQgPPuYiKiMcGdqPyAKVdgseqCjAHQVZ7AIjSjK23BAR3UA4KuNcpxctDjc+dfnwicuHT7t9aOvywRe6vYkMy2xGPFBRgAfmFuKe2Xkw6fh/TqKRsFtqFAw3RJQsQgh0eYNo64qFHdcArvT5oZYArVoFrUYFnVoFrVpS9tUq6DQqyLLAiU978G5bD0KRoS4wnVqF6lm5uH9uAR6sLERxtgF9A2H0+69ZrjqmVkmYnmvCjHwTyvPMKLMZ2U1GGYnhZhQMN0Q0UQyEIjh2oRtHWrtwuNWJz3rHeJvxKCQJKMk2ojxPCTvleSaU2YywmXTINmqRbdQix6RFll7D7jCaVBhuRsFwQ0QTkRACF7p8ONLqRMPHXXj3kx6EojK0agnZRi2ssWBy9ZJj1CIYlXGpewCfdg/gUvfYu8nUKmnY+1mNWlgNmthaC6tRE1trYTFokG3UojzXBI06PZO40dTGcDMKhhsimgwC4SiisoDpJiYcFELA5Q3hYrcPF7sHlHXPAK70+uPdWH3+cEJX2M2yGjS4v6IQyyqVcUKT5mGnNOkx3IyC4YaIprpAOKoEndjYnb6BEPr9YbgDEbj9YbgDYXiu2nb7I3AHwujxhTBwVcuQSgI+P92GZZWFWFZZiEq7hV1dNG4YbkbBcENEdGuissDpy72ob3birZbhDzstyTbgwcpC3DUtBwUWPQotBhRY9Mgz6zgBIt02hptRMNwQESXHlT4/Drc4cbjFiXfOuxC8TneXWiUhz6xDoVWPgiwl9ORm6ZCl18Bi0MTW2oT9LIMGZp1GeWi3JEEtSZAksGVoCmO4GQXDDRFR8gXCURy70I3DrU582j2ALk8QXZ4Aun2hUR92erNUkhJ2VJIElQrQqFSwmbUoyNIjP0uPAouyDG7nZymBympUJm/UaTgYerJiuBkFww0RUepEojK6fSF0eYJwegLK2h1Ez0AI3kAE3qCyuAMReANhZT8Que3JEa9Ho5Jg0qlh1mtg0qljM1Yr+/ZsA+bZLaiwW1FhtyDbOAme9zSF8PELREQ0IWjUKhRZDSiyGgBkj/l1UVnAH45CFgKyLCALDN8WAuGoQI8vqLQUeZUQ5fIGh60HH5gakYUycDpw4yd3l+YYUWG3oNJuQWWxFZV2C0pzjBBQ7kyTBYBYWQRiawHotSpYDQxG6cRwQ0REE45aJSFLP7afqJn55hteE47KGAhF4Q9F4QtFMBCMrUMRDISi8AUjuNg9gFaHBy0OD670+ePLWy3Omy7/vGIr7pubj/vmFGDxDBtnjU6xCdEttWfPHuzevRsOhwNVVVV47rnnsHTp0hGv3bdvH77//e8nHNPr9QgEAmP6LHZLERHRjfT7w/i404OWDjeaHR4l9HS4b9hdJkkYNsbIoFXhC7Py8KU5Bbh/bv6wh6UKIdA7EMZnvQP4rNcfX7f3+RGVBXSaoUd36GJr7VXr/CwdZuYrT6svyTZm7J1pk6pb6pVXXsGWLVvw/PPPo7q6Gs888wxWrlyJ1tZWFBYWjvgaq9WK1tbW+D5HzxMRUTJlG7VYMiMXS2bkxo/JskAwIsfu2gIkSPEBzlffyeXyBnH0vAtvf+zC/53rgtMTxJHWLhxp7cIvodwyv2RmLryBSDzMJGuMkU6jwow8UyzsZGFmvgkz87MwPdeE/CzdlJldOu0tN9XV1ViyZAn+8Ic/AABkWca0adOwceNGbN26ddj1+/btw+bNm9HX13dLn8eWGyIiShUhBFo7Pfi/j114+1zXsIelXq3QokeZzYgym/I8sFKbEVq1CqGIjFBERjg6tA5GZYQjAsFIFJ3uINpcXlzqGUA4ev2fdJUE5GfpY2Og9Ci0GlBkUbaLrAYUWpXb9CfqvESTpuUmFAqhsbERTzzxRPyYSqXCihUrcOzYseu+zuv1ory8HLIs4+6778avf/1rLFiwYMRrg8EggsFgfN/tdifvCxAREY1CkiRU2q2otFvxo/tmIRCO4kRbDz643IfcLN1QkMkxwqC9vXE5kaiM9r4APnF50eby4VOXD5+4fPiky4eOfj9kATg9QTg9QTRduf77aFQS8rP0sbCjhKDCqyZlNOvUMOjUMGjUMGhVMGjVMGjVMGrV0GtUEyIYpTXcuFwuRKNRFBUVJRwvKipCS0vLiK+pqKjAiy++iIULF6K/vx+//e1vcc899+DDDz9EWVnZsOt37tyJHTt2jEv5iYiIboZBq8Z9cwtw39yCpL+3Rq3C9DwTpueZ8EBF4rmoLNDtVYJNpzuATreydnqGtjvdQXT7gojIAg53AA732MayXkunUeHu6TnYv64mCd/q1qR9zM3NqqmpQU3NUIXdc889mDdvHl544QX88pe/HHb9E088gS1btsT33W43pk2blpKyEhERTQRqlaS0wFgNuLP0+rfkR6IyXN4QnJ4AnO4gOmNrpycIpzsAlzcIfzgKfziKQFhGIBxFMCwjFB3qagtFZMi3/mzWpEhruMnPz4darUZnZ2fC8c7OTtjt9jG9h1arxec//3mcP39+xPN6vR56vf62y0pERJTpNGoV7NkG2LMNN/W6qCwQCEeVJSIj3R1TaR02rdPpsGjRItTX18ePybKM+vr6hNaZ0USjUTQ1NaG4uHi8iklERESjUKskmPUa5GXpUZpjREmOMa3lSXu31JYtW1BbW4vFixdj6dKleOaZZ+Dz+eJz2Xz3u99FaWkpdu7cCQD4xS9+gS984QuYPXs2+vr6sHv3bly8eBE//OEP0/k1iIiIaIJIe7hZs2YNurq68NRTT8HhcOCuu+7Cm2++GR9kfOnSJahUQw1Mvb29+NGPfgSHwwGbzYZFixbhf//7H+bPn5+ur0BEREQTSNrnuUk1znNDREQ0+dzM7/fUmKqQiIiIpoy0d0ul2mBDFSfzIyIimjwGf7fH0uE05cKNx+MBAM51Q0RENAl5PB5kZ19/rh5gCo65kWUZ7e3tsFgsSX/g5uAEgZcvX+Z4nhRgfacW6zu1WN+pxfpOrVupbyEEPB4PSkpKEm40GsmUa7lRqVQjPqYhmaxWK/9xpBDrO7VY36nF+k4t1ndq3Wx936jFZhAHFBMREVFGYbghIiKijMJwk0R6vR7btm3js6xShPWdWqzv1GJ9pxbrO7XGu76n3IBiIiIiymxsuSEiIqKMwnBDREREGYXhhoiIiDIKww0RERFlFIabJNmzZw9mzJgBg8GA6upqnDhxIt1Fyhhvv/02vv71r6OkpASSJOG1115LOC+EwFNPPYXi4mIYjUasWLEC586dS09hJ7mdO3diyZIlsFgsKCwsxCOPPILW1taEawKBAOrq6pCXl4esrCysXr0anZ2daSrx5LZ3714sXLgwPpFZTU0N3njjjfh51vX42rVrFyRJwubNm+PHWOfJs337dkiSlLBUVlbGz49nXTPcJMErr7yCLVu2YNu2bXj//fdRVVWFlStXwul0prtoGcHn86Gqqgp79uwZ8fxvfvMbPPvss3j++efx7rvvwmw2Y+XKlQgEAiku6eTX0NCAuro6HD9+HIcOHUI4HMaXv/xl+Hy++DU//vGP8fe//x0HDhxAQ0MD2tvb8Y1vfCONpZ68ysrKsGvXLjQ2NuK9997DsmXL8PDDD+PDDz8EwLoeTydPnsQLL7yAhQsXJhxnnSfXggUL0NHREV/eeeed+LlxrWtBt23p0qWirq4uvh+NRkVJSYnYuXNnGkuVmQCIgwcPxvdlWRZ2u13s3r07fqyvr0/o9Xrx8ssvp6GEmcXpdAoAoqGhQQih1K1WqxUHDhyIX9Pc3CwAiGPHjqWrmBnFZrOJP/3pT6zrceTxeMScOXPEoUOHxP333y82bdokhODfd7Jt27ZNVFVVjXhuvOuaLTe3KRQKobGxEStWrIgfU6lUWLFiBY4dO5bGkk0NbW1tcDgcCfWfnZ2N6upq1n8S9Pf3AwByc3MBAI2NjQiHwwn1XVlZienTp7O+b1M0GsX+/fvh8/lQU1PDuh5HdXV1+OpXv5pQtwD/vsfDuXPnUFJSglmzZmHt2rW4dOkSgPGv6yn34Mxkc7lciEajKCoqSjheVFSElpaWNJVq6nA4HAAwYv0PnqNbI8syNm/ejHvvvRd33nknAKW+dTodcnJyEq5lfd+6pqYm1NTUIBAIICsrCwcPHsT8+fNx+vRp1vU42L9/P95//32cPHly2Dn+fSdXdXU19u3bh4qKCnR0dGDHjh340pe+hLNnz457XTPcENGI6urqcPbs2YQ+ckq+iooKnD59Gv39/Xj11VdRW1uLhoaGdBcrI12+fBmbNm3CoUOHYDAY0l2cjLdq1ar49sKFC1FdXY3y8nL89a9/hdFoHNfPZrfUbcrPz4darR42wruzsxN2uz1NpZo6BuuY9Z9cGzZswD/+8Q8cPnwYZWVl8eN2ux2hUAh9fX0J17O+b51Op8Ps2bOxaNEi7Ny5E1VVVfj973/Puh4HjY2NcDqduPvuu6HRaKDRaNDQ0IBnn30WGo0GRUVFrPNxlJOTg7lz5+L8+fPj/vfNcHObdDodFi1ahPr6+vgxWZZRX1+PmpqaNJZsapg5cybsdntC/bvdbrz77rus/1sghMCGDRtw8OBBvPXWW5g5c2bC+UWLFkGr1SbUd2trKy5dusT6ThJZlhEMBlnX42D58uVoamrC6dOn48vixYuxdu3a+DbrfPx4vV5cuHABxcXF4//3fdtDkkns379f6PV6sW/fPvHRRx+JdevWiZycHOFwONJdtIzg8XjEqVOnxKlTpwQA8fTTT4tTp06JixcvCiGE2LVrl8jJyRF/+9vfxJkzZ8TDDz8sZs6cKfx+f5pLPvk89thjIjs7Wxw5ckR0dHTEl4GBgfg169evF9OnTxdvvfWWeO+990RNTY2oqalJY6knr61bt4qGhgbR1tYmzpw5I7Zu3SokSRL/+c9/hBCs61S4+m4pIVjnyfSTn/xEHDlyRLS1tYmjR4+KFStWiPz8fOF0OoUQ41vXDDdJ8txzz4np06cLnU4nli5dKo4fP57uImWMw4cPCwDDltraWiGEcjv4k08+KYqKioRerxfLly8Xra2t6S30JDVSPQMQL730Uvwav98vHn/8cWGz2YTJZBKPPvqo6OjoSF+hJ7Ef/OAHory8XOh0OlFQUCCWL18eDzZCsK5T4dpwwzpPnjVr1oji4mKh0+lEaWmpWLNmjTh//nz8/HjWtSSEELff/kNEREQ0MXDMDREREWUUhhsiIiLKKAw3RERElFEYboiIiCijMNwQERFRRmG4ISIioozCcENEREQZheGGiKY8SZLw2muvpbsYRJQkDDdElFbf+973IEnSsOWhhx5Kd9GIaJLSpLsAREQPPfQQXnrppYRjer0+TaUhosmOLTdElHZ6vR52uz1hsdlsAJQuo71792LVqlUwGo2YNWsWXn311YTXNzU1YdmyZTAajcjLy8O6devg9XoTrnnxxRexYMEC6PV6FBcXY8OGDQnnXS4XHn30UZhMJsyZMwevv/76+H5pIho3DDdENOE9+eSTWL16NT744AOsXbsW3/72t9Hc3AwA8Pl8WLlyJWw2G06ePIkDBw7gv//9b0J42bt3L+rq6rBu3To0NTXh9ddfx+zZsxM+Y8eOHfjWt76FM2fO4Ctf+QrWrl2Lnp6elH5PIkqSpDx+k4joFtXW1gq1Wi3MZnPC8qtf/UoIoTypfP369Qmvqa6uFo899pgQQog//vGPwmazCa/XGz//z3/+U6hUKuFwOIQQQpSUlIif/exn1y0DAPHzn/88vu/1egUA8cYbbyTtexJR6nDMDRGl3YMPPoi9e/cmHMvNzY1v19TUJJyrqanB6dOnAQDNzc2oqqqC2WyOn7/33nshyzJaW1shSRLa29uxfPnyUcuwcOHC+LbZbIbVaoXT6bzVr0REacRwQ0RpZzabh3UTJYvRaBzTdVqtNmFfkiTIsjweRSKiccYxN0Q04R0/fnzY/rx58wAA8+bNwwcffACfzxc/f/ToUahUKlRUVMBisWDGjBmor69PaZmJKH3YckNEaRcMBuFwOBKOaTQa5OfnAwAOHDiAxYsX44tf/CL+8pe/4MSJE/jzn/8MAFi7di22bduG2tpabN++HV1dXdi4cSO+853voKioCACwfft2rF+/HoWFhVi1ahU8Hg+OHj2KjRs3pvaLElFKMNwQUdq9+eabKC4uTjhWUVGBlpYWAMqdTPv378fjjz+O4uJivPzyy5g/fz4AwGQy4d///jc2bdqEJUuWwGQyYfXq1Xj66afj71VbW4tAIIDf/e53+OlPf4r8/Hx885vfTN0XJKKUkoQQIt2FICK6HkmScPDgQTzyyCPpLgoRTRIcc0NEREQZheGGiIiIMgrH3BDRhMaecyK6WWy5ISIioozCcENEREQZheGGiIiIMgrDDREREWUUhhsiIiLKKAw3RERElFEYboiIiCijMNwQERFRRmG4ISIioozy/93/wEPsC0TWAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["\n","     \n","\n","\n","\n","\n","model_cnn.save(\"Music_Genre_10_CNN\")\n","     \n","\n","model_cnn.save(\"Music_Genre_10_CNN.h5\")\n","\n","     \n","\n","# It can be used to reconstruct the model identically.\n","reconstructed_model = keras.models.load_model(\"Music_Genre_10_CNN.h5\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JAOubbfaIi8O","executionInfo":{"status":"ok","timestamp":1684891991188,"user_tz":-60,"elapsed":3212,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"9528708b-4c43-4ce9-97fd-6d13b78edd79"},"execution_count":99,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"]}]},{"cell_type":"code","source":["history = model_cnn.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"BphXFF12BohJ","executionInfo":{"status":"error","timestamp":1684887113766,"user_tz":-60,"elapsed":2935,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"a68f0d1c-648c-4290-c7a4-d43eb847ec8b"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n"]},{"output_type":"error","ename":"InvalidArgumentError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)","\u001b[0;32m<ipython-input-41-d31301165ab5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_cnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_validation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last):\n    File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n      app.launch_new_instance()\n    File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n      app.start()\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n      self.io_loop.start()\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n      lambda f: self._run_callback(functools.partial(callback, future))\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n      ret = callback()\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n      self.ctx_run(self.run)\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n      yielded = self.gen.send(value)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n      yield gen.maybe_future(dispatch(*args))\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n      yielded = ctx_run(next, result)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n      yield gen.maybe_future(handler(stream, idents, msg))\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n      yielded = ctx_run(next, result)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n      self.do_execute(\n    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n      yielded = ctx_run(next, result)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n      result = self._run_cell(\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n      return runner(coro)\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"<ipython-input-41-d31301165ab5>\", line 1, in <cell line: 1>\n      history = model_cnn.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=50)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1685, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function\n      return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step\n      outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1051, in train_step\n      loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1109, in compute_loss\n      return self.compiled_loss(\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/compile_utils.py\", line 265, in __call__\n      loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/losses.py\", line 142, in __call__\n      losses = call_fn(y_true, y_pred)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/losses.py\", line 268, in call\n      return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/losses.py\", line 2078, in sparse_categorical_crossentropy\n      return backend.sparse_categorical_crossentropy(\n    File \"/usr/local/lib/python3.10/dist-packages/keras/backend.py\", line 5660, in sparse_categorical_crossentropy\n      res = tf.nn.sparse_softmax_cross_entropy_with_logits(\nNode: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'\nReceived a label value of 10 which is outside the valid range of [0, 10).  Label values: 1 3 1 6 0 6 5 5 9 10 6 10 0 3 9 3 5 5 3 10 10 5 10 2 3 8 0 0 3 6 10 6\n\t [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_3478]"]}]},{"cell_type":"code","source":[],"metadata":{"id":"MSfUEAm7BobW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"B3ExVjokBoLr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import json\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import matplotlib.pyplot as plt\n","import random\n","import librosa\n","import math"],"metadata":{"id":"eQ4dDdh488Df","executionInfo":{"status":"ok","timestamp":1684890264744,"user_tz":-60,"elapsed":561,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":73,"outputs":[]},{"cell_type":"code","source":["DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n","\n","def load_data(data_path):\n"," with open(data_path, \"r\") as f:\n","  data = json.load(f)\n","  X = np.array(data[\"mfcc\"])\n","  y = np.array(data[\"labels\"])\n","\n","  print(\"Data successfully loaded!\")\n","\n"," return X, y\n"],"metadata":{"id":"yyrIiLRg9E7G","executionInfo":{"status":"ok","timestamp":1684890281756,"user_tz":-60,"elapsed":564,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":76,"outputs":[]},{"cell_type":"code","source":["X, y = load_data(DATA_PATH)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RiQIUa-39jmA","executionInfo":{"status":"ok","timestamp":1684890308431,"user_tz":-60,"elapsed":16992,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"3d77b9d7-7b35-40a3-91e0-9e40a8b66386"},"execution_count":77,"outputs":[{"output_type":"stream","name":"stdout","text":["Data successfully loaded!\n"]}]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"],"metadata":{"id":"6biG8Ii09wm0","executionInfo":{"status":"ok","timestamp":1684890310326,"user_tz":-60,"elapsed":12,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":78,"outputs":[]},{"cell_type":"markdown","source":["Convert data to tensors\n"],"metadata":{"id":"j8IZKNJ591QZ"}},{"cell_type":"code","source":["X_train_tensor = torch.Tensor(X_train)\n","y_train_tensor = torch.Tensor(y_train).long()\n","X_test_tensor = torch.Tensor(X_test)\n","y_test_tensor = torch.Tensor(y_test).long()"],"metadata":{"id":"NAsW_286930S","executionInfo":{"status":"ok","timestamp":1684890313577,"user_tz":-60,"elapsed":645,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":79,"outputs":[]},{"cell_type":"code","source":["model = nn.Sequential(\n","# input layer\n","nn.Flatten(),\n","# 1st dense layer\n","nn.Linear(X.shape[1] * X.shape[2], 512),\n","nn.ReLU(),\n","\n","# 2nd dense layer\n","nn.Linear(512, 256),\n","nn.ReLU(),\n","\n","# 3rd dense layer\n","nn.Linear(256, 64),\n","nn.ReLU(),\n","\n","# output layer\n","nn.Linear(64, 10),\n","nn.Softmax(dim=1)\n",")"],"metadata":{"id":"IKkYiro498l5","executionInfo":{"status":"ok","timestamp":1684890318338,"user_tz":-60,"elapsed":687,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":["optimizer = optim.Adam(model.parameters(), lr=0.0001)\n","criterion = nn.CrossEntropyLoss()"],"metadata":{"id":"dzhikt2V-FBO","executionInfo":{"status":"ok","timestamp":1684890323214,"user_tz":-60,"elapsed":493,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}}},"execution_count":81,"outputs":[]},{"cell_type":"code","source":["# Create the model.\n","model = nn.Sequential(\n","    nn.Linear(10, 10),\n","    nn.Sigmoid()\n",")\n","\n","# Define the loss function.\n","criterion = nn.BCELoss()\n","\n","# Create the optimizer.\n","optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n","\n","# Load the data.\n","X_train = torch.randn(100, 10)\n","y_train = torch.randint(0, 2, (100,))\n","\n","# Train the model.\n","for epoch in range(100):\n","\n","    # Forward pass.\n","    outputs = model(X_train)\n","\n","    # Calculate the loss.\n","    loss = criterion(outputs, y_train.unsqueeze(1))\n","\n","    # Backpropagate the loss.\n","    optimizer.zero_grad()\n","    loss.backward()\n","\n","    # Update the parameters.\n","    optimizer.step()\n","\n","# Evaluate the model.\n","outputs = model(X_train)\n","predictions = torch.argmax(outputs, dim=1)\n","accuracy = (predictions == y_train).float().mean()\n","\n","print(accuracy)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":380},"id":"iGSdUxbs-K_0","executionInfo":{"status":"error","timestamp":1684890333130,"user_tz":-60,"elapsed":560,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"47948536-4cee-4f44-c0cf-c33fa5e3827c"},"execution_count":82,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-82-6f826d0a9a51>\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;31m# Calculate the loss.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;31m# Backpropagate the loss.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 619\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_cross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   3087\u001b[0m         \u001b[0mreduction_enum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3088\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3089\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   3090\u001b[0m             \u001b[0;34m\"Using a target size ({}) that is different to the input size ({}) is deprecated. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3091\u001b[0m             \u001b[0;34m\"Please ensure they have the same size.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Using a target size (torch.Size([100, 1])) that is different to the input size (torch.Size([100, 10])) is deprecated. Please ensure they have the same size."]}]},{"cell_type":"code","source":["import json\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import matplotlib.pyplot as plt\n","import random\n","import librosa\n","import math\n","from sklearn.preprocessing import StandardScaler\n","from keras.utils import to_categorical\n","\n","# path to json\n","DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n","\n","def load_data(data_path):\n","    with open(data_path, \"r\") as f:\n","        data = json.load(f)\n","\n","    # convert lists to numpy arrays\n","    X = np.array(data[\"mfcc\"])\n","    y = np.array(data[\"labels\"]) - 1  # Adjust target values\n","\n","    print(\"Data successfully loaded!\")\n","\n","    return X, y\n","\n","# load data\n","X, y = load_data(DATA_PATH)\n","\n","\n","# Convert labels to one-hot encoded format\n","y_categorical = to_categorical(y)\n","\n","# create train/validation split\n","X_train, X_validation, y_train, y_validation = train_test_split(X, y_categorical, test_size=0.3)\n","\n","# Update the number of classes for the output layer\n","num_classes = y_train.shape[1]\n","\n","# Convert data to tensors\n","X_train_tensor = torch.Tensor(X_train)\n","y_train_tensor = torch.Tensor(y_train).long()\n","X_validation_tensor = torch.Tensor(X_validation)\n","y_validation_tensor = torch.Tensor(y_validation).long()\n","\n","# build network topology\n","model = nn.Sequential(\n","    # input layer\n","    nn.Flatten(),\n","\n","    # 1st dense layer\n","    nn.Linear(X.shape[1] * X.shape[2], 512),\n","    nn.ReLU(),\n","\n","    # 2nd dense layer\n","    nn.Linear(512, 256),\n","    nn.ReLU(),\n","\n","    # 3rd dense layer\n","    nn.Linear(256, 64),\n","    nn.ReLU(),\n","\n","    # output layer\n","    nn.Linear(64, num_classes),\n","    nn.Softmax(dim=1)\n",")\n","\n","# Define optimizer and loss function\n","optimizer = optim.Adam(model.parameters(), lr=0.0001)\n","criterion = nn.CrossEntropyLoss()\n","\n","# Train the model\n","num_epochs = 50\n","batch_size = 32\n","num_batches = math.ceil(len(X_train_tensor) / batch_size)\n","\n","for epoch in range(num_epochs):\n","    epoch_loss = 0.0\n","    epoch_accuracy = 0.0\n","\n","    # Shuffle the data\n","    indices = torch.randperm(len(X_train_tensor))\n","    X_train_tensor = X_train_tensor[indices]\n","    y_train_tensor = y_train_tensor[indices]\n","\n","    for i in range(num_batches):\n","        # Extract batch\n","        start_idx = i * batch_size\n","        end_idx = start_idx + batch_size\n","        X_batch = X_train_tensor[start_idx:end_idx]\n","        y_batch = y_train_tensor[start_idx:end_idx]\n","\n","        # Forward pass\n","        outputs = model(X_batch)\n","        loss = criterion(outputs, torch.argmax(y_batch, dim=1))\n","\n","        # Backward pass and optimization\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","\n","        epoch_loss += loss.item()\n","        _, predicted_labels = torch.max(outputs.data, 1)\n","        correct_predictions = (predicted_labels == torch.argmax(y_batch, dim=1)).sum().item()\n","        epoch_accuracy += correct_predictions / batch_size\n","\n","    epoch_loss /= num_batches\n","    epoch_accuracy /= num_batches\n","\n","    print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.4f}\")\n","\n","# Evaluate the model on the validation set\n","model.eval()\n","with torch.no_grad():\n","    outputs = model(X_validation_tensor)\n","    _, predicted_labels = torch.max(outputs.data, 1)\n","    correct_predictions = (predicted_labels == torch.argmax(y_validation_tensor, dim=1)).sum().item()\n","    validation_accuracy = correct_predictions / len(X_validation_tensor)\n","    print(f\"Validation Accuracy: {validation_accuracy:.4f}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aXk2iokZZ_ae","executionInfo":{"status":"ok","timestamp":1684892582288,"user_tz":-60,"elapsed":145089,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"df90aff9-65d9-41b2-e64e-1b9ea55ba542"},"execution_count":101,"outputs":[{"output_type":"stream","name":"stdout","text":["Data successfully loaded!\n","Epoch 1/50, Loss: 2.1654, Accuracy: 0.2927\n","Epoch 2/50, Loss: 2.1502, Accuracy: 0.3068\n","Epoch 3/50, Loss: 2.1313, Accuracy: 0.3253\n","Epoch 4/50, Loss: 2.1195, Accuracy: 0.3376\n","Epoch 5/50, Loss: 2.1169, Accuracy: 0.3383\n","Epoch 6/50, Loss: 2.1022, Accuracy: 0.3550\n","Epoch 7/50, Loss: 2.0929, Accuracy: 0.3636\n","Epoch 8/50, Loss: 2.0918, Accuracy: 0.3672\n","Epoch 9/50, Loss: 2.0746, Accuracy: 0.3834\n","Epoch 10/50, Loss: 2.0663, Accuracy: 0.3914\n","Epoch 11/50, Loss: 2.0582, Accuracy: 0.3987\n","Epoch 12/50, Loss: 2.0451, Accuracy: 0.4115\n","Epoch 13/50, Loss: 2.0259, Accuracy: 0.4345\n","Epoch 14/50, Loss: 2.0125, Accuracy: 0.4455\n","Epoch 15/50, Loss: 2.0020, Accuracy: 0.4563\n","Epoch 16/50, Loss: 1.9817, Accuracy: 0.4793\n","Epoch 17/50, Loss: 1.9742, Accuracy: 0.4823\n","Epoch 18/50, Loss: 1.9561, Accuracy: 0.5034\n","Epoch 19/50, Loss: 1.9436, Accuracy: 0.5147\n","Epoch 20/50, Loss: 1.9302, Accuracy: 0.5288\n","Epoch 21/50, Loss: 1.9220, Accuracy: 0.5372\n","Epoch 22/50, Loss: 1.9249, Accuracy: 0.5335\n","Epoch 23/50, Loss: 1.9080, Accuracy: 0.5522\n","Epoch 24/50, Loss: 1.8951, Accuracy: 0.5651\n","Epoch 25/50, Loss: 1.8985, Accuracy: 0.5624\n","Epoch 26/50, Loss: 1.8961, Accuracy: 0.5619\n","Epoch 27/50, Loss: 1.8818, Accuracy: 0.5781\n","Epoch 28/50, Loss: 1.8651, Accuracy: 0.5945\n","Epoch 29/50, Loss: 1.8516, Accuracy: 0.6109\n","Epoch 30/50, Loss: 1.8340, Accuracy: 0.6286\n","Epoch 31/50, Loss: 1.8245, Accuracy: 0.6358\n","Epoch 32/50, Loss: 1.8107, Accuracy: 0.6535\n","Epoch 33/50, Loss: 1.8091, Accuracy: 0.6521\n","Epoch 34/50, Loss: 1.7993, Accuracy: 0.6644\n","Epoch 35/50, Loss: 1.7957, Accuracy: 0.6665\n","Epoch 36/50, Loss: 1.7846, Accuracy: 0.6752\n","Epoch 37/50, Loss: 1.7855, Accuracy: 0.6769\n","Epoch 38/50, Loss: 1.7770, Accuracy: 0.6854\n","Epoch 39/50, Loss: 1.7769, Accuracy: 0.6854\n","Epoch 40/50, Loss: 1.7674, Accuracy: 0.6939\n","Epoch 41/50, Loss: 1.7688, Accuracy: 0.6922\n","Epoch 42/50, Loss: 1.7694, Accuracy: 0.6926\n","Epoch 43/50, Loss: 1.7633, Accuracy: 0.6988\n","Epoch 44/50, Loss: 1.7550, Accuracy: 0.7058\n","Epoch 45/50, Loss: 1.7514, Accuracy: 0.7102\n","Epoch 46/50, Loss: 1.7494, Accuracy: 0.7105\n","Epoch 47/50, Loss: 1.7502, Accuracy: 0.7102\n","Epoch 48/50, Loss: 1.7436, Accuracy: 0.7162\n","Epoch 49/50, Loss: 1.7431, Accuracy: 0.7172\n","Epoch 50/50, Loss: 1.7468, Accuracy: 0.7135\n","Validation Accuracy: 0.5013\n"]}]},{"cell_type":"code","source":["import json\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import matplotlib.pyplot as plt\n","import random\n","import librosa\n","import math\n","from sklearn.preprocessing import StandardScaler\n","from keras.utils import to_categorical\n","from torch.utils.tensorboard import SummaryWriter\n","\n","# path to json\n","\n","import os\n","\n","# Specify the directory where you want to save the logs\n","log_dir = \"logs\"\n","\n","# Create the directory if it doesn't exist\n","if not os.path.exists(log_dir):\n","    os.makedirs(log_dir)\n","\n","# Initialize TensorBoard writer\n","\n","DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n","\n","def load_data(data_path):\n","    with open(data_path, \"r\") as f:\n","        data = json.load(f)\n","\n","    # convert lists to numpy arrays\n","    X = np.array(data[\"mfcc\"])\n","    y = np.array(data[\"labels\"]) - 1  # Adjust target values\n","\n","    print(\"Data successfully loaded!\")\n","\n","    return X, y\n","\n","# load data\n","X, y = load_data(DATA_PATH)\n","\n","# Convert labels to one-hot encoded format\n","y_categorical = to_categorical(y)\n","\n","# create train/validation split\n","X_train, X_validation, y_train, y_validation = train_test_split(X, y_categorical, test_size=0.3)\n","\n","# Update the number of classes for the output layer\n","num_classes = y_train.shape[1]\n","\n","# Convert data to tensors\n","X_train_tensor = torch.Tensor(X_train)\n","y_train_tensor = torch.Tensor(y_train).long()\n","X_validation_tensor = torch.Tensor(X_validation)\n","y_validation_tensor = torch.Tensor(y_validation).long()\n","\n","# build network topology\n","model = nn.Sequential(\n","    # input layer\n","    nn.Flatten(),\n","\n","    # 1st dense layer\n","    nn.Linear(X.shape[1] * X.shape[2], 512),\n","    nn.ReLU(),\n","\n","    # 2nd dense layer\n","    nn.Linear(512, 256),\n","    nn.ReLU(),\n","\n","    # 3rd dense layer\n","    nn.Linear(256, 64),\n","    nn.ReLU(),\n","\n","    # output layer\n","    nn.Linear(64, num_classes),\n","    nn.Softmax(dim=1)\n",")\n","\n","# Define optimizer and loss function\n","optimizer = optim.Adam(model.parameters(), lr=0.0001)\n","criterion = nn.CrossEntropyLoss()\n","\n","# Initialize TensorBoard writer\n","writer = SummaryWriter(log_dir=log_dir)\n","\n","# Train the model\n","num_epochs = 50\n","batch_size = 32\n","num_batches = math.ceil(len(X_train_tensor) / batch_size)\n","\n","for epoch in range(num_epochs):\n","    epoch_loss = 0.0\n","    epoch_accuracy = 0.0\n","\n","    # Shuffle the data\n","    indices = torch.randperm(len(X_train_tensor))\n","    X_train_tensor = X_train_tensor[indices]\n","    y_train_tensor = y_train_tensor[indices]\n","\n","    for i in range(num_batches):\n","        # Extract batch\n","        start_idx = i * batch_size\n","        end_idx = start_idx + batch_size\n","        X_batch = X_train_tensor[start_idx:end_idx]\n","        y_batch = y_train_tensor[start_idx:end_idx]\n","\n","        # Forward pass\n","        outputs = model(X_batch)\n","        loss = criterion(outputs, torch.argmax(y_batch, dim=1))\n","\n","        # Backward pass and optimization\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","\n","        epoch_loss += loss.item()\n","        _, predicted_labels = torch.max(outputs.data, 1)\n","        correct_predictions = (predicted_labels == torch.argmax(y_batch, dim=1)).sum().item()\n","        epoch_accuracy += correct_predictions / batch_size\n","\n","    epoch_loss /= num_batches\n","    epoch_accuracy /= num_batches\n","\n","    print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.4f}\")\n","\n","    # Write loss and accuracy to TensorBoard\n","    writer.add_scalar('Loss/train', epoch_loss, epoch)\n","    writer.add_scalar('Accuracy/train', epoch_accuracy, epoch)\n","\n","# Evaluate the model on the validation set\n","model.eval()\n","with torch.no_grad():\n","    outputs = model(X_validation_tensor)\n","    _, predicted_labels = torch.max(outputs.data, 1)\n","    correct_predictions = (predicted_labels == torch.argmax(y_validation_tensor, dim=1)).sum().item()\n","    validation_accuracy = correct_predictions / len(X_validation_tensor)\n","    print(f\"Validation Accuracy: {validation_accuracy:.4f}\")\n","\n","# Write validation accuracy to TensorBoard\n","writer.add_scalar('Accuracy/validation', validation_accuracy, num_epochs)\n","\n","# Close TensorBoard writer\n","writer.close()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"136MpzHmcvhK","executionInfo":{"status":"ok","timestamp":1684896139757,"user_tz":-60,"elapsed":139103,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"fdbee3a6-fce1-4db6-c620-036bce5bd188"},"execution_count":104,"outputs":[{"output_type":"stream","name":"stdout","text":["Data successfully loaded!\n","Epoch 1/50, Loss: 2.1492, Accuracy: 0.3029\n","Epoch 2/50, Loss: 2.0842, Accuracy: 0.3740\n","Epoch 3/50, Loss: 2.0608, Accuracy: 0.3977\n","Epoch 4/50, Loss: 2.0387, Accuracy: 0.4184\n","Epoch 5/50, Loss: 2.0269, Accuracy: 0.4307\n","Epoch 6/50, Loss: 2.0071, Accuracy: 0.4525\n","Epoch 7/50, Loss: 2.0004, Accuracy: 0.4588\n","Epoch 8/50, Loss: 1.9863, Accuracy: 0.4772\n","Epoch 9/50, Loss: 1.9723, Accuracy: 0.4902\n","Epoch 10/50, Loss: 1.9517, Accuracy: 0.5113\n","Epoch 11/50, Loss: 1.9377, Accuracy: 0.5233\n","Epoch 12/50, Loss: 1.9316, Accuracy: 0.5307\n","Epoch 13/50, Loss: 1.9148, Accuracy: 0.5484\n","Epoch 14/50, Loss: 1.9096, Accuracy: 0.5534\n","Epoch 15/50, Loss: 1.8995, Accuracy: 0.5624\n","Epoch 16/50, Loss: 1.8870, Accuracy: 0.5765\n","Epoch 17/50, Loss: 1.8735, Accuracy: 0.5905\n","Epoch 18/50, Loss: 1.8548, Accuracy: 0.6076\n","Epoch 19/50, Loss: 1.8308, Accuracy: 0.6327\n","Epoch 20/50, Loss: 1.8182, Accuracy: 0.6493\n","Epoch 21/50, Loss: 1.8084, Accuracy: 0.6591\n","Epoch 22/50, Loss: 1.7977, Accuracy: 0.6642\n","Epoch 23/50, Loss: 1.7781, Accuracy: 0.6862\n","Epoch 24/50, Loss: 1.7684, Accuracy: 0.6956\n","Epoch 25/50, Loss: 1.7651, Accuracy: 0.6989\n","Epoch 26/50, Loss: 1.7554, Accuracy: 0.7063\n","Epoch 27/50, Loss: 1.7522, Accuracy: 0.7103\n","Epoch 28/50, Loss: 1.7405, Accuracy: 0.7242\n","Epoch 29/50, Loss: 1.7383, Accuracy: 0.7250\n","Epoch 30/50, Loss: 1.7374, Accuracy: 0.7255\n","Epoch 31/50, Loss: 1.7322, Accuracy: 0.7312\n","Epoch 32/50, Loss: 1.7284, Accuracy: 0.7343\n","Epoch 33/50, Loss: 1.7272, Accuracy: 0.7357\n","Epoch 34/50, Loss: 1.7206, Accuracy: 0.7416\n","Epoch 35/50, Loss: 1.7202, Accuracy: 0.7409\n","Epoch 36/50, Loss: 1.7133, Accuracy: 0.7479\n","Epoch 37/50, Loss: 1.7091, Accuracy: 0.7530\n","Epoch 38/50, Loss: 1.7112, Accuracy: 0.7504\n","Epoch 39/50, Loss: 1.7033, Accuracy: 0.7593\n","Epoch 40/50, Loss: 1.6996, Accuracy: 0.7617\n","Epoch 41/50, Loss: 1.6964, Accuracy: 0.7644\n","Epoch 42/50, Loss: 1.6905, Accuracy: 0.7707\n","Epoch 43/50, Loss: 1.6878, Accuracy: 0.7727\n","Epoch 44/50, Loss: 1.6875, Accuracy: 0.7751\n","Epoch 45/50, Loss: 1.6808, Accuracy: 0.7811\n","Epoch 46/50, Loss: 1.6760, Accuracy: 0.7860\n","Epoch 47/50, Loss: 1.6778, Accuracy: 0.7834\n","Epoch 48/50, Loss: 1.6770, Accuracy: 0.7835\n","Epoch 49/50, Loss: 1.6719, Accuracy: 0.7898\n","Epoch 50/50, Loss: 1.6664, Accuracy: 0.7948\n","Validation Accuracy: 0.5734\n"]}]},{"cell_type":"code","source":["!pip install tensorboard"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eNtpLVLYowa_","executionInfo":{"status":"ok","timestamp":1684896228868,"user_tz":-60,"elapsed":6342,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"5c820102-08c4-4376-d01e-c63b0bbd4730"},"execution_count":105,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (2.12.2)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.4.0)\n","Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.54.0)\n","Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.17.3)\n","Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.0.0)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.4.3)\n","Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.22.4)\n","Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.20.3)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.27.1)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (67.7.2)\n","Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (0.7.0)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.8.1)\n","Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.3.0)\n","Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (0.40.0)\n","Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (5.3.0)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (0.3.0)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (1.16.0)\n","Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (4.9)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard) (1.3.1)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (1.26.15)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (2022.12.7)\n","Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (3.4)\n","Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard) (2.1.2)\n","Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.5.0)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard) (3.2.2)\n"]}]},{"cell_type":"code","source":["!tensorboard --logdir /content/logs --port=8000"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Akv1AfQGpC6b","executionInfo":{"status":"ok","timestamp":1684896646720,"user_tz":-60,"elapsed":29606,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"cc6ee14c-2c8c-4219-ddc0-7252e7906eb9"},"execution_count":112,"outputs":[{"output_type":"stream","name":"stdout","text":["2023-05-24 02:50:17.288620: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n","/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n","/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n","/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n","Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n","TensorBoard 2.12.2 at http://localhost:8000/ (Press CTRL+C to quit)\n","^C\n"]}]},{"cell_type":"code","source":["!tensorboard"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I6DefHDIp6-N","executionInfo":{"status":"ok","timestamp":1684896476007,"user_tz":-60,"elapsed":6219,"user":{"displayName":"Ahmed Haythem","userId":"12900721674845837314"}},"outputId":"dfa8fe67-2e14-4cb6-d917-4aa741a324ed"},"execution_count":110,"outputs":[{"output_type":"stream","name":"stdout","text":["2023-05-24 02:47:51.007572: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n","Error: A logdir or db must be specified. For example `tensorboard --logdir mylogdir` or `tensorboard --db sqlite:~/.tensorboard.db`. Run `tensorboard --helpfull` for details and examples.\n"]}]}]}
\ No newline at end of file
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!cd /content/drive/MyDrive/CourseWork"
+      ],
+      "metadata": {
+        "id": "U70S5cdH3nDN"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip /content/drive/MyDrive/archive.zip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hGUGy9uu3vzF",
+        "outputId": "8873cce0-7482-4a63-907c-49602bc536b1"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Archive:  /content/drive/MyDrive/archive.zip\n",
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install librosa"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YnPB83MI5NKO",
+        "outputId": "2bf4ebed-e918-45f2-b1d5-f82a14afba1d"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: librosa in /usr/local/lib/python3.10/dist-packages (0.8.1)\n",
+            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (3.0.0)\n",
+            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.22.4)\n",
+            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.10.1)\n",
+            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.2.2)\n",
+            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.2.0)\n",
+            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (4.4.2)\n",
+            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from librosa) (0.4.2)\n",
+            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (0.56.4)\n",
+            "Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.10/dist-packages (from librosa) (0.12.1)\n",
+            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (1.6.0)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from librosa) (23.1)\n",
+            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.43.0->librosa) (0.39.1)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.43.0->librosa) (67.7.2)\n",
+            "Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa) (1.4.4)\n",
+            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa) (2.27.1)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (3.1.0)\n",
+            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile>=0.10.2->librosa) (1.15.1)\n",
+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.26.15)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2022.12.7)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.0.12)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9OvhFoHz2jox",
+        "outputId": "6158a33d-2c65-4271-f927-d16a0fe10ffd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+            "/content/Data/genres_original/blues/blues.00064.wav, segment:6\n",
+            "/content/Data/genres_original/blues/blues.00064.wav, segment:7\n",
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+            "/content/Data/genres_original/blues/blues.00064.wav, segment:9\n",
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+            "/content/Data/genres_original/blues/blues.00029.wav, segment:4\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:5\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:6\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:7\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:8\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:9\n",
+            "/content/Data/genres_original/blues/blues.00029.wav, segment:10\n",
+            "/content/Data/genres_original/blues/blues.00002.wav, segment:1\n",
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+            "/content/Data/genres_original/blues/blues.00028.wav, segment:1\n",
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+            "/content/Data/genres_original/blues/blues.00012.wav, segment:9\n",
+            "/content/Data/genres_original/blues/blues.00012.wav, segment:10\n",
+            "/content/Data/genres_original/blues/blues.00074.wav, segment:1\n",
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+            "/content/Data/genres_original/country/country.00064.wav, segment:1\n",
+            "/content/Data/genres_original/country/country.00064.wav, segment:2\n",
+            "/content/Data/genres_original/country/country.00064.wav, segment:3\n",
+            "/content/Data/genres_original/country/country.00064.wav, segment:4\n",
+            "/content/Data/genres_original/country/country.00064.wav, segment:5\n",
+            "/content/Data/genres_original/country/country.00064.wav, segment:6\n",
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+            "/content/Data/genres_original/country/country.00014.wav, segment:8\n",
+            "/content/Data/genres_original/country/country.00014.wav, segment:9\n",
+            "/content/Data/genres_original/country/country.00014.wav, segment:10\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:1\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:2\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:3\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:4\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:5\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:6\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:7\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:8\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:9\n",
+            "/content/Data/genres_original/country/country.00000.wav, segment:10\n",
+            "/content/Data/genres_original/country/country.00026.wav, segment:1\n",
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+            "/content/Data/genres_original/country/country.00026.wav, segment:3\n",
+            "/content/Data/genres_original/country/country.00026.wav, segment:4\n",
+            "/content/Data/genres_original/country/country.00026.wav, segment:5\n",
+            "/content/Data/genres_original/country/country.00026.wav, segment:6\n",
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+            "/content/Data/genres_original/country/country.00026.wav, segment:10\n",
+            "\n",
+            "Processing: hiphop\n",
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+            "/content/Data/genres_original/hiphop/hiphop.00018.wav, segment:9\n",
+            "/content/Data/genres_original/hiphop/hiphop.00018.wav, segment:10\n",
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+            "/content/Data/genres_original/rock/rock.00073.wav, segment:10\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "import os\n",
+        "import math\n",
+        "import numpy as np\n",
+        "import librosa, librosa.display\n",
+        "import matplotlib.pyplot as plt\n",
+        "     \n",
+        "\n",
+        "DATASET_PATH = \"/content/Data/genres_original\"\n",
+        "JSON_PATH = \"data_10.json\"\n",
+        "SAMPLE_RATE = 22050\n",
+        "TRACK_DURATION = 30 # measured in seconds\n",
+        "SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION\n",
+        "     \n",
+        "\n",
+        "def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):\n",
+        "    \"\"\"Extracts MFCCs from music dataset and saves them into a json file along with genre labels.\n",
+        "        \"\"\"\n",
+        "\n",
+        "    # dictionary to store mapping, labels, and MFCCs\n",
+        "    data = {\n",
+        "        \"mapping\": [],\n",
+        "        \"labels\": [],\n",
+        "        \"mfcc\": []\n",
+        "    }\n",
+        "\n",
+        "    samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)\n",
+        "    num_mfcc_vectors_per_segment = math.ceil(samples_per_segment / hop_length)\n",
+        "\n",
+        "    # loop through all genre sub-folder\n",
+        "    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):\n",
+        "\n",
+        "        # ensure we're processing a genre sub-folder level\n",
+        "        if dirpath is not dataset_path:\n",
+        "\n",
+        "            # save genre label (i.e., sub-folder name) in the mapping\n",
+        "            semantic_label = dirpath.split(\"/\")[-1]\n",
+        "            data[\"mapping\"].append(semantic_label)\n",
+        "            print(\"\\nProcessing: {}\".format(semantic_label))\n",
+        "\n",
+        "            # process all audio files in genre sub-dir\n",
+        "            for f in filenames:\n",
+        "\n",
+        "\t\t# load audio file\n",
+        "                file_path = os.path.join(dirpath, f)\n",
+        "                signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)\n",
+        "\n",
+        "                # process all segments of audio file\n",
+        "                for d in range(num_segments):\n",
+        "\n",
+        "                    # calculate start and finish sample for current segment\n",
+        "                    start = samples_per_segment * d\n",
+        "                    finish = start + samples_per_segment\n",
+        "\n",
+        "                    # extract mfcc\n",
+        "                    mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)\n",
+        "                    mfcc = mfcc.T\n",
+        "\n",
+        "                    # store only mfcc feature with expected number of vectors\n",
+        "                    if len(mfcc) == num_mfcc_vectors_per_segment:\n",
+        "                        data[\"mfcc\"].append(mfcc.tolist())\n",
+        "                        data[\"labels\"].append(i-1)\n",
+        "                        print(\"{}, segment:{}\".format(file_path, d+1))\n",
+        "\n",
+        "    # save MFCCs to json file\n",
+        "    with open(json_path, \"w\") as fp:\n",
+        "        json.dump(data, fp, indent=4)\n",
+        "     \n",
+        "\n",
+        "save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import tensorflow.keras as keras\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "\n",
+        "import librosa\n",
+        "import math\n",
+        "     \n",
+        "\n",
+        "\n",
+        "# path to json\n",
+        "DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n",
+        "     \n",
+        "\n",
+        "def load_data(data_path):\n",
+        "\n",
+        "    with open(data_path, \"r\") as f:\n",
+        "        data = json.load(f)\n",
+        "\n",
+        "    # convert lists to numpy arrays\n",
+        "    X = np.array(data[\"mfcc\"])\n",
+        "    y = np.array(data[\"labels\"])\n",
+        "\n",
+        "    print(\"Data succesfully loaded!\")\n",
+        "\n",
+        "    return  X, y\n",
+        "     \n",
+        "\n",
+        "# load data\n",
+        "X, y = load_data(DATA_PATH)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vlb2BpDgBaTY",
+        "outputId": "7643f328-52ae-4991-a7f9-56029efb94de"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data succesfully loaded!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n",
+        "X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2)\n",
+        "\n",
+        "# add an axis to input sets\n",
+        "X_train = X_train[..., np.newaxis]\n",
+        "X_validation = X_validation[..., np.newaxis]\n",
+        "X_test = X_test[..., np.newaxis]\n",
+        "     \n",
+        "y_train_categorical = to_categorical(y_train)\n",
+        "y_validation_categorical = to_categorical(y_validation)\n",
+        "\n",
+        "# Update the number of classes for the output layer\n",
+        "num_classes = y_train_categorical.shape[1]\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "xCDKGt_9Boro"
+      },
+      "execution_count": 90,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "input_shape = (X_train.shape[1], X_train.shape[2], 1)\n",
+        "     \n",
+        "\n",
+        "# build the CNN\n",
+        "model_cnn = keras.Sequential()\n",
+        "\n",
+        "# 1st conv layer\n",
+        "model_cnn.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
+        "model_cnn.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n",
+        "model_cnn.add(keras.layers.BatchNormalization())\n",
+        "\n",
+        "# 2nd conv layer\n",
+        "model_cnn.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))\n",
+        "model_cnn.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))\n",
+        "model_cnn.add(keras.layers.BatchNormalization())\n",
+        "\n",
+        "# 3rd conv layer\n",
+        "model_cnn.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))\n",
+        "model_cnn.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))\n",
+        "model_cnn.add(keras.layers.BatchNormalization())\n",
+        "\n",
+        "# flatten output and feed it into dense layer\n",
+        "model_cnn.add(keras.layers.Flatten())\n",
+        "model_cnn.add(keras.layers.Dense(64, activation='relu'))\n",
+        "model_cnn.add(keras.layers.Dropout(0.3))\n",
+        "\n",
+        "# output layer\n",
+        "model_cnn.add(keras.layers.Dense(num_classes, activation='softmax'))\n",
+        "\n",
+        "# compile model\n",
+        "optimiser = keras.optimizers.Adam(learning_rate=0.0001)\n",
+        "model_cnn.compile(optimizer=optimiser,\n",
+        "                  loss='categorical_crossentropy',\n",
+        "                  metrics=['accuracy'])\n",
+        "\n",
+        "model_cnn.summary()\n",
+        "\n",
+        "\n",
+        "     "
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9OvXNYF_BomU",
+        "outputId": "bb6e2a37-0701-4f84-c2e2-c4ca61e9babc"
+      },
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_7\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d_15 (Conv2D)          (None, 128, 11, 32)       320       \n",
+            "                                                                 \n",
+            " max_pooling2d_15 (MaxPoolin  (None, 64, 6, 32)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " batch_normalization_15 (Bat  (None, 64, 6, 32)        128       \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " conv2d_16 (Conv2D)          (None, 62, 4, 32)         9248      \n",
+            "                                                                 \n",
+            " max_pooling2d_16 (MaxPoolin  (None, 31, 2, 32)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " batch_normalization_16 (Bat  (None, 31, 2, 32)        128       \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " conv2d_17 (Conv2D)          (None, 30, 1, 32)         4128      \n",
+            "                                                                 \n",
+            " max_pooling2d_17 (MaxPoolin  (None, 15, 1, 32)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " batch_normalization_17 (Bat  (None, 15, 1, 32)        128       \n",
+            " chNormalization)                                                \n",
+            "                                                                 \n",
+            " flatten_6 (Flatten)         (None, 480)               0         \n",
+            "                                                                 \n",
+            " dense_15 (Dense)            (None, 64)                30784     \n",
+            "                                                                 \n",
+            " dropout_5 (Dropout)         (None, 64)                0         \n",
+            "                                                                 \n",
+            " dense_16 (Dense)            (None, 11)                715       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 45,579\n",
+            "Trainable params: 45,387\n",
+            "Non-trainable params: 192\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = model_cnn.fit(X_train, y_train_categorical, validation_data=(X_validation, y_validation_categorical), batch_size=32, epochs=50)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c4iSXlJkGu9R",
+        "outputId": "c388e343-dd76-46cb-c3d1-3910dd15a51a"
+      },
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "188/188 [==============================] - 16s 79ms/step - loss: 2.5528 - accuracy: 0.1931 - val_loss: 2.0436 - val_accuracy: 0.2757\n",
+            "Epoch 2/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 1.9746 - accuracy: 0.3338 - val_loss: 1.6655 - val_accuracy: 0.3992\n",
+            "Epoch 3/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 1.7252 - accuracy: 0.4068 - val_loss: 1.5075 - val_accuracy: 0.4593\n",
+            "Epoch 4/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 1.5961 - accuracy: 0.4412 - val_loss: 1.3999 - val_accuracy: 0.4973\n",
+            "Epoch 5/50\n",
+            "188/188 [==============================] - 14s 77ms/step - loss: 1.4898 - accuracy: 0.4744 - val_loss: 1.3334 - val_accuracy: 0.5247\n",
+            "Epoch 6/50\n",
+            "188/188 [==============================] - 14s 77ms/step - loss: 1.3946 - accuracy: 0.5086 - val_loss: 1.2795 - val_accuracy: 0.5427\n",
+            "Epoch 7/50\n",
+            "188/188 [==============================] - 14s 77ms/step - loss: 1.3532 - accuracy: 0.5163 - val_loss: 1.2274 - val_accuracy: 0.5681\n",
+            "Epoch 8/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 1.2838 - accuracy: 0.5457 - val_loss: 1.2039 - val_accuracy: 0.5761\n",
+            "Epoch 9/50\n",
+            "188/188 [==============================] - 17s 90ms/step - loss: 1.2175 - accuracy: 0.5618 - val_loss: 1.1564 - val_accuracy: 0.6021\n",
+            "Epoch 10/50\n",
+            "188/188 [==============================] - 19s 101ms/step - loss: 1.1871 - accuracy: 0.5815 - val_loss: 1.1293 - val_accuracy: 0.6048\n",
+            "Epoch 11/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 1.1424 - accuracy: 0.6046 - val_loss: 1.1127 - val_accuracy: 0.6128\n",
+            "Epoch 12/50\n",
+            "188/188 [==============================] - 15s 82ms/step - loss: 1.1018 - accuracy: 0.6129 - val_loss: 1.0772 - val_accuracy: 0.6315\n",
+            "Epoch 13/50\n",
+            "188/188 [==============================] - 15s 78ms/step - loss: 1.0609 - accuracy: 0.6299 - val_loss: 1.0686 - val_accuracy: 0.6355\n",
+            "Epoch 14/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 1.0485 - accuracy: 0.6308 - val_loss: 1.0343 - val_accuracy: 0.6489\n",
+            "Epoch 15/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 0.9868 - accuracy: 0.6526 - val_loss: 1.0012 - val_accuracy: 0.6589\n",
+            "Epoch 16/50\n",
+            "188/188 [==============================] - 14s 75ms/step - loss: 0.9765 - accuracy: 0.6643 - val_loss: 1.0050 - val_accuracy: 0.6575\n",
+            "Epoch 17/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.9431 - accuracy: 0.6730 - val_loss: 0.9551 - val_accuracy: 0.6776\n",
+            "Epoch 18/50\n",
+            "188/188 [==============================] - 15s 78ms/step - loss: 0.9268 - accuracy: 0.6822 - val_loss: 0.9502 - val_accuracy: 0.6809\n",
+            "Epoch 19/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 0.8954 - accuracy: 0.6969 - val_loss: 0.9651 - val_accuracy: 0.6749\n",
+            "Epoch 20/50\n",
+            "188/188 [==============================] - 15s 78ms/step - loss: 0.8921 - accuracy: 0.6965 - val_loss: 0.9298 - val_accuracy: 0.6729\n",
+            "Epoch 21/50\n",
+            "188/188 [==============================] - 14s 77ms/step - loss: 0.8512 - accuracy: 0.7131 - val_loss: 0.9224 - val_accuracy: 0.6889\n",
+            "Epoch 22/50\n",
+            "188/188 [==============================] - 15s 77ms/step - loss: 0.8285 - accuracy: 0.7134 - val_loss: 0.8977 - val_accuracy: 0.6896\n",
+            "Epoch 23/50\n",
+            "188/188 [==============================] - 18s 95ms/step - loss: 0.8339 - accuracy: 0.7079 - val_loss: 0.9043 - val_accuracy: 0.6956\n",
+            "Epoch 24/50\n",
+            "188/188 [==============================] - 14s 76ms/step - loss: 0.7983 - accuracy: 0.7238 - val_loss: 0.8965 - val_accuracy: 0.7023\n",
+            "Epoch 25/50\n",
+            "188/188 [==============================] - 14s 76ms/step - loss: 0.7782 - accuracy: 0.7361 - val_loss: 0.8812 - val_accuracy: 0.6996\n",
+            "Epoch 26/50\n",
+            "188/188 [==============================] - 14s 76ms/step - loss: 0.7639 - accuracy: 0.7393 - val_loss: 0.8699 - val_accuracy: 0.7056\n",
+            "Epoch 27/50\n",
+            "188/188 [==============================] - 14s 75ms/step - loss: 0.7538 - accuracy: 0.7441 - val_loss: 0.8644 - val_accuracy: 0.7049\n",
+            "Epoch 28/50\n",
+            "188/188 [==============================] - 14s 76ms/step - loss: 0.7291 - accuracy: 0.7500 - val_loss: 0.8570 - val_accuracy: 0.7123\n",
+            "Epoch 29/50\n",
+            "188/188 [==============================] - 15s 78ms/step - loss: 0.7109 - accuracy: 0.7588 - val_loss: 0.8483 - val_accuracy: 0.7116\n",
+            "Epoch 30/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.7138 - accuracy: 0.7640 - val_loss: 0.8621 - val_accuracy: 0.7049\n",
+            "Epoch 31/50\n",
+            "188/188 [==============================] - 15s 78ms/step - loss: 0.6782 - accuracy: 0.7682 - val_loss: 0.8389 - val_accuracy: 0.7236\n",
+            "Epoch 32/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.6865 - accuracy: 0.7628 - val_loss: 0.8252 - val_accuracy: 0.7303\n",
+            "Epoch 33/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.6692 - accuracy: 0.7730 - val_loss: 0.8165 - val_accuracy: 0.7290\n",
+            "Epoch 34/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 0.6608 - accuracy: 0.7753 - val_loss: 0.8294 - val_accuracy: 0.7203\n",
+            "Epoch 35/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 0.6530 - accuracy: 0.7803 - val_loss: 0.8306 - val_accuracy: 0.7109\n",
+            "Epoch 36/50\n",
+            "188/188 [==============================] - 17s 91ms/step - loss: 0.6164 - accuracy: 0.7887 - val_loss: 0.8161 - val_accuracy: 0.7176\n",
+            "Epoch 37/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.6045 - accuracy: 0.7962 - val_loss: 0.8000 - val_accuracy: 0.7176\n",
+            "Epoch 38/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 0.5976 - accuracy: 0.7982 - val_loss: 0.8176 - val_accuracy: 0.7143\n",
+            "Epoch 39/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5964 - accuracy: 0.7975 - val_loss: 0.8169 - val_accuracy: 0.7136\n",
+            "Epoch 40/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.5850 - accuracy: 0.7960 - val_loss: 0.8232 - val_accuracy: 0.7316\n",
+            "Epoch 41/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5793 - accuracy: 0.8010 - val_loss: 0.8249 - val_accuracy: 0.7156\n",
+            "Epoch 42/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 0.5571 - accuracy: 0.8092 - val_loss: 0.7856 - val_accuracy: 0.7296\n",
+            "Epoch 43/50\n",
+            "188/188 [==============================] - 15s 79ms/step - loss: 0.5578 - accuracy: 0.8099 - val_loss: 0.7732 - val_accuracy: 0.7323\n",
+            "Epoch 44/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5236 - accuracy: 0.8239 - val_loss: 0.7671 - val_accuracy: 0.7417\n",
+            "Epoch 45/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5393 - accuracy: 0.8169 - val_loss: 0.7892 - val_accuracy: 0.7290\n",
+            "Epoch 46/50\n",
+            "188/188 [==============================] - 15s 81ms/step - loss: 0.5361 - accuracy: 0.8146 - val_loss: 0.7751 - val_accuracy: 0.7390\n",
+            "Epoch 47/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5178 - accuracy: 0.8251 - val_loss: 0.7783 - val_accuracy: 0.7303\n",
+            "Epoch 48/50\n",
+            "188/188 [==============================] - 15s 80ms/step - loss: 0.5052 - accuracy: 0.8261 - val_loss: 0.7833 - val_accuracy: 0.7363\n",
+            "Epoch 49/50\n",
+            "188/188 [==============================] - 18s 94ms/step - loss: 0.4929 - accuracy: 0.8322 - val_loss: 0.7725 - val_accuracy: 0.7356\n",
+            "Epoch 50/50\n",
+            "188/188 [==============================] - 16s 85ms/step - loss: 0.4884 - accuracy: 0.8346 - val_loss: 0.7723 - val_accuracy: 0.7417\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "def plot_history(history):\n",
+        "\n",
+        "    fig, axs = plt.subplots(2)\n",
+        "\n",
+        "    # create accuracy sublpot\n",
+        "    axs[0].plot(history.history[\"accuracy\"], label=\"train accuracy\")\n",
+        "    axs[0].plot(history.history[\"val_accuracy\"], label=\"test accuracy\")\n",
+        "    axs[0].set_ylabel(\"Accuracy\")\n",
+        "    axs[0].legend(loc=\"lower right\")\n",
+        "    axs[0].set_title(\"Accuracy eval\")\n",
+        "\n",
+        "    # create error sublpot\n",
+        "    axs[1].plot(history.history[\"loss\"], label=\"train error\")\n",
+        "    axs[1].plot(history.history[\"val_loss\"], label=\"test error\")\n",
+        "    axs[1].set_ylabel(\"Error\")\n",
+        "    axs[1].set_xlabel(\"Epoch\")\n",
+        "    axs[1].legend(loc=\"upper right\")\n",
+        "    axs[1].set_title(\"Error eval\")\n",
+        "    \n",
+        "    plt.show()\n",
+        "     "
+      ],
+      "metadata": {
+        "id": "6AUH6850XwEJ"
+      },
+      "execution_count": 96,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_history(history)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "9j5pr_VyJ5b7",
+        "outputId": "4eda1b40-1483-433e-f4ff-36140a11ae5f"
+      },
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "     \n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "model_cnn.save(\"Music_Genre_10_CNN\")\n",
+        "     \n",
+        "\n",
+        "model_cnn.save(\"Music_Genre_10_CNN.h5\")\n",
+        "\n",
+        "     \n",
+        "\n",
+        "# It can be used to reconstruct the model identically.\n",
+        "reconstructed_model = keras.models.load_model(\"Music_Genre_10_CNN.h5\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JAOubbfaIi8O",
+        "outputId": "9528708b-4c43-4ce9-97fd-6d13b78edd79"
+      },
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## In Pytorch \n"
+      ],
+      "metadata": {
+        "id": "j8IZKNJ591QZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "import matplotlib.pyplot as plt\n",
+        "import random\n",
+        "import librosa\n",
+        "import math\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from keras.utils import to_categorical\n",
+        "from torch.utils.tensorboard import SummaryWriter\n",
+        "\n",
+        "# path to json\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "# Specify the directory where you want to save the logs\n",
+        "log_dir = \"logs\"\n",
+        "\n",
+        "# Create the directory if it doesn't exist\n",
+        "if not os.path.exists(log_dir):\n",
+        "    os.makedirs(log_dir)\n",
+        "\n",
+        "# Initialize TensorBoard writer\n",
+        "\n",
+        "DATA_PATH = \"/content/drive/MyDrive/data_10.json\"\n",
+        "\n",
+        "def load_data(data_path):\n",
+        "    with open(data_path, \"r\") as f:\n",
+        "        data = json.load(f)\n",
+        "\n",
+        "    # convert lists to numpy arrays\n",
+        "    X = np.array(data[\"mfcc\"])\n",
+        "    y = np.array(data[\"labels\"]) - 1  # Adjust target values\n",
+        "\n",
+        "    print(\"Data successfully loaded!\")\n",
+        "\n",
+        "    return X, y\n",
+        "\n",
+        "# load data\n",
+        "X, y = load_data(DATA_PATH)\n",
+        "\n",
+        "# Convert labels to one-hot encoded format\n",
+        "y_categorical = to_categorical(y)\n",
+        "\n",
+        "# create train/validation split\n",
+        "X_train, X_validation, y_train, y_validation = train_test_split(X, y_categorical, test_size=0.3)\n",
+        "\n",
+        "# Update the number of classes for the output layer\n",
+        "num_classes = y_train.shape[1]\n",
+        "\n",
+        "# Convert data to tensors\n",
+        "X_train_tensor = torch.Tensor(X_train)\n",
+        "y_train_tensor = torch.Tensor(y_train).long()\n",
+        "X_validation_tensor = torch.Tensor(X_validation)\n",
+        "y_validation_tensor = torch.Tensor(y_validation).long()\n",
+        "\n",
+        "# build network topology\n",
+        "model = nn.Sequential(\n",
+        "    # input layer\n",
+        "    nn.Flatten(),\n",
+        "\n",
+        "    # 1st dense layer\n",
+        "    nn.Linear(X.shape[1] * X.shape[2], 512),\n",
+        "    nn.ReLU(),\n",
+        "\n",
+        "    # 2nd dense layer\n",
+        "    nn.Linear(512, 256),\n",
+        "    nn.ReLU(),\n",
+        "\n",
+        "    # 3rd dense layer\n",
+        "    nn.Linear(256, 64),\n",
+        "    nn.ReLU(),\n",
+        "\n",
+        "    # output layer\n",
+        "    nn.Linear(64, num_classes),\n",
+        "    nn.Softmax(dim=1)\n",
+        ")\n",
+        "\n",
+        "# Define optimizer and loss function\n",
+        "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "\n",
+        "# Initialize TensorBoard writer\n",
+        "writer = SummaryWriter(log_dir=log_dir)\n",
+        "\n",
+        "# Train the model\n",
+        "num_epochs = 50\n",
+        "batch_size = 32\n",
+        "num_batches = math.ceil(len(X_train_tensor) / batch_size)\n",
+        "\n",
+        "for epoch in range(num_epochs):\n",
+        "    epoch_loss = 0.0\n",
+        "    epoch_accuracy = 0.0\n",
+        "\n",
+        "    # Shuffle the data\n",
+        "    indices = torch.randperm(len(X_train_tensor))\n",
+        "    X_train_tensor = X_train_tensor[indices]\n",
+        "    y_train_tensor = y_train_tensor[indices]\n",
+        "\n",
+        "    for i in range(num_batches):\n",
+        "        # Extract batch\n",
+        "        start_idx = i * batch_size\n",
+        "        end_idx = start_idx + batch_size\n",
+        "        X_batch = X_train_tensor[start_idx:end_idx]\n",
+        "        y_batch = y_train_tensor[start_idx:end_idx]\n",
+        "\n",
+        "        # Forward pass\n",
+        "        outputs = model(X_batch)\n",
+        "        loss = criterion(outputs, torch.argmax(y_batch, dim=1))\n",
+        "\n",
+        "        # Backward pass and optimization\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        epoch_loss += loss.item()\n",
+        "        _, predicted_labels = torch.max(outputs.data, 1)\n",
+        "        correct_predictions = (predicted_labels == torch.argmax(y_batch, dim=1)).sum().item()\n",
+        "        epoch_accuracy += correct_predictions / batch_size\n",
+        "\n",
+        "    epoch_loss /= num_batches\n",
+        "    epoch_accuracy /= num_batches\n",
+        "\n",
+        "    print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {epoch_loss:.4f}, Accuracy: {epoch_accuracy:.4f}\")\n",
+        "\n",
+        "    # Write loss and accuracy to TensorBoard\n",
+        "    writer.add_scalar('Loss/train', epoch_loss, epoch)\n",
+        "    writer.add_scalar('Accuracy/train', epoch_accuracy, epoch)\n",
+        "\n",
+        "# Evaluate the model on the validation set\n",
+        "model.eval()\n",
+        "with torch.no_grad():\n",
+        "    outputs = model(X_validation_tensor)\n",
+        "    _, predicted_labels = torch.max(outputs.data, 1)\n",
+        "    correct_predictions = (predicted_labels == torch.argmax(y_validation_tensor, dim=1)).sum().item()\n",
+        "    validation_accuracy = correct_predictions / len(X_validation_tensor)\n",
+        "    print(f\"Validation Accuracy: {validation_accuracy:.4f}\")\n",
+        "\n",
+        "# Write validation accuracy to TensorBoard\n",
+        "writer.add_scalar('Accuracy/validation', validation_accuracy, num_epochs)\n",
+        "\n",
+        "# Close TensorBoard writer\n",
+        "writer.close()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "136MpzHmcvhK",
+        "outputId": "fdbee3a6-fce1-4db6-c620-036bce5bd188"
+      },
+      "execution_count": 104,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data successfully loaded!\n",
+            "Epoch 1/50, Loss: 2.1492, Accuracy: 0.3029\n",
+            "Epoch 2/50, Loss: 2.0842, Accuracy: 0.3740\n",
+            "Epoch 3/50, Loss: 2.0608, Accuracy: 0.3977\n",
+            "Epoch 4/50, Loss: 2.0387, Accuracy: 0.4184\n",
+            "Epoch 5/50, Loss: 2.0269, Accuracy: 0.4307\n",
+            "Epoch 6/50, Loss: 2.0071, Accuracy: 0.4525\n",
+            "Epoch 7/50, Loss: 2.0004, Accuracy: 0.4588\n",
+            "Epoch 8/50, Loss: 1.9863, Accuracy: 0.4772\n",
+            "Epoch 9/50, Loss: 1.9723, Accuracy: 0.4902\n",
+            "Epoch 10/50, Loss: 1.9517, Accuracy: 0.5113\n",
+            "Epoch 11/50, Loss: 1.9377, Accuracy: 0.5233\n",
+            "Epoch 12/50, Loss: 1.9316, Accuracy: 0.5307\n",
+            "Epoch 13/50, Loss: 1.9148, Accuracy: 0.5484\n",
+            "Epoch 14/50, Loss: 1.9096, Accuracy: 0.5534\n",
+            "Epoch 15/50, Loss: 1.8995, Accuracy: 0.5624\n",
+            "Epoch 16/50, Loss: 1.8870, Accuracy: 0.5765\n",
+            "Epoch 17/50, Loss: 1.8735, Accuracy: 0.5905\n",
+            "Epoch 18/50, Loss: 1.8548, Accuracy: 0.6076\n",
+            "Epoch 19/50, Loss: 1.8308, Accuracy: 0.6327\n",
+            "Epoch 20/50, Loss: 1.8182, Accuracy: 0.6493\n",
+            "Epoch 21/50, Loss: 1.8084, Accuracy: 0.6591\n",
+            "Epoch 22/50, Loss: 1.7977, Accuracy: 0.6642\n",
+            "Epoch 23/50, Loss: 1.7781, Accuracy: 0.6862\n",
+            "Epoch 24/50, Loss: 1.7684, Accuracy: 0.6956\n",
+            "Epoch 25/50, Loss: 1.7651, Accuracy: 0.6989\n",
+            "Epoch 26/50, Loss: 1.7554, Accuracy: 0.7063\n",
+            "Epoch 27/50, Loss: 1.7522, Accuracy: 0.7103\n",
+            "Epoch 28/50, Loss: 1.7405, Accuracy: 0.7242\n",
+            "Epoch 29/50, Loss: 1.7383, Accuracy: 0.7250\n",
+            "Epoch 30/50, Loss: 1.7374, Accuracy: 0.7255\n",
+            "Epoch 31/50, Loss: 1.7322, Accuracy: 0.7312\n",
+            "Epoch 32/50, Loss: 1.7284, Accuracy: 0.7343\n",
+            "Epoch 33/50, Loss: 1.7272, Accuracy: 0.7357\n",
+            "Epoch 34/50, Loss: 1.7206, Accuracy: 0.7416\n",
+            "Epoch 35/50, Loss: 1.7202, Accuracy: 0.7409\n",
+            "Epoch 36/50, Loss: 1.7133, Accuracy: 0.7479\n",
+            "Epoch 37/50, Loss: 1.7091, Accuracy: 0.7530\n",
+            "Epoch 38/50, Loss: 1.7112, Accuracy: 0.7504\n",
+            "Epoch 39/50, Loss: 1.7033, Accuracy: 0.7593\n",
+            "Epoch 40/50, Loss: 1.6996, Accuracy: 0.7617\n",
+            "Epoch 41/50, Loss: 1.6964, Accuracy: 0.7644\n",
+            "Epoch 42/50, Loss: 1.6905, Accuracy: 0.7707\n",
+            "Epoch 43/50, Loss: 1.6878, Accuracy: 0.7727\n",
+            "Epoch 44/50, Loss: 1.6875, Accuracy: 0.7751\n",
+            "Epoch 45/50, Loss: 1.6808, Accuracy: 0.7811\n",
+            "Epoch 46/50, Loss: 1.6760, Accuracy: 0.7860\n",
+            "Epoch 47/50, Loss: 1.6778, Accuracy: 0.7834\n",
+            "Epoch 48/50, Loss: 1.6770, Accuracy: 0.7835\n",
+            "Epoch 49/50, Loss: 1.6719, Accuracy: 0.7898\n",
+            "Epoch 50/50, Loss: 1.6664, Accuracy: 0.7948\n",
+            "Validation Accuracy: 0.5734\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install tensorboard"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eNtpLVLYowa_",
+        "outputId": "5c820102-08c4-4376-d01e-c63b0bbd4730"
+      },
+      "execution_count": 105,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: tensorboard in /usr/local/lib/python3.10/dist-packages (2.12.2)\n",
+            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.4.0)\n",
+            "Requirement already satisfied: grpcio>=1.48.2 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.54.0)\n",
+            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.17.3)\n",
+            "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.0.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.4.3)\n",
+            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.22.4)\n",
+            "Requirement already satisfied: protobuf>=3.19.6 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (3.20.3)\n",
+            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.27.1)\n",
+            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (67.7.2)\n",
+            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (0.7.0)\n",
+            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (1.8.1)\n",
+            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (2.3.0)\n",
+            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (0.40.0)\n",
+            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (5.3.0)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (0.3.0)\n",
+            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (1.16.0)\n",
+            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (4.9)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard) (1.3.1)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (1.26.15)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (2022.12.7)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (2.0.12)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard) (3.4)\n",
+            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard) (2.1.2)\n",
+            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.5.0)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard) (3.2.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!tensorboard --logdir /content/logs --port=8000"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Akv1AfQGpC6b",
+        "outputId": "cc6ee14c-2c8c-4219-ddc0-7252e7906eb9"
+      },
+      "execution_count": 112,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2023-05-24 02:50:17.288620: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+            "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
+            "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
+            "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
+            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
+            "TensorBoard 2.12.2 at http://localhost:8000/ (Press CTRL+C to quit)\n",
+            "^C\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!tensorboard"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I6DefHDIp6-N",
+        "outputId": "dfa8fe67-2e14-4cb6-d917-4aa741a324ed"
+      },
+      "execution_count": 110,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2023-05-24 02:47:51.007572: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+            "Error: A logdir or db must be specified. For example `tensorboard --logdir mylogdir` or `tensorboard --db sqlite:~/.tensorboard.db`. Run `tensorboard --helpfull` for details and examples.\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
-- 
GitLab